In multiuser MIMO networks, the spatial degrees of freedom offered by multiple antennas can be advantageously exploited to enhance the system capacity, by scheduling multiple users to simultaneously share the spatial channel. This entails a fundamental paradigm shift from single user communications, since multiuser systems can experience substantial benefit from channel state information at the transmit-ter and, at the same time, require more complex scheduling strategies and transceiver methodologies. This paper reviews multiuser MIMO communication from an algorithmic perspective, discussing performance gains, tradeoffs, and practical considerations. Several approaches including non-linear and linear channel-aware precoding are reviewed, along with more practical limited feedback schemes that require only partial channel state information. The interaction between precoding and scheduling is discussed. Several promising strategies for limited multiuser feedback design are looked at, some of which are inspired from the single user MIMO precoding scenario while others are fully specific to the multiuser setting. 1 DRAFT

Jointly optimized linear transmit beamforming and receive combining is a low complexity approach for communication in the multiuser MIMO (multiple input multiple output) broadcast channel. This paper proposes an iterative algorithm for jointly designing the beamforming and combining vectors, which enforces a zero interference requirement after combining. Since the optimization is performed at the base station with channel state information for all the users, the receive beamformers are quantized at the basestation and sent to the users via a lowrate feedforward control channel. Rate bounds are provided to estimate the impact of quantization loss on the achievable rate in Rayleigh channels is performed. Simulations show that the proposed approach using Grassmannian codebooks approaches the sum capacity of the MIMO broadcast channel.

In this paper, a novel transmission technique for the multiple-input multiple-output (MIMO) broad-cast channel is proposed that allows simultaneous transmission to multiple users with limited feedback from each user. During a training phase, the base station modulates a training sequence on multiple sets of randomly chosen orthogonal beamforming vectors. Each user sends the index of the best beamforming vector and the corresponding signal-to-interfence-plus-noise ratio for that set of orthogonal vectors back to the base station. The base station opportunistically determines the users and corresponding orthogonal vectors that maximize the sum capacity. Based on the capacity expressions, the optimal amount of training to maximize the sum capacity is derived as a function of the system parameters. The main advantage of the proposed system is that it provides throughput gains for the MIMO broadcast channel with a small feedback overhead, and provides these gains even with a small number of active users. Numerical simulations show that a 20 % gain in sum capacity is achieved (for a small number of users) over conventional opportunistic space division multiple access, and a 100 % gain (for a large number of users) over conventional opportunistic beamforming when the number of transmit antennas is four.

On the multi-antenna broadcast channel, the spatial degrees of freedom support simultaneous transmission to multiple users. Optimal multi-user transmission, known as dirty paper coding, requires non-causal channel state information (CSI) and extreme complexity and is hence not directly realizable. A more practical design, named per user unitary and rate control (PU2RC), has been proposed for emerging cellular standards. PU2RC supports multi-user simultaneous transmission, enables limited feedback, and is capable of exploiting multi-user diversity. Its key feature is an orthogonal beamforming (or precoding) constraint, where each user selects a beamformer (or precoder) from a codebook of multiple orthonormal bases. In this paper, the asymptotic throughput scaling laws for PU2RC with a large user pool are derived for different regimes. In the interference-limited regime, the throughput of PU2RC is shown to scale logarithmically with the number of users. In the normal and noise-limited regimes, the throughput is found to scale double logarithmically with the number of users and also linearly with the number of antennas at the base station. In addition, numerical results show that PU2RC achieves higher throughput and is more robust against CSI quantization errors than the popular alternative of zero-forcing beamforming if the number of users is sufficiently large.

Abstract—The downlink transmission in multi-user multipleinput multiple-output (MIMO) systems has been extensively studied from both communication-theoretic and information-theoretic perspectives. Most of these papers assume perfect/imperfect channel knowledge. In general, the problem of channel estimation is studied separately. However, in interference-limited communication systems with high mobility, the problem of channel estimation is tightly coupled with the problem of maximizing throughput of the system. In this paper, scheduling and preconditioning in the presence of reciprocal time-division duplex (TDD) training are considered. In the case of homogeneous users, a scheduling scheme is proposed and an improved lower bound on the sum capacity is derived. The problem of choosing training sequence length to maximize net throughput of the system is also studied. In the case of heterogeneous users, a modified pre-conditioning method is proposed and an optimized pre-conditioning matrix is derived. This method is combined with a scheduling scheme to further improve achievable weighted-sum rate. I.

This paper presents an opportunistic multiple-input multiple-output (MIMO) scheme that achieves increased throughput of multi-user downlink transmission in frequency-selective channels. Developed for OFDM systems, the main idea of the proposed scheme is to find the optimal subset of users for each subchannel and optimal transmit beamforming vector which would maximize the sum-rate. Assuming perfectly known channel information, an optimal solution is derived and a suboptimal solution with much lower complexity that provides comparable results to the optimal solution is also presented. The proposed scheme outperforms the existing FDMAbased and SDMA-based schemes significantly in terms of sum-rate by efficiently exploiting the time-varying nature of radio channels and multi-user diversity.

Abstract—Link adaptation is a crucial part of many modern communications systems, allowing the system to adapt the transmission and reception strategies to changes in channel conditions. One of the fundamental components of the link adaptation mechanism is signal to noise ratio (SNR) estimation, measuring the instantaneous (mostly post processing) SNR at the receiver. That is, the SNR at the decoder input, which is an important metric for the prediction of decoder performance. In linearly decoded MIMO, which is the common MIMO decoding strategy, the post processing SNR is well defined. However, this is not the case in optimal maximum likelihood (ML) decoding applied to spatial multiplexing (SM). This gap is interesting since ML decoded SM is gaining ever growing interest in recent research and practice due to the rapid increase in computation power, and available near optimal low complexity schemes. In this paper we close the gap and provide SNR estimation schemes for ML decoded SM, which are based on various approximations of the ”per stream ” error probability. The proposed methods are applicable for both horizonal and vertical decoding. Moreover, we propose a very low complexity implementation for the SNR estimation mechanism employing the ML decoder itself with negligible overhead.

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. I.

The downlink transmission in multi-user multiple antenna wireless communication systems is generally studied assuming channel state knowledge and the topic of determining this channel knowledge is considered as an unrelated topic. However, in practical interference-limited systems with mobile users, the two problems are tightly coupled, with a tradeoff existing between the two. In this paper, this coupling is explicitly characterized as follows: channel training overhead and estimation error are rigorously accounted for while determining the net system throughput. First, a transmission method with training on reverse link only is considered. Scheduling and precoding based transmission schemes are developed that effectively utilize the channel estimation process on the reverse link in improving net throughput. The schemes are applicable in the general setting of heterogeneous users with arbitrary weights assigned to these users, where the objective is to maximize net weighted-sum throughput. Next, a transmission method with forward link training in addition to reverse link channel training is considered. In this setting, a different precoding scheme is developed where the users utilize the forward pilots to estimate the effective channel gains. I.