Deterministic Pilot Pattern Placement Optimization for OFDM Sparse Channel Estimation

Nie, Yaguang

doi:10.1109/access.2020.3007916

Cited by 5 publications

(2 citation statements)

References 55 publications

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“…Initialize the butterflies population by GPS-based method in (13), and the elements of each butterfly 𝕏 𝑡 are non-repetitive positive integers belonging to the lower and upper bounds of [1, N].…”

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confidence: 99%

WITHDRAWN: Pilot Pattern Optimization of OFDM Sparse Channel Estimation Based on Enhanced Butterfly Optimization Algorithm

Deng

2022

Preprint

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Pilot pattern has an important impact on the performance of channel estimation based on compressed sensing. However, due to the limitations of time and space, the traditional enumeration methods and existing heuristic methods have some limitations in practical application. This paper proposes an enhanced butterfly optimization algorithm (EBOA) to solve this problem. EBOA is based on the butterfly optimization algorithm (BOA). In view of the problems that the BOA algorithm is prone to local optimal and slow convergence speed, EBOA improves the optimization efficiency and robustness by introducing the optimization strategy of good point set, designing adaptive switching probability and t-distribution variation factor, and applies it to pilot pattern optimization. Experiments show that compared with butterfly optimization algorithm (BOA), estimation of distribution algorithm (EDA), Particle swarm optimization (PSO), salp swarm algorithm (SSA), the EBOA algorithm proposed in this paper can generate a measurement matrix with less cross-correlation, and the corresponding optimized pilot mode is better than other methods in bit error rate and mean square error.

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confidence: 99%

WITHDRAWN: Pilot Pattern Optimization of OFDM Sparse Channel Estimation Based on Enhanced Butterfly Optimization Algorithm

Deng

2022

Preprint

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“…The probability for such an individual to modify its whole trait is essential decreases the acceptance condition that is depicted in Eq. (36).…”

Section: Passive Electro-locationmentioning

confidence: 99%

Hybrid AE and Bi-LSTM-Aided Sparse Multipath Channel Estimation in OFDM Systems

Kondepogu,

Bhattacharyya

2024

IEEE Access

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OFDM is a powerful modulation technique that efficiently transmits high-speed digital data over frequencyselective fading channels. It divides the signal into multiple subcarriers, each experiencing flat fading, and compensates for independent noise using a simple one-tap equalizer. In wireless systems, coherent detection is often employed, and accurate channel knowledge is crucial for optimizing performance. Traditional channel estimation methods, such as Least-Squares (LS), utilize randomly spaced pilot patterns, which can compromise spectral efficiency. Significant losses in BER and MSE affect many such existing methods in detrimental manner. To counter such failure, this paper proposes a new hybrid deep learning-aided sparse multipath channel estimation approach for OFDM communication systems. Additionally, a hybrid deep learning HA-Bi-LSTM model is developed by combining Bidirectional Long Short-Term Memory (Bi-LSTM) and Auto-Encoder (AE) to enhance data communication performance. Further, an efficient Grasshopper Electric Fish Optimization Algorithm (G-EFOA) is developed to optimize the parameters of AE and Bi-LSTM, resulting in superior solutions. AE handles feature extraction, while Bi-LSTM performs estimation. The proposed model aims to minimize BER and MSE values. Results show about an average 77 % improvement in BER across varied modulation schemes, along with a significant drop in MSE values by 84 %. Also, a sizable drop-in simulation time proves the contribution of the algorithm. Finally, the proposed model's effectiveness is demonstrated through performance evaluations.

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An improved pilot pattern design-based channel estimation in wireless communication using distribution ant colony optimization

Dhanasekaran,

Ramalingam,

Vivek Karthick

et al. 2023

Simulation Modelling Practice and Theory

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Deterministic Pilot Pattern Placement Optimization for OFDM Sparse Channel Estimation

Cited by 5 publications

References 55 publications

WITHDRAWN: Pilot Pattern Optimization of OFDM Sparse Channel Estimation Based on Enhanced Butterfly Optimization Algorithm

WITHDRAWN: Pilot Pattern Optimization of OFDM Sparse Channel Estimation Based on Enhanced Butterfly Optimization Algorithm

Hybrid AE and Bi-LSTM-Aided Sparse Multipath Channel Estimation in OFDM Systems

An improved pilot pattern design-based channel estimation in wireless communication using distribution ant colony optimization

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