Goal-oriented communications prioritise application-driven objectives over data accuracy, enabling intelligent next-generation wireless systems. Efficient scheduling in multi-device, multi-channel systems poses significant challenges due to high-dimensional state and action spaces. We address these challenges by deriving key structural properties of the optimal solution to the goal-oriented scheduling problem, incorporating Age of Information (AoI) and channel states. Specifically, we establish the monotonicity of the optimal state value function—a measure of long-term system performance—w.r.t. channel states and prove its asymptotic convexity w.r.t. AoI states. Additionally, we derive the greedy structure of the optimal policy w.r.t. AoI states, advancing the theoretical framework for optimal scheduling. Leveraging these insights, we propose the structure-guided unified dual on-off policy DRL (SUDO-DRL), a hybrid algorithm that combines the stability of on-policy training with the sample efficiency of off-policy methods. Through a novel structural property evaluation framework, SUDO-DRL enables effective and scalable training, addressing the complexities of large-scale systems. Numerical results show SUDO-DRL improves system performance by up to 45% and reduces convergence time by 40% compared to state-of-the-art methods. It also effectively handles scheduling in much larger systems, where off-policy DRL fails and on-policy benchmarks exhibit significant performance loss, demonstrating its scalability and efficacy in goal-oriented communications.

Wireless Human-Machine Collaboration (WHMC) represents a critical advancement for Industry 5.0, enabling seamless interaction between humans and machines across geographically distributed systems. As the WHMC systems become increasingly important for achieving complex collaborative control tasks, ensuring their stability is essential for practical deployment and long-term operation. Stability analysis certifies how the closed-loop system will behave under model randomness, which is essential for systems operating with wireless communications. However, the fundamental stability analysis of the WHMC systems remains an unexplored challenge due to the intricate interplay between the stochastic nature of wireless communications, dynamic human operations, and the inherent complexities of control system dynamics. This paper establishes a fundamental WHMC model incorporating dual wireless loops for machine and human control. Our framework accounts for practical factors such as short-packet transmissions, fading channels, and advanced HARQ schemes. We model human control lag as a Markov process, which is crucial for capturing the stochastic nature of human interactions. Building on this model, we propose a stochastic cycle-cost-based approach to derive a stability condition for the WHMC system, expressed in terms of wireless channel statistics, human dynamics, and control parameters. Our findings are validated through extensive numerical simulations and a proof-of-concept experiment, where we developed and tested a novel wireless collaborative cart-pole control system. The results confirm the effectiveness of our approach and provide a robust framework for future research on WHMC systems in more complex environments.

Physical layer security (PLS) has emerged as a promising technology to protect critical and sensitive information against unauthorized devices. To address the key challenge of acquiring channel state information (CSI) of passive eavesdroppers in PLS implementation, we propose a novel sensing-assisted PLS scheme with the aid of reflecting reconfigurable intelligent surface (STAR-RIS). It employs a self-refine sensing scheme utilizing the artificial noise (AN) signals to iteratively estimate the eavesdroppers’ positions for CSI calculation. We aim to maximize the secrecy capacity based on the sensing-estimated CSI while tracking the eavesdroppers in full-duplex (FD) mode with integrated sensing and communication (ISAC) signals comprising artificial noise (AN). This is achieved by jointly designing the beamforming vector of information signals, the beamforming vector of AN signals, and the coefficients of the STAR-RIS. To optimize these coupled variables, we introduce an alternating optimization (AO) scheme to solve the problem recursively. In particular, we tackle the non-convexity of the beamforming optimizations for information and AN signals with the successive convex approximation (SCA) scheme and adopt a semi-definite relaxation (SDR) scheme to design the reflection and refraction coefficients of the STAR-RIS. The numerical results validate that the proposed scheme ensures secure communications against multiple eavesdroppers without any prior eavesdropper channel information. In addition, the proposed scheme can significantly improve SC performance by up to 66. 7% compared to the benchmarks without the sensing-assisted function.

