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In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless powered communication network, where a UAV serves multiple energy constrained wireless sensor nodes (WSNs). In such a network, a UAV transmits wireless power to a group of WSNs which exploits the harvested power to transfer their own information towards the UAV in the uplink. For practical scenarios, a nonlinear energy harvesting saturation model is considered at WSNs. We aim to design resource allocation which maximizes the achievable sum rate in the uplink and minimizes the downlink transmit power simultaneously. The design is formulated as a multi-objective optimization problem (MOOP) which optimizes the UAV's 3D position, its transmit power, the time splitting ratio, the uplink transmission time and the beamwidth angle of the UAV. The formulated problem is a non-convex problem which is generally intractable. To address the MOOP, a weighted Tchebycheff method is proposed. The numerical results show a trade-off between the minimum power in the downlink and the maximum rate as well as energy efficiency in the uplink. [ABSTRACT FROM AUTHOR]

We consider a cognitive radio system, in which a secondary transmitter harvests energy from a primary transmitter's RF signal. The secondary transmitter, which provides decode-and-forward relaying service for the primary system, transmits its own data by using downlink non-orthogonal multiple access (NOMA). A time-switching protocol is used by the secondary transmitter to harvest energy and decode the primary transmitter's information. Our objective is to achieve maximal secondary throughput, by optimally selecting the time portion used for energy harvesting and the secondary transmitter's power allocation in NOMA transmission. In this paper, two optimization problems are formulated, in which the secondary receiver performs or does not perform successive interference cancellation (SIC), respectively. Although the two problems are nonconvex, we devise a method to transform the problems into equivalent problems under different cases. Then, we theoretically prove that the objective functions of the equivalent problems are quasiconcave, based on which we develop two-level bisection search algorithms to solve the equivalent problems. Interestingly, we show that performing SIC at the secondary receiver does not always guarantee a higher secondary throughput than the case without performing SIC. Computer simulation demonstrates that our algorithm performs better than an equal power allocation algorithm and an orthogonal multiple access algorithm. [ABSTRACT FROM AUTHOR]

This paper considers a novel problem in rechargeable wireless sensor networks (WSNs), given a set of locations with one or more targets, determine the minimum number of sensor nodes to deploy in order to ensure a given coverage quality. This problem is significant as sensor nodes are often used to monitor one or more valuable assets or critical infrastructure. We formulate the problem as an integer linear program (ILP) and use it to compute the minimum number of sensor nodes required to monitor targets in small-scale WSNs. For large-scale WSNs, we relax the integer variables of the ILP and devise three approximation algorithms: greedy round node placement, target protection node placement, and energy efficient node placement (EENP). We prove the worst case performance bound of these algorithms. We also conducted simulation to compare these algorithms against the optimal solution produced by the ILP. Our results show that the solution computed by EENP is within one percentage point from the optimal solution. [ABSTRACT FROM AUTHOR]

Energy harvesting wireless sensor networks (EH-WSNs) form the foundation of Internet of Things (IoTs) systems. Energy harvesting nodes can be deployed strategically to monitor one or more targets such as a valuable asset. However, as these nodes rely on ambient energy sources such as solar, they experience random energy arrivals. Consequently, they may exhaust their harvested energy while monitoring a target. Therefore, network operators require a robust solution that ensures all targets are monitored continuously over some time period with a given probability. In this paper, we consider three novel robust coverage requirements; each must hold with probability ($1-\epsilon$), where $\epsilon$ is the probability of failures. First, sensor nodes must not expend more than their total harvested energy over $T$ time slots. Second, the energy expenditure of each sensor node must not exceed the energy harvested in each slot. Third, the energy expenditure of sensor nodes must not exceed the energy accumulated up to the current slot. We formulate chance-constrained stochastic programs that incorporate these requirements and solve them using the sample average approximation method. We confirm via extensive simulation studies that our programs are capable of computing sensor nodes activation times that meet a given coverage failure probability. [ABSTRACT FROM AUTHOR]

In this paper, the cooperative non-orthogonal multiple access (C-NOMA) transmission with the full-duplex energy harvesting relaying (FDEHR) is investigated for enhancing the spectrum and energy efficiency. The exact outage probability is derived over Nakagami- $m$ frequency-selective fading channels. In addition, a novel near-far user pairing strategy is proposed in accordance with the geographic limitations, where the energy-harvesting-effective and successive-interference-cancelation-effective zones are demarcated according to power splitting factors, energy conversion coefficients, and power allocation parameters. The stochastic geometric model is also studied in which all users are distributed as an independent homogeneous Poisson point process. Then, the transmission probability of the C-NOMA conveyance with the FDEHR for a typical user is analyzed by utilizing the Markov chain model. Monte Carlo simulation is executed to verify the mathematical derivations. Both analytical and simulation results demonstrate that the near user possesses a lower outage probability than the far user does under the original NOMA transmission, whereas the outage performance of the far user could be improved so that he behaves better than the near user when the C-NOMA transmission with the FDEHR is applied. [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]

Radio frequency (RF) energy harvesting technique has received high attention as a promising technology for Internet of Things (IoT) based industrial systems due to its flexibility of energy supply. When multiple IoT gateways are used as directional RF energy sources, the direction and phase of RF energy transmissions influence the RF energy transmission efficiency. In this paper, we propose a phase-aware directional energy transmission algorithm (PA-DETA) where directional RF energy sources (e.g., IoT gateways) are connected to a controller and their operation parameters (i.e., RF energy transmission direction and initial phase) are decided by the controller to maximize the total harvested energy while maintaining the energy consumption in RF energy sources and the number of discarded packets due to energy depletion of harvesting nodes below certain levels. To optimize the performance of PA-DETA, we formulate a constraint Markov decision process and the optimal policy on the direction and initial phase of RF energy transmissions is obtained by a linear programming. Evaluation results demonstrate that PA-DETA with the optimal policy outperforms the comparison schemes in terms of the total harvested energy, the energy consumption in RF energy sources, and the packet discard rate. [ABSTRACT FROM AUTHOR]

