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Cisco Execs: New “Network Supercycle” as Agentic AI Workloads Reshape Telecom Infrastructure
By Alan J Weissberger
Executive Summary:
The rapid rise of agentic artificial intelligence (AI) is expected to drive material changes across data centers, service provider networks, and the broader telecom ecosystem. As agentic AI moves from chat-oriented interactions to autonomous digital agents, Cisco says that those workloads will not only increase traffic volumes, but also alter traffic characteristics in ways that place new demands on latency, security, orchestration, and distributed compute placement.
“We are entering into a Network Supercycle,” Jeetu Patel, Cisco’s president and chief product officer, said during his opening keynote at Cisco Live in Las Vegas.
As a result, network operators will need more resilient transport, edge compute, and optical capacity to support new traffic patterns and security demands.
Cisco execs pictured (left to right): Jeetu Patel, president and chief product officer; Chuck Robbins, chairman and CEO; Liz Centoni, EVP and chief customer experience officer; and Steven Clayton, SVP and chief communications officer.
Source: Jeff Baumgartner/Light Reading
AI Traffic Impact on Transport Requirements:
From a transport perspective, agentic AI traffic is likely to be more persistent, more interactive, and more latency-sensitive than conventional application traffic. Cisco has said AI-related network traffic is expected to triple over the next three years, with inference flows emerging as a major driver of load growth. That shift could place pressure on transport architectures that were optimized primarily for human-driven web, video, and enterprise application traffic
The implication for service providers is that traffic engineering will need to evolve toward finer-grained path control, stronger telemetry, and improved handling of asymmetric flows. AI sessions that span multiple exchanges between users, applications, and digital agents may also require more sophisticated policy enforcement and security integration across WAN, metro, and access layers.
Edge Compute Needs Grow:
Cisco’s remarks also point to a growing role for edge compute in telecom and cable networks. Some operators are already repurposing legacy central offices and mini data centers to support AI workloads, reflecting a broader shift toward distributed inference close to the user or device.
That architecture matters because many agentic AI use cases will be latency constrained and will not perform efficiently if all processing is centralized in distant cloud regions. Comcast and Charter have both announced AI edge strategies, underscoring how access networks can become part of the compute fabric rather than acting solely as last-mile connectivity.
For network operators, this suggests a new operational model in which compute, storage, and network functions are increasingly coordinated across regional and edge sites. In practical terms, the network becomes part of the application execution environment, not just the transport layer beneath it.
Optical Network Implications:
Optical infrastructure will likely carry much of the burden created by distributed AI deployments. As inference workloads expand across regional hubs, edge sites, and centralized clouds, operators may need higher-capacity optical transport to sustain east-west traffic between distributed compute nodes.
That points to greater demand for dense 400G and 800G interconnects, more flexible wavelength management, and lower-latency optical paths between metro aggregation points and AI facilities. The challenge is not only to scale throughput, but also to preserve path diversity, minimize jitter, and maintain predictable performance for machine-to-machine workloads that are increasingly sensitive to delay.
As AI traffic becomes more dynamic and more operationally critical, optical networks may need to be engineered with the same level of service awareness traditionally associated with enterprise transport and carrier-grade voice or mobile backhaul.
Security is a Top Priority:
Cisco cited security as a serious concern for agentic AI traffic. CEO Chuck Robbins said AI agents designed to help enterprise customers can run roughshod without a proper defense that can quickly detect, intercept and possibly “kill” them before they get out of control. It becomes an even bigger issue when they are built to be nefarious.
“AI changes the speed of defense,” Robbins said. “It’s empowering adversaries at a pace that we haven’t seen in our careers … These [AI] models are as bad as they are ever going to be …They’re only going to get better.”
Anthropic’s new Claude Mythos model, which can auto-detect and possibly exploit software vulnerabilities at scale, is now a “CEO-level discussion,” he added.
“We’re living in a post-Mythos world where security has to be fused and baked into the network,” Patel said, holding that vulnerabilities can now being attacked as soon as they arise.
“We need to reimagine security” in the AI era, Patel said, noting that AI agents will not only handle tasks locally but will be heading outside to connect to third-party agents, servers and various tools.
“Every agentic action is a routing challenge, a trust decision and a telemetry event,” Patel said. The emergence of agentic AI, he said, is shifting the security and permission focus from “access control” (for us humans) to “action control” for agents that will need to be closely monitored, controlled and, if needed, quickly intercepted.
“People don’t trust these agents right now,” Patel said later during a separate discussion with press and analysts.
These concerns also extend to AI agent identity, which Cisco is addressing with its recent agreement to acquire Astrix Security.
This extends to other types of guardrails and observability metrics, too, including the notion of “tokenomics” – essentially keeping tabs on how many tokens an AI agent could consume. If the agent is found to be overspending on tokens, it could be intercepted and shut down.
Patel suggested that, without guardrails, what a company pays for AI tokens for a year could be consumed by an agent in a week. Assessing such AI agent behavior was a key driver of Cisco’s acquisition of Galileo Technologies.
Cisco’s AI Stack:
Cisco is focused on a vertically integrated platform – starting with its Silicon One platform for data centers and enterprise devices, optics, switches, routers and access points, apps and services, and wrapped by a new Cisco Cloud Control platform announced this week. Though Cisco Cloud Control is able to provide unified access to Cisco’s tools, apps and services, such as Meraki, Catalyst and Splunk, Patel stressed that it will also be able to integrate with third parties and support an open ecosystem. Cisco is starting out with support from 52 partners, including AWS, Google Cloud, NetBrain and ServiceNow.
Telecom Market Transition:
Robbins said Cisco used AI to scan 1.8 billion lines of code in 25 different programming languages over the past eight weeks. Without AI models, that would’ve taken eight years, he said.
Patel described the industry as being at a pivotal moment, moving from chat bots to more advanced agents that function as “digital coworkers.” He noted that “These agents are going to be everywhere.”
That transition suggests telecom networks will increasingly support autonomous machine interactions at scale, with implications that extend beyond bandwidth growth into security, policy control, and distributed systems design. For operators and vendors alike, the strategic question is no longer whether AI will affect the network, but how quickly the network architecture can adapt.
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References:
https://www.lightreading.com/ai-machine-learning/cisco-ai-driving-a-network-supercycle-
Cisco report: Agentic AI to reshape WAN traffic, AI inference will be ~25% of total traffic by 2035
Cisco’s Silicon One G300 as the dominant AI networking fabric, competing with Broadcom’s Tomahawk 6 series
Will the wave of AI generated user-to/from-network traffic increase spectacularly as Cisco and Nokia predict?
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Cisco to join Stargate UAE consortium as a preferred tech partner
Cisco CEO sees great potential in AI data center connectivity, silicon, optics, and optical systems
The enterprise network stack is collapsing; AI’s impact; comparison with “Batch Pipelines Break AI Agents”
by Shashi Kiran with Alan J Weissberger, ScD
Abstract:
This article presents the primary author’s point of view on networking technology and market evolution, as experienced it directly with his customers at Nile, where he serves as Chief Marketing Officer (CMO). A key theme is overlaying the impact of AI and its implications for network and network security architecture on a new network stack. We focus specifically on the diverse complexity and heterogeneity of the LAN, while drawing inferences to other areas in the broader enterprise network.
