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Real-time energy management of hybrid electric vehicles (HEVs) is a key point for performing effective fuel economy optimization. Offline methods have been developed for energy management optimization when the drive cycle is known. Online real-time methods can provide good results but can only ensure suboptimal management. In this paper, it is assumed that information about the route is available in advance. Using this knowledge, global optimization methods can be used in real-time control to approach optimal fuel consumption while keeping the state of charge (Soc) of the batteries at a desired level. Such a method is presented in this paper. The developed strategy is implemented in a real-time experiment using the power-hardware-in-the-loop (PHIL) principle. The measured fuel consumption and the obtained battery Soc trajectory demonstrate good performance of the proposed control. [ABSTRACT FROM PUBLISHER]

This paper investigates the fundamental energy efficiency–spectral efficiency (EE–SE) relationship in a multiple-input–multiple-output (MIMO) orthogonal frequency-division multiple-access (OFDMA) broadcast channel with a practical power model considering the power consumption due to the number of admitted users, as well as the number of active transmit antennas. However, with this power model, the EE–SE tradeoff optimization problem, which jointly optimizes the transmit covariance matrices while determining the optimal admitted user set and the active transmit antenna set, is nonconvex, and hence, it is extremely difficult to solve directly. As a result, we propose an algorithm that decouples the multicarrier EE optimization problem to a set of single-carrier EE optimization problems. For the single-carrier EE optimization problem, we first investigate the EE–SE tradeoff problem with a fixed admitted user set and transmit antenna set. Under this setup, we prove that the EE–SE relationship is a quasiconcave function. Furthermore, EE is proved to be either strictly decreasing with SE or first strictly increasing and then strictly decreasing with SE. Based on these findings, we propose a two-layer resource allocation algorithm to tackle the comprehensive EE–SE tradeoff problem. Meanwhile, since admitting more users and activating more transmit antennas can achieve a higher sum rate at the cost of larger transmit-independent power consumption, there exists a tradeoff between sum-rate gain and power consumption. We therefore study the user and antenna selection approach to further explore the optimal tradeoff. Both the optimal exhaustive search and the Frobenius-norm-based dynamic selection schemes are developed to further improve the achievable EE. To further reduce the computational complexity, a strategy that chooses a fixed admitted user set for all the subcarriers is developed. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE–SE tradeoff. [ABSTRACT FROM AUTHOR]

Energy efficiency and its optimization constitute critical tasks in the design of low-power wireless networks. This paper is devoted to the error rate analysis and energy efficiency optimization of regenerative cooperative networks in the presence of multipath fading under spatial correlation. To this end, exact and asymptotic analytic expressions are first derived for the symbol error rate (SER) of $M$-ary quadrature amplitude and $M$-ary phase-shift keying modulations ( $M$-QAM and $M$- PSK), respectively, assuming a dual-hop decode-and-forward (DF) relay system, spatially correlated Nakagami- $m$ multipath fading, and maximum ratio combining (MRC) at the destination. The derived expressions are subsequently employed in quantifying the energy consumption of the considered system, incorporating both transmit energy and the energy consumed by the transceiver circuits, and in deriving the optimal power allocation (OPA) formulation for minimizing energy consumption under certain quality-of-service (QoS) requirements. A relatively harsh path-loss (PL) model, which also accounts for realistic device-to-device communications, is adopted in numerical evaluations, and various useful insights are provided for the design of future low-energy wireless networks deployments. Indicatively, it is shown that, depending on the degree of spatial correlation, severity of fading, transmission distance, relay location, and power allocation strategy, target performance can be achieved with much overall energy reduction compared with direct transmission (DT) reference. [ABSTRACT FROM AUTHOR]

