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The heterogeneous fog radio access networks (Fog-RAN), the integration of fog computing, and traditional heterogeneous radio access networks can be implemented through the next-generation wireless communication networks. However, most of the solutions are limited to the spectrum efficiency optimization, and cross-tier interference existing in the fog access points (F-APs) could affect the network performance seriously. In this paper, the user association, resource allocation (including bandwidth and power), and caching deployment are investigated in the heterogeneous Fog-RAN to consider energy efficiency and cross-tier interference mitigation. Specifically, the user association, resource allocation, and caching strategy are formulated as a non-convex optimization problem and then transformed into a convex problem, which can be solved by a proposed algorithm based on the concept of the alternating direction method of multipliers (ADMM). Then an ADMM-based algorithm is proposed to enhance the energy efficiency of the Fog-RAN. Compared with the current solutions, simulation results illustrate the proposed algorithm’s convergence and effectiveness. [ABSTRACT FROM AUTHOR]

In the past several years, unmanned aerial vehicle (UAV) have been employed to provide enhanced coverage or relay service to mobile users in a scenario with limited or even no infrastructure since they can be deployed to almost everywhere and can be manipulated at anytime. This paper studies UAV as aerial base station (BS) enabled wireless communication system, where a UAV is dispatched to provide wireless communication service to a set of ground users with difference quality-of-experience (QoE) requirements. In real world, user requirements are randomly and unevenly distributed. In addition, UAV communication coverage and on-board energy are limited and system resources are also limited (e.g., transmission power, spectrum). In order to meet the QoE of all users with limited system resources and limited UAV energy, we jointly optimize user communication scheduling, UAV trajectory, transmit power and bandwidth allocation to maximize energy-efficiency and satisfy user QoE requirement. The formulated problem is mixes integer non-convex and non-concave so it is difficult to solve. In this paper, we solvevv the problem with two steps as follows. Firstly, we transform the objective function into a tractable form. Secondly, we divide the optimal problem into four sub-optimal problems, and then use a powerful iterative algorithm with the Dinkelbach and block coordinate descent to solve the optimal problem. That is to say, the user scheduling, UAV trajectory, transmission power and bandwidth allocation are alternately optimized in each iteration. Extensive simulation results present that our proposed method can obtain higher energy efficiency than that of baselines. Specifically, the energy efficiency obtained by our proposed method is 12.5% higher than the approach that only maximizes throughput. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) and Terahertz (THz) technology are envisioned to play paramount roles in next-generation wireless communications. In this paper, we present a novel secure UAV-assisted mobile relaying system operating at THz bands for data acquisition from multiple ground user equipments (UEs) towards a destination. We assume that the UAV-mounted relay may act, besides providing relaying services, as a potential eavesdropper called the untrusted UAV-relay (UUR). To safeguard end-to-end communications, we present a secure two-phase transmission strategy with cooperative jamming. Then, we devise an optimization framework in terms of a new measure $-$ secrecy energy efficiency (SEE), defined as the ratio of achievable average secrecy rate to average system power consumption, which enables us to obtain the best possible security level while taking UUR’s inherent flight power limitation into account. For the sake of quality of service fairness amongst all the UEs, we aim to maximize the minimum SEE (MSEE) performance via the joint design of key system parameters, including UUR’s trajectory and velocity, communication scheduling, and network power allocation. Since the formulated problem is a mixed-integer nonconvex optimization and computationally intractable, we decouple it into four subproblems and propose alternative algorithms to solve it efficiently via greedy/sequential block successive convex approximation and non-linear fractional programming techniques. Numerical results demonstrate significant MSEE performance improvement of our designs compared to other known benchmarks. [ABSTRACT FROM AUTHOR]

The energy efficiency of future networks is becoming a significant and urgent issue, calling for greener network designs. However, the increasing complexity in network structure and resource space lead to growing problem scales and coupled resource dimensions, which bring great challenges in obtaining a joint solution in optimizing the energy efficiency. In this paper, we develop a multi-dimensional network model on the basis of tuple-links associated with transmission patterns (TPs) and formulate the optimization problem as a TP based scheduling problem which jointly solves transmission scheduling, routing, power control, radio, and channel assignment. In order to tackle the complexity issues, we propose a novel algorithm by exploiting the delay column generation technique to decompose the coupled problem into recursively solving a master problem for scheduling and a sub-problem for power allocation. Further, we theoretically prove that the performance gap between the proposed algorithm and the optimum is upper bounded by that for the sub-problem solution, where the latter is derived by solving a relaxed version of the sub-problem. Numerical results demonstrate the effectiveness of the multi-dimensional framework and the benefit of the proposed joint optimization in improving network energy efficiency. [ABSTRACT FROM AUTHOR]

