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The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE. [ABSTRACT FROM AUTHOR]

As Graph Convolutional Networks (GCNs) have emerged as a promising solution for graph representation learning, designing specialized GCN accelerators has become an important challenge. An analysis of GCN workloads shows that the main bottleneck of GCN processing is not computation but the memory latency of intensive off-chip data transfer. Therefore, minimizing off-chip data transfer is the primary challenge for designing an efficient GCN accelerator. To address this challenge, optimization is initialized by considering GCNs as tiled matrix multiplication. In this paper, we optimize off-chip memory access from both the in- and out-of-tile perspectives. From the out-of-tile perspective, we find optimal tile configurations of given datasets and on-chip buffer capacity, then observe the dataflow across phases and layers. Inter-layer phase fusion dataflow with optimal tile configuration reduces data transfer of intermediate outputs. From the in-tile perspective, due to the sparsity of tiles, tiles have redundant data which does not participate in computation. Redundant data load is eliminated with hardware support. Finally, we introduce an efficient GCN inference accelerator, EGCN, specialized for minimizing off-chip memory access. EGCN achieves 41.9% off-chip DRAM access reduction, 1.49× speedup, and 1.95× energy efficiency improvement on average over the state-of-the-art accelerators. [ABSTRACT FROM AUTHOR]

GPUs are widely used to accelerate the training of machine learning workloads. As modern machine learning models become increasingly larger, they require a longer time to train, leading to higher GPU energy consumption. This paper presents GPOEO, an online GPU energy optimization framework for machine learning training workloads. GPOEO dynamically determines the optimal energy configuration by employing novel techniques for online measurement, multi-objective prediction modeling, and search optimization. To characterize the target workload behavior, GPOEO utilizes GPU performance counters. To reduce the performance counter profiling overhead, it uses an analytical model to detect the training iteration change and only collects performance counter data when an iteration shift is detected. GPOEO employs multi-objective models based on gradient boosting and a local search algorithm to find a trade-off between execution time and energy consumption. We evaluate the GPOEO by applying it to 71 machine learning workloads from two AI benchmark suites running on an NVIDIA RTX3080Ti GPU. Compared with the NVIDIA default scheduling strategy, GPOEO delivers a mean energy saving of 16.2% with a modest average execution time increase of 5.1%. [ABSTRACT FROM AUTHOR]

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered IoT network, where a rotary-wing UAV adopts fly-hover-communicate protocol to successively visit IoT devices in demand. During the hovering periods, the UAV works on full-duplex mode to simultaneously collect data from the target device and charge other devices within its coverage. Practical propulsion power consumption model and non-linear energy harvesting model are taken into account. We formulate a multi-objective optimization problem to jointly optimize three objectives: maximization of sum data rate, maximization of total harvested energy and minimization of UAV’s energy consumption over a particular mission period. These three objectives are in conflict with each other partly and weight parameters are given to describe associated importance. Since IoT devices keep gathering information from the physical surrounding environment and their requirements to upload data change dynamically, online path planning of the UAV is required. In this paper, we apply deep reinforcement learning algorithm to achieve online decision. An extended deep deterministic policy gradient (DDPG) algorithm is proposed to learn control policies of UAV over multiple objectives. While training, the agent learns to produce optimal policies under given weights conditions on the basis of achieving timely data collection according to the requirement priority and avoiding devices’ data overflow. The verification results show that the proposed MODDPG (multi-objective DDPG) algorithm achieves joint optimization of three objectives and optimal policies can be adjusted according to weight parameters among optimization objectives. [ABSTRACT FROM AUTHOR]

The combination of non-orthogonal multiple access (NOMA) and intelligent reflecting surface (IRS) is an efficient solution to significantly enhance the energy efficiency of the wireless communication system. In this paper, a downlink multi-cluster NOMA network is considered, where each cluster is supported by one IRS. This paper aims to minimize the transmit power by jointly optimizing the beamforming, the power allocation and the phase shift of each IRS. The formulated problem is non-convex and challenging to be solved due to the coupled variables, i.e., the beamforming vector, the power allocation coefficient and the phase shift matrix. To address this non-convex problem, an alternating optimization based algorithm is proposed. Specifically, the primal problem is divided into two subproblems for beamforming optimization and phase shifting feasiblity, where the two subproblems are solved iteratively. Moreover, to guarantee the feasibility of the beamforming optimization problem, an iterative algorithm is proposed to search the feasible initial points. To reduce the complexity, a simplified algorithm based on partial exhaustive search for this system model is also proposed. Simulation results demonstrate that the proposed alternating algorithm can yield a better performance gain than the partial exhaustive search algorithm, NOMA with random IRS phase shift scheme and OMA-IRS scheme. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) is the key to the performance of fuel cell / battery hybrid system. At present, reinforcement learning (RL) has been introduced into this field and has gradually become the focus of research. However, traditional EMSs only take the energy consumption into consideration when optimizing the operation economy, and ignore the cost caused by power source degradations. It would cause the problem of poor operation economy regarding Total Cost of Ownership (TCO). On the other hand, most studied RL algorithms have the disadvantages of overestimation and improper way of restricting battery SOC, which would lead to relatively poor control performance as well. To solve these problems, this paper establishes a TCO model including energy consumption, equivalent energy consumption and degradation of power sources at first, then adopt the Double Q-learning RL algorithm with state constraint and variable action space to determine the optimal EMS. Finally, using hardware-in-the-loop platform, the feasibility, superiority and generalization of proposed EMS is proved by comparing with the optimal dynamic programming and traditional RL EMS and equivalent consumption minimum strategy (ECMS) under both training and unknown operating conditions. Results prove that the proposed strategy has high global optimality and excellent SOC control ability regardless of training or unknown conditions. [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]

