BER performance improvement in MIMO systems using various equalization techniques

Gupta, Bhasker; Saini, Davinder S.

doi:10.1109/pdgc.2012.6449815

Cited by 16 publications

(24 citation statements)

References 12 publications

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“…By considering a MIMO system with a transmit array of antennas and a receive array of M R antennas [11], the MIMO channel [35] at a given time instant may be represented as a matrix…”

Section: Mimo System Modelmentioning

confidence: 99%

“…MIMO CHANNEL By assuming the above channel model the product of the bandwidth and the delay spread is very small and it results in channel realizations which varies with the time and is frequency dependent that is H (f) [35].…”

Section: Mimo System Modelmentioning

confidence: 99%

“…In fact, the noise may be enhanced in the process of eliminating the interference. A zero-forcing equalizer uses an inverse filter to compensate for the channel response function [4].By assuming M T = M R and H is a full rank square matrix, we can calculate the covariance matrix [35] of the effected noise as:…”

Section: A Zero Forcingmentioning

confidence: 99%

“…We assume that the number of receive antennas is less than the number of transmit antennas MMSE at a high SNR [35] is given by ( )…”

Section: B Minimum Mean Square Error (Mmse)mentioning

confidence: 99%

“…Maximum likelihood detection [35] calculates the Euclidean distance between received signal vector and the product of all possible transmitted signal vectors with the given channel H, and finds the one with minimum distance.…”

Section: Maximum Likelihood (Ml)mentioning

confidence: 99%

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Equalization: Analysis of MIMO Systems in Frequency Selective Channel

Grover¹

2012

IJACSA

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Abstract-Due to the increased demand of wireless communication systems because of the features of the system which provides a wide coverage, high throughput and reliable services, the MIMO systems communication has come into existence. Features provided by these systems ensure the improved system coverage and increased data transmission rate by considering multiple numbers of transmitter and receiver antennas. In this paper, we are considering the equalization; a filtering approach that minimizes the error between actual output and desired output by continuous updating its filter coefficients for Rayleigh Frequency selective fading channel. We concluded that MMSE and ZF give the worst performance in Rayleigh frequency selective channel as compare to Rayleigh Flat fading Channel [35] due to a constant BER for large SNR's. We have also observed that the successive interference methods provide better performance as compare to others, but their complexity is high. ML provides the better performance in comparison to others and BER doesn't remain constant for a large SNR in this case. Simulation results shows that ML equalizer with BPSK gives better performance as compare to QPSK. Finally we concluded that Sphere decoder provides the best performance.

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“…By considering a MIMO system with a transmit array of antennas and a receive array of M R antennas [11], the MIMO channel [35] at a given time instant may be represented as a matrix…”

Section: Mimo System Modelmentioning

confidence: 99%

Section: Mimo System Modelmentioning

confidence: 99%