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Summary: The solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation. [ABSTRACT FROM AUTHOR]

Optimization of the energy efficiency, fairness, and rates of the system is a vital part of communication systems. Multiple access techniques have a huge potential to enhance such performance parameters. This paper studies the performance of NOMA and OMA systems in a singular cell environment, where the cellular users are distributed randomly, and cooperative relays are considered for better system reliability. The relay nodes forward the signals to the cell-edge users. This paper considers a practical scenario where all the relay equipment is distributed with non-uniform battery power levels. The performance of OMA and NOMA schemes is compared based on the key performance indicators: sum rate, fairness, and energy efficiency. The fairness factor determines fairness in the allocation of resources to all the system's users. The performance of the two schemes is assessed in three deployment scenarios: urban, suburban, and rural scenarios. Through numerical results, it is proved that the performance of the NOMA dominates the OMA scheme. [ABSTRACT FROM AUTHOR]

Plane spiral orbital angular momentum (PSOAM) mode-groups (MGs) and multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) serve as two emerging techniques for achieving high spectral efficiency (SE) in the next-generation networks. In this paper, a PSOAM MGs based multi-user MIMO-NOMA system is studied, where the base station transmits data to users by utilizing the generated PSOAM beams. For such scenario, the interference between users in different PSOAM mode groups can be avoided, which leads to a significant performance enhancement. We aim to maximize the energy efficiency (EE) of the system subject to the constraints of the total transmission power and the minimum data rate. This designed optimization problem is non-convex owing to the interference among users, and hence is quite difficult to tackle directly. To solve this issue, we develop a dual layer resource allocation algorithm where the bisection method is exploited in the outer layer to obtain the optimal EE and a resource distributed iterative algorithm is exploited in the inner layer to optimize the transmit power. Besides, an alternative resource allocation algorithm with Deep Belief Networks (DBN) is proposed to cope with the requirement for low computational complexity. Simulation results verify the theoretical findings and demonstrate the proposed algorithms on the PSOAM MGs based MIMO-NOMA system can obtain a better performance comparing to the conventional MIMO-NOMA system in terms of EE. [ABSTRACT FROM AUTHOR]

Vehicular networks are envisioned to deliver data transmission services ubiquitously, especially in the upcoming autonomous driving era. Accordingly, the high data traffic load poses a heavy burden to the terrestrial network infrastructure. Unmanned Aerial Vehicles (UAVs) show enormous potential to assist vehicular networks in providing services. In a dense UAV-assisted vehicular network with a large number of users, spectrum sharing is leveraged for alleviating the spectrum scarcity. However, the increasing data traffic still leads to the UAV energy consumption problem. This paper considers a specific network scenario where a UAV transmits its cached content files to vehicular users over UAV-to-vehicle (U2V) links while vehicle-to-vehicle (V2V) links reuse the U2V spectrum for safety-critical message exchanges. To improve the UAV's energy efficiency while guaranteeing users’ quality of service (QoS), we jointly optimize content placement, spectrum allocation, co-channel link pairing, and power control, which are the key factors affecting energy efficiency and QoS. The joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is solved by combining Hungarian and DDQN. We perform system performance evaluations, demonstrating that our approach can not only improve the UAV's energy efficiency while satisfying the users’ QoS requirements but also increase the timeliness of making decisions. [ABSTRACT FROM AUTHOR]

This paper studies an intelligent reflecting surface (IRS)-assisted wireless-powered communication network (WPCN), where a hybrid access point (HAP) broadcasts energy signals to multiple devices for their energy harvesting in the downlink (DL) and then the devices use the harvested energy to transmit information signals to the HAP in the uplink (UL) with the help of an IRS. In particular, we propose three types of IRS beamforming configurations, namely fully dynamic IRS beamforming (FDBF), partially dynamic IRS beamforming (PDBF), and static IRS beamforming (SBF), to strike a balance between the system performance and signaling overhead as well as implementation complexity. Moreover, we adopt a practical non-linear energy harvesting (EH) model, and leverage a power-splitting (PS) EH receiver architecture with multiple rectifiers to avoid the input radio frequency power to get stuck into the saturation regime. We aim to minimize the transmit energy consumption at the HAP by jointly optimizing the DL/UL time allocation, the HAP/devices transmit power, the PS factor, and IRS phase shifts, subject to a set of minimum throughput requirements for individual devices. To address the resulting non-convex optimization problems, a successive convex approximation (SCA) based alternating optimization algorithm is proposed. Moreover, we study the case with the ideal linear EH model and two algorithms, namely SCA-based algorithm and semidefinite relaxation (SDR) algorithm, are proposed. Simulation results demonstrate the effectiveness of our proposed designs over various benchmark schemes and also unveil the importance of the joint design of IRS beamforming and PS rectifiers for achieving energy efficient WPCNs in practice. [ABSTRACT FROM AUTHOR]

Different from conventional reflecting-only metasurfaces, intelligent omni-surfaces (IOSs) are capable of reflecting and transmitting the received signals simultaneously. As such, users located at the both sides of IOSs can be served efficiently. In this paper, a novel IOS-enhanced aerial secure offloading system is proposed in the presence of multiple ground eavesdroppers. Specifically, the IOS is adopted to prevent information leakage, improve legitimate reception quality and expand the security deployment range of unmanned aerial vehicles (UAVs). To maximize the secrecy energy efficiency (SEE) of the considered system, a non-convex resource allocation problem is formulated by allocating computing frequency, determining offloading strategy, controlling transmit power, designing phase shifts, and deploying UAV locations. The formulated problem is non-trivial and challenging to be solved directly due to the highly coupled variables. To tackle this issue, the alternating optimization technique is invoked to decouple the original one into five subproblems, and then the computing and communication settings are alternatively optimized using a low complexity iterative algorithm. Finally, simulation results demonstrate that: i) In terms of the SEE performance, our IOS-enhanced aerial secure offloading system significantly outperforms the benchmark schemes; ii) Compared with reflecting-only surfaces, IOSs can take full advantage of the flexible deployment ability of UAVs, such that their secure offloading space can be considerably expanded. [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]