6G wireless networks have raised increasing attention with computation-sensitive services such as AI Internet of things (AIoT) and mobile augmented reality/virtual reality (AR/VR) applications. Mobile edge computing (MEC) provides rich computation resources for user equipments (UE) at the edge of networks. Aided by MEC servers, computation-intensive applications that are commonly modeled as Directed Acyclic Graphs (DAG) can be performed locally and offloaded to MEC servers to enhance execution efficiency. However, it is a key issue to efficiently provide low latency with limited energy. In this paper, we investigate a multiobjective task scheduling problem in MEC-aided 6G network. Then, an improved multiobjective cuckoo search (IMOCS) algorithm is proposed to deal with a DAG-based task scheduling problem, which aims to reduce the execution latency and energy consumption of UE. Particularly, the proposed IMOCS algorithm is based on the single-objective cuckoo search algorithm and Pareto dominance. An external archive is used to record nondominated solutions, whose update strategy improves the quality of solutions by the aid of fast nondominated sorting and crowding distance sorting. Simulation results demonstrate that IMOCS algorithm outperforms other four benchmark algorithms, which can provide optimal task scheduling policy for MEC severs in 6G networks. [ABSTRACT FROM AUTHOR]

The aim of the paper is to define a limit performance of highly efficient battery electric quadricycles for urban mobility. The vehicle is employed for an energy efficiency competition in which urban concepts compete. A multi-physics (thermo-electro-mechanical) Tank To Wheels (TTW) model has been developed and validated. Given the information on the track route (track map and elevation) and on the throttle input command, the model computes the vehicle power demand and cruising speed. The model is validated by means of both indoor and outdoor experimental tests. The validated model is employed for the optimization of the transmission gear ratio and of the driving strategy to minimize the overall energy consumption on a given track. Design variables are related to the transmission gear ratio and to the throttle command profile. The algorithm aims to minimize the energy consumption, including constraints on the maximum current provided by the battery and on the maximum time available to complete the lap. Comparison with some common driving strategies confirmed the effectiveness of the proposed solution, with a foreseen 9% reduction of the vehicle overall energy demand. The single seater ultra-efficient electric vehicle, compared to other urban quadricycles, has a battery capacity ten times lower and a 50% longer driving range. [ABSTRACT FROM AUTHOR]

Online fountain codes (OFCs) have many advantages, such as low overhead, online feedback and optimal encoding, due to the feedback of the instantaneous decoding state. This paper analyzes the characteristics of underwater acoustic networks (UANs) as well as the issues of existing OFCs applied in UANs. Aiming at these issues, two optimization objectives of OFCs are put forward for UANs. In addition, a recursive OFC with limited feedback (ROFC-LF) is presented for UANs. The ROFC-LF reduces the consumption of bandwidth and energy caused by the transmission of useless coding packets. Through limited feedback, the problem of low channel utilization in UANs with half-duplex communication is solved. Furthermore, a data transmission mechanism based on the ROFC-LF for UANs is presented. The theoretical analysis and simulation results show that the proposed transmission mechanism based on the ROFC-LF scheme outperforms the existing OFC schemes in terms of overhead, computational complexity, coding efficiency and energy consumption. Consequently, the ROFC-LF is suitable for UANs with constrained resources. [ABSTRACT FROM AUTHOR]

In the cooperative vehicle infrastructure system, the road side unit (RSU) equipped with a mobile edge computing (MEC) server and sensors could provide vehicle infrastructure cooperation services for vehicles, such as optimization and cooperative driving, enhanced visibility, and so on. In view of this, the MEC server needs to fuse the sensing information from sensors on the vehicles and RSU, respectively. In the case of bad channel conditions, uploading the raw sensing information from the vehicles results in high uplink transmission latency. To deal with it, the vehicles can process the information locally and just deliver the results to the RSU. However, due to the limited computing resources on the vehicles, the processing accuracy of the raw information on the vehicles is lower than that on the MEC server. Besides, processing locally leads to higher vehicle energy consumption. Thus, in this paper, we aim to jointly optimize execution latency, processing accuracy, and energy consumption of the MEC-based cooperative vehicle infrastructure system. Firstly, we design the terminal machine learning task model and the edge machine learning task model on the vehicle side and RSU side, respectively. Then, we formulate a long-term multi-objective optimization problem. Owing to the stochastic traffic and time-varying communication conditions, we reformulate it as a Markov decision process and propose a two-stage deep reinforcement learning-based offloading and resource allocation (TDORA) strategy to determine the task offloading and the transmit power of each vehicle. Simulation results demonstrate the efficacy of the proposed strategy. [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]

