# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 9 Challenges and Future Directions
### Areas to Explore
#### 9.2.4 Artificial Intelligence and BackCom  
Artificial intelligence, machine learning, and deep neural networks are revolutionizing technology in almost every discipline of engineering. Wireless communications is not an exception. An introduction to a machine-learning-based communication system highlights different applications of machine learning to a wireless communication system architecture [231; 232; 233]. This work includes an auto-encoder, which can jointly learn the transmission and reception functions in the absence of previous knowledge. Similarly, machine learning tools equivalent to digital signal processing components of wireless communications are discussed in [235]. This work shows that a convolutional layer in deep learning is similar to a finite impulse response (FIR) filter, and a recurrent cell in a neural network resembles an infinite impulse response (IIR). Machine learning can be used with BackCom [236] to enhance the performance of a BackCom network.  
Word-Fi is an accurate handwriting system using BackCom and machine learning to recognize tiny gestures [237]. In order to maximize the network performance in an RF-powered cognitive radio network, an online reinforcement learning approach was proposed by [PERSON] et al. [238]. Furthermore, a deep learning model usually includes an input layer, deep hidden layers, and an output layer. This model learns by training. The training set includes the input and desired output for training. A deep-learning-based communication model for wireless communications can extract the relationship between the input and output, referred to as the trained model. The obtained relation can be used at the receiver for the detection of unknown transmitted information.  
A deep-learning-based algorithm was proposed for a communication system. It did not require any mathematical model for channels between transmitters and receivers [239]. This technique can extract features of channels with the help of supervised training at the receiver and reinforcement training at the transmitter. Moreover, this technique could be used for detecting features of BackCom channels in battery-free communication. Likewise, various deep-learning-based channel coding algorithms [240; 241] have been suggested for wireless communications, and these could be utilized for the extraction of tag information.  
Other authors proposed a WiFi-based gesture recognition system through the reflection of WiFi signals and machine learning techniques [196; 197]. These works improved the performance in gesture recognition. Similarly, optimization in the wireless network based on deep learning shows a 50% decrease in computation cost without any effect on optimality. Optimization using deep learning in BackCom may be an exciting area for researchers [242]. Researchers from the Massachusetts Institute of Technology (MIT) explained the utilization of Amb-BackCom (using WiFi signals) and machine learning techniques to detect heart rate and breathing conditions without physical contact. This work is just the beginning of the revolutionary wireless communications with Amb-BackCom radio signals [188]. More research can still be done by using deep learning techniques for BackCom.