Post-CCA and Reinforcement Learning Based Bandwidth Adaptation in 802.11ac Networks

Abstract

The new 802.11ac standard aims at achieving Gbps data throughput for individual users by exploiting enhanced physical-layer features, such as higher modulation levels, Multiple Input Multiple Output (MIMO), and wider bandwidths. However, the heterogeneity of bandwidth in a network can cause asymmetric interferences in which certain transmissions cannot be sensed by some other nodes. As a result, the conventional Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) may not work well in 802.11ac networks. We call this the Hidden Channel (HC) problem, which is shown to be real via experiments with USRP and WARP boards. To solve this problem, we propose bandwidth adaptation based on post-CCA , which is a clear channel assessment (CCA) procedure performed after completing a transmission. Post-CCA in wireless networks helps mimic the CSMA with Collision Detection (CSMA/CD) mechanism in the wired Ethernet, thus enhancing channel assessment capability. Using post-CCA, we propose Post-CCA based Bandwidth Adaptation (PoBA) that alters bandwidth and channel configuration dynamically by applying a reinforcement learning mechanism. Post-CCA and PoBA do not require any hardware modification and are also compliant with the 802.11 standards. PoBA is shown via simulation to increase network-wide throughput, channel utilization and fairness, and also lower packet error probability.