Unmanned aerial vehicle (UAV) energy consumption and task completion time are two significant aspects in UAV-enabled Internet of Things (IoT) data acquisition. However, while energy consumption and task completion time are not conflicting constraints, optimizing the former often does not automatically lead to optimization of the latter, and vice-versa. In this paper, we aim to further decrease UAV energy consumption while minimizing UAV task completion time. We propose a novel UAV utility function to describe task completion time for data acquisition and energy consumption for propulsion and receiver's circuits. Then, we formulate a mixed-integer nonconvex utility maximization problem, involving joint optimization of UAV trajectory, velocity, acceleration, task completion time and scheduling of IoT devices for data uploading. To tackle this problem, we decompose the original problem into two sub-problems and propose a two-layer joint task time and trajectory optimization (JTTTO) iterative algorithm, which converges in polynomial time. Accordingly, we apply block coordinate descent (BCD) and successive convex approximation (SCA) algorithms to optimize UAV trajectory, and bisection search algorithm to minimize task completion time, respectively. Simulation results show that the proposed data acquisition scheme significantly decreases the UAV's energy consumption while minimizing task completion time, compared with the other time-minimization schemes. [ABSTRACT FROM AUTHOR]

IP traffic has increased significantly in recent years, and it is expected that this progress will continue. Recent studies report that the viewing of online video content accounts for a share of 1% of the global greenhouse gas emissions. To reduce the data traffic of video streaming, the new standard Versatile Video Coding (VVC) has been finalized in 2020. In this paper, the energy efficiency of two different VVC decoders is analyzed in detail. Furthermore, we propose a design space exploration that uses an algorithm based on a greedy strategy to derive coding tool profiles that optimize the energy demand of the decoder. We show that the algorithm derives optimal coding tool profiles for a subset of coding tools. Additionally, we propose profiles that reduce the energy demand of VVC decoders and provide energy savings of more than 50% for sequences with 4K resolution. Thereby, we will also show that the proposed profiles can have a lower decoding energy demand than comparable HEVC-encoded bit streams while also having a significantly lower bit rate. [ABSTRACT FROM AUTHOR]

With the development of computer architecture, even for embedded systems, GPU devices can be integrated, providing outstanding performance and energy efficiency to meet the requirements of different industries, applications, and deployment environments. Data analytics is an important application scenario for embedded systems. Unfortunately, due to the limitation of the capacity of the embedded device, the scale of problems handled by the embedded system is limited. In this paper, we propose a novel data analytics method, called G-TADOC, for efficient text analytics directly on compression on embedded GPU systems. A large amount of data can be compressed and stored in embedded systems, and can be processed directly in the compressed state, which greatly enhances the processing capabilities of the systems. Particularly, G-TADOC has three innovations. First, a novel fine-grained thread-level workload scheduling strategy for GPU threads has been developed, which partitions heavily-dependent loads adaptively in a fine-grained manner. Second, a GPU thread-safe memory pool has been developed to handle inconsistency with low synchronization overheads. Third, a sequence-support strategy is provided to maintain high GPU parallelism while ensuring sequence information for lossless compression. Moreover, G-TADOC involves special optimizations for embedded GPUs, such as utilizing the CPU-GPU shared unified memory. Experiments show that G-TADOC provides 13.2× average speedup compared to the state-of-the-art TADOC. G-TADOC also improves performance-per-cost by 2.6× and energy efficiency by 32.5× over TADOC. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) Base Station (BS) placement optimization is an essential operational task to improve the Quality of Service (QoS) in UAV-aided wireless cellular networks. The existing approaches are almost zeroth order methods, and the few first order methods mainly ignore the allocation fairness, computational efficiency, and backhaul constraints. In this paper, we formulate the UAV placement problem as a constrained optimization problem, with the objective of maximizing the fair coverage versus energy consumption while satisfying the backhaul constraints at different time nodes. To guarantee fair QoS allocation, we introduce a novel fairness index to ensure fair communication opportunity and the novel region coverage ratio to avoid excess QoS on covered spots. An accurate and efficient proximal stochastic gradient descent based alternating algorithm that iteratively executes two optimization steps is proposed to optimize the UAV locations, which enables the fast single point-based first order methods to solve the complex problems with constraints. Experiment results manifest that the proposed algorithm performs well both in synthetic data scenario and in real city scenario. Furthermore, the proposed first order algorithm is more efficient than the existing zeroth order algorithm, typically referring to the meta-heuristic method. [ABSTRACT FROM AUTHOR]

