Deployment Of Wireless Networks Research Articles

Path Loss Models for Cellular Mobile Networks Using Artificial Intelligence Technologies in Different Environments

One of the most critical problems in a communication system is losing information between the transmitter and the receiver. WiMAX (Worldwide Interoperability of Microwave Access) technology is gaining popularity and recognition as a Broadband Wireless Access (BWA) solution. At frequencies below 11 GHz, WiMAX can operate in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. The implementation of WiMAX networks are rushed worldwide. Estimating path loss is crucial in the early stages of wireless network deployment and cell design. To anticipate propagation loss, several path loss models are available (e.g., Okumura Model Hata Model), but they are all bound by particular parameters. In this paper, we propose an MLP neural network-based path loss model with a well-structured implementation network design and grid search-based hyperparameter tuning. The proposed model optimally approximates mobile and base station path losses. Therefore, neurons number, learning rate, and hidden layers number are investigated to obtain the best model in terms of prediction accuracy. Path loss data is collected based on 14 networks in different microcellular settings. Simulations under Matlab environment showed that prediction errors were lower than standard log-distance-based path loss models.

RSSI Fingerprint Height Based Empirical Model Prediction for Smart Indoor Localization.

Smart indoor living advances in the recent decade, such as home indoor localization and positioning, has seen a significant need for low-cost localization systems based on freely available resources such as Received Signal Strength Indicator by the dense deployment of Wireless Local Area Networks (WLAN). The off-the-shelf user equipment (UE's) available at an affordable price across the globe are well equipped with the functionality to scan the radio access network for hearable single strength; in complex indoor environments, multiple signals can be received at a particular reference point with no consideration of the height of the transmitter and possible broadcasting coverage. Most effective fingerprinting algorithm solutions require specialized labor, are time-consuming to carry out site surveys, training of the data, big data analysis, and in most cases, additional hardware requirements relatively increase energy consumption and cost, not forgetting that in case of changes in the indoor environment will highly affect the fingerprint due to interferences. This paper experimentally evaluates and proposes a novel technique for Received Signal Indicator (RSSI) distance prediction, leveraging transceiver height, and Fresnel ranging in a complex indoor environment to better suit the path loss of RSSI at a particular Reference Point (RP) and time, which further contributes greatly to indoor localization. The experimentation in different complex indoor environments of the corridor and office lab during work hours to ascertain real-life and time feasibility shows that the technique's accuracy is greatly improved in the office room and the corridor, achieving lower average prediction errors at low-cost than the comparison prediction algorithms. Compared with the conventional prediction techniques, for example, with Access Point 1 (AP1), the proposed Height Dependence Path-Loss (HEM) model at 0 dBm error attains a confidence probability of 10.98%, higher than the 2.65% for the distance dependence of Path-Loss New Empirical Model (NEM), 4.2% for the Multi-Wall dependence on Path-Loss (MWM) model, and 0% for the Conventional one-slope Path-Loss (OSM) model, respectively. Online localization, amongst the hearable APs, it is seen the proposed HEM fingerprint localization based on the proposed HEM prediction model attains a confidence probability of 31% at 3 m, 55% at 6 m, 78% at 9 m, outperforming the NEM with 26%, 43%, 62%, 62%, the MWM with 23%, 43%, 66%, respectively. The robustness of the HEM fingerprint using diverse predicted test samples by the NEM and MWM models indicates better localization of 13% than comparison fingerprints.

Simultaneous wireless information and power transfer in resonant beam charging

SummaryFSO links have attracted a significant amount of interest in the recent years driven by the paramount importance of their license‐free spectrum and ease of deployment in wireless networks. Besides, the energy harvesting (EH) paradigm allows recharging terminals via ambient and/or external sources and prolongs battery lifetime operation. Thereby, facilitating free space optical (FSO) in EH networks creates potential scenarios for the future sixth‐generation (6G) communication systems. In this study, an EH system performing simultaneous wireless information and power transfer (SWIPT) while adopting FSO and resonant beam charging (RBC), RBC–SWIPT system is thoroughly analyzed. The performance of RBC‐SWIPT system is analyzed through Monte Carlo simulations, where average bit error ratio (ABER) and energy efficiency (EE) are studied and evaluated. In addition, channel capacity is derived along with the mutual information and studied over indoor line of sight (LOS) optical channel. Monte Carlo simulation results corroborate the accuracy of the derived formulas.

