Heterogeneous Wireless Communication Networks Research Articles

Comprehensive Assessment of Context-Adaptive Street Lighting: Technical Aspects, Economic Insights, and Measurements from Large-Scale, Long-Term Implementations.

This paper addresses the growing importance of efficient street lighting management, driven by rising electricity costs and the need for municipalities to implement cost-effective solutions. Central to this study is the UNI 11248 Italian regulation, which extends the European EN 13201-1 standard introduced in 2016. These standards provide guidelines for designing, installing, operating, and maintaining lighting systems in pedestrian and vehicular traffic areas. Specifically, the UNI 11248 standard introduces the possibility to dynamically adjust light intensity through two alternative operating modes: (a) Traffic Adaptive Installation (TAI), which dims the light based solely on real-time traffic flow measurements; and (b) Full Adaptive Installation (FAI), which, in addition to traffic measurements, also requires evaluating road surface luminance and meteorological conditions. In this paper, we first present the general architecture and operation of an FAI-enabled lighting infrastructure, which relies on environmental sensors and a heterogeneous wireless communication network to connect intelligent, remotely controlled streetlights. Subsequently, we examine large-scale, in-field FAI infrastructures deployed in Vietnam and Italy as case studies, providing substantial measurement data. The paper offers insights into the measured energy consumption of these infrastructures, comparing them to that of conventional light-control strategies used in traditional installations. The measurements demonstrate the superiority of FAI as the most efficient solution.

Revenue Maximization Through Cell Switching and Spectrum Leasing in 5G HetNets

Even though dynamic cell switching is a prominent approach for energy optimization in heterogeneous wireless communication networks, it results in spectrum under-utilization as the spectrum originally occupied by the base stations that are turned off remain dormant. In order to make the businesses of primary network (PN) operators, who hold the spectrum license, more profitable and sustainable as well as to avoid spectrum under-utilization, this dormant spectrum can be leased to the secondary network (SN) operators who cannot afford to purchase the spectrum license. In this study, first, the cell switching problem, which solely focuses on the amount of energy saved, is translated to a problem of revenue maximization by including the spectrum leasing concept and converting the energy saving to monetary saving from reduced electricity bills. In this regard, two spectrum demand scenarios are considered for the SN operator: delay tolerant (DT), for non-real time applications, and non-delay tolerant (NDT), for real time applications. Then, a cell switching and spectrum leasing framework based on simulated annealing algorithm is developed to maximize the revenue of the PN while respecting the quality-of-service constraints. The simulation results reveal that the DT spectrum demand is more beneficial to both PN and SN operators as it results in greater revenue for the former while the latter is able to access more spectrum to meet higher service demands. This finding suggests that if the application can tolerate delays, then it makes more sense for both PN and SN to adopt the DT scenario. In addition, it is observed that the performance of the proposed framework is very close to that of the optimal solution with a significant reduction in the computation complexity.

Guest Editorial: Introduction to the Special Section on Heterogeneous Communications Networks

The papers in this special section focus on heterogeneous communication networks. These networks, consisting of terrestrial mobile networks, satellite networks, IoT, WiFi, and etc., is a promising technique to meet the challenges in future mobile communications. Heterogeneous network resources are expected to cooperate with each other to support more efficient data transmissions and also provide more reliable services. However, current network architectures cannot efficiently exploit the benefit of heterogeneous wireless communication networks. Considering the deficiency in existing wireless networks nowadays, the most substantial amount of future system performance gains will be obtained by means of heterogeneous network infrastructure densification. Therefore, the heterogeneous communication networks should be deeply investigated to realize the full benefits of each of them.

Effective Bandwidth Prediction through Statistical Technique over Heterogeneous Networks

Real time Streaming Media (Video Streaming) applications are mostly popular on the mobiles and computers using Internet. Due to higher demand of video streaming through wireless network and mobile devices, video are being transmitted through various heterogeneous networks so as to efficiently deliver to the clients devices. This has resulted in the lower quality of real time video, since real time streaming media has quality of parameter requirements like high bandwidth, low packet loss ratio, higher delay and jitter. Streaming media such as video through heterogeneous networks has more challenges due to unreliable wireless networks and device mobility; moreover bandwidth, delay and loss are unknown in advance and are unbounded. In this paper, effective bandwidth prediction through statistical technique over heterogeneous wireless communication networks is proposed. Statistical technique offers computationally efficient bandwidth prediction with reasonably better accuracy. Especially with mobile devices with limited computational power and battery life, necessitates better bandwidth prediction with efficient but computationally simpler algorithms. Bandwidth predictions assist in selecting effective network for video streaming when various heterogeneous networks are available. Detailed bandwidth prediction algorithm is presented with use of quality of service (QoS) parameters data sets available online.

