An Efficient Bayesian PAPR Reduction Method for OFDM-Based Massive MIMO Systems

Bao, Hengyao; Fang, Jun; Chen, Zhi; Li, Hongbin; Li, Shaoqian

doi:10.1109/twc.2016.2536662

Cited by 50 publications

(39 citation statements)

References 23 publications

(51 reference statements)

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“…Inspired by , our objective is to seek a quasi‐constant magnitude solution to the linear inverse problem (14) whose maximally possible entries fall on the boundary, and the remainder lie within the specified interval [ − v , v ] in order to satisfy the MUI cancellation constraint. In the first layer of the two‐layered hierarchical prior, we assume that the coefficients of x are mutually independent such that each entry x i satisfies

p false(x_{i} false) = \{\begin{matrix} π \frac{N (x_{i}; v, α_{i 1}^{- 1})}{η_{normali 1}} + false(1 - π false) \frac{N (x_{i}; - v, α_{i 2}^{- 1})}{η_{normali 2}} & if x_{i} \in false[- v, v false], \\ 0, & otherwise \end{matrix}

which is called a truncated Gaussian prior distribution lying within the interval [ − v , v ].…”

Section: Sparse Bayesian Learning Framework For Papr Reductionmentioning

confidence: 99%

“…Update of parameter v : In the same manner, we can obtain the boundary parameter v by maximizing the complete log‐likelihood function Q with respect to v . We adopted the heuristic approach from to update v , where the parameter v is increased so as to reduce the mismatch

δ false(\overset{x}{true} false) = {‖ y - H \overset{false^}{x} ‖}_{2}^{2}

.…”

Section: Sparse Bayesian Learning Framework For Papr Reductionmentioning

confidence: 99%

“…Here, the estimated signal

\overset{bold-italicx}{true}

denotes the mean of the posterior distribution q ( x ). Motivated by , the step size Δv is given by

\frac{δ v = {(boldy - boldH {\overset{false^}{boldx}}^{false(t false)})}^{normalT} boldH γ}{{false| boldH γ false|}_{2}^{2} δ (\overset{false^}{boldx}) = {false| boldy - boldH \overset{x}{true} false|}_{2}^{2}}

…”

Section: Sparse Bayesian Learning Framework For Papr Reductionmentioning

confidence: 99%

“…where the additive noise term δ models the mismatch between y and Hx, and is assumed to be mean-zero Gaussian with variance β = 1/σ 2 , that is, pðβÞ ¼ N À δ 0; β À1 I Á . Inspired by [25], our objective is to seek a quasi-constant magnitude solution to the linear inverse problem (14) whose maximally possible entries fall on the boundary, and the remainder lie within the specified interval [ −v, v] in order to satisfy the MUI cancellation constraint. In the first layer of the two-layered hierarchical prior, we assume that the coefficients of x are mutually independent such that each entry x i satisfies…”

Section: Sparse Bayesian Learning Framework For Papr Reductionmentioning

confidence: 99%

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Computationally efficient variational Bayesian method for PAPR reduction in multiuser MIMO ‐ OFDM systems

Singh

Sarin

2019

ETRI Journal

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This paper investigates the use of the inverse‐free sparse Bayesian learning (SBL) approach for peak‐to‐average power ratio (PAPR) reduction in orthogonal frequency‐division multiplexing (OFDM)‐based multiuser massive multiple‐input multiple‐output (MIMO) systems. The Bayesian inference method employs a truncated Gaussian mixture prior for the sought‐after low‐PAPR signal. To learn the prior signal, associated hyperparameters and underlying statistical parameters, we use the variational expectation‐maximization (EM) iterative algorithm. The matrix inversion involved in the expectation step (E‐step) is averted by invoking a relaxed evidence lower bound (relaxed‐ELBO). The resulting inverse‐free SBL algorithm has a much lower complexity than the standard SBL algorithm. Numerical experiments confirm the substantial improvement over existing methods in terms of PAPR reduction for different MIMO configurations.

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p false(x_{i} false) = \{\begin{matrix} π \frac{N (x_{i}; v, α_{i 1}^{- 1})}{η_{normali 1}} + false(1 - π false) \frac{N (x_{i}; - v, α_{i 2}^{- 1})}{η_{normali 2}} & if x_{i} \in false[- v, v false], \\ 0, & otherwise \end{matrix}

which is called a truncated Gaussian prior distribution lying within the interval [ − v , v ].…”

Section: Sparse Bayesian Learning Framework For Papr Reductionmentioning

confidence: 99%

δ false(\overset{x}{true} false) = {‖ y - H \overset{false^}{x} ‖}_{2}^{2}

.…”

