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In this paper, we investigate multimode antenna selection for zero forcing receiver to maximize the overall data rate. The optimal selection scenario can be achieved by exhaustive search. However, antenna selection using exhaustive search leads to complicated computational burden. To reduce the complexity, we propose a greedy search algorithm for antenna selection. Using the proposed algorithm, the computations can be greatly reduced while the achievable data rate is nearly the same with exhaustive search. Moreover, generally fixed bit budgets are used in practical design. Hence, we propose to use water-filling bit allocation to further improve the performance of the proposed antenna selection scheme. Simulation results are provided to show the advantages of the proposed multimode antenna selection with bit allocation.

This paper proposes two recovery algorithms modified from subspace pursuit(SP) for compressed sensing problems. These algorithms can reduce the complexity of SP and maintain high recovery rate. Complexity analysis and simulation results are provided to demonstrate the improvements. Additionally this work has implemented the VLSI circuit APR of the proposed algorithm using TSMC 90 nm process. The target clock frequency is 100MHz, and the corresponding APR dimension is 11.69 2 . Based on the post-layout simulation the average power consumption is 431 mW.