# 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  
The architecture of future broadband communication systems is becoming more complex to support a variety of heterogeneous wireless services in the SSC. Furthermore, the large number of antennas and the high frequency band in next-generation broadband systems require reevaluating the transmission and reception frameworks in current cellular networks. To address technical challenges, various AI technologies, such as supervised learning, unsupervised learning, and reinforcement learning, are currently being employed in wireless communication systems [55; 56]. AI-based communication systems are thought \begin{table}
\begin{tabular}{c c c} \hline \hline
**Prime Technology** & **Key Features** & **Possible Contribution to Future SSC** \\ \hline AI-Based wireless communication & - Transmission performance improvement [38] & - Effective electricity supply and management [41] \\  & - Overcoming outdated CSI [39] & - Weather prediction from historical data [41] \\ \hline Advanced mobile edge computing & - Achieving low latency via decentralized structure [42] & - Real-time immersive service [42] \\  & - High QoS achievement [43] & - Enhanced privacy security [44] \\  & - Overload mitigation [43] & - RIS-aided zero-coverage hole networks [47] \\ \hline Non-terrestrial networks & - Reducing coverage holes [45] & - RIS-aided zero-coverage hole networks [47] \\  & - Frequency co-existence [46] & - UAV transportation service [46] \\ \hline Vision-aided wireless communication & - Environmental awareness [48] & - UAV communication systems [50] \\  & - Energy conservation [49] & - Traffic congestion mitigation [51] \\ \hline Integrated sensing and communication & - High-resolution sensing [52] & - Enabling autonomous vehicles [53] \\  & Simultaneous imaging and localization [53] & - Traffic accident detection [54] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Five prime technologies for 6G-enabled SSC. to be a promising candidate that can provide throughput enhancements for 6G networks and introduce new broadband services, distinct from conventional cellular networks. In 6G networks, the role of MIMO technology is expected to become more prominent [38; 57]. The large number of antennas makes channel state information (CSI) acquisition more complex in extremely large-scale MIMO systems at high frequencies. To address these technical challenges, the existing compressed sensing (CS) method based on sparsity-dependent assumptions needs to be reevaluated to fully exploit deep learning (DL) techniques. Consequently, DL-based CSI feedback methods, such as CsiNet (proposed in [58]), have attracted attention to improve its performance. In particular, CsiNet utilizes an encoder-decoder network structure to compress CSI and reduce the feedback overhead. By applying AI techniques, as shown in Figure 2, the CSI acquisition architecture can be redesigned to dramatically improve channel estimation and prediction performance. Previous studies focus on the aim of enhancing wireless capabilities [59; 60; 61; 62; 63]. For example, channel estimation algorithms based on recurrent neural network (RNN) have been proposed to address the channel estimation problem in fast-changing channel environments, such as UAM and LEO wireless channels [64]. In addition, a DL-based satellite channel predictor, consisting of a long-term short memory (LSTM) unit, has been proposed to eliminate the negative effects of outdated CSI in LEO satellite massive MIMO systems [65]. However, in order to effectively enable AI-powered SSC, the following technical challenges must be addressed:  
**Training data**: Acquiring sufficient training datasets is crucial because deep learning systems enhance channel estimation performance via dataset learning. However, in practical wireless communication scenarios, acquiring a large number of training samples is challenging due to the high cost of the time and frequency resources utilized for dataset training.