# Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing
## 6 Discussions for Future Works  
Our proposed framework has proven to be useful in improving the performance of the MAR system; however, there are some points for discussion or further development.  
1. Expansion on the GAI aspect: In this paper, we focus on the SR aspect of GAI to improve MAR performance. However, there are many GAI solutions to adapt to the MAR system to improve MAR performance, and the effects of these GAI solutions are designed differently from SR. In the real-world MAR environment, different GAI solutions can be applied depending on the different requirements of the application since other GAI solutions have their unique advantages and requirements [1]. For example, the diffusion model, one of the GAI algorithms, generates images from text descriptions, even when the training dataset does not include the specific images described. This algorithm can be applied to various MAR applications that require flexible qualities or specifications. If the proposed framework with SR is advanced, it may be possible to consider expanding to other GAI technologies.
2. Expansion on the problem formulation and optimization method: In this paper, we formulated a multi-objective optimization problem to balance the trade-off between latency, energy consumption, and service satisfaction. We solved this problem using the block coordinate descent method, as it allows us to address the problem mathematically with minor modifications. However, our approach has the limitation of not enabling real-time decision making. Since the real-world MAR environment is dynamic, solutions that lack real-time adaptability are not applicable to practical MAR systems. To address this, our problem can be extended to a real-time decision-making framework using a Lyapunov-based, round-wise drift-plus-cost minimization approach. Additionally, key real-world factors such as heterogeneity among MEC servers and user mobility should be incorporated. As the problem becomes more complex and advanced, the optimization solutions will also require more sophisticated techniques. As discussed in Section 2, heuristic algorithms or AI-based solutions may be well suited for solving these more complex scenarios.
3. In-depth understanding of SR adaptation to MAR framework: Although we aim to enhance the user experience for MAR users through the application of SR, there is limited analysis on the effects of SR in MAR systems, such as distortions or loss of detail in SR results, as well as the overall impact of SR on users. Unfortunately, few studies have applied SR to MAR systems, and those that have often underestimated the challenges associated with SR [24], despite extensive research addressing these challenges in other fields [35; 36]. For instance, some research suggests novel training methods using a distortion-aware network, as higher distortion in source images can degrade the performance and accuracy of SR. These studies explain that image distortion in AR is caused by the degradation process [35], changes in viewing range, and sphere-to-plane projection [36]. Moreover, as mentioned earlier, SR offers unique advantages that set it apart from other GAI technologies [1], and clearly demonstrating these differences will have important implications. In our future work, we plan to conduct more detailed analyses and evaluations to address these gaps.