Review of channel estimation for wireless communication systems

Oyerinde, Olutayo O.; Mneney, Stanley H.

doi:10.4103/0256-4602.101308

Cited by 49 publications

(30 citation statements)

References 144 publications

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“…The deployment of the noncoherent method costs about 3-4 dB loss in SNR as compared with the coherent detection method. Due to such massive loss by the noncoherent detection method, research directed toward coherent detection for providing CSI at the receiver in wireless communication systems [1, 2].…”

Section: Introductionmentioning

confidence: 99%

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Blind Channel and Data Estimation Using Fuzzy Logic-Empowered Opposite Learning-Based Mutant Particle Swarm Optimization

Asadullah

Khan

Abbas

et al. 2018

Computational Intelligence and Neuroscience

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Multiple-input and multiple-output (MIMO) technology is one of the latest technologies to enhance the capacity of the channel as well as the service quality of the communication system. By using the MIMO technology at the physical layer, the estimation of the data and the channel is performed based on the principle of maximum likelihood. For this purpose, the continuous and discrete fuzzy logic-empowered opposite learning-based mutant particle swarm optimization (FL-OLMPSO) algorithm is used over the Rayleigh fading channel in three levels. The data and the channel populations are prepared during the first level of the algorithm, while the channel parameters are estimated in the second level of the algorithm by using the continuous FL-OLMPSO. After determining the channel parameters, the transmitted symbols are evaluated in the 3rd level of the algorithm by using the channel parameters along with the discrete FL-OLMPSO. To enhance the convergence rate of the FL-OLMPSO algorithm, the velocity factor is updated using fuzzy logic. In this article, two variants, FL-total OLMPSO (FL-TOLMPSO) and FL-partial OLMPSO (FL-POLMPSO) of FL-OLMPSO, are proposed. The simulation results of proposed techniques show desirable results regarding MMCE, MMSE, and BER as compared to conventional opposite learning mutant PSO (TOLMPSO and POLMPSO) techniques.

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Section: Introductionmentioning

confidence: 99%

“…Hence, decoding of the transmitted signal would become a compressive signal problem. For a system that has a small number of high activity users, the long-term evolution is more suitable [2, 3].…”

Section: Introductionmentioning

confidence: 99%

Blind Channel and Data Estimation Using Fuzzy Logic-Empowered Opposite Learning-Based Mutant Particle Swarm Optimization

Asadullah

Khan

Abbas

et al. 2018

Computational Intelligence and Neuroscience

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“…The low-complexity algorithm presented in the preceding section requires that the indices corresponding to each orthogonal cluster of h to be known 2 . In this section, we show how to derive such knowledge from the received signal y.…”

Section: Finding the Orthogonal Clustersmentioning

confidence: 99%

Low-Complexity Bayesian Estimation of Cluster-Sparse Channels

Ballal

Al-Naffouri

Ahmed

2015

IEEE Trans. Commun.

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“…There is a large volume of research work on channel estimation and prediction in narrowband, wideband, single carrier, and multicarrier wireless system. Some of these works and algorithms can be found in [56][57][58][59][60][61] and [61][62][63][64][65], respectively. Because typical channel estimators and predictors are not error free, for practical implementation, error margin should be included in (18) and (19) to ensure that estimation/prediction error does not affect the performance of the system.…”

Section: Stationarity Parameters In Relationship To Real-time Communimentioning

confidence: 99%

Characterization and parameterization of dynamic wireless channels over long duration using evolutionary channel parameters

Chude-Okonkwo¹,

Chude-Olisah²,

Nunoo³

et al. 2015

Int J Communication

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Summary The characterization and parameterization of processes that arise in many fields of science and technology are very crucial. Of particular importance are dynamic processes whose statistics are time‐varying and are often modeled as stochastic processes. A typical example of such process is the wireless communication channel. Existing methods that are used to characterize and parameterize the dynamic stochastic wireless channel often consider short‐term duration over which the channel statistics are invariant. Conversely, this paper presents the characterization of the dynamic wireless communication channel over a long‐term duration where time/frequency channel realizations are obtained at sample intervals. To structure such channel realizations over a long duration, the idea of concatenating the ‘instantaneous’ channel realizations is presented. The resultant concatenated multivariable process is characterized using the concepts of process non‐summability and piecewise separability. Based on these concepts, the second‐order statistical parameterization of the concatenated stochastic process in both time and frequency domain is presented. The parameterization approach is based on fitting appropriate set of unit step functions that approximate the raw concatenated data using sets of evolutionary stationarity parameters. To illustrate the concepts developed in this paper, measurement‐based experiments and analysis are presented and adaptively applied to improve wideband multicarrier system performance. Copyright © 2015 John Wiley & Sons, Ltd.

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Review of channel estimation for wireless communication systems

Cited by 49 publications

References 144 publications

Blind Channel and Data Estimation Using Fuzzy Logic-Empowered Opposite Learning-Based Mutant Particle Swarm Optimization

Blind Channel and Data Estimation Using Fuzzy Logic-Empowered Opposite Learning-Based Mutant Particle Swarm Optimization

Low-Complexity Bayesian Estimation of Cluster-Sparse Channels

Characterization and parameterization of dynamic wireless channels over long duration using evolutionary channel parameters

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