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]

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]

Edge infrastructures, including edge computing servers, are increasingly powered by renewable energy and smart grid combined. Two-way energy trading allows the surplus or shortfall of renewable energy to be traded between a server and the smart grid, but is non-trivial due to randomly varying computation demands and renewables. This paper proposes a new online policy, namely, distributed online resource allocation and load management (DORL), which enables such an edge server and its serving devices to minimize their energy cost and energy consumption, respectively, in a fully distributed manner. The key idea is that we employ the stochastic dual-subgradient method to interpret the battery of the server as a virtual queue. Based on the virtual queue and task queues, the CPU frequencies of the devices and the edge server, the offloading transmit rates of the devices (to the server) and the energy trading decisions of the server (with the smart grid) are decoupled over time and among devices, and optimized on an ongoing basis. Furthermore, we prove that the DORL yields a feasible and asymptotically optimal solution with a cost-backlog tradeoff of $[\eta, 1/\eta]$. Simulations show that the DORL reduces the system cost by nearly 50%, as compared to existing benchmarks. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication is an emerging paradigm that can improve system capacity and spectral efficiency by using cooperative communication coexisting with cellular networks. In spite of these advantages, D2D communication suffers from unfair resource usage, security risks posed by eavesdroppers, and limited energy storage. To deal with these issues, in this paper, we propose a resource allocation algorithm to maximize the security-aware energy efficiency (EE) for D2D users (DUs) in a simultaneous wireless information and power transfer (SWIPT)-enabled D2D communication system with $\alpha$ fairness, where multiple random eavesdroppers are present. In particular, we formulate a multi-objective resource allocation problem by jointly optimizing the transmit power, power-splitting (PS) factors of DUs, and the sub-channel allocation factor under multiple constraints, including the maximum interference power for each cellular user, the maximum transmit power of each DU, the PS factor, and the integer sub-channel assignment. To solve the non-convex problem, an iterative algorithm is developed to obtain the sub-optimal solution. Simulation results verify that the proposed algorithm outperforms benchmark algorithms in terms of balancing secrecy EE and fairness. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has been considered as a promising paradigm to support the growing popularity of mobile devices (MDs) with similar capabilities as cloud computing. Most existing research focuses on MEC enabled by terrestrial base stations (BSs), which is unable to work in certain scenarios, e.g., disaster rescue and field operation. Some researchers have been making efforts on studying MEC assisted by unmanned-aerial-vehicles (UAVs) and developed lots of efficient scheduling algorithms. However, MEC assisted only by UAVs has limited capability and is unsuitable for heavy-computation applications. To address the issue, this paper proposes a novel UAV-and-BS hybrid enabled MEC system, where multiple UAVs and one BS are deployed to facilitate the provisioning of MEC services either directly from UAVs or indirectly from the BS. Considering maximizing the lifetime of all MDs, the energy-efficient scheduling problem is formulated as minimizing the energy consumption of all MDs by jointly optimizing UAV trajectories, task associations, computing-and-transmitting resource allocations. The optimization problem is further decomposed into three sub-problems and solved by the proposed hybrid heuristic and learning based scheduling algorithms to reduce the complexity. Experimental results show that the proposed algorithm can achieve promising performance improvements over baseline algorithms, including local-computing, random-offloading and greedy-offloading. [ABSTRACT FROM AUTHOR]

New radio in unlicensed spectrum (NR-U) and network slicing have been emerged as two promising technologies to meet with the demand of enormous traffic volume, extreme transmission speed, and highly heterogeneous service requirements in future 5 G networks. Additionally, spurred by both economic considerations and environmental concerns, green communication becomes a new prominent figure of merit in the design of future 5 G networks. In this paper, we investigate an energy consumption scheduling for unlicensed heterogeneous networks, namely NR-U and WiFi, by leveraging the network slicing technology. First, we introduce a network slice broker, which enables virtual heterogeneous network operators to request and lease resources cooperatively via signaling means. Then, we investigate a joint network selection and resource allocation scheduling to minimize the network energy consumption in real time environment, taking into account various user data rate and delay requirements. An online Lyapunov optimization algorithm has been introduced to solve the proposed problem with low computational complexity and rapid convergence. We show that there exists a tradeoff between energy consumption and traffic delay, which can be adjusted through control parameters. Numerical results are presented to confirm our analyses and demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) is a promising architecture to reduce the energy consumption of mobile devices and provide satisfactory quality-of-service to time-sensitive services. How to jointly optimize task offloading and resource allocation to minimize the energy consumption subject to the latency requirement remains an open problem, which motivates this paper. When the latency constraint is taken into account, the optimization variables, including offloading ratio, transmission power, and subcarrier and computing resource allocation, are strongly coupled. To address this issue, we first decompose the original problem into three subproblems named as offloading ratio selection, transmission power optimization, and subcarrier and computing resource allocation. Then, we propose an iterative algorithm to deal with them in a sequence. To be specific, we derive the closed-form solution of offloading ratios, employ the equivalent parametric convex programming to obtain the optimal power allocation policy, and deal with subcarrier and computing resource allocation by the primal-dual method. Simulation results demonstrate that the proposed algorithm can save 20%–40% energy compared with the reference schemes, and can converge to local optimal solutions. [ABSTRACT FROM AUTHOR]

