Dynamic Resource Allocation for 5G Network Slices Using a Hybrid DRL–GA Approach

Abstract

Dynamic resource allocation between co-existing network slices is one of the most chal- lenging problems in 5G Radio Access Network management. In a Vehicle-to-Everything (V2X) highway scenario, a URLLC slice serving safety-critical collision-avoidance messages and an eMBB slice serving high-definition cameras and passenger internet access must share a fixed 100 MHz radio pool under non-stationary, multi-regime traffic conditions. Satis- fying the strict latency constraint of the URLLC slice and the load-dependent bandwidth requirement of the eMBB slice simultaneously, at every one second decision epoch, is an NP-hard problem that classical and standalone intelligent methods cannot fully resolve. This thesis proposes a Hybrid Deep Q-Network + Adaptive Genetic Algo- rithm (Hybrid DQN+GA) framework that addresses three structural limitations iden- tified in the state of the art: the discrete action ceiling of DQN-based agents, the cold- start inefficiency of standalone Genetic Algorithms, and the absence of a bidirectional co-adaptation loop between learning and evolutionary search. At every decision epoch, the DQN provides a fast, experience-driven, discrete allocation seed; the Adaptive GA re- finer searches the continuous bandwidth interval when a constraint violation is detected, using a multi-directional warm-start strategy seeded from the DQN output and domain- knowledge candidates; and the resulting refined allocation is fed back to train the DQN, forming a self-improving co-adaptation loop. The framework is evaluated in a simulated environment with five traffic regimes Nor- mal, Rush-hour, Accident, Peak-eMBB, and Night over 500 training episodes and 100

greedy evaluation episodes, against a Static Allocation baseline and a standalone DQN. The Hybrid agent achieves an overall QoS satisfaction of 75.34% (vs. 74.10% for DQN and 65.96% for Static), an eMBB satisfaction of 53.49% (+11.0% over DQN), a per-step violation rate of 0.493 (−4.8% over DQN), and a mean reward of 7.51. During safety- critical accident events, the Hybrid agent allocates 69 MHz to the URLLC slice 19 MHz more than the DQN— maintaining a latency of 4.8 ms under near-maximum load. All decisions are produced with a computational overhead below 1 ms per epoch, confirm- ing practical deployability on a Mobile Edge Computing server co-located with the base station.