Improved signal detection for multiuser MIMO system using BD QR-LRL
01 Jan 2015-pp 1-4
TL;DR: Simulation results showed that the proposed technique confirms improved detection compared to the conventional methods for multiuser scenario.
Abstract: QR-Least reliable layer (QR-LRL) technique in fusion with the channel block diagonalization (BD) is proposed for signal detection in the multiuser multiple input and multiple output (MU-MIMO) system. Literature survey shows various precoding techniques like BD-ZF, BD-MMSE, dirty paper coding (DPC) to overcome multiuser interference. However, they suffer in the terms of either noise enhancement or complexity or sum rate capacity. It is also shown in the literature that QR-LRL, an ordered successive interference cancellation (OSIC) detector achieves hard/soft ML performance with low complexity for SM-MIMO systems. In this paper, BD and QR-LRL are associated together in order to enhance the signal detection for MU-MIMO system. Simulation results showed that the proposed technique confirms improved detection compared to the conventional methods for multiuser scenario.
Citations
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TL;DR: This paper proposes a precoding technique called modified maximum ratio transmission, which is able to deal with the problem without much complexity and tests the precoding techniques on the parameters of BER and capacity, to achieve optimum receiver performance.
Abstract: In recent years several of endeavors have been conducted in various papers to improve capacity and bit error rate performance of multiuser MIMO system. Every effort tried to increase gain of the individual stream between base station and users’ antennas while reducing the interference among multiple users and interference between streams of single user itself. All these papers adopted precoding techniques for beam forming of individual stream. Various papers have compared precoding techniques such as DPC, BD-ZF, BD-MMSE, BD-QR-MRL, and BD-QR-LRL. Block diagonalization technique is capable to reduce multiuser interference, but not able to combat interference between antennas of single user itself. BD combines with other techniques to overcome this problem. This paper also proposes a precoding technique called modified maximum ratio transmission, which is able to deal with the problem without much complexity. Simulation is done to compare all precoding technique with MRT. The paper tests the precoding techniques on the parameters of BER and capacity, to achieve optimum receiver performance. BER curve and capacity curve are plotted to conclude the paper.
References
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TL;DR: In this article, the authors examined the performance of using multi-element array (MEA) technology to improve the bit-rate of digital wireless communications and showed that with high probability extraordinary capacity is available.
Abstract: This paper is motivated by the need for fundamental understanding of ultimate limits of bandwidth efficient delivery of higher bit-rates in digital wireless communications and to also begin to look into how these limits might be approached. We examine exploitation of multi-element array (MEA) technology, that is processing the spatial dimension (not just the time dimension) to improve wireless capacities in certain applications. Specifically, we present some basic information theory results that promise great advantages of using MEAs in wireless LANs and building to building wireless communication links. We explore the important case when the channel characteristic is not available at the transmitter but the receiver knows (tracks) the characteristic which is subject to Rayleigh fading. Fixing the overall transmitted power, we express the capacity offered by MEA technology and we see how the capacity scales with increasing SNR for a large but practical number, n, of antenna elements at both transmitter and receiver.
We investigate the case of independent Rayleigh faded paths between antenna elements and find that with high probability extraordinary capacity is available. Compared to the baseline n = 1 case, which by Shannon‘s classical formula scales as one more bit/cycle for every 3 dB of signal-to-noise ratio (SNR) increase, remarkably with MEAs, the scaling is almost like n more bits/cycle for each 3 dB increase in SNR. To illustrate how great this capacity is, even for small n, take the cases n = 2, 4 and 16 at an average received SNR of 21 dB. For over 99% of the channels the capacity is about 7, 19 and 88 bits/cycle respectively, while if n = 1 there is only about 1.2 bit/cycle at the 99% level. For say a symbol rate equal to the channel bandwith, since it is the bits/symbol/dimension that is relevant for signal constellations, these higher capacities are not unreasonable. The 19 bits/cycle for n = 4 amounts to 4.75 bits/symbol/dimension while 88 bits/cycle for n = 16 amounts to 5.5 bits/symbol/dimension. Standard approaches such as selection and optimum combining are seen to be deficient when compared to what will ultimately be possible. New codecs need to be invented to realize a hefty portion of the great capacity promised.
10,526 citations
TL;DR: It is shown that the optimal transmitter adapts its signal to the state S rather than attempting to cancel it, which is also the capacity of a standard Gaussian channel with signal-to-noise power ratio P/N.
Abstract: A channel with output Y = X + S + Z is examined, The state S \sim N(0, QI) and the noise Z \sim N(0, NI) are multivariate Gaussian random variables ( I is the identity matrix.). The input X \in R^{n} satisfies the power constraint (l/n) \sum_{i=1}^{n}X_{i}^{2} \leq P . If S is unknown to both transmitter and receiver then the capacity is \frac{1}{2} \ln (1 + P/( N + Q)) nats per channel use. However, if the state S is known to the encoder, the capacity is shown to be C^{\ast} =\frac{1}{2} \ln (1 + P/N) , independent of Q . This is also the capacity of a standard Gaussian channel with signal-to-noise power ratio P/N . Therefore, the state S does not affect the capacity of the channel, even though S is unknown to the receiver. It is shown that the optimal transmitter adapts its signal to the state S rather than attempting to cancel it.
4,130 citations
"Improved signal detection for multi..." refers background or methods in this paper
...The dirty paper coding (DPC) is one such precoding scheme that involves removing possible interferences before transmission using the prior knowledge of the channel [6, 15]....
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...This performance is then compared with the BER performance of a MU-MIMO system using the conventional methods like Maximum Likelihood (ML), DPC [15], ZF [6], MMSE[14] and QR-MRL detectors with BD as the precoder for pre-processing the transmitted signals....
