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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]

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]

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) 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]

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]

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 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]

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]

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 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]

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]

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

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

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

This paper investigates the energy efficiency (EE) optimization in downlink multi-cell massive multiple-input multiple-output (MIMO). In our research, the statistical channel state information (CSI) is exploited to reduce the signaling overhead. To maximize the minimum EE among the neighbouring cells, we design the transmit covariance matrices for each base station (BS). Specifically, optimization schemes for this max-min EE problem are developed, in the centralized and distributed ways, respectively. To obtain the transmit covariance matrices, we first find out the closed-form optimal transmit eigenmatrices for the BS in each cell, and convert the original transmit covariance matrices designing problem into a power allocation one. Then, to lower the computational complexity, we utilize an asymptotic approximation expression for the problem objective. Moreover, for the power allocation design, we adopt the minorization maximization method to address the non-convexity of the ergodic rate, and use Dinkelbach’s transform to convert the max-min fractional problem into a series of convex optimization subproblems. To tackle the transformed subproblems, we propose a centralized iterative water-filling scheme. For reducing the backhaul burden, we further develop a distributed algorithm for the power allocation problem, which requires limited inter-cell information sharing. Finally, the performance of the proposed algorithms are demonstrated by extensive numerical results. [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]

Mobile edge computing (MEC) has been envisioned as a promising approach for enabling the computation-intensive yet latency-sensitive mobile Internet services in future wireless networks. In this paper, we investigate the secrecy based energy-efficient MEC via cooperative Non-orthogonal Multiple Access (NOMA) transmission. We consider that an edge-computing device (ED) offloads its computation-workload to the edge-computing server (ECS) subject to the overhearing-attack of a malicious eavesdropper. To enhance the secrecy of the ED’s offloading transmission, a group of conventional wireless devices (WDs) are scheduled to form a NOMA-transmission group with the ED for sending data to the cellular base station (BS) while providing cooperative jamming to the eavesdropper. We formulate a joint optimization of the ED’s offloaded workload, transmit-power, NOMA-transmission duration as well as the selection of the WDs, with the objective of minimizing the total energy consumption of the ED and the selected WDs, while subject to the ED’s latency-requirement and the selected WDs’ required data-volumes to deliver. Despite the nature of mixed binary and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a three-layered algorithm for solving it efficiently. To further address the fairness among different WDs, we investigate a system-wise utility maximization problem that accounts for the fairness in the WDs’ delivered data and the total energy consumption of the ED and WDs. By exploiting our previously designed layered-algorithm, we further propose a stochastic learning based algorithm for determining each WD’s optimal data-volume delivered. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of the secrecy based computation offloading via NOMA. [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]

By offloading tasks from the mobile device (MD) to its nearby deployed access points (APs), each of which is connected to one server for task processing, computation offloading can strike a balance between MD’s task execution delay and energy consumption in mobile edge computing (MEC) systems. Considering communication and computation dynamics in MEC systems, we aim to design online computation offloading mechanisms in this paper to minimize the time average expected task execution delay under the constraint of average energy consumption. Firstly, with known current channel gains between the MD and APs as well as available computing capability at MEC servers, we leverage the Lyapunov optimization framework to make an optimal one-slot decision on MD’s transmit power allocation and MEC server selection. On this basis, we then consider a more realistic scenario, where it is difficult to capture current available computing capability at MEC servers, and combine the multi-armed bandit framework for an online learning based MEC server selection algorithm. Finally, through theoretical analyses and extensive simulations, we demonstrate the near-optimality and feasibility of our proposed algorithms, and present that our proposed algorithms fully explore the interplay between communication and computation with enriched user experience and reduced energy consumption. [ABSTRACT FROM AUTHOR]

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

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

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]

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]

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]

