Hybrid Optimization for Multi‐Objective Resource Allocation and Offloading in 5G Cloud‐RAN

ABSTRACT In cloud radio access network (C‐RAN), the expense of course‐grain offloading can become unsuitable due to network conditions or a large volume of input data. The major intention of this manuscript is to design and develop an energy‐efficient resource allocation technique and offloading in 5G C‐RAN. Here, resource allocation is implemented using the newly developed poor rich firefly optimization (PRFO) algorithm, which combines poor and rich optimization (PRO) with the Firefly Algorithm (FA). The optimization problem is modeled with respect to key objectives under constraints such as high transmit power, minimum data rate, and average power of every user. Additionally, offloading is performed using the proposed Harmonic Poor Rich Firefly Optimization (HPRFO), which integrates harmonic analysis with PRFO to optimize energy, capacity, and bandwidth. The performance of HPRFO is analyzed considering various evaluation measures, like capacity, bandwidth, energy efficiency, minimum data rate, throughput, and power consumption, and is found to have achieved values of 88.706 Mbps, 129.275 Mbps, 86.876 Mbits/J, 88.877 Mbps, 90.988 Mbps, and 33.876 J/s, correspondingly.