Wireless networked control systems (WNCSs) are critical to Industry 4.0, enabling applications like drone swarms and autonomous robots. The tight interdependence between communication and control demands integrated design, yet traditional approaches treat them separately, leading to inefficiencies. Existing codesign methods often rely on simplified models for single-loop or independent multi-loop systems, overlooking the complexities of large-scale WNCSs. These include coupled control loops, time-correlated wireless channels, sensing-control trade-offs, and computational challenges. To address these challenges, we propose a practical WNCS model that captures correlated dynamics among spatially distributed sensors and actuators sharing limited wireless resources over multi-state Markov block-fading channels. To solve the resulting high-dimensional codesign problem, we develop a deep reinforcement learning (DRL) algorithm that scales efficiently by managing hybrid action spaces, capturing communication-control dependencies, and maintaining robust performance under time-correlated dynamics and resource constraints. Simulations demonstrate that our DRL approach outperforms benchmarks, providing a scalable and effective solution for large-scale industrial WNCSs.

Automatic modulation recognition (AMR) is a critical technology in wireless communications, aiming to achieve high recognition accuracy with low complexity in increasingly intricate electromagnetic environments. To tackle this challenge, in this paper, we propose a dual-branch convolution cascaded transformer network with feature assistance, termed DCTFANet. To enhance the differentiation between samples, we employ the gramian angular field (GAF) to capture potential temporal correlations between each data point. Subsequently, both I/Q sequences and GAF data are input into the model for joint signal feature extraction. The network backbone is constructed using multiple improved depthwise separable convolution (DSC) blocks, which significantly reduce computational complexity. Moreover, the backbone depth is flexibly adjustable to fully exploit local features of different data types. Finally, feature transition and the transformer encoder are used to reduce parameters and extract global feature. Experimental results on RML2016.10b show that the proposed method achieves higher recognition accuracy compared to several state-of-the-art methods, especially at low signal-to-noise ratios (SNRs), with an increase of at least 10.80% at -20dB.

We consider a joint uplink and downlink scheduling problem of a fully distributed wireless networked control system (WNCS) with a limited number of frequency channels. Using elements of stochastic systems theory, we derive a sufficient stability condition of the WNCS, which is stated in terms of both the control and communication system parameters. Once the condition is satisfied, there exists a stationary and deterministic scheduling policy that can stabilize all plants of the WNCS. By analyzing and representing the per-step cost function of the WNCS in terms of a finite-length countable vector state, we formulate the optimal transmission scheduling problem into a Markov decision process and develop a deep reinforcement learning (DRL)-based framework for solving it. To tackle the challenges of a large action space in DRL, we propose novel action space reduction and action embedding methods for the DRL framework that can be applied to various algorithms, including deep Q-network (DQN), deep deterministic policy gradient (DDPG), and twin delayed DDPG (TD3). Numerical results show that the proposed algorithm significantly outperforms benchmark policies.

Wireless-networked control system (WNCS) connecting sensors, controllers, and actuators via wireless communications is a key enabling technology for highly scalable and low-cost deployment of control systems in the Industry 4.0 era. Despite the tight interaction of control and communications in WNCSs, most existing works adopt separate design approaches. This is mainly because the co-design of control-communication policies requires large and hybrid state and action spaces, making the optimal problem mathematically intractable and difficult to be solved effectively by classic algorithms. In this article, we systematically investigate deep-learning (DL)-based estimator-control-scheduler co-design for a model-unknown nonlinear WNCS over wireless fading channels. In particular, we propose a co-design framework with the awareness of the sensor’s Age-of-Information (AoI) states and dynamic channel states. We propose a novel deep reinforcement learning (DRL)-based algorithm for controller and scheduler optimization utilizing both model-free and model-based data. An AoI-based importance sampling algorithm that takes into account the data accuracy is proposed for enhancing learning efficiency. We also develop novel schemes for enhancing the stability of joint training. Extensive experiments demonstrate that the proposed joint training algorithm can effectively solve the estimation–control–scheduling co-design problem in various scenarios and provide significant performance gain compared to separate designs and some benchmark policies.

