AI-RAN: Architecture Design and Performance Evaluation for 5G-Advanced
Dr. Mischa Dohler, Dr. Harish Viswanathan
Ericsson Research
Abstract
This paper presents the architecture design and comprehensive performance evaluation of AI-RAN for 5G-Advanced networks. We define three deployment models — centralized, distributed, and hybrid — and evaluate each across 15 KPIs including throughput, latency, energy efficiency, and operational complexity. Large-scale simulations with 500 base stations and 50,000 users show that the hybrid model achieves the best balance, improving throughput by 35% and reducing energy by 28% compared to traditional RAN.
AI Summary
- Defines three AI-RAN deployment models: centralized, distributed, and hybrid.
- Hybrid model achieves 35% throughput improvement and 28% energy reduction.
- Evaluated across 15 KPIs on a 500 base station, 50,000 user simulation.
- Provides practical deployment guidelines for operators.
Key Findings
- 1Hybrid AI-RAN offers the best performance-complexity tradeoff for most operators.
- 2Centralized model is optimal for dense urban but requires high-capacity fronthaul.
- 3Distributed model excels in edge AI latency but has lower optimization capability.
Industry Implications
Provides a roadmap for operators to adopt AI-RAN incrementally.
Informs 3GPP standardization of AI-RAN interfaces and functions.
Validates AI-RAN as a bridge from 5G-Advanced to 6G.
Read the Original Paper
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