Aerial Base Station Assisted Cellular Communication: Performance and Trade-off

The use of Aerial Base Stations (ABSs) has received a great deal of attention in academia and industry as a means to support the cellular communication traffic growth. In this article, we focus on obtaining the optimal altitude of an ABS using two criteria-maximum cell coverage area and minimum Symbol Error Rate (SER). Our study is done by using a probabilistic air-to-ground channel model, developed for low altitude aerial platforms via simulations on a commercial ray tracing software, for different scenarios like Urban High Rise, Urban, and Suburban. The probability distributions of the received power of the ground users and of the power delay profile at optimal ABS altitude are provided as a function of the size of the cell area. For the SER analysis, we present a system model based on Generalized Frequency Division Multiplexing (GFDM), in a time-frequency grid that is compatible with Long Term Evolution, by implementing parameters for low latency communication at the physical layer. The impact of “Better than Nyquist” pulses on the GFDM system is evaluated in terms of SER performance. From the presented results, a significant improvement is demonstrated compared to the traditional Nyquist pulses.

Cooperative Reinforcement Learning for Adaptive Power Allocation in Device-to-Device Communication

Mobile devices are an intrinsic part of the Internet of Things (IoT) paradigm. Device-to-device (D2D) communication is emerging as one of the viable solutions for the radio resource optimization in an IoT infrastructure. However, it also comes with the challenges associated with power allocation as it causes severe interference by reusing the spectrum with the cellular users in an underlay model. Therefore, efficient techniques are required to reduce the interference with proper power allocation. In this paper, we propose a cooperative reinforcement learning algorithm for adaptive power allocation in D2D communication which helps to provide better system throughput as well as D2D throughput with less interference. We perform cooperation by sharing the value function between devices and incorporating a neighboring factor. We design our states for reinforcement learning with appropriate application-defined variables which provide a longer observation space. We compare our work with the existing distributed reinforcement learning method and random allocation of resources. Simulation results show that the proposed algorithm outperforms the distributed reinforcement learning and the random allocation both in terms of overall system throughput as well as D2D throughput and quality of service (QoS) by adaptive power allocation.

Throughput-Aware Cooperative Reinforcement Learning for Adaptive Resource Allocation in Device-to-Device Communication

Device-to-device (D2D) communication is an essential feature for the future cellular networks as it increases spectrum efficiency by reusing resources between cellular and D2D users. However, the performance of the overall system can degrade if there is no proper control over interferences produced by the D2D users. Efficient resource allocation among D2D User equipments (UE) in a cellular network is desirable since it helps to provide a suitable interference management system. In this paper, we propose a cooperative reinforcement learning algorithm for adaptive resource allocation, which contributes to improving system throughput. In order to avoid selfish devices, which try to increase the throughput independently, we consider cooperation between devices as promising approach to significantly improve the overall system throughput. We impose cooperation by sharing the value function/learned policies between devices and incorporating a neighboring factor. We incorporate the set of states with the appropriate number of system-defined variables, which increases the observation space and consequently improves the accuracy of the learning algorithm. Finally, we compare our work with existing distributed reinforcement learning and random allocation of resources. Simulation results show that the proposed resource allocation algorithm outperforms both existing methods while varying the number of D2D users and transmission power in terms of overall system throughput, as well as D2D throughput by proper Resource block (RB)-power level combination with fairness measure and improving the Quality of service (QoS) by efficient controlling of the interference level.

Q-Learning Based Joint Energy-Spectral Efficiency Optimization in Multi-Hop Device-to-Device Communication

