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Dual-Order Resource Allocation in 5G H-CRAN Using Matching Theory and Ant Colony Optimization Algorithm

The problem of resource allocation in a Heterogeneous Cloud Radio Access Network (H-CRAN) through user association is a combinatorial optimization problem, which is addressed in this paper. The process of resource allocation is divided into two orders- namely, Primary Order Resource Allocation and Secondary Order Resource Allocation. The association process is initiated at first by using Matching Theory based on the user's Quality of Service (QoS) and the available bandwidth of the best serving base station (BS). The varying QoS demands of the users are met using Ant Colony Optimization (ACO) on the remaining channels, which improves the average data rate and throughput of the users. Hence, the proposed algorithm combines both the co¬operative game approach and the constructive greedy approach to enhance the performance metrics of the users. Results of simulations are compared with traditional resource allocation strategies to demonstrate the performance of the proposed algorithm for average data rates, access rates, and throughput.