NGMA-based intergrated communication and computing for 6G-enabled cognitive radio networks

Abstract

According to the urgent low latency and the heavy computation tasks demands required for sixth-generation (6G) wireless networks, the authors introduce the conventional resource allocation algorithms, including the game theory, artificial-intelligence (AI) methods, and matching theory enabled framework, in which the multi-access edge computing (MEC) scheme collaborative with the cloud platform to serve the primary users (PUs) and cognitive users (CUs) for next generation multiple access (NGMA). The proposed framework allows both the PUs and CUs to offload their computation tasks in a 6G-enabled cognitive radio (CR) networks, so called cloud-assisted CR-MEC networks. In particular, the fundamentals of this conceived networks based on NGMA are first introduced. Hence, a number of methods based on the resource allocation algorithms are proposed in order to improve the quality of service for the mobile users, and reduce their transmission latency as well as the energy consumptions. Moreover, the motivations, challenges, and representative models for these conventional algorithms are described for integrated-intelligent communication and computing aided NGMA networks. Furthermore, the open issues and future research directions for this conceived networks are summarised.