The integration of aerial platforms to provide ubiquitous coverage and connectivity for densely deployed terrestrial networks is expected to be a reality in the emerging sixth-generation networks. Energy-effificient and secure transmission designs are two important components for integrated terrestrial-aerial networks (ITAN). Inlight of the potential of reconfigurable intelligent surface (RIS) for significantly reducing the system power consumption and boosting information security, this paper proposes a multi-layer RIS-assisted secure ITAN architecture to defend against simultaneous jamming and eavesdropping attacks, and investigates energy-efficient hybrid beamforming for it. Specifically, with the availability of imperfect angular channel state information (CSI), we propose a block coordinate descent (BCD) framework for the joint optimization of the user’s received decoder, the terrestrial and aerial digital precoder, and the multi-layer RIS analog precoder to maximize the system energy efficiency (EE) performance. For the design of the received decoder, a heuristic beamforming scheme is proposed to convert the worst-case design problem into a min-max one and facilitate the developing a closed-form solution. For the design of the digital precoder, we propose an iterative sequential convex approximation approach via capitalizing the auxiliary variables and first-order Taylor series expansion. Finally, a monotonic vertex-update algorithm with a penalty convex-concave procedure (P-CCP) is proposed to obtain the analog precoder with satisfactory performance. Numerical results show the superiority and effectiveness of the proposed optimization framework and architecture over various benchmark schemes. [ABSTRACT FROM AUTHOR]

The foreseen development of large-scale Offshore Windfarms (OWFs) further from the shore dictates that the OWF transmission system must be optimally designed based on Dynamic Thermal Rating (DTR) in order to fully utilize the intermittent nature of the wind and to keep the offshore wind cost-competitive. In this paper, a comprehensive, DTR-based, two-stage stochastic model is presented, which has been developed for investment decision support for OWF size and HVAC transmission systems. Complex DTR models for all the critical HV components are made fit for the mixed-integer linear programming problem, while accounting for the stochasticity in wind generation and component availability. The main decisions incorporate the discrete size of OWFs, HV subsea export cable cross-sections and ratings for transformers and shunt reactors. For validation, an actual testcase OWF off the east coast of U.K. has been used. Results indicate that DTR-based iterative design of OWF and its transmission components can significantly improve the business case, even though transmission efficiency and energy delivered are not maximum for the optimal design [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]

This paper addresses the design of a torque vectoring architecture to control the four electrical machines in a four-wheel-drive (4WD) formula-type competition vehicle. The scheme includes a new yaw-rate controller and a novel optimal torque distribution algorithm. Two yaw-rate controllers are proposed: one based on $\mathcal {H}_\infty$ optimal control and another based on linear parameter varying (LPV) system concepts. Both controllers are designed using an extended bicycle model validated with experimental data. Simulation results shown the effectiveness of the proposed overall control scheme in terms of energy efficiency, cornering speed and stability no matter the high-demanding working conditions. Such an effectiveness is quantitatively demonstrated by means of several key performance indicators chosen to ease the comparison of the proposed approach with respect to other reported works. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an intelligent reflecting surface (IRS) aided millimeter wave (mmWave) communication system using hybrid precoding of subarray-connected structure at the base station. To maximize the received signal power of the user, we jointly optimize the active beamforming at the BS and passive beamforming at the IRS, respectively. Exploiting the rank-one property of mmWave channels, we transform the original problem into an approximate optimization problem and give the closed-form solution of the approximated problem. Simulation results show that our proposed scheme can effectively improve the received signal quality of the user with a high energy efficiency. [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]

Federated learning (FL) has been considered as a promising paradigm for enabling distributed training/learning in many machine-learning services without revealing users’ local data. Driven by the growing interests in exploiting FL in wireless networks, this paper studies the Non-orthogonal Multiple Access (NOMA) assisted FL in which a group of end-devices (EDs) form a NOMA cluster to send their locally trained models to the cellular base station (BS) for model aggregation. In particular, we consider that the BS adopts wireless power transfer (WPT) to power the EDs (for their data transmission and local training) in each round of FL iteration, and formulate a joint optimization of the BS’s WPT for different EDs, the EDs’ NOMA-transmission for sending the local models to the BS, the BS’s broadcasting of the aggregated model to all EDs, the processing-rates of the BS and EDs, as well as the training-accuracy of the FL, with the objective of minimizing the system-wise cost accounting for the total energy consumption as well as the FL convergence latency. In spite of the strict non-convexity of the joint optimization problem, we analytically characterize the BS’s and all EDs’ optimal processing-rates, based on which we propose a layered algorithm for finding the optimal solutions for the joint optimization problem via exploiting monotonic optimization. Numerical results validate that our algorithm can achieve the optimal solution as LINGO’s global-solver (i.e., a commercial optimization package) while significantly reducing the computation-time. Moreover, the results also demonstrate that our NOMA assisted FL can reduce the system cost compared to the benchmark FL scheme with the fixed local training-accuracy by more than 70% and the conventional frequency division multiple access (FDMA) based FL by 78%. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) and backscatter communication are two emerging technologies for low-power communication. In this paper, we consider a NOMA backscatter system, where signals from two backscatter devices are multiplexed on a frequency resource block using NOMA in each time slot. Our objective is to maximize the average energy efficiency by optimizing backscatter device pairing, reflection coefficients of backscatter devices, and the transmit power of the reader. We formulate the average energy efficiency maximization problem subject to the minimum circuit power and the minimum data rate requirements of the backscatter devices, and the transmit power constraint of the reader. The formulated problem is nonconvex. To obtain a suboptimal solution for this problem, we use alternating optimization technique and decompose the problem into two subproblems. The subproblems are solved by using fractional programming, Dinkelbach’s algorithm, and successive convex approximation method. Simulation results show that our proposed algorithm converges quickly to a suboptimal solution. Our proposed algorithm outperforms several baseline algorithms, including the genetic algorithm, fixed device pairing scheme, conventional device pairing scheme, maximum transmit power allocation scheme, and random reflection coefficient selection scheme, in terms of the average energy efficiency. The optimality gap of our proposed algorithm is investigated by comparing with the optimal scheme, in which the optimal device pairing is obtained based on exhaustive search. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) is a viable solution to remotely charge several energy receivers (ERs) in areas with intermittent access to a source of energy. However, given the limited available energy in the on-board battery of the UAV, which is mainly required to suffice propulsion energy consumption, the UAV struggles to roam seamlessly for long intervals. A key challenge is thus to efficiently use the limited battery energy, and accordingly prolong the UAV’s endurance, via controlling the underlying associated parameters such as cruising velocity. To further extend the battery lifetime, the UAV can also harness other sources of sustainable energy, such as sunlight, via solar energy harvesting. This paper studies a UAV-enabled WPT system, where a rotary-wing UAV transfers radio frequency (RF) wireless energy to several ERs at known locations. To offer uninterrupted seamless service, the UAV also harvests solar energy using solar panels. Considering the propulsion energy consumption of the UAV, we maximize the minimum average power received among all ERs over a finite cycle duration by jointly optimizing the UAV’s time allocation between harvesting and charging, flight trajectory as well as velocity, and transmit power allocation. Due to the non-convexity of the problem, we utilize the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Numerical results show that the proposed solution outperforms benchmark schemes. [ABSTRACT FROM AUTHOR]