Delta-orthogonal multiple access (D-OMA) has been recently investigated as a potential technique to enhance the spectral efficiency in the sixth-generation (6G) networks. D-OMA enables partial overlapping of the adjacent sub-channels that are assigned to different clusters of users served by non-orthogonal multiple access (NOMA), at the expense of additional interference. In this paper, we analyze the performance of D-OMA in the uplink and develop a multi-objective optimization framework to maximize the uplink energy efficiency (EE) in a multi-access point (AP) network enabled by D-OMA. Specifically, we optimize the sub-channel and transmit power allocations of the users as well as the overlapping percentage of the spectrum between the adjacent sub-channels. The formulated problem is a mixed binary non-linear programming problem. Therefore, to address the challenge we first transform the problem into a single-objective problem using Tchebycheff method. Then, we apply the monotonic optimization (MO) to explore the hidden monotonicity of the objective function and constraints, and reformulate the problem into a standard MO in canonical form. The reformulated problem can be solved by applying the outer polyblock approximation method. Our numerical results show that D-OMA outperforms the conventional non-orthogonal multiple access (NOMA) and orthogonal frequency division multiple access (OFDMA) when the adjacent sub-channel overlap and scheduling are optimized jointly. [ABSTRACT FROM AUTHOR]

Terahertz (THz) frequency band has become one of the popular researches for the short-range broadband, high-rate data communication, reliability, and strong anti-interference ability, which can realize better performance for dense vehicular network scenario. In this paper, we introduce a cache based dense vehicular networks with THz frequency link and focus on the resource allocation optimization problem of subband and power allocation by maximizing energy efficiency (EE). In order to reduce the computational complexity, the original optimization problem is formulated as a mean-field game (MFG) problem, where each cache access point (cAP) can solve its local EE optimization problem by Dinkelbach-style method and Lagrangian dual method. Different access cells interact with MFG through mean subband power. Then a global MFG based EE optimization algorithm is proposed to associate the local strategy of each cell, which guarantee the globality of the optimized solutions. The numerical results suggest the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Terahertz (THz) band has attracted considerable interest recently due to its superior high frequency and large available bandwidth. THz could act a vital part in the sixth generation (6G) mobile communication networks. In this paper, we introduce the downlink non-orthogonal multiple access (NOMA) technology into THz band small cell networks, where the total performance is optimized considering the two key enabling technologies. In order to decrease the energy consumption triggered by increasing of wireless services, we pay great attention to energy efficiency (EE) optimization and resource allocation in the THz-NOMA downlink systems by solving the subchannel assignment and power optimization. We first exploit a channel model for the THz-NOMA downlink system by using the key features of THz-NOMA networks. Then we utilize Dinkelbach-style algorithm to solve the resource allocation problem and decompose it into two subproblems. A subchannel assignment algorithm and a power optimization based on alternative direction method of multipliers (ADMM) algorithm are developed to get the solution. Finally, to embody the strengths of THz-NOMA performance, we compare our proposed schemes against the conventional schemes. Simulation results yield substantially higher EE and further prove the availability of our proposed schemes. [ABSTRACT FROM AUTHOR]

Vehicular Fog Computing (VFC) provides solutions to relieves overload cloudlet nodes, reduces service latency during peak times, and saves energy for battery-powered cloudlet nodes by offloading user tasks to a vehicle (vehicular node) by exploiting the under-utilized computation resources of nearby vehicular node. However, the wide deployment of VFC still confronts several critical challenges: lack of energy-latency tradeoff and efficient resource allocation mechanisms. In this paper, we address the challenges and provide an Energy-efficient dynamic Computation Offloading and resources allocation Scheme (ECOS) to minimize energy consumption and service latency. We first formulate the ECOS problem as a joint energy and latency cost minimization problem while satisfying vehicular node mobility and end-to-end latency deadline constraints. We then propose an ECOS scheme with three phases. In the first phase, we propose an overload cloudlet node detection policy based on resource utilization. In the second phase, we propose a computational offloading selection policy to select a task from an overloaded cloudlet node for offloading, which minimizes offloading cost and the risk of overload. Next, we propose a heuristic approach to solve the resource allocation problem between the vehicular node and selected user tasks for energy-latency tradeoff. Extensive simulations have been conducted under realistic highway and synthetic scenarios to examine the ECOS scheme's performance. In comparison, our proposed scheme outperforms the existing schemes in terms of energy-saving, service latency, and joint energy-latency cost. [ABSTRACT FROM AUTHOR]

Backscatter radio is an intriguing technology for Internet of Things applications, which enables ultra-low-power devices to communicate with each other. Most of the existing works on the combination of the backscatter communications and the “harvest-then-transfer” (HTT) protocol focus on the throughput maximization, yet the study on the energy efficiency (EE) maximization is still lacking. Driven by this observation, this paper studies the EE maximization for an RF-powered backscatter network with the HTT protocol. Multiple hybrid devices (HDs) can either use the backscatter mode by backscattering energy signals from the power beacon (PB) to the reader or use the HTT protocol to first harvest energy from the PB, and then to transmit data to the reader. The proposed scheme aims to optimize multiple parameters including PB's transmit power and time, HDs’ transmit power and time allocation between the backscatter mode and the HTT mode for maximizing the EE of all the HDs. An iterative algorithm is developed to find the optimal solution for the proposed resource allocation scheme. Simulation results demonstrate the superiority of the proposed scheme in terms of EE of all the HDs. [ABSTRACT FROM AUTHOR]

