User Coverage Research Articles

An Enhanced Deep Reinforcement Learning-based Slice Acceptance Control System (EDRL-SACS) for Cloud–Radio Access Network

In 5G networks, Network Slicing (NS) allows service providers (SPs) to create customized standalone networks on shared platforms provided by infrastructure providers (InPs). However, the challenge arises when prioritizing slice requests sent by SPs to InPs due to limited and shared resources. The Cloud Radio Access Network (C-RAN) architecture is a novel approach that supports various services in 5G networks, but efficient resource utilization and increased revenue across the three layers of C-RAN are necessary. To address this issue, we propose an Enhanced Deep Reinforcement Learning-based Slice Acceptance Control System (EDRL-SACS) technique to maximize network utility by accepting suitable requests. EDRL-SACS considers time-varying resource situations, various service requirements, and the frequency of requests from SPs to endorse model-free solutions for Slice Acceptance Control (SAC). By adding effective parameters to the deep reinforcement learning algorithm, the proposed methodology improves its efficiency and performance resulting in better resource utilization, coverage, revenue, and rejection of users’ request terms. The system’s evaluation shows that EDRL-SACS performs well and effectively maximizes the utility of the network by providing slices to SPs in response to their slice requests.

Multi-UAV Navigation for Partially Observable Communication Coverage by Graph Reinforcement Learning

In this paper, we aim to design a deep reinforcement learning (DRL) based control solution to navigating a swarm of unmanned aerial vehicles (UAVs) to fly around an unexplored target area under partial observation, which serves as Mobile Base Stations (MBSs) providing optimal communication coverage for the ground mobile users. To handle the information loss caused by the partial observability, we introduce a novel network architecture named Deep Recurrent Graph Network (DRGN), which could obtain extra spatial information through graph-convolution based inter-UAV communication, and utilize historical features with a recurrent unit. Based on DRGN and maximum-entropy learning, we propose a stochastic DRL policy named Soft Deep Recurrent Graph Network (SDRGN). In SDRGN, a heuristic reward function is elaborated, which is based on the local information of each UAV instead of the global information; thus, SDRGN reduces the training cost and enables distributed online learning. We conducted extensive experiments to design the structure of DRGN and examine the performance of SDRGN. The simulation results show that the proposed model outperforms four state-of-the-art DRL-based approaches and three heuristic baselines, and demonstrate the scalability, transferability, robustness, and interpretability of SDRGN.

Performance Analysis and Simulation of Millimeter Wave Cell-Free mMIMO Networks

Towards the 5Gnetworks and beyond, there are a lot of emerging technologies. These technologies include but not limited to; multiple input multiple output (MIMO), cell-Free networks, and millimeter wave bands. The cellular MIMO can provide a satisfied performance for users except the shadowed and the cell-edge ones. In order to overcome this disadvantage, the cell-Free networks are deployed. Through applications of distributed access points (APs), the cell-Free networks can provide a ubiquitous coverage for users as whole. Therefore, they can provide a high throughput for users. Moreover, the applications of millimeter wave bands can provide a high bandwidth and hence, a high throughput for users. In other words, the application of the cell-Free technology combined with the millimeter wave bands can extremely enlarge the users’ throughput. This is the motivation of our manuscript. The purpose of this manuscript is to provide mathematical model and simulation for the cell-Free mMIMO network, operating in the millimeter wave bands. The performance metrics can include; the spectral efficiency (SE), bit error rate (BER), and energy efficiency (EE). It is observed that the centralized cooperation among the APs can let users have a satisfied throughput even the system employs the maximal ratio combining (MRC). Furthermore, all cooperation levels, among APs, can perform well even for non-light of sight (NLOS) environment.

Construction of FANETs for user coverage and information transmission in disaster rescue scenarios

In this paper, we consider a flying ad hoc network (FANET) construction problem in a post-disaster scenario where ground users (GUs) lose contact with the outside world. The main objective of the FANET is to provide communication coverage for as many as GUs, and to transmit disaster information back to the rescue center as soon as possible. To solve this FANET construction problem, we first propose a FANET construction algorithm based on particle swarm optimization (PSO) algorithm. To improve the performance of the PSO-based FANET construction algorithm, we further model the FANET construction problem as an ordinary potential game (OPG), and propose a better-response-based FANET construction algorithm. Several simulation experiments are presented to evaluate the effectiveness of these proposed two algorithms. Simulation results demonstrate that both the PSO-based FANET construction algorithm and the better-response-based FANET construction algorithm can be used to maximize the coverage rate of GUs and minimize the average transmission delay from rescue UAVs to the gateway UAV. Moreover, the better-response-based FANET construction algorithm has a fast convergence rate, and performs better than the PSO-based FANET construction algorithm and other baselines in terms of coverage rate and average transmission delay.

