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In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA–MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm. [ABSTRACT FROM AUTHOR]

To meet the demand of green communications of ultradense mobile devices (MDs), an energy-efficient mechanism, jointly considering the computation offloading and resource management, is designed to minimize the network-wide weighted energy consumption under the delay constraints of MDs for ultradense heterogeneous networks. Such a mechanism tightly integrates with the adjustment of computation capability and transmission power of MDs. Considering that the finally formulated problem is in a nonlinear and mixed-integer form, we design an effective algorithm to solve it. Specifically, by utilizing the powerful global searching capability of genetic algorithm (GA) and the accurate local searching capability of particle swarm optimization (PSO), the adaptive GA with diversity-guided is firstly used for coarse-grained search, and then adaptive PSO is utilized for fine-grained search. After that, some detailed analyses on the convergence, computation complexity and parallel implement are provided for algorithms. Simulation results show that the designed algorithm can achieve a lower network energy consumption than other offloading algorithms in general. At the same time, the numerical simulation also reveals that the designed algorithm may be more suitable for ultradense networks than existing algorithms. [ABSTRACT FROM AUTHOR]

In heterogeneous cellular networks (HCNs), the energy reduction has a great impact on the operational cost and global carbon footprint, which has attracted more and more attention. To enhance the network energy efficiency (EE), we design two types of association mechanisms integrated with open loop power control (OLPC) for uplink code-division-multiple-access (CDMA) enabled HCNs. Specifically, they are formulated into two optimization problems that maximize the sum of users' EEs and the one of users' EE utilities. It is noteworthy that the one of these two mechanisms guarantees the user fairness by employing a logarithmic function. Considering that these formulated problems are in a fractional mixed-integer form and hard to tackle, we utilize a coalitional game to design two types of association algorithms with guaranteed convergence. To show the effectiveness of designed algorithms, another one is introduced for comparison in the simulation. As for them, we mainly investigate the impacts of user and BS densities on the association performance. [ABSTRACT FROM AUTHOR]

The unmanned aerial vehicle (UAV) assisted transmission can significantly improve the spectrum efficiency and coverage of the wireless communication network. The paper proposed a cooperative secrecy transmission mechanism, which can take advantage of transmitter signal of primary user (PU) as a dedicated radio frequency (RF) source with decode and forward (DF) UAV selection and energy harvesting (EH) under cognitive network. Specifically, the transmitting signal of PU in the cognitive network can be viewed and used as energy to drive secondary user (SU) nodes and multi-relay nodes to send signals. It was worth noting that the DF UAV relay may bring about the unexpected noise and decrease the received signal to noise ratio (SNR), therefore, the multiple UAV assisted relays selected with secrecy capacity maximization criteria under cognitive network was proposed and investigated in detail, where the destination node equipped with multiple antennas and adopted the optimal antenna selection receiver. Moreover, By using the function analysis method, the closed expressions of the system secrecy outage probability (SOP) and the probability of non-zero secrecy capacity were calculated and derived accurately. Furthermore, the asymptotic expressions of the UAV assisted relay system under the large SNR cases were analyzed in detail, where the expressions of secrecy diversity order and secrecy diversity gain of the UAV assisted relay cognitive system were obtained thoroughly. Simulation results verified the effectiveness of the proposed mechanism and the correctness of the calculation. [ABSTRACT FROM AUTHOR]

To achieve a tradeoff between spectral efficiency (SE) and energy efficiency (EE) experiences, we design an offloading mechanism to maximize the sum of weighted logarithmic utilities with respect to SE and EE for heterogeneous cellular networks, where the weighting parameters are used for adjusting the SE–EE experiences. Unlike the most existing offloading mechanisms that often try to find a tradeoff between SE and EC (energy consumption), we concentrate on a tradeoff between SE–EE experiences in our mechanism. That is to say, our mechanism directly optimizes EE and is weighted in favour of SE–EE fairness. To solve the finally formulated problem in a mixed-integer and nonlinear form, we firstly make some relaxation for the optimization objective to obtain its an upper bound. Then, we perform a decoupling operation to achieve a decomposable form of dual problem. Finally, we develop a feasible algorithm that can be well implemented in a distributed manner. Numerical results show that the designed mechanism can definitely reach a tradeoff between SE–EE experiences. [ABSTRACT FROM AUTHOR]