Energy Harvesting (EH) technology enables Device-to-Device (D2D) User Equipments (DUEs) to harvest energy from ambient energy, making contributions to green communications and breaking the single battery-powered and the regional limitations of device deployment. The joint problem of energy harvesting and resource allocation in EH-based D2D Communication Networks (EH-DCNs) is a challenge issue. In this paper, we investigate the joint problem of resource allocation and EH time slot allocation of DUEs in EH-DCNs, where the DUEs harvest energy and multiplex Cellular User Equipments (CUEs) uplink resources. A channel assignment, power allocation and EH time slot allocation problem in EH-DCNs is formulated. The goal is to maximize energy- and spectral-efficiency with α-fairness while guaranteeing the EH constraints of DUEs and the quality of service of CUEs. The formulated problem is a non-convex mixed-integer multi-objective optimization problem. In order to solve the formulated problem, the multi-objective optimization problem is transformed into a single-objective optimization problem based on the weight sum method. We propose a joint iterative algorithm based on Lagrangian dual decomposition for α > 0 and α = 0, respectively. Numerical results illustrate that the proposed algorithm achieves higher energy efficiency and spectral efficiency for different network parameter settings. [ABSTRACT FROM AUTHOR]

In order to help user terminal devices (UTDs) efficiently handle computation-intensive and time-delay sensitive computing task, multi-access edge computing (MEC) has been proposed. However, due to the differences among the performance of UTDs, and the resource limitation of MEC servers, the joint optimization between the offloading decisions of UTDs and the allocation of resources in network is still a focus of the research. This paper studies the joint computation offloading and resource allocation strategy in multi-user and multi-server scenarios. Firstly, we formulate the joint optimization problem of computation offloading and resource allocation as a mixed integer nonlinear programming (MINP) problem to minimize the energy consumption of UTDs, by constraining the offloading decision, channel selection, power allocation and resource allocation. Secondly, we propose a two-stage heuristic optimization algorithm based on genetic algorithms, which divides the joint optimization problem of computation offloading and resource allocation in two stages. Based on the coupling relationship between the offloading decision and the resource allocation scheme, we iteratively update the solution of the problem, and finally obtain the stable convergence solution of the optimization problem. Finally, the proposed algorithm is compared with other classical methods to prove the effectiveness. [ABSTRACT FROM AUTHOR]

This paper aims at investigating the tradeoff between ergodic energy efficiency (EE) and spectral efficiency (SE) for device-to-device (D2D) communications underlaying cellular networks. Assuming average-based resource allocation, we propose a multiobjective optimization problem (MOOP) approach to deal with the EE and SE maximization problem. Specifically, the formulated MOOP results in the maximization of the total ergodic sum rate of the D2D users and the minimization of the total average transmit power of the D2D transmitters. Two practical scenarios are considered, i.e., a limited interference scenario, which includes the sparsely deployed scenario, and a densely deployed scenario, where for each scenario the maximum achievable EE and SE are derived. Specifically, considering the limited interference scenario, an upper bound is imposed on the received interference across different D2D communications, and closed-form expressions for the optimal power allocation, ergodic sum rate, and maximum EE are obtained. In the general, densely deployed scenario, however, the cross D2D network interference needs to be taken into account. We provide an optimal solution for the power control based on sequential fractional programming to tradeoff between complexity and performance gain. Simulation results unveil an interesting tradeoff to strike a balance between EE and SE. [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, we study the trajectory and resource allocation design for downlink energy-efficient secure unmanned aerial vehicle (UAV) communication systems, where an information UAV assisted by a multi-antenna jammer UAV serves multiple ground users in the existence of multiple ground eavesdroppers. The resource allocation strategy and the trajectory of the information UAV, and the jamming policy of the jammer UAV are jointly optimized for maximizing the system energy efficiency. The joint design is formulated as a non-convex optimization problem taking into account the quality of service (QoS) requirement, the security constraint, and the imperfect channel state information (CSI) of the eavesdroppers. The formulated problem is generally intractable. As a compromise approach, the problem is divided into two subproblems which facilitates the design of a low-complexity suboptimal algorithm based on alternating optimization approach. Simulation results illustrate that the proposed algorithm converges within a small number of iterations and demonstrate some interesting insights: (1) the introduction of a jammer UAV facilitates a highly flexible trajectory design of the information UAV which is critical to improving the system energy efficiency; (2) by exploiting the spatial degrees of freedom brought by the multi-antenna jammer UAV, our proposed design can focus the artificial noise on eavesdroppers offering a strong security mean to the system. [ABSTRACT FROM AUTHOR]

In this paper, we consider a downlink non-orthogonal multiple access (NOMA) enabled heterogeneous small cells network (HSCN), where the macro base station simultaneously communicates with multiple small cell base stations (SBSs) through wireless backhaul. In each small cell, users are grouped by NOMA bases and then served by their respective SBS. The proposed framework considers the realistic imperfect channel state information and quality of service requirements of users. The goal is to investigate an energy-efficient joint power, and bandwidth allocation scheme, which aims to maximize the energy efficiency (EE) of the small cells in downlink NOMA-HSCN constrained by the maximum transmit power and the minimum required data rate simultaneously. The optimization problem is non-convex due to the fractional objective function and non-convex constraint and thus challenging to obtain an exact solution efficiently. To this end, the joint optimization is first decomposed into two subproblems. Then, an iterative algorithm to solve the power optimization subproblem is proposed with guaranteed convergence. Furthermore, we derive a closed-form solution for the bandwidth allocation subproblem. Simulation results reveal that the effectiveness of the proposed schemes in terms of EE compared to the existing NOMA and the orthogonal multiple access schemes. [ABSTRACT FROM AUTHOR]