Wireless body area network (WBAN) has become a promising network for continuous health monitoring of various diseases. The limited energy of sensors in WBAN cannot support the long-term work with the high requirements of Quality of Service (QoS) for health applications. Energy harvesting (EH)-powered WBAN, which can provide uninterrupted work, has attracted more attention from both academic and industry. However, the time-varying and heterogeneous EH states of different sensors become an important factor when designing the resource allocation schemes in EH-powered WBAN. In this paper, we propose a novel two-phase resource allocation scheme, which optimizes the allocation of the transmission power, the source rate and the time slots to improve the QoS performances of EH-powered WBAN. In the first phase, we investigate that the statistical knowledge of energy harvesting to measure the long-term QoS performances, and then a joint power-rate control scheme is proposed to optimize the transmission power and the source rate for ensuring the long-term QoS performance. Moreover, we design a QoS aware slot allocation scheme to dynamically adjust the time slot allocation to cope with the time-varying and heterogeneous EH states for obtaining better short-term QoS performances in the second phase. Finally, numerical simulation results demonstrate that the proposed joint power-rate-slot resource allocation scheme of EH-powered WBANs can efficiently exploit the time-varying EH to improve both the long-term and short-term QoS performances. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the buffer-aided two-way wireless energy harvesting (EH) relay network comprising two users and one relay, where two users exchange information with the help of the relay via three phases ofEH, multiple-access, and broadcast. By transforming the opportunistic scheduling design problem into an equivalent convex problem, we present the adaptive design for the buffer-aided two-way EH relay network to maximize the long-term achievable rate region. To fulfill the delay sensitive transmission requirements, a delay-aware adaptive transmission (DAAT) scheduling scheme is proposed to guarantee the average end-to-end delivery delay by employing the Lyapunov optimization framework. Our analysis discloses that the average achievable rate region of the two-way wireless EH relay network can be improved when fully considering the potentials by deploying data buffer and energy storage at the relay. There exists an inherent tradeoff among the achievable sum rate, the delivery delay, and the power consumption. It is shown that, when a certain time delay is tolerable, the DAAT scheduling scheme is able to realize a rate region arbitrarily close to the achievable rate region of the buffer-aided two-way wireless EH relay network. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the optimal throughput performance for simultaneous wireless information and power transfer relaying network in the presence of direct link between the source and the destination. By considering the direct link, system performance can be improved when the channel condition is good from source to destination. The cooperative network consists of a source, an amplify-and-forward relay, and a destination, where the relay harvests energy from the source and then uses the harvested energy to forward information to the destination. At the destination node, the signals from the direct link and relay are combined by the maximum ratio combining (MRC) method. The time switching (TS) relaying protocol is applied to enable energy harvesting and information processing simultaneously. At the same time, we made progress on analyzing the throughput of information transmission on the basis of the MRC method with power switching (PS) relaying protocol. Additionally, adaptive-MRC protocol is proposed based on the TS and PS relaying protocols, which can choose better mode to achieve the communication. The numerical analyses provide practical insights into the effect of various system parameters, such as energy harvesting time splitting ratio, source transmission rate, distance between nodes, and noise power at the destination and the relay. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) has been recently emerged as an effective way for improving the performance of wireless networks by reconfiguring the propagation environment through a large number of passive reflecting elements. This game-changing technology is especially important for stepping into the Internet of Everything (IoE) era, where high performance is demanded with very limited available resources. In this paper, we study a backscatter-assisted wireless powered communication network (BS-WPCN), in which a number of energy-constrained users, powered by a power station (PS), transmit information to an access point (AP) via backscatter and active wireless information transfer, with their communication being aided by an IRS. Using a practical energy harvesting (EH) model which is able to capture the characteristics of realistic energy harvesters, we investigate the maximization of total network throughput. Specifically, IRS reflection coefficients, PS transmit and AP receive beamforming vectors, power and time allocation are designed through a two-stage algorithm, assuming minimum mean square error (MMSE) receiver at the AP. The effectiveness of the proposed algorithm is confirmed via extensive numerical simulations. We also show that our proposed scheme is readily applicable to practical IRS-aided networks with discrete phase shift values. [ABSTRACT FROM AUTHOR]

The development of unmanned aerial vehicles (UAVs) is one of the driving forces for revolutionizing wireless communications in the modern era. Although there are many unique features of UAV networks, their widespread use is still hampered by the short battery life. As a result, most of the research efforts in this domain mainly have focused on the efficient use of energy through trajectory planning and cooperative communication. While there have been a few proposals to use energy harvesting techniques for UAV networks, a complete understanding of the performance limits of such networks is still missing in the literature and needs further investigations. Within this context, our paper provides a comprehensive performance evaluation of UAV relay networks (UAVRNs) employing non-orthogonal multiple access (NOMA) technique. Specifically, a cooperative communication system, where a UAV serves as a mobile relay, is fully considered. The UAV relay is considered to be wirelessly-powered and harvests energy from the radio signals received from a nearby base station. For the sake of comparison, both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols are considered. Subsequently, the closed-form expressions of outage probabilities and ergodic capacities are provided for each UAV relaying protocol. Extensive simulations are performed to verify the accuracy of the derived closed-form expressions. The results provided in this work characterize the outage and ergodic capacity performance of NOMA-enabled UAVRNs. [ABSTRACT FROM AUTHOR]

In this paper, novel ergodic capacity (EC) performance evaluation results of a power beacon (PB)-assisted multi-input multi-output (MIMO) wireless powered communication network are presented. In the considered system, the energy harvesting node harvests energy from the radio-frequency signals sent by the dedicated PB and uses this energy to communicate with the destination node. To accurately model the combined effect of multi-path fading and shadowing, it is assumed that the energy transfer link is subject to $\kappa$ - $\mu$ shadowed fading. Performance evaluation results are presented for two cases, depending upon the availability of channel state information (CSI) at the PB, namely, $no$ CSI and $full$ CSI. In the former case, equal power allocation is assumed, whereas, in the later case, energy beamforming is employed to increase energy transfer efficiency. For the performance evaluation of EC under $full$ CSI, a closed-form approximation for the probability density function of the maximum eigenvalue of a $\kappa$ - $\mu$ shadowed distributed random matrix is derived. For both $no$ CSI and $full$ CSI cases, lower and upper bounds on the achievable EC are derived in closed-form. Moreover, in order to obtain further insights on the impact of key parameters on the system performance, asymptotic EC expressions which become very tight at low- and high-signal-to-noise ratio regimes, are obtained. Using the proposed EC lower bound as well as these asymptotic results, simple closed-form expressions for the optimal time split that maximize the achievable EC are derived. Numerically evaluated results accompanied with Monte-Carlo simulations are further presented to corroborate the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper investigates the outage and throughput performance of full-duplex (FD) cooperative relaying (CR) based non-orthogonal multiple access system (NOMA) with energy harvesting (EH) i.e., FD-CR-EH-NOMA system. We consider a network scenario, where the base station (BS) serves two downlink users, with the near user configured as a FD relay to deliver messages to the far user. We derive analytical expressions for the outage probabilities experienced by the users and the system outage probability of FD-CR-EH-NOMA network under time switching relaying (TSR) protocol in the presence/absence of direct link from BS to the far user. Assuming imperfect successive interference cancellation (i-SIC), we derive analytical expressions for (i) optimal power allocation (OPA) factor at the BS and (ii) optimal time switching (OTS) factor at the relay that independently minimizes the system outage probability. Further, we use the alternating optimization technique to determine the jointly optimal power allocation and time switching factors that minimize the system outage probability. Furthermore, we determine the OTS factor that maximizes the asymptotic throughput of the system in the delay-limited transmission mode. With the help of numerical and simulation investigations, we show that the system outage and throughput of the considered network improve significantly under the proposed schemes. [ABSTRACT FROM AUTHOR]