In heterogeneous networks (HetNets), energy-efficient resource allocation and intercell-interference management are important issues. In this paper, we address these issues using a two-level dynamic scheme. First, we assign the MUs with the optimum number of subchannels that achieves an operator's required balance between macro users' satisfaction and maximization of network efficiency. Then, the remaining subchannels are left to be shared by a number of small cells. In the latter step, a transmit power adaptation method using a noncooperative game-theoretic approach is developed to reduce cochannel interference in the whole network. The problem is formulated by allowing multiple neighboring small cells to share each subchannel (i.e., universal frequency reuse in the small cell level). We fully characterize the pricing factor in the penalty part of the utility function. The existence and uniqueness of the Nash equilibrium (NE) are analyzed and proved. Then, a distributed iterative algorithm based on the fixed-point theorem is proposed to attain the equilibrium of the game. Simulation results are presented to show the effectiveness of the proposed scheme in different network topologies. [ABSTRACT FROM AUTHOR]

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]

Both energy efficiency (EE) and spectral efficiency (SE) are two important design criteria that can hardly be achieved simultaneously. In this paper, we investigate the EE–SE tradeoff for a two-way relaying system with amplify-and-forward (AF) strategy. Applying a unified EE–SE tradeoff metric, a power-allocation problem is formulated as the joint EE and SE maximization problem with a tradeoff factor. In particular, only statistical channel-state information (CSI) is required, and the asymmetric traffic requirements are taken into consideration in our design. Through the exploitation of the theorem of nonlinear fractional programming, a unique closed-form optimum solution is obtained, and some useful insights are also achieved. It has been proved that the provided optimal solution is Pareto optimal for EE–SE optimization. Utilizing these results, one can flexibly make the tradeoff between EE and SE by simply setting the tradeoff factor. Simulation results highlight the effect of the proposed power-allocation scheme. [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 hyperdense heterogeneous networks (HetNets), efficient intercell interference management is an important issue. In this paper, using recent advances in mean-field game (MFG) theory, we propose a novel game-theoretic approach for interference management in HetNets. The intercell interference management issue in HetNets is formulated as two nested problems: an overlay problem at the macrocell base station (MBS) level and an underlay problem at the small-cell base station (SBS) level. In the overlay problem, the MBS selects the optimal action first, which satisfies its associated users with a minimum amount of cross-layer interference, taking into account the reaction of the SBSs. The underlay problem is then formulated as a noncooperative game among the SBSs. The mean-field theory is exploited to help decouple a complex large-scale optimization problem into a family of localized optimization problems. Thus, each SBS can implement its policy by using only its local information and some macroscopic information. In addition to the achieved tradeoff between spectral efficiency (SE) and energy efficiency (EE), our approach can substantially reduce the communication overhead and convergence time of interference management in hyperdense HetNets. Simulation results are presented to show the effectiveness of the proposed scheme. [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]

Taking full advantage of both heterogeneous networks and cloud access radio access networks, heterogeneous cloud radio access networks (H-CRANs) are presented to enhance both spectral and energy efficiencies, where remote radio heads (RRHs) are mainly used to provide high data rates for users with high quality of service (QoS) requirements, whereas the high-power node (HPN) is deployed to guarantee seamless coverage and serve users with low-QoS requirements. To mitigate the intertier interference and improve energy efficiency (EE) performances in H-CRANs, characterizing user association with RRH/HPN is considered in this paper, and the traditional soft fractional frequency reuse (S-FFR) is enhanced. Based on the RRH/HPN association constraint and the enhanced S-FFR, an energy-efficient optimization problem with the resource assignment and power allocation for the orthogonal-frequency-division-multiple-access-based H-CRANs is formulated as a nonconvex objective function. To deal with the nonconvexity, an equivalent convex feasibility problem is reformulated, and closed-form expressions for the energy-efficient resource allocation solution to jointly allocate the resource block and transmit power are derived by the Lagrange dual decomposition method. Simulation results confirm that the H-CRAN architecture and the corresponding resource allocation solution can enhance the EE significantly. [ABSTRACT FROM AUTHOR]

In this paper, the power-allocation problem is solved in the context of maximizing the energy efficiency (EE) of orthogonal frequency-division multiplexing (OFDM) signaling over a two-way amplify-and-forward (AF) relay network with two sources and one relay. The EE is defined as the ratio of the system throughput over the total power consumption incorporating the transmission power, the fixed circuit power, and the dynamic circuit power, which is rate dependent. In the EE maximization (EEM) problem, the efficiency values of the power amplifiers (PAs) of the three terminals can differ from one another. It is shown that EE is a quasi-concave function of the transmission power. Based on this result, we use Dinkelbach's method for fractional programming and propose a two-step allocation (TSA) algorithm to solve the inner loop of Dinkelbach's method. Simulation results demonstrate that the proposed scheme can efficiently maximize the EE. [ABSTRACT FROM PUBLISHER]

