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Multiuser MIMO Systems with Random Transmit Beamforming

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Abstract

This paper considers the wireless downlink transmissions in a single cell environment, for which the base station (BS) is assumed to schedule its transmission to each mobile station (MS) on a time-slot basis. Only one MS is selected for transmission during each time-slot and the selected MS possibly changes from one time-slot to another. This transmission scheme is thus referred to as dynamic time-division multiple-access (D-TDMA). Random transmit beamforming with the feedback of effective signal-to-noise ratio (ESNR) was proposed by Viswanath and Tse [IEEE Transactions on Information Theory, Vol. 48, No. 6, pp. 1277–1294, 2002] for D-TDMA-based systems in which multiple transmit antennas are equipped at the BS but only single receive antenna is equipped at each MS, or the so-called “MISO” systems. It was also shown in [Viswanath and Tse, 2002] that when the number of MSs in the system becomes large, the system throughput achieved by random transmit beamforming converges to that by coherent transmit beamforming which, however, requires the complete channel state information (CSI) of each MS at the BS. This paper extends upon the work in [Viswanath and Tse, 2002] to a more general scenario for which multiple transmit antennas and multiple receive antennas are equipped at the BS and each MS, respectively, or the so-called “MIMO” systems. We also consider several linear and nonlinear receiver structures and propose novel power allocation schemes to further improve the achievable system throughput. The throughput performance of the proposed receivers and power allocations schemes is compared through computer simulations and their fast convergence to the system throughput by coherent transmit beamforming is demonstrated.

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Notes

  1. The convergence speed is defined as the closeness of the achievable throughput of one particular MS with random transmit beamforming to its capacity limit, versus the number of MSs in the system.

  2. Discussions on the selection of \(\varvec{\Gamma}^{\rm (P)}\) are postponed to Section 5.1.

  3. The receiver structures and the associated ESNR values are discussed in Section 4.

  4. For some receiver structures considered in Section 4, the power allocations in data transmission mode can be different from those used in pilot transmission mode. In these cases, the set of ESNR values and the transmission rate in data transmission mode need to be recomputed.

  5. The algorithm can be implemented by the MATLAB function [q,r,e]=qr(H), for which e is a permutation matrix giving the decoding orders.

  6. MMSE-DFE is also called “generalized decision-feedback equalizer (GDFE)” in [17].

  7. The receiver structure in this case is the MMSE-DFE receiver.

  8. The MMSE-DFE receiver in this case can be shown to be equivalent to a set of linear receivers obtained from the channel SVD.

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Authors and Affiliations

  1. Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore, 119613, Singapore

    Ying-Chang Liang

  2. Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA

    Rui Zhang

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  1. Ying-Chang Liang
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  2. Rui Zhang
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Correspondence to Ying-Chang Liang.

Additional information

Part of this work has been published in PIMRC’2004 (“Random beamforming for MIMO systems with multiuser diversity"), Vol. 1, pp. 290–294, Sept. 5–8, 2004, Barcelona, Spain.

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Liang, YC., Zhang, R. Multiuser MIMO Systems with Random Transmit Beamforming. Int J Wireless Inf Networks 12, 235–247 (2005). https://doi.org/10.1007/s10776-005-0010-1

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