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

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]

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]

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]

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

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

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

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

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

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

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

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

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

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

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

This paper addresses the resource allocation (RA) problem for orthogonal frequency division multiple access and single carrier frequency division multiple access based networks using hybrid automatic repeat request (HARQ) when considering energy efficiency (EE) as the metric to optimize. The RA is performed considering the Rayleigh channel, and assuming that only statistical channel state information is available, which fits well device-to-device (D2D) communications setup. Moreover, the use of practical modulation and coding schemes is taken into account. We formulate several RA problems with different EE criteria, subject to constraints on the minimum goodput and the maximum transmit power per-link. Based on a packet error probability approximation, we provide a framework which allows us to derive algorithms maximizing most of the common EE criteria, namely the sum of the EE of the links, the product of the EE, the EE of the link with the worst EE, and the EE of the whole network. The complexity of the proposed algorithms is also provided. The performance related to each criterion are evaluated through simulations and ranked according to the network configurations. We also illustrate on a practical example the real effectiveness of the EE criterion to achieve a better user experience than other common criteria such as the maximum sum rate and the minimum transmit power. [ABSTRACT FROM AUTHOR]

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

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

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

In this paper, we consider the resource-allocation problem for energy efficiency (EE)–spectral efficiency (SE) tradeoff. Unlike traditional research that uses the EE as an objective function and imposes constraints either on the SE or on the achievable rate, we propound a multiobjective optimization approach that can flexibly switch between the EE and SE functions or change the priority level of each function using a tradeoff parameter. Our dynamic approach is more tractable than the conventional approaches and more convenient to realistic communication applications and scenarios. We prove that the multiobjective optimization of the EE and SE is equivalent to a simple problem that maximizes the achievable rate/SE and minimizes the total power consumption. Then, we apply the generalized framework of the resource allocation for the EE–SE tradeoff to optimally allocate the subcarriers' power for orthogonal frequency-division multiplexing (OFDM) with imperfect channel estimation. Finally, we use numerical results to discuss the choice of the tradeoff parameter and study the effect of the estimation error, transmission power budget, and channel-to-noise ratio on the multiobjective optimization. [ABSTRACT FROM AUTHOR]

Energy harvesting (EH) from background radio-frequency signals has recently started to be considered as a promising means of mitigating energy deficiencies in wireless sensors that have limited battery power. In this paper, to deal with the problem of energy shortage, we consider EH and energy efficiency, which is the ratio of the amount of data transferred to the energy dissipated within the system, at the same time. We formulate a nonconvex optimization problem for determining the training interval for channel estimation, user scheduling, and power allocation strategies to maximize the energy efficiency and transform the problem into a tractable convex problem. Based on the convexity of the problem, we propose an energy-efficient resource allocation strategy using an iterative method and provide the convergence of the proposed algorithm based on nonlinear fractional programming. The simulation results show that the proposed algorithm operates in an energy-efficient way, thereby achieving maximum energy efficiency while ensuring the data rate and harvested energy requirements. [ABSTRACT FROM AUTHOR]

In this paper, a resource allocation algorithm for maximizing the energy efficiency (EE) is studied in multicell orthogonal frequency-division multiple-access (OFDMA) wireless networks. The resource allocation is designed based on imperfect channel state information (CSI). Universal frequency reuse is considered, and cochannel interference is dealt with via the cooperation of multiple base stations (BSs) sharing CSI but not user data. We formulate the energy-efficient resource allocation problem as a probabilistic mixed nonconvex optimization problem. The user scheduling, data rate adaptation, and power allocation are jointly designed to maximize the system EE, under the maximum transmitted power constraint and the outage probability constraint. An iterative algorithm is proposed in which the EE keeps improving until algorithm convergence. In each iteration, the energy-efficient power allocation optimization problem is solved by a lower bound problem and a parameterized transformation. Numerical results illustrate the convergence and the effectiveness of the proposed algorithm. In a multicell environment with cochannel interference, the proposed algorithm has large EE gains relative to the spectral efficiency (SE) loss. Compared with noncoordinated methods, the proposed method can achieve higher EE when backhaul power consumption is low. [ABSTRACT FROM AUTHOR]

