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 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 the n-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.

The ultra-dense network (UDN) incorporates a high densification of small cells, resulting in improved capacity and coverage for a fifth-generation (5G) cellular network. However, the proliferation of small cells within the 5G network has led to a notable surge in energy consumption. To improve energy efficiency in a UDN, this paper proposes a small-cell operation algorithm considering sleep mode and active mode switching for small cells. The algorithm periodically monitors the radio resource status of the macro cells and small cells for the mode-switching process. If a macro cell with a lower radio load is detected, the algorithm triggers a small cell sleep mode switching. To that end, the algorithm finds a small cell with the lowest radio load within the coverage of the macro cell. Therefore, the algorithm switches the small cell to sleep mode estimating the accumulated load in the macro. An active mode switching is triggered by the algorithm when a macro cell with a high radio load is detected. The algorithm then generates a location dataset consisting of the location information of inactive small cells and static UEs. A clustering technique is adopted on the dataset to find clusters containing at least one small cell. Considering the average reported reference signal received power (RSRP) of the UEs, a cluster is selected, and the small cells in the cluster are switched to active mode. Through system-level simulations, the performance of the proposed algorithm is evaluated. The simulation results showed that the proposed approach can ensure a higher energy efficiency than previous algorithms.

Software industries face a common problem which is the maintenance cost of industrial software systems. There are lots of reasons behind this problem. One of the possible reasons is the high maintenance cost due to lack of knowledge about understanding the software systems that are too large, and complex. Software clustering is an efficient technique to deal with such kind of problems that arise from the sheer size and complexity of large software systems. Day by day the size and complexity of industrial software systems are rapidly increasing. So, it will be a challenging task for managing software systems. Software clustering can be very helpful to understand the larger software system, decompose them into smaller and easy to maintenance. In this paper, we want to give research direction in the area of software clustering in order to develop efficient clustering techniques for software engineering. Besides, we want to describe the most recent clustering techniques and their strength as well as weakness. In addition, we propose genetic algorithm based software modularization clustering method. The result section demonstrated that proposed method can effectively produce good module structure and it outperforms the state of the art methods.