Systems combining inertial measurement unit and magnetic field information have been extensively studied for indoor positioning and navigation. However, existing research predominantly addresses static magnetic field distributions, with limited attention to user-environment interactions. Establishing the correlation between magnetic field changes and user behavior presents new challenges for enhancing spatial structure perception. This paper proposes an indoor positioning algorithm based on magnetic semantic extended Kalman filtering (EKF) for inertial navigation system. We analyze the projection characteristics of magnetic field vectors in the carrier coordinate system, revealing the inherent relationship between abrupt changes in magnetic field components and user's dynamic behavior. Based on this, we introduce fuzzy logic methods to model the relationship between abrupt changes in the magnetic field and angular velocity fluctuations, thereby accurately extracting the semantic information of the magnetic field environment. To fully utilize this semantic information, we design an EKF algorithm that integrates magnetic semantic perception information to assist the INS in state updating and error correction. The proposed algorithm not only improves positioning accuracy but also enhances the system's adaptability to dynamic environmental changes. The experimental results indicate that the maximum error of our indoor positioning system is 0.93 meters and the error is controlled within 1 meter.

Multi-modal sensing is crucial in various industrial applications, which brings new challenges for the design of sampling schemes. In multi-modal sensing systems, multiple sensors with tangled Age-of-Information (AoI) may have dramatically different requirements, while network dynamics render the distribution of transmission delay for sensing packets unknown and non-stationary. Unlike existing studies, since the long-term average AoI is not meaningful under non-stationary transmission delay, we consider the Short-Term average AoI (ST-AoI) over recently received packets, and aim to minimize the ST-AoI violation probability against different sensors’ requirements under non-stationary transmission delay. To solve this problem, we propose a Differentiated AoI Guaranteed Sampling (DAGS) scheme. The proposed DAGS scheme does not require prior assumption or knowledge about the transmission delay statistics, instead, it utilizes a dynamic linearization data model with a Pseudo-Jacobian Matrix (PJM) to characterize the hidden relationship between sampling rates and ST-AoIs for all sensors. Based on this data model, by reformulating and solving an equivalent mean squared error problem, the DAGS scheme can adjust the sampling rates of all sensors to achieve an extremely low ST-AoI violation probability. Both simulation and real-world experiment results show that the proposed DAGS scheme outperforms the existing scheme significantly, by decreasing the ST-AoI violation probability around 10 times.

This paper studies UAV detection by multiple collaborative base stations in an integrated sensing and communication (ISAC) manner. In particular, we propose a computationally efficient UAV 3D localization and velocity estimation approach based on matched filtering (MF) to process the orthogonal frequency division multiplexing (OFDM) sensing signals. The proposed method consists of two main steps: a MF-based preprocessing step at each single base station to efficiently estimate distance, velocity, and angle parameters, and a symbol-level fusion step using a grid searching approach to integrate results from multiple base stations. Compared with traditional multiple signal classification (MUSIC)-based fusion techniques, our approach reduces the overall computational complexity by more than 98.5%. Meanwhile, it demonstrates significantly higher robustness in low SNR conditions (SNR ≤ 0 dB), as evidenced by a reduction in localization error from meter-level to centimeter-level accuracy. In positive SNR conditions (SNR > 0 dB), it also improves the localization and velocity estimation accuracy by approximately 33.5% and 26.3%, respectively. These results demonstrate the practical advantage of the proposed method in real-time UAV sensing application.

In this paper, we consider reusing a rotary-wing UAV as both an airborne base station (BS) and radar to provide integrated sensing and communication (ISAC) wideband service to a ground mobile user. Specifically, the UAV transmits orthogonal frequency-division multiplexing (OFDM) signals where a part of the sub-carriers are assigned for communication purposes. We formulate an online optimization problem that jointly optimizes the UAV trajectory and power allocation of the OFDM sub-carriers to provide a balanced communication and localization service to the ground user. The problem is very challenging because of the non-convex localization accuracy metric with respect to the trajectory and transmit power. For this, we decouple the original problem into a sub-carrier power allocation sub-problem and a trajectory design sub-problem, and propose efficient algorithms to solve them respectively. Simulation results show that the proposed algorithm reduces the localization error by more than 66% at the cost of affordable decrease of communication rate compared to the representative benchmark method considered.

Industrial machine-centric video analytics applications require not only high throughput to improve video quality, but also guaranteed end-to-end latency. Adaptive BitRate (ABR) schemes play a critical role in determining the throughput and latency of real-time video streaming. However, existing ABR schemes mostly focus on congestion avoidance or Quality of Experience (QoE) optimization, which cannot provide guaranteed end-to-end latency. To fill this gap, this paper aims to maximize the throughput under guaranteed latency. We propose a Latency-Guaranteed ABR (LG-ABR) scheme. In the LG-ABR, to combat the unknown and non-stationary transmission delay for video packets, we characterize the relationship between end-to-end latency and bitrate by a dynamic linearization data model with a Pseudo-Partial Derivative (PPD) parameter. Through estimating the PPD in real-time, such dynamic relationship can be determined and used to adapt the bitrate. Numerical results show that the proposed LG-ABR significantly outperforms the existing Google Congestion Control scheme, by decreasing the latency violation probability significantly at slightly cost of link utilization reduction.