In scenarios, like critical public safety communication networks, On-Scene Available (OSA) user equipment (UE) may be only partially connected with the network infrastructure, e.g., due to physical damages or on-purpose deactivation by the authorities. In this work, we consider multi-hop Device-to-Device (D2D) communication in a hybrid infrastructure where OSA UEs connect to each other in a seamless manner in order to disseminate critical information to a deployed command center. The challenge that we address is to simultaneously keep the OSA UEs alive as long as possible and send the critical information to a final destination (e.g., a command center) as rapidly as possible, while considering the heterogeneous characteristics of the OSA UEs. We propose a dynamic adaptation approach based on machine learning to improve a joint energy-spectral efficiency (ESE). We apply a Q-learning scheme in a hybrid fashion (partially distributed and centralized) in learner agents (distributed OSA UEs) and scheduler agents (remote radio heads or RRHs), for which the next hop selection and RRH selection algorithms are proposed. Our simulation results show that the proposed dynamic adaptation approach outperforms the baseline system by approximately 67% in terms of joint energy-spectral efficiency, wherein the energy efficiency of the OSA UEs benefit from a gain of approximately 30%. Finally, the results show also that our proposed framework with C-RAN reduces latency by approximately 50% w.r.t. the baseline.

A Primer On Public Safety Communication in the Context of Terror Attacks: The NATO SPS “COUNTER-TERROR” Project

Terrorism is emerging as one of the most serious threats worldwide. Terrorist attacks are becoming more coordinated, sophisticated and hence more devastating. One of the important reasons for higher casualities is the “sluggish response time”. In some of the recent attacks, police and law enforcement agencies even after many hours were unable to have some of the basic information such as how many people are inside the attacked premises, the number of seriously injured persons, the number of terrorists, what their location is and so on.

So, one of the most important questions is “How to provide the fundamental information to public safety agencies as quickly as possible to reduce the response time following terrorist attacks”? The NATO – Science for Peace (SPS) “COUNTER-TERROR” G5482 project (2019–2021) presented in this paper investigates and proposed innovative ideas and solutionsto address this question from an information and communication technology viewpoint, including the establishment of secure D2D communication networks to quickly gather information and the use of UAVs to discover and localize weak signals.

It is envisioned that “connecting” the on-scene available (OS-A) heterogeneous devices (through multi-hop device-to-device (D2D) communication) to the nearest mobile deployed command center in an efficient way cannot only rapidly disseminate fundamental information but also aid in significantly reducing the response times and, consequently, many lives and infrastructure.

The objectives of this project are to design and evaluate efficient heterogeneous resource management by adaptive power control, throughput enhancement and interference management for device-to-device (D2D) communication. The originality is to exploit machine learning (ML) techniques to improve the existing state of the art. Further, the use of unmanned aerial vehicle (UAV) for weak signal detection and devices accurate position evaluation is an important objective. The deployed setup with UAV assisted connectivity is one of the novel contributions of this project. In addition, context aware and reliable D2D multi-hop routing and network connections to ensure high end-to-end throughout and low end-to-end energy consumption and delay is another core objective.

In order to enable the emergence of future pervasive communication systems enabling life-critical, public safety and preparedness, D2D communication can be realized both in licensed (driven by cellular spectrum) and un-licensed spectrums (used by other wireless technologies e.g., WiFi Direct, long term evolution (LTE) Direct, Bluetooth). In the licensed spectrum, smart phones can connect through LTE-A and can create multi-hop communication by exploiting UAVs (i.e., relay nodes). Whereas, in the unlicensed spectrum, smartphones can connect through WiFi Direct, LTE Direct, or LTE-U. The fundamental aspect of this work is to connect the devices (i.e., establish and maintain reliable connections) in harsh environments and existing works have to be extended and tested so that they can deal with such conditions.

So far, there have been only a few research projects specific to the context of terrorist attacks.

To enable future pervasive public safety communication systems, this work will foster the vision to achieve beyond state-of-the-art ambitious, highly innovative and challenging research and development goals. For the given context, in particular, connecting the OS-A devices in an efficient way to optimize their heterogeneous resources and improve reliability is key factor to the innovation of the proposed system.

By implementing the proposed D2D-based solution, the average response time shall be reduced by an average of 8 to 10 times. The direct positive consequences are that more lives will be saved, the number and severity of injuries will be reduced, and damages to infrastructure will be decreased.