Recently proposed splitting receivers, utilizing both coherently and non-coherently processed signals for detection, have demonstrated remarkable performance gain compared to conventional receivers in the single-antenna scenario. In this paper, focusing on a single-input multiple-output (SIMO) setup, we propose a multi-antenna splitting receiver, where the received signal at each antenna is split into an envelope detection (ED) branch and a coherent detection (CD) branch, and the processed signals from both branches of all antennas are then jointly utilized for recovering the transmitted information. We derive a closed-form approximation of the achievable mutual information (MI), in terms of the key receiver design parameters including the power splitting ratio at each antenna and the signal combining coefficients from all the ED and CD branches. We further optimize these receiver design parameters and demonstrate important design insights for the proposed multi-antenna ED-CD splitting receiver: 1) the optimal splitting ratio is identical at each antenna, and 2) the optimal combining coefficients for the ED and CD branches are the same, and each coefficient is proportional to the corresponding antenna’s channel power gain. Our numerical results also demonstrate the MI performance improvement of the proposed receiver over conventional non-splitting receivers.

Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, we focus on the transmission scheduling problem of a remote estimation system. First, we derive some structural properties of the optimal sensor scheduling policy over fading channels. Then, building on these theoretical guidelines, we develop a structure-enhanced deep reinforcement learning (DRL) framework for optimal scheduling of the system to achieve the minimum overall estimation mean-square error (MSE). In particular, we propose a structure-enhanced action selection method, which tends to select actions that obey the policy structure. This explores the action space more effectively and enhances the learning efficiency of DRL agents. Furthermore, we introduce a structure-enhanced loss function to add penalties to actions that do not follow the policy structure. The new loss function guides the DRL to converge to the optimal policy structure quickly. Our numerical experiments illustrate that the proposed structure-enhanced DRL algorithms can save the training time by 50% and reduce the remote estimation MSE by 10% to 25%, when compared to benchmark DRL algorithms. In addition, we show that the derived structural properties exist in a wide range of dynamic scheduling problems that go beyond remote state estimation.

Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, by leveraging the theoretical results of structural properties of optimal scheduling policies, we develop a structure-enhanced deep reinforcement learning (DRL) framework for optimal scheduling of a multi-sensor remote estimation system to achieve the minimum overall estimation mean-square error (MSE). In particular, we propose a structure-enhanced action selection method, which tends to select actions that obey the policy structure. This explores the action space more effectively and enhances the learning efficiency of DRL agents. Furthermore, we introduce a structure-enhanced loss function to add penalty to actions that do not follow the policy structure. The new loss function guides the DRL to converge to the optimal policy structure quickly. Our numerical results show that the proposed structure-enhanced DRL algorithms can save the training time by 50% and reduce the remote estimation MSE by 10% to 25%, when compared to benchmark DRL algorithms.

In this paper, a communication and control codesign framework is presented based on survival time, i.e., the time that a closed-loop wireless control system can continue without an anticipated message. The goal is to ensure the stability of wireless control systems with minimal resource usage. A novel interface between the controller and the scheduler is proposed, where the key communication and control parameters are analyzed for co-design, and jointly optimized. The proposed co-design framework leverages link adaptation for the communications system and sampling period adaptation for the closed-loop control system to preserve more resources. Our numerical example on closed-loop velocity control demonstrates a pronounced reduction of resources needed for control stability in contrast to the separate design paradigm that requires ultrahigh link reliability. An additional 52% reduction in resource utilization is achieved by further adapting the key parameters when the system is in survival mode.

Remote state estimation where sensors send their measurements of distributed dynamic plants to a remote estimator over shared wireless resources is essential for mission-critical applications of Industry 4.0. Existing algorithms on dynamic radio resource allocation for remote estimation systems assumed oversimplified wireless communications models and can only work for small-scale settings. In this work, we consider remote estimation systems with practical wireless models over the orthogonal multiple-access and non-orthogonal multiple-access schemes. We derive necessary and sufficient conditions under which remote estimation systems can be stabilized. The conditions are described in terms of the transmission power budget, channel statistics, and plants’ parameters. For each multiple-access scheme, we formulate a novel dynamic resource allocation problem as a decision-making problem for achieving the minimum overall long-term average estimation mean-square error. Both the estimation quality and the channel quality states are taken into account for decision making. We systematically investigated the problems under different multiple-access schemes with large discrete, hybrid discrete-and-continuous, and continuous action spaces, respectively. We propose novel action-space compression methods and develop advanced deep reinforcement learning algorithms to solve the problems. Numerical results show that our algorithms solve the resource allocation problems effectively and provide much better scalability than the literature.

