Abstract

This paper provides a comprehensive overview of critical developments in the field of multiple-input multiple-output (MIMO) wireless communication systems. The state of the art in single-user MIMO (SU-MIMO) and multiuser MIMO (MU-MIMO) communications is presented, highlighting the key aspects of these technologies. Both open-loop and closed-loop SU-MIMO systems are discussed in this paper with particular emphasis on the data rate maximization aspect of MIMO. A detailed review of various MU-MIMO uplink and downlink techniques then follows, clarifying the underlying concepts and emphasizing the importance of MU-MIMO in cellular communication systems. This paper also touches upon the topic of MU-MIMO capacity as well as the promising convex optimization approaches to MIMO system design.

Highlights

  • Multiple-input multiple-output (MIMO) wireless systems employ multiple transmit and receive antennas to increase the transmission data rate through spatial multiplexing or to improve system reliability in terms of bit error rate (BER) performance using space-time codes (STCs) for diversity maximization [1]

  • This paper provides a comprehensive overview of critical developments in the field of multiple-input multiple-output (MIMO) wireless communication systems

  • Dirty paper coding techniques based on vector precoding approach the sum capacity of the multiuser MIMO (MU-MIMO) downlink channel, which is defined as the maximum system throughput achieved by maximizing the sum of the information rates of all the users [48]

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Summary

Introduction

Multiple-input multiple-output (MIMO) wireless systems employ multiple transmit and receive antennas to increase the transmission data rate through spatial multiplexing or to improve system reliability in terms of bit error rate (BER) performance using space-time codes (STCs) for diversity maximization [1]. More recent MIMO techniques like the geometric mean decomposition (GMD) technique proposed in [2] aim at combining the diversity and data rate maximization aspects of MIMO in an optimal manner. These advantages make MIMO a very attractive and promising option for future mobile communication systems especially when combined with the benefits of orthogonal frequency-division multiplexing (OFDM) [3,4].

Current Implementation Status
V-BLAST
Single-User MIMO Techniques
Spatial Multiplexing with Cyclic Delay Diversity
Singular Value Decomposition Based MIMO Precoding
SVD Precoding with MMSE Equalization
Improved SVD Precoding Technique with Realistic Channel Knowledge
KHALID
Limited Feedback Strategies for Closed-loop MIMO Systems
MIMO over High-Speed Mobile Channels
Multiuser MIMO
The MU-MIMO Uplink
The MU-MIMO Downlink
MU-MIMO Capacity
Convex Optimization
Findings
Conclusions and Future Research

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