In this paper, we devote to maximize the secrecy energy efficiency (SEE) for a cloud radio access network where exists an eavesdropper wiretapping information from remote radio head. We first establish the system model and then formulate the power optimization problem to maximize the SEE of the system subject to the fronthaul capacity and transmit power constraints. To address this nonconvex problem, we first convert the fractional objective function into a subtractive form and two algorithms are then proposed to solve it. Simulation results indicate that the proposed the iterative optimization algorithms can obtain higher SEE and present the merit of AN in enhancing the system performance when compared with conventional methods. [ABSTRACT FROM AUTHOR]

Abstract Underwater acoustic sensor networks play a significant role in ocean observation, pollution monitoring and underwater remote control. Different from conventional sensor networks, transmission efficiency and lifetime for nodes in underwater acoustic sensor networks is vital since it is difficult to change batteries of buoy relays and underwater sensors. In this paper, two resource allocation algorithms for energy harvesting time and transmit power allocation in underwater acoustic sensor networks with single and multiple wireless energy harvesting buoy nodes are proposed to maximize the sum rate and energy efficiency. Both of the circuit power consumption and transmission power consumption are considered as the power consumption of the system. We first propose an optimal algorithm to allocate the time of energy harvesting slot for sum rate maximization. From the derivation, we show that the sum rate of the network grows as the transmission power increases. However, when the transmission power is extremely large, the energy efficiency of the network starts to decrease. Thus a joint algorithm with energy harvesting time and transmit power allocation is further provided for energy efficiency maximization. Simulation results validate the sum rate improvement under our proposed sum rate maximization algorithm and energy efficiency improvement under the proposed energy efficiency maximization algorithm. [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]

Low cost and high efficiency, defined as energy efficiency (EE) and spectral efficiency (SE), have raised more and more attention in the fifth generation (5G) communication systems due to steadily rising hardware cost, energy consumption, and mobile traffic. This paper studies the hybrid architecture of multiuser massive MIMO systems, where the digital domain utilizes the zero-forcing (ZF) precoding scheme and the analog domain uses discrete Fourier transform (DFT) processing that significantly reduces hardware cost and energy consumption. We derive analytical expressions on the total achievable SE and EE, as well as offering insight into some engineering parameters in the system performance. Our aim is to achieve low cost and high efficiency massive MIMO system, with constraints on the overall transmit power, the number of users, and the number of radio frequency (RF) chains. Results exhibit that the total achievable SE of the hybrid architectures with DFT precessing is inferior to the full digital architectures and hybrid architectures with the ideal phase shifters, but the performance attenuation can be compensated by providing the more input SNR and higher number of RF chains. Moreover, we find that the total achievable EE of hybrid architectures with DFT precessing outperforms other massive MIMO architectures that include a full digital implementation, ideal phase shifters, and a switched network. [ABSTRACT FROM AUTHOR]

The energy-efficient resource allocation problem is investigated for the downlink massive multiple-input–multiple-output (MIMO) frequency-division duplexing (FDD) system under a correlated Rayleigh fading channel. Our objective is to maximize energy efficiency by adjusting the training duration, training power, and data power, under the constraint of the total transmit energy and spectral efficiency requirement for the user, which evaluates the impacts of the training and data transmission phases simultaneously. The optimization problem is established in a complicated form, where the difficulties lie in the nonanalytic expression of the involved objective function, as well as the nonconvex nature of the optimization problem. To solve it, the deterministic equivalent approximation methodology is introduced to obtain an accurate analytical expression of the cost function. Based on this, the optimization problem in a nonconvex fractional form is transformed into an equivalent optimization problem with a parametric subtractive form, which is still nonconvex. Thus, the objective function is lower bounded by a concave function, which leads to the availability of standard convex theory, and finally, an iterative resource allocation algorithm is proposed. Moreover, we obtain the closed-form solutions using the Lambert W function for some special channel cases. Numerical results validate the benefits of the proposed scheme and illustrate the tradeoff between training and data transmission. [ABSTRACT FROM PUBLISHER]