The capability of Non-Orthogonal Multiple Access (NOMA) for minimizing interference at the receiver using successive interference cancellation (SIC) makes it as a promising solution for 5G mobile communication. Device-to-device (D2D) communication is a part of the 5G network which provides frequency reuse in cellular networks and provides a better quality of service (QoS) to co-channel users. In this paper, we investigate the resource allocation and time scheduling algorithms for a NOMA based D2D communication system. In addition, to overcome the problem of energy at D2D transmitter and receivers, wireless powered communication network (WPCN) is used. Considering these issues, the objective of the proposed scheme is to maintain energy efficient communication among D2D transmitters and receivers without compromising energy efficiency (EE) of cellular users. To solve the fractional form of an optimization problem, we employ the Dinkelbach method. A many-to-many matching game approach is used for channel allocation and successive convex approximation for low complexity (SCALE) is used to control the power of the D2D transmitter. The simulation results demonstrate that by maximizing the EE and optimizing the time and transmission power of D2D groups, the energy efficiency of the network can be significantly improved in comparison to the other competitive schemes in the existing literature. [ABSTRACT FROM AUTHOR]

Energy Harvesting Wireless Body Area Networks (EH-WBANs) where sensor nodes can harvest energy from their ambient environment are of great potential for low-power medical monitoring systems. The availability of energy in EH-WBANs, however, is challenged by uncertainty of harvested energy sources and limited charging efficiency, and thus the energy saving is still of great importance. To improve energy efficiency, this paper studies the sleep scheduling problem in EH-WBANs, which is of fundamental importance for network lifetime and connectivity while being not systematically addressed. Technically, this problem is proved to be NP-Complete, and thus needs non-trivial efforts to deal with. To that end, we present a series of approximation algorithms with proven performance from perspective of constructing minimum dominating set (DS), where energy saving and energy harvesting techniques are combined to prolong the network lifetime while guaranteeing real-time requirement of EH-WBANs. Specifically, we first propose a centralized algorithm that can construct DS with minimum size and discover the maximum number of DSs. We then design two distributed algorithms independent of prior global knowledge to improve network scalability, namely EEU and Improved EEU respectively. Theoretical analysis and extensive simulations are conducted to confirm the superiority of the proposed sleep scheduling algorithms. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is considered as a potential multiple access technique for supporting massive number of devices with high spectral efficiency for the 5 G and beyond networks. Simultaneous wireless information and power transfer (SWIPT) is considered as an efficient solution for fully autonomous and sustainable communication networks. In this paper, application of SWIPT in heterogeneous networks (HetNets) employing cooperative NOMA (CNOMA) is investigated. The HetNets consists of macro-base stations (MBS) under-laid by small-base stations (SBS). The distribution of MBSs follow Poisson point process model, while the SBS tier supports NOMA and carrier sensing for its transmission. Carrier sensing helps in reducing interference by blocking the base stations within a specific range from transmitting. In this work, the analysis at SBS tier is carried out in two phases. In the direct phase transmission, the SBS transmits the superimposed signal to a user pair, comprising of a cell-centre user (CCU) and a cell-edge user (CEU), using NOMA principle. The CCU acts as a cooperative relay to forward information to the CEU in the cooperative phase. Unlike existing literature, instead of using only the superimposed signal for energy harvesting (EH), in this work, the cooperating user (i.e. CCU) employs EH using interference from the adjacent base stations along with the EH from the superimposed signal. Expressions for outage probability and throughput are derived at the user pair served using NOMA. Comparison of the proposed EH system with existing solutions are highlighted and useful insights are drawn. Monte Carlo simulations are carried out to validate the analytical results. [ABSTRACT FROM AUTHOR]

In this paper, we consider a coexisting network of cellular transmission and device-to-device (D2D) communication, in which two users need to receive downlink signals from base station (BS), meanwhile, they want to exchange information with each other. For such a network, a hybrid cellular and bidirectional D2D cooperative non-orthogonal multiple access (HCBD-CNOMA) scheme is proposed. To characterize the performance of our proposed scheme, we derive the closed-form ergodic rate and outage probability by considering both perfect and imperfect successive interference cancellation (SIC). To acquire more insights, their corresponding asymptotic expressions are obtained as well. Moreover, considering the fact that the nodes in Internet of Things (IoT) network are usually powered by battery and thus their power is limited, we further consider the scenario that two users are energy-constraint. In this scenario, users first need to harvest energy from the transmission of BS based on time splitting (TS) or power splitting (PS) energy harvesting (EH) protocol and then use these energy to accomplish subsequent communications. Similarly, the exact ergodic rate and outage probability as well as their asymptotic expressions for this scenario are analyzed. Simulation results validate the accuracy of analytical results, and reveal the performance gain of our schemes over the benchmark schemes. [ABSTRACT FROM AUTHOR]

This paper focuses on the performance analysis of point-to-point communication, where the source node is powered with a fixed-power battery as well as a harvest-use battery. A time-switching scheme is implemented such that in each time frame the source either harvests energy or sends information to the destination node. Since switching takes place within a communication time frame, energy harvesting may not necessarily be beneficial in terms of improving the maximum achievable spectral efficiency or the energy efficiency of system. Here, we investigate and provide conditions under which harvesting can improve the system performance. We consider a scenario where the transmission power from the fixed battery is constant. Closed-form expressions for the conditions under which energy harvesting is proven beneficial are provided. The effects of important parameters, namely, time switching parameter, circuit powers, and harvested energy, on the system performance and on the condition for harvesting beneficialness, are investigated and discussed. [ABSTRACT FROM AUTHOR]

