scholarly journals A Deep Learning-Assisted Cooperative Diversity Method under Channel Aging

2020 ◽  
Author(s):  
Wei Jiang
Keyword(s):  
Deep Learning ◽  
Relay Selection ◽  
Carrier Frequency Offset ◽  
Cooperative Diversity ◽  
Channel State ◽  
Timing Offset ◽  
Multiple User ◽  
Spatially Distributed ◽  
Efficient Scheme ◽  
Single Relay

Single-relay selection is a simple but efficient scheme for cooperative diversity among multiple user devices. However, the wrong selection of the best relay due to aged channel state information (CSI) remarkably degrades its performance, overwhelming this cooperative gain. Multi-relay selection is robust against channel aging but multiple timing offset (MTO) and multiple carrier frequency offset (MCFO) among spatially-distributed relays hinder its implementation in practical systems. In this paper, therefore, we propose a deep learning-based cooperative diversity method coined predictive relay selection (PRS) that chooses a single relay with the largest predicted CSI, which can alleviate the effect of channel aging while avoiding MTO and MCFO. Performance is evaluated analytically and numerically, revealing that PRS clearly outperforms the existing schemes with a negligible complexity burden.

scholarly journals A Deep Learning-Assisted Cooperative Diversity Method under Channel Aging

2020 ◽  
Author(s):  
Wei Jiang
Keyword(s):  
Deep Learning ◽  
Relay Selection ◽  
Carrier Frequency Offset ◽  
Cooperative Diversity ◽  
Channel State ◽  
Timing Offset ◽  
Multiple User ◽  
Spatially Distributed ◽  
Efficient Scheme ◽  
Single Relay

Single-relay selection is a simple but efficient scheme for cooperative diversity among multiple user devices. However, the wrong selection of the best relay due to aged channel state information (CSI) remarkably degrades its performance, overwhelming this cooperative gain. Multi-relay selection is robust against channel aging but multiple timing offset (MTO) and multiple carrier frequency offset (MCFO) among spatially-distributed relays hinder its implementation in practical systems. In this paper, therefore, we propose a deep learning-based cooperative diversity method coined predictive relay selection (PRS) that chooses a single relay with the largest predicted CSI, which can alleviate the effect of channel aging while avoiding MTO and MCFO. Performance is evaluated analytically and numerically, revealing that PRS clearly outperforms the existing schemes with a negligible complexity burden.

scholarly journals A Simple Cooperative Diversity Method Based on Deep-Learning-Aided Relay Selection

2021 ◽  
Author(s):  
Wei Jiang ◽  
Hans Dieter Schotten
Keyword(s):  
Deep Learning ◽  
Relay Selection ◽  
Cooperative Diversity

It's been submitted to IEEE Transactions

scholarly journals A Simple Cooperative Diversity Method Based on Deep-Learning-Aided Relay Selection

2021 ◽  
Author(s):  
Wei Jiang ◽  
Hans Dieter Schotten
Keyword(s):  
Deep Learning ◽  
Relay Selection ◽  
Cooperative Diversity

It's been submitted to IEEE Transactions

scholarly journals Predictive Relay Selection: A Cooperative Diversity Scheme Using Deep Learning

2021 ◽  
Author(s):  
Wei Jiang ◽  
Hans Dieter Schotten
Keyword(s):  
Deep Learning ◽  
Fading Channels ◽  
Relay Selection ◽  
Cooperative Diversity ◽  
Spatial Diversity ◽  
Fast Time ◽  
Transmission Scheme ◽  
Opportunistic Relaying ◽  
Full Diversity ◽  
Slow Fading Channels

In this paper, we propose a novel cooperative multi-relay transmission scheme for mobile terminals to exploit spatial diversity. By improving the timeliness of measured channel state information (CSI) through deep learning (DL)-based channel prediction, the proposed scheme remarkably lowers the probability of wrong relay selection arising from outdated CSI in fast time-varying channels. It inherits the simplicity of opportunistic relaying by selecting a single relay, avoiding the complexity of multi-relay coordination and synchronization. Numerical results reveal that it can achieve full diversity gain in slow-fading channels and substantially outperforms the existing schemes in fast-fading wireless environments. Moreover, the computational complexity brought by the DL predictor is negligible compared to off-the-shelf computing hardware.

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