Exploiting the idle computation resources of mobile devices in mobile edge computing (MEC) system can achieve both channel diversity and computing diversity as mobile devices can offload their computation tasks to nearby mobile devices in addition to MEC server embedded access point (AP). In this paper, we propose a non-orthogonal multiple-access (NOMA)-aided cooperative computing scheme in a basic three-node MEC system consisting of a user, a helper, and an AP. In particular, we assume that the user can simultaneously offload data to the helper and the AP using NOMA, while the helper can locally compute data and offload data to the AP at the same time. We study two optimization problems, energy consumption minimization and offloading data maximization, by joint communication and computation resource allocation of the user and helper. We find the optimal solutions for the two non-convex problems by some proper mathematical methods. Simulation results are presented to demonstrate the effectiveness of the proposed schemes. Some useful insights are provided for practical designs. [ABSTRACT FROM AUTHOR]

With the advancement of the next generation of cellular systems, flexible mechanisms for Discontinuous Reception (DRX) are needed in order to save energy. 5G will bring heterogeneous packet sizes and traffic types, as well as an increasing need for energy efficiency. The current static DRX mechanism is inadequate to meet these needs. In this paper we exploit channel prediction to develop integer programming models. We aim to minimize the energy usage of user devices while streaming video, as well as to create extended sleep opportunities, while simultaneously preventing buffer underflows. We also develop an online algorithm to obtain an efficient solution robust to prediction errors. Our results show that using a variable DRX cycle length can reduce the energy usage by up to 60 percent and 40 percent, in the offline and online cases, respectively, compared with a static DRX configuration. Our proposed online algorithm can also reduce the number of buffer underflows by up to 97 percent compared to the offline case. Both our online and offline solutions can provide extended DRX opportunities, which is required in 5G scenarios. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has attracted both academic and industrial interest since it has been considered as one of the promising 5G technologies in order to increase connectivity and spectral efficiency. In this paper, we focus on a downlink multicarrier (MC) NOMA network, where a single base station serves a set of users through multiple subchannels. The goal is to jointly optimize energy efficiency (EE) and fairness among users with respect to the subcarrier and power allocation parameters. To achieve this with acceptable complexity, a novel greedy subcarrier assignment scheme based on the worst-user first principle is proposed. Due to the fractional form of the EE expression and the existence of interference, the power allocation problem is non-convex and NP-hard. To this end, we first transform this into an equivalent subtractive form, which is then solved by using fractional programming with sequential optimization of the inter/intra-subchannel power allocation vectors. Simulation results reveal the effectiveness of the proposed scheme in terms of EE and fairness among users compared to baseline schemes. Finally, the proposed algorithms are of fast convergence, low complexity, and insensitive to the initial values. [ABSTRACT FROM AUTHOR]

In this paper, we propose a distributed cluster formation (CF) and resource allocation (RA) framework for non-ideal non-orthogonal multiple access (NOMA) schemes in heterogeneous networks. The imperfection of the underlying NOMA scheme is due to the receiver sensitivity and interference residue from non-ideal successive interference cancellation (SIC), which is generally characterized by a fractional error factor (FEF). Our analytical findings first show that several factors have a significant impact on the achievable NOMA gain. Then, we investigate fundamental limits on NOMA cluster size as a function of FEF levels, cluster bandwidth, and quality of service (QoS) demands of user equipments (UEs). Thereafter, a clustering algorithm is developed by taking feasible cluster size and channel gain disparity of UEs into account. Finally, we develop a distributed $\alpha $ -fair RA framework where $\alpha $ governs the tradeoff between maximum throughput and proportional fairness objectives. Based on the derived closed-form optimal power levels, the proposed distributed solution iteratively updates bandwidths, clusters, and UEs’ transmission powers. Numerical results demonstrate that proposed solutions deliver a higher spectral and energy efficiency than traditionally adopted basic NOMA cluster size of two. We also show that an imperfect NOMA cannot always provide better performance than orthogonal multiple access under certain conditions. Finally, our numerical investigations reveal that NOMA gain is maximized under downlink/uplink decoupled (DUDe) UE association. [ABSTRACT FROM AUTHOR]

Based on a hierarchical cloud-fog computing-enabled paradigm, fog radio access networks (F-RANs) can provide abundant resource to support the future mobile artificial intelligent services. However, due to the differences of computation and communication capabilities at the cloud computing center, the fog computing based access points (F-APs), and the user devices, it is challenging to propose efficient computation offloading strategies to fully explore the potential of F-RANs. In this paper, we study the design of computation offloading in F-RANs to minimize the total cost with respect to the energy consumption and the offloading latency. In particular, a joint optimization problem is formulated to optimize the offloading decision, the computation and the radio resources allocation. To solve this non-linear and non-convex problem, an iterative algorithm is designed, which can be proved to converge a stationary optimal solution with polynomial computational complexity. Finally, the simulation results are provided to show the performance gains of our proposed joint optimization algorithm. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload on the base station and reduce the processing delay during the peak time. The computation tasks can be offloaded from the base station to vehicular fog nodes by leveraging the under-utilized computation resources of nearby vehicles. However, the wide-area deployment of VFC still confronts several critical challenges, such as the lack of efficient incentive and task assignment mechanisms. In this paper, we address the above challenges and provide a solution to minimize the network delay from a contract-matching integration perspective. First, we propose an efficient incentive mechanism based on contract theoretical modeling. The contract is tailored for the unique characteristic of each vehicle type to maximize the expected utility of the base station. Next, we transform the task assignment problem into a two-sided matching problem between vehicles and user equipment. The formulated problem is solved by a pricing-based stable matching algorithm, which iteratively carries out the “propose” and “price-rising” procedures to derive a stable matching based on the dynamically updated preference lists. Finally, numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme. [ABSTRACT FROM AUTHOR]

