User Grouping, Spectrum and Power Allocation for Energy Efficient MEC Aided Cognitive Radio Networks

In this paper, we study a novel integration of the Mobile edge computing (MEC) technology with cognitive radio (CR) into a holistic network, namely CR-MEC, aim to create a paradigm for communication and computing fusion in 6G networks. In this contribution, we study the comprehensive resource allocation mechanism of user grouping, power and spectrum. More specifically, multiple cognitive users (CUs) of CR-MEC networks are classified into different user groups by employing a proposed user replicator dynamical evolution (URDE) algorithm. After that, the optimal power allocation is characterized in each group for balancing the user fairness. In particular, according to the users' preference lists, multiple CUs select their suitable primary spectrum by using the two-side matching theory. Additionally, these CUs would offload their tasks to the same MEC server, by implementing either non-orthogonal multiple access (NOMA) or OMA schemes. Furthermore, we carry out the relative performance evaluations in terms of energy efficiency for the CR-MEC networks and conclude that the obtained performances based on the proposed algorithms would approach to the centralized solutions.