GAN-Enhanced Deep Reinforcement Learning for Semantic-Aware Resource Allocation in 6G Network Slicing

TL;DR

This paper introduces GAN-DDPG, a novel framework combining GANs and deep reinforcement learning to optimize resource allocation in 6G networks, addressing semantic blindness and action discretization issues.

Contribution

It presents a GAN-enhanced DDPG approach with semantic-aware rewards, improving spectral efficiency and latency in 6G network slicing.

Findings

22% spectral efficiency improvement for URLLC

20% spectral efficiency gain for eMBB

25% spectral efficiency increase for mMTC

Abstract

Sixth-generation (6G) wireless networks must support heterogeneous services: enhanced Mobile Broadband (eMBB) requiring 1 Tbps data rates, massive Machine-Type Communications (mMTC) supporting 10 million devices per km, and Ultra-Reliable Low-Latency Communications (URLLC) with 0.1-1 ms latency. Current resource allocation suffers from three limitations: (1) semantic blindness wasting 35% bandwidth on redundant data, (2) discrete action quantization, and (3) limited training diversity. This paper proposes GAN-DDPG, a Generative Adversarial Network-enhanced Deep Deterministic Policy Gradient framework integrating conditional GANs for traffic synthesis, continuous action DDPG, and semantic-aware reward optimization. Extensive simulations with statistical validation demonstrate significant improvements: 22% URLLC, 20% eMBB, 25% mMTC spectral efficiency gains (all p < 0.001) compared to baseline DDPG, with 18% latency and 31% packet loss reduction.