# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 9 Challenges and Future Directions
### Areas to Explore
#### 9.2.5 BackCom Channel Coding  
The traditional wireless communications consider channel coding and modern error correcting codes for high throughput and error correction. These modern error correcting codes include low density parity check (LDPC) codes and the most recent polar codes. The LDPC and polar codes have been adopted in 5G standards to enable reliable data transmission with high data rates. The traditional coding schemes need an encoder to encode the information into a message by adding redundancy. These encoded messages are then transmitted to a wireless channel, and the decoder then decodes the data from the received signals at the receiver end. Usually, these encoders and decoders are designed for battery-powered digital communication systems, nor for BackCom. Coding schemes for BackCom should be considered.  
[PERSON] et al. tried to improve the coding rate by proposing improved channel codes for BackCom, but there is still much room for improvement in data rates [71]. Similarly, the work in [243] shows the signal capacity could be achieved when RF signal distribution is not equiprobably backscattered by tag. Moreover, it has also been realized that the capacity of complex Gaussian signals was not exactly twice that of real Gaussian channels. [PERSON] et al. presented the effects of partitioning scattering arrays with retro-directive structures on channel capacity [244]. These structures improved the range and channel capacity, and allowed passive tags to operate in mm-wave frequencies. The latest polar codes, discovered by [PERSON], work on the phenomenon of channel polarization [245]. Polar codes are famous for their low complexity and have achieved the capacity of a binary symmetric channel. Polar codes may be used with BackCom for the improvement of data rates. Moreover, modern error correcting codes, the encoder and decoder for backscatter tags, can be an exciting research direction to enable BackCom for high data rates. Similarly, the deep-learning-based joint source-channel coding technique [240] can be used for structured data scenarios of BackCom.  
**Discussion:** The future directions for BackCom is shown in Figure 50. The recent advanced tools such as deep neural networks, machine learning, and artificial intelligence have helped us to process big data in different fields of science and technology. In the same way, the high bandwidth of mm-waves in 5G, D2D communications in IoT and modern error correcting codes can be used for sending information at higher data rates over a communication channel near the Shannon capacity. These progressive tools lead us to expect similar deep learning system models for BackCom in addition to the modern error correcting codes.  
1. It may be possible for BackCom in the near future to replace the battery-powered communication systems in IoTs and cellular communications.
2. The deep learning algorithms at the cloud radio access of 5G network architecture may execute the signal processing task for low energy systems [242]. Moreover, the signal processing work may be done by training real BackCom systems and by generating similar patterns with the help of deep neural networks or a Generative Adaptive Neural Network (GANN).
3. The modern short block length error correcting codes, such as, polar codes are adopted in the 5G control channel, and can be explored for improving data rates in BackCom [245]. This higher data rate communication at the tag side and speedy processing at the cloud radio access network can use artificial intelligence to change the future of BackCom.