Comparison of Empirical Propagation Path Loss Models for Mobile Communication

Empirical propagation models have found favor in both research and industrial communities owing to their speed of execution and their limited reliance on detailed knowledge of the terrain. In mobile communication the accuracy prediction of path losses is a crucial element during network planning and optimization. However, the existence of multiple propagation models means that there is no propagation model which is precisely and accurate in prediction of path loss fit for every environs other than in which they were designed. This paper presents few empirical models suitable for path loss prediction in mobile communication. Experimental measurements of received power for the 900 MHz GSM system are made in urban, suburban, and rural areas of Dar es Salaam, Tanzania. Measured data are compared with those obtained by five prediction models: Stanford University Interim (SUI) models [1], the COST-231 Hata model [2], the ECC-33 model [3], the ERICSSON model [4], and the HATA-OKUMURA model [5]. The results show that in general the SUI, COST-231, ERICSSON, and Hata-Okumura under-predict the path loss in all environments, while the ECC-33 model shows the best results, especially in suburban and over-predict pathloss in urban area. Keywords: Propagation pathloss, empirical models, radio coverage, mobile communications

Deep Reinforcement Learning based Handover Management for Millimeter Wave Communication

This research article published by the International Journal of Advanced Computer Science and Applications, Vol. 12, No. 2, 2021The Millimeter Wave (mm-wave) band has a broad-spectrum capable of transmitting multi-gigabit per-second date-rate. However, the band suffers seriously from obstruction and high path loss, resulting in line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions. All these lead to significant fluctu-ation in the signal received at the user end. Signal fluctuations present an unprecedented challenge in implementing the fifth gen-eration (5G) use-cases of the mm-wave spectrum. It also increases the user’s chances of changing the serving Base Station (BS) in the process, commonly known as Handover (HO). HO events become frequent for an ultra-dense dense network scenario, and HO management becomes increasingly challenging as the number of BS increases. HOs reduce network throughput, and hence the significance of mm-wave to 5G wireless system is diminished without adequate HO control. In this study, we propose a model for HO control based on the offline reinforcement learning (RL) algorithm that autonomously and smartly optimizes HO decisions taking into account prolonged user connectivity and throughput. We conclude by presenting the proposed model’s performance and comparing it with the state-of-art model, rate based HO scheme. The results reveal that the proposed model decreases excess HO by 70%, thus achieving a higher throughput relative to the rates based HO scheme

Energy Optimisation Through Path Selection for Underwater Wireless Sensor Networks

This paper explores energy-efficient ways of retrieving data from underwater sensor fields using autonomous underwater vehicles (AUVs). Since AUVs are battery-powered and therefore energy-constrained, their energy consumption is a critical consideration in designing underwater wireless sensor networks. The energy consumed by an AUV depends on the hydrodynamic design, speed, on-board payload and its trajectory. In this paper, we optimise the trajectory taken by the AUV deployed from a floating ship to collect data from every cluster head in an underwater sensor network and return to the ship to offload the data. The trajectory optimisation algorithm models the trajectory selection as a stochastic shortest path problem and uses reinforcement learning to select the minimum cost path, taking into account that banked turns consume more energy than straight movement. We also investigate the impact of AUV speed on its energy consumption. The results show that our algorithm improves AUV energy consumption by up to 50% compared with the Nearest Neighbour algorithm for sparse deployments

DEKCS: a dynamic clustering protocol to prolong underwater sensor networks

Energy consumption is a critical issue in the design of wireless underwater sensor networks (WUSNs). Data transfer in the harsh underwater channel requires higher transmission powers compared to an equivalent terrestrial-based network to achieve the same range. However, battery-operated underwater sensor nodes are energy-constrained and require that they transmit with low power to conserve power. Clustering is a technique for partitioning wireless networks into groups where a local base station (cluster head) is only one hop away. Due to the proximity to the cluster head, sensor nodes can lower their transmitting power, thereby improving the network energy efficiency. This paper describes the implementation of a new clustering algorithm to prolong the lifetime of WUSNs. We propose a new protocol called distance- and energy-constrained k-means clustering scheme (DEKCS) for cluster head selection. A potential cluster head is selected based on its position in the cluster and based on its residual battery level. We dynamically update the residual energy thresholds set for potential cluster heads to ensure that the network fully runs out of energy before it becomes disconnected. Also, we leverage the elbow method to dynamically select the optimal number of clusters according to the network size, thereby making the network scalable. Our evaluations show that the proposed scheme outperforms the conventional low-energy adaptive clustering hierarchy (LEACH) protocol by over 90% and an optimised version of LEACH based on k-means clustering by 42%

