Beamforming Through Reconfigurable Intelligent Surfaces in Single-User MIMO Systems: SNR Distribution and Scaling Laws in the Presence of Channel Fading and Phase Noise

Self-encoded spread spectrum (SESS) is a novel communication technique that derives its spreading code from the randomness of the source stream rather than using conventional pseudorandom noise (PN) code. In this paper, we propose to incorporate SESS in multiple-input multiple-output (MIMO) systems as a means to combat against fading effects in wireless channels. Orthogonal space-time block-coded MIMO technique is employed to achieve spatial diversity, and the inherent temporal diversity in SESS modulation is exploited with iterative detection. Simulation results demonstrate that MIMO-SESS can effectively mitigate the channel fading effect such that the system can achieve a bit error rate of with very low signal-to-noise ratio, from 3.3 dB for a antenna configuration to just less than 0 dB for a configuration under Rayleigh fading. The performance improvement for the case is as much as 6.7 dB when compared to an MIMO PN-coded spread spectrum system.

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Joint channel estimation and resource allocation for MIMO systems-part I: single-user analysis

IEEE Transactions on Wireless Communications ◽

10.1109/twc.2010.5403543 ◽

2010 ◽

Vol 9 (2) ◽

pp. 624-631 ◽

Cited By ~ 22

Author(s):

Alkan Soysal ◽

Sennur Ulukus

Keyword(s):

Resource Allocation ◽

Channel Estimation ◽

Mimo Systems ◽

Single User ◽

Joint Channel

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Precoding for Multiuser MIMO

Developments in Wireless Network Prototyping, Design, and Deployment ◽

10.4018/978-1-4666-1797-1.ch007 ◽

2012 ◽

pp. 130-156 ◽

Cited By ~ 1

Author(s):

Elsadig Saeid ◽

Varun Jeoti ◽

Brahim Belhaouari Samir

Keyword(s):

Mimo Systems ◽

Mimo System ◽

Multiple Input Multiple Output ◽

Achievable Rate ◽

Multiple Access Channels ◽

Multiple Input ◽

Input Multiple Output ◽

Single User ◽

A New Technique ◽

Improved Technique

Future Wireless Networks are expected to adopt multi-user multiple input multiple output (MU-MIMO) systems whose performance is maximized by making use of precoding at the transmitter. This chapter describes the recent advances in precoding design for MU-MIMO and introduces a new technique to improve the precoder performance. Without claiming to be comprehensive, the chapter gives deep introduction on basic MIMO techniques covering the basics of single user multiple input multiple output (SU-MIMO) links, its capacity, various transmission strategies, SU-MIMO link precoding, and MIMO receiver structures. After the introduction, MU-MIMO system model is defined and maximum achievable rate regions for both MU-MIMO broadcast and MU-MIMO multiple access channels are explained. It is followed by critical literature review on linear precoding design for MU-MIMO broadcast channel. This paves the way for introducing an improved technique of precoding design that is followed by its performance evaluation.

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Exploiting Channel Gain and Phase Noise for PHY-layer Authentication in Massive MIMO Systems

IEEE Transactions on Information Forensics and Security ◽

10.1109/tifs.2020.3029894 ◽

2020 ◽

pp. 1-1

Author(s):

Pinchang Zhang ◽

Jun Liu ◽

Yulong Shen ◽

Xiaohong Jiang

Keyword(s):

Phase Noise ◽

Massive Mimo ◽

Mimo Systems ◽

Channel Gain ◽

Phy Layer

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Minimum Symbol Error Probability MIMO Design under the Per-Antenna Power Constraint

Journal of Electrical and Computer Engineering ◽

10.1155/2012/290390 ◽

2012 ◽

Vol 2012 ◽

pp. 1-9

Author(s):

Enoch Lu ◽

I.-Tai Lu

Keyword(s):

Upper Bound ◽

Error Probability ◽

Minimum Distance ◽

Necessary Conditions ◽

Mimo Systems ◽

Symbol Error Probability ◽

Power Constraint ◽

Rank One ◽

Single User ◽

Numerical Approaches

Approximate minimum symbol error probability transceiver design of single user MIMO systems under the practical per-antenna power constraint is considered. The upper bound of a lower bound on the minimum distance between the symbol hypotheses is established. Necessary conditions and structures of the transmit covariance matrix for reaching the upper bound are discussed. Three numerical approaches (rank zero, rank one, and permutation) for obtaining the optimum precoder are proposed. When the upper bound is reached, the resulting design is optimum. When the upper bound is not reached, a numerical fix is used. The approach is very simple and can be of practical use.

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