The proliferation of smart vehicular terminals (VTs) and their resource hungry applications impose serious challenges to the processing capabilities of VTs and the delivery of vehicular services. Mobile Edge Computing (MEC) offers a promising paradigm to solve this problem by offloading VT applications to proximal MEC servers, while TV white space (TVWS) bands can be used to supplement the bandwidth for computation offloading. In this paper, we consider a cognitive vehicular network that uses the TVWS band, and formulate a dual-side optimization problem, to minimize the cost of VTs and that of the MEC server at the same time. Specifically, the dual-side cost minimization is achieved by jointly optimizing the offloading decision and local CPU frequency on the VT side, and the radio resource allocation and server provisioning on the server side, while guaranteeing network stability. Based on Lyapunov optimization, we design an algorithm called DDORV to tackle the joint optimization problem, where only current system states, such as channel states and traffic arrivals, are needed. The closed-form solution to the VT-side problem is obtained easily by derivation and comparing two values. For MEC server side optimization, we first obtain server provisioning independently, and then devise a continuous relaxation and Lagrangian dual decomposition based iterative algorithm for joint radio resource and power allocation. Simulation results demonstrate that DDORV converges fast, can balance the cost-delay tradeoff flexibly, and can obtain more performance gains in cost reduction as compared with existing schemes. [ABSTRACT FROM AUTHOR]

Energy efficient resource allocation in heterogeneous cloud radio access networks (H-CRANs) is considered in this paper where a set of users within a specific region is served by femto-cell access points (FAPs) and remote radio heads (RRHs) connected to the base-band unit (BBU) pool through front-haul links. For orthogonal frequency division multiple access (OFDMA) based uplink H-CRANs, we formulate an energy efficient optimization problem with the novel utility function in order to jointly assign the access point (RRH/FAP), sub-carrier, transmit power, RRH, front-haul link and BBU. Due to highly complex relation between diverse optimization variables and their effects on each other, the formulated optimization problem is non-convex and NP-hard, suffering from high computational complexity. To tackle this issue, we develop an efficient two-step iterative algorithm with low computational complexity to solve the proposed problem based on the successive convex approximation (SCA) and complementary geometric programming (CGP). The simulation results reveal that our proposed approach can effectively offload the traffic from C-RAN to low-power femto-cells, and reduce the energy consumption cost by switching off the under-utilized BBUs. [ABSTRACT FROM AUTHOR]

In this paper, we address the downlink resource (subcarriers and power jointly) allocation problem for energy harvesting-based device-to-device communication in a railway carriage communication network to improve the energy efficiency (EE) of the system. The considered problem is formulated as maximizing the weighted EE and is solved by leveraging a game-theoretic learning approach. Specifically, we first propose a new performance metric for evaluating the EE and optimize its lower bound. However, there exists an intractable issue of mixing the integer nature into the feasible region. To this end, we decompose the optimization problem into two subproblems by fixing the subcarrier and power allocations alternately. These two subproblems are formulated as two exact potential games, and the optimal properties of their solutions are analyzed. Accordingly, we respectively design a virtual distributed learning algorithm for the power control to find the optimum solution, i.e., Nash equilibrium (NE) point, based on the derived conditions of the uniqueness of NE, which can effectively accelerate convergence, and a fully distributed Max-logit algorithm for the subcarrier allocations to obtain the best NE with an arbitrarily high probability in which only local information needs to be exchanged. Through the alternation of two algorithms and iterative operation, the optimal solution to the problem is achieved. Finally, numerical results verify the effectiveness of the proposed scheme. [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]

Computation intensive and delay-sensitive applications impose severe requirements on mobile devices of providing required computation capacity and ensuring latency. Mobile edge computing (MEC) is a promising technology that can alleviate computation limitation of mobile users and prolong their lifetime through computation offloading. However, computation offloading in an MEC environment faces severe issues due to dense deployment of MEC servers. Moreover, a mobile user has multiple mutually dependent tasks, which make offloading policy design even more challenging. To address the above-mentioned problems in this paper, we first propose a novel two-tier computation offloading framework in heterogeneous networks. Then, we formulate joint computation offloading and user association problem for multi-task mobile edge computing system to minimize overall energy consumption. To solve the optimization problem, we develop an efficient computation offloading algorithm by jointly optimizing user association and computation offloading where computation resource allocation and transmission power allocation are also considered. Numerical results illustrate fast convergence of the proposed algorithm, and demonstrate the superior performance of our proposed algorithm compared to state of the art solutions. [ABSTRACT FROM AUTHOR]