This paper investigates advanced energy-efficient wireless systems in orthogonal frequency-division multiple access (OFDMA) downlink networks using coordinated multipoint (CoMP) transmissions between the base stations (BSs) in a heterogeneous network (HetNet), which is adopted by Third-Generation Partnership Project (3GPP) Long-Term Evolution (LTE)-Advanced to meet International Mobile Telecommunications-Advanced targets. HetNet CoMP has received significant attention as a way of achieving spectral efficiency (SE) and energy efficiency (EE). Usually, in the literature, the total network power consumption is restricted to the sum of the power consumption of all BSs. The significance of the power consumption of the backhaul links in wireless networks is normally omitted for its trivial effect with respect to that of the radio BSs. For SE and EE analysis of HetNet CoMP, the energy and bandwidth consumption of the backhaul is considered, without which, the investigation remains incomplete. However, SE and EE are design criteria in conflict with each other, and a careful study of their tradeoff is mandatory for designing future wireless communication systems. The EE is measured as “throughput (bits) per joule,” whereas the power consumption model includes RF transmit (radiated), circuit, and backhaul power. Furthermore, a nonideal backhaul model such as a microwave link is also investigated within intra-HetNet-CoMP (inside one cell), where an implementing fiber is not feasible. An intercell interference (ICI) coordination method is also studied to mitigate ICI. At the end, a novel resource allocation algorithm is proposed—modeled as an optimization problem—which takes into account the total power consumption, including radiated, circuit, and backhaul power, and the minimum required data rate to maximize EE. Given an SE requirement, the EE optimization problem is formulated as a constrained optimization problem. The considered optimization problem is transformed into a convex optimization problem by redefining the constraint using cubic inequality, which results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using dual decomposition with a projected gradient method. Simulations results demonstrate how backhaul has a significant impact on total power consumption and the effectiveness of the studied schemes. In addition, the results demonstrate that the proposed iterative resource allocation algorithm converges within a small number of iterations and illustrate the fundamental tradeoffs between SE and EE. Our analytical results shed light on future “green” network planning in advanced OFDMA wireless systems like those envisioned for a fifth-generation (5G) system. [ABSTRACT FROM PUBLISHER]

To enhance the power efficiency of high-power amplifiers (HPAs) with the demand of reducing energy consumption, a tone reservation (TR) technique is adopted to make orthogonal frequency-division multiplexing (OFDM) signals less sensitive to nonlinearity of HPAs. In this paper, we attempt to minimize the sensitivity from the point of signal-to-noise ratio (SNR) loss at the receiver. Specifically, the nonlinearity impact on OFDM signals is evaluated by a normalized correlation coefficient (NCC) of amplifier input and output signals. With the introduction of the NCC, we model the design of TR as an optimization problem and propose an adaptive-scaling-based gradient-project approach. Simulation results show that, in terms of bit error rate (BER) and power spectral density (PSD) performance, our proposed approach is superior to conventional schemes. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the power allocation issue for cooperative-sensing-based code-division multiple access (CDMA) cognitive radio (CR) networks. We consider a network consisting of multiple secondary users (SUs) and a secondary base station (BS) implementing a two-phase protocol. In the first phase, censor-based cooperative spectrum sensing is carried out to detect the PU's presence. When the channel is estimated to be free, SUs transmit data in the uplink to the BS in the second phase by using CDMA. We optimize the sensing parameters and transmit power of SUs jointly to minimize the total energy consumption with the constraints on SUs' quality of service (QoS) and detection probability of the PU. This is a nonconvex problem, which we represent as a monotonic optimization problem and solve by means of monotonic programming. Furthermore, we study the separate optimization problem in which sensing parameters and transmit power are optimized to minimize the energy consumption of the first phase and the second phase disjointly. Numerical results show that the proposed joint optimization method saves energy consumption significantly in lower signal-to-noise ratios (SNRs). It can introduce as high as 3.9 and 8.7 dB joule energy saving per time slot in zero-sensing SNR under the same data rate constraints of 0.5 and 1 bit/s/Hz, respectively. [ABSTRACT FROM PUBLISHER]