This paper investigates deploying semantic edge inference systems for performing a common image clarification task. In particular, each system consists of multiple Internet of Things (IoT) devices that first locally encode the sensing data into semantic features and then transmit them to an edge server for subsequent data fusion and task inference. The inference accuracy is determined by efficient training of the feature encoder/decoder using labeled data samples. Due to the difference in sensing data and communication channel distributions, deploying the system in a new environment may induce high costs in annotating data labels and re-training the encoder/decoder models. To achieve cost-effective transferable system deployment, we propose an efficient Domain Adaptation method for Semantic Edge INference systems (DASEIN) that can maintain high inference accuracy in a new environment without the need for labeled samples. Specifically, DASEIN exploits the task-relevant data correlation between different deployment scenarios by leveraging the techniques of unsupervised domain adaptation and knowledge distillation. It devises an efficient two-step adaptation procedure that sequentially aligns the data distributions and adapts to the channel variations. Numerical results show that, under a substantial change in sensing data distributions, the proposed DASEIN outperforms the best-performing benchmark method by 7.09% and 21.33% in inference accuracy when the new environment has similar or 25 dB lower channel signal to noise power ratios (SNRs), respectively. This verifies the effectiveness of the proposed method in adapting both data and channel distributions in practical transfer deployment applications.

This article addresses the challenge of accurate target localization in fifth-generation (5G) communication networks using multistatic multi-input-multi-output orthogonal frequency division multiplexing (MIMO-OFDM) waveforms. Conventional on-grid compressed sensing-based target parameter estimation methods degrade significantly when targets are located off the predefined grid points. To overcome this limitation, we propose an off-grid compressed sensing approach that uses a grid evolution technique specifically designed for the complex-valued, block sparse structure inherent in multistatic MIMO-OFDM signal. By adaptively refining the grid during the sensing process, the proposed method achieves improved target localization accuracy, particularly in off-grid scenarios. Simulation results demonstrate that this approach significantly outperforms traditional methods, enhancing localization accuracy for 5G-enabled sensor networks.

Real-time status updates play an important role in low-latency cyber-physical systems, in which the real network traffic statistics (i.e., transmission delay and/or error rate) are often unknown and non-stationary. In such cases, short-time age-of-information (ST-AoI) is more crucial than long-term average AoI, because instantaneous high ST-AoI could lead to system failures even if the long-term average AoI is low. In this paper, we propose an adaptive sampling control (ASC) scheme to ensure a low ST-AoI outage probability, defined as the probability of the average AoI in each control cycle, i.e., over a limited number of packets, exceeding a given threshold. This ASC scheme does not rely on an explicit statistical model for the non-stationary traffic behaviors. It establishes a dynamic linearization data model with a pseudo-partial derivative (PPD) parameter to capture the unknown and non-stationary traffic statistics. By estimating the PPD parameter in each control cycle, ASC can determine the sampling rates to ensure an extremely low ST-AoI outage probability. Both numerical simulation and real-world experiment show that the proposed ASC scheme significantly outperforms existing methods, reducing the ST-AoI outage probability almost by half.

The recent advance of edge computing technology enables significant sensing performance improvement of Internet of Things (IoT) networks. In particular, an edge server (ES) is responsible for gathering sensing data from distributed sensing devices, and immediately executing different sensing tasks to accommodate the heterogeneous service demands of mobile users. However, as the number of users surges and the sensing tasks become increasingly compute-intensive, the huge amount of computation workloads and data transmissions may overwhelm the edge system of limited resources. Accordingly, we propose in this paper a scalable edge sensing framework for multi-task execution, in the sense that the computation workload and communication overhead of the ES do not increase with the number of downstream users or tasks. By exploiting the task-relevant correlations, the proposed scheme implements a unified encoder at the ES, which produces a common low-dimensional message from the sensing data and broadcasts it to all users to execute their individual tasks. To achieve high sensing accuracy, we extend the well-known information bottleneck theory to a multi-task scenario to jointly optimize the information gathering and broadcast processes. We also develop an efficient two-step training procedure to optimize the parameters of the neural network-based codecs deployed in the edge sensing system. Experiment results show that the proposed scheme significantly outperforms the considered representative benchmark methods in multi-task inference accuracy. Besides, the proposed scheme is scalable to the network size, which maintains almost constant computation delay with less than 1% degradation of inference performance when the user number increases by four times.

Virtual spatial modulation (VSM) has shown potential for enhancing the energy efficiency of multiple-input multiple-output (MIMO) systems. However, the use of a limited set of virtual parallel channels in VSM results in lower spectral efficiency compared to traditional MIMO systems. To address this, we propose a composite multiple-mode VSM (C-MM-VSM) scheme, along with its optimized variants, to increase SE. The C-MMVSM extends indexing to multiple domains, mapping input bits to channel activation patterns, energy allocation patterns, and mode activation patterns. As the constellation sets are practically higher than active antennas, we propose a variant scheme, which introduces a new mapping rule between input bits and additional mode activation patterns, increasing the mode bits of the system. We also develop diversity-enhanced schemes using coordinate interleaving for improved average bit error probability (ABEP). Additionally, we propose generalized schemes that optimize all the indexes and the in-phase/quadrature extension which expands indexing to in-phase and quadrature domains, achieving double spectral efficiency. Analytical ABEP results, along with simulations, confirm that the proposed schemes outperform benchmark schemes, providing significant spectral efficiency and ABEP improvements without needing extra resources.