The article draws no information from other publications or references, except for the security breach data points derived from IDC, Gartner, and market surveys. Hence, the References listed at the end of the piece are from related IEEE Techblog posts and Nile press releases chosen by this website’s content manager.
Definitions:
The enterprise network stack is much more than a protocol stack. It is the layered architecture of physical infrastructure, forwarding devices, control protocols, management systems, and security enforcement functions that interconnect users, endpoints, workloads, and cloud services across campus, branch, WAN, data center, and cloud domains. It typically includes access, distribution, core, and edge segments, along with overlay, orchestration, telemetry, identity, and policy planes that govern how traffic is admitted, routed, segmented, monitored, and secured.
A useful way to think about the stack is in terms of planes:
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Data plane: forwards packets, enforces QoS, and applies access-control functions close to the traffic path.
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Control plane: discovers topology and capabilities, computes paths, and reacts to failures.
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Management plane: handles configuration, monitoring, troubleshooting, reporting, and performance management.
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Security stack: includes firewalls, IDS/IPS, secure web gateways, threat intelligence, and related inspection or enforcement tools.
At the device level, the stack typically includes physical media and network hardware such as cabling, Wi-Fi, NICs, switches, routers, gateways, servers, and dedicated security appliances. At higher layers, it includes protocols and services for addressing, routing, transport, application connectivity, identity, and policy enforcement, often mapped loosely to OSI/TCP-IP concepts rather than a strict textbook stack.
In an enterprise environment, the network stack extends across LAN, WAN, data center, cloud, and security domains, so “the stack” is less a single product and more an integrated system of infrastructure, software, telemetry, and policy. That is why discussions of enterprise architecture usually separate forwarding, orchestration, assurance, and security functions even when they are delivered in a unified platform.
Structural Limits of the Enterprise Network Stack:
The enterprise network stack is approaching a structural inflection which may be at a “breaking point.” That’s because what’s failing is structural and architectural, not incremental. The enterprise network stack was architected for a world that no longer exists, and most of the pain organizations feel today is the cost of pretending otherwise. The interesting question isn’t whether it breaks but rather when, and along which seams. Here’s why:
The network stack most enterprises still run was designed around five assumptions that were partly true in 2010 but mostly false in 2026. Users sit at desks on managed devices. Applications live in a corporate data center. Traffic flows north-south through a perimeter. Identity equals a user with a session. Trust derives from network location. Every one of those is gone. Users are hybrid, apps are SaaS and multi-cloud, traffic is increasingly east-west and machine-driven, identity now includes non-human agents acting with delegated authority, and zero trust has formally retired the idea that being inside the network means anything.
So, the enterprise stack isn’t failing because any single piece is bad. Rather, it’s failing because the architecture it was based on no longer matches the workload, the threat model, or the operational reality it’s asked to serve. AI is the forcing function, but the cracks were already there. The choice in front of most enterprises isn’t whether to rebuild but whether to do it deliberately or by accident. Will reinvention and self-disruption be intentional or forced?
Today, many enterprise environments represent layered extensions of legacy architectures rather than cohesive designs. AI acts as an accelerant, exposing pre-existing architectural limitations. The resulting fragmentation increases operational complexity, reduces agility, and amplifies security risk.
Complexity is a Primary Risk Vector:
Complexity has evolved from an operational burden into a primary source of systemic risk. Modern network environments often exceed the capacity for deterministic human understanding, creating conditions where failures and vulnerabilities emerge at the intersections between systems rather than within individual components.
Empirical evidence suggests that many successful breaches exploit misconfigurations and integration gaps rather than novel vulnerabilities. In this context, complexity itself becomes the effective attack surface.
This challenge is particularly acute in the LAN, which often retains legacy architectural elements, heterogeneous device ecosystems, and fragmented management models. Combined with constrained IT resources, this environment can become a disproportionate source of exposure.
Reducing complexity—through architectural simplification, integrated control planes, and automation—is therefore not merely an operational objective but a core security strategy. In AI-driven environments, simplicity directly contributes to resilience and risk reduction.
An Architectural Reset is Needed:
An architectural reset is increasingly necessary. While incremental upgrades remain feasible, their marginal returns are diminishing relative to the growing mismatch between legacy designs and emerging requirements. Many organizations continue to extend existing architectures due to cost constraints or perceived transition risks. However, this approach often compounds technical debt and increases long-term exposure. The more fundamental question is not whether incremental evolution is possible, but whether it represents effective capital allocation in the context of AI-driven workloads and threat models.
Forward-looking architectures are converging around several principles: AI-native workload support, identity-centric security, zero-trust enforcement, and tightly integrated operational models. Organizations that proactively redefine their network architectures around these principles are more likely to achieve sustainable performance, security, and operational efficiency gains.
Here are a couple of conceptual architectural constructs for a unified, secure fabric with AI orchestration, autonomous operation and service delivery, which replaces the fragmented network stack and operations of the traditional/legacy network. The first illustration is more functional; the second is a more theoretical stack. CLICK ON EACH IMAGE TO ENLARGE!
Security and the Network Fabric:
Security is neither fully “moving into” nor “remaining outside of” the network fabric; rather, it is being restructured across distinct functional planes, including identity, policy, enforcement, and detection.
Historically, network-centric security relied on in-path inspection mechanisms (e.g., firewalls, intrusion prevention systems, and proxies). This model proved difficult to scale due to encryption, cloud decentralization, and traffic patterns that bypass centralized inspection points.
In contemporary architectures, the network fabric is evolving into a high-performance enforcement plane. Policy definition and decision-making are increasingly centralized in identity and control-plane systems, while enforcement is distributed across the network and applied at line rate to identity-associated flows.
This separation of concerns improves scalability and composability. Identity-centric policy models define “who can do what,” while the network enforces those decisions efficiently and locally. The result is a more adaptable and performant security architecture.
However, the effectiveness of this approach depends on architectural discipline. Designs that treat the fabric as one component within a broader, identity-driven security framework tend to reduce complexity. Conversely, attempts to re-centralize security entirely within the network risk recreating earlier limitations in a more complex form.
AI’s Impact on Telecommunications Networks:
Artificial intelligence (AI) is influencing telecom network architectures along two orthogonal dimensions:
1.] AI introduces a new class of workloads that impose stringent and atypical requirements on network infrastructure.
AI workloads fundamentally challenge legacy network design assumptions. Traditional enterprise networks were optimized for north–south traffic patterns, human-driven interactions, and best-effort delivery models. In contrast, AI workloads generate predominantly east–west traffic, operate at machine timescales, and exhibit low tolerance for latency, jitter, and packet loss. Simultaneously, AI-enabled control and management planes enable higher degrees of automation and operational efficiency, particularly in campus and branch environments where autonomous operations are beginning to reduce manual intervention.