Interference alignment (IA) is a promising technique for interference management in wireless networks. However, the sum rate may fall short of the theoretical maximum, particularly at low signal-to-noise ratio (SNR) levels since IA mainly concentrates on mitigating the interference, instead of improving the quality of desired signal. Moreover, most of the previous works focused on improving spectrum efficiency, but the energy efficiency (EE) aspect is largely ignored. In this paper, an adaptive energy-efficient IA algorithm is proposed through power allocation (PA) and transmission-mode adaptation for green IA-based wireless networks. The PA problem for IA is first analyzed; then, we propose a PA scheme that optimizes the EE of IA-based wireless networks. When the SNR is low, the transmitted power of some users may become zero. Thus, the users with low transmitted power are turned into the sleep mode in our scheme to save energy. The transmitted power and transmission mode of the remaining active users are adapted again to further improve the EE of the network. To guarantee the interests of all the users, fairness among users is also considered in the proposed scheme. Simulation results are presented to show the effectiveness of the proposed algorithm in improving the EE of IA-based wireless networks. [ABSTRACT FROM PUBLISHER]

Economical and environmentally friendly geocast (EEFG) uses traffic signals to communicate with approaching vehicles. The communication can be signal-to-vehicle (TLS2V) and vehicle-to-vehicle (V2V). Based on the information sent, the vehicle receiving the message adapts its speed to a recommended speed (SR ) , which helps the vehicle reduce fuel consumption and emissions. Our previous paper entitled “Optimization of Fuel Cost and Emissions Using V2V Communications” develops a model to determine the optimum SR . It also proposes heuristic expressions to compute the optimum or near-optimum SR. [ABSTRACT FROM PUBLISHER]

In this paper, we present an analytical framework to improve the energy consumption of mobile nodes through traffic offloading via wireless local area networks (WLANs), taking into account the energy consumption for both data transmission and network discovery operations. More specifically, we formulate an optimization problem, according to which the network scanning period is optimized to minimize the total energy consumption and the energy consumption per transmitted bit in a scenario where a user moves with a constant, either pedestrian or vehicular, speed along a road covered by a long-range cellular network and a number of randomly deployed WLANs. The performance of the system that employs the proposed framework, which uses information on the user speed as well as on the availability and the load level of neighboring networks and performs periodic network scanning with the optimal period, is compared against a suboptimal system that does not take into consideration the user and network context information when determining the network scanning period. According to performance evaluation results, the use of the optimal network scanning period achieves significant improvement in terms of total energy consumption, energy efficiency, and network detection delay. [ABSTRACT FROM PUBLISHER]

Coordinated beamforming has been optimized to maximize the sum rate under the transmit power constraints or to minimize the transmit power under the data rate constraints. In this paper, we design a precoder that maximizes the energy efficiency (EE) of multicell multiantenna downlink network with coordination and, in the meantime, ensures the minimal data rate requirement of each user. To find a solution of such a nonconvex optimization problem, we construct a convex subset of the original constraint set and a quasi-concave lower bound of the EE. Then, we propose an iterative algorithm to maximize the lower bound of the EE within the convex subset, which is shown to rapidly converge to a local optimum of the original problem. We evaluate the EE of the proposed algorithm through simulations under different user locations, data rate requirements, and numbers of antennas. The results demonstrate that the proposed precoder performs closely to an upper bound derived from interference-free assumption. When the circuit power consumption is dominant, the proposed precoder is much more energy efficient than the transmit power minimization precoder, as well as a precoder, including an interference alignment (IA) beamformer and the optimized transmit power maximizing the EE. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of adaptive power control of a two-way amplify-and-forward (AF) relaying network based on outage probability minimization. Unlike most of the existing works, our network scenario has asymmetric traffic requirements, and each terminal in the network is subject to an individual power constraint in addition to a total power consumption constraint of the network. To fulfill power control, instantaneous or statistical channel state information (CSI) is assumed to be available at the transmitter of each terminal. By dividing the overall power control problem into a few subproblems corresponding to different power constraints, and using the traffic knowledge along with the CSI, two power control strategies, called short- and long-term strategies, are proposed, leading to closed-form solutions for individual transmit powers at the relay and the sources that are adapted to certain traffic requirements and channel conditions. Simulation results are provided to demonstrate the effectiveness of the proposed power control strategies. It is shown that the short-term strategy can achieve a significant performance gain in terms of the outage probability, regardless of symmetric or asymmetric traffics and channels, and meanwhile, the long-term strategy is more appropriate to asymmetric traffic and channel conditions. [ABSTRACT FROM AUTHOR]