Improvement of QoS through Relay Selection For Hybrid SWIPT Protocol

The exponential growth of the Internet of Things (IoT) devices has resulted in huge power consumption issues in wireless devices. Cooperative communication with simultaneous wireless information and power transfer (SWIPT) is a promising technology to improve the coverage, capacity, power consumption, and reliability of the IoT networks. Relay selection plays a pivotal role in cooperative communications for improving the quality of service (QoS) of the network. In this paper, we propose a SWIPT based hybrid protocol to improve the QoS in terms of average end-to-end outage probability through proposed relay selection. Furthermore, we investigate the impact of time switching factor α and power splitting factor λ on the average outage probability and the amount of energy harvested. The simulation results demonstrate that by selecting the best relay through proposed relay selection, we observe maximum improvement of 94 % in terms of average end-to-end outage over the worst relay at transmit power of 38 dBm. Similarly, we observe an increase in the amount of energy harvested by approximately 90% over the worse relay at transmit power of 45 dBm.

A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks

In Public Safety Networks (PSNs), the conservation of on-scene device energy is critical to ensure long term connectivity to first responders. Due to the limited transmit power, this connectivity can be ensured by enabling continuous cooperation among on-scene devices through multipath routing. In this paper, we present a Reinforcement Learning (RL) and Unmanned Aerial Vehicle-(UAV) aided multipath routing scheme for PSNs. The aim is to increase network lifetime by improving the Energy Efficiency (EE) of the PSN. First, network configurations are generated by using different clustering schemes. The RL is then applied to configure the routing topology that considers both the immediate energy cost and the total distance cost of the transmission path. The performance of these schemes are analyzed in terms of throughput, energy consumption, number of dead nodes, delay, packet delivery ratio, number of cluster head changes, number of control packets, and EE. The results showed an improvement of approximately 42% in EE of the clustering scheme when compared with non-clustering schemes. Furthermore, the impact of UAV trajectory and the number of UAVs are jointly analyzed by considering various trajectory scenarios around the disaster area. The EE can be further improved by 27% using Two UAVs on Opposite Axis of the building and moving in the Opposite directions (TUOAO) when compared to a single UAV scheme. The result showed that although the number of control packets in both the single and two UAV scenarios are comparable, the total number of CH changes are significantly different.

Experimental Characterization of Prose Direct Discovery for Emergency Scenarios

Device-to-device communication, as provided by the third generation partnership project standardization, can play a vital role in designing a reliable pervasive public safety network, which allows the user equipments to communicate directly with each other in emergency situations. In this paper, we present experimental results and evaluate the performance of proximity services-based direct discovery in terms of reliability and maximum range for outdoor and indoor emergency scenarios.

ProSe Direct Discovery: Experimental Characterization and Context-Aware Heuristic Approach to Extend Public Safety Networks Lifetime

Device-to-device communication, as provided by the third generation partnership project standardization, can play a vital role in designing a reliable pervasive public safety network, which allows the user equipment (UEs) to communicate directly with each other in emergency situations. In this paper, we analyze the performance of direct discovery, one of the features introduced by proximity services. This is examined in heterogeneous environments using the OpenAirInterface open-source software and USRP hardware platform. The experimental results highlight the suitable values for different gains and frequencies of the UEs for performing reliable baseline direct discovery in out-of-coverage scenarios. We evaluate the performance of direct discovery in terms of reliability and maximum range in outdoor and indoor scenarios. Furthermore, we propose a context-aware energy-efficient heuristic algorithm for direct discovery with the aim of extending the network lifetime in emergency scenarios. This heuristic yields significant improvements in UE lifetime (20-52%) and reduces redundant transmissions of discovery messages compared to the baseline approach.

