## 1 Introduction  
Wireless communication has undergone tremendous advancements in recent years, with backscatter communication (BC) emerging as a promising technique for low-power and low-cost wireless communication. In BC, the transmitter sends a signal to the receiver, which is reflected by a semi-passive device such as a backscatter device (BD), modulating the signal to convey information. In fact, BC allows BDs to transmit data to legitimate receivers by reflecting and modulating an existing radio frequency (RF) signal, instead of generating their own signal [1, 2, 3, 4, 5]. The BC technique uses a low-power, battery-free BD as a tag or sensor, which is equipped with a small antenna and a microcontroller. BC eliminates the need for a dedicated transmitter in the BD, which saves power and reduces the size and cost of the device, and this makes it suitable for the Internet of Things (IoT) [6, 7, 8, 9].  
However, the performance of BC systems is limited by the low signal-to-noise ratio (SNR), interference, and other factors, making it challenging to detect the backscattered signals accurately. The backscatter signal is typically weaker than the original RF signal, due to losses in the BD antenna, reflection, and transmission. As a result, the SNR at the receiver can be low, which can affect the detection performance. The low SNR can also cause interference from other sources, such as thermal noise, multipath fading, or other signals in the same frequency band. BC typically uses simple modulation schemes, such as on-off keying (OOK) or amplitude shift keying (ASK), which have a lower data rate and a higher susceptibility to noise and interference. The modulation scheme can also affect the bit-error rate (BER) performance, as some schemes are more sensitive to changes in the signal phase or frequency. BC performance can be affected by the distance between the BD and the reader. As the distance increases, the backscatter signal strength decreases, and the detection performance becomes worse [10; 11; 12; 13; 14]. This distance-dependent performance can be mitigated using a higher-power reader, a higher-gain antenna, or a larger BD antenna. BC can be susceptible to interference from other devices operating in the same frequency band. For example, if there are multiple BDs or readers in the same area, they can interfere with each other's signals, causing errors or collisions [15; 16].  
When utilizing deep learning (DL)-based approaches for signal detection in BC systems, there are two primary obstacles to overcome. Firstly, due to the low signal power received from the backscatter link in comparison to the direct link, the two hypotheses (whether the BD backscatters ambient RF source signals or not) have a minor difference, making it challenging for DL to complete the classification task. Secondly, unlike the typical classification tasks in computer vision, where a 10% error rate is acceptable, the BD signal detection task in BC systems has a lower BER requirement, usually less than 0.01. To address this challenge, our paper proposes a novel approach to improving the detection performance of BC systems by leveraging the advantage of evolutionary DL. Specifically, we focus on training deep convolutional neural networks (DCNNs) to enhance the detection accuracy of BC.