Energy-efficient transmission is crucial for future wireless communication systems and has attracted much attention. In this paper, we study coordinated beamforming optimization for multicell multiple-input–single-output (MISO) downlink systems using energy efficiency as a criterion, which is still an open problem to the best of our knowledge. The optimization problem of interest is nonconvex and in a fractional form. To solve it, we first reveal that finding the solution of the problem is equivalent to searching for a particular point on the Pareto boundary of the newly defined energy-efficiency rate tuples. Then, we propose to use a set of interference temperature (IT)-constrained beamforming optimization problems to characterize the energy-efficiency rate Pareto boundary. Based on that, two efficient iterative algorithms are developed to reach the Pareto optimality and thus to solve the primary problem. We show that the proposed algorithms can be carried out in a decentralized manner and are guaranteed to converge. Then, these algorithms are extended to consider imperfect channel state information (CSI) using the worst-case design. Numerical results are finally provided to verify the effectiveness of the proposed schemes and they exhibit the great potential of the coordinated beamforming optimization in improving the energy efficiency of the cellular network. In particular, the results also illustrate that our proposed algorithms achieve most of the achievable performance gain with a small number of iterations and thereby with limited backhaul overhead in a time-division duplexing (TDD) system. [ABSTRACT FROM AUTHOR]

Societal concerns over global warming issues and dependence on fossil fuels have caused the vehicle market share of plug-in hybrid electric vehicles (PHEVs) to grow. A large penetration of PHEVs, however, would likely put higher stress on power systems. To lessen the PHEVs' harmful impacts on the distribution grid, this paper proposes a two-stage charging control strategy modeled on a renewable-based energy hub. In the first stage, a home-based charging control method is formulated in which the charging cost of PHEVs is considered as an objective. Running this procedure by PHEV schedulers in houses, in the second stage of the proposed strategy, the PHEV coordinator agent (PCA) applies a multicriteria optimization framework to reach optimal charging plans with respect to both the vehicles owners' and system operator's perspectives. Total losses of the distribution system, total rescheduling costs, and wind energy utilization for PHEV charging are those factors that are modeled in the second stage. Once the PCA achieved optimal charging plans of PHEVs, the imposed charging demand should be reported to the energy hub manager, and optimal dispatch of the hub would be determined. [ABSTRACT FROM PUBLISHER]

One of the major considerations in the automotive industry is the reduction of hybrid electric vehicle fuel consumption and operation cost. This paper is the first to use the nondominated sorting genetic algorithm-II (NSGA-II) for power-split plug-in hybrid electric vehicle (PHEV) applications. The NSGA-II, one of the most efficient multiobjective genetic algorithms (MOGAs), simultaneously optimized operation cost, including gasoline and electricity consumption. The Pareto optimal solutions are discussed for the parameter calibrations of the rule-based control strategy as a useful guide in PHEV development, particularly in the earlier phases. The optimized operation cost at the different power-split device (PSD) gear ratios is used to determine the ideal PSD gear ratio to further minimize the operation cost. To validate the proposed strategy, dynamic PSD and powertrain models of PHEV are developed in the numerical analysis. The two typically different driving cycles, namely, the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economic Drive Schedule (HWFET), with different numbers of driving cycles, are used for control strategy optimization. [ABSTRACT FROM PUBLISHER]

