ABSTRACT With the increasing demand for high‐quality, computing‐intensive mobile services, the next‐generation networks must provide users with instantly available and sufficient computational resources. As a promising solution to this challenge, Unmanned Aerial Vehicle‐assisted Mobile Edge Computing (UAV‐assisted MEC) has gained significant attention in recent years. However, due to the limited energy available to the user equipment and UAVs, minimising the energy consumption remains a significant challenge. This article tackles the problem of energy‐efficient joint resource management and UAV trajectory optimisation in such networks by incorporating the Non‐Orthogonal Multiple Access (NOMA). To solve this non‐convex optimisation problem, it is divided into two sub‐problems, and the optimal solution to the main problem is then obtained by iteratively solving these two sub‐problems. According to the simulation results, incorporating Non‐Orthogonal Multiple Access (NOMA) method achieves a significant reduction of 44.44% in the overall utility function of the optimisation problem.

Abstract The Network Function Virtualization (NFV) technology has emerged to turn hardware‐based network functions into software‐based virtual entities. In NFV‐enabled multicast services, the data flow should be passed through a series of Virtual Network Functions (VNFs) before reaching destinations. In the problem of NFV‐enabled multicast service embedding, not only the VNFs must be deployed, but also the traffic forwarding topology must be constructed. As the exact solution of this problem suffers from high computational complexity, the heuristic or approximation approaches are used in practice. However, as a way to control the trade off between complexity and efficiency, using multistage solutions is offered in literature. In this article, some two‐stage approaches for embedding NFV‐enabled multicast services are introduced, and they are compared through analysis and simulation. Specifically, a new low‐complexity approach based on the Auxiliary VNF Graph (AVG) is proposed to determine the sub‐optimal locations for VNF placement and to construct the NFV‐based multicast routing tree. The comparative analysis shows that the proposal has acceptable performance while reducing the computational complexity significantly.

Summary Service function chaining (SFC) creates a requested orchestration for composition and placement of several service functions. One of the most critical issues for the service function providers is achieving the highest quality of service. We consider the problem of optimal Service Function composition and placement in an SFC‐enabled network with the aim of achieving maximum Quality of Service (QoS). To this end, a method of merging the composition, routing, and placement phases is introduced. A genetic algorithm is designed to find the service chain instance with the maximum QoS using grammar‐based Genetic Algorithm (GA) operators. This grammar is provided to filter invalid service compositions based on the data center, security, and mobile scenarios defined by Internet engineering task force. A chromosome in the proposed genetic algorithm corresponds to a service chain instance that matches the proposed grammar. For routing, strongly connected components (SCCs) are extracted to reduce search time in the network. Consequently, the search space is reduced for requests whose start and end nodes are placed in a SCC. Besides, the Floyd Warshall algorithm is used for routing between consecutive services. According to the results, the proposed method outperformed other methods in terms of the number of responded requests, runtime, and cost.

Transmit antenna selection (TAS) at base station has been widely studied and employed in many communication networks (such as those using massive multiple-input multiple-output (MIMO) systems). Thanks to the small size of microstrip antenna elements applicable to millimeter-wave (mmWave) frequencies, the implementation of TAS in small user equipments (UEs) employing switchable directional antennas is recently becoming popular. In this paper, we consider device-to-device (D2D) communications underlaying a cellular network wherein each D2D UE is equipped with switchable transmit antennas. By employing Generalized Bender’s Decomposition (GBD) algorithm, we obtained the solution to the problem of globally optimal transmit antenna selection and channel allocation to D2D UEs together with transmit powers of cellular and D2D UEs. Although we reformulated the non-convex primal subproblem of the proposed GBD-based method into convex form, we further managed to obtain the corresponding closed-form solution through analytical manipulations; this extensively increased (at least 60 times) the execution speed of the proposed method as shown in the simulation results.

Network Function Virtualization is a key technology that enables network operators to provide diverse communication services flexibly over a common infrastructure, resulting in a significantly reduced cost. This paper addresses the problem of optimal network function virtualization for providing multicast services in wireless mesh networks with minimal total cost. This problem is modeled in two different ways: Link-based Model (LBM) and Path-based Model (PBM). In both models, we formulate the problem as an integer linear program to find the best hosts for virtual network functions and to steer traffic across them by considering wireless interference and resource budgets. Furthermore, we propose a heuristic solution based on the decomposition of the problem into two smaller sub-problems that can be solved sequentially in two phases. In the first phase, a multicast tree for forwarding traffic is constructed, while in the second phase, the required network functions are instantiated in appropriately chosen nodes. Simulation results are presented to compare the performance and complexity of the exact solutions of link-based and path-based approaches and the proposed heuristic approach. These results demonstrate the effectiveness of the proposed path-based model and the heuristic algorithm.

The use of roadside units (RSUs) in vehicular networks can increase the efficiency of the network by providing hot spots for data dissemination between vehicles and an infrastructure-based networking integrating specialized systems. To maximize the effectiveness of the network, the locations where roadside units are deployed require special attention. In this work, we introduce a novel strategy for the deployment of roadside units in vehicular networks to maximize the number of vehicle-to-infrastructure contact opportunities. By using this strategy, we intend to provide a larger time window, increasing the vehicular communication and the QoS provided by the underlying network. As baselines, we consider the FPF and KP strategies. FPF projects the flow of vehicles in a Markovian approach, while KP is a heuristic for the 0-1 Knapsack Problem and does not consider any mobility information. Simulation results based on a realistic scenario demonstrate that the proposed method can find locations of roadside units more efficiently in comparison to FPF and KP methods in terms of the average number of satisfied vehicles (5.31% more than FPF and 35.64% more than KP).