Improving MIMO detection performance in presence of phase noise using norm difference criterion

Datta, Tanumay; Yang, Sheng

doi:10.1109/allerton.2015.7447017

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…Data detection in large scale MIMO systems using successive interference cancellation (SIC) is given in [28]. MIMO data detection in the presence of phase noise is given in [29]. Detection of LDPC coded symbols in MIMO systems is discussed in [30].…”

Section: Introductionmentioning

confidence: 99%

Data Detection in Single User Massive MIMO Using Re-Transmissions

Vasudevan¹,

Madhu²,

Singh³

2019

TOSIGPJ

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Single user massive multiple input multiple output (MIMO) can be used to increase the spectral efficiency, since the data is transmitted simultaneously from a large number of antennas located at both the base station and mobile. It is feasible to have a large number of antennas in the mobile, in the millimeter wave frequencies. However, the major drawback of single user massive MIMO is the high complexity of data recovery at the receiver. In this work, we propose a low complexity method of data detection with the help of re-transmissions. A turbo code is used to improve the bit-error-rate (BER). Simulation results indicate significant improvement in BER with just two re-transmissions as compared to the single transmission case. We also show that the minimum average SNR per bit required for error free propagation over a massive MIMO channel with re-transmissions is identical to that of the additive white Gaussian noise (AWGN) channel, which is equal to −1.6 dB. Index TermsChannel capacity, massive MIMO, turbo codesThe authors are with the

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Section: Introductionmentioning

confidence: 99%

Data Detection in Single User Massive MIMO Using Re-Transmissions

Vasudevan¹,

Madhu²,

Singh³

2019

TOSIGPJ

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“…Proposition 1. Let A A A and b b b be defined as in (12). Then we have the following approximation of the log-likelihood function ln…”

Section: A Proposed Approximation Of the Likelihood Functionmentioning

confidence: 99%

“…Related Work. Receiver design with phase noise mitigation has been extensively investigated in the past years (see [11], [12] and references therein). More complex channel models, including multi-carrier systems (e.g., OFDM) and time-correlated phase noises (e.g., the Wiener process) have also been considered.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, joint data detection and phase noise estimation algorithms have been proposed in [13], [11]. A phase noise estimation based scheme to improve the system performance for smaller alphabets has been proposed in [12]. However, the challenging problem of signal detection in MIMO phase noise channels using higher order modulation, where August 9, 2017 DRAFT performance is extremely sensitive to phase noise, has not been addressed adequately before.…”

Section: Introductionmentioning

confidence: 99%

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An Approximate ML Detector for MIMO Channels Corrupted by Phase Noise

Combes¹,

Yang²

2018

IEEE Trans. Commun.

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We consider the multiple-input multiple-output (MIMO) communication channel impaired by phase noises at both the transmitter and receiver. We focus on the maximum likelihood (ML) detection problem for uncoded single-carrier transmission. We derive an approximation of the likelihood function, based on which we propose an efficient detection algorithm. The proposed algorithm, named self-interference whitening (SIW), consists in 1) estimating the self-interference caused by the phase noise perturbation, then 2) whitening the said interference, and finally 3) detecting the transmitted vector. While the exact ML solution is computationally intractable, we construct a simulation-based lower bound on the error probability of ML detection. Leveraging this lower bound, we perform extensive numerical experiments demonstrating that SIW is, in most cases of interest, very close to optimal with moderate phase noise.More importantly and perhaps surprisingly, such near-ML performance can be achieved by applying only twice the nearest neighbor detection algorithm. In this sense, our results reveal a striking fact: near-ML detection of phase noise corrupted MIMO channels can be done as efficiently as for conventional MIMO channels without phase noise. Index TermsMIMO systems, phase noise, maximum likelihood detection, probability of error.The authors are with the

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“…The 2 × 2 MIMO phase noise channel with independent transmit and receive phase noises at each antenna was studied in [12], where the authors showed that the multiplexing gain is 1 2 for a specific class of input distributions. For general MIMO channels with separate phase noises, estimation and detection algorithms have been proposed in [13], [14]. However for such channels, even the multiplexing gain is unknown, to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

On the Multiplexing Gain of Discrete-Time MIMO Phase Noise Channels

Yang¹,

Shamai²

2017

IEEE Trans. Inform. Theory

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Abstract-The capacity of a point-to-point discrete-time multi-input-multiple-output (MIMO) channel with phase uncertainty (MIMO phase noise channel) is still open. As a matter of fact, even the pre-log (multiplexing gain) of the capacity in the high signal-to-noise ratio (SNR) regime is unknown in general. We make some progresses in this direction for two classes of such channels. With phase noise on the individual paths of the channel (model A), we show that the multiplexing gain is 1 2 , which implies that the capacity does not scale with the channel dimension at high SNR. With phase noise at both the input and output of the channel (model B), the multiplexing gain is upper-bounded by }, where nt and nr are the number of transmit and receive antennas, respectively. The multiplexing gain is enhanced to 1 2 min{nt, nr} without receive phase noise, and to 1 2 min{2nt − 1, nr} without transmit phase noise. In all the cases of model B, the multiplexing gain scales linearly with min{nt, nr}. Our main results rely on the derivation of non-trivial upper and lower bounds on the capacity of such channels.

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Improving MIMO detection performance in presence of phase noise using norm difference criterion

Cited by 5 publications

References 14 publications

Data Detection in Single User Massive MIMO Using Re-Transmissions

Data Detection in Single User Massive MIMO Using Re-Transmissions

An Approximate ML Detector for MIMO Channels Corrupted by Phase Noise

On the Multiplexing Gain of Discrete-Time MIMO Phase Noise Channels

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