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.

With the development of the sixth-generation (6G) communication networks, the scale of internet of things (IoT) devices is rapidly expanding. However, the limited computational and energy resources have become the major bottlenecks constraining IoT devices in processing complex tasks and providing high-quality services. In order to solve this problem, a novel unmanned aerial vehicle (UAV)-mounted reconfigurable intelligent surfaces (RIS) assisted mobile edge computing (MEC) with backscatter system is proposed. In this paper, a system energy efficient (EE) maximization problem is formulated by jointly optimizing the reflection coefficients, computational resources, time allocation, RIS phase shifts, and UAV trajectories while satisfying the task constraints, energy causality constraints, and trajectory constraints. To solve the established non-convex optimization problem, a three-stage alternating optimization algorithm is developed based on the Dinkelbach algorithm. The efficient solution of each subproblem is realized by leveraging the Lagrangian dual method, combined with the sub-gradient descent and successive convex approximation techniques. Furthermore, the closed-form expressions for the CPU computation frequency, backscatter reflection coefficient, and RIS phase-shift coefficients are derived. Simulation results show that the proposed scheme achieves superior system EE compared with the benchmark and simplified schemes.

Ambient backscatter communications (AmBC), where a backscatter transmitter (BT) modulates and reflects ambient signals to a backscatter receiver (BR), have been deemed a low-power communication technology for the Internet of Things. Previous work on symbol detection in AmBC assumed perfect time synchronization (TS), which is unrealistic in practice. The residual TS errors (RTSE) causepartial sample mismatch, degrading symbol detection performance. To address this, we propose a new AmBC symbol detection framework that incorporates the BT’s current and adjacent symbols, as well as channel coefficients. Using energy detector (ED) as a case study, we derive both exact and approximate bit error rate (BER) expressions. Our results show that the ED’s BER performance degrades significantly under RTSE, with the symbol detection threshold optimized under the assumption of perfect TS. We then derive a closed-form expression for a near-optimal symbol detection threshold that minimizes BER under RTSE. To estimate the required parameters for the detection threshold, we propose a novel method exploiting the attributes of the BR’s received signal samples. The analytical results are verified by simulation results.

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.

In a single-antenna fluid antenna system (FAS), the transceiver dynamically selects the antenna port with the strongest instantaneous channel to enhance link reliability. However, deriving accurate yet tractable performance expressions under the fully correlated fading channel remains challenging, primarily due to the absence of a closed-form distribution for the FAS channel. To address this gap, this paper develops a novel performance evaluation framework for FAS under fully correlated Rayleigh fading by modeling the FAS channel using extreme value distributions (EVDs). We first justify the suitability of EVDs and model the FAS channel as a Gumbel distribution, whose parameters, estimated via the maximum likelihood (ML) criterion, are expressed as functions of the number of ports and antenna aperture size. Closed-form approximate expressions for the outage probability (OP) and the ergodic capacity (EC) are then derived. Simulation results show that the Gumbel distribution provides simple yet sufficiently accurate expressions for EC, although slight deviations in OP accur in the low-probability region of practical interest. To further improve accuracy, the FAS channel is modeled using the generalized extreme value (GEV) distribution, yielding closed-form expressions for OP and EC based on ML-estimated parameters. Simulation results confirm that the GEV distribution provides superior accuracy compared to the Gumbel, particularly for OP, while both EVD-based approaches offer computationally efficient and analytically tractable tools for evaluating FAS performance under the fully correlated fading channel.

This paper investigates covert communication in a hybrid-functional (HF) reconfigurable intelligent surface (RIS) assisted symbiotic radio (SR) system, where each element of RIS can adaptively switch between passive reflection and covert signal modulation mode. Since covert communication fundamentally relies on low transmission power to ensure undetectability, we try to minimize the transmit power while ensuring the covert constraint, and the joint optimization of active beamforming (BF) at access point, passive BF and mode selection at HF-RIS can be formulated as a mixed-integer fractional programming (MIFP) problem. By decomposing the original problem into the continuous beamformer design and the discrete mode selection problems, we further develop a binary search-assisted alternative beamformer optimization algorithm to obtain a stable solution of the MIFP problem. Numerical results demonstrate the superiority of the adaptive RIS mode selection covert-enhanced design over existing benchmark schemes and reveal useful insights to guide RIS mode selection design for different SR paradigms.