In this paper, weighted sum energy efficiency (WSEE) in uplink and downlink of a multi-user non-orthogonal multiple access (NOMA) system is considered. we adopt a more realistic power consumption model where signal processing power is modeled as a linear function of transmit power. Rather than the well-known network-centric global energy efficiency (GEE) metric, which is a pseudo-concave (PC) function, the weighted sum energy efficiency metric is considered, which is not PC in general. To find the optimum user power allocation Dinkelbach-like algorithm is adopted, by which each individual fractional EE function is converted to a parametric function where under some conditions on the weights falls into a class of convex optimization problems and it is solved in the dual domain. The dual variables are updated using a sub-gradient and cutting plane-based algorithm, which here ellipsoid method is used. Since the optimum solution restricts user weights, a low complexity suboptimum algorithm that does not consider any condition on user's weights is proposed. The problem is non-convex in general; hence, epigraph form followed by successive convex approximation (SCA) is used to deal with that problem. Results demonstrate that with the user-oriented metric, one can provide different priorities to users, and by choosing proper weights entail fairness among users. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is one of the most significant technologies to meet the demand of high spectral efficiency (SE) in the fifth generation (5G) cellular networks. The utilization of simultaneous wireless information and power transfer (SWIPT) contributes to prolonging the battery life of the mobile users (MUs) and enhancing the system energy efficiency (EE), especially in the NOMA scenario where the inter-user interference can be reused for energy harvesting (EH). In this paper, we study the achievable data rate maximization problem for the downlink multi-carrier NOMA (MC-NOMA) network with power splitting (PS)-based SWIPT, in which power allocation and PS control are jointly optimized with the limitation of available power budget as well as the requirement for EH. The considered non-convex optimization problem is arduous to tackle, resulting from the presence of the coupled variables and the inter-user interference. To cope with the problem, a decoupled approach is developed, in which the power allocation and PS control are separated and the corresponding sub-problems are respectively solved through Lagrangian duality method. Furthermore, an alternative approach based on deep learning is proposed, which is capable of effectively obtaining the approximate optimal solution according to the empirical data. Simulation results confirm the effectiveness of the proposed schemes, and demonstrate the superiority of the combination of PS-based SWIPT with MC-NOMA over SWIPT-aided single-carrier NOMA (SC-NOMA) and SWIPT-aided orthogonal multiple access (OMA). [ABSTRACT FROM AUTHOR]

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

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

This paper aims at investigating the tradeoff between ergodic energy efficiency (EE) and spectral efficiency (SE) for device-to-device (D2D) communications underlaying cellular networks. Assuming average-based resource allocation, we propose a multiobjective optimization problem (MOOP) approach to deal with the EE and SE maximization problem. Specifically, the formulated MOOP results in the maximization of the total ergodic sum rate of the D2D users and the minimization of the total average transmit power of the D2D transmitters. Two practical scenarios are considered, i.e., a limited interference scenario, which includes the sparsely deployed scenario, and a densely deployed scenario, where for each scenario the maximum achievable EE and SE are derived. Specifically, considering the limited interference scenario, an upper bound is imposed on the received interference across different D2D communications, and closed-form expressions for the optimal power allocation, ergodic sum rate, and maximum EE are obtained. In the general, densely deployed scenario, however, the cross D2D network interference needs to be taken into account. We provide an optimal solution for the power control based on sequential fractional programming to tradeoff between complexity and performance gain. Simulation results unveil an interesting tradeoff to strike a balance between EE and SE. [ABSTRACT FROM AUTHOR]

