With increasing density and heterogeneity in unlicensed wireless networks, traditional MAC protocols, such as carrier-sense multiple access with collision avoidance (CSMA/CA) in Wi-Fi networks, are experiencing performance degradation. This is manifested in increased collisions and extended backoff times, leading to diminished spectrum efficiency and protocol coordination. Addressing these issues, this paper proposes a deep-learning-based MAC paradigm, dubbed DL-MAC, which leverages spectrum sensing data readily available from energy detection modules in wireless devices to achieve the MAC functionalities of channel access, rate adaptation and channel switch. First, we utilize DL-MAC to realize a joint design of channel access and rate adaptation. Subsequently, we integrate the capability of channel switch into DL-MAC, enhancing its functionality from single-channel to multi-channel operation. Specifically, the DL-MAC protocol incorporates a deep neural network (DNN) for channel selection and a recurrent neural network (RNN) for the joint design of channel access and rate adaptation. We conducted real-world data collection within the 2.4 GHz frequency band to validate the effectiveness of DL-MAC, and our experiments reveal that DL-MAC exhibits superior performance over traditional algorithms in both single and multi-channel environments and also outperforms single-function approaches in terms of overall performance. Additionally, the performance of DL-MAC remains robust, unaffected by channel switch overhead within the evaluated range.

翻译：随着非授权无线网络密度与异构性的日益增加，传统MAC协议（如Wi-Fi网络中采用载波侦听多路访问/冲突避免的CSMA/CA协议）正面临性能下降的问题。这表现为冲突加剧与退避时间延长，导致频谱效率降低与协议协调性减弱。针对这些问题，本文提出一种基于深度学习的MAC范式——DL-MAC，该范式利用无线设备能量检测模块可便捷获取的频谱感知数据，实现信道接入、速率适配与信道切换的MAC功能。首先，我们运用DL-MAC实现信道接入与速率适配的联合设计；随后，将信道切换能力整合至DL-MAC中，使其功能从单信道操作扩展至多信道操作。具体而言，DL-MAC协议包含用于信道选择的深度神经网络（DNN）以及用于信道接入与速率适配联合设计的循环神经网络（RNN）。我们在2.4 GHz频段进行了实际数据采集以验证DL-MAC的有效性，实验结果表明：DL-MAC在单信道与多信道环境中均表现出优于传统算法的性能，且在整体性能上超越单一功能方案。此外，在评估范围内，DL-MAC的性能保持稳健，未受信道切换开销的影响。