Abstract
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 of our proposed joint power control and blind beamforming system.
Highlights
Over the past few decades, wireless communications and networking have witnessed an unprecedented growth, and have become pervasive much sooner than anyone could have imagined
We present a novel joint power control and blind beamforming algorithm for a multicell multiantenna system
Simulation results illustrate that our algorithm converges to the desired solution and is more robust to channel estimation error compared with traditional joint power control and training-based beamforming algorithm
Summary
Over the past few decades, wireless communications and networking have witnessed an unprecedented growth, and have become pervasive much sooner than anyone could have imagined. Simulation results illustrate that our algorithm converges to the desired solution and is more robust to channel estimation error compared with traditional joint power control and training-based beamforming algorithm. Where Gdki is path loss, αdki is fading coefficient, Pkd is transmit power, akdi(θkdi) is the ith base station array response vector to the signal from the dth mobile in the kth cell at direction θkdi, gkd(t) is shaping function, sdk (t) is message symbol, τki is the delay, and ni(t) is thermal noise vector. If the channel responses hdii can be estimated, the beamforming vector can be calculated by the MVDR method, which minimizes the total interferences at the output of a beamformer, while the gain for the desired dth user in the ith cell is kept as a constant. The algorithm assumes the knowledge of SINR and directions of the desired signals or the perfect measurements of channel responses, which are very difficult to get in practice
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