Editorial para el Número Especial del Taller Andino de Comunicaciones Inalámbricas y sus Aplicaciones

Nowadays, wireless communications are present in the daily activities of modern society and leverage the fast growth of new services, and this trend is likely to increase for the foreseeable future. However, wireless networks have become more complex because of the number of different services that must support. Therefore, research is more important than ever to ensure the successful deployment and implementation of wireless networks and related services. This special issue includes the papers presented at the second Andean workshop on wireless communications and its applications, which show the research carried out in the Andean region to develop this important telecommunications area.

ANN-based model for multiband path loss prediction in built-up environments

Path loss propagation models are critically needed for optimum planning and deployment of wireless communication networks. However, the complexity exhibited by the propagated signals makes the prediction of the received losses difficult in built-up environments. There is however a new paradigm shift towards the application of computational methodologies, such as the Artificial Neural Networks (ANN), for multi-band path loss prediction. In this paper, we have developed a new ANN-based model for path loss prediction. The model was developed using large scale path loss data collected across 485 base stations in 6 urban cities of Nigeria, West Africa. The data collection, which spanned a period of 9 years, were taken over open areas, sub urban and urban environments, and the bands considered were 89.3 MHz, 103.5 MHz, 203.25MHz, 429.25 MHz, 529.25MHz, 615.25MHz, 629.25MHz, 900MHz, 1800 MHz and 2100MHz. The developed model was validated using independent path loss data across different frequencies, environments; and, distances and the results were compared with the popular empirical models such as Hata, COST 231 and Egli models, and to previously published ANN-based multi-frequency models. The global performance of 4.81 dB was obtained in terms of RMSE value with an R-value of 0.96, thus outperforming the existing ANN-based path loss models that were developed for multiple frequencies. Based on these findings, the proposed model can be deployed across all categories since the average RMSE values are all within the acceptable thresholds. Furthermore, the model is multi-frequency, thus will be suitable for multiple and complex environments, and usable for both short- and long-range wireless communication networks

Survey on UAV Deployment and Trajectory in Wireless Communication Networks: Applications and Challenges

A new era of the fifth-generation (5G) networks is realized to satisfy user demands on higher data rate and massive connectivity for information sharing and utilization. The vertical applications such as vehicle-to-everything (V2X) communications, industrial automation, smart factory, smart farm and smart cities require ultra-fast communications and wide service range. Coverage extension is a key issue to support the required demands on higher performance, but requires an additional deployment of base or relay stations. Therefore, an efficient solution needs to be cost-effective and easy, in order to deploy more stations. An unmanned aerial vehicle (UAV) has been considered as a candidate to overcome these issues because it is much more cost-effective than the ground stations and does not require network or cell replanning, thereby enhancing the network coverage without additional excessive deployment procedures of the existing networks. UAVs will play important roles in 5G and beyond networks assisting as macro base stations, relay stations, small cells, or a moving aggregator. The performance of UAV wireless networks highly depends on the position or the trajectory of UAVs and the resource managements of entire networks. Recently, there have been extensive studies on performance analysis, UAV deployment, UAV trajectory and resource management of UAV wireless networks to achieve the required demands on performance. This paper surveys research conducted for the UAV deployment and trajectory to construct UAV wireless networks for the coverage extension, the throughput improvement and the resource management for different use cases and scenarios, so as to encourage further studies in this area.

LCMQSINABM: Design of a Low-power hybrid Consensus Method for QoS-aware Sidechain-based IoT Networks via Augmented Bioinspired computing Models

