Symbol detection is critical for ambient backscatter communication (AmBC) and its optimal detection threshold is sensitive to the distribution of the signal from the ambient radio frequency source. Existing work mainly focuses on symbol detection under the assumption of a single ambient RF source (S-ARF). However, in the space-air-ground integrated networks, multiple ambient RF sources (M-ARFs), including satellites, unmanned aerial vehicles and terrestrial base stations, often reuse the same RF resources, which makes the distribution of M-ARFs signals differ from that of an S-ARFs signal. In this paper, we investigate symbol detection for AmBC in the presence of M-ARFs. To this end, we model the M-ARFs signal as an aggregate signal with log-normally distributed power. On this basis, we formulate a maximum likelihood (ML) detector and obtain a closed-form approximate threshold using Hermite-Gauss Quadrature, which is effective under specific parameter conditions. Furthermore, to enhance generality and analyze the impact of aggregate signal parameters, we construct an energy detector (ED) and propose a novel log-energy detector (LED), for which we derive the expressions for bit error rate (BER) and near-optimal thresholds. Simulation results show that the aggregate signal can significantly degrade the BER performance when directly using the detection threshold assuming an S-ARF. In contrast, the ML and ED based on the proposed detection framework can mitigate the performance degradation, and LED can achieve superior BER performance.

Symbiotic Backscatter Communication (SBC) has emerged as a spectrum-efficient and low-power communication technology, where backscatter devices (BDs) modulate and reflect incident radio frequency (RF) signals from primary transmitters (PTs). While previous studies have assumed a circularly symmetric complex Gaussian (CSCG) distribution for the BD’s signal, this assumption may not be practical because the high complexity of generating CSCG signals is not supported by the low-cost BD. In this paper, we address this gap by investigating SBC for two low-complexity modulation schemes, i.e.,M-ary amplitude-shift keying (MASK) andM-ary phase-shift keying (MPSK), where BD’s signals inherently deviate from CSCG distribution. Our goal is to derive the achievable rate of the PT and BD under the MASK/MPSK and to design MASK/MPSK modulation scheme for maximizing the PT’s rate. Towards this end, we first derive the expressions of both the PT’s rate and BD’s rate. Theoretical results reveal that whether or not the BD improves the PT’s rate depends on the phase of MASK/MPSK modulation, while the BD’s rate is independent of this phase. We then formulate two optimization problems to maximize the PT’s rate by adjusting the phase under the MASK and MPSK modulation schemes, respectively, and derive the optimal phases for each modulation scheme in closed forms. Given that the optimal phase is continuous and thus impractical for real-world BDs, we also propose a practical circuit design that enables BDs to select a discrete phase close to the theoretical optimum. Simulation results demonstrate that the optimal phase of MASK/MPSK can ensure an improvement in the PT’s rate and the performance gain between the ideal continuous phase and the practical implementation with few discrete phases is negligible, and reveal that a low-order ASK modulation is better than a low-order PSK for the BD in terms of improving PT’s rate, especially when the direct link is not significantly weaker than the backscatter link in SBC.

Ambient backscatter communication (AmBC) enables ultra-low-power, low-cost and massive connectivity. However, practical AmBC systems suffer from symbol timing offset (STO) due to propagation delay and backscatter receiver (BR) activation latency, while conventional correlation-based synchronization methods are inapplicable because ambient radio frequency sources are non-cooperative. Moreover, residual STO (RSTO) inevitably remains due to the finite synchronization sequence, which degrades symbol detection performance. To address these challenges, we first design a specialized synchronization sequence with alternating “0” and “1” bits at the backscatter device to induce observable sampling errors at the BR. Based on this, we propose a pilot-aided, sampling-error-aware maximum likelihood estimation (PSE-MLE) method for STO estimation and compensation, which exploits the statistical variations in the received synchronization signal. After STO compensation, the remaining RSTO is statistically modeled as a discrete bilateral Laplace distribution, with its parameter estimated via ridge regression. Leveraging this prior information, we further develop a Bayesian average energy detector (ave-ED) and derive closed-form expressions for both the detection threshold and bit error rate. Simulation and experimental results on a practical AmBC platform validate the effectiveness of the proposed methods.