This paper addresses the resource allocation (RA) problem for orthogonal frequency division multiple access and single carrier frequency division multiple access based networks using hybrid automatic repeat request (HARQ) when considering energy efficiency (EE) as the metric to optimize. The RA is performed considering the Rayleigh channel, and assuming that only statistical channel state information is available, which fits well device-to-device (D2D) communications setup. Moreover, the use of practical modulation and coding schemes is taken into account. We formulate several RA problems with different EE criteria, subject to constraints on the minimum goodput and the maximum transmit power per-link. Based on a packet error probability approximation, we provide a framework which allows us to derive algorithms maximizing most of the common EE criteria, namely the sum of the EE of the links, the product of the EE, the EE of the link with the worst EE, and the EE of the whole network. The complexity of the proposed algorithms is also provided. The performance related to each criterion are evaluated through simulations and ranked according to the network configurations. We also illustrate on a practical example the real effectiveness of the EE criterion to achieve a better user experience than other common criteria such as the maximum sum rate and the minimum transmit power. [ABSTRACT FROM AUTHOR]

In the area of energy-efficient (EE) resource allocation, only limited work has been done on consideration of both transmitter and receiver energy consumption. In this paper, we propose a novel EE resource-allocation scheme for orthogonal frequency-division multiple-access (OFDMA) networks, where both transmitter energy consumption and receiver energy consumption are considered. In addition, different quality-of-service (QoS) requirements, including minimum-rate guarantee service and best effort service, are taken into account. The time slot, subcarrier (frequency), and power-allocation policies are jointly considered to optimize system EE. With all these considerations, the EE resource-allocation problem is formulated as a mixed combinatorial and nonconvex optimization problem, which is extremely difficult to solve. To reduce the computational complexity, we tackle this problem in three steps. First, for a given power allocation, we obtain the time–frequency resource unit (RU) allocation policy via binary quantum-behaved particle swarm optimization (BQPSO) algorithm. Second, under the assumption of known RU allocation, we transform the original optimization problem into an equivalent concave optimization problem and obtain the optimal power-allocation policy through the Lagrange dual approach. Third, an iteration algorithm is developed to obtain the joint time–frequency power–resource-allocation strategy. We validate the convergence and effectiveness of the proposed scheme by extensive simulations. [ABSTRACT FROM PUBLISHER]

This paper considers a multiuser simultaneous wireless information and power transfer system with a nonlinear energy harvesting model, in which a multiantenna base station estimates the downlink channel state information (CSI) via uplink pilots. Each single-antenna user is equipped with a power splitter. Three crucial issues on resource management for this system include: 1) power-efficient improvement, 2) user-fairness guarantee, and 3) nonideal channel reciprocity effect mitigation. Potentially, a resource allocation scheme to address jointly such issues can be devised by using the framework of multiobjective optimization. However, the resulting problem might be complex to solve since the three issues hold different characteristics. Therefore, we propose a novel method to design the resource allocation scheme. In particular, the principle of our method relies on structuralizing mathematically the issues into a cross-layer multilevel optimization problem. On this basis, we then devise solving algorithms and closed-form solutions. Moreover, to instantly adapt the CSI changes in practice while reducing computational burdens, we propose a closed-form suboptimal solution to tackle the problem. Finally, we provide numerical results to show the achievable performance gains using the optimal and suboptimal solutions, and then validate the proposed resource allocation scheme. [ABSTRACT FROM AUTHOR]

In this paper, a new approach to energy harvesting and data transmission optimization in a heterogeneous-based multi-class and multiple resource wireless transmission wireless sensor network system that focus on monitoring water and its quality is presented. Currently, energy is a scarce resource in wireless sensor networks due to the limited energy budget of batteries, which are typically employed for powering sensors. Once the available energy of a particular sensor node battery is depleted, such sensor node becomes inactive in a network. As a consequence, such node may not be able to participate in the transmission of the application signal in the uplink stage of the network, resulting in a lack of ability to communicate vital signals in a timely manner. Energy scarcity has been a long standing problem in wireless sensor network applications. To address this problem, energy harvesting from intended radiofrequency power source is considered in this work. However, wirelessly powered wireless sensor network systems are confronted by unfairness in resource allocation problem, as well as interference problem in multiple energy resource transmissions. These problems adversely impact the performance of the system in the context of the harvested energy by the sensor nodes, sensors information transmission rates, and the overall system throughput rate. These problems are tackled in this paper by formulating a sum-throughput maximization problem to reduce system energy consumption and enhance the system overall throughput rate. The throughput optimization problem is formulated as a non-convex function. Through the exploitation of the problem structure, it is converted to a convex function. The mathematical models of the optimization problem are validated through numerical simulations. The simulation results reveal that the proposed wireless powered sensor network system outperforms an existing wireless powered sensor network system, by comparison of the numerical simulations of this work to the numerical simulations of the existing WPSN system, regardless of the distances of the sensor nodes to the IPS and the base station. Also, the newly proposed method performs efficiently using parameters that include path-loss exponent impact, performance comparison of systems, convergence based on iteration, comparison based on unequal network distances to the BS, transmission power impact on the attainable throughput and on fraction of energy consumed on information transmissions, and influence of different number of nodes in the network classes. [ABSTRACT FROM AUTHOR]

