Unmanned Aerial Vehicle Base Stations Research Articles

Seamless connectivity with 5G enabled unmanned aerial vehicles base stations using machine programming approach

AbstractDeployment of a small unmanned aerial vehicle (UAV) mounted 5G base station is a promising solution for providing seamless network connectivity to users in a modern, data‐centric thrust areas. The key challenge is to find the location, the height and the optimum number of mounts. A machine programming based approach is proposed here for optimal placement of UAV‐mounted base station. The location of the deployment is determined using three clustering algorithms such as K‐means, K‐medoids, and fuzzy cluster means. Different sets of UAV‐mounted base stations have been deployed with variable user density at different heights. The impact on the network performance has been quantified through measurements of received power, signal to interference plus noise ratio (SINR), and path loss per active user equipment (UEs). To gain further insights, a scenario where UEs are connected only to the terrestrial base station, that is, the network is devoid of any sort of UAV mounted base station is evaluated. Numerical computations reaffirm that the proposed technique reduces the average path loss of the active UEs. Moreover, the use of height‐mounted base station also alleviates the issues arising due to low SINR values. The said technique shows immense potential in terms of seamless connectivity to end users in events of emergency and remote deployment scenarios, where ground‐based base station is not possible. The big transition of on‐ demand connectivity for 5G networks shall be benefitted from purpose‐built UAV infrastructure with specific locations or areas in mind.

On the Performance of a UAV-Aided Wireless Network Based on NB-IoT

In recent years, interest in Unmanned Aerial Vehicles (UAVs) as a means to provide wireless connectivity has substantially increased thanks to their easy, fast and flexible deployment. Among the several possible applications of UAV networks explored by the current literature, they can be efficiently employed to collect Internet-of-Things (IoT) data because the non-stringent latency and small-size traffic type is particularly suited for UAVs’ inherent characteristics. However, the implications coming from the implementation of existing technology in such kinds of nodes are not straightforward. In this article, we consider a Narrow Band IoT (NB-IoT) network served by a UAV base station. Because of the many configurations possible within the NB-IoT standard, such as the access structure and numerology, we thoroughly review the technical aspects that have to be implemented and may be affected by the proposed UAV-aided IoT network. For proper remarks, we investigate the network performance jointly in terms of the number of successful transmissions, access rate, latency, throughput and energy consumption. Then, we compare the obtained results on different and known trajectories in the research community and study the impact of varying UAV parameters such as speed and height. Moreover, the numerical assessment allows us to extend the discussion to the potential implications of this model in different scenarios. Thus, this article summarizes all the main aspects that must be considered in planning NB-IoT networks with UAVs.

Special Issue on Unmanned Aerial Vehicle (UAV)-Enabled Green Communications and Networking

Supporting explosive data traffic and massive connections is the most critical challenge of 5G and beyond networks. To overcome this challenge, an unmanned aerial vehicle (UAV) can be exploited as a promising communication platform in future wireless networks due to its inherent advantage of flexibility, mobility, hovering and low cost. In particular, the air-to-air and air-to-ground channels are much better than the ground-to-ground channels when a UAV is used as a base station. In many cases, the communication between a UAV base station and a user equipment can benefit from the line-of-sight (LoS) channel in between. Under such circumstances, mmWave transmission becomes practicable for UAV communications, which could provide much higher data rate.

Improving UAV base station energy efficiency for industrial IoT URLLC services by irregular repetition slotted-ALOHA

In the past few years, the continual improvement of 5G technology has supported powerful IoT (Internet of Things) and IIoT (Industrial IoT) devices which have been used to provide a wide range of services. One of the service types that has been noticed recently is URLLC (Ultra-Reliable Low-Latency Communication) service that requires highly reliable communication and low latency bounds. In this paper, intending to meet the latency and reliability requirements of IIoT users located outdoor in a hard-to-reach area, we propose to use a UAV-BS (Unmanned Aerial Vehicle Base Station) for air-to-ground (A2G) communications. Despite the very fact that UAV-BSs have been used widely, yet they have some shortcomings in flight time. So we proposed an energy-efficient trajectory design method to reduce the UAV-BS’s energy consumption for both the communication and mobility functions while fulfilling the application’s reliability and latency requirements. We proposed a UMC-IRSA (UAV-BS Multi-Channel Irregular Repetition Slotted-ALOHA) method to adapt the New Radio (NR) distinct frame structure for IIoT users. The IIoT nodes are clustered by the UMC-IRSA method (based on Mahalanobis distance) to decrease the UAV-BS energy consumption. The simulation results show that the UMC-IRSA clustering method combined with the Q-Learning algorithm for clusters serving decreases the UAV-BS energy consumption for flying in fixed altitude. The reduction in energy consumption provided by using the combination of UMC-IRSA and Q-Learning, in comparison to the combination of UMC-IRSA and Random Serving, combination of CRP (Chinese Restaurant Process) and Q-Learning, and CRP and Random Serving is 19%, 24%, and 31% respectively.