Mutualistic symbiotic radio (MSR) that allows a primary link and a backscatter link to share resources and benefit each other has been investigated mainly for long-packet communications. In the Internet of Things (IoT), where short packets are often transmitted, the design of MSR needs to consider the distinct features of short packet transmissions. In this work, we study a multi-IoT-node MSR network supporting both long and short packet transmissions, where a primary transmitter sends long packets to a cooperative receiver (CR), while multiple IoT nodes sequentially backscatter their short packets to the CR. We first derive the lower bound expression for the primary transmission rate by considering the error probabilities (EPs) of the IoT nodes’ short-packet communications. On this basis, we propose a resource allocation scheme to maximize the weighted sum throughput of all IoT nodes, subject to the constraints on the quality-of-service requirement and energy causality for each IoT node, as well as the throughput gain for the primary transmission. Specifically, the weighted sum throughput maximization problem is formulated by jointly optimizing the short-packet blocklength, power reflection coefficient and EP of each IoT node, and is solved by proposing an iterative algorithm based on the block coordinate decent method. Simulation results confirm the quick convergence of the proposed iterative algorithm and show that our proposed scheme outperforms the existing scheme in terms of the weighted sum throughput of all IoT nodes.

This paper investigates the secrecy performance of an energy-causality wirelessly powered backscatter communication (WPBC) network under Nakagami-m fading channels. We first define a performance metric called as the strict secrecy outage probability (S2OP), which accounts for both the energy-causality constraint of the backscatter device (BD) and whether the BD’s transmitted codewords can be decoded by the legitimate receiver. We then derive closed-form expressions for the S2OP and the reliable transmission probability under on-off fixed-rate secure transmission scheme (FRTS) and on-off adaptive-rate secure transmission scheme (ARTS). We also derive the asymptotic expressions of the S2OP. Numerical simulations validate the theoretical analysis and demonstrate the impact of key system parameters on secrecy performance for both FRTS and ARTS.

Symbiotic backscatter communication radio (SBCR) systems enable spectrum-efficient communication by allowing backscatter devices (BDs) to modulate and reflect primary transmitter (PT) signals. However, prior studies often assume circularly symmetric complex Gaussian (CSCG) signals for BDs, which is impractical for low-complexity BDs. This paper addresses this gap by investigating SBCR systems where the BD adopts one of two popular modulation schemes in wireless communications, i.e., binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) modulation schemes. We derive the PT’s rates for these modulation schemes, highlighting the critical role of BD’s modulation phase in enhancing the PT’s rate. We then formulate two optimization problems to maximize the PT’s rate by optimizing the BD’s reflection phases for BPSK and QPSK, respectively, and derive closed-form expressions for the optimal phases. Simulations validate our theoretical analysis, and reveal that BPSK modulation achieves a higher PT’s rate than that of QPSK.

Although coexisting passive reconfigurable intelligent surface (RIS) and active relay have been considered for enhancing long-packet communications, their potential in supporting short-packet communications (SPC) where the direct link is blocked has not been sufficiently studied. Motivated by this, this work investigates the performance of the coexisting passive RIS and active relay-assisted SPC network, where a source (S) transmits its short-packet data to the destination (D) with the help of a passive RIS and an active relay (R). More specifically, in the first time slot of the two-hop transmission, S transmits its short-packet signal to both the RIS and R. The RIS then reflects the incident signal towards D. If D is unable to decode the short-packet information while R successfully decodes it, R forwards the decoded signal to D with the assistance of the RIS in the second time slot. Following a decode-and-forward (DF) relaying protocol, we analytically derive the closed-form expression of the block error rate (BLER) for the short-packet transmission. Simulation results verify the correctness of the derived expression and show that the combination of the passive RIS and active relay can enhance the BLER performance as compared with the benchmarks that employ only a passive RIS or an active relay.

