Learning-Based Multi-Channel Access in 5G and Beyond Networks With Fast Time-Varying Channels

We propose a learning-based scheme to investigate the dynamic multi-channel access (DMCA) problem in the fifth generation (5G) and beyond networks with fast time-varying channels wherein the channel parameters are unknown. The proposed learning-based scheme can maintain near-optimal performance for a long time, even in the sharp changing channels. This scheme greatly reduces processing delay, and effectively alleviates the error due to decision lag, which is cased by the non-immediacy of the information acquisition and processing. We first propose a psychology-based personalized quality of service model after introducing the network model with unknown channel parameters and the streaming model. Then, two access criteria are presented for the living streaming model and the buffered streaming model. Their corresponding optimization problems are also formulated. The optimization problems are solved by learning-based DMCA scheme, which combines the recurrent neural network with deep reinforcement learning. In the learning-based DMCA scheme, the agent mainly invokes the proposed prediction-based deep deterministic policy gradient algorithm as the learning algorithm. As a novel technical paradigm, our scheme has strong universality, since it can be easily extended to solve other problems in wireless communications. The real channel data-based simulation results validate that the performance of the learning-based scheme approaches that derived from the exhaustive search when making a decision at each time-slot, and is superior to the exhaustive search method when making a decision at every few time-slots.

I. Introduction

With the popularity of applications in high-speed railways and highways, high-mobility environments attract great attention in the fifth generation (5 G) and beyond mobile communications, leading to many new challenges, such as fast time-varying channels, frequent handovers, and complex channel environments [1]. At the same time, it is an important ability of 5G and beyond networks to efficiently support ultra-reliable and low-latency communication (URLLC) services for autonomous driving and vehicle-to-everything (V2X) scenarios [2]. Providing stable, reliable, and fast data transmission service is also a crucial target in high-mobility wireless communications. As a most important component in achieving the above targets [3], dynamic multi-channel access (DMCA), should be greatly improved [4], since it can also effectively promote system throughputs [5].