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Unmanned aerial vehicles (UAVs) are considered as a promising method to provide service for Internet of Remote Things (IoRT). The incorporation of non-orthogonal multiple access (NOMA) into UAV-assisted networks provides a possibility for massive IoRT devices access. In this paper, we investigate the downlink transmission UAV-assisted wireless communication system based on NOMA in IoRT networks, where UAVs fly vertically to provide services for IoRT devices. In order to transmit as much data as possible, we aim to maximize the energy efficiency (EE) of the UAV by jointly optimizing NOMA user scheduling, UAV trajectory and power allocation. Due to the coupling between variables, the derived problem is mixed integer optimization and non-convex. To solve this challenging problem, we first decouple the variables and split the original problem into three subproblems, i.e., user scheduling, trajectory planning, and power allocation. Then, a double-loop iterative algorithm is proposed to solve the corresponding subproblems based on the Dinkelbach method and successive convex approximation (SCA) techniques. Finally, simulation results give some insights e.g., NOMA outperforms OMA in both spectral efficiency and energy efficiency and the proposed NOMA-based EE-oriented scheme is approximate three times energy efficient than benchmark schemes. [ABSTRACT FROM AUTHOR]

As mobile devices are widely used and various applications emerge, users have higher demands on data rates and computing power. Software Defined Network (SDN) can configure and manage various devices in the network through a centralized control controller, making the network more flexible. In an SDN-enabled edge computing environment, dense multiple access devices make mobile devices handover frequently, and mobile devices handover between different access points becomes an inevitable problem. To address this problem, we propose an Access Point (AP) handover strategy based on the signal strength and traffic load. The scheme uses the global view and centralized control capability of the SDN controller to obtain, manage, and analyze information, then calculate the weights and compare them, and finally develop the handover policy. On the other hand, to improve system resource utilization and meet the performance demands of different applications, MEC systems need to allocate computing and communication resources appropriately to keep users' Quality-of-Service (QoS) experience. We propose a joint optimization strategy for computing and communication resources based on the Lagrange multiplier method. The policy calculates and analyzes the task execution latency and energy consumption of edge servers and local terminals, and develops an optimization scheme for sub-channel allocation and resource allocation. It aims to reduce latency and energy consumption as much as possible. The results of the experiments in this paper illustrate that the proposed AP handover scheme which is on the basis of received signal strength indicator (RSSI) and traffic load can effectively improve the task completion time and energy consumption performance. The proposed joint optimization strategy of computing and communication resources based on the Lagrange multiplier method can effectively improve energy consumption and delay performance. [ABSTRACT FROM AUTHOR]

This paper discusses the challenges of mobile edge computing with low latency and energy consumption caused by the explosive growth of communication traffic and data generated by mobile devices. To address the issue of selecting edge servers, a multi-user-oriented edge server selection strategy is proposed. The strategy minimizes the weighted sum of network latency and total energy consumption and develops an improved SPEA2-based algorithm to choose the most suitable edge server. The resource allocation problem is also considered, and a resource allocation strategy is proposed that maximizes the energy efficiency ratio. A DQN-based resource allocation strategy is devised to find the best resource allocation strategy. Experimental results demonstrate that the proposed server selection strategy reduces system overhead in terms of energy consumption and latency while improving resource utilization. The resource allocation strategy improves the computational efficiency of edge servers while reducing latency and total energy consumption. [ABSTRACT FROM AUTHOR]

In very crowded areas, a large number of LTE users contained in a single cell will try to access services at the same time causing high load on the Base Station (BS). Some users may be blocked from getting their requested services due to this high load. Using a two-hop relay architecture can help in increasing the system capacity, increasing coverage area, decreasing energy consumption, and reducing the BS load. Clustering techniques can be used to configure the nodes in such two-layer topology. This paper proposes a new algorithm for relay selection based on the Basic Sequential Algorithmic Scheme (BSAS) along with power control protocol. Unlike other capacity improving techniques such as small cells and relay stations this approach does not require additional infrastructure. Instead, users themselves will act as a temporary relay stations. Modifications are implemented to the original BSAS to make it suitable for LTE environment and to improve its performance. The protocol for resource allocation and power control is implemented assuming a multi cell scenario. The algorithm is compared to other relaying and clustering schemes in addition to the conventional LTE. The simulation results show that the proposed algorithm has improved system capacity and energy consumption compared to other existing clustering/relaying schemes. [ABSTRACT FROM AUTHOR]