In this paper, we consider wireless-powered secure communication with an energy harvesting receiver, which is allowed to harvest energy from the transmitted signals but not to decode information, and there is thus a requirement to keep the information secret from this potential eavesdropper. Considering co-channel interference among signal links, we find the optimal transmit power to maximize the sum rate of the signal links, while ensuring the requirements of information secrecy and energy harvesting. Due to the non-convexity of the optimization problem formulated here, we first derive suboptimal solutions using an iterative algorithm based on a dual method. In order to address the limitations caused by the use of the iterative algorithm, i.e., long computation time and suboptimality, we design an efficient deep neural network (DNN) framework and a novel training strategy as a means of combining supervised and unsupervised training. Specifically, the DNN is first pre-trained using labeled training data with the suboptimal solutions obtained from the iterative algorithm in a supervised manner; further training is then applied to the DNN using a well designed loss function in an unsupervised manner to enhance the training performance. Simulation results reveal that the proposed scheme achieves a near-optimal performance with a lower computation time than existing schemes. We also verify that the pre-training and the new loss function are effective in improving the speed of training of the DNN. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the performance of a hybrid receiver-based simultaneous wireless information and power transfer (SWIPT) two-way relay network over Nakagami-m faded channels. A hybrid receiver is adopted at the relay node that utilizes both the time switching (TS) and power splitting (PS) protocols for energy harvesting and information transmission. Amplify-and-forward relaying protocol is utilized at the relay node to process the information in half-duplex mode. In the considered network, impact of a practical non-linear power amplifier (NLPA) at the relay node is considered. Selection combining is performed at the destination node to utilize the direct-link and relay assisted signals. For the considered system, the analytical expressions of outage probability (OP) and asymptotic OP are derived over Nakagami-m faded channels for both the integer and non-integer value of m. The analytical expression of the system throughput and energy efficiency of the considered network are also derived. Further, the analytical expression of ergodic capacity is derived in terms of the Meijer-G function. By utilizing a cumulative distribution function based approach, analytical expressions of average symbol error rate (ASER) for general order hexagonal quadrature amplitude modulation (QAM), general order rectangular QAM, and 32-cross QAM schemes are derived. For different QAM constellations, a comparative ASER analysis is also illustrated. Furthermore, the impact of TS factor, PS ratio, energy conversion efficiency, NLPA, and threshold data-rates on the considered network is also highlighted. Finally, Monte-Carlo simulations are performed for validation of the derived analytical expressions. [ABSTRACT FROM AUTHOR]

This paper investigates a novel joint content caching and energy harvesting scheme to enhance unmanned aerial vehicle (UAV) communications in Internet of Things non-orthogonal multiple access (NOMA) network, where a UAV is acted as an aerial relay to serve users on demand. In particular, a neoteric gravitational search-based multi-constraint optimization algorithm is designed to maximize the throughput of the served users by jointly optimizing power allocation, energy harvesting, and time scheduling schemes. The algorithm engages two main characteristics: 1) classification of multiple constraints, which is for reducing the complexity of the problem solving process due to multiple iterations, and 2) design of force sets, which aims to enhance the global search for the optimal solution at the feasible region boundary. Finally, simulations are provided to show the effective convergence and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Integrating power transfer into wireless communications to support simultaneous wireless information and power transfer (SWIPT) is a promising technique in energy-constrained wireless networks. While most existing work on SWIPT focuses on capacity–energy characterization, the benefits of cooperative transmission for SWIPT have been investigated far less. In this paper, we consider SWIPT in an orthogonal frequency-division multiplexing (OFDM) relaying system, where a source node transfers information and a fraction of power simultaneously to a relay node, and the relay node uses the harvested power from the source node to forward the source information to the destination. To support the simultaneous information and energy cooperation, we first propose a transmission protocol, assuming that the direct link between the source and destination does not exist, i.e., power-splitting (PS) relaying protocol, where the relay node splits the received signal power in the first hop into two separate parts: one for information decoding and the other for energy harvesting. Then, we consider the case that the direct link between the source and destination is available, and the transmission mode adaptation (TMA) protocol is proposed, where transmission can be completed by cooperative mode and direct mode simultaneously (over different subcarriers). In the direct mode, when the source transmits signal to the destination, the destination receives the signal as information, and the relay node concurrently receives the signal for energy harvesting. Joint resource-allocation problems are formulated to maximize the system throughput. By using the Lagrangian dual method, we develop efficient algorithms to solve the nonconvex optimization problems. [ABSTRACT FROM AUTHOR]

This paper considers an online multiantenna precoder design for energy harvesting transmitter based on finite-alphabet inputs and statistical channel state information (CSI). It formulates the problem by maximizing the sum of average mutual information (AMI) of multiple time slots in one channel frame with causal energy constraint. The formulated problem is a \mbox2Nt^2- dimensional stochastic dynamic programming (SDP) problem with a nonconcave objective function, where Nt is the number of transmit antennas. The \mbox2Nt^2- dimensional SDP problem is prohibitively complex and prevents the development of online algorithm for multiantenna systems. We provide equivalence between the multidimensional SDP problem and a one-dimensional power choice problem. Solving the one-dimensional equivalence decreases the computational burden extraordinarily without loss of optimality. We separate the one-dimensional problem into two cases: 1) the discrete-battery-state–discrete-power-choice (DBDP) problem and 2) the continuous-battery-state–continuous-power-choice (CBCP) problem. We also develop numerical algorithms to solve them, respectively. We further analyze the computational complexity of the developed algorithms and demonstrate the significant performance gains offered by the proposed method when compared with other methods. [ABSTRACT FROM AUTHOR]

