Small cells were introduced to support high data-rate services and for dense deployment. Owing to user equipment (UE) mobility and small-cell coverage, the load across a small-cell network recurrently becomes unbalanced. Such unbalanced loads result in performance degradation in throughput and handover success, and can even cause radio link failure. In this paper, we propose a mobility load balancing algorithm for small-cell networks by adapting network load status and considering load estimation. To that end, the proposed algorithm adjusts handover parameters depending on the overloaded cells and adjacent cells. Resource usage depends on signal qualities and traffic demands of connected UEs in Long Term Evolution (LTE). Hence, we define a resource block-utilization ratio as a measurement of cell load, and employ an adaptive threshold to determine overloaded cells, according to the network load situation. Moreover, to avoid performance oscillation, the impact of moving loads on the network is considered. Through system-level simulations, the performance of the proposed algorithm is evaluated in various environments. Simulation results show that the proposed algorithm provides a more balanced load across networks (i.e. smaller standard deviation across the cells) and higher network throughput than previous algorithms.

In a heterogeneous network (HetNet) with ultra-dense small-cell deployment, fast-moving users may experience frequent handovers between small cells owing to the small coverage areas of those cells. Moreover, due to shadowing and channel fading, static or slow-moving users can also experience frequent handovers, called ping-pongs. Such redundant handovers are unacceptable, and can cause packet losses or packet delays leading to a poor user experience. To solve these unwanted handover problems, we propose a frequent handover mitigation algorithm for ultra-dense HetNets. Based on mobility behavior, the proposed algorithm categorizes frequent handover-experience users as either fast-moving or ping-pong users. Fast-moving users are then handed over to the macro layer, and ping-pong users are managed by adjustment of handover parameters. However, if ping-pongs cannot be avoided through handover-parameter adjustment, ping-pong users are handed over to the macro layer as well. Simulation results show that the proposed algorithm can reduce 79.56% of the overall handovers, and increase network throughput by 10.82%.

In this paper, we propose a distributed mobility robustness optimization algorithm to minimize handover failures due to radio link failures by adjusting time-to-trigger and offset parameters. According to the reason for failure, the algorithm classifies handover failure into three categories (too late, too early, and wrong cell), and simultaneously optimizes three handover parameters according to the dominant failure. Moreover, the algorithm considers handover failures to each neighboring cell and adjusts handover parameters individually. Via simulation, we show how the proposed algorithm adaptively optimizes the parameters and outperforms previous algorithms in various mobile environments.

In this paper, we analyze vertical handovers in heterogeneous networks. Vertical handovers are an important ramification of user mobility in a heterogeneous wireless network since they directly impact network performance, such as throughput, packet delay, or call blocking probability. However, the study of vertical handovers faces many difficulties when it comes to analysis, including network modeling which is extremely complicated due to topology diversity. To tackle such analytic challenges, we propose a stochastic geometric analysis scheme on user mobility and small cell networks, in order to capture the impacts of different topologies. We derive a theoretical expression for the expected number of handovers that occur and a corresponding approximation. Our analysis shows that the number of vertical handovers is determined as a concave function of small cell radius r when given the small cell density. Via simulations our analysis is verified in various wireless environments.

We propose a load balancing algorithm for a multi-RAT (radio access technology) network including a non-terrestrial network (NTN) and a terrestrial network (TN). Fifth generation (5G) and beyond-5G networks consider NTNs to provide connectivity and data delivery to large numbers of user equipments (UEs). However, previous load balancing algorithms do not consider the coexistence of NTNs and TNs and ignore the different resource allocation units in a multi-RAT network. Hence, we define a radio resource utilization ratio (RRUR) as a common load metric to measure the cell load of each RAT and employ an adaptive threshold to determine overloaded cells. The proposed algorithm consists of two steps to overcome the uneven load distribution across 5G cells: intra-RAT load balancing and inter-RAT load balancing. Based on the RRUR of a cell, the algorithm first performs intra-RAT load balancing by offloading the appropriate edge UEs of an overloaded cell to underutilized neighboring cells. If the RRUR of the cell is still higher than a predefined threshold, then inter-RAT load balancing is performed by offloading the delay-tolerant data flows of UEs to a satellite link. Furthermore, the algorithm estimates the impact of moving loads to the target cell load to avoid unnecessary load balancing actions. Simulation results show that the proposed algorithm not only distributes the load across terrestrial cells more evenly but also increases network throughput and the number of quality of service satisfied UEs more than previous load balancing algorithms.