Energy loss due to the complete equipping and overstatement of infrastructure networks has recently prompted internet service providers to reduce energy consumption. Increasing energy costs and environmental awareness have recently raised concerns about grid power consumption and the underlying structure of services; therefore, one of the top priorities for service providers is to reduce energy consumption. In this paper, a method based on the differential evolution optimization algorithm is introduced to reduce the energy consumption costs, network load balancing, and execution time. To increase the convergence speed in the optimization algorithm, two Mamdani fuzzy inference systems have been used which are used in the optimization objective function to determine the fitness amount of each allocation. To better understand proposed algorithm behavior, the results of the differential evolution algorithm are compared with the AGA and MPGA methods. Simulation results show that the proposed algorithm achieves more than 30% performance improvement in comparison with the previous algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we devise a resource allocation scheme for secure communications in a device to device (D2D) communication scenario underlaying an existing orthogonal frequency division multiple access cellular network in which there are some eavesdroppers overhearing the information transmission of cellular network, i.e., from base station (BS) to cellular users. Given the uncertainty of perfect values of channel state information and channel distribution information (CDIs) for the transmitters such as BS and transmitters of D2Ds, the required knowledge about these parameters is feedbacked via some channels of limited capacity. The devised resource management scheme is written in an optimization problem whose solution is subcarrier allocations, transmit power allocations, and determination of the fading space boundaries and CDIs' boundaries, and are obtained using the particle swarm optimization method. We aim at maximizing the weighted sum of average rate of D2D pairs and average energy efficiency of them while the average secrecy rate of cellular network should be maintained above a predefined threshold. We consider the case where D2D pairs use only their batteries and the case where in addition to the battery, D2D pairs can harvest the energy and use it for the transmission purpose. Using simulations, we evaluate the proposed framework in different situations. [ABSTRACT FROM AUTHOR]

In this paper, we propose an energy-efficient resource allocation algorithm for multiple radio access technologies (multi-RAT) networks, where the user equipments (UEs) transmit data over multiple radio interfaces for exploiting the complementary advantages of different RATs. In this scenario, the resource allocation is formulated as a stochastic energy efficiency (EE) maximization problem. More specifically, the time average quality of service constraint is considered to provide the flexible resource allocation over the time-varying fading channels. The virtual queue is introduced for each UE to deal with the time average transmission requirement. By adopting Lyapunov optimization technique and fractional programming theory, the non-concave EE maximization is converted into a mixed integer nonlinear optimization (MINO) problem. After that, the continuity relaxation and Lagrange dual methods are used to find the solution of the MINO problem. Then, we develop an EE-based dynamic joint subcarrier and power allocation algorithm, which does not require any prior knowledge of the channel state information. In addition, the performance bounds of the EE and virtual queue are provided. Our simulation results show that the performance of the proposed algorithm is better than other general algorithms. [ABSTRACT FROM AUTHOR]

With the constant increase of throughput demands, maximizing the wireless network capacity has always been a crucial issue. Network densification becomes one of the adopted strategies to guarantee a high Quality of Experience (QoE). These new access points are included in the traditional 5G coverage scheme and offload the network in dense areas where the demand is high. A mobile may be located in the coverage area of several base stations which implies the need to extend traditional scheduling to a multicell context. This leads to the emergence of new issues concerning resource allocation strategies. When one user is suitable to receive resources from different cells, a well designed users' distribution is essential to limit unbalanced and overloaded cells. The main contribution of this paper is to propose a pre-scheduler called Multi-Cell Pre-Scheduler (MCPS) that adequately allocates user traffic between different access points, intervening before the scheduling application. Compatible with any type of simple cell existing schedulers, MCPS adapts schedulers to multi-cell context and improves their performance by delaying the system congestion and making the system more robust to unexpected traffic peaks. Performance evaluations show that the use of MCPS solution allows to increase the throughput capacity and energy efficiency while improving QoE fairness between users. [ABSTRACT FROM AUTHOR]

