Downlink Scheduling and Resource Allocation for Cognitive Radio MIMO Networks

Abstract

Cognitive radio is regarded as the ideal candidate for enhancing the efficiency of spectrum usage for next-generation wireless systems. In fact, this emerging technology allows unlicensed cognitive users to transmit over frequency bands that are initially owned by license holders through the use of dynamic spectrum sharing. In this paper, we propose a novel algorithm that efficiently solves the problem of spectrum sharing and user scheduling in a cognitive downlink multi-input-multi-output system (MIMO). We study the scenario where primary receivers do not allow any interference from a multiantenna cognitive base station, which serves cognitive users. Using graph theory, we model, formulate, and develop an algorithm that finds near-optimal spectrum sharing with the objective of approaching the maximum achievable secondary sum rate. Since the formulated graph-coloring problem is shown to be NP-hard, we design a low-complexity greedy algorithm. Following, we add the well-known proportional fairness to the proposed algorithm to ensure time-based fairness and to efficiently resolve the fairness/sum rate tradeoff. The problem is also formulated as a binary integer programming problem to find the optimal coloring solution. Computer simulations show that the proposed algorithm is able to achieve near-optimal performances with low computational complexity.