Wireless Backhaul Research Articles

Collaborative Cloud-Edge-End Task Offloading in MEC-Based Small Cell Networks With Distributed Wireless Backhaul

Collaborative cloud-edge-end computing is a promising solution to support computation-intensive and latency-sensitive tasks by utilizing rich computing resources of cloud datacenters and low access delay of mobile edge computing (MEC) servers. Compared with traditional cloud computing and MEC, the cloud-edge environment has a stronger heterogeneity of servers and networks, resulting in significant differences between servers in the computation speed and access delay. However, few studies on cloud-edge-end task offloading focused on the characteristic of 5G heterogeneous networks in the cloud-edge environment. In this paper, we study the task offloading problem for collaborative cloud-edge-end computing in MEC-enabled small cell networks with low-cost distributed wireless backhaul. We aim to minimize the energy consumption of all user devices (UDs) via jointly optimizing the offloading decision, UDs’ transmission power, and the allocation of spectrum and computation resources. To solve the non-convex problem, we decouple the original problem into three subproblems, and design an efficient method with solving these three subproblems iteratively to obtain a high-quality solution. The simulation results indicate that our proposed method can lead to significant reduction in the energy consumption of all UDs compared with other conventional methods.

Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks

The deployment of non-terrestrial networks (NTNs) is envisioned to achieve global coverage for 6G and beyond. In addition to space nodes, aerial NTN nodes such as high-altitude platform stations (HAPSs) and rotary-wing unmanned aerial vehicles (UAVs) could be deployed, based on the intended coverage and operational altitude requirements. NTN nodes have the potential to support both wireless access and backhauling. While the onboard base station provides wireless access for the end users, the backhauling link connects the airborne/space-borne base station to the core network. With its high data transmission capability comparable to fiber optics and its ability to operate in the interference-free optical spectrum, free space optical (FSO) communication is ideally suited to backhauling requirements in NTNs. In this paper, we present a comprehensive tutorial on airborne FSO backhauling. We first delve into the fundamentals of FSO signal transmission and discuss aspects such as geometrical loss, atmospheric attenuation, turbulence-induced fading, and pointing errors, all of which are critical for determining received signal levels and related link budget calculations. Then, we discuss the requirements of airborne backhaul system architectures, based on use cases. While single-layer backhaul systems are sufficient for providing coverage in rural areas, multi-layer designs are typically required to establish connectivity in urban areas, where line of sight (LoS) links are harder to maintain. We review physical layer design principles for FSO-based airborne links, discussing both intensity modulation/direct detection (IM/DD) and coherent modulation/coherent demodulation (CM/CD). Another critical design criteria for airborne backhauling is self-sustainability, which is further discussed in our paper. We conclude the paper by discussing current challenges and future research directions. In this context, we discuss reconfigurable intelligent surfaces (RIS) and spatial division multiplexing (SDM), for improved performance and an extended transmission range. We emphasize the importance of advanced handover techniques and scalability issues for practical implementation. We also highlight the growing role of artificial intelligence/machine learning (AI/ML) and their potential applications in the design and optimization of future FSO-based NTNs.

Optimal Resource Management for Hierarchical Federated Learning Over HetNets With Wireless Energy Transfer

Remote monitoring systems analyze the environment dynamics in different smart industrial applications, such as occupational health and safety, and environmental monitoring. Specifically, in industrial Internet of Things (IoT) systems, the huge number of devices and the expected performance put pressure on resources, such as computational, network, and device energy. Distributed training of Machine and Deep Learning (ML/DL) models for intelligent industrial IoT applications is very challenging for resource limited devices over heterogeneous wireless networks (HetNets). Hierarchical Federated Learning (HFL) performs training at multiple layers offloading the tasks to nearby Multi-Access Edge Computing (MEC) units. In this paper, we propose a novel energy-efficient HFL framework enabled by Wireless Energy Transfer (WET) and designed for heterogeneous networks with massive Multiple-Input Multiple-Output (MIMO) wireless backhaul. Our energy-efficiency approach is formulated as a Mixed-Integer Non-Linear Programming (MINLP) problem, where we optimize the HFL device association and manage the wireless transmitted energy. However due to its high complexity, we design a Heuristic Resource Management Algorithm, namely H2RMA, that respects energy, channel quality, and accuracy constraints, while presenting a low computational complexity. We also improve the energy consumption of the network using an efficient device scheduling scheme. Finally, we investigate device mobility and its impact on the HFL performance. Our extensive experiments confirm the high performance of the proposed resource management approach in HFL over HetNets, in terms of training loss and grid energy costs.