Remote state estimation, where many sensors send their measurements of distributed dynamic plants to a remote estimator over shared wireless resources, is essential for mission-critical applications of Industry 4.0. Most of the existing works on remote state estimation assumed orthogonal multiple access and the proposed dynamic radio resource allocation algorithms can only work for very small-scale settings. In this work, we consider a remote estimation system with non-orthogonal multiple access. We formulate a novel dynamic resource allocation problem for achieving the minimum overall long-term average estimation mean-square error. Both the estimation quality state and the channel quality state are taken into account for decision making at each time. The problem has a large hybrid discrete and continuous action space for joint channel assignment and power allocation. We propose a novel action-space compression method and develop an advanced deep reinforcement learning algorithm to solve the problem. Numerical results show that our algorithm solves the resource allocation problem effectively, presents much better scalability than the literature, and provides significant performance gain compared to some benchmarks.

In this paper, we focus on the problem of remote state estimation in wireless networked cyber-physical systems (CPS). Information from multiple sensors is transmitted to a central gateway over a wireless network with fewer channels than sensors. Channel and power allocation are performed jointly, in the presence of a denial of service (DoS) attack where one or more channels are jammed by the attacker through the transmission of spurious signals. The attack policy is unknown and the central gateway has the objective of minimizing state estimation error with maximum energy efficiency. Therefore, the problem involves a novel combination of discrete and continuous action spaces. In addition, the state and action spaces have high dimensionality and the channel states are not fully known to the defender. We propose a novel model-free and off-policy deep reinforcement learning algorithm to address the problem. The proposed algorithm shows promise in solving online complex CPS problems, outperforming some other existing benchmark algorithms.

Although the unique advantages of optical and synthetic aperture radar (SAR) images promote their fusion, the integration of complementary features from the two types of data and their effective fusion remains a vital problem. To address that, a novel framework is designed based on the observation that the structure of SAR images and the texture of optical images look complementary. The proposed framework, named SOSTF, is an unsupervised end-to-end fusion network that aims to integrate structural features from SAR images and detailed texture features from optical images into the fusion results. The proposed method adopts the nest connect-based architecture, including an encoder network, a fusion part, and a decoder network. To maintain the structure and texture information of input images, the encoder architecture is utilized to extract multi-scale features from images. Then, we use the densely connected convolutional network (DenseNet) to perform feature fusion. Finally, we reconstruct the fusion image using a decoder network. In the training stage, we introduce a structure-texture decomposition model. In addition, a novel texture-preserving and structure-enhancing loss function are designed to train the DenseNet to enhance the structure and texture features of fusion results. Qualitative and quantitative comparisons of the fusion results with nine advanced methods demonstrate that the proposed method can fuse the complementary features of SAR and optical images more effectively.

Wireless networked control systems (WNCSs) provide a key enabling technique for Industrial Internet of Things (IIoT). However, in the literature of WNCSs, most of the research focuses on the control perspective and has considered oversimplified models of wireless communications that do not capture the key parameters of a practical wireless communication system, such as latency, data rate, and reliability. In this article, we focus on a WNCS, where a controller transmits quantized and encoded control codewords to a remote actuator through a wireless channel, and adopt a detailed model of the wireless communication system, which jointly considers the interrelated communication parameters. We derive the stability region of the WNCS. If and only if the tuple of the communication parameters lies in the region, the average cost function, i.e., a performance metric of the WNCS, is bounded. We further obtain a necessary and sufficient condition under which the stability region is n -bounded, where n is the control codeword blocklength. We also analyze the average cost function of the WNCS. Such analysis is nontrivial because the finite-bit control-signal quantizer introduces a nonlinear and discontinuous quantization function that makes the performance analysis very difficult. We derive tight upper and lower bounds on the average cost function in terms of latency, data rate, and reliability. Our analytical results provide important insights into the design of the optimal parameters to minimize the average cost within the stability region.

