A Novel LS-LMMSE Channel Parameter Tuning Approach using Particle Swarm Optimization in MIMO-OFDM

Agarkar, Poonam T.; Narole, N. G.; Hajare, Pratik R.; Bawane, N. G.

doi:10.1109/icctct.2018.8550946

Cited by 2 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RWBS proved less complex and able to achieve perfect channel estimation without requiring a large dataset [203][204][205]. PSO had its computational complexity varying according to the channel coefficients demanding a few iterations (100 at maximum) to overcome LMMSE estimate [206][207][208][209]. A comparison among some of the discussed evolutionary algorithms is available in [194].…”

Section: Complexity Discussionmentioning

confidence: 99%

“…Some evolutionary algorithms are the genetic algorithm (GA), repeated weighted boosting search (RWBS), particle swarm optimization (PSO), differential evolution algorithms (DEA), and colony optimization [194,195]. Among those approaches, GA [196][197][198][199][200][201][202], RWBS [203][204][205], and PSO [206][207][208][209] have been applied to channel estimation in multicarrier systems. Evolutionary algorithms are also exploited mainly in pilot pattern placement optimization, which is out of the scope of this work [210,211].…”

Section: Evolutionary Algorithmmentioning

confidence: 99%

“…Inspired by this behavioral algorithm, some works have inserted it in the channel estimation context for OFDM and MIMO-OFDM systems. The channel parameters are estimated using iterative linear estimators and delivered to the PSO algorithm that works with it to improve the BER [206]. The BER comparison against the LS and least minimum mean squared error (LMMSE) estimator has shown that the proposal equals performance, mainly with the latter.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

See 2 more Smart Citations

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

View full text Add to dashboard Cite

Multicarrier modulation allows for deploying wideband systems resilient to multipath fading channels, impulsive noise, and intersymbol interference compared to single-carrier systems. Despite this, multicarrier signals suffer from different types of distortion, including channel noise sources and long- and short-term fading. Consequently, the receiver must estimate the channel features and compensate it for data recovery based on channel estimation techniques, such as non-blind, blind, and semi-blind approaches. These techniques are model-based and designed with accurate mathematical channel models encompassing their features. Nevertheless, complex environments challenge accurate mathematical channel estimation modeling, which might neither be accurate nor correspond to reality. This impairment decreases the system performance due to the channel estimation accuracy loss. Fortunately, (AI) algorithms can learn the relationship among different system variables using a model-driven or model-free approach. Thereby, AI algorithms are used for channel estimation by exploiting its complexity without unrealistic assumptions, following a better performance than conventional techniques under the same channel. Hence, this paper comprehensively surveys AI-based channel estimation for multicarrier systems. First, we provide essential background on conventional channel estimation techniques in the context of multicarrier systems. Second, the AI-aided channel estimation strategies are investigated using the following approaches: classical learning, neural networks, and reinforcement learning. Lastly, we discuss current challenges and point out future research directions based on recent findings.

show abstract

Section: Complexity Discussionmentioning

confidence: 99%

Section: Evolutionary Algorithmmentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…For frequency selective channels, an efficient technique is to use a multi-carrier modulation in which the blocks of information are modulated by the Fourier transform [2]. This technique known as orthogonal frequency division multiplexing (OFDM), showed to be very efficient [3]. The information is transmitted on N different sub-carriers [4], every sub-carrier having an interval of time multiplied by N [5].…”

Section: Introductionmentioning

confidence: 99%

Analysis of cyclic prefix length effect on ISI limitation in OFDM system over a Rayleigh-fading multipath

Zanafi

Aknin

2021

IJECE

View full text Add to dashboard Cite

<span>In this work, the influence of the cyclic prefix on the performance of the OFDM system is studied. We worked out an OFDM transceiver using a 16 QAM modulation scheme, a comparison of the BER for various lengths of the cyclic prefix has been achieved, and the influence of the noise introduced in the channel has been highlighted, for both a Gaussian and Rayleigh noise. The simulation was carried out on MATLAB where the curves of the BER for various lengths of the cyclic prefix are given and compared. We also adopted as a metric the QAM constellation to show the dispersion of the carriers as a consequence of the transmission channel, the mitigation of this effect by the CP is noticeable.</span>

show abstract

A Novel LS-LMMSE Channel Parameter Tuning Approach using Particle Swarm Optimization in MIMO-OFDM

Cited by 2 publications

References 11 publications

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Analysis of cyclic prefix length effect on ISI limitation in OFDM system over a Rayleigh-fading multipath

Contact Info

Product

Resources

About