Latency Optimization of UAV-Enabled MEC System for Virtual Reality Applications Under Rician Fading Channels

This letter considers an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system, where UAV base stations (UBSs) are deployed to cache, process, and deliver virtual reality (VR) content from a cloud server to VR users (VRUs). Under Rician fading channel model, we optimize various resource allocation parameters, e.g., association of VRUs with UBSs, caching policy, computing-capacity allocation, and location of UBSs, with the objective of minimizing the maximum latency, subject to computing, caching, and power constraints at the UBSs. The problem is non-convex and solved by using alternating optimization and successive convex approximation techniques. Our simulation results clearly show the importance of the proposed joint optimization algorithm, in terms of performance improvement, over different benchmark schemes.

Efficient algorithms for decision making and coverage deployment of connected multi-low-altitude platforms

Unmanned aerial vehicles (UAVs) have gained significantly in popularity in recent years, and they are coupled with the emerging Internet-of-Things, 5G, and mobile edge computing. Their application domains will continue to expand. UAVs can deliver various IoT-driven applications (e.g., video surveillance UAVs-based applications) anytime and anywhere. Indeed, the rapid deployment of such applications in different environments (e.g., urban or wildlife surveillance/sensing environments) is one of the critical factors to support the emergence of new services and applications (e.g., 5G/B5G UAVs base station, civil and commercial IoT applications). However, several research challenges need to be addressed before the latter can be deployed in the real world. In this paper, we propose a novel approach that enables efficient coverage using a set of UAVs. We define two multi-objective sub-problems. The first sub-problem allows selecting a minimum number of appropriate UAVs for deployment as Low-Altitude-Platforms (LAP) over an area of interest from an extensive collection of options. We model this sub-problem as multi-criteria decision-making (MCDM) problem considering various UAV features and user constraints and solve it using a novel multi-criteria selection algorithm named SAGA. The second sub-problem is a coverage optimization problem that enables controlling UAVs’ hover locations and use gimbal-mounted rotating cameras or multi-lens cameras to increase the coverage accordingly. We propose a strategy aiming at optimizing hovering locations of UAVs and then rotating their cameras. The process comprises two techniques to solve the latter sub-problem: (1) an improved preference-guided genetic algorithm named NSPGGA; (2) a hybrid heuristic DCXGA built upon three algorithmic techniques: divide-and-conquer, greedy search, and exhaustive search. We carried out comparative analyses with seven MCDM methods and ten multi-objective evolutionary/swarm intelligence algorithms. The proposed algorithms outperformed the benchmarking techniques and showed remarkable results, e.g., the selection algorithm SAGA exhibits a high success rate, accuracy, and consistency. The preference-guided genetic algorithm NSPGGA achieves better efficiency, and it is four times faster than the famed NSGA-II. Finally, the hybrid heuristic DCXGA allows having more significant imaging coverages with few camera rotations. The aforementioned vital results are validated through diverse and intensive simulation scenarios.

DOA Estimation Based on Weighted l1-norm Sparse Representation for Low SNR Scenarios.

In this paper, a weighted l1-norm is proposed in a l1-norm-based singular value decomposition (L1-SVD) algorithm, which can suppress spurious peaks and improve accuracy of direction of arrival (DOA) estimation for the low signal-to-noise (SNR) scenarios. The weighted matrix is determined by optimizing the orthogonality of subspace, and the weighted l1-norm is used as the minimum objective function to increase the signal sparsity. Thereby, the weighted matrix makes the l1-norm approximate the original l0-norm. Simulated results of orthogonal frequency division multiplexing (OFDM) signal demonstrate that the proposed algorithm has s narrower main lobe and lower side lobe with the characteristics of fewer snapshots and low sensitivity of misestimated signals, which can improve the resolution and accuracy of DOA estimation. Specifically, the proposed method exhibits a better performance than other works for the low SNR scenarios. Outdoor experimental results of OFDM signals show that the proposed algorithm is superior to other methods with a narrower main lobe and lower side lobe, which can be used for DOA estimation of UAV and pseudo base station.