ABSTRACT Relay‐assisted symbiotic radio (SR) has been recently proposed to overcome the blocking of the direct link from the primary transmitter (PT) or backscatter node (BN) to the destination node (DN). However, the energy efficiency (EE), which is an important performance metric for SR networks, has been largely ignored in existing studies of the relay‐assisted SR. To fill the gap, this work maximizes the EE of a relay‐assisted parasitic SR network, which comprises a PT, a BN, a relay node (RN), and a DN. More specifically, we formulate a mixed‐integer programming optimization problem that maximizes the system EE by jointly optimizing the transmit power of the PT, the power reflection coefficient of the BN, the transmit power and the power allocation ratio at the RN as well as the successive interference cancellation (SIC) decoding order at the DN. We decompose the formulated non‐convex problem into two subproblems corresponding to the two different SIC decoding orders, respectively. For each subproblem, we convert its objective function from a fractional form into a subtractive form by using a Dinkelbach‐based method, and then utilize the block coordinate descent (BCD) method to further decouple it into two subsubproblems that are proved to be convex. Based on the obtained solutions, we devise an iterative algorithm to solve each subproblem by solving its two subsubproblems alternately. The optimal solution to the subproblem with a higher system EE returns a near‐optimal solution to the original problem. Simulation results demonstrate the rapid convergence of the proposed algorithms and validate the significant advantages of our proposed algorithms over the baseline schemes.

Energy-efficient computation is an inevitable trend for unmanned aerial vehicles (UAV)-enabled wireless powered mobile edge computing (MEC), while it has not been investigated when the hybrid passive and active transmissions (ATs) are considered for Internet of Things (IoT) nodes’ task offloading. In this paper, we study the computation energy efficiency (EE) fairness among IoT nodes in a UAV-enabled wireless powered MEC network with hybrid passive and ATs, where the UAV serves as a dynamic energy source to support IoT nodes for backscatter communication (BackCom) and AT. Specifically, we formulate an optimization problem to maximize the computation EE of the worst IoT node by jointly optimizing the UAV’s transmit power and trajectory, the IoT nodes’ BackCom time and reflection coefficients, the IoT nodes’ AT power and time, as well as the IoT nodes’ local computing time and frequencies. The formulated problem is highly non-convex and difficult to be solved optimally. To address it, we first obtain the closed-form expressions for the UAV’s transmit power and the IoT nodes’ local computing time by means of the proof by contradiction to simplify the problem, and then propose a Dinkelbach-based iterative algorithm to obtain the solution of other optimization variables. Specifically, based on the Dinkelbach’s method, the original fractional problem is transformed into the problem with the subtractive objective function. Then we further decouple the transformed problem into two subproblems based on the block-coordinated-decent (BCD) method and solve the transformed problem by the proposed BCD-based iterative algorithm, where the above two subproblems are solved by means of the existing convex optimization tools and the proposed successive convex approximation (SCA)-based iterative algorithm alternatively. Simulation results show that the proposed algorithms have a fast rate of convergence and that the proposed scheme outperforms other baseline schemes in terms of the computation EE fairness.

Most of works on backscatter communication (BackCom) assume the availability of a direct link from the BackCom transmitter to its receiver and the long-packet transmission for BackCom. However, the above assumptions may be inapplicable in some practical Internet of Things (IoT) applications, e.g., industrial automation. Motivated by this, this paper proposes and studies a relay assisted short-packet BackCom network, where a full-duplex relay (R) with two antennas and an energy self-sustaining IoT node form a monostatic short-packet BackCom paradigm in the first phase while R forwards the IoT’s short-packet information to the information receiver (IR) in the second phase via a decode-and-forward (DF) or amplify-and-forward (AF) relaying protocol. For a given DF or AF relaying protocol, we propose to optimize the power allocation factor of two antennas at R in the second phase to minimize the IoT node’s average block error rate (BLER), and derive the optimal solution in the closed form. With the optimal power allocation factor, we derive the analytical expressions of the IoT node’s average BLER under the DF and AF protocols while considering the residual self-interference at R and the energy causal constraint at the IoT node. Simulation results verify the correctness of the derived expressions and reveal the impacts of various parameters such as the IoT node’s short-packet blocklength and power reflection coefficient on the average BLER. It is found that when the short-packet blocklength increases, the average BLER decreases until it reaches a certain value.