As a nonverbal form of communication, gestures convey information through bodily movements and postures. Gesture recognition provides a more intuitive and natural human-computer interaction (HCI) experience, making it an integral component of the field of HCI. Recently, Wi-Fi-based gesture recognition has become a popular direction in research and applications due to its low cost, privacy-friendly nature, and convenience. However, due to the differences in the distribution of gesture data between the known and target environments, deploying the gesture recognition model in a new environment may induce high costs in annotating data labels and retraining the model. To achieve a cost-effective transferable gesture recognition model, we propose an efficient Wi-Fi-based gesture recognition domain-adaptive method [label-free domain-adaptive wireless gesture recognition (LAGER)] that can maintain high recognition accuracy in a new environment without the need for labeled samples. Specifically, LAGER divides the cross-domain Wi-Fi gesture recognition problem into two interrelated subproblems, where we iteratively apply a pseudo-label-guided feature alignment and feature augmentation method in a latent space by leveraging the wisdom of unsupervised domain adaptation. To minimize the negative impact of erroneous pseudo-labels in the early training stage, we introduce a preheated training technique that separates the training process into two parts associated with different training strategies. We evaluate our method on the Widar3.0 data set and compare the performance under various cross-domain settings with several representative benchmark methods. The proposed LAGER evidently outperforms all the benchmark methods. In particular, compared to the cross-domain recognition method used in Widar3.0, the proposed LAGER achieves 6.89%–7.83% higher average accuracy in different cross-domain experiments considered.

Target indoor localization has become an attractive research topic due to its importance in location-based applications in wireless networks for sensing, controlling, and communicating. In TDOA-based localization models, timestamp packets must be exchanged between anchor nodes and target nodes. Timestamp measurements are susceptible to random transmission delays and packet loss in indoor environments, resulting in inaccurate positioning accuracy. In this paper, we propose a linear fusion indoor localization scheme based on TDOA with low-rank approximation to improve target localization accuracy and robustness. In an asynchronous localization model, we first formulate the indoor localization problem from incomplete and noisy timestamp measurements as a low-rank matrix completion problem. Furthermore, the proposed linear fusion algorithm is used to further optimize the localization accuracy by weighting multiple localization rounds. Simulation and experimental results indicate that the proposed method is more effective than the existing methods in the presence of packet loss and random delays.

Simple authentication protocols based on conventional physical unclonable functions (PUFs) are vulnerable to modeling attacks and other security threats. This article proposes an arbiter PUF based on a linear feedback shift register (LFSR-APUF). Different from the previously reported linear feedback shift register (LFSR) for challenge extension, the proposed scheme feeds the external random challenges into the LFSR module to obfuscate the linear mapping relationship between the challenge and response. It can prevent attackers from obtaining valid challenge–response pairs (CRPs), increasing its resistance to modeling attacks significantly. A 64-stage LFSR-APUF has been implemented on a field programmable gate array (FPGA) board. The experimental results reveal that the proposed design can effectively resist various modeling attacks, such as logistic regression (LR), evolutionary strategy (ES), artificial neuro network (ANN), and support vector machine (SVM) with a prediction rate of 51.79% and a slight effect on the randomness, reliability, and uniqueness. Further, a lightweight authentication protocol is established based on the proposed LFSR-APUF. The protocol incorporates a low-overhead, ultralightweight, novel private bit conversion Cover function that is uniquely bound to each device in the authentication network. The proposed authentication protocol not only resists spoofing attacks, physical attacks, and modeling attacks effectively but also ensures the security of the entire authentication network by transferring important information in encrypted form from the server to the database even when the attacker completely controls the server.

In this paper, we consider a massive ultrareliable and low-latency communication (mURLLC) network with multiple antennas at each transmitter. We formulate a distributed beamforming and power control problem by minimizing the logarithm-based average utility of decoding error probability for the worst link over different network topologies and channels, where the policy is represented by a graph neural network (GNN). To reduce signaling overhead and computation delay for distributed inference, we first develop a GNN for mURLLC (G4U) framework, where the graph embedding of each node is updated according to its previous graph embedding. In addition, we represent the local message of each node by the amplitude and phase of its pilot signal, such that the graph convolution can be accomplished efficiently by broadcasting the pilot signals. To further reduce the overall latency, we propose the pipeline G4U (PG4U), where each node determines its policy solely based on the channel state information acquired in the previous frames. The feedforward neural networks in PG4U for graph convolution can be executed efficiently during data transmission. To train the GNNs in mURLLC where the decoding error probability is small, we develop a novel loss function based on the asymptotic expression of the GaussianQ-function. Simulation results show that G4U and PG4U are scalable to a different number of links. They can outperform the existing GNN and other policies significantly in terms of the QoS outage probability. Moreover, PG4U is suitable for mURLLC networks with short frame durations and highly correlated channels, while G4U is suitable for moderate frame durations with low channel correlation coefficients.

In future B5G/6G broadband communication systems, non-linear signal distortion caused by the impairment of transmit power amplifier (PA) can severely degrade the communication performance, especially when uplink users share the wireless medium using non-orthogonal multiple access (NOMA) schemes. This is because the successive interference cancellation (SIC) decoding technique, used in NOMA, is incapable of eliminating the interference caused by PA distortion. Consequently, each user’s decoding process suffers from the cumulative distortion noise of all uplink users. In this paper, we establish a new and tractable DPD-PA distortion signal model based on real-world measurements, where the distortion noise power is a polynomial function of PA transmit power diverging from the oversimplified linear function commonly employed in existing studies. Applying the proposed signal model, we characterize the capacity rate region of multi-user uplink NOMA by optimizing the user transmit power. Our findings reveal a significant contraction in the capacity region of NOMA, attributable to polynomial distortion noise power. For practical engineering applications, we formulate a general weighted sum rate maximization (WSRMax) problem under individual user rate constraints. We further propose an efficient power control algorithm to attain the optimal performance. Numerical results show that the optimal power control policy under the proposed non-linear PA model achieves on average 13% higher throughput compared to the policies assuming an ideal linear PA model. Overall, our findings demonstrate the importance of accurate PA distortion modeling to the performance of NOMA and provide efficient optimal power control method accordingly.