Automated Self-Optimization in Heterogeneous Wireless Communications Networks

Traditional single-tiered wireless communications networks cannot scale to satisfy exponentially rising demand. Operators are increasing capacity by densifying their existing macro cell deployments with co-channel small cells. However, cross-tier interference and load balancing issues present new optimization challenges in channel sharing heterogeneous networks (HetNets). One-size-fits-all heuristics for allocating resources are highly suboptimal, but designing ad hoc controllers requires significant human expertise and manual fine-tuning. In this paper, a unified, flexible, and fully automated approach for end-to-end optimization in multi-layer HetNets is presented. A hill climbing algorithm is developed for reconfiguring cells in real time in order to track dynamic traffic patterns. Schedulers for allocating spectrum to user equipment are automatically synthesized using grammar-based genetic programming. The proposed methods for configuring the HetNet and scheduling in the time–frequency domain can address ad hoc objective functions. Thus, the operator can flexibly tune the tradeoff between peak rates and fairness. Far cell edge downlink rates are increased by up to 250% compared with non-adaptive baselines. Alternatively, peak rates are increased by up to 340%. The experiments illustrate the utility and future potential of natural computing techniques in software-defined wireless communications networks.

Optimized Cell Planning for Network Slicing in Heterogeneous Wireless Communication Networks

We propose a cell planning scheme to maximize the resource efficiency of a wireless communication network while considering quality-of-service requirements imposed by different mobile services. In dense and heterogeneous cellular 5G networks, the available time-frequency resources are orthogonally partitioned among different slices, which are serviced by the cells. The proposed scheme achieves a joint optimization of the resource distribution between network slices, the allocation of cells to operate on different slices, and the allocation of users to cells. Since the original problem formulation is computationally intractable, we propose a convex inner approximation. Simulations show that the proposed approach optimizes the resource efficiency and enables a service-centric network design paradigm.

A novel downlink semi-persistent packet scheduling scheme for VoLTE traffic over heterogeneous wireless networks

Long-term evolution (LTE) is becoming the first choice of mobile network operators (MNOs) when constructing a wireless network infrastructure because of its high data rate, high throughput, and low latency. These significant advancements are necessary for satisfying the delivery of a wide-range of mobile applications and managed network resources. However, deploying a new LTE network or a transition from current legacy cellular networks to LTE can take several years to roll out. In the meantime, working in a heterogeneous wireless communication network looks inevitable. This paper investigates Voice over LTE (VoLTE) Quality of Service (QoS) under a heterogeneous wireless communication scenario. The contributions of this paper are twofold. First, a novel downlink (DL) semi-persistent scheduling scheme is proposed to reduce VoLTE end-to-end delay and increase system capacity. Second, an extensive network simulation model has been designed and implemented to evaluate the proposed scheme. The performance of the proposed scheme is compared with the performance of two relevant and well-known DL packet scheduling methods. The simulation results confirm that the proposed scheme is able to reduce VoLTE end-to-end delay and achieve a better system capacity than current methods, and maintain the desired VoLTE QoS.

Waterfall Traffic Classification: A Quick Approach to Optimizing Cascade Classifiers

Heterogeneous wireless communication networks, like 4G LTE, transport diverse kinds of IP traffic: voice, video, Internet data, and more. In order to effectively manage such networks, administrators need adequate tools, of which traffic classification is the basis for visualizing, shaping, and filtering the broad streams of IP packets observed nowadays. In this paper, we describe a modular, cascading traffic classification system—the Waterfall architecture—and we extensively describe a novel technique for its optimization—in terms of CPU time, number of errors, and percentage of unrecognized flows. We show how to significantly accelerate the process of exhaustive search for the best performing cascade. We employ five datasets of real Internet transmissions and seven traffic analysis methods to demonstrate that our proposal yields valid results and outperforms a greedy optimizer.

Multilevel pattern mining architecture for automatic network monitoring in heterogeneous wireless communication networks

The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks. This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data. This architecture leverages and combines existing frequent itemset discovery over data streams, association rule deduction, frequent sequential pattern mining, and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.