With the advancement of wireless energy harvesting and transfer technologies, e.g., radio frequency (RF) energy, mobile nodes are fully untethered as energy supply is more ubiquitous. The mobile nodes can receive energy from wireless chargers, which can be static or mobile. In this paper, we introduce the use of a mobile energy gateway that can receive energy from a fixed charging facility, as well as move and transfer energy to other users. The mobile energy gateway aims to maximize the utility by optimally taking energy charging/transferring actions. We formulate the optimal energy charging/transferring problem as a Markov decision process (MDP). The MDP model is then solved to obtain the optimal energy management policy for the mobile energy gateway. Furthermore, the optimal energy management policy obtained from the MDP model is proven to have a threshold structure. We conduct an extensive performance evaluation of the MDP-based energy management scheme. The proposed MDP-based scheme outperforms several conventional baseline schemes in terms of expected overall utility. [ABSTRACT FROM AUTHOR]

In this paper, we study resource allocation for multiuser multiple-input–single-output secondary communication systems with multiple system design objectives. We consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multiobjective optimization framework for the design of a Pareto-optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference-power-leakage-to-transmit-power ratio minimization. The proposed framework takes into account a quality-of-service (QoS) requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information (CSI) of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The proposed framework includes total harvested power maximization and interference power leakage minimization as special cases. The adopted multiobjective optimization problem is nonconvex and is recast as a convex optimization problem via semidefinite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP-relaxed problem. Moreover, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes but unveil an interesting tradeoff among the considered conflicting system design objectives as well. [ABSTRACT FROM AUTHOR]

Energy harvesting makes use of energy from the environment. However, since harvesting energy depends on natural conditions, it is not a stable energy source. As a result, the energy from the power grid is often included to serve as a supplementary source to regulate the overall energy supply of the system. Further, the power from the power grid is often subject to the constraints of peak power and the energy budget. These constraints lead to more difficulties in solving optimal power allocation problems. In this paper, we extend our recently proposed geometric water-filling (GWF) and recursive GWF (RGWF) algorithms to solve the throughput maximization problem and transmission completion time minimization problems for this kind of hybrid energy source system. Our investigation shows that the optimal power allocation for throughput maximization is the result of a sequence of water-filling algorithms for smart power grid and harvested energy, in that order, followed by a power adjustment step of the power from the grid. The allocation order is not commutative for an optimal solution due to the specific structure of the target problems. The proposed algorithms can compute the exact (optimal) solutions to the problems via finite computation with low computational complexity. Numerical examples are presented to illustrate the detailed procedures to efficiently obtain the optimal power allocation solutions using the proposed algorithms. The results also illustrate that the composite operation of the two water-fillings is noncommutative. [ABSTRACT FROM AUTHOR]

Compared to battery-powered wireless nodes having a constant but limited power supply, wireless nodes having energy harvesting (EH) capability may greatly prolong the network's sustainability. However, the energy usage policies (EUPs) have to be carefully designed according to the characteristics of the random power supply gleaned from the environment. In this paper, we carry out the outage analysis of a point-to-point (P2P) network relying on an EH transmitter, which has a finite energy buffer (EB) for transmission over a fading channel when having random energy arrival rates. A discrete Markov chain (DMC) model is proposed for characterizing the energy state of the EB, which is then used for quantifying the outage probability (OP) over the fading channels. Then, we propose both a novel 2-D and a low-complexity 1-D search algorithm for finding the specific EUPs, which are capable of minimizing the OP for the P2P network considered. It is shown that the EUP found by both algorithms outperforms the state-of-the-art EUPs disseminated in the open literature. Furthermore, we consider a multiple-access network having $M$ EH-aided sources, where we propose a distributed EUP optimization (DEUPO) algorithm and then minimize the OP relying on the local optimization of each EH-aided source. [ABSTRACT FROM AUTHOR]

Energy harvesting technique is a potential way for relay node energy supply in cooperative networks with respect to deployment flexibility and maintenance charge reduction. However, unlike traditional power source, it is intuitive that the fluctuation of harvested energy flow can degrade the benefit resulted from relay-aided cooperative transmission. To this end, this paper focuses on the performance analysis of an energy harvesting relay-aided cooperative network under slow fading channel from a perspective of outage behavior and attempts deriving the closed-form expression of outage probability of the proposed cooperative protocol. Compared with simple direct transmission protocol, it is proved that a multiplicative gain for improving system performance in terms of minimizing outage probability can be obtained by energy harvesting relay-aided cooperative transmission. In particular, if there are multiple available relay nodes around the environment or the energy-exhausted probability of relay node is small, it is very profitable to employ relay-aided cooperative transmission protocol. [ABSTRACT FROM PUBLISHER]

Recent years have witnessed several new promising technologies to power wireless sensor networks, which motivate some key topics to be revisited. By integrating sensing and computation capabilities to the traditional radio-frequency identification (RFID) tags, the Wireless Identification and Sensing Platform (WISP) is an open-source platform acting as a pioneering experimental platform of wireless rechargeable sensor networks. Different from traditional tags, an RFID-based wireless rechargeable sensor node needs to charge its onboard energy storage above a threshold to power its sensing, computation, and communication components. Consequently, such charging delay imposes a unique design challenge for deploying wireless rechargeable sensor networks. In this paper, we tackle this problem by planning the optimal movement strategy of the mobile RFID reader, such that the time to charge all nodes in the network above their energy threshold is minimized. We first propose an optimal solution using the linear programming (LP) method. To further reduce the computational complexity, we then introduce a heuristic solution with a provable approximation ratio of (\mbox1+\theta )/(\mbox1-\varepsilon) by discretizing the charging power on a 2-D space. Through extensive evaluations, we demonstrate that our design outperforms the set-cover-based design by an average of 24.7%, whereas the computational complexity is \mathcalO((N/\varepsilon)^2). Finally, we consider two practical issues in system implementation and provide guidelines for parameter setting. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the optimal recursive power-allocation policies with energy harvesting wireless nodes equipped with multiple antennas in a fading channel. This optimization problem includes several complex matrices as optimization variables. As a difference, existing optimization theory and methods have been designed to solve these problems over real space. Naturally, the optimization variables have been assumed points in the real space. We proposed a transform approach and designed the algorithms for solving the throughput maximization problem and transmission completion time minimization problem for a multiple-input–multiple-output (MIMO) system. The algorithms were further extended to solve the throughput maximization problem of a hybrid system with both harvesting energy and grid power. Numerical results illustrated the algorithm steps and significant efficiency of the proposed algorithms. To the best of the authors' knowledge, there are no existing algorithms reported in the open literature to obtain exact solutions to the proposed problems. Significant features of the proposed algorithms are that 1) they provide the exact optimal solutions via efficient finite computation and that 2) optimality of the proposed algorithms is strictly proven. [ABSTRACT FROM PUBLISHER]