Deployment Optimization of UAV-Aided Networks Through a Dynamic Tunable Model

In the existing work of unmanned aerial vehicle base station (UAV-BS) deployment, where serving radius (SR) is fixed regardless of resource waste and overlapping interference, dynamic tunability of SR has not been considered. In this letter, a dynamic tunable model where SR can be properly adjusted is proposed, based on which a semi-progressive offloading deployment scheme devoted to UAV number and overlapping interference optimization is raised. Specifically, UAV is deployed around macro base stations (MBSs), where a certain proportion ( $\gamma $ ) of the ground terminals (GTs) are covered first with the principle of minimum overlap and maximum coverage, then through tunability of SR, the rest GTs are covered without increasing UAV number. Moreover, the base station expands its SR to cover the closest uncovered GT through fine adjustment of power (MBS) or antenna down-tilt (UAV), after which surrounding UAVs are also adjusted to reduce overlapping interference. The results show our proposed scheme reduces 17.26% UAV quantity compared with the cluster method and decreases 71.75% interference compared with the core-set method.

Array antenna equipped UAV‐BS for efficient low power WSN and its theoretical analysis

In a wireless sensor network consisting of a large number of sensor nodes (SNs), an efficient communication scheme is crucial to boost data collection efficiency. Furthermore, SN is required to communicate by the low transmission power to prolong its battery life. However, SNs located at the edge of the communication area may fall into the outage as the signal-to-noise power ratio fails to surpass a certain threshold. Moreover, signal collision due to the interferers may deteriorate the packet delivery rate. An unmanned aerial vehicle-base station (UAV-BS) is an attractive solution to lower outage probability and enhance communication efficiency. Here, outage probability and capturing failure probability performance of a rotational angle division multiple access (RADMA), which is realized by UAV-BS with an array antenna, are analytically evaluated. The numerical performance evaluation is conducted to elucidate the performance improvement of RADMA under low power wide-area network setup. Numerical simulation results show that RADMA can suppress the packet loss ratio 77%, shorten the transmission time 25%, and reduce the required transmission energy 20%.

Computation Efficiency Maximization and QoE-Provisioning in UAV-Enabled MEC Communication Systems

Since the traditional mobile edge computing (MEC) server is fixed at the edge of the network, it is likely to cause severe propagation loss between the edge server and users. However, ground users can be served when the MEC server is integrated into the unmanned aerial vehicle (UAV) base station (BS). Nevertheless, there are three main factors, including the limited system radio resources, the finite UAV energy and severe interference between the uplink and downlink, that make it challenging for the system to guarantee quality-of-experience of users in the downlink while ensuring the minimum amount of offloading data for users in the uplink. Therefore, this paper investigates computation-efficiency maximization. We jointly optimize computing scheduling, UAV 3D trajectory, bandwidth allocation and transmission power control to maximize the amount of offloaded data, minimize UAV energy consumption, and simultaneously guarantee the QoE of users in the downlink. Due to the nonconvex and nonconcave objective function and the coupling between variables, a multi-stage alternative optimization algorithm is proposed to solve the problem. The simulation results have demonstrated that the computation-efficiency obtained by the proposed scheme is higher than that of other benchmark schemes and meets the QoE demands of users under insufficient resources.

