Analysis of Dynamic Channel Bonding in Dense Networks of WLANs

Dynamic Channel Bonding (DCB) allows for the dynamic selection and use of multiple contiguous basic channels in Wireless Local Area Networks (WLANs). A WLAN operating under DCB can enjoy a larger bandwidth, when available, and therefore achieve a higher throughput. However, the use of larger bandwidths also increases the contention with adjacent WLANs, which can result in longer delays in accessing the channel and consequently, a lower throughput. In this paper, a scenario consisting of multiple WLANs using DCB and operating within carrier-sensing range of one another is considered. An analytical framework for evaluating the performance of such networks is presented. The analysis is carried out using a Markov chain model that characterizes the interactions between adjacent WLANs with overlapping channels. An algorithm is proposed for systematically constructing the Markov chain corresponding to any given scenario. The analytical model is then used to highlight and explain the key properties that differentiate DCB networks of WLANs from those operating on a single shared channel. Furthermore, the analysis is applied to networks of IEEE 802.11ac WLANs operating under DCB-which do not fully comply with some of the simplifying assumptions in our analysis-to show that the analytical model can give accurate results in more realistic scenarios

An Adaptive Common Control Channel MAC with Transmission Opportunity in IEEE 802.11ac

Spectral utilization is a major challenge in wireless ad hoc networks due in part to using limited network resources. For ad hoc networks, the bandwidth is shared among stations that can transmit data at any point in time. It  is important to maximize the throughput to enhance the network service. In this paper, we propose an adaptive multi-channel access with transmission opportunity protocol for multi-channel ad hoc networks, called AMCA-TXOP. For the purpose of coordination, the proposed protocol uses an adaptive common control channel over which the stations negotiate their channel selection based on the entire available bandwidth and then switch to the negotiated channel. AMCA-TXOP requires a single radio interface so that each station can listen to the control channel, which can overhear all agreements made by the other stations. This allows parallel transmission to multiple stations over various channels, prioritizing data traffic to achieve the quality-of-service requirements. The proposed approach can work with the 802.11ac protocol, which has expanded the bandwidth to 160 MHz by channel bonding. Simulations were conducted to demonstrate the throughput gains that can be achieved using the AMCA-TXOP protocol. Moreover, we compared our protocol with  the IEEE 802.11ac standard protocols

Multi-Armed Bandits for Spectrum Allocation in Multi-Agent Channel Bonding WLANs

While dynamic channel bonding (DCB) is proven to boost the capacity of wireless local area networks (WLANs) by adapting the bandwidth on a per-frame basis, its performance is tied to the primary and secondary channel selection. Unfortunately, in uncoordinated high-density deployments where multiple basic service sets (BSSs) may potentially overlap, hand-crafted spectrum management techniques perform poorly given the complex hidden/exposed nodes interactions. To cope with such challenging Wi-Fi environments, in this paper, we first identify machine learning (ML) approaches applicable to the problem at hand and justify why model-free RL suits it the most. We then design a complete RL framework and call into question whether the use of complex RL algorithms helps the quest for rapid learning in realistic scenarios. Through extensive simulations, we derive that stateless RL in the form of lightweight multi-armed-bandits (MABs) is an efficient solution for rapid adaptation avoiding the definition of broad and/or meaningless states. In contrast to most current trends, we envision lightweight MABs as an appropriate alternative to the cumbersome and slowly convergent methods such as Q-learning, and especially, deep reinforcement learning