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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]

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]

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]

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]

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 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 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]

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]

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]

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]

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]

This paper analyzes the problem of securing the transmissions of multiple mobile users against eavesdropping attacks. We propose and optimize the deployment of a single unmanned aerial vehicle (UAV), which serves as an aerial relay (AR) between the user cluster and the base station. The focus is on maximizing the secrecy energy efficiency by jointly optimizing the uplink transmission powers of the ground users and the position of the UAV. The joint optimization problem is nonconvex; therefore we split it into two subproblems and solve them using an iterative algorithm. [ABSTRACT FROM AUTHOR]

The stationary supercapacitor energy storage system (SCESS) is one of effective approaches for the utilization of train's regenerative braking energy in urban rail systems. In this paper, the capacity configuration of SCESSs, the no-load voltage of substation, the control of onboard braking resistors and train operation diagrams are considered comprehensively. Based on the equivalent circuit model, the effects of traction power system parameters on the energy transmission between powering trains, braking trains and SCESSs are analyzed, and the relationships between the train operating parameters and system energy consumptions are revealed. A multi-variable synthetic optimization method is proposed to optimize the SCESS capacity, train operation diagrams and traction power system parameters collaboratively. In order to reduce the search space and improve the efficiency of the optimization algorithm, a hierarchical multi-objective optimization model is established, based on which the design variables and the control variables are optimized iteratively. And as the traffic density has a great impact on the regenerative braking energy distribution of the system, the optimization objective function fully considers the frequency distribution of headways throughout the day. Combined the Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) with the traction power flow simulator, the flowchart of the two-stage optimization algorithm is designed, and the pareto set of the multi-objective problem is obtained. The advantages of reducing system energy consumptions and configuration cost under the proposed algorithm are verified based on case studies of Beijing Batong Line. [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]

Mobile edge computing (MEC) is a promising solution to support emerging delay-sensitive mobile applications, such as self-driving, augment/virtual reality, and various Internet of Things (IoT) applications. By deploying MEC servers at network edge, e.g., close to cellular base stations (BSs), the computational tasks generated by these applications can be offloaded to edge nodes (ENs) and be quickly executed there. At the same time, with the projected large number of IoT devices, the communication and computational resources allocated to each user can be quite limited, making it challenging to provide low-latency MEC services. In this paper, we investigate the problem of task partitioning and user association in an MEC system, aiming to minimize the average latency of all users. We assume that each task can be partitioned into multiple subtasks that can be executed on local devices (e.g., vehicles), MEC servers, and/or cloud servers; each user can be associated with one of the nearby ENs. The subtasks can be independent of or dependent on each other. For each case, we formulate the joint optimization of task partitioning ratios and user association as a mixed integer programming problem. Each problem is solved by decomposing it into two subproblems. The lower-level subproblem is task partitioning under a given user association, which can be solved optimally. The higher-level subproblem is user association, we propose a dual decomposition-based approach and a matching-based approach to derive near-optimal solutions. Simulation results show that compared to benchmark schemes, the proposed schemes reduce the average latency by about 50% and 40% for the cases of independent and dependent subtasks, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of wireless service provisioning through a rotary-wing UAV which can serve as an aerial base station (BS) to communicate with multiple ground terminals (GTs) in a boost demand area. Our objective is to optimize the UAV control for maximizing the UAV.s energy efficiency, in which both aerodynamic energy and communication energy are considered while ensuring the communication requirements for each GT and backhaul link between the UAV and the terrestrial BS. The mobility of the UAV and GTs lead to time-varying channel conditions that make the environment dynamic. We formulate a nonconvex optimization for controlling the UAV considering the practical angle-dependent Rician fading channels between the UAV and GTs, and between the UAV and the terrestrial BS. Traditional optimization approaches are not able to handle the dynamic environment and high complexity of the problem in real-time. We propose to use a deep reinforcement learning-based approach namely Deep Deterministic Policy Gradient (DDPG) to solve the formulated nonconvex problem of UAV control with continuous action space that takes into account the real-time of the environment including time-varying UAV-ground channel conditions, available onboard energy of the UAV, and the communication requirement of the GTs. However, the DDPG method may not achieve good performance in an unstable environment and will face a large number of hyperparameters. We extend our approach to use the Trust Region Policy Optimization (TRPO) method that can improve the performance of the UAV compared to the DDPG method in such a dynamic environment. [ABSTRACT FROM AUTHOR]

