A Review on Training and Blind Equalization Algorithms for Wireless Communications

Ahmed, Sheeraz; Khan, Yousaf; Wahab, Asad

doi:10.1007/s11277-019-06495-8

Cited by 15 publications

(4 citation statements)

References 15 publications

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“…In this experiment, PTE vs the channel noise, expressed in snr, is explored. From figure 9, it can be seen that PTE increases nonlinearly with the Rician path gain coefficient pg (2), as it contributes a product term as indicated in figure 2. On the other hand, PTE decreases as the channel snr increases, which implies a decrease in the noise level compared to the signal strength.…”

Section: Pte Vs the Channel Noisementioning

confidence: 95%

“…The basic task of a wireless communication receiver is to faithfully recover the transmitted data despite the distortions due to channel impairments. Conventionally, the channel distortion effects are compensated using channel equalization techniques [1][2] and noise cancellation methods [3][4]. However, the equalization process may be incomplete when the channel state is challenging to estimate due to environmental fluctuations and unseen inter-channel interferences.…”

Section: ░ 1 Introductionmentioning

confidence: 99%

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Design of a Deep Learning based Intelligent Receiver for a Wireless Communication System

M.N.,

R. Kounte,

Ravindra

2024

IJEER

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In communication systems, deep learning techniques can provide better predictions than model-based methods when the hidden features of the problem are prone to deviating substantially from the formulated assumptions. Severe signal impairments due to multipath fading and higher channel noise levels degrade the performance of conventional receivers. To overcome this, a novel intelligent receiver based on a deep learning network is presented, achieving better performance in terms of reduced bit error rate than a standalone conventional receiver. The experimental result shows that the relative decrement in the symbol error ratio due to the proposed method is about 9 percent compared to the traditional receiver when the Rician channel fading is relatively high.

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Section: Pte Vs the Channel Noisementioning

confidence: 95%

Section: ░ 1 Introductionmentioning

confidence: 99%

Design of a Deep Learning based Intelligent Receiver for a Wireless Communication System

M.N.,

R. Kounte,

Ravindra

2024

IJEER

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“…The schematic flowchart of HAWOA-CMA is shown in Figure 4. Using formula (17) to update the positions Using formula (14) to update the positions Using formula (8) to update the positions Using formula (17) to…”

Section: The Constant Modulus Blind Equalization Algorithm Based On H...mentioning

confidence: 99%

“…Blind equalization indicates that the prior information equalization channel characteristics brought by the receiving sequence are only used in the equalization process without the help of the training sequence so that the output sequence of the equalizer is constantly close to the sending sequence [14]. Blind equalization is divided into Bussgang blind equalization algorithm, algorithms based on higher order spectrum estimation and algorithms based on neural networks, fuzzy theory, wavelet theory and other theories [13].…”

Section: Introductionmentioning

confidence: 99%

A Wireless Channel Equalization Method Based on Hybrid Whale Optimization: For Constant Modulus Blind Equalization System

Wang,

Zhang,

Sun

et al. 2023

Mathematics

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This paper proposes a wireless channel equalization method applied to the constant modulus blind equalization system, which addresses the slow convergence and strong randomness in the initialization of equalizer weights in the constant modulus blind equalization algorithm (CMA) by introducing a hybrid arithmetic whale optimization algorithm (HAWOA). The mean square error in the CMA is utilized as the cost function for the HAWOA to obtain a more effective initial weights for the equalizer. To validate the superiority of the hybrid arithmetic whale constant modulus blind equalization algorithm, tests are conducted on the equalization system using 16QAM and 64QAM signals. The simulation results demonstrate that the proposed algorithm achieved better initial weights compared to the CMA and the constant modulus blind equalization algorithm based on the whale optimization algorithm. It can obtain the desired mean square error with a lower symbol error rate in fewer iterations. Furthermore, the hybrid arithmetic whale constant modulus blind equalization algorithm exhibited faster convergence in optimizing initial weights, effectively enhancing the equalization performance of the CMA in wireless channels while ensuring timeliness.

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Load based dynamic channel allocation model to enhance the performance of device-to-device communication in WPAN

Logeshwaran,

Shanmugasundaram,

Lloret

2024

Wireless Netw

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The modern communication network has advanced to such an extent that it is now possible for devices within a wireless personal area network (WPAN) to communicate among themselves directly. However, the limited shared radio resources of a WPAN lead to numerous issues, such as cross-layer interference and data collisions, which wind up affecting the quality of communication. A load based dynamic channel allocation (LB-DCA) model has been proposed to enhance the performance of device-to-device communication in WPAN. This model uses several control schemes in collaboration with interference estimation and channel load balancing mechanisms to allocate and manage the radio resources efficiently. The objective of this model is to achieve high throughput, low interference and low energy consumption. The control schemes implemented are based on distributed coordination and a cell-splitting approach. These schemes are utilized to estimate the channel usage and number of active nodes in a network. The interference estimation is done by using a new efficiency formula. Further, channel load balancing takes into account the hops and load factor values. The proposed model obtained 98.58% CSI, 95.86% MCC, 96.35% delta-P, 97.96% FMI, 99.83% BMI, 21.52% enhanced spectrum efficiency, 16.38% enhanced scalability, 18.79% enhanced signal quality, 18.64% enhanced power control and 18.89% enhanced energy efficiency.

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A Review on Training and Blind Equalization Algorithms for Wireless Communications

Cited by 15 publications

References 15 publications

Design of a Deep Learning based Intelligent Receiver for a Wireless Communication System

Design of a Deep Learning based Intelligent Receiver for a Wireless Communication System

A Wireless Channel Equalization Method Based on Hybrid Whale Optimization: For Constant Modulus Blind Equalization System

Load based dynamic channel allocation model to enhance the performance of device-to-device communication in WPAN

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