A 3-dimensional fast machine learning algorithm for mobile unmanned aerial vehicle base stations

<p>The 5G technology is predicted to achieve the unoptimized millimeter Wave (mmWave) of 30-300 GHz bands. This unoptimized band because of the loss of mm-Wave bands, like path attenuation and propagation losses. Nonetheless, because of: (i) directional transmission paving way for beamforming to recompense for the path attenuation, and (ii) sophisticated placement concreteness of the base stations (BS) is the best alternative for array wireless communications in mmWave bands (that is to say 100-150 m). The advance in technology and innovation of unmanned aerial vehicles (UAVs) necessitates many opportunities and uncertainties. UAVs are agile and can fly all complexities if the terrains making ground robots unsuitable. The UAV may be managed either independently through aboard computers or distant controlled of a flight attendant on pulverized wireless communication links in our case 5G. Although a fast algorithm solved the problematic aspect of beam selection for 2-dimensional scenarios. This paper presents 3-dimensional scenarios for UAV. We modeled beam selection with environmental responsiveness in millimeter Wave UAV to accomplish close optimum assessments on the regular period through learning from the available situation.</p>

Trajectory Design and Access Control for Air–Ground Coordinated Communications System With Multiagent Deep Reinforcement Learning

Unmanned-aerial-vehicle (UAV)-assisted communications has attracted increasing attention recently. This article investigates air–ground coordinated communications system, in which trajectories of air UAV base stations (UAV-BSs) and access control of ground users (GUs) are jointly optimized. We formulated this optimization problem as a mixed cooperative–competitive game, where each GU competes for the limited resources of UAV-BSs to maximize its own throughput by accessing a suitable UAV-BS, and UAV-BSs cooperate with each other and design their trajectories to maximize the defined <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fair throughput</i> to improve the total throughput and keep the GU fairness. Moreover, the action space of GUs is discrete, while that of UAV-BS is continuous. To tackle this hybrid action space issue, we transform the discrete actions into continuous action probabilities and propose a multiagent deep reinforcement learning (MADRL) approach, named air–ground probabilistic multiagent deep deterministic policy gradient (AG-PMADDPG). With well-designed rewards, AG-PMADDPG can coordinate two types of agents, UAV-BSs and GUs, to achieve their own objectives based on local observations. Simulation results demonstrate that AG-PMADDPG can outperform the benchmark algorithms in terms of throughput and fairness.

On-demand UAV base station deployment for wireless service of crowded tourism areas

With the support of wireless network, tourists in tourism areas could enjoy various tourism information search and smart tourism–related services. However, due to the limited capacity of wireless networks, in peaking seasons, tourism area crowding in local areas could result in emergency and temporary wireless network congestion. While increasing infrastructure investment (e.g., densifying base stations) is desiring for peak seasons, it can be a waste of resource for significantly shrunk tourist arrival in off seasons. In response to the temporary network congestion offloading demand, this paper proposes an on-demand coverage solution based on unmanned aerial vehicle (UAV) base stations. Firstly, taking the air-to-ground channel characteristic into account, we define the effective coverage radius, based on which the optimal altitude of UAV BS is derived. Then, to tackle the inherent challenge of irregular tourist distribution issue in tourism areas, an automatic UAV BS deployment algorithm is designed to determine the minimal number of UAV BSs and their two-dimensional coordinates simultaneously. Simulation results show that the proposed solution could realize efficient UAV BS on-demand deployment.

개별 노드의 위치 및 접속 가능성을 고려한 효율적 UAV 기지국 배치

무인기 기지국(UAV-BS) 형태로 무선 셀룰러 네트워크를 제공하는 연구가 최근 많이 진행되고 있다. 본 논문은 각 노드의 위치와 접속 확률을 고려하여 전력 효율적인 UAV-BS 배치 방식을 제안한다. 본 논문의 목표는 각 노드가 네트워크에 접속할 확률이 다르므로 UAV-BS에서 각 노드까지의 네트워크 트래픽이 다른 것을 고려하여 UAV-BS를 배치하여 UAV-BS의 평균 전송 전력을 최소화하는 것이다. UAV-BS는 사용자에게 신뢰할 수 있는 통신을 제공하기 위한 가시선(LOS) 연결를 보장할 수 있을 만큼 충분히 높이 비행해야 한다. 반면, 또 다른 측면으로 무인기의 송신 전력을 절약하기 위해 낮게 비행해야 한다. 따라서 본 논문에서는 이 두 가지 제약조건을 고려하면서 Weber 문제로 공식화하고 거리와 가중치를 함수로 해결하는 3단계 알고리즘을 제안한다. 시뮬레이션을 통해 본 논문의 제안 방식의 성능을 평가하며, 그 결과는 기존에 제안된 maximal coverage 알고리즘보다 낮은 평균 전송 전력을 소비한다는 것을 보여준다.

