Multiuser orthogonal space-division multiplexing with iterative water-filling algorithm

The problem of multiuser multiplexing with a MIMO sub system for each individual user is considered. We demonstrate that the capacity performance of the null space based spatial multiplexing schemes can be improved with iterative power allocation within the iterative design process. We considered water-filling based local and global power allocation and demonstrate that both schemes outperform the existing null space based spatial diversity technique in terms of mean capacity and outage capacity

A GMD-based precoding scheme for downlink multiuser multistream MIMO channels

In order to obtain a good balance of bit error rate (BER) across channels, the geometric mean decomposition (GMD) is introduced to replace the singular value decomposition (SVD) for precoding in the downlink of a multiuser multistream multiple-input multiple-output (MIMO) system. By combining GMD with a block diagonalization method, we obtain two kinds of precoding schemes: iterative nullspace-directed GMD and non-iterative nullspace-directed GMD. Considering their respective advantages and disadvantages, a mixed nullspace-directed GMD is proposed to solve the convergence related problems of the iterative method. Furthermore, the computational complexity of the mixed scheme is similar to the iterative scheme under the same conditions. The simulation results show that the average BER performance of the block diagonalization method based on GMD is better than the same method based on SVD, and the mixed nullspace-directed GMD outperforms the iterative nullspace-directed GMD and the non-iterative nullspace-directed GMD

Alternating Minimization for Wideband Multiuser IRS-Aided MIMO Systems Under Imperfect CSI

© 2023 IEEE. This version of the article has been accepted for publication, after peer review. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The Version of Record is available online at: https://doi.org/10.1109/TSP.2023.3336166[Abstract]: This work focuses on wideband intelligent reflecting surface (IRS)-aided multiuser MIMO systems. One of the major challenges of this scenario is the joint design of the frequency-dependent base station (BS) precoder and user filters, and the IRS phase-shift matrix which is frequency flat and common to all the users. In addition, we consider that the channel state information (CSI) is imperfect at both the transmitter and the receivers. A statistical model for the imperfect CSI is developed and exploited for the system design. A minimum mean square error (MMSE) approach is followed to determine the IRS phase-shift matrix, the transmit precoders, and the receiving filters. The broadcast (BC)- multiple access channel (MAC) duality is used to solve the optimization problem following an alternating minimization approach. Numerical results show that the proposed approach leads to substantial performance gains with respect to baseline strategies that neglect the inter-user interference and do not optimize the IRS phase-shift matrix. Further performance gains are obtained when incorporating into the system design the statistical information of the channel estimation errors.This work was supported by Grants PID2019-104958RB-C42 (ADELE), PID2022-137099NB-C42 (MADDIE), and BES-2017-081955 funded by MCIN/AEI/10.13039/501100011033. José P. González-Coma thanks the Defense University Center at the Spanish Naval Academy for all the support provided for this research

User grouping and resource allocation in multiuser MIMO systems under SWIPT

This paper considers a broadcast multiple-input multiple-output (MIMO) network with multiple users and simultaneous wireless information and power transfer (SWIPT). In this scenario, it is assumed that some users are able to harvest power from radio frequency (RF) signals to recharge batteries through wireless power transfer from the transmitter, while others are served simultaneously with data transmission. The criterion driving the optimization and design of the system is based on the weighted sum rate for the users being served with data. At the same time, constraints stating minimum per-user harvested powers are included in the optimization problem. This paper derives the structure of the optimal transmit covariance matrices in the case where both types of users are present simultaneously in the network, particularizing the results to the cases where either only harvesting nodes or only information users are to be served. The trade-off between the achieved weighted sum rate and the powers harvested by the user terminals is analyzed and evaluated using the rate-power (R-P) region. Finally, we propose a two-stage user grouping mechanism that decides which users should be scheduled to receive information and which users should be configured to harvest energy from the RF signals in each particular scheduling period, this being one of the main contributions of this paper.Peer ReviewedPostprint (published version

Transmit-Receive Generalized Spatial Modulation Based on Dual-layered MIMO Transmission

We propose a novel scheme for downlink multiuser multiple-input multiple-output (MIMO) systems, called dual-layered transmit-receive generalized spatial modulation (DL-TR-GSM). The proposed scheme is based on the concept of dual-layered transmission (DLT) which uses two receive antenna power levels instead of receive antenna activation/inactivation to transmit data in the receive spatial domain. Hence, in order to minimize the bit error rate (BER) for DL-TR-GSM, the optimal ratio between the two power levels is determined. To further characterize DL-TR-GSM, we fully derive the computational complexity and show a significant computational complexity reduction as well as a required hardware complexity reduction of DL-TR-GSM, compared to a state-of-the-art benchmark scheme. Simulation results confirm the performance advantages of DL-TR-GSM

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