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29 Sep 1998
TL;DR: This paper describes a wireless communication architecture known as vertical BLAST (Bell Laboratories Layered Space-Time) or V-BLAST, which has been implemented in real-time in the laboratory and demonstrated spectral efficiencies of 20-40 bps/Hz in an indoor propagation environment at realistic SNRs and error rates.
Abstract: Information theory research has shown that the rich-scattering wireless channel is capable of enormous theoretical capacities if the multipath is properly exploited In this paper, we describe a wireless communication architecture known as vertical BLAST (Bell Laboratories Layered Space-Time) or V-BLAST, which has been implemented in real-time in the laboratory Using our laboratory prototype, we have demonstrated spectral efficiencies of 20-40 bps/Hz in an indoor propagation environment at realistic SNRs and error rates To the best of our knowledge, wireless spectral efficiencies of this magnitude are unprecedented and are furthermore unattainable using traditional techniques
3,925 citations
"Improved signal detection for multi..." refers methods in this paper
...VBLAST, another type of the signal detection method, is an OSIC technique that offers a good accordance between the complexity and performance [7]....
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...The OSIC detector has gone through an endless research lately [3-4, 7-12]....
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TL;DR: While the proposed algorithms are suboptimal, they lead to simpler transmitter and receiver structures and allow for a reasonable tradeoff between performance and complexity.
Abstract: The use of space-division multiple access (SDMA) in the downlink of a multiuser multiple-input, multiple-output (MIMO) wireless communications network can provide a substantial gain in system throughput. The challenge in such multiuser systems is designing transmit vectors while considering the co-channel interference of other users. Typical optimization problems of interest include the capacity problem - maximizing the sum information rate subject to a power constraint-or the power control problem-minimizing transmitted power such that a certain quality-of-service metric for each user is met. Neither of these problems possess closed-form solutions for the general multiuser MIMO channel, but the imposition of certain constraints can lead to closed-form solutions. This paper presents two such constrained solutions. The first, referred to as "block-diagonalization," is a generalization of channel inversion when there are multiple antennas at each receiver. It is easily adapted to optimize for either maximum transmission rate or minimum power and approaches the optimal solution at high SNR. The second, known as "successive optimization," is an alternative method for solving the power minimization problem one user at a time, and it yields superior results in some (e.g., low SNR) situations. Both of these algorithms are limited to cases where the transmitter has more antennas than all receive antennas combined. In order to accommodate more general scenarios, we also propose a framework for coordinated transmitter-receiver processing that generalizes the two algorithms to cases involving more receive than transmit antennas. While the proposed algorithms are suboptimal, they lead to simpler transmitter and receiver structures and allow for a reasonable tradeoff between performance and complexity.
3,291 citations
"Improved signal detection for multi..." refers methods in this paper
...For DPC, BD-ZF and BD-MMSE such BER is not attained within 19 dB range....
[...]
...Literature survey shows various precoding techniques like BD-ZF, BD-MMSE, dirty paper coding (DPC) to overcome multiuser interference....
[...]
...To minimize it, equalization is done using the linear detectors like zero forcing (ZF) and minimum mean square error (MMSE) [5-6]....
[...]
...2 and 3, it is clear that proposed method performs relatively better than BD-ZF, BD-MMSE and BD QR-MRL when compared on the same error floor (apx....
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...On comparing BD QR-LRL with BD-ZF, BD-MMSE and BD QR-MRL, it is found that in spite of being more complex than BD-ZF and BD-MMSE and of similar complexity as that of BD QR-MRL, it still provides the better performance than any of these methods....
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Book•
16 Nov 2010TL;DR: In this article, the authors provide a comprehensive introduction to the theory and practice of wireless channel modeling, OFDM, and MIMO, using MATLAB programs to simulate the various techniques on a wireless network.
Abstract: MIMO-OFDM is a key technology for next-generation cellular communications (3GPP-LTE, Mobile WiMAX, IMT-Advanced) as well as wireless LAN (IEEE 802.11a, IEEE 802.11n), wireless PAN (MB-OFDM), and broadcasting (DAB, DVB, DMB). In MIMO-OFDM Wireless Communications with MATLAB, the authors provide a comprehensive introduction to the theory and practice of wireless channel modeling, OFDM, and MIMO, using MATLAB programs to simulate the various techniques on MIMO-OFDM systems. One of the only books in the area dedicated to explaining simulation aspects Covers implementation to help cement the key concepts Uses materials that have been classroom-tested in numerous universities Provides the analytic solutions and practical examples with downloadable MATLAB codes Simulation examples based on actual industry and research projects Presentation slides with key equations and figures for instructor use MIMO-OFDM Wireless Communications with MATLAB is a key text for graduate students in wireless communications. Professionals and technicians in wireless communication fields, graduate students in signal processing, as well as senior undergraduates majoring in wireless communications will find this book a practical introduction to the MIMO-OFDM techniques. Instructor materials and MATLAB code examples available for download at www.wiley.com/go/chomimo
1,413 citations
"Improved signal detection for multi..." refers background or methods in this paper
...For DPC, BD-ZF and BD-MMSE such BER is not attained within 19 dB range....
[...]
...The dirty paper coding (DPC) is one such precoding scheme that involves removing possible interferences before transmission using the prior knowledge of the channel [6, 15]....
[...]
...Literature survey shows various precoding techniques like BD-ZF, BD-MMSE, dirty paper coding (DPC) to overcome multiuser interference....
[...]
...To minimize it, equalization is done using the linear detectors like zero forcing (ZF) and minimum mean square error (MMSE) [5-6]....
[...]
...2 and 3, it is clear that proposed method performs relatively better than BD-ZF, BD-MMSE and BD QR-MRL when compared on the same error floor (apx....
[...]