Resource Allocation Strategies for Cognitive and Cooperative Mimo Communications: Algorithm and Protocol Design

Abstract

Dynamic Spectrum Access (DSA) and multi-input multi-output (MIMO) communications are among the most promising solutions to address the ever-increasing wireless demand. Cognitive radio (CR) is the enabling technology for DSA. In this dissertation, we propose several resource allocation strategies for multiuser and cooperative MIMO communications in the context of DSA/CR systems and wireless sensor networks (WSNs). First, to maximize the Cognitive MIMO (CMIMO) network throughput, we develop a low-complexity distributed algorithm that configures the transmit antenna radiation directions and allocates power to all data streams over both frequency and space/antenna dimensions. We formulate the joint power, spectrum allocation, and MIMO beamforming problem as a noncooperative game. We prove that the game always admits at least one Nash Equilibrium (NE). To improve the efficiency of this NE (i.e., network throughput), we derive user-dependent pricing policies that force MIMO transmitters to steer their beams away from nearby unintended receivers. Second, we propose beamforming games (with and without pricing policies) that jointly improve the power and spectrum efficiency while meeting various rate demands. We derive sufficient conditions under which a given rate-demand profile can be supported. To account for user fairness, we develop a channel assignment and power allocation mechanism based on the Nash Bargaining solution. The proposed scheme allows CMIMO links to first propose their rate demands, and then cooperate and bargain in the process of determining their channel assignment, power allocation, and "precoding" matrices. In the context of WSNs where energy efficiency is a key design metric, we propose a cooperative MIMO framework. The framework partitions a WSN into various clusters in which several single-antenna sensors cooperate and form a virtual MIMO node so as to conserve power through harvesting MIMO's diversity gain. Extensive simulations show that our proposed schemes achieve significant throughput and energy efficiency improvement compared with state-of-the-art designs.