3-D Dynamic UAV Base Station Location Problem

We address a dynamic covering location problem of an unmanned aerial vehicle base station (UAV-BS), in which the location sequence of a single UAV-BS in a wireless communication network is determined to satisfy data demand arising from ground users. This problem is especially relevant in the context of smart grid and disaster relief. The vertical movement ability of the UAV-BS and nonconvex covering functions in wireless communication restrict utilizing classical planar covering location approaches. Therefore, we develop new formulations to this emerging problem for a finite time horizon to maximize the total coverage. In particular, we develop a mixed-integer nonlinear programming formulation that is nonconvex in nature and propose a Lagrangean decomposition algorithm (LDA) to solve this formulation. Because of the high complexity of the problem, the LDA is still unable to find good local solutions to large-scale problems. Therefore, we develop a continuum approximation (CA) model and show that CA would be a promising approach in terms of both computational time and solution accuracy. Our numerical study also shows that the CA model can be a remedy to build efficient initial solutions for exact solution algorithms. Summary of Contribution: This paper addresses a facet of mixed integer nonlinear programming formulations. Dynamic facility location problems (DFLPs) arise in a wide range of applications. However, classical DFLPs typically focus on the two-dimensional spaces. Emerging technologies in wireless communication and some other promising application areas, such as smart grids, have brought new location problems that cannot be solved with classical approaches. For practical reasons, many research attempts to solve this new problem, especially by researchers whose primary research area is not OR, have seemed far from analyzing the characteristics of the formulations. Rather, solution-oriented greedy heuristics have been proposed. This paper has two main objectives: (i) to close the gap between practical and theoretical sides of this new problem with the help of current knowledge that OR possesses to solve facility location problems and (ii) to support the findings with an exhaustive computational study to show how these findings can be applied to practice.

On the Physical Layer Security of the Cooperative Rate-Splitting-Aided Downlink in UAV Networks

Unmanned Aerial Vehicles (UAVs) have found compelling applications in intelligent logistics, search and rescue as well as in air-borne Base Station (BS). However, their communications are prone to both channel errors and eavesdropping. Hence, we investigate the max-min secrecy fairness of UAV-aided cellular networks, in which Cooperative Rate-Splitting (CRS) aided downlink transmissions are employed by each multi-antenna UAV Base Station (UAV-BS) to safeguard the downlink of a two-user Multi-Input Single-Output (MISO) system against an external multi-antenna Eavesdropper ( $Eve$ ). Realistically, only Imperfect Channel State Information (ICSI) is assumed to be available at the transmitter. Additionally, we consider a realistic total power constraint and guarantee the specific Quality of Service (QoS) requirements of the legitimate users. To handle the worst-case channel uncertainty of the legitimate users and an external $Eve$ , we conceive a robust secure resource allocation algorithm, which maximizes the minimum worst-case secrecy rate of the legitimate users. Based on the CRS principle, the transmitter splits and encodes the messages of legitimate users into common as well as private streams and the user having stronger CSI is asked to help the cell-edge user by opportunistically forwarding its decoded common message. In contrast to the existing schemes adopted in the literature for ensuring secure transmission of the first cooperative phase only, in our proposed solution the common message has a twin-fold mission. Explicitly, apart from serving as the desired message, it also acts as Artificial Noise (AN) for drowning out $Eve$ without consuming extra power. This is in stark contrast to the conventional AN designs. In the second phase, the pure AN is directed towards the $Eve$ , deploying a robust Maximum Ratio Transmitter (MRT) beamformer at the UAV-BS. To solve the resultant non-convex optimization problem we resort to the Sequential Parametric Convex Approximation (SPCA) method together with a bespoke initialization algorithm to avoid any failure due to infeasibility. Our simulation results confirm that the proposed secure transmission scheme outperforms the existing cooperative benchmarkers.

