Lightweight Deep Reinforcement Learning Model for Energy-Efficient Resource Allocation in Edge Computing

Abstract

A lightweight digital twin model for a single 6G cell operating in the D-band (140 GHz) with a 1 GHz bandwidth is presented in this work with the goal of assessing the cell's capacity, coverage, and terminal time in order to support extended reality (XR) applications. With a tangent dispersion of 3 dB and a path exponent of n = 2.2, the model is based on the free-space loss equation as per ITU-R Recommendation P.52 5. The instantaneous capacity is determined using the Shannon-Hartley theorem. Three XR sessions are created every minute using a Poisson method, and their durations are determined by an exponential distribution (mean of 12 0 seconds). In accordance with 3 GPP and Ericsson guidelines for normal XR loads, the bit needs per user are randomly selected to fall between 40 and 12 0 Mb/s. The average coverage was around 92 %, the average cell capacity was approximately 5.1 Gb/s, and the edge capacity (lowest quintile) was approximately 230 Mb/s, according to fifty statistical forecasts. Additionally, the 95th percentile round-trip latency was 3.9 ms, which is significantly less than the permitted maximum (10– 20 ms) for immersive XR research. These findings suggest that modest XR loads may be supported by a 250-meter cell with a high-gain antenna layout without the need to immediately lower the radius or raise the transmitted power. However, the model remains theoretical and simplified, excluding geometric blockage and cell overlap in complex metropolitan environments.