Online reinforcement learning for adaptive interference coordination

Approaching the cell switch-off problem in 5G ultra-dense networks with dynamic multi-objective optimization

A lightweight cell switching and traffic offloading scheme for energy optimization in ultra-dense heterogeneous networks

One of the major capacity boosters for 5G networks is the deployment of ultra-dense heterogeneous networks (UDHNs). However, this deployment results in tremendousincrease in the energy consumption of the network due to the large number of base stations (BSs) involved. In addition to enhanced capacity, 5G networks must also be energy efficient for it to be economically viable and environmentally friendly. Dynamic cell switching is a very common way of reducing the total energy consumption of the network but most of the proposed methods are computationally demanding which makes them unsuitable for application in ultra-dense network deployment with massive number of BSs. To tackle this problem, we propose a lightweight cell switching scheme also known as Threshold-based Hybrid cEllswItching Scheme (THESIS) for energy optimization in UDHNs. The developed approach combines the benefits of clustering and exhaustive search (ES) algorithm to produce a solution whose optimality is close to that of the ES (which is guaranteed tobe optimal), but is computationally more efficient than ES and as such can be applied for cell switching in real networks even when their dimension is large. The performance evaluation shows that the THESIS produces a significant reduction in the energy consumption of the UDHN and is able to reduce the complexity of finding a near-optimal solution from exponential to polynomial complexity.

A Lightweight Cell Switching and Traffic Offloading Scheme for Energy Optimization in Ultra-Dense Heterogeneous Networks.

This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future. 

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A survey on binary metaheuristic algorithms and their engineering applications

As a large number of small base stations have been deployed in dense heterogeneous networks, the increasing cost of energy has become a challenging issue. In this paper, we propose an optimized clustering-based sleep strategy to release the power consumption and interference in the system. First, we group the small base stations with large interference as a cluster in which the binary particle swarm optimization (BPSO) algorithm is used to formulate a sleep strategy for small base stations in the clusters. Since the small base stations between clusters have less interference, the whole spectrum resources can be shared in all clusters and the orthogonal spectrum resources are allocated for the small base stations in the same cluster. The strategy effectively restrains the co-tier interference in the system. Furthermore, a separate sleep strategy is applied for each cluster to improve the sleep and activation efficiency of the small base stations. The simulation results show that the proposed sleep strategy can improve the satisfaction of user equipment and reduce the power consumption. As the increasing number of user equipment, the outage probability of the system can be reduced effectively.

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Optimized sleep strategy based on clustering in dense heterogeneous networks

To solve the problem of load imbalance and system interference in heterogeneous networks (HetNets), an energy efficiency (EE) optimized strategy based on enhanced intercell interference coordination (eICIC) technology is proposed. The optimization of the EE of the system is established by taking into account four components which are the connection between the users and base stations (BSs), almost blank subframe (ABS) ratio, resource allocation in the frequency domain, and the power transmission of the macro BSs (MBSs). In our optimized strategy, the user’s optimal BS connection status are determined according to the overall EE change of the system, and the allocation of subframes and resource blocks of the MBSs both in the time domain and frequency domain are optimized, respectively. We use the Gauss–Seidel method to iteratively solve the optimization problem. Simulation results show that the optimized strategy outperforms other solutions in terms of EE and user fairness in average. The proposed optimization strategy increases the system EE by 24.2% and 19.1%, respectively, when compared with the maximal reference signal received power (MAX-RSRP) and the low power-ABS (LP-ABS) strategies. The Jain fairness index of our proposed optimization strategy is averagely increased by 33.8% and 23.4%, respectively. In addition, the proposed strategy can further improve the user satisfaction and the performance in the system.

Hao Wang

Papers

Optimized sleep strategy based on clustering in dense heterogeneous networks

Energy Efficiency Optimization Based on eICIC for Wireless Heterogeneous Networks