In the area of energy-efficient (EE) resource allocation, only limited work has been done on consideration of both transmitter and receiver energy consumption. In this paper, we propose a novel EE resource-allocation scheme for orthogonal frequency-division multiple-access (OFDMA) networks, where both transmitter energy consumption and receiver energy consumption are considered. In addition, different quality-of-service (QoS) requirements, including minimum-rate guarantee service and best effort service, are taken into account. The time slot, subcarrier (frequency), and power-allocation policies are jointly considered to optimize system EE. With all these considerations, the EE resource-allocation problem is formulated as a mixed combinatorial and nonconvex optimization problem, which is extremely difficult to solve. To reduce the computational complexity, we tackle this problem in three steps. First, for a given power allocation, we obtain the time–frequency resource unit (RU) allocation policy via binary quantum-behaved particle swarm optimization (BQPSO) algorithm. Second, under the assumption of known RU allocation, we transform the original optimization problem into an equivalent concave optimization problem and obtain the optimal power-allocation policy through the Lagrange dual approach. Third, an iteration algorithm is developed to obtain the joint time–frequency power–resource-allocation strategy. We validate the convergence and effectiveness of the proposed scheme by extensive simulations. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the energy-efficient job-dispatching algorithm for transcoding as a service (TaaS) in a multimedia cloud. We aim to minimize the energy consumption of service engines in the cloud while achieving low delay for TaaS. We formulate the job-dispatching problem as a constrained optimization problem under the framework of Lyapunov optimization. Using the drift-plus-penalty function, we propose an online algorithm that dispatches the transcoding jobs to service engines, with an objective to Reduce Energy consumption while achieving the QUEue STability (REQUEST). We first characterize the fundamental tradeoff between energy consumption and queue delay for the REQUEST algorithm numerically and obtain its performance bound theoretically. Second, we study the robustness of the REQUEST algorithm, with numerical results indicating that the REQUEST algorithm is robust to the inaccuracy of estimating the transcoding time. Third, we compare the performance of the REQUEST algorithm with the other two algorithms, i.e., the Round Robin and Random Rate algorithms. By simulation and real trace data, we show that by appropriately choosing the control variable, the REQUEST algorithm outperforms the Round Robin and Random Rate algorithms, with smaller time average energy consumption and time average queue length. The proposed REQUEST algorithm can be applied in cloud-assisted multimedia transcoding service. [ABSTRACT FROM PUBLISHER]

In this paper, we first develop an analytical framework for the total energy consumption of an amplify-and-forward (AF) multihop network employing M-ary quadrature amplitude modulation (MQAM) while satisfying an average bit error rate (BER) requirement at the destination over Rayleigh fading channels. Based on this framework, we then establish the conditions under which a dual-hop relay network is always more energy efficient when compared with a reference single-hop network. Moreover, the impact of the relay's location and the energy resource allocation between the relay and the source on the possible energy savings is also analyzed. Since a realistic power consumption model is considered, it is shown that there exists an optimal number of relays for the linear multihop relay network and that the maximum energy efficiency (EE) is obtained through the joint optimization of the number of relays and the constellation size. Considering this fact, we first formulate the optimization problem, and then, this problem is transformed into a convex form by relaxing constraints to be defined over the real numbers (instead of taking only discrete values). The new closed-form optimal solution that involves only elementary functions is derived for this relaxed problem. It is also demonstrated that the optimized multihop network has the potential not only to reduce the total energy consumption but to satisfy the peak-power constraint simultaneously at the expense of more delay as well. Finally, our analysis shows that significant energy savings are possible through joint optimization not only for delay-tolerant networks but for delay-limited networks as well, and up to about 55% energy savings are achievable over the optimized single-hop system. [ABSTRACT FROM PUBLISHER]