In future B5G/6G wideband communication systems, non-linear signal distortion caused by the impairment of transmit power amplifier (PA) can severely degrade the communication performance. The performance impact is especially significant when uplink users share the wireless medium using Non-orthogonal Multiple Access (NOMA) scheme. This is because the successive interference cancellation (SIC) information decoding technique of NOMA cannot eliminate the interference caused by the PA non-linear distortion, such that the decoding of each user will suffer from the aggregate distortion noise of all the uplink users. In this paper, we study the impact of PA non-linear distortion on the performance of uplink NOMA. In particular, we first establish a new PA distortion signal model based on real-world measurements, where the distortion noise power is a polynomial function of PA transmit power, instead of a simplified linear function in most existing studies. Under the proposed signal model, we then accurately characterize the capacity region of a two-user uplink NOMA by optimizing the user transmit power. We show that the polynomial distortion noise power significantly shrinks the achievable capacity region of NOMA. This indicates that existing studies may have overestimated the communication performance of NOMA in practical wideband systems. Besides, the non-linear noise power also leads to a rather different optimal power allocation strategy to attain maximum throughput. Simulation results show that, for a PA following the polynomial distortion noise power model, the proposed optimal power allocation method achieves on average 12.2% higher sum throughput than that obtained from ideal PA model. Overall, our results demonstrate the importance of accurate PA distortion modeling to the performance of NOMA and provide an efficient power allocation method to attain the optimal performance.

Accurate indoor crowd counting (ICC) is a key enabler to many smart home/office applications. Recent development of the WiFi-based ICC technology relies on detecting the variation of wireless channel state information (CSI) caused by human motions and has gained increasing popularity due to its low hardware cost, reliability under all lighting conditions, and privacy preservation in sensing data processing. To attain high estimation accuracy, existing WiFi-based ICC methods often require a large amount of labeled CSI training data samples for each application domain, i.e., a particular WiFi transceiver or background deployment. This makes large-scale deployment of the WiFi-based ICC technology across dissimilar domains extremely difficult and costly. In this article, we propose a Domain-Agnostic and Sample-Efficient wireless indoor crowd Counting (DASECount) framework that suffices to attain robust cross-domain detection accuracy given very limited data samples in new domains. DASECount leverages the wisdom of the few-shot learning (FSL) paradigm consisting of two major stages: 1) source domain meta training and 2) target domain meta testing. Specifically, in the meta-training stage, we design and train two separate convolutional neural network (CNN) modules on the source domain data set to fully capture the implicit amplitude and phase features of CSI measurements related to human activities. A subsequent knowledge distillation procedure is designed to iteratively update the CNN parameters for better generalization performance. In the meta-testing stage, we use the partial CNN modules to extract low-dimension features out of the high-dimension input target domain CSI data. With the obtained low-dimension CSI features, we can even use very few amounts of target domain data samples (e.g., 5-shot samples) to train a lightweight logistic regression (LR) classifier, and attain very high cross-domain ICC accuracy. Experiment results show that the proposed DASECount method achieves over 92.68%, and on average 96.37% detection accuracy in a 0–8 people counting task under various domain setups, which significantly outperforms the other representative benchmark methods considered.

Under the outbreak of the COVID-19 pandemic, respiration state monitoring plays an important role in assisting respiratory disease diagnosis and treatment. Thanks to the nonintrusive nature and low deployment cost, Wi-Fi-based wireless respiration state monitoring methods have gained increasing popularity. By analyzing the variation of channel state information (CSI) of Wi-Fi signals, the respiration states of a target person under the wireless coverage, such as cough, sneeze, and yawn, can be accurately detected. A major problem of the current wireless respiration state monitoring methods is being overly domain-dependent. That is, a sensing algorithm fine-tuned to a specific device placement and background setting (i.e., a domain) can result in drastic drop in detection accuracy when applied to a dissimilar new domain. To enhance the robustness of wireless sensing and reduce the sensing cost across different domains, we propose in this article a domain-adaptive respiration state monitoring system (ResMon) that achieves highly accurate cross-domain detection performance while requiring very limited labeled samples in the new domain. In a nutshell, the proposed ResMon consists of a source domain meta-training stage and a target domain meta-testing stage. In the meta-training stage, we leverage the rich source domain labeled data set to train an embedding model as a feature extractor of high-dimensional CSI data measurements. In particular, we apply the statistical Bayesian deep learning technique to improve the generalization performance of the embedding model in cross-domain applications. In the meta-testing stage, we combine the embedding model with a few-shot learning technique to train a domain-specific classifier using very limited labeled samples in the target domain. Experiment results show that the proposed ResMon can achieve on average 87.26% cross-domain detection accuracy in a 4-class respiration state classification task using only five labeled samples per class, which significantly outperforms the considered benchmark methods.

Maneuvering-target tracking has always been an important and challenge work because the unknown and changeable motion-models can easily lead to the failure of model-driven target tracking. Recently, many neural network methods are proposed to improve the tracking accuracy by constructing direct mapping relationships from noisy observations to target states. However, limited by the coverage of training data, those data-driven methods suffer other problems, such as weak generalization abilities and unstable tracking effects. In this paper, a digital twin system for maneuvering-target tracking is built, and all kinds of simulated data are created with different motion-models. Based on those data, the features of noisy observations and their relationship to target states are found by two specially designed neural networks: one eliminates the observation noises and the other one predicts the target states according to the noise-limited observations. Combining the above two networks, the state prediction method is proposed to intelligently predict targets by understanding the information of motion-model hidden in noisy observations. Simulation results show that, in comparison with the state-of-the-art model-driven and data-driven methods, the proposed method can correctly and timely predict the motion-models, increase the tracking generalization ability and reduce the tracking root-mean-squared-error by over 50% in most of maneuvering-target tracking scenes.

