# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 1 Introduction  
In recent years, with the development of modern digital society, new services and applications such as smart cities, smart houses, digital Internet of Everything, and wired/wireless distributed sensing systems have been constantly emerging, putting forward significantly higher requirements for communication capability [1].  
Surprisingly, ambient backscatter communication (AmBC) has been one of the most promising paradigms for the modern communication technologies [2]. Particularly, the reciprocal communication among energy-free equipment is achieved with backscattering radio frequency (RF) signals [3, 4]. Different from the traditional backscatter systems [5], the requirements for a part of dedicated infrastructures are weakened with the appearance and growth of AmBC. Furthermore, the exploration of the scarce and expensive wireless spectrum is improved by the available AmBC signals. Particularly, AmBC signal detection is a fundamental and difficult problem to be solved.  
So far, there has been some research focused on AmBC signal detection that can be roughly divided into three categories: the physical methods [6], the statistical methods [7], and the learning methods [8]. However, there are several common thorny problems on most of the approaches above: (1) the obtained parameters are incomplete; (2) the direct link interference is serious; (3) the hide intrinsic representations and characteristics are difficult to extract. These issues above make it difficult for the most existing AmBC signal detection methods to be applied well in practice. Therefore, efficient and practical AmBC signal detection methods are urgently needed in both the theoretical and industrial fields.  
It is worth noting that deep learning, especially deep reinforcement learning (DRL), has been widely used in wireless communications [9, 10, 11]. Learning rules are ingeniously developed, and intrinsic features are learned autonomously from the environment byDRL, which is difficult to obtain by traditional methods in wireless communication systems. It has been proven that DRL may be an effective way to perform the design, operation, and optimization of the reconfigurable intelligent surface (RIS)-based wireless communication systems.  
Therefore, in this paper, the RIS-assisted multi-antenna AmBC signal detection is investigated, and an efficient signal detection method is proposed based on DRL and RIS. Specifically, the contributions of this work can be summarized as follows:  
1. An RIS-assisted multi-antenna AmBC signal model is developed, which can achieve information transmission and energy collection cooperatively.
2. A twin delayed deep deterministic (TD3) AmBC signal detection method is developed based on deep reinforcement learning and reconfigurable intelligent surface.
3. Extensive quantitative and qualitative experiments are performed, showing that the presented method is more compelling than the state-of-the-art comparison approaches.  
This paper is organized as follows. The related studies are given in Section 2. The AmBC system model and the problem formulation are shown in Section 3. The proposed solutions are described in Section 4. The experimental results, comparisons, and analysis are presented in Section 5. The conclusions are offered in Section 6.