UAV-Assisted Wireless Localization for Search and Rescue

In the aftermath of disasters, localization of trapped victims is imperative to ensure their safety and rescue. This article presents a novel localization and path planning approach that uses unmanned aerial vehicles (UAVs). The UAVs can extract one-hop neighbor information from the devices that may have run out of power by using directed wireless power transfer (WPT). The one-hop neighbor information corresponds to range measurements, which may or may not contain noise. For the noiseless case, we present a customized online graph traversal approach that minimizes the search energy of the UAV and the number of unlocalized nodes. The lower limits on the various performance aspects of this joint approach are presented. For a noiseless case, the results of UAV travel distance and cells searched show a decreasing trend with an increase in the number of maximum neighbors. These curves approximately approach their corresponding lower limits when the number of maximum neighbors is increased beyond 9. For the case of noisy range measurements, using the same objective function and graph traversal algorithm, the probabilistic region for search is determined that gives the least probability of flip errors. To this end, we further optimize the UAV flight path and its search energy in the probabilistic region through clustering. The proposed method is able to achieve linear scaling of the area searched with respect to the noise level. For a given noise level and increasing number of nodes, the UAV search energy with clustering can reduce the energy cost to 70%.

UAV and SWIPT Assisted Disaster Aware Clustering and Association

In an event of a disaster, the connectivity of on-scene available User Equipment (UE) to the first responders is important because of the unavailability of conventional networks. Therefore, in this paper, considering the deployment of both the Unmanned Aerial Vehicle (UAV) and Mobile Command Center (MCC), we investigate end to end connectivity of UEs to the MCC in terms of the outage. Specifically, various disaster aware clustering schemes are proposed that utilize the UAV and MCC position for the association. These schemes include multiple degrees of freedom to manage intra-cluster distances along with the flexibility to restructure the clusters. In addition, we assume the provision of simultaneous wireless information and power transfer (SWIPT) at Cluster Heads (CHs) through the UAV and MCC. The results show that the association of a UE to MCC or UAV prior to clustering can be optimized to achieve better performance. Without SWIPT at CH, the minimum distance metric to the UAV provides less outage. However, with SWIPT a weighted compromise between intra-cluster distance and CH distance to the UAV achieves less outage. We applied our proposed methods on a real man-made disaster scenario layout and determined their efficacy. INDEX TERMS Public safety networks (PSNs), energy harvesting, clustering, SWIPT.

Managing Critical Nodes in UAV assisted Disaster Networks

Device-to-device (D2D) communications serve as an alternative to cellular networks to enable communication for public safety networks (PSNs). A key requirement for PSNs is to offer alternative access to reach the responders if the communication infrastructure is partially or completely damaged due to a natural or man-made disaster. In this paper, we propose a novel unmanned aerial vehicle (UAV) assisted solution to ensure energy-efficient D2D connectivity in the disaster zone in the presence of critical nodes (CNs). The results show that the minimum average outage is achieved when the UAV is placed at the center of the region associated with the UAV, however, this scenario completely changes with the presence of CNs. Initially, with an increase in the number of CNs, the optimal UAV position shifts from the centre, however, increasing CNs causes the UAV to converge back to the center of the UAV associated region. To cater the service requirements of CNs, we analyze the impact of increasing the mobile command center (MCC) coverage range and study its impact on UAV placement by varying the ratio of CNs and non-CNs. We found that the average outage probability decreases with the increase of the MCC range.

A Configurable Radio Jamming Prototype for Physical Layer Attacks Against Malicious Unmanned Aerial Vehicles

The goal of this paper is to design and prototype a radio jamming system that is able to interfere the communication drone-remote control, in particular, disabling the motion control system. The drone adopted in the experimental session is the AEE Toruk AP10 Pro, characterized by a digital wireless control system centered at 868MHz. We have created a configurable jamming prototype for limit as much as possible the interference with other radio systems and study the effect of the signal band on the motion control system. We will present our system with both simulation and experimental validation.

A Deep Learning Approach for LoS/NLoS Identification via PRACH in UAV-assisted Public Safety Networks

The high mobility of Unmanned Aerial Vehicles (UAVs) and their capability to rapidly deploy Aerial Base Stations (ABS) in areas where the terrestrial network becomes unavailable is a key enabler for Public Safety Networks. In our work we introduce a model in order to identify Line of Sight (LoS) and Non-Line of Sight (NLoS) conditions for User Equipments (UEs) that attempt a connection to an ABS through the Physical Random Access Channel (PRACH) based on Convolutional Neural Networks (CNNs). Our method limits the number of antennas employed with respect to other methods that were developed for traditional approaches, while achieving higher than 80% accuracy for SNR of -20 dB. Finally, we study the impact of UAV’s height on the accuracy of our method and we compare it with typical computationally efficient methods based on the delay spread with and without the aid of beamforming.

