Detection techniques for MIMO spatial multiplexing systems

Seethaler, D.; Artes, H.; Hlawatsch, Franz

doi:10.1007/bf03054042

Cited by 18 publications

(18 citation statements)

References 20 publications

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“…First, the ZF and MMSE detectors are the basic building blocks of advanced MIMO communication architectures (e.g., layered space-time architectures (Foschini, 1996;Seethaler, Artés & Hlawatsch, 2004) and joint transmit-receive equalizers (Palomar & Lagunas, 2003;Jiang, Li & Hager, 2005) ), and have been extensively addressed in the MIMO literature (Jankiraman, 2004;Biglieri et al, 2007;Heath Jr & Lozano, 2018). Second, they have low computational complexity compared to the (optimum) maximum likelihood (ML) detector, and their performance can be very close to the ML performance for a well-conditioned MIMO channel, i.e., its condition number is near to unity (see Seethaler, Artés & Hlawatsch (2005) for more details).…”

Section: Related Workmentioning

confidence: 99%

On the secrecy performance of transmit-receive diversity and spatial multiplexing systems

Maichalernnukul

2019

PeerJ Computer Science

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Emerging from the information-theoretic characterization of secrecy, physical-layer security exploits the physical properties of the wireless channel for security purpose. In recent years, a great deal of attention has been paid to investigating the physical-layer security issues in multiple-input multiple-output (MIMO) wireless communications. This paper analyzes the secrecy performance of transmit-receive diversity system and spatial multiplexing systems with zero-forcing equalization and minimum mean-square-error equalization. Specifically, exact and asymptotic closed-form expressions are derived for the secrecy outage probability of such MIMO systems in a Rayleigh fading environment, and the corresponding secrecy diversity orders and secrecy array gains are determined. Numerical results are presented to corroborate the analytical results and to examine the impact of various system parameters, including the numbers of antennas at the transmitter, the legitimate receiver, and the eavesdropper. These contributions bring about valuable insights into the physical-layer security in MIMO wireless systems.

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Section: Related Workmentioning

confidence: 99%

On the secrecy performance of transmit-receive diversity and spatial multiplexing systems

Maichalernnukul

2019

PeerJ Computer Science

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“…Knowing the exact ML solution is desired since it serves as a benchmark to assess how various detectors perform relative to the optimum solution [12]. When n T is large (tens to hundreds), computing the exact ML solution becomes infeasible due to the exponential complexity.…”

Section: Optimum Detectorsmentioning

confidence: 99%

A Primer on MIMO Detection Algorithms for 5G Communication Network

Idowu-Bismark

Okokpujie

Idachaba

et al. 2018

IRECAP

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In the recent past, demand for large use of mobile data has increased tremendously due to the proliferation of hand held devices which allows millions of people access to video streaming, VOIP and other internet related usage including machine to machine (M2M) communication. One of the anticipated attribute of the fifth generation (5G) network is its ability to meet this humongous data rate requirement in the order of 10s Gbps. A particular promising technology that can provide this desired performance if used in the 5G network is the massive multiple-input, multiple-output otherwise called the Massive MIMO. The use of massive MIMO in 5G cellular network where data rate of the order of 100x that of the current state of the art LTE-A is expected and high spectral efficiency with very low latency and low energy consumption, present a challenge in symbol/signal detection and parameter estimation as a result of the high dimension of the antenna elements required. One of the major bottlenecks in achieving the benefits of such massive MIMO systems is the problem of achieving detectors with realistic low complexity for such huge systems. We therefore review various MIMO detection algorithms aiming for low computational complexity with high performance and that scales well with increase in transmit antennas suitable for massive MIMO systems. We evaluate detection algorithms for small and medium dimension MIMO as well as a combination of some of them in order to achieve our above objectives. The review shows no single one detector can be said to be ideal for massive MIMO and that the low complexity with optimal performance detector suitable for 5G massive MIMO system is still an open research issue. A comprehensive review of such detection algorithms for massive MIMO was not presented in the literature which was achieved in this work.

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“…if H n,m channel coefficient is independent Maximum diversity is available, because each information data b m is transmitted over N R independent scalar fading channels H n,m , n = 1,…, N R [6] . If larger is N R, smaller is the possibility that all of channels fade at a time and thus the data detection reliability can be increased.…”

Section: System Modelmentioning

confidence: 99%

“…In equalization detection, an estimation of transmitted information b is made as d = Gv with an "equalization matrix" G. The detected information vector is obtained as = { }, where Q {•} indicate the component wise according to the symbol [6]. For the zero-forcing equalizer, the equalization matrix G is given by pseudoinverse of H, it is given by G = # = ( H) -1 H H .…”

Section: Equalization Based Detectionmentioning

confidence: 99%