# Key Technologies for 6G-Enabled Smart Sustainable City
## 4 Key Candidate Technologies for Future SSC
### Prime Technologies for SSC
#### 4.2.1 AI-based Wireless Communication
**High-speed scenario**: High-mobility scenarios in SSC pose challenges to 6G networks in effectively delivering new applications and services. For example, the base station must accurately reconstruct the CSI and even predict future CSI to mitigate channel aging issues. However, there is insufficient research on DL-based feedback and CSI prediction techniques that effectively address scenarios with fast channel changes. A DL-based feedback scheme should be carefully designed to ensure effective communication, even in highly mobile environments. **Generalization**: In DL-based wireless communications, neural networks undergo training using CSI samples that adhere to a specific distribution of channel environments. However, the stability of channel environments is not guaranteed because UEs do not always remain in a fixed cell and frequently move to other cells. The communication systems, developed based on specific channel parameters, experience significant performance degradation when the channel changes rapidly. The DL-based CSI feedback architecture needs to be carefully designed to ensure flexibility in coping with various channel and network conditions. Figure 2: Illustration of AI-based channel estimation and CSI feedback. **Balancing complexity and performance**: The high complexity and increased computational demands of DL-based communication systems make them unsuitable for UEs with limited computational power. Although neural network compression techniques can reduce complexity, this still poses challenges for small-scale devices with limited computational power, such as IoT sensors that are essential for building 6G smart cities. Addressing the tradeoff between performance and complexity is vital for enabling computationally cost-effective AI solutions in SSC. **Security and privacy**: Security and privacy challenges in SSC networks necessitate reevaluating cybersecurity frameworks to provide wireless services with enhanced security, particularly when dealing with sensitive data [66]. For example, the exponential growth of widespread IoT connectivity has introduced vulnerabilities that result in an increase in malicious attacks [67]. To establish a secure SSC, it is crucial to tackle the security and privacy challenges associated with IoT devices in SSC networks. To shed light on how AI-based wireless communication can be constructively exploited in SSC, we discuss its potential benefits in the following. * _Improve transmission performance_: In the future 6G era, a multitude of UEs and machines will be connected to the internet through small devices equipped with AI, resulting in a hyper-connected society. The number of IoT terminals worldwide is increasing at a tremendous rate and is expected to grow even more in the future [68]. The rapid increase in IoT terminals can lead to the problem of increasing the amount of feedback. The CSI feedback method based on DL is considered a viable solution to effectively deal with the overhead problem caused by this rapid increase in feedback. * _Low compression ratio and reduced time complexity_: One of the problems with traditional CS methods is their dependence on channel sparsity. To overcome this problem, DL can be used to take advantage of huge amounts of training samples and train multi-layer neural networks. One of the structures used to do this is an autoencoder. An autoencoder consists of an encoder, that leverages training data to compress the original channel matrix into a codeword, and a decoder, that learns the inverse transformation from the codeword to the original channel form. High-compression ratios and reduced time complexity can be achieved using these autoencoder structures. * _High throughput and efficiency_: It is necessary to address the outdated CSI problem between the LEO satellite and UT to enable future transportation methods in SSC, such as UAM. A possible solution to predict CSI from channel estimates is to exploit the DL-based time series prediction model [69].