通过深度强化学习利用 eMBB 数据实现分散式无补助 mMTC 流量复用

Giovanni Di Gennaro;Amedeo Buonanno;Gianmarco Romano;Stefano Buzzi;Francesco A. N. Palmieri
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摘要

本文探讨了在同一上行链路时频 RG 中联合复用增强型移动宽带(eMBB)和大规模机器型通信(mMTC)流量的问题。鉴于潜在的大量用户所带来的挑战,必须重点关注利用人工智能适应特定信道条件的多路接入策略。我们通过深度强化学习(DRL)方法开发了一种 mMTC 代理,用于以分散方式生成免授权跳频流量,同时假设存在潜在的 eMBB 流量动态。在此 DRL 框架内,对两种可能的深度神经网络进行了有条不紊的比较,使用不同的生成模型来确定它们在各种应用场景中的内在能力。分析结果表明,长短期记忆网络特别适合所需的任务,尽管后者需要完全了解底层统计数据,但其鲁棒性始终非常接近潜在上限。
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Decentralized Grant-Free mMTC Traffic Multiplexing With eMBB Data Through Deep Reinforcement Learning
This paper addresses the problem of joint multiplexing of enhanced Mobile Broadband (eMBB) and massive Machine-Type Communications (mMTC) traffic in the same uplink time-frequency RG. Given the challenge posed by a potentially large number of users, it is essential to focus on a multiple access strategy that leverages artificial intelligence to adapt to specific channel conditions. An mMTC agent is developed through a Deep Reinforcement Learning (DRL) methodology for generating grant-free frequency hopping traffic in a decentralized manner, assuming the presence of underlying eMBB traffic dynamics. Within this DRL framework, a methodical comparison between two possible deep neural networks is conducted, using different generative models employed to ascertain their intrinsic capabilities in various application scenarios. The analysis conducted reveals that the Long Short-Term Memory network is particularly suitable for the required task, demonstrating a robustness that is consistently very close to potential upper-bounds, despite the latter requiring complete knowledge of the underlying statistics.
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