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.

We investigate multiple access offloading designs toward weighted peak age of information (PAoI) and energy consumption optimization in mobile edge computing (MEC) systems. Two partial offloading strategies based on orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are proposed, which balance the delay and spectral efficiency for weighted PAoI-energy minimization. Efficient algorithms are then devised to jointly optimize the transmit power, the task splitting factor, and the time allocation. Numerical results show that the proposed algorithm outperforms conventional benchmark strategies.

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.

This paper investigates the physical layer security for a double-reconfigurable intelligent surface (DRIS) aided wireless-powered communication (WPC) system in the presence of an eavesdropper, where one RIS (termed as RIS-1) is deployed between power station (PS) and information user (U) while the other RIS (termed as RIS-2) is deployed between U and access point (AP). Moreover, an idle user acts as a cooperative jamming user (J) to emit the artificial noise to improve the transmission security of U. According to different types in energy harvesting (EH) and information transmission (IT)/noise transmission (NT) of U/J, we propose four DRIS enabled wireless-powered cooperative jamming transmission schemes based on jointly enhancing EH and IT of U (namely DRIS-1), jointly enhancing EH of U and NT of J (namely DRIS-2), jointly enhancing EH of J and IT of U (namely DRIS-3), and jointly enhancing EH and NT of J (namely DRIS-4), respectively. We analyze connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) of the proposed four schemes, respectively. The results show that DRIS-1 scheme has the highest diversity gain among the four schemes. Besides, the gain of the number of reflecting elements at RIS-1 has the same order as that at RIS-2 in DRIS-1 scheme and DRIS-4 scheme. In DRIS-2 scheme, the gains of the number of reflecting elements at RIS-1 and RIS-2 are two powers and one power, respectively, while the opposite is true in DRIS-3 scheme. In addition, DRIS-1 scheme achieves the best COP, while DRIS-4 scheme achieves the best SOP. DRIS-1 scheme, DRIS-3 scheme, and DRIS-4 scheme respectively achieve the best EST in the low, middle, and high region of transmission power or number of RIS elements.

Existing works on mutualistic backscatter communications (MBC) rely on the existence of direct links connecting both the primary user (PU) and the backscatter device (BD) to the destination node (D), rendering them ineffective when these links are blocked. While relay technology offers a solution, the performance of relay-assisted MBC and whether the backscatter link still benefits the primary link or not remain unclear. Additionally, the energy-harvesting capability of BD has been largely ignored in the study of MBC. This paper investigates a relay-assisted MBC network, where a decode-and-forward relay (R) forwards the messages of a PU and an energy constrained BD to D. We propose three forwarding schemes for R, i.e., orthogonal time-division multiple access (OMA), non-orthogonal multiple access (NOMA) with static power allocation (PA), and NOMA with dynamic PA. Considering the energy-causality constraint of BD, we derive the outage probabilities and diversity gains of the primary and backscatter links for each forwarding scheme. Computer simulation verifies the correctness of our analytical results and reveals that different from conventional MBC, in the relay-assisted MBC, whether or not the backscatter link improves the primary link’s outage performance depends on the specific forwarding scheme employed, as well as the location of R and the power reflection coefficient of BD.

