MAQ: A Multiple Model Predictive Congestion Control Scheme for Cognitive Radio Networks

Abstract

In this paper, we investigate the problem of robust congestion control in infrastructure-based cognitive radio networks (CRN). We develop an active queue management algorithm, termed MAQ, which is based on multiple model predictive control. The goal is to stabilize the TCP queue at the base station under disturbances from the time-varying service capacity for secondary users. The proposed MAQ scheme is validated with extensive simulation studies under various types of background traffic and system/network configurations. It outperforms two benchmark schemes with considerable gains in all the scenarios considered.