# Key Technologies for 6G-Enabled Smart Sustainable City
## 4 Key Candidate Technologies for Future SSC
### Prime Technologies for SSC
#### 4.2.4 Vision-Aided Wireless Communication  
Recently, vision-aided wireless communication (VAWC) has received much attention as a key enabling technology in realizing 6G-enabled SSC due to its capability to manipulate beamforming using visual and radio information collected by cameras in mmWave and THz bands [101], as shown in Figure 5. VAWC uses machine learning techniques to analyze the data collected by cameras. The aim of VAWC is to understand both the invisible and visible situations of mobile users and to predict their future locations [102]. One major bottleneck of exploiting the mmWave and THz bands is that these higher frequency bands tend to be vulnerable to blockage due to their high directivity and severe path loss. Due to its ability to mitigate intermittent blockage, VAWC can be considered a prime technology for achieving seamless connectivity in non-stationary scenarios where user equipment (UE) moves rapidly and the variation in path angle and time delay is not negligible. Environmental awareness technologies are crucial to supporting emerging services such as UAM, smart factories, and smart farms. Since emerging services would demand not only high data rates but also low latency and high-precision positioning capabilities [48], VAWC can play a pivotal role in the development of SSC applications by identifying the location of obstacles from acquired image information [48]. To fully enjoy the benefits of VAWC in various smart city domains such as telemedicine and autonomous driving, we need to address the following challenges. **Building datasets**: To utilize VAWC, an extensive collection of visual data sets is necessary to show a comprehensive, balanced, and diverse representation. Building such a dataset demands substantial time and financial resources, ensuring that the data represents all potential environmental scenarios. Consequently, building satisfactory datasets in VAWC remains a major challenge [103]. **Selection of deep learning techniques**: The utilization of various deep learning techniques in VAWC, such as encoder-decoder architectures, generative adversarial neural networks (GANs), and transformers, has proven to be effective. For example, encoder-decoder architectures have been widely used in semantic segmentation and sequence-to-sequence tasks. In addition, transformers using attention mechanisms are robust in processing unordered data [104]. It is worth noting that identifying efficient DL techniques to address specific environments with visual data remains a major challenge. **Training for environmental compatibility**: One of the important challenges in VAWC is to ensure environmental compatibility in different wireless environments [48]. To this end, the training of various datasets collected from different environments is necessary, but this process requires substantial time and effort. As a potential solution, transfer learning can be considered [105]. This approach involves adapting a pre-trained VAWC model trained on common visual characteristics, by utilizing a small amount of target environment data to create a VAWC model adaptable to the new environment. **Privacy issue**: VAWC systems can collect a wide range of personal identification information during the process of visual information collection. The development of VAWC should be approached with consideration for network security and privacy issues, as it extensively observes the daily activities of mobile users [106]. Addressing privacy concerns in VAWC poses a significant challenge, requiring mechanisms to assess data security risks and ensure privacy. In the following, we discuss the potential benefits of using VAWC in SSC. * _Maximizing beamforming gain_: Unlike conventional codebook-based beam management, VAWC can effectively improve the beamforming accuracy by using the UE's angle information obtained via RGB images at base stations. In [101], it is shown that the orientation error of DL-based object detection is below 0.5 degrees, which can be used to maximize the beamforming gain using VAWC.