Distributed power allocation is important for interference-limited wireless networks with dense transceiver pairs. In this paper, we aim to design low signaling overhead distributed power allocation schemes by using graph neural networks (GNNs), which are scalable to the number of wireless links. We first apply the message passing neural network (MPNN), a unified framework of GNN, to solve the problem. We show that the signaling overhead grows quadratically as the network size increases. Inspired from the over-the-air computation (AirComp), we then propose an Air-MPNN framework, where the messages from neighboring nodes are represented by the transmit power of pilots and can be aggregated efficiently by evaluating the total interference power. The signaling overhead of Air-MPNN grows linearly as the network size increases, and we prove that Air-MPNN is permutation invariant. To further reduce the signaling overhead, we propose the Air message passing recurrent neural network (Air-MPRNN), where each node utilizes the graph embedding and local state in the previous frame to update the graph embedding in the current frame. Since existing communication systems send a pilot during each frame, Air-MPRNN can be integrated into the existing standards by adjusting pilot power. Simulation results validate the scalability of the proposed frameworks, and show that they outperform the existing power allocation algorithms in terms of sum-rate for various system parameters.

Abstract In this paper, we investigate the joint design of transmit waveform and receive filter for colocated Multiple‐Input Multiple‐Output radar equipped with one‐bit digital‐to‐analogue converters (DACs). The problem is formulated as maximising the output signal‐to‐interference‐plus‐noise ratio in the presence of signal‐dependent interferences, subject to a discrete constraint imposed by the waveform quantised with one‐bit DACs. To cope with the challenging non‐convex problem, an alternating maximisation framework is developed to optimise the transmit waveform and receive filter vectors in an iterative manner. More specifically, for a given transmit waveform vector, the analytical expression of the receive filter vector is first derived. Then, by fixing the receive filter, a biconvex relaxation method is employed to tackle the non‐convex problem with respect to the transmit waveform vector. The performance of the proposed approach is demonstrated by numerical simulations in different circumstances.

With the acceleration of a new round of global scientific, technological, and industrial revolution, the next generation of information and communication technology, i.e., 6G, will inject new momentum into industry transformation and upgrading, as well as into economic innovation and development.This will subsequently promote a global industrial integration.Wireless communication will be ubiquitous in all areas of future society, supporting novel applications with various performance requirements, such as immersive-or interactive-experience applications requiring a large bandwidth, autonomous driving and vehicle-to-everything applications requiring ultrahigh reliability and ultra-low latency, and applications for industrial Internet requiring massive machine-type connectivity.Facing the challenges of the post-Moore and post-pandemic era, wireless communication needs breakthroughs in network architecture to improve the intelligence, security, robustness, bandwidth, and heterogeneity.With this background, several important tendencies have emerged in the development of 6G wireless communications

Mobile edge computing (MEC) has recently become a prevailing technique to alleviate the intensive computation burden in Internet of Things (IoT) networks. However, the limited device battery capacity and stringent spectrum resource significantly restrict the data processing performance of MEC-enabled IoT networks. To address the two performance limitations, we consider in this paper an MEC-enabled IoT system with a wireless device (WD) replenishing its battery by means of energy harvesting (EH) and opportunistically accessing the licensed spectrum of an overlaid primary communication link to offload its sensing data to an MEC server (MS) for edge processing. Under time-varying fading channel, random energy arrivals, and stochastic ON-OFF state of the primary link, we aim to design an online algorithm to jointly control the cognitive data sensing rate and processing method (i.e., local and edge processing) without knowing future system information. In particular, we aim to maximize the long-term average sensing rate of the WD subject to quality of service (QoS) requirement of primary link, average power constraint of MS and data queue stability of both MS and WD. We formulate the problem as a multi-stage stochastic optimization and propose an online algorithm named PLySE that applies the perturbed Lyapunov optimization technique to decompose the original problem into per-slot deterministic optimization problems. For each per-slot problem, we derive the closed-form optimal solution of data sensing and processing control to facilitate low-complexity real-time implementation. Interestingly, our analysis finds that the optimal solution exhibits an threshold-based structure related to the current energy state, secondary queueing backlogs and primary link activity. Simulation results collaborate with our analysis and demonstrate more than 46.7% data sensing rate improvement of the proposed PLySE over representative benchmark methods.

To enable user diversity while balancing tradeoffs between cost and flexibility, we exploit hybrid analog-digital beamforming for multi-user mobile systems. By combining array and spatial signal-processing techniques, highly directional beams can be formed with high beamforming gain to achieve sufficient link budget. In this way, fine selection of codewords for analog beamforming is essential to ensure the uplink rate, which may increase the latency of establishing a reliable communication link, especially for mobile millimeter-wave communication systems. In order to guarantee the reliability of communication, adaptive data-driven beam tracking is proposed to find the suitable beamformer/combiner pair to achieve the given signal-to-interference-plus-noise ratios constraint. Unlike the model-based approach, the proposed approach is dependent only on the real-time measurement data based on a dynamic linearization representation of a time-varying pseudo-gradient parameter estimation procedure. Numerical analyses show that the proposed beam tracking algorithm can achieve good tracking performance with lower training overhead compared with traditional schemes.

