Channel Statistics-Based RF Pre-Processing with Antenna Selection

    •  Sudarshan, P.; Mehta, N.B.; Molisch, A.F.; Zhang, J., "Channel Statistics-Based RF Pre-Processing with Antenna Selection", IEEE Transactions on Wireless Communications, ISSN: 1536-1276, Vol. 5, No. 12, pp. 3501-3511, December 2006.
      BibTeX Download PDF
      • @article{Sudarshan2006dec,
      • author = {Sudarshan, P. and Mehta, N.B. and Molisch, A.F. and Zhang, J.},
      • title = {Channel Statistics-Based RF Pre-Processing with Antenna Selection},
      • journal = {IEEE Transactions on Wireless Communications},
      • year = 2006,
      • volume = 5,
      • number = 12,
      • pages = {3501--3511},
      • month = dec,
      • issn = {1536-1276},
      • url = {}
      • }
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  • Research Areas:

    Electronics & Communications, Wireless Communications

We introduce two novel joint radio-frequency (RF)-baseband designs for receivers in a MIMO system with Nt transmit antennas, Nr receive antennas, but only L less-than Nr RF chains at the receiver. The joint design introduces an RF pre-processing matrix that processes the signals from the different antennas, and is followed by selection (if necessary), down-conversion, and further processing in the baseband. The schemes are similar to conventional antenna selection in that they use fewer RF chains than antenna elements, but achieve superior performance by exploiting the spatial correlation of the received signals. The first of our proposed designs uses an L x Nr RF pre-processing matrix that outputs only L streams followed by baseband signal processing, and, thus, eliminates the need for a selection switch. The second one uses an Nr x Nr RF pre-processing matrix that outputs Nr streams and is followed by a switch that selects L streams for baseband signal processing. Both spatial diversity and spatial multiplexing systems are considered and the optimum pre-processing matrices are derived for all cases. To accommodate practical RF design constraints, which prefer a variable phase-shifter-based implementation, a sub-optimal phase approximation is also introduced. Performance better than conventional antenna selection and close to the full complexity receiver is observed in both single cluster and multi-cluster wireless channels. A beam-pattern-based geometric intuition is also developed to illustrate the effectiveness of the optimal solution.