Caching content on the edge of a network can effectively localize traffic, reduce network latency, and improve network throughput. In this paper, we propose a posteriori caching mechanism rather than the mainstream apriority theory, in which the content placement strategy is determined based on the identical distribution of content popularity and user preference before the theoretical analysis of the placement gain is validated. We primarily investigate the optimal caching strategy subjected to the constraint of storage capacity in a heterogeneous network, where a macro base station (MBS), small base stations (SBSs), and user terminals (UTs) are integrated for proactive content storage in the physical layer. To maximize the local hit rate while reducing the transmission delay, we first analyze the optimal strategy by converting content placement into a 0–1 knapsack problem and address the optimization problem using the method of Lagrangian multipliers. We then determine the request probability for each user by exploiting the context information of the user's request history, user similarity, and social ties to achieve reasonably well-optimized caching performance. The caching policy is further optimized into a low-complexity heuristic algorithm with the knowledge of request probability and the optimal copy volumes. Finally, the simulation results show that the proposed cooperative caching algorithm improves the performance metric in terms of hit rate and transmission delay under different benchmarks. [ABSTRACT FROM AUTHOR]

In this paper, we present an efficient allocation of power and channel so that power-drained primary users can harvest energy from secondary systems through wireless power transfer, and in return, the secondary system can have full access to the licensed spectrum for a certain length of time. Considering partial channel state information between primary and secondary systems, we formulate an optimization problem consisting of probabilistic constraint. By introducing a confidence level and using support vector machine, we convert the probabilistic constraint to deterministic form. We also implement a particle swarm optimization technique to solve the resource allocation optimization problem, while guaranteeing the required data rate for both primary and secondary systems. The results verify that the proposed scheme enables a higher number of primary users to efficiently harvest energy, and it also increases total primary capacity. [ABSTRACT FROM AUTHOR]

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]

Cooperation between the fog and the cloud in mobile cloud computing environments could offer improved offloading services to smart mobile user equipment (UE) with computation intensive tasks. In this paper, we tackle the computation offloading problem in a mixed fog/cloud system by jointly optimizing the offloading decisions and the allocation of computation resource, transmit power, and radio bandwidth while guaranteeing user fairness and maximum tolerable delay. This optimization problem is formulated to minimize the maximal weighted cost of delay and energy consumption (EC) among all UEs, which is a mixed-integer non-linear programming problem. Due to the NP-hardness of the problem, we propose a low-complexity suboptimal algorithm to solve it, where the offloading decisions are obtained via semidefinite relaxation and randomization, and the resource allocation is obtained using fractional programming theory and Lagrangian dual decomposition. Simulation results are presented to verify the convergence performance of our proposed algorithms and their achieved fairness among UEs, and the performance gains in terms of delay, EC, and the number of beneficial UEs over existing algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a fair and energy-efficient resource allocation framework in spectrum-sharing wireless networks with quality-of-service guarantees. Consider the tradeoff between energy efficiency and spectral efficiency, the multiobjective problem of spectral efficiency and energy efficiency is transformed into a problem that minimizes the total power consumption and maximizes the achievable utility, subject to power constraints and rate outage probability constraints. We then analyze the complexity of the considered problem; particularly, the optimization problem is NP-Hard when $0 < \alpha < 1$ and $\alpha =0$ and is convex for other values of the fairness index $\alpha$. After that, we adopt the successive convex approximation (SCA) approach to approximate and transform the NP-hard nonconvex optimization problem into a sequence of convex programs and propose two iterative SCA-based resource allocation algorithms. Extensive simulation results are presented to demonstrate the effectiveness and outperformance of the proposed algorithms over existing frameworks. [ABSTRACT FROM PUBLISHER]

The recent paradigm of small cell dual-connectivity (DC) provides a promising solution to facilitate mobile users’ (MUs’) traffic offloading in heterogeneous networks. With DC, an MU can flexibly schedule its traffic to macrocell base station (mBS) and offload data to small-cell access point (sAP). However, a malicious node might intentionally eavesdrop the MU's offloaded data, which could lead to the secrecy exposure. In this paper, we investigate the optimal resource allocation for the MUs’ traffic offloading via DC with guaranteed secrecy. First, we study a single-MU single-sAP case and formulate a joint optimization of the MU's traffic scheduling, power allocation, and bandwidth usage for traffic offloading, which aims to minimize the MU's overall resource usage including the power consumption and bandwidth usage. Although the joint optimization problem is nonconvex, we propose an efficient algorithm to obtain the optimal offloading solution. Second, by using the single-MU's optimal offloading solution, we study the multi-MU multi-sAP case and formulate an optimal offloading-selection problem that aims to maximize the overall served MUs’ throughput with guaranteed secrecy, while taking into account the mBS's and sAPs’ limited bandwidths and the sAPs’ limited backhaul capacities. Despite the NP-hardness of the formulated offloading-selection problem, we propose an efficient heuristic algorithm to achieve the offloading-selection solution. Numerical results are provided to validate the performance gain of the proposed traffic offloading schemes with guaranteed secrecy. [ABSTRACT FROM PUBLISHER]

Tri-generation systems with the aim of recycling heat dissipation of equipment and importing the heat into the energy production cycle have been considered by researchers recently because of increasing energy efficiency and decreasing environmental pollution. Many studies have been done in the field of tri-generation systems, but the studies have been more focused on centralized energy sources, such as, steam and gas turbines. Thus, required researches to move the sources of tri-generation systems toward renewable energy resources are not perfect enough. Moreover, the type of operation strategy, which is one of the important issues in investigating tri-generation system, is usually depended on assistant resources, such as, local power grid. In this paper, a novel tri-generation system based on wind and solar renewable energy resources and natural gas as the system prime movers is evaluated. Furthermore, a different operation strategy is considered to minimize the need to auxiliary sources and so the ability to use the system in remote regions, independently. Hence, wind turbines, photovoltaic (PV) modules, and solid oxide fuel cells (SOFCs) are considered as prime movers of the system. Moreover, a battery bank and heat storage tanks (HSTs) are deployed to balance the fluctuations in produced energy by wind and solar renewable resources. Hence, thermal demand management (TDM) and electrical demand management (EDM) operation strategies are considered simultaneously and defined as two possible functions to achieve a system with the ability to operate independently. In order to obtain the optimized solutions for each system component, a genetic optimization algorithm is applied to solve a resources allocation problem. The possibility of implementation in distant regions, considerable reduction in fuel consumption and pollution are the advantages of the proposed system. The numerical results demonstrate that the fossil fuels consumption and the pollution, respectively, are reduced up to 154 and 207 times more than common separated production (SP) systems. Moreover, power grid and other auxiliary systems requirements are reduced to less than 1%. [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]