This paper investigates the resource-sharing problem in vehicular networks, including both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication links. A novel underlaying resource-sharing communication mode for vehicular networks is proposed, in which different V2V and V2I communication links are permitted to access the same resources for their individual data transmission. To solve the resource-sharing problem in vehicular networks, we, for the first time, apply graph theory and propose the following two interference graph-based resource-sharing schemes: 1) the interference-aware graph-based resource-sharing scheme and 2) the interference-classified graph-based resource-sharing scheme. Compared with the traditional orthogonal communication mode in vehicular networks, the proposed two resource-sharing schemes express better network sum rate. The utility of the proposed V2V and V2I underlaying communication mode and the two proposed interference graph-based resource-sharing schemes are verified by simulations. [ABSTRACT FROM PUBLISHER]

Motivated by the increasing interest in energy-efficient communication systems, the relation between energy efficiency (EE) and spectral efficiency (SE) for multiple-input–multiple-output (MIMO) systems is investigated in this paper. To provide insights into the design of practical MIMO systems, we adopt a realistic power model and consider both independent Rayleigh fading and semicorrelated fading channels. We derived a novel and closed-form upper bound (UB) for the system EE as a function of SE. This UB exhibits great accuracy for a wide range of SE values and, thus, can be utilized for explicit assessment of the influence of SE on EE and for analytically addressing the EE optimization problems. Using this tight EE UB, our analysis unfolds two EE optimization issues: Given the number of transmit and receive antennas, an optimum value of SE is derived, such that the overall EE can be maximized, and given a specific value of SE, the optimal number of antennas is derived for maximizing the system EE. [ABSTRACT FROM AUTHOR]

Spectrum sensing is an important aspect of cognitive radio networks (CRNs). Secondary users (SUs) should periodically sense the channels to ensure primary-user (PU) protection. Sensing with cooperation among several SUs is more robust and less error prone. However, cooperation also increases the energy spent for sensing. Considering the periodic nature of sensing, even a small amount of savings in each sensing period leads to considerable improvement in the long run. In this paper, we consider the problem of energy-efficient (EE) spectrum sensing scheduling with satisfactory PU protection. Our model exploits the diversity of SUs in their received signal-to-noise ratio (SNR) of the primary signal to determine the sensing duration for each user/channel pair for higher energy efficiency. We model the given problem as an optimization problem with two different objectives. The first objective is to minimize the energy consumption, and the second objective is to minimize the spectrum sensing duration to maximize the remaining time for data transmission. We solve both problems using the outer linearization method. In addition, we present two suboptimal but efficient heuristic methods. We provide an extensive performance analysis of our proposed methods under various numbers of SUs, average channel SNR, and channel sampling frequency. Our analysis reveals that all proposals with an energy minimization perspective provide significant energy savings compared with a pure transmission-time maximization (TXT) technique. [ABSTRACT FROM PUBLISHER]

This paper focuses on the development of computational algorithms for determining online energy-based driving guidance for an electric vehicle (EV) endowed with regenerative breaking system capabilities. A predictive decision support system is designed to optimally distribute energy flow between the instantaneous power demand requested by the driver for the powertrain engine and the different auxiliaries relating to comfort performance, such as the heating system. The proposed methodology uses an online particle swarm optimization (PSO) algorithm to search for a global optimum relative to specific objective functions, which take into account battery autonomy, driving comfort indexes, and travel time. Our methodology has been validated for a heavy motorized quadricycle vehicle using hardware-in-loop (HIL) simulations, for which the energy management system has been implemented in a digital signal processing (DSP) board communicating through a controller area network (CAN) protocol. [ABSTRACT FROM PUBLISHER]

In this paper, based on the prey model in foraging theory, the BIO-inspired Cross-layer (BIOX) communication and coordination protocol is introduced for wireless sensor and actor networks (WSANs). BIOX permits each sensor node to autonomously determine its next-hop selection and channel access strategy using bio-inspired next-hop selection and channel access profitability measures. Based on these profitability measures, BIOX provides optimal performance in energy-efficient and reliable sensor–actor communication. Furthermore, using task allocation profitability measure, BIOX also guarantees stable allocation of available tasks in a way that each task is accomplished by an actor node within a bounded time delay. Performance evaluations reveal that BIOX significantly prolongs the network lifetime while providing highly reliable sensor–actor communication and effective task allocation for actor nodes. [ABSTRACT FROM PUBLISHER]