Blockchain-based Secure Data Transmission for UAV Swarm using Modified Particle Swarm Optimization Path Planning Algorithm

With the rapid development of unmanned aerial vehicles assisted applications enabled with a communication system, they are open to various malicious attacks. As a new form of flying things, they can access the network for better communication via the aerial base station. Most of the Unmanned aerial vehicles assisted flying objects' optimal path selection schemes does not consider the path deviation. In path deviation attacks, secure data transmission are not addressed in existing works. The secure communication process between Unmanned aerial vehicles and base station are exploited through security-based attacks. Moreover, path loss issue leads to multicast packet loss and unsecured broadcast. The existing network architecture setup does not fulfill the secure data communication and privacy issues. In this paper, Blockchain is utilized to investigate the secure communication between Unmanned aerial vehicles to Wireless Unmanned aerial vehicles Base stations. Since the destination information is dynamic under an uncertain environment, it will cause a delay in data communication. Unmanned aerial vehicles are more vulnerable to security attacks. The proposed blockchain-based architecture supports secure data communication in Unmanned aerial vehicles uncertain environments. To improve network security, this paper designs a modified particle swarm optimization method for better path selection. Through these experimental results, a blockchain-based data communication scheme is outer performed concerning network security.

3D Location and Resource Allocation Optimization for UAV-Enabled Emergency Networks Under Statistical QoS Constraint

Unmanned aerial vehicles (UAVs) as aerial base stations have attracted great attention in emergency communication networks due to flexible deployment. With the popularization of smart devices, the demand for multimedia services is increasing in disaster relief. Therefore, it is very important to significantly improve throughput of unmanned aerial vehicle base station (UAV-BS) while ensuring quality-of-service (QoS) of multimedia traffic. In this paper, we consider a UAV-BS to serve a group of users in the downlink who have different statistical delay-bound QoS requirements in an emergency situation. We formulate a problem to maximize the sum statistical-QoS-guaranteed throughput (effective capacity) of all users by jointly optimizing the UAV's 3D location, power and bandwidth allocation under each user's statistical QoS requirement constraint. The formulated problem is a non-linear non-convex optimization problem, which is very difficult to solve. To this end, we propose an efficient iteration algorithm based on the block coordinate descent and successive convex optimization techniques to solve it. Specifically, we decouple the primary problem into three sub-problems, which can be approximated as easy-to-solve convex optimization problems. In each iteration, three sub-problems are alternately optimized. Finally, numerical simulation results prove the effectiveness of our proposed algorithm compared with benchmarks.

3D Deployment of Unmanned Aerial Vehicle‐Base Station Assisting Ground‐Base Station

Unmanned aerial vehicles (UAVs), also named as drones, have become a modern model to provide a quick wireless communication infrastructure. They have been used when conventional base stations’ capacity is suffering in some extreme cases such as congestion inside the cell or a special event. This paper proposes an efficient three‐dimension (3D) placement of a single UAV‐assisted wireless network in such cases. Our proposed model assists the ground base station (GBS) using the UAV to serve arbitrary distributed users considering the impact of the obstacle blockage over the well‐known air‐to‐ground (A2G) path model. This work is aimed at optimizing the percentage of available bandwidth that must be provided to the UAV in order to maximize the number of served users. In addition, it finds the 3D placement of the UAV base station (UAVBS) that maximizes the number of served users, each with maximum quality‐of‐service (QoS). The exhaustive search and particle swarm optimization (PSO) algorithms are used to find the problem’s solution.

SkyCore

The advances in unmanned aerial vehicle (UAV) technology have empowered mobile operators to deploy LTE (long-term evolution) base stations (BSs) on UAVs and provide on-demand, adaptive connectivity to hotspot venues as well as emergency scenarios. However, today's evolved packet core (EPC) that orchestrates LTE's radio access network (RAN) faces fundamental limitations in catering to such a challenging, wireless, and mobile UAV environment, particularly in the presence of multiple BSs (UAVs). In this work, we argue for and propose an alternate, radical edge EPC design, called SkyCore that pushes the EPC functionality to the extreme edge of the core network---collapses the EPC into a single, lightweight, self-contained entity that is colocated with each of the UAV BS. SkyCore incorporates elements that are designed to address the unique challenges facing such a distributed design in the UAV environment, namely the resource constraints of UAV platforms, and the distributed management of pronounced UAV and UE mobility. We build and deploy a fully functional version of SkyCore on a two-UAV LTE network and showcase its (i) ability to interoperate with commercial LTE BSs as well as smartphones, (ii) support for both hotspot and stand-alone multi-UAV deployments, and (iii) superior control and data plane performance compared to other EPC variants in this environment.