5G communication resource allocation strategy based on edge computing

Abstract

Aiming at the problem that most of the existing strategies cannot deal with the explosive growth of user data after the popularization of 5G communication technology, this paper proposes a 5G communication resource allocation strategy based on edge computing. First, the 5G communication network is virtualized to build mobile edge computing system with multiple mobile user equipment and multiple servers. Then, the communication model and calculation model of system are established. Besides, the optimization objectives are proposed based on this, which can minimize total user cost and maximize the success rate of task offloading and successful execution rate. Finally, the resource allocation problem is formalized and modelled as Markov model. The objective is to minimize the ratio of overall system energy consumption to task execution time and delay constraints, the optimal communication resource allocation scheme is obtained by solving it based on Q-learning algorithm. The experimental demonstration of proposed strategy is carried out based on the python simulation platform. Experimental results show that the total system cost and average delay of proposed strategy do not exceed 500 W and 1000 ms under the experimental conditions; its overall performance is better than other comparison strategies, can meet the interest requirements of users and service providers.