Surveying pervasive public safety communication technologies in the context of terrorist attacks

Existing public safety networks (PSNs) are not designed to cope with disasters such as terrorist attacks, consequently leading to long delays and intolerable response times. First responders’ life threats when accessing the attacked zone are more severe in comparison to other disasters and the accuracy of basic information such as the number of terrorists, the number of trapped people, their locations and identity, etc., is vital to the reduction of the response time. Recent technologies for PSNs are designed to manage natural disaster scenarios; these are not best suited for situations like terrorist attacks because a proper communication infrastructure is required for operating most of the classical PSNs. This serious concern makes it highly desirable to develop reliable and adaptive pervasive public safety communication technologies to counter such a kind of emergency situation. Device-to-device (D2D) communication can be a vital paradigm to design PSNs that are fit for dealing with terrorist attacks thanks to long-term evolution (LTE)-sidelink, which could allow the devices that people carry with themselves in the attacked zone to communicate directly. To our best knowledge, this is the first survey paper on public safety communication in the context of terrorist attacks. We discuss PSN scenarios, architectures, 3rd generation partnership project (3GPP) standards, and recent or ongoing related projects. We briefly describe a system architecture for disseminating the critical information, and we provide an extensive literature review of the technologies that could have a significant impact in public safety scenarios especially in terrorist attacks, such as beamforming and localization for unmanned aerial vehicles (UAVs), LTE sidelink for both centralized (base-station assisted) and decentralized (without base-station) architectures, multi-hop D2D routing for PSN, and jamming and anti-jamming in mobile networks. Furthermore, we also cover the channel models available in the literature to evaluate the performance of D2D communication in different contexts. Finally, we discuss the open challenges when applying these technologies for PSN.

On the impact of clustering for Energy critical Public Safety Networks

In the event of man made disasters the critical infrastructure is not available. In the absence of any centralized control it is important to devise energy efficient techniques for routing. This paper presents a comparison of clustering techniques for enabling energy-aware routing in Device to Device (D2D) communication for Public Safety Networks. This work compares different clustering schemes in terms of throughput, energy consumption, residual energy and number of dead nodes through extensive simulations. Clustering with gateway outperforms both clustering without gateway and no clustering by 90% and 100%, respectively in terms of throughput. Similarly an improvement of 50% and 100% is observed in terms of energy consumption over the other two schemes. Clustering with a provision of gateway nodes is an energy efficient mechanism for D2D communication since it increases the overall network lifetime.

A Study on Beamforming for Coverage of Emergency Areas from UAVs

In this paper we present a study about the use of beamforming (BF) for devices discovery from flying platforms as the Unmanned Aerial Vehicles (UAVs). This type of application is meant to be exploited in emergency scenarios characterized by the absence of a network infrastructure; the purpose is to search and identify the devices (and consequently the persons) involved in a critical scenario in a limited area without the possibility of connecting to a mobile network. The use of an antenna array from the UAV is supposed to increase the sensitivity towards devices with weak signals and/or difficult propagation conditions. Our preliminary results indicate the effectiveness of a scanning method based on BF techniques for discovering and detecting User Equipment (UEs) on the ground. The results provide an insight on the capability level of BF solutions in these conditions w.r.t. to the size of the area to be covered.

Impact of Power Allocation on Device-to-Device Discovery Processes

Device to device (D2D) communications is one of the key-technologies for advanced releases of LTE and 5G. The centralized base station (or eNodeB), which controls everything in traditional mobile networks, cannot be the only solution when the number of mobile users and devices increase, causing service outages, low spectral efficiency, and high latency. An important technology that can help to solve some of the issues related to traffic overhead and to fulfill the requirements of 5G is D2D communication. In this paper, we focus on D2D communication in a 5G decentralized emergency scenario, where decentralized means that there is no communication or control from the eNodeB. One of the main issues in D2D communications, is the efficiency of the discovery process between couples of devices able to interconnect directly, especially in the decentralized case. In this context, we explore and compare the impact of different power allocation strategies with increasing numbers of D2D devices and different system parameters.