Fifth-generation small cell backhaul capacity enhancement and large-scale parameter effect

<span lang="EN-US">The proliferation of handheld devices has continued to push the demand for higher data rates. Network providers will use small cells as an overlay to macrocell in fifth-generation (5G) for network capacity enhancement. The current cellular wireless backhauls suffer from the problem of insufficient backhaul capacity to cater to the new small cell deployment scenarios. Using the 3D digital map of Lagos Island in the Wireless InSite, small cells are deployed on a street canyon and in high-rise scenarios to simulate the backhaul links to the small cells at 28 GHz center frequency and 100 MHz bandwidth. Using a user-defined signal to interference plus noise ratio-throughput (SINR-throughput) table based on an adaptive modulation and coding scheme (MCS), the throughput values were generated based on the equation specified by 3GPP TS 38.306 V15.2.0 0, which estimates the peak data rate based on the modulation order and coding rate for each data stream calculated by the propagation model. Finding shows achieved channel capacity is comparable with gigabit passive optical networks (GPON) used in fiber to the ‘X’ (FTTX) for backhauling small cells. The effect of channel parameters such as root mean squared (RMS) delay spread and RMS angular spread on channel capacity are also investigated and explained.</span>

Implementation of mmWave long‐range backhaul for UAV‐BS

AbstractUncrewed aerial vehicles (UAVs) have become a vital element in nonterrestrial networks, especially with respect to 5G communication systems and beyond. The use of UAVs in support of 4G/5G base station (uncrewed aerial vehicle base station [UAV‐BS]) has proven to be a practical solution for extending cellular network services to areas where conventional infrastructures are unavailable. In this study, we introduce a UAV‐BS system that utilizes a high‐capacity wireless backhaul operating in millimeter‐wave frequency bands. This system can achieve a maximum throughput of 1.3 Gbps while delivering data at a rate of 300 Mbps, even at distances of 10 km. We also present the details of our testbed implementation alongside the performance results obtained from field tests.

Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks

An unmanned aerial vehicle (UAV), with the advantages of mobility and easy deployment, serves as a relay node in wireless networks, which are known as UAV relay networks (URNs), to support user equipment that is out of service range (Uo) or does not have a direct communication link from/to the base station (BS) due to severe blockage. Furthermore, URNs have become crucial for delivering temporary communication services in emergency states or in disaster areas where the infrastructure is destroyed. The literature has explored single transmissions from one BS to a UAV to establish a wireless backhaul link in the URN; however, there exists a possibility of Uo outages due to severe interference from an adjacent BS, causing an overall throughput degradation of user equipment (UE) in the URN. In this paper, to improve the signal-to-interference-plus-noise ratio (SINR) of a wireless backhaul link, avoid an outage of Uo, and guarantee a reliable relay transmission, simultaneous transmissions from multiple BSs (e.g., macrocell BSs (mBSs) and small cell BSs (sBSs)) is considered. An outage probability is analyzed, and an optimal transmit time allocation algorithm is proposed to maximize the throughput of the UE and guarantee a reliable relay transmission. Simulation results demonstrate that simultaneous transmissions from multiple BSs in the URN leads to higher throughput and reliable transmission without an Uo outage compared to a single transmission in the URN from a single BS (e.g., mBS or sBS), and the optimization of transmit time allocation is essential in the URN.

Framework for fast and low-complexity deployment of UAVs-assisted communication

Unmanned Aerial Vehicles (UAVs) can work as aerial base stations along with the ground base stations to enhance capacity and provide ubiquitous coverage in 5G and beyond communication systems. UAVs have numerous features that distinguish them from the traditional fixed ground network infrastructure. These features include more flexibility, lower costs, faster integration, and better channel conditions with users owing to the UAVs maneuverability and adaptability to varying network conditions. This paper addresses UAVs positioning and radio resource management, which are two critical design issues that greatly impact the system performance and user experience in UAVs-assisted communication scenarios. Our objective is maximizing the achieved sum rate of a system consisting of a ground base station and a number of UAVs operating as aerial base stations under constraints on the minimum rate requirements of users, the capacity of wireless backhaul links between the UAVs and the ground base station, and the power budgets of the base stations. In doing so, we optimize the UAVs locations, the users-to-base stations association, the access link channels assignment, and the power allocation. The problem is a mixed-integer non-convex optimization, which is NP-hard to solve, and consequently we propose a fast low-complexity algorithm that achieves high performance compared to the common block coordinate descent-based solution approach and at much less execution time.

