en.Wedoany.com Reported - Federated Wireless has launched a platform called Spectrum AI, which enhances the capacity of shared CBRS networks by directly addressing the physical radio frequency (RF) challenges.

Operators deploying the platform on hundreds of commercial Citizens Broadband Radio Service (CBRS) networks report capacity expansions of up to five times. These hardware-agnostic performance gains are achieved without additional spectrum acquisition, new base station construction, or supplementary physical infrastructure.

The software architecture brings physical AI into the real world, directing computing power toward three physical variables—propagation conditions, interference dynamics, and spectrum allocation—that determine the overall performance of wireless networks.

Currently, most AI tools in the telecommunications sector are primarily used to automate high-level workflows, operating strictly above the network layer. The system built by Federated Wireless operates specifically at the RF layer, the foundational level where engineers design, coordinate, and optimize wireless hardware. The platform's accuracy improves with each real-world deployment it processes.

Spectrum AI, as an automated spectrum management system, aims to deliver step-change gains in both spectrum efficiency and spectrum performance. Direct results widely measured in actual deployments provide operators with usable, high-quality spectrum.

Engineering teams operating real commercial networks can extract up to 50% more usable high-quality spectrum capacity through machine-driven coordination, achieving greater efficiency from existing spectrum allocations. Simulation speeds during the planning phase have been accelerated by 100 to 1000 times.

The platform offers propagation modeling accuracy exceeding 90%, with precision maintained within sub-0.5 decibels. RF engineers can use these metrics to plan networks that rely on precise coverage and interference predictions. In active, real-world shared spectrum environments, interference coordination accuracy can be improved by up to 20 decibels.

Operators pursuing equivalent coverage and capacity targets report a reduction in required sites of up to 50%. Reducing the total number of physical sites can lower the total deployment cost for each new infrastructure project by up to 40%. Improving the underlying economics of each construction project changes the commercial viability of large-scale CBRS and shared spectrum deployments.

Iyad Tarazi, CEO of Federated Wireless, stated: "Most AI in the wireless industry operates above the physical layer to automate workflows, manage operations, or optimize software. Spectrum AI works at the RF and propagation level, the physical foundation that actually determines coverage, interference, and capacity."

"Wireless networks contain far more potential capacity than operators have been able to access so far. The spectrum has always been there, but the computational tools to discover and use it at scale have not. Spectrum AI changes that. Operators can now treat spectrum coordination as a continuous optimization problem rather than a one-time engineering task, and every network the platform touches becomes smarter, more efficient, and more profitable over time."

Most AI systems designed for wireless network management primarily rely on simulation environments for training data. Federated Wireless abandoned the simulation-only approach, building its data architecture on production telemetry data derived from hundreds of real commercial networks.

The platform operates on five foundational capabilities. It leverages real-time spectrum coordination data continuously generated across the largest commercial shared spectrum coverage area in the United States, cross-referenced with over a decade of national geospatial intelligence from actual spectrum allocations, interference events, and propagation observations. This historical dataset provides a commercially relevant training foundation that simulation models cannot replicate.

Business intelligence and Coverage IQ data supplement this baseline by providing address-level coverage, service eligibility, and market intelligence. This insight directly links RF performance to user acquisition and revenue outcomes, grounding all analysis in first-principles RF modeling.

Deepak Das, Vice President of Solutions and Products at Federated Wireless, explained: "The accuracy provided by Spectrum AI comes from AI-native propagation models based on RF physics, continuously refined through measurements in real wireless environments."

"Each deployment feeds propagation data, interference observations, and deployment outcomes back into the platform, creating a compound intelligence advantage that benefits every operator on the system. For operators, this directly translates into more capacity from the spectrum they already have, fewer site builds, and better economics for every deployment."

The platform works in conjunction with the Adaptive Network Planner (ANP) to manage two parts of an operator's network lifecycle. Spectrum AI handles active hardware by improving networks already transmitting data over the air.

Engineering teams rely on ANP to plan new networks before committing capital expenditure. ANP applies the same underlying physical intelligence to network planning, evaluating coverage, capacity, and economics with physical precision.

The initial software version is specifically designed to provide spectrum conflict resolution and performance improvements for the CBRS and 6 GHz spectrum bands. The release version also includes enhanced antenna support capabilities.

Federated Wireless designed the system for medium-to-large operators deploying hardware in shared spectrum environments. This primary operator group includes Priority Access License users maintaining protected allocations and General Authorized Access users operating in dynamic CBRS networks. Enterprises managing networks constrained by existing protection requirements or high coordination needs can use the system to address physical interference while maximizing data throughput.

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