This paper investigates the energy-efficient power allocation strategy for multi-user mmWave non-orthogonal multiple access (NOMA) system with different hybrid precoding (HP) structures. Particularly, we consider the fully-access HP structure and the subarray HP structure, respectively. For these two structures, the finite resolution analog precoder is realized through a low-power switches and inverters (SI) network. In the NOMA system, multiple users are first paired according to their channel correlation and gain difference. Following this, we propose hybrid precoding schemes for the low-power SI-based HP structures. Then, a power allocation (PA) problem is formulated to maximize the energy efficiency of the system, subject to per-cluster’ power constraints and per-user’ quality of service constraints. To solve the non-convex PA optimization problem, we devise a suboptimal solution with two-stage PA strategies. Furthermore, the PA problem aiming at conditionally maximizing the spectrum efficiency is also analyzed. Simulation results show that the proposed mmWave-NOMA can achieve higher energy efficiency than the existing mmWave-NOMA scheme and the conventional mmWave orthogonal multiple access scheme. [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]

In the face of the unprecedented demand for higher data rates while coping with the constraints from physical size (i.e., windward area), it is profitable to deploy compact planar arrays (CPAs) on the base stations (BSs). But massively densified antenna deployment not only leads to antenna mutual coupling (MC), but also consumes additional energy due to the equipment of more radio frequency (RF) chains. This paper studies energy efficiency (EE) optimization for massive multi-input multi-output (MIMO) wireless systems with a CPA in the presence of MC. In comprehensive consideration of the spectral efficiency (SE) and power consumption, a joint optimization problem of beamforming and antenna subset (AS) is proposed. Our motivation is to relieve the effects of MC and reduce the relevant energy consumption by constructing irregular AS. Particularly, considering the nonconvexity of the objective and the discrete 0-1 constraints, an available iterative algorithm, named the energy-efficient beamforming and AS (EE-BF&AS) algorithm, is developed. Numerical results illustrate that when MC is modeled, the proposed EE-BF&AS algorithm outperforms conventional related approaches in EE. By using the AS switching strategy, the irregular AS can be constructed to make MC less serious and let the relevant energy consumption decrease. [ABSTRACT FROM AUTHOR]

This paper investigates and analyzes the number of reflecting elements for guaranteed energy- and spectral-efficient intelligent reflecting surface (IRS)-assisted communication systems. As opposed to previous works where the energy efficiency (EE)/the spectral efficiency (SE) maximization or the EE-SE tradeoff was considered, our goal is to minimize the number of reflecting elements in the IRS-assisted system. Besides, both the EE and the SE constraints are considered in the number minimization problem, which has not been addressed in existing works. However, both the EE and the SE performance do not admit exact closed-form expressions due to the non-convexity incurred by joint beamforming and phase shift design. In order to make the optimization problem tractable, we resort to their performance bounds for problem reformulation. By decomposing the problem into two sub-problems, we are able to derive closed-form expressions for the minimum number of reflecting elements, and both the coherent phase shift (CPS)-oriented solution and the random phase shift (RPS)-oriented solution are proposed for comparison. In order to shed light on the practical design, the relationship between the derived number of reflecting elements is established for both schemes, and the upper bounds on both the EE and the SE thresholds and the placement of the IRS to achieve only one reflecting element are obtained. Simulation results confirm the validity of our analysis on the minimum number of reflecting elements and effectiveness of both schemes. [ABSTRACT FROM AUTHOR]