This article considers a wireless networked control system (WNCS) consisting of a dynamic system to be controlled (i.e., a plant), a sensor, an actuator, and a remote controller for mission-critical Industrial Internet of Things (IIoT) applications. A WNCS has two types of wireless transmissions, i.e., the sensor's measurement transmission to the controller and the controller's command transmission to the actuator. In the literature of WNCSs, the controllers are commonly assumed to work in a full-duplex (FD) mode by default, i.e., being able to simultaneously receive the sensor's information and transmit its own command to the actuator. In this article, we consider a practical half-duplex (HD) controller, which introduces a novel transmission-scheduling problem for WNCSs. A frequent scheduling of sensor's transmission results in a better estimation of plant states at the controller and thus a higher quality of control command, but it leads to a less frequent/timely control of the plant. Therefore, considering the overall control performance of the plant in terms of its average cost function, there exists a fundamental tradeoff between the sensor's and the controller's transmissions. We formulate a new problem to optimize the transmission-scheduling policy for minimizing the long-term average cost function. We derive the necessary and sufficient condition of the existence of a stationary and deterministic optimal policy that results in a bounded average cost in terms of the transmission reliabilities of the sensor-to-controller and controller-to-actuator channels. Also, we derive an easy-to-compute suboptimal policy, which notably reduces the average cost of the plant compared to a naive alternative-scheduling policy.

Wireless networked control systems for the Industrial Internet of Things (IIoT) require low-latency communication techniques that are very reliable and resilient. In this article, we investigate a coding-free control method to achieve ultralow latency communications in single-controller-multiplant networked control systems for both slow- and fast-fading channels. We formulate a power allocation problem to optimize the sum cost functions of multiple plants, subject to the plant stabilization condition and the controller's power limit. Although the optimization problem is a nonconvex one, we derive a closed-form solution, which indicates that the optimal power allocation policy for stabilizing the plants with different channel conditions is reminiscent of the channel-inversion policy. We numerically compare the performance of the proposed coding-free control method and the conventional coding-based control methods in terms of the control performance (i.e., the cost function) of a plant, which shows that the coding-free method is superior in a practical range of signal-to-noise ratios.

Real-time remote estimation is critical for mission-critical applications including industrial automation, smart grid and tactile Internet. In this paper, we propose a hybrid automatic repeat request (HARQ)-based real-time remote estimation framework for linear time-invariant (LTI) dynamic systems. Considering the estimation quality of such a system, there is a fundamental tradeoff between the reliability and freshness of the sensor's measurement transmission. We formulate a new problem to optimize the sensor's online transmission control policy for static and Markov fading channels, which depends on both the current estimation quality of the remote estimator and the current number of retransmissions of the sensor, so as to minimize the long-term remote estimation mean squared error (MSE). This problem is non-trivial. In particular, it is challenging to derive the condition in terms of the communication channel quality and the LTI system parameters, to ensure a bounded long-term estimation MSE. We derive a sufficient condition of the existence of a stationary and deterministic optimal policy that stabilizes the remote estimation system and minimizes the MSE. Also, we prove that the optimal policy has a switching structure, and accordingly derive a low-complexity suboptimal policy. Numerical results show that the proposed optimal policy significantly improves the performance of the remote estimation system compared to the conventional non-HARQ policy.

For future Internet-of-Things based Big Data applications, data collection from ubiquitous smart sensors with limited spectrum bandwidth is very challenging. On the other hand, to interpret the meaning behind the collected data, it is also challenging for an edge fusion center running computing tasks over large data sets with a limited computation capacity. To tackle these challenges, by exploiting the superposition property of multiple-access channel and the functional decomposition, the recently proposed technique, over-the-air computation (AirComp), enables an effective joint data collection and computation from concurrent sensor transmissions. In this paper, we focus on a single-antenna AirComp system consisting of K sensors and one receiver. We consider an optimization problem to minimize the computation mean-squared error (MSE) of the K sensors' signals at the receiver by optimizing the transmitting-receiving (Tx-Rx) policy, under the peak power constraint of each sensor. Although the problem is not convex, we derive the computation-optimal policy in closed form. Also, we comprehensively investigate the ergodic performance of the AirComp system, and the scaling laws of the average computation MSE (ACM) and the average power consumption (APC) of different Tx-Rx policies with respect to K. For the computation-optimal policy, we show that the policy has a vanishing ACM and a vanishing APC with the increasing K.

