Abstract
In a highly dense heterogeneous cellular network, the loads across cells are uneven due to random deployment of cells and the mobility of user equipments (UEs). Such unbalanced loads result in performance degradation such as throughput and handover success. In order to solve the uneven load problem for better network performance, we propose a cluster-based mobility load-balancing algorithm for heterogeneous cellular networks. Traditional mobility load balancing (MLB) schemes that consider only the adjacent neighbors cannot provide enough improvement in network performance. On the other hand, the previous MLB schemes consider neighbors in the entire network suffer from unnecessary MLB actions. However, in the load balancing process, the proposed algorithm considers overloaded cells and their neighbors within then-tiers. First, the algorithm models the network as a directed multi-graph and constructs clusters taking the overloaded cells and their n-tier neighbors. Therefore, by adjusting cell individual offset parameters of the cells in the clusters the algorithm achieves load balancing locally. Since load balancing is performed inside the clusters, the network can be optimized more efficiently by avoiding unnecessary MLB actions. Simulations show that the proposed algorithm distributes the load across the network more evenly than other MLB algorithms, and in a low UE velocity scenario, it increases the overall network throughput by 6.42% compared to a non-optimized network without an MLB algorithm.
Highlights
The wireless data demand has increased exponentially as the use of smart gadgets and applications has increased
Algorithms may offload users from an overloaded cell to inappropriate neighboring cells as unnecessary cells were considered for load balancing, which may lead to network performance degradation
For mobility load balancing in a cellular network, an MLB algorithm tunes the Ocn, Ocp, and Off parameters of the overloaded cells, so that intentional handovers take place and edge user equipments (UEs) are moved to low-loaded neighboring cells
Summary
The wireless data demand has increased exponentially as the use of smart gadgets and applications has increased. The threshold-based multitraffic load-balancing algorithm proposed in [8] considered traffic-variant UEs to adopt to varying load in a network. Algorithms may offload users from an overloaded cell to inappropriate neighboring cells as unnecessary cells were considered for load balancing, which may lead to network performance degradation. These previous efforts are not able to provide effective load balancing. To enhance the performance of the network as well as to increase the load balancing opportunity, this paper proposes an MLB algorithm considering n-tier heterogeneous neighbors of an overloaded cell. The cluster-based load balancing algorithm is proposed, followed by the performance evaluation, and Section VI provides the conclusions for the paper. A high value for ρ in a cell implies a high load condition in that particular cell and a low value implies that the cell is underutilized with enough available PRBs for allocation
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