In this paper, the relationship between the energy efficiency in terms of the total consumed power per bit and the spectral efficiency is studied for dual-hop cooperative relaying systems consisting of multiple single-antenna amplify-and-forward relays. Considering the source and relay transmit antenna gains, the path-loss fading and the power consumption model as a whole, a new metric to measure the energy to spectral-efficiency trade-off (ESET) is formulated. Based on the convexity analysis of the new cost function, an analytical expression of the minimum per-bit power consumed by the whole relay system is derived as a general framework of the best ESET for given number of relays and relay-to-source power allocation. The relay-to-source power ratio is then further optimized, leading to a closed-form solution to relay design and power allocation. Numerical simulation results validating the theoretical analysis and the derived optimal ESET are provided. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, the joint source-relay power allocation issue in green dual-hop single-antenna relaying systems is investigated from the energy efficiency point of view. By considering a practical power consumption model, the optimal transmit power of the source and that of the relay are derived in the form of analytical expressions based on the maximisation of energy efficiency with a guarantee of the required spectral efficiency. The novelty of the work lies in that the highly complicated optimisation problem is solved by deliberately manipulating the Lagrangian function using the properties of the Lambert function. It is shown that the optimal relay-to-source power ratio is adaptive to the ratio of the instantaneous two-hop channel gains. Numerical simulations have illustrated the effectiveness of our proposed scheme. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The mass machine type communication (mMTC) in 5G requires the technical support of the wireless sensor network (WSN). Compared with homogeneous WSNs, heterogeneous WSNs provide more flexibility and are more in line with the reality. For heterogeneous WSNs, the use of proper routing methods can prolong the lifetime as well as the stability of the system. This paper studies an energy heterogeneous WSN, where nodes can cooperate with each other to improve the energy efficient of the network. We focus on the network routing within distributed energy-efficient clustering (DEEC) protocol, a typical networking/communication protocol for heterogeneous WSNs. First, we derive a proposition on the choice of cooperative single-hop or multi-hop modes, which gives a sufficient condition on cooperative multi-hop transmission. Furthermore, we present two lemmas, which are the conditions on the distance threshold for mode selection. Based on the above, a novel adaptive cooperative routing algorithm combined with DEEC is proposed. Simulation results show that the proposed routing algorithm outperforms the benchmark schemes and can effectively reduce the network energy consumption and prolong the network lifetime. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a cooperative Internet of Things (IoT) communication network with decode-and-forward relay nodes that can be eavesdropped, where the relay node harvests energy from the received signal power to charge its battery, which is used to retransmit the received signal to the destination. The secrecy communication of the energy harvesting Gaussian eavesdropping channel has been studied. The expression of secrecy energy efficiency for the cooperative IoT system with a battery-enabled relay has been derived, which depends on energy absorption rate and energy harvesting time. Then we perform analytical and numerical analysis on the performance on secrecy energy efficiency. Our analytical results reveal that the secrecy energy efficiency has a maximum under constraints. Finally, we demonstrate that our proposed model and methods are feasible from simulation results based on proposed secrecy energy efficiency maximization algorithm. [ABSTRACT FROM AUTHOR]

• The jointly optimization can be significantly higher than the literature. • The proposed scheme's performance is better than only transmit power optimization. • The energy efficiency decreases with parameter a and b increasing. • The joint optimization can improve the overall system energy efficiency. • The proposed scheme can reduce the system BER effectively and significantly. For the ambient backscatter communication system, a joint optimization algorithm based on the energy efficiency maximization criterion was proposed in this paper by designing a two-hop wireless information and energy transmission scheme. The source side of this system was configured with multiple antennas and employed Max Ratio Transmission (MRT) precoding method. The relay node (hot spots or tag node) utilized the power split approach to transmit information and harvest energy. In allusion to the established transmission system with large scale nodes, a joint optimization algorithm was applied to the number of source antennas, the transmission power and power splitting factor to obtain the maximum energy efficiency of the system. Using the high SNR approximation method transformed the non-convex into convex to solve the non-convex problem in the optimization target. The optimal solution of joint optimization for transmit power, antennas' number and power splitting factor was derived by using Lambert function function. Finally, the simulation results show that the proposed joint design achieves significant energy efficiency gain as compared to other optimized schemes. [ABSTRACT FROM AUTHOR]