Security is one of the primary issues in any wireless network deployment, because, wireless nodes are susceptible to a wide-variety of internal & external attacks. These include improper authentication & access control, distributed denial of service (DDoS), sybil, spoofing, spying, masquerading, etc. Securing networks against these attacks requires an immutable, transparent, traceable, and distributed computing security model, that has minimum overheads. Most of these characteristics are fulfilled via use of blockchains, which assist in low-complexity deployment of security & hashing models. But the delay needed to scale these blockchains increases exponentially w.r.t. chain length, due to which researchers split the main blockchain into multiple sidechains. A wide variety of models are proposed by researchers for sidechain formation, and most of them are consensus dependent, which limits the scalability. Moreover, energy needed to mine blocks for these sidechains depends directly on the consensus model, encryption model, hash rules, and length of the blockchain. Thus, models proposed for sidechain formation are context-specific, and have limited scalability performance when used for multiple blockchain types. To overcome these limitations, and maintain high security performance, this text proposes design of a Low-power hybrid Consensus Method for QoS-aware Sidechain-based IoT Networks via Augmented Bioinspired computing Models. This method uses a combination of Proof-of-Work (PoW), Proof-of-Stake (PoS) & Proof-of-Authority (PoA) based consensus models, which assists in reducing its mining delay. The PoW model allows selection of nodes with higher performance, PoS allows selection of nodes with higher stake, and PoA ensures better control of IoT devices. Selection of these consensus models is done via use of an Improved Genetic Algorithm (IGA) model, that evaluates the power needed for mining, and minimizes it using a rule-based method. This is combined with a sidechain creation model, that assists in improving QoS performance via dynamically splitting the main blockchain into performance-specific sidechains. These sidechains are categorized into low-power, low-delay, and high-throughput sidechains, which are formed via Elephant Herding Optimization (EHO) model. Due to combination of IGA for consensus selection, and EHO for sidechain creation, the proposed model is able to reduce energy needed for mining by 15.4% when compared with various state-of-the-art models. It is also able to improve mining speed by 5.3%, while maintaining high security performance under different IoT Network attacks. Due to this performance enhancement, the proposed model is capable of being deployed for a wide variety of low-delay healthcare IoT, high-throughput industrial IoT, and low energy home IoT application deployments.

Joint Optimization of Edge Computing Server Deployment and User Offloading Associations in Wireless Edge Network via a Genetic Algorithm

Of late years, a growing number of mobile devices are demanding computation-intensive tasks with low delay requirements. Edge computing, where mobile devices offload their tasks to servers installed on the edge nodes of a network closer to the mobile devices, enables faster completion of the computation-intensive tasks. To offer low delay with a limited number of servers, efficient computing server deployment and user offloading associations resulting from the deployment are consequential. However, the existing research on server deployment and user offloading associations is restricted to heuristic algorithms failing to address joint optimization due to its prohibitive complexity. In contrast, this paper studies the joint design of computing server deployment and user offloading associations in wireless edge networks with wireless backhaul, enabling broadband transmission at a lower cost than the existing wired backhaul. Leveraging the evolutionary concept of a genetic algorithm, we devise a novel algorithm to solve the problem and minimize the average service delay while satisfying the delay requirements of individual users. The simulation results show that the proposed algorithm outperforms the conventional random search or heuristic algorithms.

Path Loss Modeling for Wireless Network Deployment in Water Surface Environments

The efficient channel model is an extremely important cornerstone for the deployment and optimization of wireless networks. At present, many channel models are presented and discussed extensively. However, the channel model for water surface is rarely mentioned. To meet the need for water environmental protection, more and more sensors will be deployed on the water surface to establish the sensor network so as to monitor water quality in real time. Therefore, wireless channel modeling of water surface is becoming more and more exigent. Nevertheless, the propagation of signal on the water surface may meet more complicated reflection and scattering, and this aggravates the difficulty of channel modeling. In order to tackle this problem, we analyze related propagation theory and some representative path-loss models in detail, then propose a channel model in water surface environments. To validate the efficiency of the channel model, we set up an experimental platform and measure the attenuation of the RF signal above a pool filled with fresh water. The experimental result shows that the proposed model is more consistent with the characteristics of the attenuation of the signal on water surface. This result can be applied into the deployment of wireless sensor networks on the water surface.

5G network deployment and the associated energy consumption in the UK: A complex systems’ exploration

Investing in the communication infrastructure transition requires significant scientific consideration of challenges, prioritisation, risks and uncertainties. To address these challenges, a bottom-up approach was used to demonstrate the future of wireless network transmission and deployment. This study developed an agent-based model to explore the future deployment of non-standalone 5G networks, synthesizing multi-dimensional data visualization. In particular, this research took the UK as an example to investigate the spatiotemporal dynamic characteristics of 5G evolution, and further analysed the energy consumption and carbon footprint of 5G networks, as well as the consequent change in the operating expenses pattern. The simulation results show that 700 MHz and 26 GHz will play an important role in 5G deployment in the UK, which allow base stations to meet short-term and long-term data traffic demands respectively. Furthermore, due to the geopolitical restrictions and embargos, telecommunications may face additional costs of £0.63bn to £1.19bn when deploying 5G radio access networks. Network densification may cause some environmental and economic problems. Take a medium demand scenario as an example, it is found that the electricity consumed by the 5G radio access network will account for more than 2.1% of the total electricity generation, and indirectly lead to 990,404 tonnes carbon emissions in 2030.