Performance Analysis of Cellular Ultradense IoT Networks With Wireless Backhauls

The rising era of smart living requires unprecedented advancements in cellular networks to support the communications of the Internet of Things (IoT), referred to as Machine-Type Communication (MTC). Hence, we consider an Ultra-Dense Network (UDN) environment supported by wireless backhaul links and investigate the achievable performance gains for MTC. By doing so, we avoid the complexity, cost, and/or infeasibility of providing fiber backhaul links for the massive number of Small Cells (SCs) found in UDNs. We utilize the Millimeter Wave (mmWave) band to support the communications between the IoT Devices (IoTDs) and their serving SCs. By doing so, the excess available bandwidth can be used to support a massive number of IoTDs while the propagation characteristics of the mmWave signals can be exploited to mitigate the severe Inter-Cell Interference (ICI) found in UDNs. In this regard, we formulate a mathematical framework using tools from stochastic geometry to derive analytical expressions for the density of supported IoTDs and the average capacities per SC on both the Access Link (AL) and the Backhaul Link (BH). In addition, we obtain a tight lower bound of the average capacity per SC under the considered wireless limited-capacity backhaul links. The obtained results show the existence of an optimal active SC density that maximizes SC utilization.

UAV-Assisted Wireless Backhaul Networks: Connectivity Analysis of Uplink Transmissions

With the proliferation of wireless communication technologies, user equipments (UEs) in rural or disaster areas have data-transmission demand to upload their data to the core network. However, current networks lack coverage in rural or disaster areas due to the absence or damage of/to infrastructures. To address this issue, a promising solution is employing unmanned aerial vehicles (UAVs) as relays to assist the wireless backhaul of UEs to remote ground base stations (GBSs). For convenience, we call these networks as UAV-assisted wireless backhaul networks (UABNs). This paper aims to investigate the uplink transmission performance in UABNs. In particular, we analyze the connectivity of the two-hop uplink path from a reference UE to a remote GBS via a reference UAV. Compared with previous studies that mostly analyze single-hop transmissions, the investigation of the path connectivity of UABNs is more complex because of the location variation of UAVs as well as the complexity of the interference at the two-hop path. Considering the distribution of UEs, we exploit stochastic geometry to establish a theoretical model to analyze the path connectivity of UABNs. In our model, UEs form clusters according to a Poisson Cluster Process (PCP) and one UAV serves one UE cluster. Based on our model, the connectivity of a two-hop uplink path is finally derived by limiting the signal-to-noise-plus-interference (SINR) above a threshold. Theoretical values of the connectivity of UABNs match with simulation results, confirming the accuracy of the proposed analytical model. Our results also offer insightful implications for constructing and configuring UABNs.

Energy-Efficiency Optimization for Multiple Access in NOMA-Enabled Space–Air–Ground Networks

Due to the flexible deployment of unmanned aerial vehicles (UAVs) and the wide-area coverage of satellites, the space-air-ground (SAG) communication network can provide flexible and pervasive connectivity, especially in remote areas. In this work, we investigate the uplink transmission in a SAG network, where the non-orthogonal multiple access mechanism is adopted at the UAVs to enhance the number of access from ground user equipments (UEs) and a low earth orbit satellite offers the wireless backhaul for UAVs. In particular, the energy efficiency (EE) of the considered network is maximized by optimizing the user association (UA), the power allocation (PA), and the UAV 3D trajectory jointly with the consideration of the movement of the satellite. To tackle the formulated problem, by leveraging the block coordinate descent (BCD) method, we develop a joint UA, PA, and UAV trajectory (namely, JUPT) optimization algorithm, i.e., the original problem is decomposed into three subproblems, and the subproblems are solved iteratively until convergence. Specifically, we propose to include the virtual UEs in the system and develop a low-complexity matching algorithm to effectively solve the UA problem. A successive convex approximation (SCA)-based Dinkelbach algorithm is then adopted to address the PA problem. Later, with the introduction of the auxiliary variables, the UAV 3D trajectory subproblem is iteratively solved by the SCA method. Our numerical results demonstrate the superiority of the proposed JUPT algorithm, which obtains significantly higher EE compared to the benchmark schemes. Moreover, the rapid convergence of the JUPT algorithm is verified.