Energy-efficiency (EE) is critical for device-to-device (D2D) enabled cellular networks due to limited battery capacity and severe cochannel interference. In this paper, we address the EE optimization problem by adopting a stable matching approach. The NP-hard joint resource allocation problem is formulated as a one-to-one matching problem under two-sided preferences, which vary dynamically with channel states and interference levels. A game-theoretic approach is employed to analyze the interactions and correlations among user equipments (UEs), and an iterative power allocation algorithm is developed to establish mutual preferences based on nonlinear fractional programing. We then employ the Gale–Shapley algorithm to match D2D pairs with cellular UEs, which is proved to be stable and weak Pareto optimal. We provide a theoretical analysis and description for implementation details and algorithmic complexity. We also extend the algorithm to address scalability issues in large-scale networks by developing tie-breaking and preference-deletion-based matching rules. Simulation results validate the theoretical analysis and demonstrate that significant performance gains of average EE and matching satisfactions can be achieved by the proposed algorithm. [ABSTRACT FROM AUTHOR]

In heterogeneous wireless networks (HetNet), the multihomed user equipment (UE) utilizes multiple access options (AO) simultaneously and aggregate the offered resources from different AOs so as to support the same application, such as media streaming. Two important challenges of the resource optimization in this context are: 1) determining the connection results between UEs and AOs, and 2) determining the amount of radio resources that AOs should allocate to UEs. In this paper, we investigate the energy-efficient resource optimization for the HetNet with multihomed UEs. First, the energy-efficient resource optimization is formulated as an energy efficiency (EE) maximization problem. Second, the nonconcave objective function of EE maximization is converted by a fractional programming theory into a convex optimization problem. Considering that the connection results between UEs and AOs correspond to certain network topology configurations, the convex optimization problem is decomposed into a topology building problem and a resource allocation problem, which are proved as combination optimization and mixed integer nonlinear optimization, respectively. Then, the Markov approximation framework and continuity relaxation method are employed for solving the two problems, respectively. Finally, a joint topology building and resource allocation algorithm are proposed for optimizing the AOs radio resource energy efficiently. Simulation results validate the theoretical analysis of our proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, joint resource allocation and power control for energy-efficient device-to-device (D2D) communications underlaying cellular networks are investigated. The resource and power are optimized for maximization of the energy efficiency (EE) of D2D communications. Exploiting the properties of fractional programming, we transform the original nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form. Then, an efficient iterative resource allocation and power control scheme is proposed. In each iteration, part of the constraints of the EE optimization problem are removed by exploiting the penalty function approach. We further propose a novel two-layer approach, which allows finding the optimum at each iteration by decoupling the EE optimization problem of joint resource allocation and power control into two separate steps. In the first layer, the optimal power values are obtained by solving a series of maximization problems through root finding, with or without considering the loss of cellular users' rates. In the second layer, the formulated optimization problem belongs to a classical resource-allocation problem with single allocation format, which admits a network flow formulation so that it can be solved to optimality. Simulation results demonstrate the remarkable improvements in terms of EE by using the proposed iterative resource allocation and power control scheme. [ABSTRACT FROM AUTHOR]

Multistream carrier aggregation (MSCA) is a promising technique to boost data rates by enabling users to connect with multiple base stations (BSs) simultaneously. In this paper, we investigate joint user association and resource allocation for MSCA systems to achieve energy efficiency (EE) balance among different BSs. We aim at maximizing the weighted summation of EE values for different BSs, which is modeled as a nonconvex combinatorial sum-of-ratios optimization problem. To develop an upper bound algorithm, we first relax the combinatorial variables and then transform the problem into a series of convex optimization problems by the successive convex approximation (SCA) method. We also propose a low-complexity suboptimal algorithm, which divides the original optimization problem into two steps: user association and channel allocation, as well as power allocation. Numerical results show that the proposed algorithms can achieve flexible EE tradeoff among BSs, and the EE can be significantly improved with MSCA. [ABSTRACT FROM AUTHOR]

Abstract: A mobile grid incorporates mobile devices into Grid systems. But mobile devices at present have severe limitations in terms of processing, memory capabilities and energy. Minimizing the energy usage in mobile devices poses significant challenges in mobile grids. This paper presents energy constrained resource allocation optimization for mobile grids. The goal of the paper is not only to reduce energy consumption, but also to improve the application utility in a mobile grid environment with a limited energy charge, ensuring battery lifetime and the deadlines of the grid applications. The application utility not only depends on its allocated resources including computation and communication resources, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of allocated resources and consumed energy. Energy constrained resources allocation optimization is formulated as a utility optimization problem, which can be decomposed into two subproblems, the interaction between the two sub-problems is controlled through the use of a pricing variable. The paper proposes a price-based distributed energy constrained resources allocation optimization algorithm. In the simulation, the performance evaluation of our energy constrained resources allocation optimization algorithm is conducted. [Copyright &y& Elsevier]