Symbol rate is one of the most important parameters in signal demodulation process. In real-time signal processing, traditional symbol rate estimation algorithms for the Multiple Phase Shift Keying (M-PSK) and the Multiple Quadrature Amplitude Modulation (M-QAM) are based on the Fourier transform of signal’s complex envelope. At the low signal-to-noise ratio (SNR), the accuracy of symbol rate estimation can be improved by increasing the number of symbols as much as possible. However, this improvement is infeasible in many applications such as the energy-limited Internet of Things devices and sporadic noncooperative transmissions. In this paper, we propose a data-driven bandpass filter (BPF) design scheme for accurate estimation of symbol rate under low SNR with only a small number of symbols available. The proposed scheme considerably improves the estimation performance by optimizing the BPF design using the equivalent dynamic linearization model with time-varying pseudo-partial derivatives. Specifically, the proposed scheme iteratively optimizes the upper and lower cut-off frequencies of the BPF based on the measured complex envelope spectrum until achieving the optimal BPF. Therefore, the peaks of the complex envelope spectrum are extracted as the estimate of the symbol rate by applying the optimal BPF. Experimental results indicate the promise of the proposed scheme as an efficient symbol rate estimator for sporadic signal at low SNR and with a small number of symbols.

Clock synchronization has been a challenging task in the design of wireless networks because the synchronization accuracy suffers from uncertain transmission delay and/or packet loss due to poor wireless channel conditions. To address this problem, this paper develops robust clock synchronization schemes based on the matrix completion theory to improve the accuracy in presence of random transmission delay and packet loss. We propose a matrix completion-based maximum likelihood estimator (MC-MLE) to estimate the clock offset and clock skew under Gaussian transmission delay model. Thanks to the denoise feature of matrix completion, the proposed schemes outperform the maximum likelihood estimator (MLE) in the presence of packet loss and random transmission delay. That is, the proposed schemes are closer to the Cramer-Rao lower bound (CRLB) than the MLE when the timestamp packets are randomly corrupted. The robustness and accuracy of the proposed schemes are validated by numerical results.

Signal classification plays an important role in cognitive communication networks to identify and avoid interference. Contrary to traditional cooperative spectrum sensing based on binary hypothesis testing, we study a network of cognitive radios that jointly perform linear cooperation based signal classification via M-ary hypothesis testing. To maximize the probability of successful classification subject to constraints on individual probabilities of misclassification, we divide the problem into M independent binary hypothesis testing subproblems in parallel before selecting the hypothesis that is most likely true. Furthermore, we consider a problem that maximizes the probability of successful classification subject to a constraint on the total probability of misclassification. We reformulate such an optimization problem into two different subproblems, where the optimal solution is obtained by alternating the two optimization sub-problems iteratively. Numerical simulations demonstrate the near-optimality of the proposed methods with low computational complexity for the cooperative signal classification problems.

Receiver sensitivity is one of the most important performance metrics in communication systems. Industry has been using exhaustive search in a specified range to identify the sensitivity of a receiver. However, such an exhaustive search is neither efficient in terms of measurement time, nor accurate due to the fixed step-size. In this paper, a data-driven approach is proposed to measure the receiver sensitivity based on a dynamic linearization representation of a time-varying pseudo-gradient parameter estimation procedure. Unlike the model-based approach, the proposed data-driven approach is dependent only on the input and output measurement data. In addition, we derive the minimum number of test packets needed to satisfy the desired confidence level for packet error rate estimation. By adapting the number of test packets, we are able to further reduce the measurement time. Numerical analyses and experimental results show that the proposed data-driven sensitivity measurement can achieve good estimation performance as well as reduce measurement time.

Receiver sensitivity is one of the most important parameters in determining the overall performance of a radio frequency communication system. The traditional receiver sensitivity measurement is to use exhaustive search in a specified range to identify the receiver's sensitivity. However, such an exhaustive search is neither efficient in terms of measurement time, nor accurate due to the fixed step-size. In this paper, a data-driven approach is proposed to measure the receiver sensitivity based on a dynamic linearization representation of a time-varying pseudo-gradient parameter estimation procedure. Unlike the model-based approach, the proposed data-driven approach is dependent only on the input and output measurement data. Furthermore, an analytical approach is presented to derive the minimum number of test packets needed to satisfy the desired confidence level for packet error rate estimation. By adapting the number of test packets, the proposed approach can converge to the exact receiver sensitivity with less time. A theoretical analysis of the proposed approach is provided and further validated by numerical analyses and real-world experimental verification. These analyses show that the proposed data-driven sensitivity measurement can a achieve good estimation performance within a few iterations as well as reduce measurement time.

In distributed inference, cooperation among networked agents can be exploited to enhance the performance of each individual agent. In this paper, we consider signal classification over a network of agents, where each agent observes a certain signal under a particular signal-to-noise ratio (SNR). Each agent produces a statistic that summarizes its observations over a time period and then forwards it to a fusion center for identifying the type of signal in a global manner. A linear cooperation strategy for signal classification is formulated as maximizing the classification probability subject to constrained misclassification probabilities. We show that this problem can be transformed into a convex problem under some conditions and linear cooperation is a simple but effective strategy that can greatly enhance the performance of signal classification over networked agents.

Signal classification plays an important role in spectrum sensing for cognitive radios to identify and avoid interference from other wireless devices. In this paper, we study a network of cognitive radios that jointly perform signal classification via cooperation. We propose a simple but effective linear cooperation scheme to fuse pre-processed measurements collected from spatially distributed cognitive radios. Our objective is to maximize the probability of successful classification subject to some constraints on the probabilities of misclassification. By applying a divide-and-conquer strategy and new constraint relaxation methods, we are able to derive the closed-form expressions for the optimal weight coefficient for each contributing cognitive radio. The design of such a cooperative signal classification system is further studied through numerical simulation.