In this paper, we study resource allocation for a multicarrier-based cognitive radio (CR) network. More specifically, we investigate the problem of secondary users’ energy-efficiency (EE) maximization problem under secondary total power and primary interference constraints. First, assuming cooperation among the secondary base stations (BSs), a centralized approach is considered to solve the EE optimization problem for the CR network where the primary and secondary users are using either orthogonal frequency-division multiplexing (OFDM) or filter bank based multicarrier (FBMC) modulations. We propose an alternating-based approach to solve the joint power-subcarrier allocation problem. More importantly, in the first place, subcarriers are allocated using a heuristic method for a given feasible power allocation. Then, we conservatively approximate the nonconvex power control problem and propose a joint successive convex approximation-Dinkelbach algorithm (SCADA) to efficiently obtain a solution to the nonconvex power control problem. The proposed algorithm is shown to converge to a solution that coincides with the stationary point of the original nonconvex power control subproblem. Moreover, we propose a dual decomposition-based decentralized version of the SCADA. Second, under the assumption of no cooperation among the secondary BSs, we propose a fully distributed power control algorithm from the perspective of game theory. The proposed algorithm is shown to converge to a Nash-equilibrium (NE) point. Moreover, we propose a sufficient condition that guarantees the uniqueness of the achieved NE. Extensive simulation analyses are further provided to highlight the advantages and demonstrate the efficiency of our proposed schemes. [ABSTRACT FROM AUTHOR]

In order to enable low-latency computation-intensive applications for mobile user equipments (UEs), computation offloading becomes critical necessary. We tackle the computation offloading problem in a mixed fog and cloud computing system, which is composed of an long term evolution-advanced (LTE-A) small-cell based fog node, a powerful cloud center, and a group of UEs. The optimization problem is formulated into a mixed-integer non-linear programming problem, and through a joint optimization of offloading decision making, computation resource allocation, resource block (RB) assignment, and power distribution, the maximum delay among all the UEs is minimized. Due to its mixed combinatory, we propose a low-complexity iterative suboptimal algorithm called BTFA based joint computation offloading and resource allocation algorithm (FAJORA) to solve it. In FAJORA, first, offloading decisions are obtained via binary tailored fireworks algorithm; then computation resources are allocated by bisection algorithm. Limited by the uplink LTE-A constraints, we allocate feasible RB patterns instead of RBs, and then distribute transmit power among the RBs of each pattern, where Lagrangian dual decomposition is adopted. Since one UE may be allocated with multiple feasible patterns, we propose a novel heuristic algorithm for each UE to extract the optimal pattern from its allocated patterns. Simulation results verify the convergence of the proposed iterative algorithms, and exhibit significant performance gains could be obtained compared with other algorithms. [ABSTRACT FROM AUTHOR]

We study the resource allocation and management issues related to heterogeneous wireless systems made up of several radio access technologies (RATs) that collectively provide a unified wireless network to a diverse set of users through co-ordination managed by a centralized global resource controller (GRC). We assume that the user devices are multimodal, which makes it possible for each device to use any available access point (AP)/base station (BS) of a RAT at any given time. Through detailed protocol level simulations performed in ns-2, we show an increase in spectral efficiency of up to 99 percent and an increase in short-term fairness of up to 28.5 percent for two greedy sort-based user device-to-AP/BS association algorithms implemented at the GRC compared to a distributed solution used in practice today where each user makes his/her own association decision. While the increase in overhead due to re-associations for a centralized solution grows only slightly (by up to 4.1 percent) compared to a distributed solution, we find the performance increase in spectral efficiency and short-term fairness attributes come at the cost of an order of magnitude increase (of up to 794 percent) in energy consumption. [ABSTRACT FROM PUBLISHER]