Dominant coverage for target users at the lowest cost under competitive propagation in social networks

Dominant coverage for target users at the lowest cost under competitive propagation in social networks

FlexBeamOpt: Hybrid solution methodologies for high‐throughput GEO satellite beam laydown and resource allocation

SummaryModern satellite communication systems are required to serve heterogeneous and geographically dispersed user demands with limited resources. In this paper, we investigate methodologies for dynamic resource allocation in Geosynchronous Earth Orbit (GEO) High‐throughput Satellite (HTS) systems. We designed three solution approaches FlexBeamOpt v1, FlexBeamOpt v2, and FlexBeamOpt v3, each as a hybridization of custom heuristics, integer linear programming, and/or constraint programming. We test the performance of the three approaches on 12 test instances that vary in user distribution (realistic, random, and clustered), user numbers (500 vs. 5000 users), and demand distribution (uniform vs. random). We observed that FlexBeamOpt v1 consistently outperformed FlexBeamOpt v2 and FlexBeamOpt v3 in terms of demand coverage and number of users covered for realistic and random user distribution test instances but at the cost of computation time. FlexBeamOpt v3 is the fastest in these instances. For clustered user distribution instances, FlexBeamOpt v3 performed better in terms of demand coverage and number of users covered, at the cost of using more beams. For these test instances, FlexBeamOpt v2 is the fastest in terms of computation time while providing a comparable solution quality.

Joint Optimization of Service Fairness and Energy Consumption for 3D Trajectory Planning in Multiple Solar-Powered UAV Systems

In this paper, we study the three-dimensional (3D) trajectory optimization problems of unmanned aerial vehicles (UAV) with a solar energy supply, aiming to provide communication coverage for mobile users on the ground. In general, the higher UAVs fly, the more solar energy they collect, but the smaller the range of coverage they could achieve, and vice versa. How to plan optimal trajectories for UAVs so that more users can be encompassed, while allowing UAVs to collect enough solar energy, is a challenging issue. Moreover, we also consider how geographically fair coverage for each ground user can be achieved. To solve these problems, we designed a multiple solar-powered UAV (SP-UAV) energy consumption model and a fairness model, while designed an observation space, state space, action space, and reward function. Then, we proposed a multiple SP-UAV 3D trajectory optimization algorithm based on deep reinforcement learning (DRL). Our algorithm is able to balance the energy consumption of UAVs to extend the system’s lifetime, while avoiding both collisions and flying out of communication range. Finally, we trained our model through simulation experiments and conducted comparative experiments and analysis based on real network topology data. The results show that our algorithm is superior to the existing typical algorithms in coverage, fairness, and lifetime.

UAV-Aided Computation Offloading in Mobile-Edge Computing Networks: A Stackelberg Game Approach

Unmanned aerial vehicles (UAVs) are considered as a promising method to provide additional computation capability and wide coverage for mobile users (MUs), especially when MUs are not within the communication range of the infrastructure. In this article, a UAV-aided mobile-edge computing (MEC) network, including one UAV-MEC server, one BS-MEC server, and several MUs, is investigated for computation offloading, in which the edge service provider (ESP) manages two kinds of servers. It is considered that MUs have a large number of computation tasks to conduct, while the ESP has idle computational resources. MUs can choose to offload their tasks to the ESP to reduce their pressure and cost, and the ESP can make a profit by selling computational resources. The interaction among the ESP and MUs is modeled as a Stackelberg game, and both the ESP and MUs want to maximize their utility. The proposed game is analyzed by using the backward induction method, and it is proved that a unique Nash equilibrium can be achieved in the game. Then, a gradient-based dynamic iterative search algorithm (GDISA) is proposed to get the approximate optimal solution. Finally, the effectiveness of GDISA is verified by extensive simulations, and the results show that GDISA performs better than other benchmark methods under different scenarios.