This paper considers an intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) network, where multiple users decode data and harvest energy from the transmitted signal of a transmitter. The proposed design framework exploits the cost-effective IRS to establish favorable communication environment to improve the fair energy efficient. In particular, we study the max-min energy efficiency (EE) of the system by jointly designing the transmit information and energy beamforming at the base station (BS), phase shifts at the IRS, as well as the power splitting (PS) ratio at all users subject to the minimum rate, minimum harvested energy, and transmit power constraints. The formulated problem is non-convex and thus challenging to be solved. We propose two algorithms namely penalty-based and inner approximation (IA)-based to handle the non-convexity of the optimization problem. As such, we divide the original problem into two sub-problems and apply the alternating optimization (AO) algorithm for both proposed algorithms to handle it iteratively. In particular, in the penalty-based algorithm for the first sub-problem, the semi-definite relaxation (SDR) technique, difference of convex functions (DC) programming, majorization-minimization (MM) approach, and fractional programming theory are exploited to transform the non-convex optimization problem into a convex form that can be addressed efficiently. For the second sub-problem, a penalty-based approach is proposed to handle the optimization on the phase shifts introduced by the IRS with the proposed algorithms. For the IA-based method, we jointly optimize beamforming vectors and phase shifts while the PS ratio is solved optimally in the first sub-problem. Simulation results verify the effectiveness of the IRS, which can significantly improve the system EE as compared to conventional benchmark schemes and also unveil a trade-off between convergence and performance gain for the two proposed algorithms. [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]

This paper is concerned with the efficient movement control of multiple drone cells for communication coverage. Although many works have been developed to cope with this problem, but only few of them have investigated the three-dimensional (3-D) continuous movement control of multiple drone cells. In this paper, a problem of 3-D continuous movement control of multiple drone cells is formulated with an objective of maximizing the energy-efficient communication coverage of drone-cell networks while preserving the network connectivity. To mitigate this challenging problem, an energy-efficiency and continuous-movement-control algorithm (E $^2$ CMC), which is based on an emerging deep reinforcement learning method, is proposed. In E $^2$ CMC, an energy-efficiency reward function considering the energy consumption, the sum of quality-of-service (QoS) requirements of users as well as the coverage fairness is first designed. Next, E $^2$ CMC learns to adjust drone cells’ locations continuously by interacting with an environment in a sequence of observations, actions, and rewards. Furthermore, E $^2$ CMC will reduce the reward drastically as long as the networks are disconnected. Simulation results verify the superiority of the proposed learning algorithm on deploying multiple drone cells to provide the communication coverage. [ABSTRACT FROM AUTHOR]

This paper investigates the secrecy energy efficiency (SEE) for a two-tier heterogeneous cellular network having a macro cell and multiple small cells, where the macro cell consists of a macro base station (MBS), a macro user and an eavesdropper and meanwhile each small cell comprises of a small base station (SBS), a small user and an eavesdropper. We consider the use of artificial noise (AN) signals emitted at MBS and SBSs at the expense of transmit power to defend against eavesdroppers. For the sake of obtaining a better balance between the secrecy rate (SR) and energy efficiency of heterogeneous cellular networks, we propose an SEE maximization (SEEM) scheme in small cells through jointly optimizing the beamformers of confidential signals and AN at MBS and SBSs, called joint beamforming based SEEM (JBF-SEEM) while guaranteeing the SR requirements and the power constraints for MBS and each SBS. However, the proposed JBF-SEEM problem is a complicated optimization problem because of the non-convex fractional form. Thus, we firstly transform the original objective function in fractional form into a subtractive one through applying the Dinkelbach's method, and then the non-convex subtractive form is approximated into a convex optimization problem by combining the penalty function and the difference of two-convex functions approach. Furthermore, we propose a two-tier iterative JBF-SEEM algorithm to obtain the near-optimal beamforming solution. Numerical results show that the JBF-SEEM algorithm is convergent while achieving a better SEE performance than conventional approaches. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) play a significant role in various 5G or Beyond-5G (B5G)-enabled Internet-of-Things (IoT) applications. However, the UAV performance in an air-ground cooperative network is significantly affected by its mobility and air-to-ground (A2G) communication and computation behaviors. In this paper, a UAV-oriented computation offloading system is investigated, where the UAV desires to complete its onboard computation demands with the assistance of a ground edge-computing infrastructure, i.e., a road-side unit (RSU). The objective is to maximize the energy efficiency of the UAV. Specifically, a non-convex constrained optimal control problem is formulated to optimize the overall energy efficiency of UAV by jointly considering the coupled effects of UAV's longitudinal mobility, A2G communication, and computation dynamics. To address the coupled complexity and non-convexity of the original problem, a primal decomposition approach is developed to transform the problem into a convex subproblem and a primary problem, and then a closed-form optimal transmission power control is derived by solving the subproblem, which is dependent on mobility information. By embedding the closed-form optimal power control into the primary problem, a gradient projection-based iterative algorithm is proposed to obtain a joint optimal solution for both the longitudinal acceleration control and the power control, the feasibility and convergence of which is also theoretically proven. Extensive simulations have been conducted to validate the effectiveness of the proposed method in terms of constraint satisfaction and convergence speed, and comparative results also demonstrate that it can outperform other benchmark methods in terms of global energy efficiency. [ABSTRACT FROM AUTHOR]

