Weighted DFT Codebook for Multiuser MIMO in Spatially Correlated Channels.
ABSTRACT This paper proposes a novel codebook for multiuser multiple-input multiple-output systems under spatially correlated channels. Existing codebooks designed for correlated channels either require accurate channel statistics which is not favorable for practical systems, or only perform well in highly correlated channels. In this paper, we first analyze the per-user rate loss led by using DFT codebook, then propose a two-level codebook, named weighted DFT codebook. It consists of a DFT-based codebook and a Grassmannian linear packing codebook. The proposed scheme does not need accurate channel statistics and can adapt to both correlated and uncorrelated scenarios. Simu- lation results show significant performance gain of the proposed codebook over the existing codebooks in various correlated channels. Index Terms—Limited feedback, MU-MIMO, WDFT, Code- book
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ABSTRACT: Multiple-input multiple-output (MIMO) wireless systems provides capacity much larger than that provided by traditional single-input single-output (SISO) wireless systems. Beamforming is a low complexity technique that increases the receive signal-to-noise ratio (SNR), however, it requires channel knowledge. Since in practice channel knowledge at the transmitter is difficult to realize, we propose a technique where the receiver designs the beamforming vector and sends it to the transmitter by transmitting a label in a finite set, or codebook, of beamforming vectors. A codebook design method for quantized versions of maximum ratio transmission, equal gain transmission, and generalized selection diversity with maximum ratio combining at the receiver is presented. The codebook design criterion exploits the quantization problem's relationship with Grassmannian line packing. Systems using the beamforming codebooks are shown to have a diversity order of the product of the number of transmit and the number of receive antennas. Monte Carlo simulations compare the performance of systems using this new codebook method with the performance of systems using previously proposed quantized and unquantized systems.IEEE Transactions on Information Theory 01/2003; 49:2735-2747. · 2.65 Impact Factor
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ABSTRACT: Abstract—The DFT-based beamforming weight-vector code-book is considered as an effective design for spatially correlated channels. In this paper, we demonstrate that when the antenna elements are uniformly spaced as well as linearly arranged, and the channels are spatially correlated, the codewords in a DFT-based beaforming weight-vector codebook approximately match the distribution of the optimal beamforming weight-vectors. As a result, the DFT-based codebook is indeed effective. Furthermore, we also demonstrate that if the antenna elements are uniformly spaced and circulary arranged, the statistical distribution of the optimal beamforming weight-vectors becomes different. We will demonstrate that in this scenario the DFT-based codebook will no longer outperform the Grassmannian codebook, which has not been shown in previous studies. Finally, an algorithm is proposed for constructing the DFT-based precoding matrix, which outperforms the conventional algorithm by ensuring the orthogonality of the precoding matrix.01/2010;
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ABSTRACT: This paper considers the effect of spatial correlation between transmit antennas on the sum-rate capacity of the MIMO broadcast channel (i.e., downlink of a cellular system). Specifically, for a system with a large number of users n, we analyze the scaling laws of the sum-rate for the dirty paper coding and for different types of beamforming transmission schemes. When the channel is i.i.d., it has been shown that for large n, the sum rate is equal to M log log n + M log P/M + o(1) where M is the number of transmit antennas, P is the average signal to noise ratio, and o(1) refers to terms that go to zero as n rarr infin. When the channel exhibits some spatial correlation with a covariance matrix R (non-singular with tr(R) = M), we prove that the sum rate of dirty paper coding is M log log n + M log P/M + log det(R) + o(1). We further show that the sum-rate of various beamforming schemes achieves M log log n + M log P/M + M log c + o(1) where c les 1 depends on the type of beamforming. We can in fact compute c for random beamforming proposed in M. Sharif et al. (2005) and more generally, for random beamforming with preceding in which beams are pre-multiplied by a fixed matrix. Simulation results are presented at the end of the paperInformation Theory, 2006 IEEE International Symposium on; 08/2006