Energy Efficiency Optimization in Distributed MIMO vRAN via Cross-Layer Link Abstraction
Virtualized radio access networks (vRAN) run the compute-intensive multiple-input multiple-output (MIMO) baseband as software on shared servers, which makes energy efficiency (EE) a primary design objective. Distributed MIMO vRAN consumes power across virtualized distributed unit (vDU) baseband, fronthaul transport, and per-radio-unit operation. We build a power model that resolves these three components. We then develop a framework that jointly selects modulation, transmission rank, and per-subcarrier power to maximize system EE. Exponential effective SNR mapping induces a convex per-subcarrier power constraint, which yields a convex power minimization problem with a closed-form waterfilling-like solution. We show that radio frequency-only models underestimate the spectral efficiency range where single-input multiple-output (SIMO) transmission saves power, and our power model extends this range by 24%. We further extend the framework to a traffic-aware setting with realistic user trajectories from the multi-agent transport simulator. We propose a traffic-aware strategy that switches each radio unit among MIMO, SIMO, and sleep modes based on demand. Simulation results over 3GPP NR compliant fading channels show that, after a one-time offline calibration, the framework predicts link performance without further link-level simulation. The proposed framework achieves higher average EE than a traffic-agnostic always-on MIMO baseline, while maintaining comparable throughput at peak hours.