Wireless-powered communication and reconfigurable intelligent surface (RIS) can complement each other for increasing energy utilization and spectrum efficiency by reconfiguring the surrounding radio environment, however, which has not been sufficiently studied by the existing works. In this paper, we propose a joint radio resource and passive beamforming optimization scheme for a downlink RIS-assisted wireless-powered communication network with a harvest-then-transmit protocol to improve system energy efficiency (EE). In the considered model, the single-antenna wireless devices (WDs) harvest wireless energy from a multi-antenna dedicated power station (PS) through the RIS in the downlink and transmit their independent information to a single-antenna receiver in the uplink by a time-division-multiple-access mode. Our goal is to maximize the total EE of all WDs. To make full use of the beamforming gain provided by both the PS and the RIS, we jointly optimize the active beamforming of the PS and the passive beamforming of the RIS. To deal with the challenging non-convex optimization problem with multiple coupled variables, we first consider fixing the passive beamforming, and converting the remaining radio resource allocation problem into an equivalent convex problem which is solved by using Lagrange dual theory. Then, we fix the optimized resource allocation parameters and optimize the passive beamforming of the RIS by using a semidefinite programming method. Simulation results demonstrate that the proposed algorithm achieves higher EE compared to the conventional schemes. [ABSTRACT FROM AUTHOR]

Simultaneous lightwave information and power transfer (SLIPT) is considered as an effective method of transferring information and energy via illuminated lightwaves. In this paper, we investigate the effect of non-linear responsivity on the SLIPT/radio frequency (RF) performance. We develop a tractable mathematical model for the non-linearity of responsivity and use it to study the end-to-end signal-to-interference-plus-noise ratio (SINR). Based on that, we derive the closed-form expressions for the probability density function and cumulative distribution function of the output SINR at the receiver. These statistical distributions are then used to derive the closed-form expressions of the outage probability of SLIPT/RF system over Nakagami- $m$ fading channels. Numerical results indicate that the simplified model used in previous studies leads to inaccurate performance evaluation results, and that our model provides more accurate evaluation. We also verify the analytical findings by simulation. [ABSTRACT FROM AUTHOR]

Using a practical relay model, in which an energy transmitter (ET) transmits energy to a sensory node S and an information receiver D, information freshness or age of information (AoI) under outdated-channel-state-information (oCSI) severity and line-of-sight (LoS) power is examined in this paper. The sensory node—equipped with a finite-size-battery capacitor—harvests energy from the ET to sustain its operation. Rician fading under non-oCSI and oCSI environments is employed to study the AoI, in which benefits and severity of LoS power and oCSI will be elaborated. The proposed AoI results exactly reduce to existing ones under Rayleigh fading, which validates this work. Simulation results are additionally deployed to validate the proposed AoI results. Impact of parameters such as capacitor capacity, system incoherence coefficient and spectral efficiency on AoI under specific scenarios is also discussed. [ABSTRACT FROM AUTHOR]

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

This paper considers the power allocation of a single multiple-input–multiple-output (MIMO) wireless link with hybrid energy harvesting and grid power supply. We optimize the power allocation to minimize the power grid energy consumption while guaranteeing users' quality of service (QoS), which is defined as transmitting a certain number of bits in finite time horizon. First, we solve the offline throughput maximization problem and propose the optimal solution called two-stage spatial–temporal water filling (ST-WF), which allocates harvested energy in the first stage and then power grid energy in the second stage. Based on this, we solve the offline power grid energy minimization problem by utilizing its equivalence to the throughput maximization problem. Then, we use the Markov decision process (MDP) to get the optimal online power grid energy minimization policy. Furthermore, we propose two near-optimal algorithms with reduced complexity, namely, the constant water level algorithm and the bit-aware water level algorithm. We evaluate the performance of the proposed offline and online algorithms by numerical simulations, showing that the bit-aware water level algorithm performs closely to the optimal online solution. [ABSTRACT FROM PUBLISHER]

In this paper, we pursue a unified study on smart grid and coordinated multipoint (CoMP)-enabled wireless communication by investigating a new joint communication and energy cooperation approach. We consider a practical CoMP system with clustered multiple-antenna base stations (BSs) cooperatively communicating with multiple single-antenna mobile terminals (MTs), where each BS is equipped with local renewable energy generators to supply power, as well as a smart meter to enable two-way energy flow with the grid. We propose a new energy cooperation paradigm, where a group of BSs dynamically shares their renewable energy for more efficient operation via locally injecting/drawing power to/from an aggregator with a zero effective sum energy exchanged. Under this new energy cooperation model, we consider the downlink transmission in one CoMP cluster with cooperative zero-forcing (ZF)-based precoding at the BSs. We maximize the weighted sum rate for all MTs by jointly optimizing the transmit power allocations at cooperative BSs and their exchanged energy amounts subject to a new type of power constraints featuring energy cooperation among BSs with practical loss ratios. Our new setup with BSs' energy cooperation generalizes the conventional CoMP transmit optimization under BSs' sum-power or individual-power constraints. It is shown that, with energy cooperation, the optimal throughput is achieved when all BSs transmit with all of their available power, which is different from the conventional CoMP schemes without energy cooperation where BSs' individual power constraints may not all be tight at the same time. This result implies that some harvested energy may be wasted with no use in the conventional setup due to the lack of energy sharing among BSs, whereas the total energy harvested at all BSs is efficiently utilized for throughput maximization with the proposed energy cooperation, thus leading to a new energy cooperation gain. Finally, we validate our results by simulations under various practical setups and show that the proposed joint communication and energy cooperation scheme substantially improves the downlink throughput of CoMP systems powered by smart grid and renewable energy, as compared with other suboptimal designs without communication and/or energy cooperation. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the power allocation of the physical layer and the buffer delay of the upper application layer in energy harvesting green networks. The total power required for reliable transmission includes the transmission power and the circuit power. The harvested power (which is stored in a battery) and the grid power constitute the power resource. The uncertainty of data generated from the upper layer, the intermittence of the harvested energy, and the variation of the fading channel are taken into account and described as independent Markov processes. In each transmission, the transmitter decides the transmission rate and the allocated power from the battery, and the rest of the required power will be supplied by the power grid. The objective is to find an allocation sequence of transmission rate and battery power to minimize the long-term average buffer delay under the average grid power constraint. A stochastic optimization problem is formulated accordingly to find such transmission rate and battery power sequence. Furthermore, the optimization problem is reformulated as a constrained Markov decision process (MDP) problem whose policy is a 2-D vector with the transmission rate and the power allocation of the battery as its elements. We prove that the optimal policy of the constrained MDP can be obtained by solving the unconstrained MDP. Then, we focus on the analysis of the unconstrained average-cost MDP. The structural properties of the average optimal policy are derived. Moreover, we discuss the relations between elements of the 2-D policy. Next, based on the theoretical analysis, the algorithm to find the constrained optimal policy is presented for the finite-state-space scenario. In addition, heuristic policies (two deterministic policies and a mixed policy) with low complexity are given for the general state space. Finally, simulations are performed under these policies to demonstrate their effectiveness. [ABSTRACT FROM PUBLISHER]