2.] AI is increasingly being embedded within the network itself, enhancing operations, optimization, fault diagnosis/recovery and security functions. The interaction between these roles is driving many of the architectural shifts observed today. Today, wide-area networks (WANs) must interconnect AI-intensive data center environments with distributed enterprise domains, effectively bridging heterogeneous traffic models and service requirements.
AI-Driven Changes in Traffic and Risk:
AI is reshaping both the structure of network traffic and its associated risk profile. From a traffic perspective, flows are becoming increasingly east–west, bursty, and machine-generated, with reduced visibility due to encryption and abstraction layers. From a security standpoint, AI introduces new classes of actors (e.g., non-human identities and autonomous agents), as well as new attack vectors, including adversarial AI and data exfiltration via model interactions.
These shifts are tightly coupled. The same properties that define AI-driven traffic—distribution, dynamism, and opacity—also complicate detection and enforcement. As a result, security architectures are evolving toward:
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Identity-centric models that extend zero-trust principles to non-human entities.
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Data loss prevention mechanisms adapted to AI-generated and AI-consumed data flows.
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Fine-grained segmentation within network fabrics, subject to latency constraints.
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Increased reliance on AI-driven detection and response systems to counter AI-enabled threats.
Importantly, these dynamics vary across network domains (LAN, WAN, and data center/cloud), requiring domain-specific adaptations while maintaining consistent policy frameworks.
Alignment with “Why Batch Pipelines Break AI Agents: The Case For Streaming-First Network Operations:”
The key points made in this article are highly consistent with the above referenced IEEE Techblog post written by Shazia Hasnie, Ph.D. Both articles treat AI as an architectural forcing function: Shazia’s article focuses on the data/telemetry layer, while this post extends the same logic to the broader enterprise network stack. The core claim in both pieces is that legacy architectures were built for human-operated, latency-tolerant workflows, not autonomous AI systems. In the Shazia’s article, batch pipelines fail because they deliver stale, incomplete, and inconsistent context to AI agents. Here, the same mismatch appears at the network level, where legacy enterprise designs were optimized for north–south traffic, perimeter trust, and static operational assumptions. Both arguments are fundamentally about architectural mismatch rather than isolated product shortcomings.
A particularly strong point of overlap is the emphasis on real-time context. Shazia’s article argues that AI agents require continuous data freshness and an ordered event stream to function safely, while this piece frames AI networking as a shift toward machine-timescale traffic, streaming telemetry, and identity-aware enforcement. In both cases, the network is no longer just a transport layer; it becomes part of the control loop that determines whether AI decisions are accurate and timely.
The failure models are also similar. Shazia identifies five failure modes of batch-to-agent mismatch: stale data, memory gaps, delete blindness, schema fragility, and coordination failure. While not using that taxonomy explicitly, we share the same underlying diagnosis by arguing that complexity, fragmentation, and legacy operational models are now the primary sources of risk. Our discussion of east–west traffic, non-human identities, zero trust, and observability mirrors Shazia’s broader point that autonomous systems fail when their surrounding infrastructure cannot preserve state, sequence, and policy consistency.
These two articles work well together because they address different layers of the same transition. The first article is mainly about the data plane of AI operations—how telemetry, event streams, and agent inputs must move from batch to streaming to avoid operational failure. This article is about the network and security architecture around that data plane—how the enterprise stack, LAN, WAN, and fabric must evolve to support AI-native workloads and enforcement. Hence, the reader can consider the two articles companion pieces.
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About the Author:
Shashi Kiran has nearly 30 years of experience in network, security and cloud technologies, primarily as an operator and executive in public and private B2B companies, where he has held global product management and marketing positions. He’s adopting a protopian view of AI, while being both fascinated and frightened by it at the same time.
Shashi is currently the CMO at Nile, whose network architecture aligns with what AI-era networks require: identity-centric control, embedded security, and autonomous operations. He previously held executive roles at Cisco, Check Point Software, Broadcom and other venture backed startups, and is based in San Jose, CA. He can be reached at http://www.linkedin.com/in/
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References:
Why Batch Pipelines Break AI Agents: The Case For Streaming-First Network Operations
Nile launches a Generative AI engine (NXI) to proactively detect and resolve enterprise network issues
Fiber Optic Networks & Subsea Cable Systems as the foundation for AI and Cloud services
Dell’Oro: Bright Future for Campus Network As A Service (NaaS) and Public Cloud Managed LAN
Cisco Plus: Network as a Service includes computing and storage too
https://nilesecure.com/press-releases/networking-and-security-in-higher-ed
https://nilesecure.com/press-releases/nile-powers-black-hat-mea-2025-with-zero-reported-incidents
Will 2026 be the “Year of the AI Ontology” for telecoms?
Overview:
For the telecommunications industry, many pundits say 2026 will be the year of “AI Ontology [1.],” primarily because a standardized knowledge plane is now seen as the “ultimate driver” for reaching higher levels of network autonomy. Industry experts from companies like Telstra and Amdocs emphasize that for agentic AI to move from isolated pilots to enterprise-scale operations, it requires a structured, explainable, and typed world model—an ontology—to unify data across fragmented systems.
Note 1. An ontology in AI is a formal, machine-readable framework that defines the concepts, properties, and relationships within a specific domain to enable knowledge sharing, reasoning, and semantic understanding. It structures data into a network of “things” (classes) rather than just files, acting as a “Rosetta stone” that allows AI systems to understand context, infer conclusions, and act on data.
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Several network providers are adopting a “standardized, ontology-driven knowledge plane” to enable agentic AI to operate across traditionally siloed network systems. This shift in 2026, is driven by the need for Level 4 and 5 network autonomy, where agents require a common language to reason about network states and business intents.
1. Mark Sanders, Telstra’s chief architect, talked about the emergence of a structured, explainable knowledge plane that removes silo barriers between agents, freeing them up to become the workhorses of network automation. “We think for the autonomous network to reach level four or five is going to require a standardized, ontology-driven approach on the knowledge plane,” said Sanders at a recent Ericsson conference, touting this approach as the ultimate driver in next-level autonomous networks.
2. For BT, agentic AI is already yielding tangible results in IT service desks, especially as organizations shift from assistance to execution, according to Girish Mahajan, senior leader for mobile AI data/automation. In particular, AI agents have reduced trouble ticket resolution times. “It has reduced the time of the manual effort, and it has also increased efficiency of the service desk,” he said. However, same autonomy that drives value also introduces unpredictability.
“The outcome of agentic AI is something unpredictable because it’s continuously adapting during execution,” he said, adding a call for better design principles. “We need reflection-based architecture, and we need better AI/human collaboration. AI agents should learn from their actions and should work along with humans in their day-to-day.”
3. For Vodafone, work has revolved around lighthouse projects: small-scale efforts to demonstrate the value of a larger business use case.
“It’s quite a mundane use case around energy cost recovery. So obviously, energy is a huge operational expense for our industry,” said Simon Norton, digital/OSS engineering director, Vodafone Group. “It’s very complex, especially when you’re working in that multi-market environment, to manually compare line by line with energy bills against your own data sets.”
