The GD-based approach used in the Li-GD algorithm presents several challenges and limitations. One significant challenge is the sensitivity to the choice of hyperparameters, such as the learning rate. An inappropriate learning rate can lead to slow convergence or even divergence, making it difficult to find the optimal trade-off between inference delay, QoE, and resource consumption.
Another limitation is the potential for local minima in the optimization landscape. The gradient descent method may converge to a suboptimal solution rather than the global optimum, particularly in complex, non-convex optimization problems typical in edge intelligence scenarios. This can hinder the algorithm's ability to achieve the best possible performance.
To address these challenges, alternative optimization techniques can be explored. For instance, evolutionary algorithms, such as genetic algorithms or particle swarm optimization, can be employed to search the solution space more effectively and avoid local minima. These techniques do not rely on gradient information and can explore a broader range of solutions, potentially leading to better overall performance.
Additionally, reinforcement learning (RL) approaches can be integrated into the resource allocation framework. By treating the resource allocation problem as a sequential decision-making process, RL can adaptively learn optimal strategies based on user feedback and changing conditions, thus improving the algorithm's robustness and adaptability.