Section: Sparse Bayesian Learning Framework For Papr Reductionmentioning

confidence: 99%

“…Here, the estimated signal

\overset{bold-italicx}{true}

denotes the mean of the posterior distribution q ( x ). Motivated by , the step size Δv is given by

\frac{δ v = {(boldy - boldH {\overset{false^}{boldx}}^{false(t false)})}^{normalT} boldH γ}{{false| boldH γ false|}_{2}^{2} δ (\overset{false^}{boldx}) = {false| boldy - boldH \overset{x}{true} false|}_{2}^{2}}

…”

Section: Sparse Bayesian Learning Framework For Papr Reductionmentioning

confidence: 99%

Section: Sparse Bayesian Learning Framework For Papr Reductionmentioning

confidence: 99%

See 2 more Smart Citations

Computationally efficient variational Bayesian method for PAPR reduction in multiuser MIMO ‐ OFDM systems

Singh

Sarin

2019

ETRI Journal

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“…Researchers have focused on PAPR reduction using Bayesian approaches and on evaluating spectrum sensing, mean square errors and successful reconstruction rates. 33,34 The cyclostationary detection method is used for the detection of primary signals. Signals are reconstructed when specified signals are obtained from a cognitive radio user.…”

Section: Mimo-ofdmmentioning

confidence: 99%

Secure cognitive radio‐based synchronized transmission of 5G signals using massive MIMO‐OFDM‐ES

Pari

Karuppiah

Islam

2018

Int J Communication

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Summary A cognitive radio network (CRN) can address spectrum scarcity issues to achieve higher levels of spectral efficiency. Fifth‐generation (5G) technologies are advanced telecommunication technologies that are built into CRNs. Network performance is enhanced when using a massive multiple input multiple output (MIMO) system that tolerates a large number of users based on the use of multiple antennas. Several previous research works have been conducted on 5G CRNs, although these works have failed to mitigate interference and the peak‐to‐average power ratio (PAPR). In this paper, a 5G CRN with massive MIMO that ensures secure data transmission is developed. Security is ensured via the standardized key–based secrecy method supported by a certificate authority. A CRN fusion centre (FC) plays a vital role in decision making. Fusion centre rotation is applied to mitigate interference. Massive MIMO‐OFDM effective signalling is used to address PAPR problems. The proposed CRN is experimentally modelled using network simulator 3, and its performance is evaluated. Our simulation results reveal improvements in channel capacity, a reduction in the PAPR and bit error rates, and improvements in spectral efficiency.

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Hybrid Zadoff‐Chu and multilateral piecewise exponential companding transform–based PAPR reduction technique in OFDM systems

Manjula

Muralidhara

2019

Int J Communication

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SummaryThe orthogonal frequency‐division multiplexing (OFDM) is a multicarrier modulation system that is used to transmit the large volume of data to the receiver. Reducing the peak‐to‐average power ratio (PAPR) in OFDM system is one of the demanding and crucial task in recent days. For this reason, various precoding and companding mechanisms are developed in the traditional works, but it remains with the limitations of increased complexity, reduced performance, and nonlinear distortion. The reduction of PAPR is achieved by minimizing the companding distortion with the enhancement of the bit error rate (BER) performance significantly. Then, in order to avoid clipping in OFDM, a multilateral piecewise exponential companding transform (MPECT) method has been utilized rather than using piecewise exponential companding transform (PEC) where PAPR is getting reduced. The OFDM is very sensitive to synchronizing error. To overcome this sensitivity, employ the Zadoff‐Chu sequence to carrier frequency offsets. Zadoff‐Chu matrix transform (ZCMT) has numerous merits among the other ODFM systems such as the improvement in the performance of the channels that are fading away and provides an ideal periodic autocorrelation and a constant magnitude periodic cross correlation. Both of these techniques provide improvement in the ODFM systems. To get more efficiency, this paper aims to develop a hybrid technique by integrating the ZCMT and MPECT techniques for reducing the PAPR in OFDM systems. Further, convolutional encoding is applied for better BER and PAPR. The simulation results of the proposed ZCMT‐MPECT technique are evaluated and compared with the conventional OFDM and other precoding methods.

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An Efficient Bayesian PAPR Reduction Method for OFDM-Based Massive MIMO Systems

Cited by 50 publications

References 23 publications

Computationally efficient variational Bayesian method for PAPR reduction in multiuser MIMO ‐ OFDM systems

Computationally efficient variational Bayesian method for PAPR reduction in multiuser MIMO ‐ OFDM systems

Secure cognitive radio‐based synchronized transmission of 5G signals using massive MIMO‐OFDM‐ES

Hybrid Zadoff‐Chu and multilateral piecewise exponential companding transform–based PAPR reduction technique in OFDM systems

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