In a wirelessly-powered communication network (WPCN), an energy access point (E-AP) supplies the energy needs of the network nodes through radio frequency wave transmission, and the nodes store their received energy in their batteries for possible data transmission. In this paper, we propose an online control policy for energy transfer from the E-AP to the wireless nodes and for data transfer among the nodes. With our proposed control policy, all data queues of the nodes are stable, while the average energy consumption of the network is shown to be within a bounded gap of the minimum energy required for stabilizing the network. Our proposed policy is designed using a quadratic Lyapunov function to capture the limitations on the energy consumption of the nodes imposed by their battery levels. We show that under the proposed control policy, the backlog level in the data queues and the stored energy level in the batteries fluctuate in small intervals around some constant levels. Consequently, by imposing a negligible average data drop rate, the data buffer size and the battery capacity of the nodes can be significantly reduced. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel hybrid user pairing (HUP) scheme in multiuser multiple-input single-output non-orthogonal multiple access networks with simultaneous wireless information and power transfer. In this system, two information users with distinct channel conditions are optimally paired while energy users perform energy harvesting (EH) under non-linearity of the EH circuits. We consider the problem of jointly optimizing user pairing and power allocation to maximize the overall spectral efficiency (SE) and energy efficiency (EE) subject to user-specific quality-of-service and harvested power requirements. A new paradigm for the EE-EH trade-off is then proposed to achieve a good balance of network power consumption. Such design problems are formulated as the maximization of non-concave functions subject to the class of mixed-integer non-convex constraints, which are very challenging to solve optimally. To address these challenges, we first relax binary pairing variables to be continuous and transform the design problems into equivalent non-convex ones, but with more tractable forms. We then develop low-complexity iterative algorithms to improve the objectives and converge to a local optimum by means of the inner approximation framework. Simulation results show the convergence of proposed algorithms and the SE and EE improvements of the proposed HUP scheme over state-of-the-art designs. In addition, the effects of key parameters such as the number of antennas and dynamic power at the BS, target data rates, and energy threshold, on the system performance are evaluated to show the effectiveness of the proposed schemes in balancing resource utilization. [ABSTRACT FROM AUTHOR]

In this paper, we study the trajectory and resource allocation design for downlink energy-efficient secure unmanned aerial vehicle (UAV) communication systems, where an information UAV assisted by a multi-antenna jammer UAV serves multiple ground users in the existence of multiple ground eavesdroppers. The resource allocation strategy and the trajectory of the information UAV, and the jamming policy of the jammer UAV are jointly optimized for maximizing the system energy efficiency. The joint design is formulated as a non-convex optimization problem taking into account the quality of service (QoS) requirement, the security constraint, and the imperfect channel state information (CSI) of the eavesdroppers. The formulated problem is generally intractable. As a compromise approach, the problem is divided into two subproblems which facilitates the design of a low-complexity suboptimal algorithm based on alternating optimization approach. Simulation results illustrate that the proposed algorithm converges within a small number of iterations and demonstrate some interesting insights: (1) the introduction of a jammer UAV facilitates a highly flexible trajectory design of the information UAV which is critical to improving the system energy efficiency; (2) by exploiting the spatial degrees of freedom brought by the multi-antenna jammer UAV, our proposed design can focus the artificial noise on eavesdroppers offering a strong security mean to the system. [ABSTRACT FROM AUTHOR]

This correspondence paper proposes a novel low-complexity radio-frequency (RF) precoding and combining scheme for wideband multiuser millimeter wave hybrid-array systems, targeting at maximizing the system energy efficiency. We first derive a nearly-optimal fully-connected RF precoder, via minimizing the correlation across different users and subcarriers. We then extend the optimization solution to subarray-based architectures by exploiting the unitary matrix feature of subarrays. With the obtained phase values of the precoder, we optimize the power allocation on each subcarrier of the baseband precoder. Simulation results are provided and validate the effectiveness of our proposed hybrid precoding scheme. [ABSTRACT FROM AUTHOR]

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

In this paper, we study the optimal power allocation for a radio frequency (RF)-powered cooperative network and propose energy-saving time switching relaying (ES-TSR) and energy-saving power splitting relaying (ES-PSR) protocols with an iterative energy-saving (ES) algorithm. In particular, we develop the analytical expressions of the channel capacity and the total energy consumption for the considered cooperative network. We formulate the system energy efficiency maximization problems and then provide the corresponding solutions by deriving the source's optimal transmit power in terms of energy fraction consumed with time switching (TS) factor and power splitting (PS) factor for the proposed ES-TSR and ES-PSR protocols, respectively, in each transmission block. From the past approaches, the optimal power allocation to maximize the system energy efficiency of the RF-powered cooperative networks has been studied under the assumption that the relay must utilize all of its harvested power in each transmission block. This work considers optimal power allocation to maximize the system energy efficiency for the case when the relay node can save energy for the next transmission block. Our simulation results show that the proposed ES-TSR and ES-PSR protocols outperformed the existing TS-based and PS-based relaying protocols with $\text{14.22}\%$ and $\text{8.4}\% $ , respectively, in paticular when the number of transmission blocks $\boldsymbol{L} = 10$. [ABSTRACT FROM AUTHOR]