Signalized intersections play a vital role in addressing the issue of transportation efficiency and vehicle fuel economy in urban areas. Meanwhile, with the development of Connected and Automated Vehicles (CAVs), the mixed traffic environment composed of traffic participants with differing intelligent levels will become an important stage of the intelligent transportation system. Considering the changes in the mixed traffic environment, this paper proposes a Coupled Vehicle-Signal Control (CVSC) method to optimize the traffic signal timing and driving trajectories of CAVs at the same time, with the goals of traffic efficiency improvement and energy saving respectively. The signal timing is continuously optimized to minimize the total delay at the intersection. CAVs generate eco-driving trajectories using the received signal timing information and the planned arrival time to reduce fuel consumption. Finally, simulation experiments were been carried out to verify the control effect of the proposed CVSC. On the one hand, the influence of the involved parameters on the optimization results was analyzed and discussed. On the other hand, the proposed CVSC method was compared with the traditional CACC control and the classic eco-driving model, GlidePath. The research results show that the proposed CVSC method can effectively improve the performance of signalized intersections. When the penetration rate of CAVs is greater than 40%, this method can save fuel consumption by 6%-14% and increase the average speed by 1%-5%. [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]

Energy efficiency is extremely important for electric vehicles to improve their driving range. As the motor output energy directly acts on the tire, the tire slip energy generated by tire excessive slip will reduce the effective utilization of motor output energy, resulting in the reduction of energy efficiency, especially in the case of low friction coefficient, or even vehicle instability in some changes, such as slalom change, etc. Therefore, this paper designs an integrated chassis control and verifies the energy utilization. First, based on model predictive control (MPC), an integrated chassis controller is proposed by explicitly incorporating both motor energy and tire slip energy in the main objective function. Second, the reference control actions of the torque distribution method optimized for the motor energy, which facilitates solution-search process of MPC. Then, a semi-empirical UniTire tire slip energy model is proposed for derivation of the tire dissipation energy. Acceleration and stability tests are conducted by four in-wheel independent motor-drive electric vehicles (4WIMD-EVs) on winter proving ground, respectively. The acceleration change results show that both tire dissipation energy and motor output energy are well suppressed to achieve energy-efficient, and the energy utilization ratio of tire dissipation energy to motor output energy is only 35% to achieve stable acceleration. The slalom change verifies vehicle stability, and the results show that the slip ratio of four wheels only reaches to 0.04, and the motor output energy of four wheels is effectively utilized to generate differential yaw moment. [ABSTRACT FROM AUTHOR]

This paper presents an energy-efficient downlink precoding scheme in a multi-cell Massive MIMO system. We approach the precoder design problem to maximize the system energy efficiency by jointly considering power control, interference management, antenna switching and user throughput in a cluster of base stations. This is computationally difficult as it requires solving a sparsity-inducing non-convex optimization problem, which is NP-hard. To alleviate the solution complexity, first a stochastic smooth approximation of zero-norm is applied in the antenna power management to enable fast, gradient-based algorithms. For efficient convergence, we develop a novel optimization algorithm combining augmented multiplier (AM) and quadratic programming (QP), and show how this scheme permits decentralized implementation by offloading parts of the computation to the individual base stations to reduce communication overhead. We provide theoretical proof that the proposed algorithm converges both locally and globally under realistic assumptions. Numerical results confirm that our method achieves higher energy efficiency with a superior convergence rate compared to different types of existing methods, and illustrate the relationship between energy efficiency performance and system design parameters. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) networks have recently developed to integrate renewable sources for providing sustainable and low-cost computation/energy services. However, one challenging problem is how to guarantee the stable and effective services for numerous devices under highly intermittent renewable generations and variable computation/energy demands. In this paper, we consider to co-deploy the edge servers and energy storage at micro Base Station (BS) in each edge sub-network. In the considered scenario, we first determine the number of edge servers and energy storage units at different sub-edge networks in a long-term period. Then, we jointly design the offloading and demand response policies for all micro BSs in a centralized manner. Considering the variabilities of renewable energy and computation/energy demands, the system cost is minimized while meeting the energy demand-supply balance and renewable portfolio standards. Furthermore, a distributed optimization problem is formulated to find the optimal offloading parameters, the amount of charging/discharging energy and utilized renewable energy of the micro BS without handing over all operational information. Numerical results show that the proposed deployment and policies can provide effective offloading and energy saving strategies for the proposed mobile edge network. [ABSTRACT FROM AUTHOR]