To address the limitation on the rate of secondary users (SUs) in traditional mutualistic symbiotic radio (SR) systems, this paper proposes a new mutualistic SR with hybrid active-passive communications. Unlike the traditional mutualistic SR, where SUs only perform passive backscatter communications (BC), in the proposed mutualistic SR, each SU conveys information via passive BC and active communications (AC) alternatively. To exploit the potential advantages of the proposed SR, we formulate a non-convex problem to maximize the total rate of all SUs by jointly optimizing the transmit power of the primary user (PU), the reflection coefficient and transmit power of each SU, and the time allocation for each SU to perform passive BC and AC. With proof by contradiction, auxiliary variables and successively convex approximation (SCA), we transform the original problem into a convex one and solve it by proposing an iterative algorithm. The simulation results demonstrate that the proposed iterative algorithm converges quickly. Furthermore, the performance evaluation of the proposed mutualistic SR system demonstrates its superiority over the traditional mutualistic SR in terms of the rate achieved by SUs under the same constraints.

This article proposes a novel unmanned aerial vehicle (UAV)-enabled wireless-powered mobile edge computing (WP-MEC) network, where several Internet of Things (IoT) nodes use the energy harvested from the UAV’s radio frequency signals to support the local computation and the hybrid active–passive communications-based task offloading. Two weighted sum computation bits (WSCB) maximization problems are formulated under the partial and binary offloading, respectively, by jointly optimizing the local computing frequencies and time, the IoT nodes’ reflection coefficients, the IoT nodes’ transmit powers, the UAV’s trajectory, etc., subject to the quality-of-service and energy-causality constraints per IoT node, the speed constraint of the UAV, etc. Since the formulated problems are highly nonconvex, two iterative algorithms are proposed to solve the formulated problems under two modes. Simulation results demonstrate that the proposed iterative algorithms have a fast convergence rate, and the proposed schemes achieve higher WSCB than several baseline schemes.

In this article, we propose a novel system model for a wireless-powered mobile edge computing (MEC) network, where the Internet of Things (IoT) nodes perform partial offloading to the MEC server via hybrid backscatter communication (BackCom) and active radio (AR) following an orthogonal frequency division multiple access protocol, and maximize the system computation bits (SCBs). For the case of the system having more subchannels than IoT nodes, we formulate the SCB maximization problem that requires the joint optimization of the transmit power and time, subchannel allocation, computation frequency, and time of the MEC server, as well as the IoT nodes’ BackCom time and reflection coefficients, transmit power and time for AR-based offloading, local computing time and frequencies, subject to the MEC server’s computation capacity and the Quality of Service (QoS) and energy-causality constraints of each IoT node. By applying the proof by contradiction and time-sharing relaxation, we transform the formulated problem into a convex one and then solve it by using the existing convex tools. For the case of the system having less subchannels than IoT nodes, we propose a dynamic subchannel allocation scheme that allows each IoT node to choose one task-offloading mode from three modes: 1) HAPR; 2) BackCom only; and 3) AR only, while ensuring that no more than one IoT node occupies a subchannel at any time. The SCB is maximized by first determining the subchannel allocation and mode selection of each IoT node and then optimizing the remaining resource allocation for the MEC server and all IoT nodes under the obtained subchannel assignment and mode selection. Simulations validate the superior performance of the proposed schemes over several benchmark schemes from the SCB perspective.

In mutualistic cooperative ambient backscatter communications (AmBC), Internet-of-Things (IoT) device sends its information to a desired receiver by modulating and backscattering the primary signal, while providing beneficial multipath diversity to the primary receiver in return, thus forming a mutualism relationship between the AmBC and primary links. We note that the hardware impairments (HIs), which are unavoidable in practical systems and may significantly affect the transmission rates of the primary and AmBC links and their mutualism relationships, have been largely ignored in the study of mutualistic cooperative AmBC networks. In this paper, we consider a mutualistic cooperative AmBC network under HIs, and study the impacts of HIs on the achievable rates of the primary link and the AmBC link. In particular, we theoretically prove that although HIs degrades the rate of each link, the mutualism relationship between the AmBC and primary links is maintained, i.e., the rate of the primary link in the mutualistic cooperative AmBC network is still higher than that without the AmBC link. The closed-form rate expressions of both the AmBC and primary links are derived. Computer simulations are provided to validate our theoretical analysis.