Urban vehicle-to-vehicle (V2V) link scheduling with shared spectrum is a challenging problem. Its main goal is to find the scheduling policy that can maximize system performance (usually the sum capacity of each link or their energy efficiency). Given that each link can experience interference from all other active links, the scheduling becomes a combinatorial integer programming problem and generally does not scale well with the number of V2V pairs. Moreover, link scheduling requires accurate channel state information (CSI), which is very difficult to estimate with good accuracy under high vehicle mobility. In this paper, we propose an end-to-end urban V2V link scheduling method called Map2Schedule, which can directly generate V2V scheduling policy from the city map and vehicle locations. Map2Schedule delivers comparable performance to the physical-model-based methods in urban settings while maintaining low computation complexity. This enhanced performance is achieved by machine learning (ML) technologies. Specifically, we first deploy the convolutional neural network (CNN) model to estimate the CSI from street layout and vehicle locations and then apply the graph embedding model for optimal scheduling policy. The results show that the proposed method can achieve high accuracy with much lower overhead and latency.

The rise of deep learning enables spectrum sensing to make decisions only based on the observed data which brings about a lot of flexibility. However, the existing data-driven spectrum detection methods handle the data in Euclidean space well but ignore the laten structure connections of the signal. To characterize this information, we propose a graph construction mechanism based on the similarity measure of the random signal and further convert the received signal into graph topology. On this basis, we propose a graph neural network based detector that consists of the construction of graph topology, offline training and online detection. The proposed data-driven method does not need any priori knowledge of the observed signal. Simulation results demonstrate that whether there is noise uncertainty or not, the proposed method is robust and performs better than the existing deep learning based methods.

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.

Driven by the ever-increasing requirements of ultrahigh spectral efficiency, ultralow latency, and massive connectivity, the forefront of wireless research calls for the design of advanced next-generation multiple access schemes to facilitate the provisioning of these stringent demands. This inspires the embrace of nonorthogonal multiple access (NOMA) in future wireless communication networks. Nevertheless, the support of massive access via NOMA leads to additional security threats due to the open nature of the air interface, the broadcast characteristic of radio propagation, and the intertwined relationship among paired NOMA users. To address this specific challenge, the superimposed transmission of NOMA can be explored as new opportunities for security-aware design; for example, multiuser interference inherent in NOMA can be constructively engineered to benefit communication secrecy and privacy. The purpose of this tutorial is to provide a comprehensive overview of the state-of-the-art physical layer security techniques that guarantee wireless security and privacy for NOMA networks, along with the opportunities, technical challenges, and future research trends.

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.

In this paper, we investigate a covert cooperative backscatter communication (BackCom) network under residual hardware impairments (RHIs) that are unavoidable in practical radio frequency transceivers and may have an impact on the communication covertness and reliability. By exploiting the availability of statistical channel state information, we derive analytic expressions for the eavesdropper's detection error probability (DEP) and system outage probability (SOP) to evaluate the system covertness and communication reliability, respectively. Using the derived results, we prove that RHIs can improve the communication covertness but significantly degrade the outage performance. We also show that under RHIs and extremely high transmit signal-to-noise levels, the system outage performance has a non-zero asymptote, while the DEP still approaches one, similar to the scenario with perfect hardware. Computer simulations corroborate our analytical results.

In this paper, a rate-splitting multiple access (RSMA) scheme is proposed to aid a mobile edge computing (MEC) system where multiple randomly deployed users offload their computation tasks to a MEC server. Considering that users are divided into the center and edge groups, a cognitive radio (CR)-inspired rate-splitting is designed to enable the paired users to simultaneously offload to MEC server. Under the CR principles, the rate-splitting parameters are jointly designed to attain the maximum achievable rate for the secondary user, meanwhile maintaining the primary user’s offloading performance same as in orthogonal multiple access. For the case of the paired users with fixed locations, we derive a closed-form expression for the successful computation probability (SCP) achieved by the RSMA-aided MEC (RSMA-MEC) scheme and formulate a SCP maximization problem to obtain the optimal offloading parameters. To reveal the impact of the user locations on the offloading performance of the RSMA-MEC system, various distance-based user pairing schemes are investigated by invoking stochastic geometry techniques. We provide closed-form expressions for the SCPs achieved by the user pairing schemes to characterize the offloading performance. Pros and cons of the user locations to maximize the SCP are highlighted. Simulation results verify the accuracy of the analytical results and clarify the superior offloading performance achieved by the RSMA-MEC scheme, which attains a higher SCP than the existing schemes.