This paper studies the problem of joint allocation of subchannel, transmission power, and phase duration in the relay-enhanced bidirectional orthogonal frequency-division multiple access time division duplex systems. The challenges of this resource allocation problem arise from the complication of multiple-phase assignments within a subchannel since the relay station can provide an additional signal path from the base station to the user equipments (UEs). Existing research work does not fully consider all the influential factors to achieve feasible resource allocation for the relay-based networks. Since energy consumption is one of the principal issues, the energy minimization resource allocation (EMRA) schemes are proposed in this paper to design the allocation of subchannel, power, and phase duration for the UEs with the consideration of UE's quality-of-service (QoS) requirements. Both the four-phase and two-phase bidirectional relaying assignments and the network coding technique are adopted to obtain the suboptimal solutions for the proposed EMRA schemes. Different weights are designed for the UEs to achieve the minimization of weighted system energy for the relay-enhanced networks. Simulation results show that the proposed EMRA schemes can provide comparably better energy conservation and outage performance with QoS support. [ABSTRACT FROM AUTHOR]

Distributed satellite cluster network is a novel structure in spatial information network. This paper investigates the spectrum-power tradeoff algorithm for distributed satellite cluster (DSC), which enables the master satellite (MS) to offload data traffic to the slave satellite (SS). The SS provides power resource to the master satellite users (MSUs) with bandwidth as compensation. The purpose of the proposed algorithm is to maximize the energy efficiency (EE) of the SS while ensuring the QoS of the DSC. Considering data rate and bandwidth constraints of the MS, the SS jointly allocates bandwidth and power resources based on the served MSU set, meanwhile, data rate and power constraints of the SS should be also satisfied. We first prove that compensating bandwidth of a MSU can only be used by one slave satellite user. Then objective function is simplified to the equivalent subtractive form that can be solved by convex optimization method. Besides, we discuss the MSU selection conditions and employ them to the proposed spectrum-power tradeoff algorithm. The numerical results validate the performance improvement of the proposed algorithm and demonstrate the impact of power, bandwidth and data rate on the EE. [ABSTRACT FROM AUTHOR]

In this paper, the probability distributions of per user downlink data rate, spectral efficiency (SE) and energy efficiency (EE) are analytically derived for a heterogeneous network model with cell-edge located small cells. The high accuracies of analytically derived cumulative distribution functions (CDF) are verified using distributions obtained via simulations. CDF expressions are then used in order to optimize key performance indicators (KPI) which are selected here as 10th percentile downlink data rate ( R 10 ), spectral efficiency ( S E 10 ) and energy efficiency ( E E 10 ). In addition to optimizing KPIs separately, we also investigate the variation of the KPIs with respect to each other employing the analytically derived distributions. The results show that the resource allocation parameter values maximizing R 10 are very close to the values that maximize E E 10 . However, the values that are optimal for E E 10 and R 10 are not optimal for S E 10 , which demonstrates the EE and SE trade-off in HetNets. [ABSTRACT FROM AUTHOR]

In this paper, we devote to maximize the secrecy energy efficiency (SEE) for a cloud radio access network where exists an eavesdropper wiretapping information from remote radio head. We first establish the system model and then formulate the power optimization problem to maximize the SEE of the system subject to the fronthaul capacity and transmit power constraints. To address this nonconvex problem, we first convert the fractional objective function into a subtractive form and two algorithms are then proposed to solve it. Simulation results indicate that the proposed the iterative optimization algorithms can obtain higher SEE and present the merit of AN in enhancing the system performance when compared with conventional methods. [ABSTRACT FROM AUTHOR]

With increasing demand in multimedia applications and high data rate services, energy consumption of wireless devices has become a problem. At the user equipment side, high-level energy consumption brings much inconvenience, especially for mobile terminals that cannot connect an external charger, due to an exponentially increasing gap between the available and required battery capacity. Motivated by this, in this paper we consider uplink energy-efficient resource allocation in very large multi-user MIMO systems. Specifically, both the number of antenna arrays at BS and the transmit data rate at the user are adjusted to maximize the energy efficiency, in which the power consumption accounts for both transmit power and circuit power. We proposed two algorithms. Algorithm1, we demonstrate the existence of a unique globally optimal data rate and the number of antenna arrays by exploiting the properties of objective function, then we develop an iterative algorithm to obtain this optimal solution. Algorithm2, we transform the considered nonconvex optimization problem into a convex optimization problem by exploiting the properties of fractional programming, then we develop an efficient iterative resource allocation algorithm to obtain this optimal solution. Our simulation results did not only show that the the proposed two algorithms converge to the solution within a small number of iterations, but demonstrated also the performances of the proposed two algorithms are close to the optimum. Meanwhile, it also shows that with a given number iterations the performance of proposed algorithm1 is superior to proposed algorithm2 under small p. On the contrary, the performance of proposed algorithm2 is superior to proposed algorithm1 under large p. [ABSTRACT FROM AUTHOR]