Dedicating Cellular Infrastructure for Aerial Users: Advantages and Potential Impact on Ground Users

A new generation of aerial vehicles is hopeful to be the next frontier for the transportation of people and goods, becoming even as important as ground users in the communication systems. To enhance the coverage of aerial users, appropriate adjustments should be made to the existing cellular networks that mainly provide services for ground users by the down-tilted antennas of the terrestrial base stations (BSs). It is promising to up-tilt the antennas of a subset of BSs for serving aerial users through the mainlobe. With this motivation, in this work, we use tools from stochastic geometry to analyze the coverage performance of the adjusted cellular network (consisting of the up-tilted BSs and the down-tilted BSs). Correspondingly, we present exact and approximate expressions of the signal-to-interference ratio (SIR)-based coverage probabilities for users in the sky and on the ground, respectively. Numerical results verify the analysis accuracy and clarify the advantages of up-tilting BS antennas on the communication connectivity of aerial users without the potential adverse impact on the quality of service (QoS) of ground users.

Scattering and Eavesdropping in Terahertz Wireless Link by Wavy Surfaces

Wireless communication at terahertz (THz) frequencies is viewed as one of the potentials for future 6G wireless systems. However, the path of secure data transmission by THz links is facing many complex challenges. Of that, scattering-induced eavesdropping threat remains one of the most critical but less explored. With the updating of carrier frequency into the THz range, the wavelength becomes comparable to the surface roughness of typical objects in our daily life. Surface scattering, which is negligible in current wireless networks, becomes serious and would lead to desperate signal eavesdropping. For a non-line-of-sight (NLOS) link achieved by surface reflection, how does the scattering affect link security at the physical layer? Such direct information is crucial for the inspection of security threats and proposing of possible solutions. Using metallic wavy surfaces, we characterize possibilities of signal eavesdropping caused by bistatic scattering, by several unmodulated channels at different frequencies. We also demonstrate a real-time data transmission for this eavesdropping configuration, with a data rate of 4 gigabits/s by 16 quadrature amplitude modulation. We observe that successful signal interception from an NLOS link happens, even when transceiver designs incorporating backscatter measures are considered. This is likely to be a common security threat for any THz communication systems in which NLOS channels are employed to achieve wider user coverage.

Resource allocation method for Mobility‐Aware and Multi‐UAV‐Assisted mobile edge computing systems with energy harvesting

AbstractDue to the limited coverage of base station (BS) and battery capacity of mobile users, the resource allocation strategy in multiple unmanned aerial vehicles (UAVs)‐assisted edge computing system with nonlinear energy harvesting is investigated in this paper. The cooperation between BS and multi‐UAV is considered, which can provide extensive coverage for users with mobility. Mobile users can simultaneously offload computation bits to the BS and the UAV, and mobile users harvest energy from BS and UAV. Meanwhile, the mobility of users is taken into account. Moreover, an echo state network (ESN)‐based prediction algorithm is utilized for predicting the future positions of mobile users. Therefore, the UAV can reach the predicted users' positions in advance to ensure the continuity of communication. The objective of the resource allocation strategy is to maximize the energy efficiency by jointly optimizing bandwidth allocation, computation resources, the trajectory of UAV, and transmitting power of mobile users. Then, the resource allocation problem is formulated as a mixed‐integer nonlinear programming problem. The quantum‐behaved particle swarm optimization (QPSO) algorithm is used to solve the problem. Simulation results demonstrate that the proposed strategy can achieve higher energy efficiency than other benchmark strategies. In addition, QPSO algorithm outperforms the standard particle swarm optimization algorithm and genetic algorithm in terms of energy efficiency.

Cell-Free Massive MIMO With Finite Fronthaul Capacity: A Stochastic Geometry Perspective

In this work, we analyze the downlink performance of a cell-free massive multiple-input-multiple-output system with finite capacity fronthaul links between the centralized baseband unit and the access point (APs). Conditioned on the user and AP locations, we first derive an achievable rate for a randomly selected user in the network that captures the effect of finite fronthaul capacity as a compression error. From this expression, we establish that for the traditional cell-free architecture where each AP serves all the users in the network, the achievable rate becomes zero as the network size grows. Hence, to have a meaningful analysis, for the traditional architecture, we model the user and AP locations as two independent binomial point processes over a finite region and provide an accurate theoretical result to determine the user rate coverage. In contrast, for an asymptotically large network, we consider a user-centric architecture where each user in the network is served by a specified number of nearest APs that limits the fronthaul load. For this architecture, we model the AP and user locations as two independent Poisson point processes (PPPs). Since the rate expression is a function of the number of users served by an AP, we statistically characterize the load in terms of the number of users per AP. As the exact derivation of the probability mass function of the load is intractable, we first present the exact expressions for the first two moments of the load. Next, we approximate the load as a negative binomial random variable through the moment matching method. Using the load results along with appropriate distance distributions of a PPP, we present an accurate theoretical expression for the rate coverage of the typical user. From the analyses, we conclude that for the traditional architecture when the AP transmit power is relatively high, a more collocated antenna deployment is preferred. Further, for the user-centric architecture, the energy efficiency of the system is a concave function of the number of antennas per AP.

Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium.

Fixed wireless access (FWA) provides a solution to compete with fiber deployment while offering reduced costs by using the mmWave bands, including the unlicensed 60 GHz one. This paper evaluates the deployment of FWA networks in the 60 GHz band in realistic urban and rural environment in Belgium. We developed a network planning tool that includes novel backhaul based on the IEEE 802.11ay standard with multi-objective capabilities to maximise the user coverage, providing at least 1 Gbps of bit rate while minimising the required network infrastructure. We evaluate diverse serving node locations, called edge nodes (EN), and the impact of environmental factors such as rain and vegetation on the network design. Extensive simulation results show that defining a proper EN's location is essential to achieve viable user coverage higher than 95%, particularly in urban scenarios where street canyons affect propagation. Rural scenarios require nearly 75 ENs per km2 while urban scenarios require four times (300 ENs per km2) this infrastructure. Finally, vegetation can reduce the coverage by 3% or increment infrastructure up to 7%, while heavy rain can reduce coverage by 5% or increment infrastructure by 15%, depending on the node deployment strategy implemented.

Responsible and human centric AI-based insurance advisors

The domain about what it means to give responsible and human centric recommendations in the context of Artificial Intelligence (AI)-based insurance has not yet been fully explored. In this article, we therefore, first provide an in-depth analysis and perform a systematic literature review on (i) the specifications and requirements for such systems from a regulation point of view, (ii) instructions on which data they can rely upon, (iii) which recommender techniques can be used for developing such an advisor, (iv) off-the-shelf components for the trustworthy, responsible, and ethical behavior of this AI-empowered tool. Then, we present a novel approach, based on AI, to suggest insurance coverage for users and families, as well as instructions on how to design such a system. The solution, as proposed in our paper, will be transparent, trustworthy, and responsible to the final users and thus, hopefully, better accepted by customers. After describing a possible system design and architecture, we critically discuss the challenges and opportunities for the deployment of such systems in insurance companies.

Scaled and nonlinear multi-objective model for Downlink and Uplink exposure in massive MIMO

When designing wireless networks with a large number of wireless broadband devices, it is important to take into account the induced Uplink (UL) and Downlink (DL) exposure from base station broadcasting. This work focuses on the reduction of power consumption, low downlink and uplink electromagnetic exposure, and the optimization of locations and power levels of large Multiple Input–Multiple Output-Long Term Evolution base stations (MIMO-LTE). Additionally, it anticipates achieving maximum user coverage. In order to handle the power consumption and Electro Magnetic Field (EMF) exposure, a Nonlinear and Scaled Weight objective model (NLSW) is defined (DL and UL exposure). An enhanced Particle Swarm Optimization (PSO) technique is employed to resolve this optimization issue. Finally, DL exposure, UL exposure, and power performance of a NLSW are validated compared to the traditional linear model. Increased antenna leads to a 25% reduction in DL dosage. The minimum BS deployed in the area with the number of antenna elements until it reaches the ideal value is related to the UL exposure maximization.

