M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders

Lu, Conghui; Chen, Peng; Zhong, Hua; Wang, Mengyuan

doi:10.3390/electronics11081233

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Resulting from the UAVs' location uncertainty, the additional path loss damages communication links between the leader UAVs and the follower UAVs. Thus, perfect CSI knowledge of the UAVs cannot be guarantee, and the system performance is degraded because of the imperfect CSI [7,31]. In view of this, multiple antennas performing beamforming can be employed to improve spectral efficiency in multi-UAV jitter communication systems.…”

Section: Introductionmentioning

confidence: 99%

Robust Channel Estimation Scheme for Multi-UAV MmWave MIMO Communication with Jittering

Chen

2023

Electronics

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In unmanned aerial vehicle (UAV)-assisted millimeter-wave (mmWave) communications, the communication performance is significantly degraded by UAV jitter. We formulate a UAV-assisted mmWave channel model with hybrid beamforming for the impacts of UAV jitter. Then, we derive the distribution of angle of arrivals/departures (AOAs/AODs) with random fluctuation of the UAV attitude angle. We develop an iterative reweight-based robust scheme as the super-resolution AOAs/AODs estimation method. Specifically, we introduce the partially adaptive momentum (Padam) estimation method to optimize the objective function of the jittering UAV mmWave massive multi-input multioutput (MIMO) system. Finally, compared with existing channel estimation schemes, the proposed UAV mmWave channel estimation method can achieve robust super-resolution performance in AOAs/AODs and path gains estimation with numerical results. Therefore, the proposed channel estimation scheme is very suitable for UAV mmWave massive MIMO communications with jittering.

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Section: Introductionmentioning

confidence: 99%

Robust Channel Estimation Scheme for Multi-UAV MmWave MIMO Communication with Jittering

Chen

2023

Electronics

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Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization

Gao,

Xie,

Wang

et al. 2024

Electronics

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Recently, researchers have achieved the goal of using unified architecture to achieve multiple modulation modes under specific conditions. However, existing research still suffers from the problem of large resource and time overhead. This paper proposes an adaptive modem based on a vector quantization (VQ) long short-term memory autoencoder (LSTM-AE), designed to implement modulation and demodulation of signals from the second to thirty-second order in a lightweight way. By leveraging the memory capacity of the LSTM module and the compression capability of the autoencoder, the model is able to support multi-order modulation methods. This study used the Adam optimizer for training and testing on a simple dataset extended by adding AWGN noise only and made modifications based on the MSE loss function to balance the accuracy and training speed of each part of the model. Experiments demonstrate that the proposed method not only achieves comparable modem performance to existing frameworks for second to thirty-second order signals, but also significantly reduces the number of parameters and training time. Experimental results indicate that the proposed methodology not only matches the performance of existing frameworks for signals ranging from the second to the thirty-second order, but also employs merely 79.6% of the average parameter count and a mere 7.4% of the average training duration. This represents a substantial reduction in resource expenditure.

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M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders

Cited by 2 publications

References 22 publications

Robust Channel Estimation Scheme for Multi-UAV MmWave MIMO Communication with Jittering

Robust Channel Estimation Scheme for Multi-UAV MmWave MIMO Communication with Jittering

Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization

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