In this paper, single- and multi- user Resource Allocation (RA) optimization problems considering transmit power and minimum rate constraint of Mobile Station (MS) for maximizing MS' energy efficiency, measured as bits-per-joule (bpj), are addressed. Assume channel state information of all MSs is known by base station. We propose uplink RA algorithms, performing subcarrier assignment and power allocation, for optimizing bpj of MS in a single-cell OFDMA-based cellular network for both single- and multi- user scenarios. In the single-user case, we propose RA algorithms, which utilize the closed-form solution derived by applying Lambert-W function and an iterative approach based on Karush-Kuhn-Tucker conditions respectively to achieve optimal bpj of MS. In the multi-user case, centralized iterative multi-user RA algorithms for maximizing sum of MS' bpj, performing joint subcarrier assignment and power allocation iteratively, are proposed by utilizing the proposed single-user RA schemes. In particular, tradeoffs between energy efficiency and spectral efficiency are fully investigated, and the influence of MS' power and minimum rate constraints on bpj performance is also studied. The effectiveness of proposed algorithms is presented by numerical experiments. Numerical results demonstrate the proposed algorithms can enhance bpj significantly with limited loss of total throughput compared to the sum-rate maximization algorithm (in Moretti et al., IEEE Trans Veh Technol 60(4):1788-1798, ). [ABSTRACT FROM AUTHOR]

Device to device (D2D) communication that provides high data rate proximity based direct communication between users, along with simultaneous wireless information and power transfer (SWIPT) that extracts energy from the received RF power, can achieve high energy and spectral efficiencies together with low latency communication. Heterogeneous networks, that employ efficient frequency reuse, provide high gains in the coverage and capacity of the cellular networks. SWIPT helps in converting the harmful interference, incurred by the frequency reuse of D2D tier underlaying the Hetnet, into energy that can be stored for the future. This work presents a resource allocation and power control scheme for SWIPT enabled underlay D2D networks, designed to improve the energy efficiency and the amount of energy harvested while ensuring the minimum required data rates for the users. Resource allocation is performed using a stable many-to-one matching game model inspired from Gale-Shapley algorithm. Two techniques of SWIPT, namely power splitting SWIPT and time splitting SWIPT, are considered and the resource allocation schemes are designed for both. Simulation results demonstrate the superior performance of the proposed algorithm compared to an existing work in single tier network with power splitting SWIPT. The work is extended to a typical 5G HetNet scenario, and extensive simulations are done to compare the performance of the two SWIPT architectures in the HetNet scenario. [ABSTRACT FROM AUTHOR]

Heterogeneous networks (HetNets) provide the demand for high data rates. In this study, we analyze the coexistence of femtocells and device-to-device (D2D) communication with macrocells. Interference management and decreasing energy consumption are two important issues in HetNets. To this end, we propose an efficient fractional frequency reuse (FFR)-based spectrum partitioning scheme to reduce the cross-tier interference. We also propose to use different optimization problems for resource allocation in different tiers. For this purpose, an energy efficient optimization problem is applied to D2D user equipment. Further, an optimization problem based on the spectral efficiency, i.e., throughput, is considered for macrocell and femtocell tiers. These problems are modeled as a non-cooperative game that results in low computational complexity. Iterative algorithms with fast convergence are used to solve the optimization problems. It is shown that applying different optimizations on different tiers leads to better performance than considering the same optimization for all tiers. The results indicate that the proposed FFR structure and optimization problems improve system performance. We also analyze the tradeoff between energy efficiency and spectral efficiency of the introduced structure. [ABSTRACT FROM AUTHOR]