A Distance-Weighted Dynamic Bandwidth Allocation Algorithm for Improved Performance in Long-Reach Passive Optical Networks for Next Generation Networks

In recent years, there has been an increasing trend towards extending the coverage of passive optical networks (PONs) over large geographical areas. Long-reach PONs (LRPONs) are capable of extending the distance covered by PONs from 20 km to 100 km, leading to cost savings in the network operation by reducing the number of central offices. They have become widely deployed due to their ability to provide high-speed, long-distance data transmission over optical fibers. In addition, the next generation of optical access networks are expected to provide high-capacity mobile and wireless backhauling over a wide coverage area. However, this extended reach also requires the design of efficient dynamic bandwidth allocation (DBA) schemes to address the performance degradation caused by the increased propagation delay in LRPONs. The DBA schemes commonly used for upstream traffic transmission in traditional PONs are not well-suited for use in LRPONs due to their inefficiency in bandwidth utilization due to the increased round-trip time (RTT) between the optical line terminal (OLT) and the optical network unit (ONU). In this study, we present an efficient DBA algorithm, the Distance-Weighted Bandwidth Allocation DWDBA Algorithm, specifically enhanced for multi-wavelength LRPONs. Our DBA algorithm utilizes a scheduling policy that assigns weight vectors to Optical Network Units (ONUs) based on their distance from the Optical Line Terminal (OLT), sorting them accordingly without penalizing any ONU due to their distance. The DWDBA takes the laser tuning time into consideration. We conducted extensive simulations to evaluate the performance of the proposed algorithm under various scenarios and compared it to the IPACT algorithm. The results of the simulations show that the proposed algorithm outperformed the IPACT algorithm in terms of bandwidth utilization and queue delay.

Energy-Efficient Ground Base Station Antenna Array System for Wireless Back-Hauling and Two State Charging of Drone Base Stations

A ground base station (GBS) antenna array system (AAS) geometry and feed technique are proposed for 360 degree azimuth beam scan, elevation beam scan, azimuth beam-width control, elevation beam-width control, and in existing AASs these four capabilities do not coexist. Firstly, the results confirm that the number of active radio-frequency (RF) switches in the proposed AAS feed network is reduced by more than 99 %, and the AAS achieves a power consumption reduction of up to 50 % without fully connected (FC) architecture and 35 % with FC architecture compared to the existing AAS. Also, an independent GBS AAS azimuth and elevation plane beam-width control can be achieved from 65 to 0.01 degrees which makes it suitable for GBS-assisted wireless back-hauling plus wireless charging of unmanned aerial vehicle base stations (UAV-BSs), dynamic GBS sectorisation order control, and millimeter wave beam tracking. Also, a parallel concurrent UAV-BS battery charging configuration with power source swap capability is proposed to charge UAV-BS using the GBS back-haul and onboard power amplifier (PA) system as the power sources. The results show that the proposed charging configuration improves hovering time by 180.7 % compared to the existing onboard PA system configuration for battery charging. Furthermore, the proposed charging configuration can recharge the battery of the inactive state UAV-BS to increase the state of charge of two 16 Ampere hour batteries to 80 % in 180.13 minutes, and UAV-BS’s inactive state charging was not proposed in the existing onboard PA system configuration for UAV-BS’s battery charging.

Topology Planning Using Q-Learning for Microwave-Based Wireless Backhaul Networks

Today, a microwave-based solution is the one commonly used for wireless backhaul networks as it has high capacity and can be easily deployed. For such a microwave-based wireless backhaul network, a well-designed topology is important for efficient capacity utilization and high-quality mobile services. In an earlier work, we presented an approach for topology planning for microwave-based wireless backhaul networks, where an integer linear programming (ILP) model was formulated and a heuristic algorithm was also developed, subject to various constraints. However, the previous heuristic algorithm may merely lead to local optima, and moreover, only a single set of weight factors was considered for the optimization. This cannot guarantee joint optimization over all the relevant performance aspects. We overcome these drawbacks here by employing the Q-learning technique for this topology planning problem. This consists of (a) re-optimizing an initial topology based on a specific set of weight factors and (b) finding an optimal set of these weight factors. To accelerate the Q-learning process, we also develop a parallel Q-learning system to find the optimal set of weight factors. Simulation results indicate that the Q-learning based approach can jointly optimize multiple system objectives and outperforms our earlier heuristic algorithm. The parallel learning system can also significantly expedite the learning process.