Receiver sensitivity is one of the most important parameters in determining the overall performance of a communication system. Industry has been using exhaustive search in a specified range to identify the achieved receiver sensitivity for wireless devices. Such an exhaustive search scheme is neither efficient in terms of measurement time, nor accurate due to the fixed stepsize. In this paper, we propose a fast sensitivity measurement method for wireless communication systems based on bisection search, where the number of packets needed to estimate the packet error rate (PER) is optimized for the required confidence level. Specifically, we present a new analytic approach to derive the minimum number of packets needed to satisfy the desired confidence level, with the number of test packets further adapted to speed up the measurement of the receiver sensitivity.

This study presents a comprehensive analysis to highlight advantages and disadvantages, in terms of channel capacity and computational complexity (CC), of a so‐called clustered‐orthogonal frequency division multiplexing (OFDM) scheme for power line communication (PLC) technologies for access networks. By taking into account filtering, decimation and upsampling techniques, the implementations of two transmitter schemes, named (·)‐I and (·)‐II, and three receivers ones, named (·)‐I, (·)‐II and (·)‐III, that can be easily derived from the hermitian symmetric OFDM (HS‐OFDM) scheme are discussed. Numerical results show that the clustered‐OFDM schemes based on HS‐OFDM provide the same bit‐error‐rate performance as that of HS‐OFDM, double sideband‐OFDM and single sideband‐OFDM. Also, clustered‐OFDM based on the combination of (·)‐II and (·)‐III offers the lowest CC for both baseband and passband data communications. Further, it is demonstrated that the clustered‐OFDM schemes can trade off channel capacity for CC, which can give rise to low‐priced transceivers for PLC technologies. Finally, a comparative analysis of clustered‐OFDM and orthogonal frequency division multiple access (OFDMA) points out the scenarios in which clustered‐OFDM can be competitive if the complexity of the OFDM transceiver is a primary consideration.

This paper presents a simple approach for matrix inversion by using dichotomous coordinate descent (DCD) algorithm. The idea of the approach is that the DCD algorithm obtains separately the individual columns of the inverse of the matrix. Owing to the low complexity of hardware implementation of the individual DCD algorithm, a block of DCD processors can be adopted to obtain the columns of the inverse of the channel correlation matrix in parallel with reduced hardware occupation.

Spectrum sensing is one of the enabling functionalities for cognitive radio systems to operate in the spectrum white space. To protect the primary incumbent users from interference, the cognitive radio is required to detect incumbent signals at very low signal-to-noise ratio (SNR). In this paper, we study a spectrum sensing technique based on spectral correlation for detection of television (TV) broadcasting signals. The basic strategy is to correlate the periodogram of the received signal with the a priori known spectral features of the primary signal. We show that this sensing technique is asymptotically equivalent to the likelihood ratio test (LRT) at very low SNR, but with less computational complexity. That is, the spectral correlation-based detector is asymptotically optimal according to the Neyman-Pearson criterion. From the system design perspective, we analyze the effect of the spectral features on the spectrum sensing performance. Through the optimization analysis, we obtain useful insights on how to choose effective spectral features to achieve reliable sensing. Simulation results show that the proposed sensing technique can reliably detect analog and digital TV signals at SNR levels as low as -20 dB.

Spectrum sensing is one of the enabling functionalities for cognitive radio (CR) systems to operate in the spectrum white space. To protect the primary incumbent users from interference, the CR is required to detect incumbent signals at very low signal-to-noise ratio (SNR). In this paper, we present a spectrum sensing technique based on correlating spectra for detection of television (TV) broadcasting signals. The basic strategy is to correlate the periodogram of the received signal with the a priori known spectral features of the primary signal. We show that according to the Neyman-Pearson criterion, this spectra correlation-based sensing technique is asymptotically optimal at very low SNR and with a large sensing time. From the system design perspective, we analyze the effect of the spectral features on the spectrum sensing performance. Through the optimization analysis, we obtain useful insights on how to choose effective spectral features to achieve reliable sensing. Simulation results show that the proposed sensing technique can reliably detect analog and digital TV signals at SNR as low as -20 dB.

As an enabling functionality of overlay cognitive radio networks, spectrum sensing needs to reliably detect licensed signal in the band of interest. To achieve reliable sensing, we propose a linear fusion scheme for distributed spectrum sensing to combine the sensing results from multiple spatially distributed cognitive radios. The optimal linear fusion design is formulated into a nonconvex optimization problem. We show that the optimal solution of such a nonconvex problem can be solved via semi-definite programming reformulation.

In this paper, a box-constrained MIMO detector is considered that allows simple FPGA implementation and provides improvement in the detection performance compared to the MMSE detector. The box-constrained detector is implemented using dichotomous coordinate descent iterations. We investigate the design throughput against the BER performance and the design complexity in terms of the number of logic slices. The proposed design requires as few as 637, 658, and 667 slices for 4 times 4, 8 times 8, and 16 times 16 MIMO systems, respectively, which is significantly less than that required by known designs of the MMSE detector.

Spectrum sensing is an essential functionality that enables cognitive radios to detect spectral holes and opportunistically use under-utilized frequency bands without causing harmful interference to primary networks. Since individual cognitive radios might not be able to reliably detect weak primary signals due to channel fading/shadowing, this paper proposes a cooperative wideband spectrum sensing scheme, referred to as spatial-spectral joint detection, which is based on a linear combination of the local statistics from spatially distributed multiple cognitive radios. The cooperative sensing problem is formulated into an optimization problem, for which suboptimal but efficient solutions can be obtained through mathematical transformation under practical conditions.