Mutualistic cooperative ambient backscatter communications (AmBC) have been proposed to improve the spectrum and energy efficiencies of Internet-of-Things (IoT) systems, where a primary link (from a primary transmitter to a primary receiver) and an AmBC link (from an IoT device to the same primary receiver) form a mutualism relationship. We note that hardware impairments (HIs), which are unavoidable in practical systems and may significantly affect the transmission rates of the primary and AmBC links and their mutualism relationships, have been largely ignored in the study of mutualistic cooperative AmBC networks. In this paper, we study a mutualistic cooperative AmBC network with HIs at all the active transceivers and a non-linear energy harvesting circuit at each IoT device. We derive closed-form rate expressions for both the AmBC and primary links and theoretically prove that the mutualism relationship between the AmBC and primary links is maintained under HIs, i.e., the rate of the primary link in the mutualistic cooperative AmBC network is still higher than that without the AmBC link. To maximize the weighted sum rate of all links in a cooperative AmBC network under HIs, we propose two resource allocation schemes for two scenarios with a single link and multiple AmBC links, respectively. For the single AmBC link case, we derive the optimal transmit power of the primary transmitter and the optimal power reflection coefficient of the IoT device in closed forms. For the scenario with multiple AmBC links, the weighted-sum-rate maximization problem is transformed into a convex one and solved with convex optimization tools. Computer simulations validate our theoretical results and that our proposed schemes outperform the benchmark schemes in terms of the weighted sum rate.

In this paper, we consider a backscatter-assisted wireless powered mobile edge computing (MEC) network, where multiple Internet-of-Things (IoT) nodes harvest energy from the energy signals transmitted by a power beacon (PB) and utilize the harvested energy for local computing and task offloading via hybrid backscatter communication (BackCom) and active transmission (AT). Considering the limited computation capacity of the MEC server and the quality-of-service (QoS) and energy-causality constraints per IoT node, we propose two resource allocation schemes to maximize the total computation bits of all the IoT nodes and the system computation energy efficiency (EE), respectively, by jointly optimizing the computation frequency and time of the MEC server and each IoT node, the transmit power of the PB and each IoT node, and the BackCom power reflection coefficient and the time for energy harvesting (EH), BackCom, and AT of each IoT node. The non-convex computation bits maximization problem is transformed to a convex one by introducing a series of auxiliary variables and proof by contradiction, and then solved by the existing convex tools. The system computation EE maximization is a non-convex nonlinear programming problem. We propose a two-layer iterative algorithm to solve it optimally and devise a reduced-complexity iterative algorithm to solve it sub-optimally by leveraging the block coordinate decent technique. Computer simulations validate the convergence of the proposed iterative algorithms and their superior performance over the benchmark schemes in terms of the computation bits or EE.

Transmission delay is critical to time-sensitive and power-limited Internet of Things (IoT) systems. This work proposes a hybrid transmission scheme, which enables energy-constrained sensor nodes (SNs) to deliver data to an information fusion via a hybrid of backscatter communications (BackCom) and wireless powered active communications (WPAC). Considering a non-linear energy harvesting (EH) model for each SN, we formulate a non-convex optimization problem to minimize the total transmission time of all SNs while satisfying the minimum throughput requirement for each SN, by jointly optimizing the time allocation between BackCom and active communications (AC), the transmit power and the power reflection coefficients of each SN, and the transmit power of the energy source (ES). We first determine the optimal transmit power of the ES by contradiction and then transform the non-convex problem into a convex one by introducing a series of auxiliary variables. We theoretically prove that the minimum total transmission time is achieved when each SN exhausts all the harvested energy and does not work in the pure BackCom mode. Simulation results show that the proposed scheme achieves a much shorter total transmission time than the existing schemes, e.g., binary transmission scheme, WPAC, and pure BackCom.