Unmanned aerial vehicles (UAVs) with integrated computing platforms can be used to provide computing offloading services for ground user equipments (UEs) with limited local computing capabilities, especially in remote areas. In this paper, we focus on the task offloading in an aerial edge network (AEN) assisted by a UAV. We aim at minimizing the sum energy consumption of all UEs by the joint optimization of the task offloading decisions and the UAV position under the constraints of the latency and the total energy of UAV. The formulated optimization problem is a mixed-integer nonconvex problem and involves coupling of many optimization variables. To address this challenge, we first transform the original optimization problem into a linear convex optimization problem via reformulation linearization technology, and then the alternating direction method of multipliers (ADMM) algorithm is proposed to achieve the approximate optimal solution. Numerical results confirm that the proposed ADMM algorithm can effectively reduce the total of energy consumption of UEs and ensure the continuous operation of the UEs.

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.

In this article, we propose a wireless powered opportunistic cooperative backscatter communication network, where an Internet of Things (IoT) node conveys information to its associated receiver via backscatter communications with the help of a hybrid access point (HAP) in each transmission block. The HAP provides energy signals for the IoT node in the first half transmission block, and continues to do or relays the IoT node’s signal to the receiver via decode-and-forward protocol in the second half transmission block. We investigate under which condition the HAP serves as the relay node in the second half transmission block in terms of the achievable throughput. To this end, a mixed integer non-convex optimization is formulated to maximize the throughput of the IoT node by optimizing the power reflection coefficient (PRC) of the IoT node and the operation mode of the HAP during the second half transmission block, while meeting the energy-causality constraint of the IoT node. We derive the closed-form expressions for the optimal PRC and operation mode, based on which a scheme is proposed to determine the optimal operation mode of the HAP. Simulations validate the derived results and study the impacts of various parameters on the optimal operation mode and throughput.

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.

Wireless powered communication via backscatter and/or harvest-then-transmit (HTT) has been considered a promising solution to connecting nodes in the Internet-of-things (IoT) networks. However, the harvested energy at an IoT node is heavily limited by the distance between the node and the power beacon (PB) due to the high propagation loss. In this paper, we propose to employ an unmanned aerial vehicle (UAV) as a mobile PB to provide energy signals on demand to the IoT nodes, which convey their information to a reader via backscattering or active transmission using the harvested energy. We maximize the total energy efficiency (EE) of all the IoT nodes powered by the UAV by jointly optimizing the UAV’s transmit power and trajectory, the IoT nodes’ backscatter reflection coefficients and their transmit power for active transmission, and the time allocation between backscattering and active transmission. To solve the formulated non-linear fractional programming problem, we use the generalized fractional programming theory and a block coordinated decent method to decompose it into two sub-problems: one optimizes the communication resource allocation under a fixed UAV trajectory, and the other optimizes the UAV trajectory for given communication resource allocation. We then devise a Dinkelbach-based iterative algorithm to solve the two sub-problems by employing a Lagrangian dual method and a successive convex programming technique, respectively and iteratively. Simulation results show that our proposed iterative algorithm converges very fast, and the optimized UAV-enabled hybrid backscatter-HTT communication achieves a much higher EE of all the IoT nodes than the benchmark schemes including the UAV-enabled backscatter, UAV-enabled HTT, and hybrid BackCom-HTT with a fixed PB.

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.