This paper considers a wireless networked control system (WNCS) consisting of a dynamic system to be controlled (i.e., a plant), a sensor, an actuator and a remote controller for mission-critical Industrial Internet of Things (IIoT) applications. A WNCS has two types of wireless transmissions, i.e., the sensor's measurement transmission to the controller and the controller's command transmission to the actuator. In the literature of WNCSs, the controllers are commonly assumed to work in a full-duplex mode by default, i.e., can simultaneously receive the sensor's information and transmit its own command to the actuator. In this work, we consider a practical half- duplex controller, which introduces a novel transmission-scheduling problem for WNCSs. A frequent schedule of the sensor's transmission results in a better estimation of the plant states at the controller and thus a higher quality of the control command, but it leads to a less frequent/timely control of the plant. Therefore, considering the overall control performance of the plant, i.e., the average cost function of the plant, there exists a fundamental tradeoff between the sensor's and controller's transmission. We formulate a new problem to optimize the transmission-scheduling policy so as to minimize the long-term average cost function. We derive the necessary and sufficient condition of the existence of a stationary and deterministic optimal policy that results in a bounded average cost in terms of the transmission reliability of the sensor- to- controller and controller-to-actuator channels. Also, we derive an easy-to-compute suboptimal policy, which notably reduces the average cost of the plant compared to a naive alternative-scheduling policy.

Wireless networked control systems for Industrial Internet of Things (IIoT) require low latency communication techniques. In this paper, we investigate a coding-free control method to achieve ultra-low latency communications in single-controller-multi-plant networked control systems. We formulate a power allocation problem to optimize the sum cost functions of multiple plants, subject to the plant stabilization condition and the controller's power limit. Although the optimization problem is a non-convex one, we derive a closed-form solution, which indicates that the optimal power allocation policy for stabilizing the plants with different channel conditions is reminiscent of the channel-inversion policy. Also, we numerically compare the performance of the proposed coding-free control method and the conventional coding-based control methods in terms of the cost function of a plant, which shows that the coding-free method is superior in a practical range of SNRs.

Real-time remote estimation is critical for mission-critical applications including industrial automation, smart grid, and the tactile Internet. In this paper, we propose a hybrid automatic repeat request (HARQ)-based real-time remote estimation framework for linear time-invariant (LTI) dynamic systems. Considering the estimation quality of such a system, there is a fundamental tradeoff between the reliability and freshness of the sensor's measurement transmission. When a failed transmission occurs, the sensor can either retransmit the previous old measurement such that the receiver can obtain a more reliable old measurement, or transmit a new but less reliable measurement. To design the optimal decision, we formulate a new problem to optimize the sensor's online decision policy, i.e., to retransmit or not, depending on both the current estimation quality of the remote estimator and the current number of retransmissions of the sensor, so as to minimize the long-term remote estimation mean-squared error (MSE). This problem is non-trivial. In particular, it is not clear what the condition is in terms of the communication channel quality and the LTI system parameters, to ensure that the long-term estimation MSE can be bounded. We give a sufficient condition of the existence of a stationary and deterministic optimal policy that stabilizes the remote estimation system and minimizes the MSE. Also, we prove that the optimal policy has a switching structure, and derive a low-complexity suboptimal policy. Our numerical results show that the proposed optimal policy notably improves the performance of the remote estimation system compared to the conventional non-HARQ policy.

In this paper, we propose an ambient backscatter multiple-access system, in which a receiver (Rx) simultaneously detects the information sent from an active transmitter (Tx) and a passive Tag. Specifically, the information-carrying signal sent by the Tx arrives at the Rx through two wireless channels: one is the direct Tx-Rx channel, and the other is the backscatter channel, i.e., the Tx- Tag-Rx channel, which further carries the Tag's information due to the multiplicative backscatter operation at the Tag. The proposed multiple-access scheme introduces a new channel model named as the multiplicative multiple-access channel (M-MAC), which has not been addressed before. We study the achievable rate region and the capacity region of the M-MAC, and prove that the achievable rate region of the M-MAC is strictly larger than that of the conventional time-sharing one (i.e., the M- MAC capacity region is strictly convex) in many cases, including the high SNR case and the typical case that the direct channel is much stronger than the backscatter channel. Moreover, the numerical results have also validated this phenomenon under a practical range of SNR and channel conditions. The proposed multiple-access scheme is an attractive technique to improve the throughput of ambient backscatter communication systems.

