Receive Combining vs. Multi-Stream Multiplexing in Downlink Systems with Multi-Antenna Users

In downlink multi-antenna systems with many users, the multiplexing gain is strictly limited by the number of transmit antennas $N$ and the use of these antennas. Assuming that the total number of receive antennas at the multi-antenna users is much larger than $N$, the maximal multiplexing gain can be achieved with many different transmission/reception strategies. For example, the excess number of receive antennas can be utilized to schedule users with effective channels that are near-orthogonal, for multi-stream multiplexing to users with well-conditioned channels, and/or to enable interference-aware receive combining. In this paper, we try to answer the question if the $N$ data streams should be divided among few users (many streams per user) or many users (few streams per user, enabling receive combining). Analytic results are derived to show how user selection, spatial correlation, heterogeneous user conditions, and imperfect channel acquisition (quantization or estimation errors) affect the performance when sending the maximal number of streams or one stream per scheduled user---the two extremes in data stream allocation. While contradicting observations on this topic have been reported in prior works, we show that selecting many users and allocating one stream per user (i.e., exploiting receive combining) is the best candidate under realistic conditions. This is explained by the provably stronger resilience towards spatial correlation and the larger benefit from multi-user diversity. This fundamental result has positive implications for the design of downlink systems as it reduces the hardware requirements at the user devices and simplifies the throughput optimization.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 11 figures. The results can be reproduced using the following Matlab code: https://github.com/emilbjornson/one-or-multiple-stream

Channel estimation in massive MIMO systems

Last years were characterized by a great demand for high data throughput, good quality and spectral efficiency in wireless communication systems. Consequently, a revolution in cellular networks has been set in motion towards to 5G. Massive multiple-input multiple-output (MIMO) is one of the new concepts in 5G and the idea is to scale up the known MIMO systems in unprecedented proportions, by deploying hundreds of antennas at base stations. Although, perfect channel knowledge is crucial in these systems for user and data stream separation in order to cancel interference. The most common way to estimate the channel is based on pilots. However, problems such as interference and pilot contamination (PC) can arise due to the multiplicity of channels in the wireless link. Therefore, it is crucial to define techniques for channel estimation that together with pilot contamination mitigation allow best system performance and at same time low complexity. This work introduces a low-complexity channel estimation technique based on Zadoff-Chu training sequences. In addition, different approaches were studied towards pilot contamination mitigation and low complexity schemes, with resort to iterative channel estimation methods, semi-blind subspace tracking techniques and matrix inversion substitutes. System performance simulations were performed for the several proposed techniques in order to identify the best tradeoff between complexity, spectral efficiency and system performance

Transmit antenna selection and user selection in multiuser MIMO downlink systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonMultiuser multiple input multiple output (MU-MIMO) systems play essential role in improving throughput performance and link reliability in wireless communications. This improvement can be achieved by exploiting the spatial domain and without the need of additional power and bandwidth. In this thesis, three main issues which are of importance to the data rate transmission have been investigated. Firstly, antenna selection in MU-MIMO downlink systems has been considered, where this technique can be e fficiently used to reduce the complexity and cost caused by radio frequency chains, associated with antennas, while keeping most of the diversity advantages of the system. We proposed a transmit antenna selection algorithm which can select an optimal set of antennas for transmission in descending order depending on the product of eigenvalues of users' effective channels. The capacity achieved by the proposed algorithm is about 99:6% of the capacity of the optimum search method, with much lower complexity. Secondly, user selection technology in MU-MIMO downlink systems has been studied. Based on the QR decomposition, we proposed a greedy suboptimal user selection algorithm which adopts the product of singular values of users' effective channels as a selection metric. The performance achieved by the proposed algorithm is identical to that of the capacity-based algorithm, with significant reduction in complexity. Finally, a proportional fairness scheduling algorithm for MU-MIMO downlink systems has been proposed. By utilising the upper triangular matrix obtained by applying the QRD on the users' effective channel matrices, two selection metrics have been proposed to achieve the scheduling process. The first metric is based on the maximum entry of the upper triangular matrix, while the second metric is designed using the ratio between the maximum and minimum entries of the triangular matrix multiplied by the product of singular values of effective channels. The two metric provide significant degrees of fairness. For each of these three issues, a different precoding method has been used in order to cancel the interuser interference before starting the selection process. This allows to investigate each precoding design separately and to evaluate the computational burden required for each design.Ministry of Higher Education-Ira

Two-Stage Subspace Constrained Precoding in Massive MIMO Cellular Systems

We propose a subspace constrained precoding scheme that exploits the spatial channel correlation structure in massive MIMO cellular systems to fully unleash the tremendous gain provided by massive antenna array with reduced channel state information (CSI) signaling overhead. The MIMO precoder at each base station (BS) is partitioned into an inner precoder and a Transmit (Tx) subspace control matrix. The inner precoder is adaptive to the local CSI at each BS for spatial multiplexing gain. The Tx subspace control is adaptive to the channel statistics for inter-cell interference mitigation and Quality of Service (QoS) optimization. Specifically, the Tx subspace control is formulated as a QoS optimization problem which involves an SINR chance constraint where the probability of each user's SINR not satisfying a service requirement must not exceed a given outage probability. Such chance constraint cannot be handled by the existing methods due to the two stage precoding structure. To tackle this, we propose a bi-convex approximation approach, which consists of three key ingredients: random matrix theory, chance constrained optimization and semidefinite relaxation. Then we propose an efficient algorithm to find the optimal solution of the resulting bi-convex approximation problem. Simulations show that the proposed design has significant gain over various baselines.Comment: 13 pages, accepted by IEEE Transactions on Wireless Communication

Multi-cell cooperation for future wireless systems

Portuguese CADWIN - PTDC/EEA TEL/099241/2008Portuguese Foundation for Science and Technology (FCT