Swarm intelligence‐based deep ensemble learning machine for efficient channel estimation in MIMO communication systems

Manasa, B; Venugopal, Pulidindi

doi:10.1002/dac.5152

Cited by 4 publications

(3 citation statements)

References 44 publications

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“…The proposed DHHO algorithm is mainly for enlarging the superiority of the channel estimation in the MIMO channel while reducing the factors like BER, SNR, and MSE. Optimization is required in channel estimation because it selects the best cost function values for efficient data communication in the channel of MIMO 35 . HHO 34 and the DOA 36 heuristic algorithms are selected in channel estimation for balancing the exploitation and exploration phases and also to reduce convergence problems that occur in deep learning networks.…”

Section: Fitness Methods and Proceeding Stages Used In The Channel Es...mentioning

confidence: 99%

Efficient Channel Estimation in Multiple Input Multiple Output Communication Systems using Hybrid Intelligence‐based Deep Learning Model using Horse Dingo Optimizer

Manasa

Venugopal

2023

Trans Emerging Tel Tech

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The estimation of the channel in multiple input multiple output (MIMO) systems over wireless communication has faced many challenges, such as high bit rate transmission with less time complexity, low error rate, and high convergence rate. And also, there is some potential for enhancing the system performance regarding channel efficiency diversity. MIMO system uses a huge count of antennas on the receiver and the transmitter side. This increases the cost requirements and power consumption. For achieving coherent detection in the MIMO system, optimization such as water filling and beam forming are required to find the proper channel. Hence, multiple transceivers used in both the transmitter side and receiver side create a channel estimation issue that is complicated when analyzed to a single input, single output system. Therefore, several solutions have been recommended to decrease the computational cost and power consumption in channel estimation of MIMO systems. This work intends to propose a novel deep structured architecture to handle the channel estimation problem in a MIMO system. The major objective of the developed method is to validate the channel coefficients of MIMO at the transmitter according to the obtained SNR feedback data packet from an acceptor with the help of the hybrid intelligence‐based deep learning model (HI‐DLM). For both scenarios, the HI‐DLM is introduced, in which the convolution neural network (CNN) is integrated with other deep‐structured architecture. Especially, the fully connected layers of CNN are restored by the long short‐term memory. In the HI‐DLM‐aided channel estimation, the hyperparameter optimization is accomplished by the novel dingo‐horse herd optimization for reducing the mean square error and bit error rate to acquire the estimated channel. The corresponding algorithm is validated on the communication of the MIMO system by experimentations. Thus, it is revealed that the offered algorithm provides better convergence and less computational cost.

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Section: Fitness Methods and Proceeding Stages Used In The Channel Es...mentioning

confidence: 99%

Efficient Channel Estimation in Multiple Input Multiple Output Communication Systems using Hybrid Intelligence‐based Deep Learning Model using Horse Dingo Optimizer

Manasa

Venugopal

2023

Trans Emerging Tel Tech

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“…Table 4 lists several common contrastive loss functions and their definitions. In sequence labeling tasks, conditional random field loss considers the transition probabilities between labels, effectively modeling the dependencies among labels, and has been widely applied in scenarios such as named entity recognition and part-of-speech tagging [6]. Therefore, designing appropriate loss functions tailored to task characteristics is of paramount importance for improving the performance of deep natural language processing models.…”

Section: Loss Function Optimizationmentioning

confidence: 99%

Optimization Strategies for Deep Learning Models in Natural Language Processing

Yao,

Yuan

2024

JTPES

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Deep learning models have achieved remarkable performance in the field of natural language processing (NLP), but they still face many challenges in practical applications, such as data heterogeneity and complexity, the black-box nature of models, and difficulties in transfer learning across multilingual and cross-domain scenarios. In this paper, corresponding improvement measures are proposed from four perspectives: model structure, loss functions, regularization methods, and optimization strategies, to address these issues. Extensive experiments on three tasks including text classification, named entity recognition, and reading comprehension confirm the feasibility and effectiveness of the proposed optimization solutions. The experimental results demonstrate that introducing innovative mechanisms like Multi-Head Attention and Focal Loss, and judiciously applying techniques such as LayerNorm and AdamW, can significantly improve model performance. Finally, this paper also explores model compression techniques, providing new insights for deploying deep models in resource-constrained scenarios.

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“…In 2021, Manasa et al 30 have introduced the swarm intelligence‐based deep ensemble learning machine (SI‐DELM) for estimating the MIMO channel coefficients at a transmitter based on the received SNR feedback information. Here, the three neural networks were used for the SI‐DELM.…”

Section: Literature Surveymentioning

confidence: 99%

Intelligent channel estimation in millimeter wave massive MIMO communication system using hybrid deep learning with heuristic improvement

Suneetha

Satyanarayana

2023

Int J Communication

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The major goal of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems is to get effective channel state information (CSI). Most of the recent works use nuclear norm theory for recovering the low-rank scheme of channels. Some suboptimal solutions to the rank minimization problem can occur while addressing the nuclear norm-based convex problem, which degrades the accuracy of channel estimation. Some works recover the channel with the assumption of the mmWave channel using an over-complete dictionary. On the other hand, the accuracy of available CSI may openly influence the efficiency of mmWave communications. The main intention of this paper is to develop an enhanced channel estimation model with an optimized hybrid deep learning model. Here, the integration of deep neural network (DNN) and long short-term memory (LSTM) form the hybrid deep learning model termed optimized D-LSTM, which is modified by the opposition searched explorationbased Harris hawks optimization (OE-HHO). The input to the proposed hybrid deep learning is taken as the correlation among the received signal vectors and the measurement matrix for predicting the beam space channel amplitude.Finally, the successful channel estimation is observed by deep hybrid learning by the experimental outcomes, which also demonstrate that the proposed channel estimation model overwhelms the conventional models in terms of Normalized Mean-Squared Error (NMSE) and spectral efficiency. The experimental results show that the designed OE-HHO method obtains 9.2%, 8.9%, 8.65%, and 0.47% progressed than DA, DHOA, GWO, and HHO, respectively. Therefore, higher efficiency is observed by OE-HHO based mmWave MIMO communication system.

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Swarm intelligence‐based deep ensemble learning machine for efficient channel estimation in MIMO communication systems

Abstract: Multiple-input multiple-output (MIMO) technology is much significant for

Cited by 4 publications

References 44 publications

Efficient Channel Estimation in Multiple Input Multiple Output Communication Systems using Hybrid Intelligence‐based Deep Learning Model using Horse Dingo Optimizer

Efficient Channel Estimation in Multiple Input Multiple Output Communication Systems using Hybrid Intelligence‐based Deep Learning Model using Horse Dingo Optimizer

Optimization Strategies for Deep Learning Models in Natural Language Processing

Intelligent channel estimation in millimeter wave massive MIMO communication system using hybrid deep learning with heuristic improvement

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