Emerging smart Internet-of-Things (IoT) applications are increasingly relying on mobile-edge computing (MEC) networks, where the energy efficiency (EE) of computation is one of the most pertaining issues. In this article, considering the limited computation capacity at the MEC server and a practical nonlinear energy harvesting (EH) model for IoT devices, we propose a scheme to maximize the system computation EE (CEE) of a wireless power transfer (WPT) enabled nonorthogonal multiple access (NOMA)-based MEC network by jointly optimizing the computing frequencies and execution time of the MEC server and the IoT devices, the offloading time, the EH time and the transmit power of each IoT device, as well as the transmit power of the power beacon (PB). We formulate the joint optimization into a nonlinear fractional programming problem and devise a Dinkelbach-based iterative algorithm to solve it. By means of convex theory, we derive closed-form expressions for parts of the optimal solutions, which reveal several instrumental insights into the maximization of the system CEE. In particular, the system CEE increases as the optimal computing frequencies of both the IoT devices and the MEC server decrease, and the system CEE is maximized when the MEC server and the IoT devices use the maximum allowed time to complete their computing tasks. Simulation results demonstrate the superiority of the proposed scheme over benchmark schemes in terms of system CEE.

Ambient backscatter communications (AmBackComs) have been recognized as a spectrum- and energy-efficient technology for the Internet of Things, as it allows passive backscatter devices (BDs) to modulate their information into the legacy signals, e.g., cellular signals, and reflect them to their associated receivers while harvesting energy from the legacy signals to power their circuit operation. However, the co-channel interference between the backscatter link and the legacy link and the nonlinear behavior of energy harvesters at the BDs have largely been ignored in the performance analysis of AmBackComs. Taking these two aspects, this article provides a comprehensive outage performance analysis for an AmBackCom system with multiple backscatter links, where one of the backscatter links is opportunistically selected to leverage the legacy signals transmitted in a given resource block. For any selected backscatter link, we propose an adaptive reflection coefficient (RC), which is adapted to the nonlinear energy harvesting (EH) model and the location of the selected backscatter link, to minimize the outage probability of the backscatter link. In order to study the impact of co-channel interference on both backscatter and legacy links, for a selected backscatter link, we derive the outage probabilities for the legacy link and the backscatter link. Furthermore, we study the best and worst outage performances for the backscatter system where the selected backscatter link maximizes or minimizes the signal-to-interference-plus-noise ratio (SINR) at the backscatter receiver. We also study the best and worst outage performances for the legacy link where the selected backscatter link results in the lowest and highest co-channel interference to the legacy receiver, respectively. Computer simulations validate our analytical results and reveal the impacts of the co-channel interference and the EH model on the AmBackCom performance. In particular, the co-channel interference leads to the outage saturation phenomenon in AmBackComs, and the conventional linear EH model results in an overestimated outage performance for the backscatter link.

Non-orthogonal multiple access (NOMA) based mobile edge computing (MEC) networks can enhance delay-constrained computation. Most of the existing works focus on the energy or delay minimization, and the study on NOMA based MEC in terms of successful computation probability has been limited, which motivates this work. In particular, randomly deployed edge computing users are modeled as the homogeneous Poisson Point Process and are divided into two groups namely center group (C-group) and edge group (E-group) for uplink NOMA grouping. We propose a new hybrid offloading scheme in a NOMA-based MEC network that can operate in three different modes, namely partial offloading, complete local computation, and complete offloading. We firstly consider a NOMA grouping scenario where a user from the C-group and a user from the E-group are each given a fixed location. The probability that the computation can be completed within the given delay budget is derived and the optimal parameters, i.e., the time for offloading, the power allocation, and the offloading ratios for the two fixed users, are obtained. It reveals that the optimal offloading ratios are determined by the difference of the computational capability between the edge computing user and the MEC server, and that the locations of users have a big impact on the successful computation probability. Inspired by this, we further study three distance-dependent NOMA grouping schemes in the MEC offloading. Specifically, we provide closed-form mathematical expressions of the successful computation probability and its partial optimal solutions for these three schemes. Simulation results verify the accuracy of the analytical results and compare the performance of the proposed offloading scheme with the existing schemes. Insights on the pros and the cons of different user selection schemes are also provided.

