Resource Management of Heterogeneous Cellular Networks With Hybrid Energy Supplies: A Multi-Objective Optimization Approach

Heterogeneous cellular networks with hybrid energy supplies can relieve traffic pressure and reduce grid energy consumption. In heterogeneous cellular networks, rational resource management can help improve system performances. In general, more than one performance is expected to do well, but there can exist a trade-off among different performance metrics, thus making resource management a multi-objective problem. The existing solution usually transforms a multi-objective problem into another single-objective problem by assigning weights for various objectives. However, it is difficult to know the exact weights in advance, and different systems call for different requirements for objectives. Hence, a multi-objective optimization approach based on the gravitational search algorithm (GSA) is proposed to find a series of Pareto optimal solutions. The decision-makers can select an appropriate solution according to the system requirement. In this work, three different multi-objective GSA-based algorithms are proposed to determine user association and power control, with the goal to optimize the traffic load balancing among small base stations and grid energy consumption per unit throughput simultaneously. The complexity of the proposed algorithms is analyzed, and simulations compare the performances of the proposed algorithms and the benchmark algorithm. Experimental results reveal the feasibility and effectiveness of this approach.

Automated summary

A multi-objective optimization approach based on the gravitational search algorithm is proposed in heterogeneous cellular networks to find a series of Pareto optimal solutions, addressing the trade-off problem in resource management. The proposed algorithms aim to simultaneously optimize traffic load balancing and grid energy consumption, with experimental results demonstrating the feasibility and effectiveness of this approach.