In this paper, we consider the downlink channel of multi-user multi-input single-output （MU-MISO） system in cognitive radio network, where the cognitive base station （CBS） resort to beamforming scheme to relief co...In this paper, we consider the downlink channel of multi-user multi-input single-output （MU-MISO） system in cognitive radio network, where the cognitive base station （CBS） resort to beamforming scheme to relief co-channel interference. The design criterion is to minimize the transmit power at CBS, subject to the signal-to-interference-plus-noise-ratio （SINR） constraints of cognitive users （CUs） and the interference constraints at primary users （PUs）. Standard conic optimization packages can handle the problem, however, the complexity is very high and optimization packages are not always available. Basing on the karush-kuhn-tucker （KKT） conditions of the converted optimization problem, we proposed an iteration algorithm. Simulation results reveal that the proposed algorithm can converge to the optimal beamforming vectors that lead to minimum transmit power with all constraints satisfied.展开更多
基金supported by Major National Science and Technology Programs (2009ZX03007-001)the National Basic Research Program (2009CB320401)the Hi-Tech Research and Development Program of China (2009AA011501-2)
文摘In this paper, we consider the downlink channel of multi-user multi-input single-output （MU-MISO） system in cognitive radio network, where the cognitive base station （CBS） resort to beamforming scheme to relief co-channel interference. The design criterion is to minimize the transmit power at CBS, subject to the signal-to-interference-plus-noise-ratio （SINR） constraints of cognitive users （CUs） and the interference constraints at primary users （PUs）. Standard conic optimization packages can handle the problem, however, the complexity is very high and optimization packages are not always available. Basing on the karush-kuhn-tucker （KKT） conditions of the converted optimization problem, we proposed an iteration algorithm. Simulation results reveal that the proposed algorithm can converge to the optimal beamforming vectors that lead to minimum transmit power with all constraints satisfied.