Multi-view videos (MVVs) provide immersive viewing experience, at the cost of traffic load increase for wireless networks. In this paper, we would like to optimize MVV transmission in a multiuser wireless network by exploiting both natural multicast opportunities and view synthesis-enabled multicast opportunities. Specifically, we first establish a mathematical model to specify view synthesis at the server and each user, and characterize its impact on multicast opportunities. This model is highly nontrivial and fundamentally enables the optimization of view synthesis-based multicast opportunities. For given video quality requirements of all users, we consider the optimization of view selection, transmission time and power allocation to minimize the average weighted sum energy consumption for view transmission and synthesis. In addition, under the energy consumption constraints at the server and each user respectively, we consider the optimization of view selection, transmission time and power allocation and video quality selection to maximize the total utility. These two optimization problems are challenging mixed discrete-continuous optimization problems. For the first problem, we propose an algorithm to obtain an optimal solution with reduced computational complexity by exploiting optimality properties. For each problem, to reduce computational complexity, we also propose a low-complexity algorithm to obtain a suboptimal solution, using Difference of Convex (DC) programming. Finally, numerical results show the advantage of the proposed solutions over existing ones, and demonstrate the importance of the optimization of view synthesis-enabled multicast opportunities in MVV transmission. [ABSTRACT FROM AUTHOR]

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

The placement of Unmanned Aerial Vehicle (UAV) based aerial Base station (BS) has emerged as a viable solution to the contemplated high traffic demands of the future wireless networks. Nevertheless, the practical realization of UAVs for wireless coverage provisioning demands efficient utilization of its limited onboard energy reserves. This paper studies the power-domain Non-Orthogonal Multiple Access (NOMA) scheme for energy-efficient placement of the UAV-BS. The objective of maximized information bits per unit energy consumption of the UAV-BS is developed assuming a multi-Quality of Service (QoS) scenario. Also, the efficiency analysis is carried out considering the composite of energies consumed for the signal transmission and hover operation of the UAV-BS. The formulated Non-Linear Fractional Problem (NLFP) as a function of transmission power and altitude is solved by employing an alternating optimization technique based on a nested Dinkelbach structure. An investigation into the user pairing scheme also sheds new light on its effect upon the constrained feasible altitude range of the UAV-BS. Consequently, a computationally efficient approach is devised to render solution space based on the identified subset of user pairs. The numerical analyses of the proposed scheme signify a substantial increase in the energy efficiency of the NOMA based UAV-BS in comparison to the baseline scheme of Orthogonal Multiple Access (OMA). Thus, the improved energy efficiency enables enhanced coverage for the UAV-BS. [ABSTRACT FROM AUTHOR]

In order to provide dynamic adaptation of the performance/energy tradeoff, systems today rely on heterogeneous multicore architectures (different micro-architectures on a chip). These systems are limited to single-ISA approaches to enable transparent migration between the different cores. To offer more tradeoff, we can integrate statically scheduled micro-architecture and use dynamic binary translation (DBT) for task migration. However, in a system where performance and energy consumption are a prime concern, the translation overhead has to be kept as low as possible. In this paper, we present Hybrid-DBT, an open-source, hardware accelerated DBT system targeting VLIW cores. Three different hardware accelerators have been designed to speed-up critical steps of the translation process. Experimental study shows that the accelerated steps are two orders of magnitude faster than their software equivalent. The impact on the total execution time of applications and the quality of generated binaries are also measured. [ABSTRACT FROM AUTHOR]