In this paper, we discuss three different design criteria for a distributed antenna system (DAS). They include maximizing throughput under the constraint of the overall transmit power, minimizing the overall transmit power while guaranteeing the minimum spectral efficiency (SE) requirements, and maximizing energy efficiency (EE) under the constraints of minimum SE requirements and overall transmit power. The optimization problems for the first two criteria are signomial programming (SP) problems when considering the interference among the mobile stations (MSs), which are nonlinear nonconvex and do not lead to the closed-form solutions directly. Exploiting the nature of geometric mean, we transform the SP problems into geometric programming (GP) problems, which can be solved by turning them into convex problems. For the third criterion, using the theorem of fractional programming, we first transform the fractional optimization problem into an equivalent optimization problem in a subtractive form, which is an SP problem. We then obtain its solution by transforming the SP to the GP problem as we have done for the previous two problems. Based on these design criteria, three power allocation algorithms are developed for the downlink multiuser DAS. Depending on the application environments, we can use the first and second criteria to achieve the highest throughput and to save energy, respectively, while balancing throughput and energy consumption using the third criterion. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy consumption issue in cognitive radio (CR) networks. We aim to maximize the energy efficiency of the CR network while considering practical restrictions, including the power budget of the system, the interference thresholds of the primary users (PUs), the rate requirements of the secondary users, and the fairness among them. Particularly, due to the lack of explicit support from the PU system, perfect channel state information may not be acquired. Thus, the interference constraint is posed as chance-constrained form and tackled by Bernstein approximation. Then, we convert the optimization task into a quasi-convex problem via relaxing the integer variables, followed by a simple rounding technique to yield feasible subchannels assignment. We derive a fast algorithm to distribute power among subchannels by exploiting the structure of the power-allocation problem. Moreover, we give an efficient heuristic algorithm for subchannels assignment, which reduces the computation load dramatically. Simulation results show that both our proposed resource allocation schemes perform well in practical scenarios. The energy efficiency obtained by the integer subchannels assignment and the fast power distribution achieves more than 98% of the upper bound. On the other hand, the proposed heuristic subchannels assignment with optimal power allocation achieves a good tradeoff between computation complexity and energy efficiency. [ABSTRACT FROM AUTHOR]

Considering the recent exponential growth in the amount of information processed in Big Data, the high energy consumed by data processing engines in datacenters has become a major issue, underlining the need for efficient resource allocation for more energy-efficient computing. We previously proposed the Best Trade-off Point (BToP) method, which provides a general approach and techniques based on an algorithm with mathematical formulas to find the best trade-off point on an elbow curve of performance vs. resources for efficient resource provisioning in Hadoop MapReduce. The BToP method is expected to work for any application or system which relies on a trade-off elbow curve, non-inverted or inverted, for making good decisions. In this paper, we apply the BToP method to the emerging cluster computing framework, Apache Spark, and show that its performance and energy consumption are better than Spark with its built-in dynamic resource allocation enabled. Our Spark-Bench tests confirm the effectiveness of using the BToP method with Spark to determine the optimal number of executors for any workload in production environments where job profiling for behavioral replication will lead to the most efficient resource provisioning. [ABSTRACT FROM AUTHOR]

Because the energy consumption is growing rapidly, green radio, which lays emphasis on the energy efficiency (EE) in wireless networks, is becoming increasingly important. As potential paradigms for future wireless network design, cognitive radio (CR) is a promising technology to solve the spectrum shortage and inefficiency issues, while cooperative relay is capable of improving spectral efficiency by combating severe fading in wireless environment. In this paper, we investigate the energy-efficient power allocation problem in orthogonal frequency division multiplexing (OFDM)-based relaying CR networks. We develop a general framework to maximize the overall EE of the CR system, under the constraints of transmission power budget, traffic demands, and the interference constraints of the primary users. Our problem formulation is a nonconvex optimization task, and it is hard to obtain the optimal solution. We first convert our formulated problem into a convex optimization problem via its hypograph form, which can be solved by the barrier method. Then we further speed up the computation of Newton step during the barrier method, significantly reducing the complexity of the algorithm by exploiting its special structure. Numerical results validate that our method can exploit the overall EE of CR systems, while the algorithm converges efficiently and stably. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]