In this paper, we consider a novel ambient backscatter multiple-access system, where a receiver (Rx) simultaneously detects the signals transmitted from an active transmitter (Tx) and a backscatter tag. Specifically, the information-carrying signal sent by the Tx arrives at the Rx through two wireless channels: the direct channel from the Tx to the Rx and the backscatter channel from the Tx to the tag and then to the Rx. The received signal from the backscatter channel also carries the tag's information because of the multiplicative backscatter operation at the tag. This multiple-access system introduces a new channel model referred to as backscatter multiplicative multiple-access channel (BM-MAC). We analyze the achievable rate region of the BM-MAC and prove that its region is strictly larger than that of the conventional time-division multiple-access scheme in many cases, including, e.g., the high SNR regime and the case when the direct channel is much stronger than the backscatter channel. Hence, the multiplicative multiple-access scheme is an attractive technique to improve the throughput for ambient backscatter communication systems. Moreover, we analyze the detection error rates for coherent and noncoherent modulation schemes adopted by the Tx and the tag, respectively, in both synchronous and asynchronous scenarios, which further bring interesting insights for practical system design.

Future Internet-of-Things (IoT) is expected to wirelessly connect tens of billions of low- complexity devices. Extending the finite battery life of massive number of IoT devices is a crucial challenge. The ultra-low-power backscatter communications (BackCom) with the inherent feature of RF energy harvesting is a promising technology for tackling this challenge. Moreover, many future IoT applications will require the deployment of dense IoT devices, which induces strong interference for wireless information transfer (IT). To tackle these challenges, in this paper, we propose the design of a novel multiple-access scheme based on time-hopping spread-spectrum (TH-SS) to simultaneously suppress interference and enable both two-way wireless IT and one-way wireless energy transfer (ET) in coexisting backscatter reader-tag links. The performance analysis of the BackCom network is presented, including the bit-error rates for forward and backward IT and the expected energy-transfer rate for forward ET, which account for non-coherent and coherent detection at tags and readers, and energy harvesting at tags, respectively. Our analysis demonstrates a tradeoff between energy harvesting and interference performance. Thus, system parameters need to be chosen carefully to satisfy given BackCom system performance requirement.

In this paper, we propose a novel splitting receiver, which involves a joint processing of coherently and non-coherently received signals. Using a passive RF power splitter, the received signal at each receiver antenna is split into two streams, which are then processed by a conventional coherent detection (CD) circuit and a power-detection (PD) circuit, respectively. The streams of the signals from all the receiver antennas are then jointly used for information detection. We show that the splitting receiver creates a 3-D received signal space due to the joint coherent and non-coherent processing. We analyze the achievable rate of a splitting receiver, which shows that the splitting receiver provides a rate gain of 3/2 compared with either the conventional (CD-based) coherent receiver or the PD-based non-coherent receiver in the high SNR regime. We also analyze the symbol error rate (SER) for practical modulation schemes, which shows that the splitting receiver achieves asymptotic SER reduction by a factor of at least √M-1 for M-QAM compared with either the conventional (CD-based) coherent receiver or the PD-based non-coherent receiver.

Future Internet-of-Things (IoT) is expected to wirelessly connect billions of low-complexity devices. For wireless information transfer (IT) in IoT, high density of IoT devices and their ad hoc communication result in strong interference, which acts as a bottleneck on wireless IT. Furthermore, battery replacement for the massive number of IoT devices is difficult if not infeasible, making wireless energy transfer (ET) desirable. This motivates: 1) the design of full-duplex wireless IT to reduce latency and enable efficient spectrum utilization and 2) the implementation of passive IoT devices using backscatter antennas that enable wireless ET from one device (reader) to another (tag). However, the resultant increase in the density of simultaneous links exacerbates the interference issue. This issue is addressed in this paper by proposing the design of full-duplex backscatter communication (BackCom) networks, where a novel multiple-access scheme based on time-hopping spread-spectrum is designed to enable both one-way wireless ET and two-way wireless IT in coexisting backscatter reader-tag links. Comprehensive performance analysis of BackCom networks is presented in this paper, including forward/backward bit-error rates and wireless ET efficiency and outage probabilities, which accounts for energy harvesting at tags, non-coherent and coherent detection at tags and readers, respectively, and the effects of asynchronous transmissions.