This paper presents three energy-efficient power allocation (PA) schemes for the downlink of a distributed multiple-input multiple-output (D-MIMO) system with beamforming at distributed antenna ports and antenna selection (AS) at a mobile station over composite Rayleigh channels. Subject to maximum power constraints, a constrained maximization problem on energy efficiency (EE) is formulated. Based on theoretical analysis, the optimal beamforming is shown to be aligned with the channel direction that renders the joint design problem to be a PA optimization one. It is shown that the optimization problem is strictly pseudo-concave with a unique maximum. Thus, an optimal PA scheme based on a gradient projection method is proposed to obtain the global solution. To speed up the gradient projection method, a closed-form initialization is derived. Furthermore, a low-complexity optimal PA schemes based on the Karush–Kuhn–Tucker conditions is proposed. This optimal scheme has a closed-form PA expression for a certain condition or else computes the PA by the Lambert function or the bisection method, and gives the same EE performance as that of the optimal gradient-based scheme. The performances of these two optimal schemes are optimal and verified to be valid in contrast to the inaccurate optimal scheme in the literature. For large maximum power, the closed-form initial PA will become the optimal solution and can be considered as a suboptimal solution with no iterative computation. Making use of the tight upper and lower bounds of the Lambert function, a very low-complexity suboptimal PA scheme is developed. Simulation results show that the proposed suboptimal scheme can achieve nearly optimal performance. Maximum EE and maximum channel power, which are optimal and suboptimal criteria, are adopted for antenna selection. The simple suboptimal criterion can provide EE performance very close to that of the optimal one. The D-MIMO with AS is shown to give considerable EE gain over the single receive antenna system. Moreover, the proposed three PA schemes can be used for multiuser diversity D-MIMO systems to achieve superior EE performance. [ABSTRACT FROM AUTHOR]

In this paper, we optimize the energy efficiency (bits/s/Hz/J) of device-to-multi-device (D2MD) wireless communications. While the device-to-device scenario has been extensively studied to improve the spectral efficiency in cellular networks, the use of multicast communications opens the possibility of reusing the spectrum resources also inside the groups. The optimization problem is formulated as a mixed integer non-linear joint optimization for the power control and allocation of resource blocks (RBs) to each group. Our model explicitly considers resource sharing by letting co-channel transmission over a RB (up to a maximum of $r$ transmitters) and/or transmission through $s$ different channels in each group. We use an iterative decomposition approach, using first matching theory to find a stable even if sub-optimal channel allocation, to then optimize the transmission power vectors in each group via fractional programming. In addition, within this framework, both the network energy efficiency and the max-min individual energy efficiency are investigated. We characterize numerically the energy-efficient capacity region, and our results show that the normalized energy efficiency is nearly optimal (above 90% of the network capacity) for a wide range of minimum-rate constraints. This performance is better than that of other matching-based techniques previously proposed. [ABSTRACT FROM AUTHOR]

Recently, the concept of network virtualization and network slicing attracted significant attention from both industry and academia as a key component of the evolving 5G architecture to allow the efficient entrance of vertical industries and tackle increased aggregate traffic by flexible network re-configurability. However, the potential price to be paid for facilitating network slicing in a multi-tenant virtual network is the underutilization of the scarce wireless network resources due to the different tenant requirements and the inherent dynamics of the traffic. A potential way to avoid such sacrifice of radio resources is to allow efficient inter-tenant resource sharing. To this end, this paper proposes a novel optimization framework for flexible inter-tenant resource sharing embedded with transmission power control to aggressively improve network capacity, the utilization of wireless access resources, user data rate as well as energy efficiency. More specifically, we define two novel resource sharing mechanisms called tight coupling and loose coupling, respectively, via mixed integer linear programming formulations. Furthermore, two resource and power joint allocation algorithms are designed to solve the optimization problem in polynomial time. Based on 3GPP network parameterization, a rigorous analysis via a wide set of numerical investigations reveal that significant gains in network throughput, individual user rate, and energy efficiency can be achieved compared with current baseline network slicing methods and constant power resource sharing algorithms. [ABSTRACT FROM AUTHOR]