This paper presents a new stochastic optimization framework for user association and task offloading in mobile edge computing (MEC) networks with spatial and temporal variations of computing power, channel quality and connection capacity between different MEC servers. The new framework minimizes a quadratic penalty function which balances the energy consumption and fairness of the devices. Lyapunov optimization is first applied to eliminate the time coupling of the framework, leading to a mixed-integer program (MIP) of user association and offloading scheduling at every time slot. While solving the scheduling using linear programming, we convert the user association to a minimum cost maximum flow problem by interpreting edge servers and devices as two disjoint vertexes. We solve the minimum-cost maximum flow problem efficiently by using the Ford-Fulkerson algorithm. Corroborated by simulations, the proposed approach is asymptotically optimal and outperforms alternative approaches in terms of energy saving and fairness. [ABSTRACT FROM AUTHOR]

In this paper, an aerial heterogeneous wireless network consists of a high altitude platform (HAP) and low altitude platforms (LAPs) as aerial base stations (ABSs) in a downlink orthogonal division multiple access (OFDMA) transmission is investigated. Two types of spectrum sharing, including spectrum underlay access (SUA) and spectrum overlay access (SOA) are employed to evaluate the inter-tier interference. Distributed problem formulation and solving method are used to reduce the computational complexity, communication overhead and system latency. Subcarrier assignment and power allocation in spectral efficiency (SE), energy efficiency (EE) and transmission power optimization problems are accomplished by the proposed two-layer algorithm. In the proposed algorithm, first, subcarrier assignment based on constraints of the optimization problems is carried out to cover both SUA and SOA with low computational complexity. Second, the proposed algorithm performs power allocation. The simulation results show the relation between spectrum reuse and inter-tier interference, such that system SE, EE and the number of served users are maximized when both spectrum reuse and inter-tier interference management are performed together. [ABSTRACT FROM AUTHOR]

In subway systems, increasing attention has been paid to improve energy efficiency due to remarkable energy consumption for rail transportation operation. The optimization of single-train driving control and multi-train operation synergy are two critical and closely related aspects for energy saving, but they are rarely solved in an integrative manner. The comprehensive study on these two aspects is a complex, large-scale, multi-variable and constrained task, which involves nested, coupled and mutual-input optimization processes. This paper proposes an energy-efficient integrated model, which includes optimizing single-train operation control in an inner model and multi-train operation synergy, committing to the utilization of regenerative braking energy (RBE) in an outer model. A parallelized particle swarm optimization based on Map-Reduce (PPSO-MR) algorithm is proposed to solve the integrated model to reduce the execution time. Two concepts of temporal-spatial record and time shift are introduced to derive the movement of a train at inter-stations for the operation adjustment of multiple trains on a subway line. Experimental results show that the proposed control model solved by the holistic optimization approach can achieve lower energy consumption with fast execution time. This study provides the novel model and algorithm to obtain energy-saving schedules serving the multi-train operation coordination. [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]

Energy efficiency is a critical issue in the management of data centers, which form the backbone of cloud computing. Virtual resource management has a significant impact on improving the energy efficiency of data centers. Despite the progress in this area, virtual resource management has been considered mainly at two separate levels: application assignment and virtual machine placement. It has not been well-investigated in a unified framework for both levels, limiting further improvement in the energy efficiency of data centers. To address this issue, this paper proposes the virtual resource management problem for energy efficiency as a constrained optimization problem. Then, this paper simplifies the problem through profile-guided task classification and problem decomposition for complexity reduction and improved energy efficiency. After that, a three-phase framework and algorithms are presented for profiling and profile updating, task classification and application assignment, and successive virtual machine placement. Experimental studies show energy savings of 8–12% by the three-phase framework compared to the existing technique. [ABSTRACT FROM AUTHOR]

This paper presents a novel method, advisory dynamic programming (AD-DP), for power management of a fuel cell hybrid vehicle (FCHEV). The presented method embraces a new understanding of vehicle states as driver-dependent and -independent states in time domain to define a suitable state space to be used for dynamic programming. Driver-dependent states are defined in terms of multiple characteristic parameters for vehicle speed and power demand, corporately. Driver-independent states are defined in terms of discrete values for supercapacitor's state of charge $SoC_{sc}$. Transition costs between all states in the state space are calculated offline and tabulated in look-up tables for online implementation. A state predictive model is developed based on the transition statistics of driver-dependent states for a suitable number of driving cycles. Backward calculation of the total transition cost for the predicted horizon in state space is used to define optimal power split strategy for the powertrain. The formulation of optimal control problem, in terms of situation-based solutions related to vehicle states, enables a significant reduction of computational steps and hence addresses the main challenge of real-time applications. The algorithm is adapted in terms of optimization horizon and number of discrete states for DIS to suit the real-time application. Experimental application of AD-DP, using an emulation test-rig, is conducted over different driving cycles. The obtained results reveal an improvement in energy efficiency up to 29% compared to the adaptive rule-based method. The contribution of this paper can be identified as: first, development of a corporate definition for vehicle states, that can be further implemented in optimization-based power management methods. Second, the formulation of an adaptive DP that requires lower computational steps and hence suits real-time applications in hybrid electric vehicles. [ABSTRACT FROM AUTHOR]

