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RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning.

Feng Jing^1,2,3, Hailin Zhang^1,3, Mei Gao^1,2,3

¹School of Telecommunication Engineering, Xidian University, Xi'an 710126, China.

Sensors (Basel, Switzerland)

|August 26, 2022

Summary

This summary is machine-generated.

This study introduces a new method for detecting signals in ambient backscatter communication (AmBC) using reconfigurable intelligent surfaces (RIS) and deep reinforcement learning. The approach enhances signal detection accuracy in AmBC systems.

Keywords:

ambient backscatter communication deep reinforcement learning multi-antenna reconfigurable intelligent surface signal detection

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Area of Science:

Wireless Communication
Signal Processing
Machine Learning

Background:

Signal detection is a critical challenge in ambient backscatter communication (AmBC) systems.
Existing methods may struggle with the complexity and efficiency required for modern AmBC applications.

Purpose of the Study:

To propose a novel multi-antenna AmBC signal detection method.
To enhance the performance of AmBC systems through advanced techniques.

Main Methods:

Development of an efficient multi-antenna AmBC system utilizing reconfigurable intelligent surfaces (RIS) for simultaneous information transmission and energy collection.
Implementation of a smart twin delayed deep deterministic (TD3) algorithm, a deep reinforcement learning approach, for signal detection.

Main Results:

The proposed RIS-based AmBC system demonstrates simultaneous information transmission and energy harvesting capabilities.
The TD3-based signal detection method shows superior performance compared to existing methods in extensive experiments.

Conclusions:

The integration of RIS and deep reinforcement learning offers a compelling solution for improving signal detection in AmBC.
The proposed method significantly advances the efficiency and effectiveness of ambient backscatter communication systems.