Maximisation of the sum-rate of secondary users (SUs), equipped with multi-antenna transmitters and receivers, is considered in the context of multi-input-multi-output antenna multi-band cognitive radio networks (MIMO...
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Maximisation of the sum-rate of secondary users (SUs), equipped with multi-antenna transmitters and receivers, is considered in the context of multi-input-multi-output antenna multi-band cognitive radio networks (MIMO-MB-CRNs) with coexisting primary users (PUs). The total interference power introduced to the PUs is constrained to maintain reliable communication for them. An interference channel configuration is considered for ad hoc networking, where the receivers treat the interference from undesired transmitters as noise. Using game theory approach, the strong duality in convex optimisation and the primal decomposition method, a low complexity semi-distributed algorithm is proposed for spectrum sharing and power allocation for MIMO-MB-CRNs. The key idea behind the algorithm is the introduction of a diagonal block pricing factor matrix for each SU link. This matrix regulates network interference by encouraging SU links to work in a more altruistic manner. The proposed algorithm is configured in two iterativealgorithms, Jacobi and gauss-seidelalgorithms, and their performance is investigated through simulations. The simulation results showed that the proposed algorithm dramatically improves sum-rate and achieves higher energy efficiency, compared with other algorithms.
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