Vodafone’s AI agents, therefore, have been automatically ingesting bills and comparing them to identify any tariff anomalies.
“It’s mundane but actually super valuable,” said Norton, who stressed operators should find a project with a clear value proposition and get it out into production quickly. “You build the credibility, you start to get the funding into the system, and it buys you the time to work on that longer-term strategy.”
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- From Assistant to Doer: AI is evolving from a “helper” that provides insights to a “doer” that autonomously observes, decides, and executes actions within governed boundaries.
- Multi-Agent Orchestration: 2026 will see the rise of coordinated multi-agent ecosystems. These systems require an ontology to ensure that a “planner agent” can accurately break down goals for specialized “worker agents” without semantic confusion.
- Intent-Based Orchestration: To ensure network stability, telcos are adopting intent-based orchestration layers. These layers use ontologies to provide the deterministic, model-driven framework necessary to ground agent actions in real-world business intent.
- Network Autonomy: CSPs are aiming for TM Forum Level 3 or 4 autonomy by late 2026, using agents to turn intent into outcomes in live networks.
- Operational Leverage: Rather than massive headcount cuts, agentic AI is providing “operational leverage,” allowing teams to manage growing network complexity with the same workforce.
- Measurable ROI: Investments are focusing on high-impact areas like autonomous incident handling (30-40% cost reduction) and predictive maintenance (up to 40% fewer outages).
- Structured Knowledge Plane: Operators are shifting toward a standardized, ontology-driven knowledge plane to remove silo barriers between agents. This allows multiple specialized agents to collaborate on “broader, bigger outcomes” like root cause analysis across billing, CRM, and network systems.
- Enabling Agentic Autonomy: While 2025 focused on “agentic AI” as a buzzword, 2026 is about the foundational infrastructure—specifically graph-based data systems and digital twins—that gives agents the “executable semantics” they need to plan and act safely.
- Unified Truth for Agents: Without a central ontology, horizontal AI platforms often suffer from “agent drift,” where different agents interpret the same business logic (e.g., “unlimited plan”) differently, leading to billing and provisioning errors.
Ericsson’s View:
Hassan Iftikhar, Ericsson’s head of product domain data & analytics, called for better hyperscaler collaboration on scale, foundational cloud, and AI capabilities.
“The AI tooling, the security framework, we use those to industrialize and put agents into production… It’s pretty much an ecosystem that works together,” he said. At the panel, the data head revealed the vendor’s role in the agentic ecosystem through the use case of one operator needing help with catalog management, as well as scarce developer skills.
“They wanted to take the pain out of product configuration. So we designed a multi-agentic system where it basically helps product managers and marketers to configure and publish new instances through an actual language. So very complex catalog engineering, which can take weeks, is reduced to hours where you can search for reuse and launch.”
Iftikhar also revealed an OSS tool to help one operator’s engineers to diagnose and resolve issues within their operational instances – resulting in an agent that was seemingly too autonomous for the client.
“We put this use case together, basically taking an intent from an operations engineer, such as data diagnostics, and into it, we built the ability to take remediation actions automatically. What we sort of decided from that was a bit of a step too far to just throw that to an operations department for it to autonomously take steps. So we actually had to go in and build guardrails to limit that capability to a human oversight capability.”
“I think what we learned is that we have to sort of build that confidence in the team step by step before we can actually go to fully autonomous operation. Our learning from adjusting that use case was to be practical and adapt very quickly to what the business really needs.”
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References:
https://www.sdxcentral.com/analysis/has-telco-already-faced-the-year-of-ai-agents/
The Financial Trap of Autonomous Networks: Scaling Agentic AI in the Telecom Core
Telecom operators investing in Agentic AI while Self Organizing Network AI market set for rapid growth
Nokia to showcase agentic AI network slicing; Ericsson partners with Ookla to measure 5G network slicing performance
T-Mobile US announces new broadband wireless and fiber targets, 5G-A with agentic AI and live voice call translation
Ericsson integrates Agentic AI into its NetCloud platform for self healing and autonomous 5G private networks
Agentic AI and the Future of Communications for Autonomous Vehicles (V2X)
AWS to deploy AI inference chips from Cerebras in its data centers; Anapurna Labs/Amazon in-house AI silicon products
Analysis and Impact of Blockbuster FCC ban on foreign made WiFi routers
On March 23rd, the Federal Communications Commission (FCC) updated its Covered List to prohibit the sale of foreign made consumer-grade (WiFi) routers to be sold in the U.S. The FCC’s Covered List is a list of communications equipment and services that are deemed to pose an unacceptable risk to the national security of the U.S. or the safety and security of U.S. persons. This FCC decision follows a determination by an Executive Branch interagency body, which concluded those devices pose unacceptable risks to U.S. national security and the safety of its citizens. . The new FCC restriction applies strictly to new foreign made router models, meaning retailers can continue marketing previously approved units and consumers can operate their existing equipment without interruption.
Impact:
TP-Link, Netgear, and Asus are currently among the top-selling Wi-Fi router brands in the U.S. consumer market. Estimates for early 2026 indicate that TP-Link alone holds approximately 35% of the U.S. consumer router market share, while Netgear and Asus collectively account for another 25%. The TP-Link Archer AXE75 is frequently rated the best router for most users due to its Wi-Fi 6E speed and reasonable price.
AXE5400 Tri-Band Gigabit Wi-Fi 6E Router
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Linksys and Ubiquiti are American-based companies, but their hardware is produced by contract manufacturers overseas in locations like China, Vietnam, and Taiwan. Similarly, Amazon eero and Google Nest mesh routers are not made in the U.S.
–>Hence, these companies ability to sell new WiFi router models in the U.S. is now facing strict regulatory hurdles.
Quotes:
FCC Chairman Brendan Carr said: “I welcome this Executive Branch national security determination, and I am pleased that the FCC has now added foreign-produced routers, which were found to pose an unacceptable national security risk, to the FCC’s Covered List. “Following President Trump’s leadership, the FCC will continue to do our part in making sure that US cyberspace, critical infrastructure, and supply chains are safe and secure.”
Bogdan Botezatu, director of Threat Research at cybersecurity firm Bitdefender, says this ban is a step to harden the cybersecurity readiness of U.S. households, given ongoing geopolitical tensions. “Consumer routers sit at the edge of every home network, which makes them an attractive target and a strategic risk if compromised at scale,” he says. Asked whether he thinks the risk is real, Botezatu says the risk is real, though there’s no easy way to prove intent. “[Internet of Things] devices, including routers, are a weak point across the internet.”
“Virtually all (WiFi) routers are made outside the United States, including those produced by US-based companies like TP-Link, which manufactures its products in Vietnam,” a spokesperson from TP-Link tells WIRED. “It appears that the entire router industry will be impacted by the FCC’s announcement concerning new devices not previously authorized by the FCC.”
- Reduced Product Availability: New, high-performance routers manufactured outside the U.S. will not receive the necessary approval to be imported or sold, restricting future consumer choices.
