Optimum Vector Perturbation Minimizing Total MSE in Multiuser MIMO Downlink
ABSTRACT We propose a new vector precoding for the down-link of multiuser multiple-input multiple-output (MIMO) systems. The proposed scheme can be thought as the minimum mean square error (MMSE) version of the vector perturbation technique. We generalize the vector perturbation by allowing the data vector to be perturbed by an arbitrary vector. After the modulo operation at receivers, the remaining part of the perturbation is dealt with as the co-channel interference. We derive the total mean square error (MSE) of the received signal, and find the optimum perturbation vector that minimizes the total MSE. Simulation results show that our scheme outperforms the other compared schemes at all signal-to-noise ratios (SNRs), while the computational complexity is not increased.
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ABSTRACT: We introduce a transmit preprocessing technique for the downlink of multiuser multiple-input multiple-output (MIMO) systems. It decomposes the multiuser MIMO downlink channel into multiple parallel independent single-user MIMO downlink channels. Some key properties are that each equivalent single-user MIMO channel has the same properties as a conventional single-user MIMO channel, and that increasing the number of transmit antennas of the multiuser system by one increases the number of spatial channels to each user by one. Simulation results are also provided and these results demonstrate the potential of our technique in terms of performance and capacity.IEEE Transactions on Wireless Communications 02/2004; · 2.42 Impact Factor
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ABSTRACT: In this paper, we consider a scenario where a source node wishes to broadcast two confidential messages for two respective receivers via a Gaussian MIMO broadcast channel. A wire-tapper also receives the transmitted signal via another MIMO channel. First we assumed that the channels are degraded and the wire-tapper has the worst channel. We establish the capacity region of this scenario. Our achievability scheme is a combination of the superposition of Gaussian codes and randomization within the layers which we will refer to as Secret Superposition Coding. For the outerbound, we use the notion of enhanced channel to show that the secret superposition of Gaussian codes is optimal. We show that we only need to enhance the channels of the legitimate receivers, and the channel of the eavesdropper remains unchanged. Then we extend the result of the degraded case to non-degraded case. We show that the secret superposition of Gaussian codes along with successive decoding cannot work when the channels are not degraded. we develop a Secret Dirty Paper Coding (SDPC) scheme and show that SDPC is optimal for this channel. Finally, we investigate practical characterizations for the specific scenario in which the transmitter and the eavesdropper have multiple antennas, while both intended receivers have a single antenna. We characterize the secrecy capacity region in terms of generalized eigenvalues of the receivers channel and the eavesdropper channel. We refer to this configuration as the MISOME case. In high SNR we show that the capacity region is a convex closure of two rectangular regions. Comment: 23 pages, 2 figures03/2009;
Article: Capacity limits of MIMO channels[show abstract] [hide abstract]
ABSTRACT: We provide an overview of the extensive results on the Shannon capacity of single-user and multiuser multiple-input multiple-output (MIMO) channels. Although enormous capacity gains have been predicted for such channels, these predictions are based on somewhat unrealistic assumptions about the underlying time-varying channel model and how well it can be tracked at the receiver, as well as at the transmitter. More realistic assumptions can dramatically impact the potential capacity gains of MIMO techniques. For time-varying MIMO channels there are multiple Shannon theoretic capacity definitions and, for each definition, different correlation models and channel information assumptions that we consider. We first provide a comprehensive summary of ergodic and capacity versus outage results for single-user MIMO channels. These results indicate that the capacity gain obtained from multiple antennas heavily depends on the available channel information at either the receiver or transmitter, the channel signal-to-noise ratio, and the correlation between the channel gains on each antenna element. We then focus attention on the capacity region of the multiple-access channels (MACs) and the largest known achievable rate region for the broadcast channel. In contrast to single-user MIMO channels, capacity results for these multiuser MIMO channels are quite difficult to obtain, even for constant channels. We summarize results for the MIMO broadcast and MAC for channels that are either constant or fading with perfect instantaneous knowledge of the antenna gains at both transmitter(s) and receiver(s). We show that the capacity region of the MIMO multiple access and the largest known achievable rate region (called the dirty-paper region) for the MIMO broadcast channel are intimately related via a duality transformation. This transformation facilitates finding the transmission strategies that achieve a point on the boundary of the MIMO MAC capacity region in terms of the transmission strategies of the MIMO broadcast dirty-paper region and vice-versa. Finally, we discuss capacity results for multicell MIMO channels with base station cooperation. The base stations then act as a spatially diverse antenna array and transmission strategies that exploit this structure exhibit signifi- cant capacity gains. This section also provides a brief discussion of system level issues associated with MIMO cellular. Open problems in this field abound and are discussed throughout the paper.IEEE Journal on Selected Areas in Communications 06/2003; 21(5):684 - 702. · 3.12 Impact Factor