Energy-aware scheduling in a manufacturing environment with real-time energy pricing is a challenging problem. The purpose of this paper is to study a nonpreemptive scheduling problem on a single-machine to minimize the total tardiness and total energy cost under time-of-use electricity tariffs, which is a mixed-integer multi-objective mathematical programming model. To achieve these objectives, we develop several new holistic genetic algorithms. The proposed model is solved via several methods including weighted sum method and multiobjective genetic algorithms based on dominance rank (GA-1), weighted sum aggregation (GA-2), dominance ranking procedure and crowding distance comparison (GA-3), and heuristic approach (GA-H). This paper illustrates that operations management, which is typically observed as a significant technique that allows manufacturing to operate in challenging data enabled environment in an industrial internet of things ecosystem, can also carefully optimize a production process to improve energy cost in order to manage environmental challenges. Thus, the findings provide information on when to start each job. We provide detailed experimental results evaluating the performance of the proposed algorithms. In a case study, we illustrate how the results of the multiobjective model could be utilized in decision making using the technique for order preference by similarity to ideal solution method. [ABSTRACT FROM AUTHOR]

This paper proposes a peer-to-peer energy market platform based on the new concept of multiclass energy management, to coordinate trading between prosumers with heterogeneous (i.e., beyond purely financial) preferences. Power networks are undergoing a fundamental transition, with traditionally passive distribution network consumers becoming “prosumers”; proactive consumers that actively manage their production and consumption of energy. The paper introduces the new concept of energy classes, allowing energy to be treated as a heterogeneous product, based on attributes of its source, which are perceived by prosumers to have value. Examples include generation technology, location in the network and owner's reputation. The proposed peer-to-peer energy market platform coordinates trading between subscribed prosumers and the wholesale electricity market, to minimize costs associated with losses and battery depreciation, while providing added value by accounting for the prosumers’ individual preferences for the source/destination of the energy they consume/produce. The decomposable structure of the multiclass energy management problem is exploited to devise a distributed price-directed optimization mechanism, providing scalability and prosumer data privacy. Receding horizon model predictive control allows the prosumers to adjust their planned power flows based on the wholesale energy price, and up-to-date renewable generation and load predictions. [ABSTRACT FROM AUTHOR]

This paper is concerned with the energy efficiency maximization problem in multiple-input multiple-output interference channels. In the first part of this paper, we design the transmit beamforming matrices based on three approaches. In the first approach, each user tries to maximize their own energy efficiency, resulting in what we call the selfish method. The maximization problem corresponding to the selfish method can be implemented in a distributed manner with a semi-closed-form solution. The second approach aims to maximize the weighted sum-energy efficiency; we refer to it as the unselfish method. The proposed unselfish method has the following advantages. 1) It is easily implementable as the problem can be divided into several sub-problems and solved in a distributed manner. 2) It results in a quadratically constrained quadratic program with a semi-closed-form solution. Also, we consider fairness with the minimum-energy efficiency maximization problem, an approach that results in what we call the worst-case method. In the second part, general energy efficiency maximization in the presence of norm-bounded channel uncertainty is investigated which leads to a semi-definite program; the resulting method is called the robust method. Finally, the effect of the system parameters is evaluated through simulations. [ABSTRACT FROM AUTHOR]

High-speed train systems have proven to be very flexible and attractive systems that can be developed under various circumstances and in different contexts and cultures. As a result, high-speed trains are widely deployed around the world. Providing more reliable and higher data rate communication services for high-speed trains has become one of the most urgent challenges. With vast amounts of spectrum available, the millimeter wave (mmWave) system is able to provide transmission rates of several gigabits per second for high-speed trains. At the same time, mmWave communication also suffers from high attenuation; thus, higher energy efficiency should be considered. This paper proposes an energy-efficient power-control scheme of train–ground mmWave communication for high-speed trains. Considering a beam switching method for efficient beam alignment, we first establish the position-prediction model, realistic direction antenna model, and receive power model. And, then we allocate the transmission power rationally by using the power minimization algorithm while ensuring the total amount of transmission data. Based on this, this paper also develops a hybrid optimization scheme and finds the limit of total energy consumption when the number of segments goes to infinity. Using simulation with various system parameters and taking velocity estimation error into account, we demonstrate the superior performance of our schemes. [ABSTRACT FROM AUTHOR]