- Higher Costs: The, “This ruling has the potential to significantly disrupt the U.S. consumer router market,” according to, likely resulting in increased prices for consumers as companies grapple with new regulatory requirements.
- Shift in Manufacturing: Router manufacturers, including those targeting the U.S. market, will likely need to shift production to the U.S. to satisfy security concerns and bypass the ban, says PC Magazine.
- Security Focus: The ban targets vulnerabilities in foreign hardware and firmware.
- No Impact on Existing Devices: Consumers can continue to use routers they currently own
References:
https://www.wired.com/story/us-government-foreign-made-router-ban-explained/
U.S. Weighs Ban on Chinese made TP-Link router and China Telecom
China backed Volt Typhoon has “pre-positioned” malware to disrupt U.S. critical infrastructure networks “on a scale greater than ever before”
WSJ: T-Mobile hacked by cyber-espionage group linked to Chinese Intelligence agency
Trump and FCC crack down on China telecoms; supply chain security at risk
Semtech LoRa® PHY technology enables Amazon Sidewalk to expand while supporting fixed and mobile IoT endpoints
Introduction:
Semtech Corporation, a leading provider of high-performance semiconductor, Internet of Things (IoT) systems and cloud connectivity service solutions, is the creator and primary owner of the intellectual property (IP) for LoRa® technology, providing the Physical layer chips (PHY transceivers) used in LoRaWAN – the very popular Low Power Wide Area Network (LPWAN) for IoT endpoints.
The Camarillo, CA based company last week announced that LoRa® technology will continue to serve as the core radio modulation for Amazon Sidewalk across all markets in this year’s Sidewalk international expansion. Sidewalk’s global expansion officially begins in Canada and Mexico with further expansion to other international regions is scheduled for later in 2026. The network is projected to expand to over 30 new countries by year’s end.
Amazon Sidewalk is increasingly viewed as a commercial success in terms of infrastructure deployment and technical capability, transitioning from a niche smart home feature to a broad, LoRa-based Low Power Wide Area Network (LPWAN). While it faced initial skepticism regarding privacy and adoption, the network now boasts massive, passive coverage of over 95% of the U.S. population and is undergoing rapid international expansion.
Architectural role of LoRa in Sidewalk:
LoRa is the de facto wireless platform of LPWANs for IoT. Semtech’s LoRa chipsets connect sensors to the Cloud and enable real-time communication of data and analytics that can be utilized to enhance efficiency and productivity. LoRa devices enable smart IoT applications that solve some of the biggest challenges facing our planet: energy management, natural resource reduction, pollution control, and infrastructure efficiency.
Amazon Sidewalk aggregates spectrum in unlicensed bands and combines multiple physical layers, with Semtech’s LoRa modulation providing the long‑range, low‑power tier for neighborhood‑scale coverage beyond home Wi‑Fi and short‑range Personal Area Networks (PANs). By using ONLY LoRa as the core wide‑area PHY, Sidewalk evolves from a home‑centric LAN into a geographically distributed WAN that can support both fixed and mobile IoT endpoints across dense residential environments.
Network scale and coverage:
Sidewalk already covers roughly 95% of the U.S. population, making it one of the largest license‑free, consumer‑facing LPWA deployments, and the 2026 roadmap extends the footprint into Canada and Mexico first, followed by additional international markets later in the year. This expansion effectively turns Sidewalk into a multi‑continent overlay network, leveraging existing consumer premises equipment and LoRa‑enabled endpoints to provide persistent connectivity without requiring dedicated operator‑grade RAN build‑outs.
Technology differentiation vs other LPWAN options:
NB-IoT (included in ITU-R M.2150 IMT 2020 RIT/SRIT standard) holds the largest LPWAN share at roughly 54%–58% of total LPWAN connections, due to massive adoption in China which accounts for approximately 84% of all global NB-IoT connections. Outside of China, LoRaWAN is the clear market leader with a 41% share of connections. As of late 2025, there are over 125 million LoRaWAN end devices deployed globally, growing at a 25% annual rate. It is the preferred choice for private IoT networks, specifically in smart buildings, agriculture, and industrial asset tracking.
LoRa’s combination of long range, ultra‑low power operation, and mature ecosystem (silicon, gateways, and cloud stacks) gives Sidewalk a differentiated profile relative to alternatives such as narrowband cellular IoT and other unlicensed LPWAN modulation methods. For Amazon, anchoring Sidewalk on LoRa reduces RF and protocol fragmentation on the end‑device side while preserving flexibility to layer higher‑level Sidewalk services and security on top of the underlying LoRa/LoRaWAN protocol stack.
Market and ecosystem context:
Amazon Sidewalk now sits alongside large industrial and enterprise LoRaWAN networks, reinforcing LoRa’s position as the leading low‑power wide‑area connectivity technology in unlicensed spectrum. The LoRaWAN IoT connectivity market is forecast to grow from about 10.7 billion USD in 2025 to 44.8 billion USD by 2030 (33.1% CAGR), while LoRaWAN deployments have surpassed 125 million devices globally with a 25% CAGR, signaling a robust runway for Sidewalk‑class Massive IoT use cases.
Implications for device and service design:
For device OEMs and service providers, Amazon’s decision effectively de‑risks LoRa as a long‑term connectivity bet for consumer and prosumer IoT, given Sidewalk’s trajectory to tens of millions of active devices worldwide. Vendors integrating LoRa‑based designs can now target both traditional LoRaWAN operator networks and the Sidewalk ecosystem, enabling common hardware platforms to support smart home, safety, environmental monitoring, and asset‑tracking applications at neighborhood and city scale.
LoRa Enables Sidewalk’s Technical Evolution:
Chirp spread spectrum (CSS) modulation in LoRa technology provides the technical foundation enabling Amazon Sidewalk’s new capabilities:
- Enhanced Network Density: LoRa multi-spreading factor capability optimizes longer range and shorter time-on-air, supporting higher device concentrations in urban environments while maintaining reliable connectivity.
- Location-Based Services: Unique location accuracy service that combines the power of Wi-Fi, Bluetooth Low Energy (BLE) and GPS enables a new class of location aware devices that don’t need expensive cellular solutions for asset tracking applications.
- Hub-Less Deployments: Utilized for both out-of-band-diagnostics as well as signaling radio for battery-powered cameras, LoRa lowers the need for hubs/repeaters, reducing infrastructure complexity for consumers while extending effective coverage areas.
Proven Heritage of LoRa in Massive IoT Networks:
Semtech’s LoRa technology has been deployed by more than 170 major mobile network operators globally, with over 500 million connected devices across smart cities, utilities, logistics, unmanned aircraft systems, and industrial applications. This proven deployment heritage provides the technical foundation and ecosystem maturity required for Amazon Sidewalk’s global expansion.
The technology’s long-range capability, extending connectivity up to several kilometers from Sidewalk bridge devices, combined with its ability to penetrate buildings and operate in dense urban environments makes it uniquely suited for neighborhood-scale networks. LoRa provides free, long-range connectivity that consumers can rely on for years of battery-powered operation.
