Multiple-input multiple-output cross-layer antenna selection and beamforming for cognitive networks

Beamforming techniques can be used to suppress co-channel interference in radio devices. In a cognitive setting, beamforming can be beneficial as it can be applied to cancel interference among co-located primary users and cognitive users. In this study, the authors propose an antenna selection algorithm combined with zero-forcing beamforming to improve the throughput of cognitive multiple-input multiple-output (MIMO) radios. The algorithm consists of two phases. First, cognitive nodes apply antenna selection approach to achieve high transmission efficiency among communicating pairs. Cognitive nodes then exploit the spatial opportunities of MIMO systems and employ beamforming to cancel interference between cognitive and primary users. In that, the authors maximise an objective function for the system throughput where precoding is applied on the transmitted spatial multiplexed signals. Numerical results show the advantages offered by the proposed algorithm under different system scenarios.

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