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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Joint Power Control and Blind Beamforming over Wireless Networks: A Cross Layer Approach | EURASIP Journal on Applied Signal Processing 2004 5 751-761 2004 Hindawi Publishing Corporation Joint Power Control and Blind Beamforming over Wireless Networks A Cross Layer Approach Zhu Han Department of Electrical and Computer Engineering and Institute for Systems Research University of Maryland College Park MD 20742 USA Email hanzhu@glue.umd.edu Farrokh R. Farrokhi Department of Electrical and Computer Engineering and Institute for Systems Research University of Maryland College Park MD 20742 USA Email frfarrokhi@yahoo.com K. J. Ray Liu Department of Electrical and Computer Engineering and Institute for Systems Research University of Maryland College Park MD 20742 USA Email kjrliu@umd.edu Received 6 January 2003 Revised 9 September 2003 Traditional joint power control and beamforming achieve the targeted signal-to-interference-noise ratio SINR at the receivers by assuming the knowledge of the measurements of channel parameters and SINR. Blind beamforming is an effective technique for beamforming and channel estimation without the need of training sequences thus not consuming extra bandwidth. In this paper we propose a novel joint power control and blind beamforming algorithm that reformulates the power control problem in such a way that it does not need any prior knowledge and additional measurements in the physical layer. In contrast to the traditional schemes that optimize SINR and as a result minimize bit error rate BER our proposed algorithm achieves the desired BER by adjusting a quantity available from blind beamforming. By sending this quantity to the transmitter through a feedback channel the transmit power is iteratively updated in a distributed manner in the wireless networks with cochannel interferences CCIS . Our proposed algorithm is more robust to estimation errors. We have shown in both analysis and simulation that our algorithm converges to the desired solution. In addition a Cramer-Rao lower bound CRB is derived to compare with the performance