This letter focuses on the design of an optimal resource allocation scheme to maximize the energy efficiency (EE) in a simultaneous wireless information and power transfer (SWIPT) enabled two-way decode-and-forward (DF) relay network under a non-linear energy harvesting model. In particular, we formulate an optimization problem by jointly optimizing the transmit powers of two source nodes, the power-splitting (PS) ratios of the relay, and the time for the source-relay transmission, under multiple constraints including the transmit power constraints at sources and the minimum rate requirement. Although the formulated problem is non-convex, an iterative algorithm is developed to obtain the optimal resource allocation. Simulation results verify the proposed algorithm and show that the designed resource allocation scheme is superior to other benchmark schemes in terms of EE.

Simultaneous wireless information and power transfer (SWIPT) has been recognized as a promising approach to improving the performance of energy constrained networks. In this paper, we investigate a SWIPT based three-step two-way decode-and-forward (DF) relay network with a non-linear energy harvester equipped at the relay. As most existing works require instantaneous channel state information (CSI) while CSI is not fully utilized when designing power splitting (PS) schemes, there exists an opportunity for enhancement by exploiting CSI for PS design. To this end, we propose a novel heterogeneous PS scheme, where the PS ratios are dynamically changed according to instantaneous channel gains. In particular, we derive the closed-form expressions of the optimal PS ratios to maximize the capacity of the investigated network and analyze the outage probability with the optimal dynamic PS ratios based on the non-linear energy harvesting (EH) model. The results provide valuable insights into the effect of various system parameters, such as transmit power of the source, source transmission rate, and source to relay distance on the performance of the investigated network. The results show that our proposed PS scheme outperforms the existing schemes.

Simultaneous wireless information and power transfer (SWIPT) is a promising technique to prolong the lifetime of energy‐constrained relay systems. Most previous works optimize power‐splitting (PS) scheme based on a linear or a simple two‐piecewise linear energy harvesting (EH) model, while the employed EH model may not characterize the properties of practical EH harvesters well. This leads to a mismatch between the existing PS scheme and the practical EH harvester available for relay systems. Motivated by this, this paper is devoted to the design of PS scheme in a nonlinear EH amplify‐and‐forward energy‐constrained relay system in the presence of a direct link between the source and the destination. In particular, we formulate an optimization problem to maximize the system capacity according to the instantaneous channel state information, subject to a nonlinear EH model based on the logistic function. The objective function of the formulated problem is proven to be unimodal and there is no closed‐form expression for the optimal PS ratio due to the complexity of logistic function. In order to reduce overhead cost of optimizing PS ratio, a simpler nonlinear EH model based on the inverse proportional function is employed to replace the nonlinear EH model based on the logistic function and we further derive the closed‐form expression for the optimal PS ratio. Simulation results reveal that a higher system capacity can be achieved when the PS scheme is optimized based on nonlinear EH models instead of the linear EH model, and that there is only a marginal difference between the capacity under the two optimal PS schemes optimized for two different nonlinear EH models.

In this paper, we investigate profit maximization problem in wireless powered communication (WPC) systems, where the profit is defined as the difference between the revenue earned from uplink data transmission and total energy cost of the system. Since the efficiencies of energy harvesting (EH) circuits and battery storage are both non-linear, conventional linear models will lead to suboptimal resource allocation. In this paper, we propose to maximize the profit of a WPC system under nonlinear energy harvesting and storage models. In order to solve the non-convex optimization problem, we develop a piecewise linear EH model, which is more accurate than conventional linear EH models and existing non-linear EH models. Numerical results show that the power and time allocation design based on the non-linear energy harvesting and storage models outperforms that based on existing linear models in terms of the profit.

In this paper, we study a radio frequency (RF) wireless energy transfer (WET) enabled multiple input multiple output (MIMO) system which consists of a base station (BS) and a user equipment (UE) with a finite capacity battery. A time slotted transmission pattern is considered. Each slot can be divided into two phases, downlink (DL) WET and uplink (UL) wireless information transmission (WIT). The battery State-Of-Charge (SOC) which is defined as the ratio of energy available in the current battery to battery capacity is considered. Given a fixed SOC, the maximum acceptable charging power of the battery is determined, which provides an idea for the BS to reduce energy loss at the UE by adjusting transmission power. The optimization problem is formulated to minimize the transmission energy. The problem is proved convex and a power and time allocation algorithm is proposed to achieve an optimal solution. The numerical results show that our proposed algorithm has better performance than other existing schemes.