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The unprecedented growth of internet contents, specially social media, makes a challenge to the load of cellular networks. Latency is one of the most important metrics at end-users. To this end, we propose a resource allocation (RA) algorithm to design both caching and delivery policies with the aim of minimizing total latency of mobile users (MUs), where in the caching phase, the content placement is investigated and in the delivery phase, we allocate radio resources (i.e., transmit powers and subcarriers) in a multi-cell orthogonal frequency division multiple access (OFDMA)-based network communicating with a data center via backhaul links. In order to achieve an efficient caching policy, we propose an optimization problem to minimize the latency of MUs subject to maximum delivery deadline, maximum allowable transmit power of each base station (BS) and data center, and exclusive subcarrier assignment constraints. Hence, we devise an iterative algorithm to solve the main optimization problem and prove that the proposed approach converges to a near-optimal solution, when the number of iterations increases. Moreover, simulation results illustrate that devising the transmission-aware caching policy can significantly improve the performance, compared to the conventional proactive caching policies which are only based on the popularity of contents and the storage capacity of BSs. [ABSTRACT FROM AUTHOR]

In this paper, cooperative cognitive multiple access schemes with harvesting capability are considered for improving the energy efficiency and spectrum utilization. We consider three scenarios, namely, orthogonal multiple access (OMA), time variable-OMA (OMA- α) and power-domain non-orthogonal multiple access (PD-NOMA), and compare their performance. There exists a secondary network underlying the primary network helps as relay to improve the performance of primary network and gain the opportunity of transmission of own data. Our objective is to maximize the energy efficiency while a minimum transmission data rate for primary network, energy usage causality, and the battery overflow constraints should be satisfied. The proposed problems are highly non-convex, and therefor, we exploit Dinkelbach's Algorithm and successive convex approximation (SCA) method to overcome this bottleneck. The simulation results reveal that the performance of PD-NOMA scheme in some simulation is approximately up to 66% and 25% better than the performance of OMA- α and OMA, respectively, in the same environment. [ABSTRACT FROM AUTHOR]

Summary: The highly dynamic nature of the current network traffics makes the network managers to exploit the flexibility of the state‐of‐the‐art paradigm called SDN. In this way, there has been an increasing interest in hybrid networks of SDN‐MPLS. In this paper, a new traffic engineering architecture for SDN‐MPLS network is proposed. To this end, OpenFlow‐enabled switches are applied over the edge of the network to improve flow‐level management flexibility while MPLS routers are considered as the core of the network to make the scheme applicable for existing MPLS networks. The proposed scheme re‐assigns flows to the Label‐Switched Paths (LSPs) to highly utilize the network resources. In the cases that the flow‐level re‐routing is insufficient, the proposed scheme re‐computes and re‐creates the undergoing LSPs. To this end, we mathematically formulate two optimization problems, ie, i) flow re‐routing and ii) LSP re‐creation, and propose a heuristic algorithm to improve the performance of the scheme. Our experimental results show the efficiency of the proposed hybrid SDN‐MPLS architecture in traffic engineering superiors traditionally deployed MPLS networks. [ABSTRACT FROM AUTHOR]

In this paper, we consider unmanned aerial vehicles (UAVs) equipped with a visible light communication (VLC) access point and coordinated multipoint (CoMP) capability that allows users to connect to more than one UAV. UAVs can move in 3-dimensional (3D) at a constant acceleration, where a central server is responsible for synchronization and cooperation among UAVs. The effect of accelerated movement in UAV is necessary to be considered. Unlike most existing works, we examine the effects of variable speed on kinetics and radio resource allocations. For the proposed system model, we define two different time scales. In the frame, the acceleration of each UAV is specified, and in each slot, radio resources are allocated. Our goal is to formulate a multi-objective optimization problem where the total data rate is maximized, and the total communication power consumption is minimized simultaneously. To handle this multi-objective optimization, we first apply the scalarization method and then apply multi-agent deep deterministic policy gradient (MADDPG). We improve this solution method by adding two critic networks together with two-stage resource allocation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel resource management scheme that jointly allocates the transmit power and computational resources in a centralized radio access network architecture. The network comprises a set of computing nodes to which the requested tasks of different users are offloaded. The optimization problem minimizes the energy consumption of task offloading while takes the end-to-end-latency, i.e., the transmission, execution, and propagation latencies of each task, into account. We aim to allocate the transmit power and computational resources such that the maximum acceptable latency of each task is satisfied. Since the optimization problem is non-convex, we divide it into two sub-problems, one for transmit power allocation and another for task placement and computational resource allocation. Transmit power is allocated via the convex-concave procedure. In addition, a heuristic algorithm is proposed to jointly manage computational resources and task placement. We also propose a feasibility analysis that finds a feasible subset of tasks. Furthermore, a disjoint method that separately allocates the transmit power and the computational resources is proposed as the baseline of comparison. A lower bound on the optimal solution of the optimization problem is also derived based on exhaustive search over task placement decisions and utilizing Karush–Kuhn–Tucker conditions. Simulation results show that the joint method outperforms the disjoint method in terms of acceptance ratio. Simulations also show that the optimality gap of the joint method is less than 5%. [ABSTRACT FROM AUTHOR]