用生成对抗网络提升6G网络切片的智能资源分配效率
GAN-Enhanced Deep Reinforcement Learning for Semantic-Aware Resource Allocation in 6G Network Slicing
- 引入条件GAN合成多类业务流量，增强训练多样性
- 连续动作策略使资源分配更精准，频谱效率提升20%以上
- 适合研究6G网络智能化与通信系统联合优化的读者
第六代（6G）无线网络需支持多样化服务：增强型移动宽带（eMBB）要求1 Tbps数据速率，海量机器类通信（mMTC）支持每平方公里1000万设备，超可靠低时延通信（URLLC）要求0.1–1毫秒延迟。当前资源分配存在三大缺陷：（1）语义盲区导致35%带宽浪费于冗余数据；（2）离散动作量化；（3）训练多样性不足。本文提出GAN-DDPG框架，融合条件GAN进行流量生成、连续动作深度确定性策略梯度（DDPG）及语义感知奖励优化。大量仿真实验结合统计验证表明：相比基线DDPG，URLLC、eMBB、mMTC频谱效率分别提升22%、20%、25%（均p < 0.001），时延降低18%，包丢失率下降31%。
原文摘要 · Abstract (English)
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.
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