Linear processing and sum throughput in the multiuser MIMO downlink

Abstract

We consider linear precoding and decoding in the downlink of a multiuser multiple-input, multipleoutput (MIMO) system, wherein each user may receive more than one data stream. We propose several mean squared error (MSE) based criteria for joint transmit-receive optimization and establish a series of relationships linking these criteria to the signal-to-interference-plus-noise ratios of individual data streams and the information theoretic channel capacity under linear minimum MSE decoding.In particular, we show that achieving the maximum sum throughput is equivalent to minimizing the product of MSE matrix determinants (PDetMSE). Since the PDetMSE minimization problem does not admit a computationally efficient solution, a simplified scalar version of the problem is considered that minimizes the product of mean squared errors (PMSE). An iterative algorithm is proposed to solve the PMSE problem, and is shown to provide near-optimal performance with greatly reduced computational complexity. Our simulations compare the achievable sum rates under linear precoding strategies to the sum capacity for the broadcast channel.

Near-Optimal Waveform Design for Sum Rate Optimization in Time-Reversal Multiuser Downlink Systems

“…Some of these works [5]- [7] directly optimize the sum rate in the downlink, and some works [6], [8], [9] exploit the uplink-downlink duality [10]- [13] to iteratively optimize the sum rate. Such an iterative solution based on virtual uplink first appeared in [14], [15].…”

“…In [8], the receiver is assumed to know the transmit power allocation, and thus, the receiver is able to normalize the received signal with the transmit power allocation and the resulting problem is shown to be convex. In [9], the weighted sum rate maximization is modelled into minimizing the product of MSE, and sequential quadratic programming is used to locate a local optimum of the minimization. Most previous works on beamforming for multiuser MIMO downlink channels assume flat fading and do not consider the ISI introduced by multipath.…”

Block triangularization: A new linear precoding strategy for Gaussian MIMO BC

“…It has been previously reported in several works (e.g., [3] and [12]) that there is a significant gap between (nonlinear) DPC and linear precoding strategies especially at high SNR. However, it is not known whether this gap is firm by a fundamental difference between the two strategies or is just from local optima due to the non-convexity of linear precoding optimization problems and hence reducible.…”

“…However, it is not known whether this gap is firm by a fundamental difference between the two strategies or is just from local optima due to the non-convexity of linear precoding optimization problems and hence reducible. Moreover, it has been observed that BD and other sophisticated linear precoding strategies tends to be equivalent in sum rate at high SNR (e.g., [12]). …”

Efficient QR-Based Multi-Mode Precoding for Limited Feedback Multi-User MIMO Systems

“…An extension of the SU-MIMO system to the multi-user MIMO (MU-MIMO) system was intended to provide space-division multiple access [10,11]. However, designing multi-mode precoding for MU-MIMO to improve detection performance is even more challenging due to non-cooperative users (i.e., mobile stations) and the complex computations as the number of users grows [12]. Overcoming these difficulties is the motivation for this paper.…”