RAN Telemetry for AI: Data Quality, Timing and Feature Governance
Build a trustworthy RAN telemetry pipeline for AI with clear timestamps, quality checks, feature definitions and privacy-aware data handling.
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Special Coverage: 6G AI-RAN and Intelligent Radio Access Networks. Educational analysis. Performance depends on the complete workload and configuration; future 6G concepts are not guarantees of commercial capability.
Published articles
Cloud RAN and Distributed Compute in 6G Networks
How centralized, regional and edge compute placement changes performance, resilience and operating economics.
Read article →AI-Native RAN Architecture: Planning the Path Toward 6G
A practical guide to AI-native RAN architecture, control boundaries, model execution, failure handling and future 6G planning.
Read article →The 6G RAN Architecture: From Radio Sites to Distributed Intelligence
A system-level view of radio, compute, transport and control functions that will shape the next generation of radio access networks.
Read article →Inference Routing and Orchestration: Sending Each Request to the Right Model
How gateways and model routers select endpoints based on cost, latency and policy.
Read article →Cloud, Enterprise and Edge: Where AI Inference Runs
Examine placement choices from centralized cloud to enterprise and edge.
Read article →GPUs, Accelerators and Specialized Inference Silicon
Compare general-purpose GPUs and purpose-built inference processors.
Read article →KV Cache and Memory Bandwidth: Why Inference Is Not Just About FLOPS
How cached attention state and data movement affect language-model serving.
Read article →Latency, Throughput and Utilization: Measuring Inference Performance
A guide to the core metrics and trade-offs shaping production inference.
Read article →Prefill and Decode: The Two Phases Behind Every Generated Answer
Understand the distinct compute and memory demands of prompt processing and token generation.
Read article →What Is AI Inference? The Shift from Model Training to Real-World Use
A practical introduction to inference and the infrastructure required to serve trained models.
Read article →Coming soon
AI Beam Management: Evaluating Prediction Without Overstating Gains
Evaluate AI beam management using representative mobility, measurement overhead, recovery behavior and complete radio-service outcomes.
The Role of Radio Units in an AI-Native 6G RAN
Radio-unit capabilities, RF constraints, synchronization and the boundary between physical radio and software intelligence.
AI-Assisted Radio Scheduling: Protecting Deadlines and Fairness
Assess AI-assisted radio scheduling with realistic load, inference deadlines, service fairness, safeguards and reproducible baseline comparisons.
Distributed Units and Centralized Units: Functional Placement for 6G
A practical framework for deciding which RAN functions belong at the site, edge or regional cloud.
RAN Energy Optimization with AI: Measuring Savings Without Losing Service
Measure AI-driven RAN energy optimization with consistent boundaries, service-quality constraints, demand context and safe operational limits.
6G Fronthaul and Midhaul: Capacity, Timing and Interoperability
Transport design requirements for distributed radio architectures, including bandwidth, latency, synchronization and operations.
AI-and-RAN Infrastructure: Sharing Accelerators Without Breaking Radio Timing
Evaluate shared AI and RAN compute through resource isolation, admission control, timing budgets and realistic contention tests.
Massive MIMO, Beamforming and Advanced 6G Radio Design
How antenna arrays, beam management and propagation assumptions affect real-world 6G coverage and capacity.
AI-on-RAN Edge Inference: Measuring the Complete Application Path
Plan AI-on-RAN edge inference with end-to-end latency measurement, data dependencies, capacity limits and clear service ownership.
Spectrum Strategy for 6G Radio Access Networks
The engineering and commercial trade-offs across low-, mid-, high- and potential sub-terahertz spectrum.
O-RAN RIC and AI-RAN: Separating Control Interfaces from AI Claims
Understand how O-RAN control concepts relate to AI-RAN, including execution placement, application compatibility and safe control authority.
AI-Native Scheduling and Resource Allocation in the 6G RAN
How learning-assisted schedulers can coordinate radio resources while preserving deterministic service objectives.
RAN Model Lifecycle Management: Versioning, Deployment and Rollback
Manage AI models in the RAN with traceable artifacts, staged deployment, compatibility checks, monitoring and verified rollback.
Open RAN Interoperability and the 6G Migration Path
The practical role of open interfaces, multi-vendor integration, conformance testing and lifecycle accountability.
AI-RAN Security: Protecting Models, Telemetry and Control Authority
Review AI-RAN security across telemetry integrity, model artifacts, service identities, authorization and incident recovery.
Energy-Efficient 6G RAN: From Sleep Modes to Intelligent Power Control
How operators can reduce network energy use without sacrificing coverage, service continuity or resilience.
Digital Twins and Simulation for AI-RAN: Testing the Limits of a Model
Use AI-RAN digital twins and simulation responsibly with calibration, held-out validation, scenario coverage and explicit transfer limitations.
Integrated Sensing and Communications at the RAN Layer
How radio infrastructure may support sensing functions alongside communications, and what validation is required.
AI-RAN Benchmarking: From Model Scores to Reproducible Network Evidence
Design AI-RAN benchmarks with equivalent baselines, complete measurement boundaries, quality constraints and transparent reporting.
Edge Computing and Local Breakout in 6G Radio Networks
The relationship between radio access, edge platforms, application placement and latency-sensitive services.
AI-RAN Economics: Utilization, TCO and Edge Service Assumptions
Assess AI-RAN economics using service obligations, realistic shared capacity, integration costs and transparent commercial scenarios.
RAN Automation: From SON Features to Closed-Loop Operations
How telemetry, policy, automation and human oversight can evolve into measurable closed-loop network operations.
An AI-RAN Deployment Readiness Review for Future 6G Projects
Review AI-RAN deployment readiness across data, models, radio constraints, shared infrastructure, security and operational ownership.
Resilience Engineering for Distributed 6G Radio Access
Designing for site failure, transport disruption, power constraints, software faults and graceful service degradation.
Quantization, Batching and Caching: Practical Ways to Improve Inference Economics
Review common optimization techniques and their quality and latency trade-offs.
Planning a 6G RAN: A Deployment and Investment Readiness Framework
A structured method for evaluating architecture, spectrum, sites, transport, compute, operations and lifecycle cost.
The Network Matters: Inference Fabrics and Data Movement
Why interconnects, topology and data movement matter in distributed AI systems.
Power, Cooling and Data Center Design for AI Inference
Facility considerations that influence cost, density and scalability.
Operating Inference in Production: Observability, Reliability and Security
Monitoring, service objectives, resilience, access controls and governance.
The Economics of Inference: Cost per Token, Request and Outcome
Evaluate inference costs beyond hardware prices and token rates.
Agentic AI and the New Shape of Inference Demand
Why multi-step agent workflows create distinct request patterns and resource needs.
Planning an Inference Architecture: Questions to Ask Before You Buy
A decision framework for platforms, accelerators, cloud services and operations.
The Evolving AI Inference Stack: From Silicon to Service
How hardware, networking, software and operations combine to deliver AI services.
6G AI-RAN Explained: AI-for-RAN, AI-and-RAN and AI-on-RAN
Understand the three AI-RAN categories, their infrastructure implications and how to evaluate claims about future 6G radio access networks.
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