Performance of an adaptive PSO partial parallel interference canceller for CDMA communication systems

Cheng, Chia-Hsin; Hsu, Hsi-Chou; Huang, Yung-Fa; Wen, Jyh‐Horng; Hsu, Ling-Cheng

doi:10.4108/icst.wicon2010.8626

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…Each user's pilot OFDM symbol is multiplied by a user-specific spreading code before it is transmitted. In the proposed system, orthogonal Walsh-Hadamard-Transform (WHT) [1], [8] based spreading codes of length L are used. Note that no spreading is applied to the data OFDM symbols.…”

Section: Iterative Joint Channel Estimation and Partial Parallel Intementioning

confidence: 99%

“…At the k th subcarrier of the nth OFDM symbol received by the Pelement receiver antenna array, we have the received complex signal vector x, which is constituted by the superposition of the independently faded signals associated with the L mobile users and contaminated by the AWGN, expressed as (1) ,s (2) ,…,s (L) ] and the ( × 1) dimensional vector n= [ , , … ] are the received, transmitted and noise signals, respectively. The ( × )-dimensional matrix , which contains the frequency-domain channel transfer functions (FD-CHTFs) of the L users, are given by…”

Section: System Overviewmentioning

confidence: 99%

“…The detection algorithm can be outlined as follows [1] i) Set the initial particles for both PCFs and FD-CHTFs. ii) Initialize the PSO parameters; velocity vi is initialized to zero, pbest is initialized for each particle position and gbest is initialized for the global best position.…”

Section: Pso-assisted Joint Fd-chtf and Data Optimizationmentioning

confidence: 99%

“…Numerous multiuser detection (MUD) schemes have been presented for MIMO SDMA-OFDM systems during these years; such as the Maximum-Likelihood (ML) MUD, the Zero-Forcing (ZF) and Minimum Mean Squared Error (MMSE) [6], [7]. In the literature, various suboptimal nonlinear MUDs have also been presented, such as "Successive Interference Canceller (SIC) [7]" or "Parallel Interference Canceller (PIC) [9], [1]". In contrast to ZF and MMSE MUDs that detect the users' signals in a separate manner, in SIC and PIC MUDs, all detected signals generated during the previous detection iteration are demodulated and fed back to the input of the MUD in successive or parallel manner for the next iteration.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

SA and PSO assisted joint scheme of channel estimation and PPIC for MIMO-SDMA/OFDM system

Shaalan

Elbarbary

Dessouki

et al. 2013

Proceedings of the 16th ACM International Conference on Modeling, Analysis &Amp; Simulation of Wireless and Mobile Systems

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In this paper, a new scheme of Joint Channel Estimation and Partial Parallel Interference Cancellation using Simulating Annealing (SA) or Particle Swarm Optimization (PSO) algorithms is proposed. This scheme is proposed for multiuser Multiple-Input Multiple-Output /Space Division Multiple Access (MIMO-SDMA) Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed scheme tries to reduce the cancellation error in Conventional Parallel Interference Cancellation (CPIC) detection schemes used in MIMO SDMA-OFDM system. The SA and PSO algorithms are employed in searching the optimal weights of the PPIC for multiple access interference cancellation. The proposed scheme is shown to provide a performance improvement as compared to MMSE and CPIC detectors especially in an overloaded scenario where number of users is high as compared to the Base Station (BS) antennas. In this scenario, channel estimation becomes more challenging, owing to the increased number of independent transmitter-receiver links to be estimated and the constraint imposed by the rank of the MIMO channel matrix. The proposed scheme provides a reasonable solution to the multiuser MIMO channel estimation problem and multiuser detection in the above-mentioned overloaded scenario.

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Section: Iterative Joint Channel Estimation and Partial Parallel Intementioning

confidence: 99%

Section: System Overviewmentioning

confidence: 99%

Section: Pso-assisted Joint Fd-chtf and Data Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SA and PSO assisted joint scheme of channel estimation and PPIC for MIMO-SDMA/OFDM system

Shaalan

Elbarbary

Dessouki

et al. 2013

Proceedings of the 16th ACM International Conference on Modeling, Analysis &Amp; Simulation of Wireless and Mobile Systems

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“…PSO is a population-based heuristic global optimization algorithm, which originated in modeling the social behavior of bird flocks and fish schools. It has been applied to a variety of technical optimization problems, including channel and parameter estimation [8][9][10][11][12][13] as well as data detection [14] and multiuser detection [15]. Unfortunately, a fair evaluation of PSO is rather difficult due to the wide range of available modifications and the fact that the algorithm is often tuned to optimum performance for a specific optimization problem by empirical measures.…”

Section: Introductionmentioning

confidence: 99%

On Particle Swarm Optimization for MIMO Channel Estimation

Knievel

Hoeher

2012

Journal of Electrical and Computer Engineering

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Evolutionary algorithms, in particular particle swarm optimization (PSO), have recently received much attention. PSO has successfully been applied to a wide range of technical optimization problems, including channel estimation. However, most publications in the area of digital communications ignore improvements developed by the PSO community. In this paper, an overview of the original PSO is given as well as improvements that are generally applicable. An extension of PSO termed cooperative PSO (CPSO) is applied for MIMO channel estimation, providing faster convergence and, thus, lower overall complexity. Instead of determining the average iterations needed empirically, a method to calculate the maximum number of iterations is developed, which enables the evaluation of the complexity for a wide range of parameters. Knowledge of the required number of iterations is essential for a practical receiver design. A detailed discussion about the complexity of the PSO algorithm and a comparison to a conventional minimum mean squared error (MMSE) estimator are given. Furthermore, Monte Carlo simulations are provided to illustrate the MSE performance compared to an MMSE estimator.

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Integrated Swarming Computing Paradigm for Efficient Estimation of Channel Parameters in MIMO System

Ali

Tanoli

et al. 2020

Wireless Pers Commun

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Performance of an adaptive PSO partial parallel interference canceller for CDMA communication systems

Cited by 6 publications

References 19 publications

SA and PSO assisted joint scheme of channel estimation and PPIC for MIMO-SDMA/OFDM system

SA and PSO assisted joint scheme of channel estimation and PPIC for MIMO-SDMA/OFDM system

On Particle Swarm Optimization for MIMO Channel Estimation

Integrated Swarming Computing Paradigm for Efficient Estimation of Channel Parameters in MIMO System

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