Differentiable architecture search plays a prominent role in Neural Architecture Search (NAS) and exhibits preferable efficiency than traditional heuristic NAS methods, including those based on evolutionary algorithms (EA) and reinforcement learning (RL). However, differentiable NAS methods encounter challenges when dealing with non-differentiable objectives like energy efficiency, resource constraints, and other non-differentiable metrics, especially under multi-objective search scenarios. While the multi-objective NAS research addresses these challenges, the individual training required for each candidate architecture demands significant computational resources. To bridge this gap, this work combines the efficiency of the differentiable NAS with metrics compatibility in multi-objective NAS. The architectures are discretely sampled by the architecture parameter α within the differentiable NAS framework, and α are directly optimised by the policy gradient algorithm. This approach eliminates the need for a sampling controller to be learned and enables the encompassment of non-differentiable metrics. We provide an efficient NAS framework that can be readily customized to address real-world multi-objective NAS (MNAS) scenarios, encompassing factors such as resource limitations and platform specialization. Notably, compared with other multi-objective NAS methods, our NAS framework effectively decreases the computational burden (accounting for just 1/6 of the NSGA-Net). This search framework is also compatible with the other efficiency and performance improvement strategies under the differentiable NAS framework.

As mobile communication technologies advance, mobile networks become increasingly complex, and user requirements become increasingly diverse. To satisfy the diverse demands of users while improving the overall performance of the network system, the limited wireless network resources should be efficiently and dynamically allocated to them based on the magnitude of their demands and their relative location to the base stations. We separated the problem into four constrained subproblems, which we then solved using a safe reinforcement learning method. In addition, we design a reward mechanism to encourage agent cooperation in distributed training environments. We test our methodology in a simulated scenario with thousands of users and hundreds of base stations. According to experimental findings, our method guarantees that over 95% of user demands are satisfied while also maximizing the overall system throughput.