Multi-armed Bandit-based Channel Bonding for Off-body Communication in IEEE 802.15.6 Cognitive Radio Wireless Body Area Networks

Off-body communication in Cognitive Radio Wireless Body Area Networks (CRWBANs) must cope with different types of interference and dynamic traffic loads. Existing static channel assignment schemes cannot handle dynamic traffic loads with higher data rate requirements. One of the major problems experienced by CRWBANs is contention across wireless body area networks, thereby affecting the network throughput. Channel bonding has been successfully applied to different networks, such as Wireless Local Area Networks (WLANs), cognitive radio sensor networks, and wireless sensor networks. However, its use for the off-body communication in IEEE 802.15.6-based CRWBANs has not been investigated. This article proposes a multi-armed bandit-based bonded channel algorithm (MAB-BCA) with Upper Confidence Bound (UCB) for channel bonding to improve the off-body network capacity. The MAB-BCA based on UCB algorithm maximizes the capacity of off-body communication in CRWBANs. It demonstrates significant performance improvement over Static Channel Assignment (SCA) and Reinforcement Learning—Channel Assignment Algorithm (RL-CAA) in terms of average throughput and comparable bit error rate and dissatisfaction probability.