This paper proposes a holistic framework of data-driven distributionally robust joint chance constrained economic dispatch (ED) optimization, which seamlessly incorporates deep learning-based optimization for effective utilization of renewable energy in power systems. By leveraging a deep generative adversarial network (GAN), an f-divergence-based ambiguity set of wind power distributions is constructed as a ball centered around the probability distribution induced by a generator neural network. In particular, the GAN is well suited for capturing complicated spatial and temporal correlations of wind power. Based upon this ambiguity set, a distributionally robust joint chance constrained ED model is developed to hedge against distributional uncertainty present in multiple constraints, without assuming a perfectly known probability distribution. The proposed deep learning based ED optimization framework greatly mitigates the conservatism inflicting on distributionally robust individual chance constrained optimization. Theoretical a priori bound on the required number of synthetic wind power data generated by GAN is explicitly derived for the multi-period ED problem to guarantee a predefined risk level. The effectiveness and scalability of the proposed approach are demonstrated in the six-bus and IEEE 118-bus systems by comparing with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Virtual power plant (VPP) has gradually become a key technology to the increasing penetration of renewable energy. The uncertainty and variability of renewable energy and load demand pose significant challenges to the efficiency and stability of VPP's operation. In this paper, a data-driven two-stage stochastic robust optimization (SRO) scheduling model is proposed for a VPP considering wind power, solar power, load demand, and market price uncertainties. The objective is to maximize the profit of the VPP in the energy market and minimize the total system cost of the VPP in the reserve market under the worst-case realization of the uncertainties. The Dirichlet process mixture model (DPMM) and variational inference algorithm are employed for constructing the data-driven uncertainty ambiguity set considering the correlations among multiple uncertainties. The tailored column-and-constraint generation algorithm is developed to solve the SRO model iteratively by reformulating the second stage with the application of the Karush-Kuhn-Tucker conditions. Results from a case study illustrate the effectiveness and superiority of the proposed model. [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]

As an attractive alternative to traditional and large satellite, small satellite cluster (SSC) with low cost and short development time will play an important role in Earth observation. Generally, SSC networks for Earth observation are energy-limited, the design of the joint data downloading and resource management scheme remains challenging. On the one hand, with the ever-increasing amount of sensing data, multiple gateway stations and relay satellites have been deployed to receive the sensing data from SSC. The problem that where to download the sensing data should be addressed. On the other hand, due to the limited communication, storage, computing and power supply capacities of small satellite, an efficient multi-dimensional resource management scheme should be adopted. In this paper, the joint data downloading scheduling and multi-dimensional resource management problem for satellite cluster networks with relay satellites is investigated, which is formulated as a long-term optimization problem to minimize the energy consumption and guarantee the data drop rate performance. Then, this problem is decomposed into a series of mixed-integer programming problems by Lyapunov optimization theory. Leveraging many-to-many matching theory with empty node and the simplex method, an online joint data downloading and resource management (OJDDRM) scheme is proposed. Simulation results validate the proposed OJDDRM scheme's performance and demonstrate that there exists a balance point for the proposed OJDDRM scheme. Under the balance point, the data drop rate is zero and the energy consumption can be reduced compared with the existed schemes. [ABSTRACT FROM AUTHOR]

The supercapacitor-based regenerative braking system heightens energy utilization efficiency of the motor. However, the power mismatch between the energy storage unit and the motor would cause the supercapacitor to absorb energy from the power grid and unnecessary energy loss. In this paper, a multi-parameter collaborative power prediction control of the motor-driven system is proposed to solve the power mismatch. Mechanism analysis of energy flow is investigated. Afterward, a power prediction model of the supercapacitor is established based on various signals of certain time sequences. The modeling is carried out by discrete forms, and the collaboration among multi-parameters is considered. The performance optimization function is deduced from the perspective of the minimum energy consumption principle. The storage/release energy of the supercapacitor can be fully coordinated with the energy of the power grid and the power of the motor to maintain the dynamic supply-demand power balance. Simulation and experimental results verify the effectiveness of the proposed control scheme. The energy utilization efficiency is increased by 20% in comparison with the typical control strategy under the recommended operating condition. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS), a recently introduced technology for future wireless communication systems, enhances the spectral and energy efficiency by intelligently adjusting the propagation conditions between base stations (BSs) and mobile equipments (MEs). An RIS consists of many low-cost passive reflecting elements that are optimized to improve the quality of the received signal. In this paper, we study the problem of power control at the BS and RIS optimization for application to physical-layer broadcasting. Our goal is to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BS and the phase shifts of the passive elements at the RIS. Furthermore, to help validate the proposed optimization methods, we derive lower bounds to quantify the average transmit power at the BS as a function of the number of MEs, the number of RIS elements, and the number of antennas at the BS. The simulation results demonstrate that the average transmit power at the BS is close to the lower bound in an RIS-aided system, and is significantly lower than the average transmit power in conventional schemes without an RIS. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is a promising solution to build a programmable wireless environment with reconfigurable passive elements, which can achieve high spectral and energy efficiency. In this paper, we investigate the ergodic capacity of RIS-assisted multi-user multiple-input single-output (MISO) wireless systems in both uplink and downlink scenarios. Unlike most of prior works, where instantaneous channel state information (CSI) is assumed, we consider the realistic scenario with only statistical CSI. For both scenarios, we first present an analytical expressions for the ergodic sum capacity of the system. Based on which, the joint power control (or transmit beamforming) and phase shift design problem maximizing the ergodic sum capacity is formulated. Capitalizing on the alternating direction method of multipliers (ADMM), fractional programming (FP) and alternating optimization (AO) methods, efficient suboptimal solutions are obtained for the non-convex design problems. Simulation results are presented to validate the accuracy of the analytical ergodic sum capacity expressions and evaluate the impact of key system parameters such as CSI, Rician $K$ factor, number of RIS elements, and RIS location on the ergodic capacity performance. The findings suggest that the proposed statistical CSI design achieves decent performance compared with the instantaneous CSI based design. Moreover, a signal hot spot can be created when placing the RIS close to the users. [ABSTRACT FROM AUTHOR]