In this correspondence paper, we investigate the resource allocation problem for D2D communications underlaying wireless powered communication networks, where multiple D2D pairs harvest energy from a power station equipped with multiple antennas and then transmit information signals simultaneously over the same spectrum resource. The aim is to maximize the sum throughput via joint time scheduling and power control, while satisfying the energy causality constraints. The formulated nonconvex problem is first transformed into a nonlinear fractional programming problem with a tactful reformulation. Then, by leveraging difference of two convex functions programming, a suboptimal solution to the nonconvex problem is obtained by iteratively solving a sequence of convex problems. Simulation results demonstrate that the proposed scheme works well in different scenarios and can significantly improve the system throughput compared with the-state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

This paper considers a simultaneous wireless information and power transfer (SWIPT) based decode-and-forward relaying network, where the “harvest-then-forward” strategy is employed. We focus on designing optimal static and dynamic transmission schemes of joint time allocation and power splitting to conduct the relay in terms of outage performance and ergodic performance, respectively. In particular, the analytical expressions for the outage probability and ergodic capacity are derived to determine the optimal static power splitting (PS) and time allocation (TA) ratios. Moreover, we study the dynamic transmission scheme and formulate two joint optimization problems to minimize the outage probability and maximize the instantaneous channel capacity. Considering that the two optimization problems are nonconvex, a split-step iterative method is proposed to obtain the optimal dynamic PS ratio and TA ratio for minimizing the outage probability, and an alternate convex optimization method is employed to solve the problem of maximizing the instantaneous channel capacity. Simulation results verify the advantages of the proposed schemes over three peer schemes. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of joint resource allocation and admission control for an energy harvesting (EH)-based cooperative orthogonal frequency-division multiple access network, where multiple source–destination pairs and an EH amplify-and-forward relay coexist. Specifically, the relay first employs the power splitting technique to scavenge energy from radio frequency signals emitted by source nodes and then leverages the harvested energy to forward data for source nodes when necessary. Under imperfect channel state information, we first design a distributed robust resource allocation scheme to efficiently allocate system subcarriers, and the relay's and source nodes’ transmit powers together with selecting the suitable power splitting ratios for the relay. Then, for the admission control, which is important but has hardly been studied for EH networks, we propose a fuzzy-logic-based admission control policy to determine the network access of each source–destination pair, if not all source–destination pairs’ quality-of-service (QoS) requirements can be satisfied. In particular, considering the EH nature of the relay, this policy not only considers the QoS and the priority of each source–destination pair, but also takes account of the contribution of each source–destination pair to the relay's energy harvesting, and the energy efficiency to utilize the relay's energy. Finally, we evaluate and verify the performance of the proposed resource allocation and admission control schemes through numerical experiments. [ABSTRACT FROM PUBLISHER]

This paper investigates simultaneous wireless information and power transfer (SWIPT) in the ad hoc network, where access points (APs) with multiple antennas can transfer energy as well as information using a common spectrum resource. The receiving nodes decode the incoming information data (information decoding, ID) and/or harvest the RF energy (energy harvesting, EH), where two different receiving node architectures—time switching (TS) and power splitting (PS)—are considered. By using a stochastic geometry approach, we aim to analyze the transmission capacity and the harvested energy per unit area as performance measures of the SWIPT in the ad hoc network. From the analytical results, we can derive the optimal node densities of ID nodes and EH nodes for TS architectures and, furthermore, optimize the power splitting ratio jointly with the node density for the PS architectures such that the harvested energy per unit area is maximized when the target transmission capacity is given. The proposed node density optimization gives us a useful insight into the design of SWIPT-based ad hoc network, where we can show how densely the APs/nodes can be deployed in a given area and furthermore, find that the ad hoc network with PS receiving nodes outperforms that with TS receiving nodes for a large target transmission capacity, and vice versa. [ABSTRACT FROM AUTHOR]

In this paper, we consider a cooperative network, where relays are capable of simultaneous wireless information and power transfer with a power splitting scheme, and study the optimal design for power splitting ratio at each relay, downlink power allocation, channel assignment, as well as relay association to maximize the weighted sum rate with both amplify-and-forward and decode-and-forward protocols. In essence, such a resource allocation problem is a mashup of continuous-variable optimization and combinatorial optimization. To solve such a challenging problem, we first investigate the optimal solution for power-split ratio in single-user cooperative communication, based on which we propose a dynamic programming solution by exploiting the optimal substructure property of the original problem. To further reduce computational complexity, we propose a polynomial-time heuristic algorithm by leveraging local search strategy, which decomposes the original problem into three subproblems (i.e., downlink power allocation, channel assignment, and relay association) and alternately solves each one given the other two such that the weighted sum rate can be iteratively improved. Simulation results show that the proposed resource allocation scheme outperforms a baseline scheme based on max-sum criterion. Moreover, the proposed local search solution is fairly close in performance to the dynamic programming solution under various parameter settings while the computational complexity is significantly reduced. [ABSTRACT FROM PUBLISHER]

