Minimum neighbour and extended Kalman filter estimator: a practical distributed channel assignment scheme for dense wireless local area networks

Abstract

Dense deployments of wireless local area networks (WLANs) are becoming a norm in many cities around the world. However, increased interference and traffic demands can severely limit the aggregate throughput achievable unless an effective channel assignment scheme is used. In this work, a simple and effective distributed channel assignment (DCA) scheme is proposed. It is shown that in order to maximise throughput, each access point (AP) simply chooses the channel with the minimum number of active neighbour nodes (i.e. nodes associated with neighbouring APs that have packets to send). However, application of such a scheme to practice depends critically on its ability to estimate the number of neighbour nodes in each channel, for which no practical estimator has been proposed before. In view of this, an extended Kalman filter (EKF) estimator and an estimate of the number of nodes by AP are proposed. These not only provide fast and accurate estimates but can also exploit channel switching information of neighbouring APs. Extensive packet level simulation results show that the proposed minimum neighbour and EKF estimator (MINEK) scheme is highly scalable and can provide significant throughput improvement over other channel assignment schemes.