In this paper, we design a computing, communication and caching scheme for edge caching-based video streaming in order to improve the network performance. Firstly, we optimize the system's energy efficiency and delay with the aid of network function virtualization. Then, a dynamic edge caching decision is developed, and based on Lyapunov optimization, an alternating resource optimization algorithm is proposed for allocating the optimal subcarrier and power resources, video caching and computing resources. Our numerical results show that the proposed scheme outperforms both the traditional caching scheme as well as the least frequently used (LFU)-40% regime, and strikes a compelling tradeoff between the energy efficiency and delay. [ABSTRACT FROM AUTHOR]

This paper considers a cooperative cognitive radio network with two primary users (PUs), and two secondary users (SUs) that enables two-way communications of primary, and secondary systems in conjunction with non-linear energy harvesting based simultaneous wireless information, and power transfer (SWIPT). With the considered network, SUs are able to realize their communications over the licensed spectrum while extending relay assistance to the PUs. The overall bidirectional end-to-end transmission takes place in four phases, which include both energy harvesting (EH), and information transfer. A non-linear energy harvester with a hybrid SWIPT scheme is adopted in which both power-splitting, and time-switching EH techniques are used. The SUs aid in relay cooperation by performing an amplify-and-forward operation, whereas selection combining technique is adopted at the PUs to extract the intended signal from multiple received signals broadcasted by the SUs. Accurate outage probability expressions for the primary, and secondary links are derived under the Nakagami- $m$ fading environment. Further, the system behavior is analyzed with respect to achievable system throughput, and energy efficiency. Since the performance of the considered system is strongly affected by the spectrum sharing factor, and hybrid SWIPT parameters, particle swarm optimization is implemented to optimize the system parameters so as to maximize the system throughput, and energy efficiency. Simulation results are provided to corroborate the performance analysis, and give useful insights into the system behavior concerning various system/channel parameters. [ABSTRACT FROM AUTHOR]

In this paper, we focus on a wireless powered communication network where a hybrid access point can wirelessly activate the power of source nodes, and receive their information. Considering the time-switching protocol that implies energy harvesting, and energy usage are separated by time, a novel graphics-based multi-epoch power adjustment (GB-MPA) strategy is proposed to maximize the sum-throughput over a finite time horizon consisting of multiple transmission epochs. The proposed strategy relies on graphical constraint conditions, and allows the elimination of restrictions corresponding to the objective of throughput maximization. Moreover, the proposed GB-MPA strategy can be combined with the time-switching factor optimization by employing an alternating optimization procedure for performance enhancement. Simulation results show the accuracy of the proposed strategy, reduced complexity compared with the conventional convex optimization method, and the existing multi-epoch power adjustment strategy, and the compatibility with the alternating optimization procedure for developing the joint power, and time allocation scheme. [ABSTRACT FROM AUTHOR]

Combining intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) is an effective solution to enhance communication coverage and energy efficiency. In this paper, we focus on an IRS-assisted NOMA network and propose an energy-efficient algorithm to yield a good tradeoff between the sum-rate maximization and total power consumption minimization. We aim to maximize the system energy efficiency by jointly optimizing the transmit beamforming at the BS and the reflecting beamforming at the IRS. Specifically, the transmit beamforming and the phases of the low-cost passive elements on the IRS are alternatively optimized until the convergence. Simulation results demonstrate that the proposed algorithm in IRS-NOMA can yield superior performance compared with the conventional OMA-IRS and NOMA with a random phase IRS. [ABSTRACT FROM AUTHOR]

