Spectrum Allocation and Power Management using Markov Chains and Beamforming in Underlay Cognitive Radios

The exponential growth of wireless spectrum usage in the past decades has led to spectrum scarcity. Cognitive radio technology has been proposed to improve spectrum usage among licensed and unlicensed users by dynamically allocating spectrum. Since licensed users are the primary users of the spectrum, it is imperative to ensure that the primary users' signal to interference and noise ratio and throughput demands are met. In order to tackle this issue, we make use of Markov chains to model the dynamic spectrum allocation process of a cognitive radio system. Scalability and complexity limits constrain Markov models. To tackle the same, we have utilized the novel concept of beamforming to generate more focused streams of data to limit the interference caused by secondary users (or unlicensed users) in the communication region of primary users. The paper proposes an algorithm to utilise the techniques of Markov chain models and beamforming in tandem with each other for appropriate usage of spectrum in underlay mode. Simulation results carried out based on a network scenario shows considerable improvement, in terms of network usage and primary user throughput, in comparison with network usage with no proper allocation of spectrum.