In this paper, we investigate the performance of simultaneous wireless information and power transfer (SWIPT) in a point-to-point system, adopting practical M-ary modulation. We take into account the fact that the receiver's radio-frequency (RF) energy harvesting circuit can only harvest energy when the received signal power is greater than a certain sensitivity level. For both power-splitting (PS) and time-switching (TS) schemes, we derive the energy harvesting performance as well as the information decoding performance for the Nakagami-m fading channel. We also analyze the performance tradeoff between energy harvesting and information decoding by studying an optimization problem, which maximizes the information decoding performance and satisfies a constraint on the minimum harvested energy. Our analysis shows that (i) for the PS scheme, modulations with high peak-to-average power ratio achieve better energy harvesting performance, (ii) for the TS scheme, it is desirable to concentrate the power for wireless power transfer in order to minimize the non-harvested energy caused by the RF energy harvesting sensitivity level, and (iii) channel fading is beneficial for energy harvesting in both PS and TS schemes.

Energy harvesting (EH) provides a means of greatly enhancing the lifetime of wireless sensor nodes. However, the randomness inherent in the EH process may cause significant delay for performing sensing operations and transmitting sensed information to the sink. Unlike most existing studies on the delay performance of EH sensor networks, where only the energy consumption of transmission is considered, we consider the energy costs of both sensing and transmission. Specifically, we consider an EH sensor that monitors some status property and adopts a harvest-then-use protocol to perform sensing and transmission. To comprehensively study the delay performance, we consider two complementary metrics and analytically derive their statistics: 1) update age-measuring the time taken from when information is obtained by the sensor to when the sensed information is successfully transmitted to the sink, i.e., how timely the updated information at the sink is, and 2) update cycle-measuring the time duration between two consecutive successful transmissions, i.e., how frequently the information at the sink is updated. Our results show that the consideration of sensing energy cost leads to an important tradeoff between the two metrics: more frequent updates result in less timely information available at the sink.

In this paper, we propose using a wireless-powered friendly jammer to enable secure communication between a source node and destination node, in the presence of an eavesdropper. We consider a two-phase communication protocol with fixed-rate transmission. In the first phase, wireless power transfer is conducted from the source to the jammer. In the second phase, the source transmits the information-bearing signal under the protection of a jamming signal sent by the jammer using the harvested energy in the first phase. We analytically characterize the long-term behavior of the proposed protocol and derive a closed-form expression for the throughput. We further optimize the rate parameters for maximizing the throughput subject to a secrecy outage probability constraint. Our analytical results show that the throughput performance differs significantly between the single-antenna jammer case and the multiantenna jammer case. For instance, as the source transmit power increases, the throughput quickly reaches an upper bound with single-antenna jammer, while the throughput grows unbounded with multiantenna jammer. Our numerical results also validate the derived analytical results.

Realistic modeling of energy consumption is crucial for accurate performance analysis of wireless-powered sensor nodes. In this paper, we analyze the performance of wireless-powered sensor transmissions taking into account both the energy cost of sensing and transmission. We consider a sensor that is harvesting energy from an ambient radio-frequency (RF) signal and using this energy to perform sensing and transmission. Since energy harvesting is time-varying in nature, it introduces a delay in the sensor transmissions. We study two delay-related metrics, one measuring how frequent the sensed information is updated at the sink and the other measuring the time taken from the sensing operation to successful transmission of sensed information. We analytically characterize the statistical behavior of both metrics and find an important tradeoff between them. In particular, our results illustrate that more frequent update of sensed information at the sink increases the time taken from the sensing operation to successful transmission of sensed information.