A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

The concept of smart cities is to enhance the life quality of residents and provide efficient services by integrating advanced information and communication technologies, autonomous robots, Internet of Things (IoT) devices, etc. Unmanned Aerial Vehicles (UAVs) are a class of autonomous flying mobile robots that bring a lot of benefits to smart cities due to their mobility, accessibility, autonomy, and many other advantages. Their integration allows for accomplishing hard and complex tasks that humans or other entities are not able to complete. In most applications, multiple connected UAVs are required to build a network under which missions are completed and tasks are shared. This network can be established by finding the optimal UAV placement that meets some requirements such as user coverage, UAV connectivity, energy, and load distribution. In this paper, we present a hybrid algorithm, called IMRFO-TS, based on the hybridization of Improved Manta Ray Foraging Optimization (IMRFO) with the Tabu Search (TS) algorithm for solving the UAV placement problem in a smart city. First, the tangential control strategy is incorporated into the original MRFO algorithm to enhance its convergence speed and explore the search space effectively. Second, The TS algorithm is hybridized with the IMRFO algorithm to increase the exploitation capability of IMRFO and improve the best solution (placement quality) obtained after each iteration. The performance of the proposed IMRFO-TS algorithm is validated using 52 benchmarks considering the fitness value, coverage, connectivity, energy consumption, and load distribution parameters. Compared to eight well-known optimization meta-heuristics such as the original MRFO, TS algorithm, Bat Algorithm (BA), Firefly Algorithm (FA), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Reptile Search Algorithm (RSA), the results of the experiments revealed the significant superiority of the proposed IMRFO-TS algorithm by obtaining promising solutions (optimal positions of UAVs) in the majority of the cases.

IMPLEMENTATION OF UNMANNED AERIAL VEHICLES AS FLYING BASE STATIONS TO ASSIST 5G NETWORKS

Current wireless communication networks are not able to accommodate the increase in broadband data and are currently encountering fundamental challenges like higher data rate and Quality of Service (QoS) requirements, energy efficiency and excellent end-to-end performance and user coverage in overcrowded areas and hotspots whilst maintaining extremely low latency and high bandwidth. The deployment of 5G networks aims to address such challenges by introducing multiple advancements to the network and implementing new technologies to evolve new radio networks. This will primarily be done by introducing the 5G New Radio, which is the radio technology that is being developed to support the 5G technologies that will solve the problems mentioned previously. With the New Radio implementation, the next generation networks will accommodate the growing data rates. The networks are expected to attain a mobile data volume per unit area that is 1,000 times higher than current networks. Over 10-100 times the number of current connected devices is expected to be accommodated by 5G networks. Coverage is primarily the crucial problem with 5G networks, requiring the densification of urban areas with heterogeneous networks and the deployment of more closely packed terrestrial MBSs. However, this is not cost-effective and can be more complex as terrestrial network replanning will be required. The issue can be overcome by integrating UAVs into the network infrastructure as FBSs.

Beamspace Selection in Multi-User Massive MIMO

In this paper, we propose new Discrete Fourier Transform (DFT)-based beamspace selection algorithms for Massive Multiple Input, Multiple Output (MIMO) receiver operating in realistic multi-user (MU) scenarios. In practical uplink scenarios, there is a power ratio between signals received from different users that complicates performance analysis in MU cases. Our algorithms are inspired by a proportional fair approach to allocating spatial resources for target users. Thus, we analyze performance in terms of coverage (for low-power users) and capacity (for high-power users) and consider the implementation complexity to highlight feasible algorithms. Simulations with realistic non-line-of-sight scenarios generated by the QuaDRiGa 2.0 demonstrate our methods outperform other DFT-based alternatives, such as the beamspace selection based on the projection power maximization.

Joint Communication, Computing, and Caching Resource Allocation in LEO Satellite MEC Networks

Driven by the urgent requirement of ubiquitous and reliable coverage for global users, the low earth orbit (LEO) satellite network has attracted numerous attentions from the academic and industry circles. By deploying multi-access edge computing (MEC) servers in LEO satellites, computation offloading and content caching services can be provided for remote Internet-of-Things (IoT) devices without proximal servers. In this paper, the joint optimization of computation offloading, radio resource allocation and caching placement in LEO satellite MEC networks are investigated. The problem is formulated to minimize the total delay of all ground IoT devices while ensuring the energy, computing and caching constraints. To solve this mixed-integer and non-convex problem, a Lagrange dual decomposition (LDD)-based algorithm is proposed to obtain the closed-form optimal solution. Then, a heuristic algorithm is proposed to further reduce the computation complexity. Numerical results validate that both algorithms are effective compared to the optimal exhaustive search, the full local computing and the full MEC methods. Besides, the offloading ratio and the average delay of all IoT devices with different numbers and computing capacities of devices and satellites are also demonstrated.

A Hybrid metaheuristic Algorithm for Edge Site Deployment with User Coverage Maximization and Cost Minimization

A Hybrid metaheuristic Algorithm for Edge Site Deployment with User Coverage Maximization and Cost Minimization