Indoor Genetic Algorithm-Based 5G Network Planning Using a Machine Learning Model for Path Loss Estimation

Accurate wireless network planning is crucial for the deployment of new wireless services. This usually requires the consecutive evaluation of many candidate solutions, which is only feasible for simple path loss models, such as one-slope models or multi-wall models. However, such path loss models are quite straightforward and often do not deliver satisfactory estimations, eventually impacting the quality of the proposed network deployment. More advanced models, such as Indoor Dominant Path Loss models, are usually more accurate, but as their path loss calculation is much more time-consuming, it is no longer possible to evaluate a large set of candidate deployment solutions. Out of necessity, a heuristic network planning algorithm is then typically used, but the outcomes heavily depend on the quality of the heuristic. Therefore, this paper investigates the use of Machine Learning to approximate a complex 5G path loss model. The much lower calculation time allows using this model in a Genetic Algorithm-based network planning algorithm. The Machine Learning model is trained for two buildings and is validated on three other buildings, with a Mean Absolute Error below 3 dB. It is shown that the new approach is able to find a wireless network deployment solution with an equal, or smaller, amount of access points, while still providing the required coverage for at least 99.4% of the receiver locations and it does this 15 times faster. Unlike a heuristic approach, the proposed one also allows accounting for additional design criteria, such as maximal average received power throughout the building, or minimal exposure to radiofrequency signals in certain rooms.

Fundamental Wireless Performance of a Building

Over 80% of wireless traffic already takes place in buildings. Like water, gas, and electricity, wireless communication is becoming one of the most fundamental utilities of a building. It is well known that building structures have a significant impact on in-building wireless networks. If we seek to achieve the optimal network performance indoors, the buildings should be designed with the objective of maximizing wireless performance. So far, wireless performance has not yet been considered when designing a building. In this paper, we introduce a novel and interdisciplinary concept of building wireless performance (BWP) to a wide audience in both wireless communications and building design, emphasizing its broad impacts on wireless network development and deployment, and on building layout/material design. We first give an overview of the BWP evaluation framework proposed in our state-of-the-art works and explain their interconnections. Then, we outline the potential research directions in this exciting research area to encourage further interdisciplinary research.

Design Challenges and Possible Solutions for 5G SIW MIMO and Phased Array Antennas: A Review

With the increasing demand for high-speed data, rapid deployments of the 5G terrestrial heterogeneous wireless networks are expected worldwide in the next decade. In such networks, sub-6 GHz macro-cells overlapped by mmWave small-cells are being used to cater to densely populated regions. As a result, several challenges arise with the antenna design technologies used at the mobile terminal, access points, or backhaul/front haul levels. These challenges are being addressed using multiple-input multiple-output (MIMO), massive-MIMO, and phased array antenna technologies. In order to implement these antenna technologies, considering the expanding 5G scenario, substrate integrated waveguide (SIW) offers a viable solution due to its low-cost, low-profile, high-power handling, low transmission loss, and ease of integration with the radio circuits; therefore, the SIW plays a vital role in developing the modern radio systems. The proposed study aimed to provide a comprehensive overview of SIW MIMO and phased array antennas operating in the 5G sub-6 GHz and mmWave bands. It deliberates the specific issues related to the band of operations and challenges in designing different antenna structures. After careful investigation and detailed analysis, this paper identified existing research gaps and suggested possible antenna design solutions for prospective researchers who intend to explore further the aforementioned promising area and present future research directions.