Spiral-Resonator-Based Frequency Reconfigurable Antenna Design for Sub-6 GHz Applications

This paper presents a novel frequency reconfigurable antenna design for sub-6 GHz applications, featuring a unique combination of antenna elements and control mechanisms. The antenna is composed of an outer split-ring resonator loaded with an inner spiral resonator, which can be adjusted through the remote control of PIN diode or Single Pole Double Throw (SPDT) switches. The compact antenna, measuring 22 × 16 × 1.6 mm3, operates in broadband, or tri-band mode depending on the ON/OFF states of switches. The frequency reconfigurability is achieved using two BAR64−02V PIN diodes or two CG2415M6 SPDT switches acting as RF switches. SPDT switches are controlled remotely via Arduino unit. Additionally, the antenna demonstrates an omni-directional radiation pattern, making it suitable for wireless communication systems. Experimental results on an FR-4 substrate validate the numerical calculations, confirming the antenna’s performance and superiority over existing alternatives in terms of compactness, wide operating frequency range, and cost-effectiveness. The proposed design holds significant potential for applications in Wi-Fi (IEEE 802.11 a/n/ac), Bluetooth (5 GHz), ISM (5 GHz), 3G (UMTS), 4G (LTE), wireless backhaul (4G and 5G networks), WLAN (IEEE 802.11 a/n/ac/ax), 5G NR n1 band, and Wi-Fi access points due to its small size and easy control mechanism. The antenna can be integrated into various devices, including access points, gateways, smartphones, and IoT kits. This novel frequency reconfigurable antenna design presents a valuable contribution to the field, paving the way for further advancements in wireless communication systems.

Secrecy Performance Analysis of Heterogeneous Networks With Unreliable Wireless Backhaul and Imperfect Channel Estimation

Wireless backhaul is an economical and flexible alternative to wired backhaul, however, it experiences impairments. This research proposes a new secure heterogeneous system model with unreliable wireless backhaul and imperfect channel estimation over Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> fading channel. To improve the system secrecy performance, we propose three small-cell transmitter selection schemes under channel estimation imperfections and wireless backhaul impairments, namely Optimum Selection (OS), Sub-optimum Selection (SS) and Minimum-Eavesdropping Selection (MES). Novel closed-form expressions of secrecy outage probability (SOP) are derived for these three selection schemes in both practical and ideal scenarios. The novel theoretical analysis and simulation results show the impact of wireless backhaul uncertainties and channel estimation errors on system secrecy performance. In addition, we investigate how the number of small-cell transmitters affects the system secrecy performance. The asymptotic behaviour is provided to obtain insights into the system performance. The analytical derivations and asymptotic expressions are validated by Monte Carlo simulations. Our novel theoretical analysis can guide different physical layer security (PLS) designs.

Joint Communication and Sensing Design in Coal Mine Safety Monitoring: 3-D Phase Beamforming for RIS-Assisted Wireless Networks

This paper investigates the resource allocation of a reconfigurable intelligent surface (RIS)-aided joint communication and sensing (JCAS) system in a coal mine scenario. In the JCAS system, an RIS is implemented at the corner of the zigzag tunnels to improve the complicated wireless environment, where ground obstacles frequently block direct links. In addition, a wireless backhaul base station with a limited energy budget is deployed in the depth of the mine to sense the target area and provide internet of things (IoT) services and communication services for users. Furthermore, a data center is placed on the ground to analyze the obtained data and route the communication data. Under this deployment, a joint optimization problem of RIS phase shift matrix, RIS element switches, and area sensing time is proposed. We aim to maximize the successful sensed bits under total completion time, and maximum transmit power constraints. In order to solve this problem, an iterative algorithm is proposed. The successive convex approximation (SCA) based algorithm is used for the RIS phase shift matrix optimization subproblem. For the sensing time optimization subproblem, the quadratic approximation method is proposed to optimize the number of area perceptions. The coordinate descent method is utilized to optimize the RIS element switches. Simulation results show that the energy efficiency is improved by up to 38%, and 7% increases the specific data size compared with the benchmark solutions.