Smart, and green cities impose stringent requirements on spectral efficiency (SE), and energy efficiency (EE) of vehicular networks. For the current vehicular ad-hoc networks (VANETs), vehicle's mobility leads to rapid topology changes, and high channel uncertainty. However, clustering schemes for establishing stable clusters, and robust power control (RPC) combating with channel fluctuation are investigated independently. In this paper, joint clustering, and RPC schemes are proposed to optimize the SE, and EE of the involved VANETs. Via the same fixed-length slot, the synchronized interference constraints of cluster heads (CHs) are formed, and offer conditions for RPC. Due to the random channel fluctuations, all CHs’ synchronized interference constraints are formulated as probability constraints. Besides, a pricing-based utility which avoids the separate optimization between SE, and EE is introduced, and the price's impact on the tradeoff between them is involved. Since the probability constraints are intractable, and the unified utility is nonconvex, the Bernstein approximation, and successive convex approximation (SCA) are used to transform the problem into a tractable convex one. Through dual decomposition, two RPC algorithms are proposed to determine the optimal solutions for the fixed price C, and the optimal price C*, respectively. Numerical simulations are used to evaluate the algorithmic performances in high-dynamic system, and the results show that the proposed algorithms are effective. The validity of the clustering method, and the proposed RPC scheme is further verified by comparisons. [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]

Next-generation wireless communications are expected to provide reliable high-rate connectivity. The recent concept of fog-enabled architecture comes to rescue by invoking a substantial amount of data storage close to end-user devices for meeting these challenges. Thus, caching popular contents at heterogeneous devices, e.g., fog nodes (FNs) or fog access points (FAPs), constitutes a promising technique of reducing both the traffic and the energy consumption of the backhaul links. Therefore, in this paper, we propose an energy-efficient caching and node association algorithm for cache-aided fog networks. First, we solve the problem of energy-efficient content caching and delivery in the FNs/FAPs in conjunction with a fixed node association strategy, where the FNs communicate either with other FNs by node-to-node (N2N) communications or with the FAPs in their proximity. In both caching scenarios, we investigate the relationship between the caching probability of the file and the energy-efficient content delivery by formulating the associated energy efficiency (EE) optimization problem under caching memory constraints. Then, we derive a joint modulation mode allocation strategy and caching policy for each content caching node and conceive a joint node association and caching algorithm for maximizing the EE. Finally, we quantify both the overall EE and throughput for demonstrating that the proposed caching and transmission strategy achieves significant performance improvements. [ABSTRACT FROM AUTHOR]

Energy efficiency of three common hybrid automatic repeat request (HARQ) schemes, including Type I HARQ, HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR), is analyzed and joint power allocation and rate selection to maximize the energy efficiency is investigated in this paper. Unlike prior literature, time-correlated fading channels are considered and two widely concerned quality of service (QoS) constraints, i.e., outage and goodput constraints, are also considered in the optimization, which further differentiates this paper from prior ones. Using a unified expression of asymptotic outage probabilities, optimal transmission powers and optimal rate are derived in closed forms to maximize the energy efficiency while satisfying the QoS constraints. These closed-form solutions then enable a thorough analysis of the maximal energy efficiencies of various HARQ schemes. It is revealed that with low-outage constraint, the maximal energy efficiency achieved by Type I HARQ is $\frac{1}{4\ln 2}$ b/J, while HARQ-CC and HARQ-IR can achieve the same maximal energy efficiency as $\frac{\kappa _\infty }{4\ln 2}$ b/J, where $\kappa _\infty = 1.6617$. Moreover, time correlation in the fading channels has a negative impact on the energy efficiency, whereas large maximal allowable number of transmissions is favorable for the improvement of energy efficiency. The effectiveness of the energy-efficient optimization is verified by extensive simulations and the results also show that HARQ-CC can achieve the best tradeoff between energy efficiency and spectral efficiency among the three HARQ schemes. [ABSTRACT FROM AUTHOR]

This paper investigates the integrated optimization of internal powertrain energy management and external vehicles coordination for multiple autonomous and connected hybrid electric vehicles (HEVs). Most existing research on hybrid vehicles powertrain energy management only considers a single vehicle scenario and is independent from vehicle coordination control. With the advancement of connected and autonomous vehicles, optimizing both the vehicle level coordination and powertrain level power management together can bring in substantial benefits in the overall energy efficiency. In this paper, to enable such an integrated optimization scheme, first, the models of the external vehicle level dynamics and internal powertrain dynamics are built for each vehicle. Second, by integrating the dynamics of these two interactive levels together, an augmented system is formed. Then, the HEVs energy management problem and the decentralized vehicle coordination for a set of HEVs are addressed in a unified framework using dynamic programming. The results show that, as a result of the integrated optimization, the vehicles are able to operate close to the most fuel-efficient region, and the battery can avoid being depleted or over charged while maintaining the same state of charge at the beginning and the end. Moreover, much less fuel consumption can be achieved for the driving cycle considered, compared with the separated optimization method. [ABSTRACT FROM PUBLISHER]