Power-Efficient Inference for Radio Access Networks
Strategies for reducing the energy cost of AI inference across accelerators, memory, cooling and network sites.
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Special Coverage: Research on inference workloads in radio access networks, including edge placement, accelerator selection, model optimization and operational measurement.
Published articles
AI-RAN Scheduling: Applying Machine Learning to Radio Resource Decisions
How AI models may support scheduling, traffic prediction and resource allocation in radio access networks.
Read article →Low-Latency AI Inference in RAN: Meeting Real-Time Network Constraints
How inference latency, jitter, scheduling and execution placement affect AI workloads supporting radio access networks.
Read article →GPU vs NPU for RAN Inference: Choosing the Right AI Accelerator
A practical comparison of GPUs, NPUs and other accelerators for inference workloads associated with radio networks.
Read article →Edge AI Inference for 6G: Processing Intelligence Close to Users
How edge AI inference can reduce data movement and support location-sensitive applications in future 6G networks.
Read article →What Is Inference RAN? Bringing AI Inference Into the Radio Network
An introduction to inference RAN and how AI models can support radio optimization, network operations and edge services.
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Distributed Inference Across RAN, Edge and Cloud
How AI inference workloads can be distributed between radio sites, regional edge facilities and centralized cloud infrastructure.
Model Compression for AI Inference at the Network Edge
How quantization, pruning, distillation and other model optimization methods can reduce inference resource requirements.
Real-Time Channel Estimation With AI in Radio Networks
How machine learning may assist channel estimation and radio signal processing, with attention to accuracy and implementation constraints.
Inference Orchestration: Managing AI Workloads Across Network Sites
How orchestration systems can deploy, scale, update and recover inference services across distributed telecom infrastructure.
Multi-Tenant AI Inference in Telecom Edge Infrastructure
Design considerations for sharing inference accelerators across operators, applications and enterprise workloads.
Observability for RAN Inference: Metrics, Traces and Model Quality
How observability can connect infrastructure health, inference performance, model quality and radio-service outcomes.
AI Model Lifecycle Management for Inference RAN
A practical guide to model training, validation, deployment, monitoring, rollback and retirement in radio network environments.
RIC, xApps and rApps: Where Inference Fits in Open RAN Automation
How inference services can integrate with RAN Intelligent Controllers, xApps, rApps and open network management frameworks.
The Economics of Inference RAN: Cost, Capacity and Business Value
How operators can evaluate the financial and operational trade-offs of deploying AI inference in radio access networks.
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