Reinforcement Learning Approaches to Improve Spatial Reuse in Wireless Local Area Networks

Abstract

The ubiquitous deployment of IEEE 802.11 based Wireless Local Area Networks (WLANs) or WiFi networks has resulted in dense deployments of Access Points (APs) in an effort to provide wireless links with high data rates to users. This, however, causes APs and users/stations to experience a higher interference level. This is because of the limited spectrum in which WiFi networks operate, resulting in multiple APs operating on the same channel. This in turn affects the signal-tonoise-plus interference ratio (SINR) at APs and users, leading to low data rates that limit their quality of service (QoS). To improve QoS, interference management is critical. To this end, a key metric of interest is spatial reuse. A high spatial reuse means multiple transmissions are able to transmit concurrently, which leads to a high network capacity. One approach to optimize spatial reuse is by tuning the clear channel access (CCA) threshold employed by the carrier sense multiple access with collision avoidance (CSMA/CA) medium access control (MAC) protocol. Specifically, the CCA threshold of a node determines whether it is allowed to transmit after sensing the channel. A node may increase its CCA threshold, causing it to transmit even when there are other ongoing transmissions. Another parameter to be tuned is transmit power. This helps a transmitting node lower its interference to neighboring cells, and thus allows nodes in these neighboring cells to transmit as well. Apart from that, channel bonding can be applied to improve transmission rate. In particular, by combining/aggregating multiple channels together, the resulting channel has a proportionally higher data rate than the case without channel bonding. However, the issue of spatial reuse remains the same whereby the focus is to maximize the number of concurrent transmissions across multiple channels