Fair Computation Offloading for RSMA-Assisted Mobile Edge Computing Networks

Rate splitting multiple access (RSMA) provides a flexible transmission framework that can be applied in mobile edge computing (MEC) systems. However, the research work on RSMA-assisted MEC systems is still at the infancy and many design issues remain unsolved, such as the MEC server and channel allocation problem in general multi-server and multi-channel scenarios as well as the user fairness issues. In this regard, we study an RSMA-assisted MEC system with multiple MEC servers, channels and devices, and consider the fairness among devices. A max-min fairness computation offloading problem to maximize the minimum computation offloading rate is investigated. Since the problem is difficult to solve optimally, we develop an efficient algorithm to obtain a suboptimal solution. Particularly, the time allocation and the computing frequency allocation are derived as closed-form functions of the transmit power allocation and the successive interference cancellation (SIC) decoding order, while the SIC decoding order is obtained heuristically, and the bisection search and the successive convex approximation methods are employed to optimize the transmit power allocation. For the MEC server and channel allocation problem, we transform it into a hypergraph matching problem and solve it by matching theory. Simulation results demonstrate that the proposed RSMA-assisted MEC system outperforms current MEC systems under various system setups.

When NOMA Meets AIGC: Enhanced Wireless Federated Learning

Wireless federated learning (WFL) enables devices to collaboratively train a global model via local model training, uploading and aggregating. However, WFL faces the data scarcity/heterogeneity problem (i.e., data are limited and unevenly distributed among devices) that degrades the learning performance. In this regard, artificial intelligence generated content (AIGC) can synthesize various types of data to compensate for the insufficient local data. Nevertheless, downloading synthetic data or uploading local models iteratively takes a lot of time, especially for a large amount of devices. To address this issue, we propose to leverage non-orthogonal multiple access (NOMA) to achieve efficient synthetic data and local model transmission. This paper is the first to combine AIGC and NOMA with WFL to maximally enhance the learning performance. For the proposed NOMA+AIGC-enhanced WFL, the problem of jointly optimizing the synthetic data distribution, two-way communication and computation resource allocation to minimize the global learning error is investigated. The problem belongs to NP-hard mixed integer nonlinear programming, whose optimal solution is intractable to find. We first employ the block coordinate descent method to decouple the complicated-coupled variables, and then resort to our analytical method to derive an efficient low-complexity local optimal solution with partial closed-form results. Extensive simulations validate the superiority of the proposed scheme compared to the existing and benchmark schemes such as the frequency/time division multiple access based AIGC-enhanced schemes.