On the Uniqueness of the Quasi-Moment-Method Solution to the Pathloss Model Calibration Problem

Investigations in this paper focus on establishing the uniqueness properties of the Quasi-Moment-Method (QMM) solution to the problem of calibrating nominal radiowave propagation pathloss prediction models. Nominal (basic) prediction models utilized for the investigations, were first subjected to QMM calibrations with measurements from three different propagation scenarios. Then, the nominal models were recast in forms suitable for Singular Value Decomposition (SVD) calibration before being calibrated with both the SVD and QMM algorithms. The prediction performances of the calibrated models as evaluated in terms of Root Mean Square Prediction Error (RMSE), Mean Prediction Error (MPE), and Grey Relational Grade-Mean Absolute Percentage Error (GRG-MAPE) very clearly indicate that the uniqueness of  QMM-calibrations of basic pathloss models is more readily observable, when the basic models are recast in forms specific to SVD calibration. In the representative case of calibration with indoor-to-outdoor measurements, RMSE values were recorded for QMM-calibrated nominal models as 5.2639dB for the ECC33 model, and 5.3218dB for the other nominal models. Corresponding metrics for the alternative (rearranged) nominal models emerged as 5.2663dB for the ECC33 model and 5.2591dB for the other models. A similar general trend featured in the GRG-MAPE metrics, which for both SVD and QMM calibrations of all the alternative models, was recorded as 0.9131, but differed slightly (between 0.9138 and 0.9196) for the QMM calibration of the nominal models. The slight differences between these metrics (due to computational round-off approximations) confirm that when the  components of basic models are linearly independent, the QMM solution is unique. Planning for wireless communications network deployment may consequently select any basic model of choice for QMM-calibration, and hence, identify relative contributions to pathloss by the model’s component parts.

Optimization Analysis of Sustainable Solar Power System for Mobile Communication Systems

Cellular mobile technology has witnessed tremendous growth in recent times. One of the challenges facing the operators to extend the coverage of the networks to meet the rising demand for cellular mobile services is the power sources used to supply cellular towers with energy, especially in remote. Thus, switch from the conventional sources of energy to a greener and sustainable power model became a target of the academic and industrial sectors in many fields; one of these important fields is the telecommunication sector. Accordingly, this study aims to find the optimum sizing and techno-economic investigation of a solar photovoltaic scheme to deploy cellular mobile technology infrastructure cleanly and sustainably. The optimal solar-powered system is designed by employing the energy-balance procedures of the HOMER software tool. The problem objective is considered in terms of cost, but the energy system is constrained to meet the power demand reliably. Process simulations were performed to determine the optimum sizing, performance and monetary cost of the power system, using long-term meteorological datasets for a case study site with defined longitude (31° 25′ E) and latitude (30° 06′ N). From the observed results, the total net present cost (NPC) of the proposed system is $28,187. Indeed, these outcomes can provide profound economic, technical, and ecological benefits to cellular operators. It also ensures a sizeable reduction in greenhouse gas that supports sustainable green wireless network (WN) deployment in remote areas.

A Secure Optimization Routing Algorithm for Mobile Ad Hoc Networks

Mobile ad hoc networks (MANET) are self-organizing, rapidly deployable wireless networks excellent for outdoor events, communications in places lacking radio infrastructure, disasters, and military activities. Because network topologies are flexible and dynamic, security may be the most vulnerable point in the network, open to attacks including eavesdropping, routing, and application changes. MANET has more security flaws than quality of service (QoS). It is thus recommended to use intrusion detection, which regulates system to detect further security problems. Monitoring for intrusions is crucial for prevention and additional security against unwanted access. The loss of a mobile node’s power source may affect the node’s ability to forward packets, which is reliant on the system’s overall life. In this paper, the Bacteria for Aging Optimization Algorithm (BFOA), which finds the ideal hops in advancing the routing, is utilized to offer a trust-based protected and energy-efficient navigation in MANETs using a trust-based protected and energy-efficient navigation algorithm. The fuzzy clustering algorithm is activated first, and the Cluster Heads (CHs) are selected depending on the value of indirect, direct, and recent trust that each CH has. In addition, value nodes were discovered based on trust levels. Moreover, the CHs are engaged in multi hop routing, and the selection of the ideal route is based on the projected protocol, which selects the best routes based on latency, throughput, and connection within the course’s boundaries. Even without an attack, compared to the exiting methods EA-DRP & EE-OHRA the proposed secure optimization routing (BFOA) algorithm produced a minimum energy of 0.10 m joules, a minimal latency of 0.0035 m sec, a maximum throughput of 0.70 bps, and an 83 percent detection rate, with enhanced results obtained by using a selective packet dropping attack