A MAC Protocol Design for Maximizing End-to-End Throughput and Fairness Guarantee in Chain-Based Multi-Hop Wireless Backhaul Networks

Chain-based multi-hop wireless backhaul networks (WBN) have attracted considerable attention in next-generation networks since it can cost-effectively extend the service coverage area. The major challenge pertaining to such networks is the designing of a high-performance medium access control (MAC) scheme to raise the end-to-end throughput and ensure fairness. In this paper, we propose a novel MAC protocol, that is aimed at resolving the well-known location-dependent unfairness problem of chain-based networks, maximizing spatial reuse, and most importantly, achieving exceptional end-to-end throughput. Our scheme provides an elaborate control mini-slots design that resolves the different relay-traffic load problem of nodes at various positions near or far from the gateway and further provides local-traffic fairness among all nodes. Moreover, based on the traffic analysis, we provide a four-node group-transmission methodology. By using the neighbor-nodes echoes (broadcasts) the reservation-signals design, the chain-based WBN is allowed to extend to numerous nodes for concurrent and collision-free data transmission through distributed scheduling. Extensive simulation and analytic results also demonstrate that our proposed MAC scheme achieves exceptional system throughput which is approximately 1.6 times better than previous works for a four-node-chain network and also attains high system performance under a wide range of traffic loads and various system parameters.

RIS-aided Multi-hop Backhauling for 5G/6G UAV-assisted Access Points

Drones are considered to be an important part of future 6G telecommunication systems. Thanks to their quick deployment potential, they provide additional connectivity options in the form of a flying hotspot. However, in such use cases, they typically require a wireless backhaul link to facilitate their proper operation, which might be a challenging task in dense urban environments. One of the potential methods that may be relied upon to connect such nodes is the integrated access and backhaul (IAB) approach, where part of the spectrum allocated to users accessing the base station is used for wireless backhauling. Thus, in this work, we consider the problem of establishing a multi-hop wireless backhaul link following the IAB concept, with the aid of drone relay stations (DRSs) and reconfigurable intelligent surfaces (RISs). We formulate the problem of coverage improvement with a fixed number of relays, assuming certain throughput requirements for the backhaul link. The simulations show that the use of RISs offers a coverage improvement in such a scenario or a reduction in the number of nodes involved in ensuring the required backhaul performance.

Transmitter selection for secrecy against colluding eavesdroppers with backhaul uncertainty

AbstractDue to the exponential growth of interconnected devices and reduced cell coverage, beyond fifth‐generation networks will be dense. Thus, instead of wired backhaul, wireless backhaul will be cost‐effective and flexible. For security in multi‐transmitter systems, sub‐optimal and optimal transmitter selection schemes exist. However, including backhaul activity knowledge available (BKA) and backhaul activity knowledge unavailable (BKU) cases and transmitter selection schemes, there is no generalized secrecy analysis method. Moreover, evaluation of the ergodic secrecy rate (ESR) of the optimal selection schemes is impossible using existing solution approaches. To address these, two sub‐optimal and optimal transmitter selection schemes are proposed for a small‐cell multi‐transmitter system in BKU or BKA cases in the presence of multiple colluding eavesdroppers. The distribution of the ratio of the destination channel SNR and eavesdropping channel SNR is derived, thereby providing different secrecy performance metrics uniformly irrespective of selection schemes and BKU or BKA cases. Simplified asymptotic expressions are provided to elucidate the influence of the system parameters and of the backhaul reliability. It is observed that the secrecy performance improves when the knowledge of backhaul link activity is utilized, and the improvement is most noticeable when the backhaul is highly unreliable. It is also observed that while the secrecy performance degrades with an increasing number of eavesdroppers, neither the asymptotic saturation value of the secrecy outage probability nor the rate of improvement of the ESR with signal‐to‐noise‐ratio depends on the number of eavesdroppers.

Development of an Ultra-Long-Range Wireless Backhaul Solution Using ATSC 3.0

In rural and remote locations Internet access remains a challenge. Fiber optics cannot always be implemented due to cost and geographical terrain. Microwave and satellite links are expensive. An ultra-long-range wireless backhaul (ULRWB) solution using the ATSC 3.0 NextGen TV system is proposed, which can provide a backbone infrastructure for delivering inexpensive high-speed communications to rural and remote communities. The proposed ULRWB is based on the ATSC 3.0 Physical Layer (ATSC A/322) standard with all the advantages this modern advanced standard brings, including making the most efficient use of RF spectrum.