This paper focuses on the secure transmission of wireless-powered full-duplex (FD) relay systems, where a multiantenna source communicates with a single-antenna destination with the help of a FD relay in the presence of a single-antenna eavesdropper. It is assumed that the FD relay is wireless energy harvesting-enabled, adopting both transmit and receive antennas to harvest energy in a time switching (TS) mode. As the objective of this paper is to maximize the system secrecy rate through jointly designing the energy beamforming vector, the information beamforming vector and the TS coefficient, an optimization problem is formulated. The formulated problem is proved to be nonconvex and the challenge to solve which is to concurrently solve out the three variables. To cope with this difficulty, an iterative algorithm is proposed to convert the formulated optimization problem into three convex subproblems, based on which the closed form solutions for the beamforming vectors are derived and the TS coefficient is obtained. Convergence property of the iterative method is analyzed. Simulations are also done to verify the theoretical derivations in terms of the convergence speed and the secrecy rate. Results reveal that the secrecy rate performance of exploiting transmit antenna together with receive antenna for energy harvesting at the FD relay outperforms only receive antenna case. Moreover, although there exists loopback interference between antennas, the FD relaying can always substantially boost the secrecy rate compared with half-duplex relaying architecture. [ABSTRACT FROM PUBLISHER]

A multiple-input multiple-output two-way amplify-and-forward relay network with energy harvesting is considered in this paper. Under the transmit power constraints at sources and relay, a beamforming design problem is formulated, which minimizes the total mean square error while still guaranteeing sufficient energy harvested at the sources. Since this problem is nonconvex, an alternating optimization method based on generalized singular value decomposition (GSVD) is proposed and the algorithm is proved to be convergent. Moreover, based on the successive convex approximation, an iterative refinement is further designed to improve the solution quality of the GSVD scheme. Numerical results show good performance of the proposed schemes. [ABSTRACT FROM PUBLISHER]

In this paper, we study short-packet communications in wireless-powered cognitive Internet-of-Things (IoT) networks with multiple primary receivers (PRs). The considered system can be applied for small factory automations, where a source and multiple relays harvest energy from a multi-antenna dedicated power beacon (PB) to send short packets to a robot destination for controlling purposes under cognitive radio constraint imposed by PRs. We propose an opportunistic relay selection (ORS) scheme to maximize the end-to-end signal-to-noise ratio in cognitive IoT systems. Closed-form expressions for the average block error rate (BLER) of the proposed system are obtained, based on which the performance floor analysis, goodput, and energy efficiency (EE) are also carried out. Relying on analytical results, we develop a deep learning framework for the BLER prediction with high accuracy and short execution time. Simulation results show the BLER, goodput, and EE improvements of the ORS scheme over conventional relay selection schemes. Moreover, the developed deep learning-based evaluation model achieves the equivalent performance as the ORS scheme in terms of BLER, goodput, and EE, while remarkably reducing the execution time in cognitive IoT systems. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of cooperative communication between relays and base station in an advanced Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) framework, under the assumption that the relays are supplied by electric power drawn from energy harvesting (EH) sources. In particular, we focus on the relay selection, with the goal to guarantee the required performance in terms of capacity. In order to maximize the data throughput under the EH constraint, we model the transmission scheme as a nontransferable coalition formation game, with characteristic function based on an approximated capacity expression. Then, we introduce a powerful mathematical tool inherent to coalitional game theory, namely: The Shapley value to provide a reliable solution concept to the game. The selected relays will form a virtual dynamically-configured MIMO network that is able to transmit data to destination using efficient space-time coding techniques. Numerical results, obtained by simulating the EH-powered cooperative MIMO-OFDM transmission with algebraic space-time coding, prove that the proposed coalitional game-based relay selection allows us to achieve performance very close to that obtained by the same system operated by guaranteed power supply. The proposed methodology is finally compared with some recent related state-of-the-art techniques showing clear advantages in terms of link performance and goodput. [ABSTRACT FROM AUTHOR]

Integrating simultaneous wireless information and power transfer (SWIPT) into cooperative cognitive radio networks (CCRN) can increase both energy and spectral efficiencies. In contrast to most of existing works that consider only relay transmission, this paper studies opportunistic relaying along with dynamic energy harvesting in SWIPT-based CCRN, where a secondary transmitter (ST) can act as relay for primary transmission and a secondary receiver (SR) has the energy harvesting function. We propose a new framework that can unify the direct transmission, relay transmission and energy harvesting. Under the proposed framework, we first assume the transmit power of the ST is fixed and formulate the optimization problem of maximizing the ergodic capacity for SR while meeting the ergodic capacity for the primary receiver and average harvested energy for SR, by joint transmission mode selection and transmit power splitting ratio selection. Then, for the case when the transmit power of the ST can be adjusted, we investigate the joint optimization of transmit power control, transmit power splitting ratio, and transmission mode selection. The two problems are nonconvex and we propose the optimal policies by using the dual Lagrangian method. Simulations validate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

In this paper, we consider relay-assisted simultaneous wireless information and power transfer (SWIPT) for two-hop cooperative transmission, where a half-duplex multiantenna relay adopts decode-and-forward relaying strategy for information forwarding. The relay is assumed to be energy-free and needs to harvest energy from the source node. By embedding power splitting (PS) at each relay antenna to coordinate the received energy and information, joint problem of determining PS ratios and power allocation at the multiantenna relay node is formulated to maximize the end-to-end achievable rate. We show that the multiantenna relay is equivalent to a virtual single-antenna relay in such a SWIPT system, and the problem is optimally solved with closed form. To reduce the hardware cost of the PS scheme, we further propose the antenna clustering scheme, where the multiple antennas at the relay are partitioned into two disjoint groups, which are exclusively used for information decoding and energy harvesting, respectively. Optimal clustering algorithm is first proposed but with exponential complexity. Then, a greedy clustering algorithm is introduced with linear complexity and approaches to the optimal performance. Several valuable insights are provided via theoretical analysis and simulation results. [ABSTRACT FROM PUBLISHER]

In this paper, we study a dual-hop wireless powered communication network, where the communication between a hybrid access point (HAP) and a number of users is assisted by energy-constrained relays, which need to harvest energy from radio-frequency (RF) signals broadcast by the HAP. In order to power the relays for assisting the uplink communication, the HAP broadcasts a dedicated energy signal in the downlink. Each relay harvests energy from this signal and utilizes the harvested energy in forwarding its corresponding user's data to the HAP. In downlink communication, relays exploit a power-splitting strategy and use the information signal broadcast by the HAP for both energy harvesting and information forwarding. Under this setup, we are interested in maximizing the total uplink and downlink throughput. First, we propose an algorithm for solving uplink throughput maximization problem, which optimizes time allocations for energy and information transfer. For downlink throughput maximization, we obtain a near-optimal solution by optimizing time allocations and power splitting ratios iteratively. Finally, we conduct numerical simulations to evaluate the performance of the proposed algorithms. [ABSTRACT FROM PUBLISHER]