Due to the limited battery capacity in mobile devices, simultaneous wireless information and power transfer (SWIPT) has been proposed as a promising solution to improve the energy efficiency (EE) in Internet-of-Things (IoT) networks, i.e., device-to-device (D2D) networks, by allowing mobile devices to harvest energy from ambient radio-frequency (RF) signals. However, the nonlinear behavior of RF energy harvesters has largely been ignored in the existing works on SWIPT. In this article, we propose to maximize the sum EE of all D2D links in a D2D underlaid cellular network by optimizing the resource and power allocation based on a nonlinear energy harvesting (EH) model. Toward this end, we first propose a prematching algorithm to divide the D2D links into a SWIPT-enabled group and a non-EH group that cannot meet the EH circuit sensitivity. We then develop a two-layer iterative algorithm to jointly optimize the D2D transmission power and the power splitting ratio to maximize the EE for each SWIPT-enabled D2D link. On this basis, we build the preference lists for both SWIPT-enabled D2D links and cellular user equipment (CUE), and propose a one-to-one constraint stable matching algorithm to maximize the sum EE of all SWIPT-enabled D2D links by optimizing the spectrum resource sharing between D2D links and CUEs. The sum EE of non-EH D2D links is maximized through an iterative power control algorithm and a one-to-one stable matching algorithm. Simulation results show that our proposed algorithms achieve a much higher sum EE than the existing matching-based energy-efficient resource allocation scheme for SWIPT-enabled D2D networks.

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 study focuses on spectrum sensing under Laplacian noise. To mitigate the negative effects caused by the heavy‐tailed behaviour of Laplacian noise, the fractional lower order moments (FLOM) technology is employed to pre‐process the received samples before spectrum sensing. Through exploiting the asymmetrical difference between the distribution for the FLOM of received samples in the absence and presence of primary users, the authors formulate the spectrum sensing problem under Laplacian noise as a unilateral goodness‐of‐fit (GoF) test problem. Based on this test problem, they propose a new GoF‐based detector, which is called a unilateral left‐tail Anderson Darling (ULAD) detector. The analytical expressions for the theoretical performance, in terms of false‐alarm and detection probabilities, of the ULAD are derived. Moreover, a closed‐form expression for the optimal detection threshold is also derived to minimise the total error rate. Simulation results are provided to validate the theoretical analyses and to demonstrate the superior performance of the proposed detector than others.

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.

Non‐orthogonal multiple access (NOMA) has been proposed as a promising technique for the fifth generation wireless networks. In this study, the authors investigate the performance of a cooperative relaying scheme using NOMA (termed NOMA‐AF‐CRS), where two sources communicate with their corresponding destinations over the same frequency simultaneously through a shared amplify‐and‐forward (AF) relay. First, the closed‐form approximations are derived for the outage probability. The analytical results are further evaluated in the high signal‐to‐noise ratio region to characterise the diversity order achieved by the system. Then, the ergodic sum capacity of NOMA‐AF‐CRS is analysed and an upper bound to the ergodic sum capacity is derived. Finally, simulation results are presented to verify the proposed analysis and demonstrate the advantages of NOMA‐AF‐CRS over the conventional orthogonal multiple access techniques.

Power splitting (PS) scheme design is one of the most important challenges in simultaneous wireless information and power transfer-based two-way multiplicative amplify-and-forward relay networks. In this letter, we propose a novel dynamic asymmetric PS (DAPS) scheme to minimize the system outage probability by exploiting the asymmetric instantaneous channel gains between the relay node and the destination nodes. As the formulated optimization problem is a nonconvex problem and difficult to solve, we reformulate it as a fractional programming problem and propose a Dinkelbach-based iterative algorithm to obtain the optimal asymmetric PS ratios. Both the analytical and simulation results demonstrate that the proposed DAPS scheme with the same channel state information overhead can significantly reduce the system outage probability as compared with the existing static symmetric PS scheme.

This paper investigates an energy‐constrained two‐way multiplicative amplify‐and‐forward (AF) relay network, where a practical nonlinear energy harvesting (NLEH) model is equipped at the relay to realize simultaneous wireless information and power transfer (SWIPT). We focus on the design of dynamic power splitting (DPS) strategy, in which the PS ratio is able to adjust itself according to the instantaneous channel state information (CSI). Specifically, we first formulate an optimization problem to maximize the outage throughput, subject to the NLEH. Since this formulated problem is nonconvex and difficult to solve, we further transfer it into an equivalent problem and develop a Dinkelbach iterative method to obtain the corresponding solution. Numerical results are given to verify the quick convergence of the proposed iterative method and show the superior outage throughput of the designed DPS strategy by comparing with two peer strategies designed for the linear energy harvesting (LEH) model.

