Dynamic MAC Protocol for Wireless Spectrum Sharing via Hyperdimensional Self-Learning

Abstract

In wireless networks, dynamic spectrum access is the key to improving spectrum utilization and increasing channel capacity. Since the channels in wireless networks are highly correlated, they require intelligent algorithms to dynamically handle multi-channel access. Reinforcement Learning (RL) algorithms are introduced as effective techniques to optimize network performance. However, current RL methods heavily rely on a computationally intensive deep neural network (DNN) that is not friendly for edge devices. In this paper, we propose HD-RL, a dynamic wireless channel-sharing solution that utilizes brain-inspired lightweight hyperdimensional computing as the learning engine. HD-RL mimics important brain functionalities towards high-efficiency and noise-tolerant computation. HD-RL naturally encodes and memorizes prior knowledge to provide the near-optimal policy for channel throughput and $\alpha $ -fairness in the wireless network. Our evaluation shows that HD-RL achieves maximum throughput and fairness while significantly improving efficiency compared to the state-of-the-art DNN-based RL algorithms. In particular, HD-RL achieves more than $20\times $ speedup for reaching the fairness objective. On average, the speedup of convergence time is more than $10\times $ compared to the baseline. Our results also indicate that HD-RL has substantially higher robustness against possible hardware failure, e.g., up to 40% dimension loss in the model.

Keywords

• Brain-inspired computing
• hyperdimensional computing
• medium access control
• reinforcement learning