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Journal Article

A Novel detection algorithm for performanceanalysis of MIMO-OFDM systems usingequalizer over a Rayleigh fading channel

TL;DR: In this paper antenna configuration is used and QPSK modulation is treated here for simulation purpose, and signal-noise ratio (SNR) curve of Constant modulus algorithm(CMA) equalizer exceeds that of ZF, MMSE and ML equalizer.
Abstract: Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing(OFDM) systems have recently emerged as key technology in wireless communication systems for increasing data rate and system performance. The effect of fading and interference can be combated to increase the capacity of the link. MIMO systems uses Multiple Transmit and Multiple Receive antennas which exploit the multipath propagation in rich scattering environment. The matrix channel plays a pivotal role in the throughput of a MIMO link since the modulation, data rate, power allocation and antenna weights are dependent on the channel gain. When data rate is transmitted at high bit rate, the channel impulse response can extend over many symbol periods which leads to Inter-Symbol Interference(ISI). ISI always caused an issue for signal recovery in wireless communication. In order to reduce the complexity of MIMO system, various detection algorithm such as Zero forcing(ZF), Minimum Mean Square Error(MMSE), Maximum Likelihood(ML) and a novel algorithm namely Constant Modulus Algorithm(CMA) are proposed that reduce bit error rate(BER) via spatial multiplexing. QPSK modulation is treated here for simulation purpose.Simulations are done by MatLab that shows BER vs. signal-noise ratio (SNR) curve of Constant modulus algorithm(CMA) equalizer exceeds that of ZF, MMSE and ML equalizer. In this paper antenna configuration is used.

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Citations
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Proceedings ArticleDOI
01 Sep 2016
TL;DR: A small length polar, encoded for a MIMO (Multiple-Input Multiple-Output) systems with soft output MMSE-SIC (Minimum Mean Square Error-Successive Cancellation) detection, is applied to improve the coded performance while reducing the complexity.
Abstract: Polar codes are proven capacity-achieving and are shown to have equivalent or even better finite length performance than turbo/LDPC codes under some improved decoding algorithm over the Additive White Gaussian Noise (AWGN) channels. Polar coding is based on the so-called channel polarization phenomenon induced by a transform over the underlying binary-input channel. The channel polarization is found to be universal in many signal processing problems and is applied to the coded modulation schemes. In this paper, a small length polar, encoded for a MIMO (Multiple-Input Multiple-Output) systems with soft output MMSE-SIC (Minimum Mean Square Error-Successive Cancellation) detection, is applied to improve the coded performance while reducing the complexity. In order to prove this theory, we compare the proposed MMSE-SIC BER to Zero Forcing (ZF) and Maximum Likelihood (ML) by using 2*2 MIMO systems into Rayleigh channel with BPSK (Binary Phase-Shift Keying) modulation. Simulation results show that MMSE-SIC complexity is lower than the two others detections. We show that the performance of the proposed approach using polar code (128, 64) at 10−2 BER (Bit Error Rate) is around 3dB i.e. 0,66% compared to the optimal ML, while ZF performance is the worst.

5 citations

Journal Article
TL;DR: In this paper antenna 2x2 configuration is used and a Novel algorithm namely ALMMSE is proposed that reduce Bit Error Rate (BER) by using spatial multiplexing and Signal to Noise Ratio (SNR) curve of equalizer exceeds that of ZF, ZFSIC, ZF-SIC-OO, MMSE , MMSE-S IC, MMSA, ML, LS and AlMMSE equalizer.
Abstract: Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems have emerged as wide application technology in wireless communication systems for increasing data rate and the system performance. The effect of fading & interference can be reduced to increase the capacity of the link. MIMO systems uses multiple (input) Transmit and multiple (output) Receive antennas which exploit the multipath propagation in the rich scattering environment. The matrix channel plays very pivotal role in the throughput of a MIMO link since the modulation, data rate, power allocation & the antenna weights are the various dependent parameters on the channel gain. When data rate is been transmitted at high bit rate, the channel impulse response can be extended over many symbol periods which leads to Inter-Symbol Interference (ISI). ISI always causing an issue for signal recovery in wireless communication. In order to reduce complexity of MIMO system, various detection algorithm such as Zero Forcing (Z

2 citations

References
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Journal ArticleDOI
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

