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Summary: The next‐generation networks are expected to support diverse latency‐sensitive services, that is, auto‐driving, telemedicine, and so on. Deploying these services in current hardware‐based networks faces significant challenges. The emergence of network function virtualization (NFV) and software defined network (SDN) makes network deployment flexible and agile. In order to meet the strict latency constraints, joint VNF deployment and resource allocation problems are needed. In this paper, we consider the resource‐delay interpendency and formulate the joint VNF deployment and resource allocation problems as linear programming (LP) problems. Discrete spider monkey optimization (DSMO) algorithm is proposed to solve this problem, which imitates spider monkeys' fission and fusion social behavior during foraging. The two‐phase decision update mechanism greatly increases the diversity of the spider monkey decision population and promotes the algorithm to perform a global search of the solution space. Evaluation results show that the proposed solutions achieve superior performance in terms of latency, CPU utilization and service acceptance rate. [ABSTRACT FROM AUTHOR]

Summary: In heterogeneous networks (HetNets), sharply increasing data traffic exacerbates the rapid growth in the network energy consumption and at the same time brings tremendous pressure on the backhaul links. Aiming at improving the network energy efficiency (EE) and relieving backhaul data volume traffic pressure, in this paper, we investigate energy efficient resource allocation which restricts small cell data traffic by imposing different kinds of backhaul capacity constraints (BCCs) on small cells, including small cell BCC (SBCC) and macrocell BCC (MBCC). We formulate the joint resource block (RB) and power allocation problem as a fractional programming problem. By using variable relaxation and replacement, we convert this nonconvex optimization problem into a concave–convex fractional programming problem, which can further be converted into a parametric convex problem and solved by convex optimization theory. Based on the Dinkelbach and dual decomposition methods, we propose iterative resource allocation algorithms under different BCCs, where RB and power are jointly allocated among users in each cell. Numerical results reveal that the proposed algorithms have good convergence performance and also evaluates the impact of both SBCC and MBCC on network performance. [ABSTRACT FROM AUTHOR]

Summary: Beam space multiple‐input multiple‐output (B‐MIMO) systems operating at mmWave frequency in downlink uses different beam selection techniques for reducing the required number of radio frequency chains without apparent performance loss. In this paper, a joint beam selection and user association scheme with the objective of sum rate maximization is proposed. The proposed work on beam selection is based on a norm and uncorrrelation based approach, which considers channel correlation among the users so as to avoid inter‐user interference. Two different methods for beam user association with one modelled as a balanced assignment problem based on average sum assignment method and the other method that associates an optimal beam to every user by considering their channel gain values are also proposed along with beam selection. The proposed beam assignment methods are less complex and efficient in finding a suitable beam to every user within the cell. Simulation results show that the proposed method accomplish comparable performance in terms of sum rate and energy efficiency with appreciably low computational complexity relative to the existing methods in a correlated environment. [ABSTRACT FROM AUTHOR]

Summary: Network function virtualization (NFV) provides a simple and effective mean to deploy and manage network and telecommunications' services. A typical service can be expressed in the form of a virtual network function–forwarding graph (VNF‐FG). Allocating a VNF‐FG is equivalent to place VNFs and virtual links onto a given substrate network considering resources and quality‐of‐service (QoS) constraints. The deployment of VNF‐FGs in large‐scale networks, such that QoS measures and deployment cost are optimized, is an emerging challenge. Single‐objective VNF‐FGs allocation has been addressed in existing literature; however, there is still a lack of studies considering multiobjective VNF‐FGs allocation. In addition, it is not trivial to obtain optimal VNF‐FGs allocation due to its high computational complexity even in case of single‐objective VNF‐FGs allocation. Genetic algorithms (GAs) have been proved its ability in coping with multiobjective optimization problems; thus, we propose a GA‐based scheme to solve multiobjective VNF‐FGs allocation problem in this paper. The numerical results confirm that the proposed scheme can provide near Pareto‐optimal solutions within a short execution time. [ABSTRACT FROM AUTHOR]

Summary: Recently, the routing problem in vehicular ad hoc networks is one of the most vital research. Despite the variety of the proposed approaches and the development of communications technologies, the routing problem in VANET suffers from the high speed of vehicles and the repetitive failures in communications. In this paper, we adjusted the well‐known K‐medoids clustering algorithm to improve the network stability and to increase the lifetime of all established links. First, the number of clusters and the initial cluster heads will not be selected randomly as usual, but based on mathematical formula considering the environment size and the available transmission ranges. Then the assignment of nodes to clusters in both k‐medoids phases will be carried out according to several metrics including direction, relative speed, and proximity. To the best of our knowledge, our proposed model is the first that introduces the new metric named "node disconnection frequency." This metric prevents nodes with volatile and suspicious behavior to be elected as a new CH. This screening ensures that the new CH retains its property as long as possible and thus increases the network stability. Empirical results confirm that in addition to the convergence speed that characterizes our adjusted K‐medoids clustering algorithm (AKCA), the proposed model achieves more stability and robustness when compared with most recent approaches designed for the same objective. [ABSTRACT FROM AUTHOR]