Building on CES 2026 Momentum:
Ring showcased its expanded product portfolio using LoRa at CES 2026, introducing comprehensive sensor families for security, safety and home automation. These products join the growing network of devices powered on Sidewalk, including water leak and freeze detection sensors, wearable devices and environmental monitoring solutions, all leveraging the connectivity advantages of LoRa.
The Sidewalk network’s architecture—combining LoRa for long-range communication with Bluetooth Low Energy for device setup—creates a robust, resilient IoT infrastructure that can scale to support millions of devices while maintaining the ultra-low power consumption critical for battery-operated sensors and cameras.
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About Semtech:
Semtech Corporation (Nasdaq: SMTC) is a leading provider of high-performance semiconductor, IoT systems and cloud connectivity service solutions dedicated to delivering high-quality technology solutions that enable a smarter, more connected and sustainable planet. Our global teams are committed to empowering solution architects and application developers to develop breakthrough products for the infrastructure, industrial and consumer markets.
Amazon’s Sidewalk Low Power WAN is a dud, despite four announced partnerships
From LPWAN to Hybrid Networks: Satellite and NTN as Enablers of Enterprise IoT – Part 2
Enterprise IoT and the Transformation of UK Telecom Business Models – Part 1
Empowering Low-Power Wide-Area Networks to Meet the IoT Challenge
NTT’s IoT Services for Sustainability supported by new LoRaWAN low power network
IoT for Smart Cities: LoRa with Semtech Silicon as a leading LPWAN
LoRa Alliance and OMS Group integrate smart metering standards which may result in massive scale for IoT
LoRaWAN gains momentum: NEC LoRaWAN server + New Zealand nationwide network
LoRaWAN and Sigfox lead LPWANs; Interoperability via Compression
New LoRaWAN™ network for IoT in Saudi Arabia from Actility and machinestalk
LPWAN to Application standardization within the IETF
Taara Lightbridge Pro: an ultra reliable wireless optical communications system for 5G mobile backhaul
Google moonshot factory X graduate Taara [1.] is launching Lightbridge Pro, a wireless optical communications system designed to deliver 99.999% (“five nines”) carrier-grade uptime for 20 Gbps backhaul, addressing weather-related reliability issues in Free Space Optical Communication (FSOC).
Lightbridge Pro is designed for seamless integration into carrier-grade networks, including mobile backhaul and mission-critical infrastructure. By integrating intelligent optical switching directly into the hardware, it automatically reroutes traffic to fiber or RF backups during, for example, heavy fog or rain.
Note 1. Tara says that for the last eight years, they have been developing novel technology that uses beams of light to deliver high-speed, secure connectivity where fiber and wireless can’t – bringing abundant access to everyone, everywhere.
“As demand for data soars, existing connectivity solutions are reaching their limits. What if we could harness the power of light to deliver a better, faster, more efficient connection, without the need for cables?” Mahesh Krishnaswamy, Founder and CEO.
Key Features and Impact:
Carrier-Grade Reliability: Lightbridge Pro is purpose-built for high-availability requirements of 5G mobile backhaul,, and city-wide network providers.
Intelligent Switching: The system ensures seamless, near-instantaneous, switching between optical and backup connections (like RF) to maintain continuity.
Performance: It delivers up to 20 Gbps full-duplex capacity, bridging gaps where fiber installation is too costly or difficult.
Global Application: Already deployed in over 20 countries, the technology is used in dense urban, rural, and disaster recovery scenarios.
Operational Efficiency: The system includes comprehensive Fault, Configuration, Accounting, Performance, and Security (FCAPS) management, suitable for integration with existing Operations and Business Support Systems (OSS/BSS).
Deployment and Use Cases: Tara’s platform is aimed at large-scale network operators and mission-critical communications, particularly in dense urban environments or rough terrain where laying fiber is not economically viable.
Current Partners: Taara Lightbridge is already deployed in more than 20 countries, from dense urban cores to remote terrain to disaster recovery scenarios. Carriers already using Taara’s technology include Airtel, T-Mobile, SoftBank, Digicel, and Liquid Intelligent Technologies. T-Mobile previously deployed Taara units for high-capacity backhaul at Coachella and the Albuquerque Balloon Festival.
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Taara is showcasing these new solutions at Mobile World Congress (MWC) Barcelona 2026 where the start-up will also be announcing a new photonics-based wireless optical system designed for even greater density and scalability.
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Editor’s Analysis:
Taara’s Lightbridge Pro looks like a serious, carrier-minded evolution of free-space optics (FSO) for 5G backhaul, but its real value will hinge on how well the “five nines” claim holds up under diverse atmospheric and operational conditions in the field.
Risks and open questions:
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SLA realism: “Five nines” across mixed optical/RF paths is a strong claim; operators will want multi-year availability data by climate region, plus clear modeling of residual outage during extreme events where both optical and RF paths can degrade.
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Operational complexity: Even with integrated switching and FCAPS, adding a new transport technology introduces planning, monitoring, and skillset overhead versus staying on homogeneous fiber/microwave.
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Regulatory and spectrum: Where RF is the backup, spectrum licensing, interference management, and coordination with existing microwave/E‑band layers will affect total cost and deployment speed, and those aspects are not detailed in the product material.
Overall assessment:
For 5G mobile backhaul, Lightbridge Pro is best viewed as a targeted tool for high-value, hard-to-fiber routes, and for rapid-capacity or temporary deployments, rather than a universal replacement for fiber or microwave. If Taara’s integrated protection switching performs as advertised at scale, it meaningfully advances FSO from “interesting niche” to a credible part of a multi-layer transport strategy for carriers and city-scale operators.
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References:
https://www.taaraconnect.com/post/introducing-lightbridge-pro#
https://www.taaraconnect.com/product/lightbridge-pro
https://www.taaraconnect.com/about
Google X spin-out Taara and Digicomm International partner to offer high speed wireless communications
Dell’Oro: Mobile Core Networks +15% in 2025; Ookla: Global Reality Check on 5G SA and 5G Advanced in 2026
Palo Alto Networks and Google Cloud expand partnership with advanced AI infrastructure and cloud security
Google’s Project Suncatcher: a moonshot project to power ML/AI compute from space
Google Cloud announces TalayLink subsea cable and new connectivity hubs in Thailand and Australia
India 5G subscribers top 400 Million with rapid adoption continuing without 5Gi
With over 400 million 5G subscribers, India now ranks #2 globally (China is #1 [1.]). What’s even more remarkable is the speed of adoption after 5G spectrum auctions were repeatedly delayed. Jyotiraditya Scindia, India’s union minister for communications and development of the North Eastern Region, said the country is “setting new global benchmarks in scale, speed and digital transformation.”
According to figures cited by the minister, the country’s 5G subscriber base now exceeds that of other major markets, including the United States with around 350 million users, the European Union with 200 million, and Japan with 190 million. China remains the global leader, with more than 1.2 billion 5G connections.