Journal ArticleDOI
08 Nov 2004
TL;DR: The paper explores various physical layer research challenges in MIMO-OFDM system design, including physical channel measurements and modeling, analog beam forming techniques using adaptive antenna arrays, and signal processing algorithms used to perform time and frequency synchronization, channel estimation, and channel tracking in M IMO- OFDM systems.
Abstract: Orthogonal frequency division multiplexing (OFDM) is a popular method for high data rate wireless transmission. OFDM may be combined with antenna arrays at the transmitter and receiver to increase the diversity gain and/or to enhance the system capacity on time-varying and frequency-selective channels, resulting in a multiple-input multiple-output (MIMO) configuration. The paper explores various physical layer research challenges in MIMO-OFDM system design, including physical channel measurements and modeling, analog beam forming techniques using adaptive antenna arrays, space-time techniques for MIMO-OFDM, error control coding techniques, OFDM preamble and packet design, and signal processing algorithms used to perform time and frequency synchronization, channel estimation, and channel tracking in MIMO-OFDM systems. Finally, the paper considers a software radio implementation of MIMO-OFDM.

1,475 citations

Proceedings ArticleDOI
01 Dec 2003
TL;DR: A convolutionally coded MIMO-OFDM system with EM-based channel estimation and a QRD-M data detection algorithm and the bit error rate performance of the systems is 9 (or 5) dB better than that of uncoded (or coded) BLAST systems.
Abstract: The use of multiple antennas at both the transmitter and receiver can significantly increase the channel capacity. These systems are called the multiple-input multiple-output (MIMO) systems. By using orthogonal frequency division multiplexing (OFDM) transmission techniques, the MIMO-OFDM system can achieve high spectral efficiency, which makes it an attractive candidate for high-data-rate wireless applications. In this paper, we propose a convolutionally coded MIMO-OFDM system with EM-based channel estimation and a QRD-M data detection algorithm. In our systems, one training symbol is transmitted from each transmit antenna for the MIMO channel estimation at the receiver. With the channel estimates available, we apply the QRD-M algorithm on the estimated channel matrix for suboptimal data detection with reasonable computational cost. The bit error rate (BER) and packet error rate (PER) performance of the MIMO-OFDM systems are compared. In the simulations, the bit error rate performance of our systems is 9 (or 5) dB better than that of uncoded (or coded) BLAST systems.

100 citations

Proceedings ArticleDOI
09 Jul 2006
TL;DR: This work proposes, here, new perfect space-time block codes for parallel MIMO channels based on OFDM, which is linear information preserving codes achieving the diversity-multiplexing gain (D-M) tradeoff.
Abstract: The problem of designing space-time codes on the MIMO quasi-static channel have received considerable attention these last years. We now know how to design perfect space-time block codes, that is linear information preserving codes achieving the diversity-multiplexing gain (D-M) tradeoff (F. Oggier et al., 2004) (P. Elia et al., 2005). Recent standards using multiple antennas terminals such as IEEE 802.11n or IEEE 802.16e, for example, are based on OFDM. By using interleaving, such OFDM systems can be seen as parallel MIMO quasi-static channels. We propose, here, new perfect space-time block codes for parallel MIMO channels

53 citations

Proceedings ArticleDOI
01 Sep 2016
TL;DR: A small length polar, encoded for a MIMO (Multiple-Input Multiple-Output) systems with soft output MMSE-SIC (Minimum Mean Square Error-Successive Cancellation) detection, is applied to improve the coded performance while reducing the complexity.
Abstract: Polar codes are proven capacity-achieving and are shown to have equivalent or even better finite length performance than turbo/LDPC codes under some improved decoding algorithm over the Additive White Gaussian Noise (AWGN) channels. Polar coding is based on the so-called channel polarization phenomenon induced by a transform over the underlying binary-input channel. The channel polarization is found to be universal in many signal processing problems and is applied to the coded modulation schemes. In this paper, a small length polar, encoded for a MIMO (Multiple-Input Multiple-Output) systems with soft output MMSE-SIC (Minimum Mean Square Error-Successive Cancellation) detection, is applied to improve the coded performance while reducing the complexity. In order to prove this theory, we compare the proposed MMSE-SIC BER to Zero Forcing (ZF) and Maximum Likelihood (ML) by using 2*2 MIMO systems into Rayleigh channel with BPSK (Binary Phase-Shift Keying) modulation. Simulation results show that MMSE-SIC complexity is lower than the two others detections. We show that the performance of the proposed approach using polar code (128, 64) at 10−2 BER (Bit Error Rate) is around 3dB i.e. 0,66% compared to the optimal ML, while ZF performance is the worst.

5 citations