In this paper, we consider the simultaneous wireless information and power transfer assisted cooperative nonorthogonal multiple access (SWIPT-CNOMA) system, in which the near NOMA users with strong channel conditions serve as energy harvesting (EH) relays to help the far NOMA users with poor channel conditions. For the considered system, a new power splitting (PS) protocol for EH relay is proposed and its impact on the outage performance of both near and far NOMA users is studied. The analytical expressions of both outage probability and system throughput are further derived for delay-sensitive transmission mode. Simulation results are provided to verify the derived results and reveal the advantages of the proposed protocol.

Inspired by random matrix theory, a quantity of eigenvalue based cooperative spectrum sensing methods have been proposed. The results are based on the asymptotical assumptions in need of large numbers of users and samples, which result in inferior performance with a few users. In this paper, sensing methods based on maximum eigenvalue and minimum eigenvalue of LDLT decomposition are proposed respectively with a view to improve the accuracy of decision threshold by means of hard decision criterion. The corresponding expressions of false alarm probability are also derived. Finally, both theoretical analyses and simulations demonstrate that the proposed two methods perform better than the existing eigenvalue based sensing methods for accurate decision threshold.

In this paper, a joint time allocation and power splitting (JTAPS) scheme is considered for the decode- and-forward (DF) energy harvesting (EH) relaying network, where the ``harvest-then-forward" strategy is employed. We focus on designing an optimal JTAPS scheme to conduct the relay under statistical/instantaneous channel state information (CSI) in terms of outage performance. For the statistical CSI, the expressions of the outage probability and outage capacity are derived to determine the optimal power splitting (PS) ratio and time allocation (TA) ratio. For the instantaneous CSI, a joint PS ratio and TA ratio optimization problem is formulated to minimize the outage probability. Considering that the optimization problem is non- convex, a split-step iterative method is proposed to obtain the optimal PS ratio and TA ratio. Finally, simulation results are given to illustrate the advantage of JTAPS scheme with respect to the outage performance.

This paper considers a non-linear energy harvesting (EH) model, better reflecting the properties of practical circuits compared to the linear model, in an amplify-and-forward (AF) relaying network. We focus on the design of relay's power splitting scheme to optimize the system outage performance, and derive the optimal power splitting ratios with both perfect and imperfect channel state information (CSI), respectively. Using the dynamic power splitting scheme, we find that the system outage performance is enhanced in the low signal-to-noise ratio (SNR) regime while converging to an upper bound in the high SNR regime. This finding is significant since it illustrates that the nonlinearity of practical energy harvester brings changes to the design of relaying networks. Simulation results are provided to support our work.

This paper considers a three-step two-way network utilizing an energy harvesting (EH) multiplicative amplify-and-forward (AF) relay. Based on the instantaneous channel state information (CSI), we study a dynamic power splitting (DPS) scheme on how the relay adjusts the power splitting (PS) ratio, achieving a real-time trade-off between the harvested energy and the power of the retransmitted signal. Taking into account a guarantee that both terminals decode the information at the same time, we aim to optimize the worse end-to-end signal-to-noise ratio (SNR) of the two links. As this optimization problem is non-convex and difficult to be solved, an equivalent problem is formulated and an efficient Dinkelbach-based iterative algorithm is proposed to obtain the optimal PS ratio. Simulation results show that the proposed DPS scheme outperforms the exsiting static PS scheme and highlight the effectiveness of the proposed iterative algorithm.

Spectrum sensing based on absolute value cumulating (AVC sensing) has attracted much attention due to its effectiveness in the presence of Laplaican noise and simpleness of implementation. In this paper, we investigate its detection performance and optimal detection threshold. Specifically, based on the derived mean and variance of the test statistic, an accurate expression of the detection probability is given. Using the accurate expression, an optimization problem is formulated to minimize the total error rate of AVC sensing by optimizing the detection threshold with a constraint on false alarm probability, and the optimal detection threshold is derived. Numerical results are provided to support our work.