Coverage Analysis for Indoor-Outdoor Coexistence for Millimetre-Wave Communication

Milimeter-wave (mm-wave) communication, which has already been a part of the fifth generation of mobile communication networks (5G), would result in ultra dense small cell deployments due to its limited coverage characteristics. In such an environment, outdoor base stations (BS) will get closer to the buildings, in which users are covered and served by indoor small cells that in turn degrades the user Quality of Experience (QoE) owing to the increased interference caused by the outdoor BSs. In this paper, indoor coverage analysis is conducted by considering a scenario, which includes a multi-storey building and two identical indoor femtocell and outdoor BS operating at 28 GHz. During the simulations, impacts of the outdoor BS's transmit power and distance to the building on the indoor coverage are investigated. In addition, various material types, namely one layer brick, International Telecommunication Union (ITU) 28 GHz concrete, ITU 28 GHz glass, and ITU 28 GHz wood, for the building walls are tested. Results reveal that dielectric properties of the materials are the key factors in determining the severity of the interference caused by the outdoor BS, paving the way for including the effects of material type in network designing and smart city planning

A survey of machine learning applications to handover management in 5G and beyond

Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The state-of-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance the capacity due to their easy implementation, pop up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed. However, because the UAVs mostly have limited energy storage, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed; conventional and machine learning (ML). Such classification helps understand the state of the art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of Communications Society (OJ-COMS

Intelligent handover decision scheme using double deep reinforcement learning

Handovers (HOs) have been envisioned to be more challenging in 5G networks due to the inclusion of millimetre wave (mm-wave) frequencies, resulting in more intense base station (BS) deployments. This, by its turn, increases the number of HOs taken due to smaller footprints of mm-wave BSs thereby making HO management a more crucial task as reduced quality of service (QoS) and quality of experience (QoE) along with higher signalling overhead are more likely with the growing number of HOs. In this paper, we propose an offline scheme based on double deep reinforcement learning (DDRL) to minimize the frequency of HOs in mm-wave networks, which subsequently mitigates the adverse QoS. Due to continuous and substantial state spaces arising from the inherent characteristics of the considered 5G environment, DDRL is preferred over conventional -learning algorithm. Furthermore, in order to alleviate the negative impacts of online learning policies in terms of computational costs, an offline learning framework is adopted in this study, a known trajectory is considered in a simulation environment while ray-tracing is used to estimate channel characteristics. The number of HO occurrence during the trajectory and the system throughput are taken as performance metrics. The results obtained reveal that the proposed method largely outperform conventional and other artificial intelligence (AI)-based models

Coverage and throughput analysis of an energy efficient UAV base station positioning scheme

Recently, the use of unmanned aerial vehicles (UAVs) for wireless communications has attracted much research attention. However, most applications of UAVs for wireless communication provisioning are not feasible as researchers fail to consider some vital aspects of their deployment, especially the energy requirements of both the UAV and communication system. The considerable energy consumption overhead involved in flying or hovering UAVs makes them less appealing for green wireless communications. Therefore, in this work, we examine the feasibility of an alternative energy-efficient deployment scheme where UAVs can be made to land-on designated locations, also known as landing stations (LSs). The idea of LS makes the UAV-based wireless communication more durable and advantageous, since the total energy consumption is reduced by minimizing the flying/hovering energy consumption, which, in turn, enables diverse set of applications including emergency and pop-up networking. We evaluate the impact of the separation distance between these LSs and the Optimal Hovering Position (OHP) on the network performance. Specifically, we develop mathematical frameworks to model the relationship between UAV power consumption, coverage probability, throughput, and separation distance. Numerical results reveal that a significant energy reduction can be achieved when the LS concept is exploited with a slight compromise in coverage probability and throughput. However, the choice of a suitable LS location depends on the users’ service requirements, transmit power, and frequency band utilized