Note 1. China has over 1.2 billion 5G subscribers as of late 2025, representing over 60% of all mobile connections in the country, driven by massive infrastructure rollout and strong adoption across major operators like China Mobile, China Telecom, and China Unicom, making it the global leader in 5G penetration.
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Bharti Airtel and Reliance Jio Infocomm spearheaded the launch, becoming the first carriers to operationalize 5G networks and achieving significant subscriber acquisition, with each surpassing the 50 million user milestone within their initial year of service [1]. Vodafone Idea subsequently entered the 5G market with launches in specific cities during 2025.
State-owned telecom provider BSNL is projected to launch 5G services within the current year [1]. This deployment is slated to exclusively utilize India’s indigenously developed telecom technology stack, a collaborative effort involving the Centre for Development of Telematics (C-DOT), Tejas Networks, and Tata Consultancy Services (TCS).
As of the close of 2025, the national 5G infrastructure comprised 518,854 operational base stations, marking a substantial increase from approximately 464,990 recorded at the start of the year [1]. The Department of Telecommunications (DoT) reported the deployment of 4,112 new 5G base transceiver stations (BTS) in December 2025 alone, contributing to the year-end cumulative total
- Infrastructure Growth: Rapid BTS deployment, exceeding 4,100 new installations in December 2025 alone, demonstrates intense network densification, with coverage now reaching most districts.
- Vodafone Idea’s Entry: Vi, after initial delays, commenced its phased 5G service introduction in select cities during 2025.
- BSNL’s Indigenous Strategy: The state-owned operator is slated to launch 5G using India’s homegrown stack (C-DOT, Tejas, TCS), showcasing self-reliance in telecom technology.
- Market Dynamics: The rapid expansion aims to unlock enterprise and consumer use cases, positioning India as a significant global 5G player, despite ongoing discussions about monetization and infrastructure investment.
Technical & Deployment Highlights:
- Architecture: A mix of 5G SA (Jio) and 5G NSA (Airtel) is prevalent, with SA offering lower latency and true 5G capabilities.
- Spectrum: Operators utilize various bands, including sub-6 GHz (3.3 GHz, 26 GHz) for broad coverage and capacity.
- Deployment Pace: Driven by ministerial targets, operators installed BTS at an accelerated pace, focusing initially on high-revenue urban centers.
This author deeply regrets that the Telecommunications Standards Development Society India (TSDSI)’s 5Gi RIT specification, included as part of the ITU-R M.2150 IMT 2020 RIT/SRIT standard, was not implemented in India. On January 25, 2022, TSDSI told the Telecommunication Engineering Center (TEC) under the DoT not to proceed with the adoption of 5Gi as a national 5G standard. TSDSI added that it “does not intend to further update 5Gi specifications.”
References:
OpenSignal: real world 5G deployment in India, market status & what happened to 5Gi?
Nokia Executive: India to Have Fastest 5G Rollout in the World; 5Gi/LMLC Missing!
LightCounting & TÉRAL RESEARCH: India RAN market is buoyant with 5G rolling out at a fast pace
Nokia’s Bell Labs to use adapted 4G and 5G access technologies for Indian space missions
Reliance Jio in talks with Tesla to deploy private 5G network for the latter’s manufacturing plant in India
Communications Minister: India to be major telecom technology exporter in 3 years with its 4G/5G technology stack
India to set up 100 labs for developing 5G apps, business models and use-cases
Adani Group to launch private 5G network services in India this year
Adani Group planning to enter India’s long delayed 5G spectrum auction
At long last: India enters 5G era as carriers spend $ billions but don’t support 5Gi
5G in India to be launched in 2023; air traffic safety a concern; 5G for agricultural monitoring to be very useful
Bharti Airtel to launch 5G services in India this August; Reliance Jio to follow
5G Made in India: Bharti Airtel and Tata Group partner to implement 5G in India
India government wants “home-grown” 5G; BSNL and MTNL will emerge as healthy
5G in India dependent on fiber backhaul investments
Hindu businessline: Indian telcos deployed 33,000 5G base stations in 2022
Arm Holdings unveils “Physical AI” business unit to focus on robotics and automotive
- Cloud and AI: Focused on data center and AI infrastructure solutions.
- Edge: Encompassing mobile devices, personal computing, and related technologies.
- Physical AI: Integrating its automotive business with robotics initiatives.
- Market Opportunity: Acknowledged the significant growth potential in robotics, from industrial automation to humanoid robots, driven by AI advancements.
- Synergy with Automotive: Combined robotics and automotive within the unit due to shared technical needs, such as power efficiency, safety, and sensor technology.
- Strategic Reorganization: Positioned Physical AI as a third core business line, alongside Cloud & AI and Edge (mobile/PC), to better focus resources and expertise.
- Customer Demand: Responding to existing customers (like automakers and robotics firms such as Boston Dynamics) who are integrating more AI into physical devices.
- Enhancing Real-World Impact: Aims to deliver solutions that fundamentally improve labor, productivity, and potentially GDP, moving AI from data centers to physical interactions
2026 Consumer Electronics Show Preview: smartphones, AI in devices/appliances and advanced semiconductor chips
Marvell shrinking share of the RAN custom silicon market & acquisition of XConn Technologies for AI data center connectivity
Groq and Nvidia in non-exclusive AI Inference technology licensing agreement; top Groq execs joining Nvidia
The Internet of Things (IoT) explained along with ARM’s role in making it happen!
Sebastian Barros: Using telecom networks for weather sensing requires a strategic telco shift from connectivity providers to ecosystem orchestrators
Telecom networks for weather sensing can be facilitated by using existing microwave links and 4G/5G signals as virtual sensors, detecting changes in signal strength and timing caused by rain, humidity, and temperature, effectively turning vast infrastructure into a dense, real-time atmospheric monitoring system for improved forecasting and disaster alerts, notes Sebastian Barros on Substack. By analyzing signal attenuation, telecom networks create high-resolution weather maps, complementing traditional methods like radar. Yet very few network operators or vendors have attempted to use telecommunications infrastructure for dense atmospheric sensing. The data exists but is rarely activated, processed, or disclosed.
Global Navigation Satellite System (GNSS) [1.] based atmospheric estimation, rain attenuation on microwave links, and radio refractivity effects have been studied for more than 20 years. The physics is well understood and already embedded in network planning and synchronization systems. There are eight million radio sites span cities, roads, ports, factories, and borders. Every site has power, compute, backhaul, antennas, timing, and regulatory protection. Today, the network only provides connectivity. Integrated Sensing and Communication (ISAC) starts to change that. It repurposes radio waves for radar-like sensing, including presence detection, velocity, Doppler shift, and range.
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Note 1. Global Navigation Satellite System (GNSS) is the umbrella term for satellite constellations like the U.S.’s GPS, Russia’s GLONASS, the EU’s Galileo, and China’s BeiDou, which provide global positioning, navigation, and timing (PNT) services. GNSS receivers use signals from these orbiting satellites to calculate precise locations on Earth, offering increased accuracy and reliability compared to relying on a single system, enabling applications from smartphone navigation to autonomous vehicles and precision agriculture.