Toward Secure Localization in Randomly Deployed Wireless Networks

Being able to accurately locate wireless devices, while guaranteeing high-level of security against spoofing attacks, benefits all participants in the localization chain (e.g., end users, network operators, and location service providers). On the one hand, most of existing localization systems are designed for innocuous environments, where no malicious adversaries are present. This makes them highly susceptible to security threats coming from interferers, attacks, or even unintentional errors (malfunctions), and thus, practically futile in hostile settings. On the other hand, existing secure localization solutions make certain (favorable) assumptions regarding the network topology (e.g., that the target device lies within a convex hull formed by reference points), which restrict their applicability. Therefore, this work addresses the problem of target localization in randomly deployed wireless networks in the presence of malicious attackers, whose goal is to manipulate (spoof) the estimation process and disable accurate localization. We propose a low-complex solution based on clustering and weighted central mass to detect attackers, using only the bare minimum of reference points, after which we solve the localization problem by a bisection procedure. The proposed method is studied from both localization accuracy and success in attacker detection point of views, where closed-form expressions for upper and lower bounds on the probability of attacker detection are derived. Its performance is validated through computer simulations, which corroborate the effectiveness of the proposed scheme, outperforming the state-of-the-art methods.

Improving security and privacy in VANET network using node authentication

The security of vehicular ad hoc networks is a serious problem (VANETs). Group- based communication on VANETs is a well-studied issue. Creating a group and distributing a key to a large number of fast-moving vehicles for secure communication is a significant security problem for VANETs. Distributed keys in VANETs enable the secure participation of vehicles that were not present when the group was created. Using polynomial interpolation, this study provides a new distributed key technique for safe group administration in VANET. This allows attackers to either steal the identity of the legal intermediary node or directly join the identified data transfer path. As a result, VANETs are particularly vulnerable to assaults. Also, the number of VANET applications has increased significantly in recent years due to advances in handheld device technology and wireless network deployment technologies. The flooding assault might target the targeted node or the network as a whole. This exploit targets network resources such when two hostile nodes (attackers) spread incorrect network information to their neighbors during routing table building, they become intermediate nodes on the designated route and discard data packets during communication. The proposed approach not only offers a distributed key for vehicles to form, join, and interact in the group, but it also provides and controls key distribution to ensure the security of VANETs. The experimental findings show that the suggested system is a safe option for group communication in VANETs.

Unmanned Aerial Vehicles and Multi-Access Edge Computing

Multi-access edge computing processes and stores data at the edge of the network, brings computing resources closer to the end users, significantly reduces latency for computation-intensive and time-sensitive applications. Unmanned aerial vehicles (UAVs) can accomplish rapid deployment of wireless networks as mobile base stations. Compared to terrestrial communications, UAVs have the outstanding advantage of short-range line-of-sight links, which can improve the network performance significantly. In addition, with its flexibility and mobility, UAVs can be quickly deployed to support wireless communications in remote areas with complex environments, such as disaster areas and emergency zones. UAVs equipped with computing servers provide a new paradigm for multi-access edge computing. They have attracted wide attention from both industry and academia recently. However, there are many critical technical challenges yet to be addressed toward aerial computing. In this issue of IEEE Wireless Communications, we are pleased to present a special issue on “Aerial Computing: Drones for Multi-access Edge Computing” with a collection of 13 articles, to bring together researchers, industry practitioners, and individuals working on related areas to address some of these technical challenges for UAVs and multi-access edge computing. Thanks to the guest editors, J. Zheng, A. Anpalagan, M. Guizani, Y. Wu, N. Zhang, X. Chen, and F. R. Yu, who did an excellent job editing this special issue for our readers. Please stay tuned for new developments in the research area of UAVs and multi-access edge computing and read the editorial and the papers in this special issue.

A Survey on Energy Harvesting Wireless Networks: Channel Capacity, Scheduling, and Transmission Power Optimization

This paper presents a survey on energy harvesting (EH) wireless communication networks focusing on channel capacity, transmission schemes, and power optimization. While many network researchers focus on energy management policies addressing the intermittency and randomness of the EH processes, but the channel capacity, and transmission power optimization have not been fully explored yet. In this paper, we provide a review and analysis of channel capacity, offline and online transmission schemes, and power optimization from an information theory perspective. By reviewing and analyzing wireless networking literature, we found that EH is a technologically feasible and economically viable paradigm for cost-effectiveness in the design and deployment of next-generation wireless networks. Finally, we identify open research problems and future research directions in the emerging field of EH wireless networks. We expect this study to stimulate more research endeavors to build energy-efficient scalable next-generation wireless network systems.