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We consider the Signal-to-Interference plus Noise Ratio(SINR) balancing problem in-volving joint beamfoming and power allocation in the Cognitive Radio(CR) network,wherein the Single-Input Multi-Output Multiple Access Channels(SIMO-MAC) are assumed.Subject to two sets of constraints:the interference temperature constraints of Primary Users(PUs) and the peak power constraints of Cognitive Users(CUs),a low-complexity joint beamforming and power allocation algo-rithm called Semi-Decoupled Multi-Constraint Power Allocation with Constraints Preselection(SDMCPA-CP) for SINR balancing is proposed.Compared with the existing algorithm,the proposed SDMCPA-CP can reduce the number of matrix inversions and matrix eigen decompositions signifi-cantly,especially when large numbers of PUs and CUs are active,while still providing the optimal balanced SINR level for all the CUs.
We consider the Signal-to-Interference plus Noise Ratio (SINR) balancing problem in-volving joint beamfoming and power allocation in the Cognitive Radio (CR) network, where the Single-Input Multi-Output Multiple Access Channels Assigned. Subject to two sets of constraints: the interference temperature constraints of Primary Users (PUs) and the peak power constraints of Cognitive Users (CUs), a low-complexity joint beamforming and power allocation algo-rithm called Semi-Decoupled Multi-Constraint Power Allocation with Constraints Preselection (SDMCPA-CP) for SINR balancing is proposed. Compared with the existing algorithm, the proposed SDMCPA-CP can reduce the number of matrix inversions and matrix eigen decompositions signifi-cantly, especially when large numbers of PUs and CUs are active, while still providing the optimal balanced SINR level for all the CUs.