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- Receiver: Your end point receiving device (phone, car navigation, etc.) picks up signals from several of these satellites.
- Calculation: By measuring the time it takes for signals to arrive from at least four satellites, the receiver performs complex calculations (trilateration) to pinpoint your exact position.
Telecom networks can expose not just connectivity but structured awareness: object motion, crowd flow, anomalies, risks, and environmental state. In real time, across every continent. The infrastructure already exists. What’s missing is the architecture and the will to build the sensing layer on top of it. With dense, real-time sensing already in place, telecom can expose environmental intelligence through Open Gateway APIs, just as it exposes location or quality today. No new hardware. No new towers. Just activation, inference at the edge, and exposure.
- Data Siloing: The data produced by ISAC currently remains locked within the physical (PHY) and Media Access Control (MAC) layers of the network. It is primarily used for internal network optimization and is not exposed to external applications or platforms.
- Lack of Abstraction and APIs: There are no standard abstraction layers or Application Programming Interfaces (APIs) that would allow external systems (e.g., weather services, autonomous navigation systems, urban infrastructure management) to access and interpret the raw sensing data.
- Absence of Data Fusion Standards: There is no standard methodology to fuse the output from ISAC with data from other sensing modalities (e.g., vision, audio, thermal sensors). This prevents the creation of a comprehensive, multimodal sensing mesh.
- Missing Marketplace: The lack of standardized access and integration means there is no marketplace for this valuable data, which stifles innovation and collaboration across different industries that could benefit from real-world awareness information.
References:
https://sebastianbarros.substack.com/p/telecom-built-the-worlds-best-weather
https://sebastianbarros.substack.com/p/telco-network-as-a-sensor-is-a-huge
https://www.linkedin.com/feed/update/urn:li:activity:7413260481743769600/
https://www.euspa.europa.eu/eu-space-programme/galileo/what-gnss
2025 Year End Review: Integration of Telecom and ICT; What to Expect in 2026
Smart electromagnetic surfaces/RIS: an optimal low-cost design for integrated communications, sensing and powering
Deutsche Telekom: successful completion of the 6G-TakeOff project with “3D networks”
Smart electromagnetic surfaces/RIS: an optimal low-cost design for integrated communications, sensing and powering
Researchers at Xidian University in China have pioneered a smart electromagnetic surface that converts ambient electromagnetic waves into electrical power, marking a potential leap in stealth and wireless technologies. This meta-surface innovation merges advanced electromagnetic engineering with communication principles, enabling self-powered systems for demanding applications. The self-sustaining electronic system integrates wireless information transfer and energy harvesting and has the potential to upend the dynamics of electronic warfare.
The surface facilitates simultaneous energy harvesting and data transmission, drawing power from radar or environmental signals without traditional batteries. Xidian’s team highlights its role in “electromagnetic cooperative stealth,” where networked platforms collaboratively minimize radar cross-sections and sensor detectability. Prototypes demonstrate viability for real-time wave manipulation, building on metasurface designs that dynamically adjust phase and amplitude.
The researchers said this included investigating “electromagnetic cooperative stealth,” where multiple entities work together to reduce their visibility to radar and electromagnetic sensors. In electronic warfare, the technology flips the script on radar threats: stealth aircraft could harvest enemy beams for propulsion or comms, reducing logistical vulnerabilities. This cooperative approach extends to multi-asset formations, enhancing collective invisibility across spectra.
Early tests align with broader Reconfigurable Intelligent Surfaces (RIS), a two-dimensional reflecting surface. RIS advancements facilitate beam steering up to ±45° with low side lobes. According to a paper published in the IEEE Internet of Things magazine last year, RIS could also be used in anti-jamming technology, unmanned aerial vehicle communication and radio surveillance – all of which are difficult to do using older optimization tools.
Reconfigurable intelligent surfaces can also be configured to create intentional radio “dead zones” to mitigate interference and reduce the risk of eavesdropping, according to German electronics manufacturer Rohde & Schwarz. The European Space Agency has further highlighted RIS as a candidate technology for satellite-to-ground communications, where controllable reflection and redirection of signals could help route links around physical obstacles.
For telecommunications, the surface promises 6G breakthroughs like integrated sensing and powering satellites or base stations. China’s lead here could accelerate reconfigurable networks, improving coverage in non-line-of-sight scenarios. Ongoing refinements target complex interactions for higher precision. By including sensing, communication and power into one hardware platform, the device could allow for a range of advanced applications while reducing eavesdropping and interference.
“Ultimately, it is expected to have a broad impact on 6G communications, the Internet of Things, intelligent stealth and other related fields,” the team said in a paper published in the peer-reviewed journal National Science Review last month. Many scientists say that a key area for next-generation wireless communications will be the transmission channel.
Researchers from Fudan University, the University of Sydney and the Commonwealth Scientific and Industrial Research Organization note that, when combined with artificial intelligence (AI), this technology could significantly enhance the security of air-to-ground Internet of Things (IoT) links.
In their latest publication, the Xidian University team describes RIS as a “powerful solution” for future wireless networks, citing its low cost, high programmability and ease of deployment. However, for 6G systems, RIS must support both communication and sensing on a unified hardware platform by integrating data transmission and radar-like functionality to lower cost and optimize spectrum and hardware resource utilization.
Addressing this requirement will demand architectures that can jointly manipulate both scattered electromagnetic waves and actively radiated signals. The researchers propose that an electromagnetic all-in-one radiation–scattering RIS architecture could provide a viable path to meeting this dual-control challenge. “This achieves significant savings in physical space and cost while ensuring multifunctionality across diverse application scenarios,” the team said. The RIS system could also work in a receiver mode to harvest wireless energy to be used to power the meta-surface itself or charge other electronic devices, the paper added.
It could be used for line-of-sight wireless communication, where there is a direct, unobstructed path between a transmitter and receiver, as well as non-line-of-sight wireless communication, in which there is no direct visual link due to physical barriers like buildings.
The proposed RIS “stands out as the optimal low-cost design” for integrated communication and sensing. “In the future, this architecture could enable the development of environment-adaptive integrated sensing and communication systems, micro base stations and relay integrated systems, as well as self-powered sensing systems,” the team said.
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References:
https://ieeexplore.ieee.org/document/10907868
Nokia sees new types of 6G connected devices facilitated by a “3 layer technology stack”
Electromagnetic Signal & Information Theory (ESIT): From Fundamentals to Standardization-Part I.
Electromagnetic Signal and Information Theory (ESIT): From Fundamentals to Standardization-Part II.
IMT Vision – Framework and overall objectives of the future development of IMT for 2030 and beyond
ITU-R WP5D: Studies on technical feasibility of IMT in bands above 100 GHz
Summary of ITU-R Workshop on “IMT for 2030 and beyond” (aka “6G”)
Excerpts of ITU-R preliminary draft new Report: FUTURE TECHNOLOGY TRENDS OF TERRESTRIAL IMT SYSTEMS TOWARDS 2030 AND BEYOND
Juniper Research: Global 6G Connections to be 290M in 1st 2 years of service, but network interference problem looms large