A Machine Learning Approach to Achieving Energy Efficiency in Relay-Assisted LTE-A Downlink System

In recent years, Energy Efficiency (EE) has become a critical design metric for cellular systems. In order to achieve EE, a fine balance between throughput and fairness must also be ensured. To this end, in this paper we have presented various resource block (RB) allocation schemes in relay-assisted Long Term Evolution-Advanced (LTE-A) networks. Driven by equal power and Bisection-based Power Allocation (BOPA) algorithm, the Maximum Throughput (MT) and an alternating MT and proportional fairness (PF)-based SAMM (abbreviated with Authors’ names) RB allocation scheme is presented for a single relay. In the case of multiple relays, the dependency of RB and power allocation on relay deployment and users’ association is first addressed through a k-mean clustering approach. Secondly, to reduce the computational cost of RB and power allocation, a two-step neural network (NN) process (SAMM NN) is presented that uses SAMM-based unsupervised learning for RB allocation and BOPA-based supervised learning for power allocation. The results for all the schemes are compared in terms of EE and user throughput. For a single relay, SAMM BOPA offers the best EE, whereas SAMM equal power provides the best fairness. In the case of multiple relays, the results indicate SAMM NN achieves better EE compared to SAMM equal power and BOPA, and it also achieves better throughput fairness compared to MT equal power and MT BOPA.

Cell Coverage Analysis of a Low Altitude Aerial Base Station in Wind Perturbations

The use of Unmanned Aerial Vehicles (UAVs) as Aerial Base Station (ABSs) is emerging as an effective technique to provide high capacity wireless networks to ground users. In this paper, cell coverage of a low altitude UAV is investigated for supporting such networks. An analytical framework for cell coverage area of an ABS is provided for Suburban, Urban and Urban high rise environments using a solid angle approach including radio link propagation effects in air-to-ground channel obtained from ray tracing simulations. Here, we account for the change in Euler angles such as roll, pitch and yaw due to perturbations by wind gusts or intentional maneuvers which leads to an increase in the geometrical coverage area by approximately 40-50 %, given same transmission power and antenna gain of the ABS.

Device-to-Device Discovery and Localization Assisted by UAVs in Pervasive Public Safety Networks

Device-to-device (D2D) can be a key paradigm to design Pervasive Public safety Networks (PPNs) which could allow the User equipments (UEs) to communicate directly in disaster scenarios. Recently, the use of Unmanned Aerial Vehicles (UAVs) has been suggested in PSNs to enhance situational awareness and disseminate critical information to the deployed Base Station (BS) by providing reliable connectivity. In this paper, we are interested in direct discovery, one of the functions provided by Proximity Services (ProSe). We consider a disaster situation when no core network is available and transmit the discovery message over UAV-to-UE link. Simulation results are presented and discussed based on the root-MUSIC algorithm to locate the affected UE assisted by UAV, achieving a meter accuracy at over 200 m. Furthermore, we analyse the performance of the link by calculating Packet Error Ratio (PER) and throughput, achieving up to 11 Mbps.

Channel Characterization at 2.4 GHz for Aerial Base Station

The paradigm shift towards high data rate demands of mobile users in IMT-2020 commonly known as 5G, led to the possibility of using Aerial Base Stations (ABS) to fulfill such requirements. However, for implementation of ABS, an appropriate air-to-ground channel model is needed. It is an important factor to incorporate the understanding of the channel fading behavior before designing the system. In this article, we present novel channel propagation results obtained from ray tracing simulations for different environments, such as Suburban, Urban and Urban-High-Rise, according to ITU Radio-communication parameters. The details of different channel characteristics such as Spatial Correlation and Cumulative Distribution Function for Small Scale Parameters as Delay Spread and Angle-of-Arrival are presented for different ABS heights. We also focus on various channel modeling approaches and frameworks for 3D channel models.