Spectrum sensing is an essential enabling functionality for cognitive radio networks to detect spectrum holes and opportunistically use the under-utilized frequency bands without causing harmful interference to legacy networks. This paper introduces a novel wideband spectrum sensing technique, called multiband joint detection, which jointly detects the signal energy levels over multiple frequency bands rather than consider one band at a time. The proposed strategy is efficient in improving the dynamic spectrum utilization and reducing interference to the primary users. The spectrum sensing problem is formulated as a class of optimization problems in interference limited cognitive radio networks. By exploiting the hidden convexity in the seemingly non-convex problem formulations, optimal solutions for multiband joint detection are obtained under practical conditions. Simulation results show that the proposed spectrum sensing schemes can considerably improve the system performance. This paper establishes important principles for the design of wideband spectrum sensing algorithms in cognitive radio networks.

Spectrum sensing is a key enabling functionality in cognitive radio (CR) networks, where the CRs act as secondary users that opportunistically access free frequency bands. Due to the effects of channel fading, individual CRs may not be able to reliably detect the existence of a primary radio, who is a licensed user for the particular band. In this paper, we present optimal cooperation strategies for spectrum sensing to combat the effects of destructive channels and malfunctioning devices. Our approach conducts spectrum sensing based on the linear combination of local test statistics from individual secondary users. We propose two optimization schemes to control the combining weights, and compare their performance. Our first approach is to optimize the probability distribution function of the global test statistics at the fusion center. For the second scheme, we maximize the global detection sensitivity under constraints on the false alarm probability. Simulation results illustrate the significant cooperative gain achieved by the proposed strategies.

We consider an estimation network of many distributed sensors, where each senor takes a noisy measurement of some unknown parameter. Due to energy limitation, the network selects only a subset of sensors for data fusion as long as the distortion is tolerable. In this paper, we present a sampling framework based on linear minimum variance unbiased estimation. The framework enables the system to achieve a desired estimation fidelity level and to improve the network lifetime. Simulations illustrate the effectiveness of the proposed sampling schemes.

This paper considers an estimation network of many distributed sensors with a certain correlation structure. Due to limited communication resources, the network selects only a subset of sensor measurements for estimation as long as the resulting fidelity is tolerable. We present a distributed sampling and estimation framework based on innovations diffusion, within which the sensor selection and estimation are accomplished through local computation and communications between sensor nodes. In order to achieve energy efficiency, the proposed algorithm uses a greedy heuristics to select a nearly minimum number of active sensors in order to ensure the desired fidelity for each estimation period. Extensive simulations illustrate the effectiveness of the proposed sampling scheme.

The emergence of wireless sensor networks has imposed many challenges on network design such as severe energy constraints, limited bandwidth and computing capabilities. This kind of networks necessitates network protocol architectures that are robust, energy-efficient, scalable, and easy for deployment. This paper proposes a robust energy-aware clustering architecture (REACA) for large-scale wireless sensor networks. We analyze the performance of the REACA network in terms of quality-of-service, asymptotic throughput capacity, and power consumption. In particular, we study how the throughput capacity scales with the number of nodes and the number of clusters. We show that by exploiting traffic locality, clustering can achieve performance improvement both in capacity and in power consumption over general-purpose ad hoc networks. We also explore the fundamental trade-off between throughput capacity and power consumption for single-hop and multi-hop routing schemes in cluster-based networks. The protocol architecture and performance analysis developed in this paper provide useful insights for practical design and deployment of large-scale wireless sensor network.

The emergence of wireless sensor networks has imposed many challenges on network design such as severe energy constraints, limited bandwidth and computing capabilities. This kind of networks necessitates network protocol architectures that are robust, energy-efficient, scalable, and easy for deployment. This paper proposes a robust energy-aware clustering architecture (REACA) for large-scale wireless sensor networks. We analyze the performance of the REACA network in terms of quality-of-service, asymptotic throughput capacity, and power consumption. In particular, we study how the throughput capacity scales with the number of nodes and the number of clusters. We show that by exploiting traffic locality, clustering can achieve performance improvement both in capacity and in power consumption over general-purpose ad hoc networks. We also explore the fundamental trade-off between throughput capacity and power consumption for single-hop and multi-hop routing schemes in cluster-based networks. The protocol architecture and performance analysis developed in this paper provide useful insights for practical design and deployment of large-scale wireless sensor network.

We propose a robust energy-aware clustering architecture for large-scale wireless sensor networks and analyze its performance in terms of throughput capacity and power consumption. The results show that clustered networks can achieve performance improvement by exploiting traffic locality and spatial separation.

Earliest deadline first (EDF) has become one of the most promising scheduling schemes for providing quality-of-service (QoS) differentiation over high speed networks. We study the deadline violation (loss) probability at an EDF scheduling switch. An analytical framework based on the dominant time scale (DTS) has been developed for estimating the deadline violation probabilities of the aggregated traffic and the individual flows. This enables us to determine whether a given flow can meet its deadline with the required loss probability. As shown by simulations using real network traffic, the developed asymptotic approximations are accurate enough to predict the real metrics. The framework can serve as the basis for the design of call admission control (CAC) mechanisms which are targeted to provide statistical guarantees on transmission delays and/or loss.

Future high speed packet-switching networks deploying integrated service (IntServ) or differentiated service (DiffServ) architectures are expected to provide heterogeneous quality-of-service (QoS) guarantees for a variety of applications. Call admission control (CAC) plays a critical role in achieving this goal and is an integration of the traffic models, scheduling disciplines, and QoS specifications. Its major task is to decide whether a new connection should be granted while the QoS requirements of all the connections are to be satisfied. However, it is well known that developing an effective and efficient CAC algorithm for a stochastic system such as an EDF scheduler is generally very difficult due to the intractability of per-class QoS analysis. A robust control mechanism is necessary for the long-range dependent traffic with infinite variance. In this paper, we present an admission control algorithm for probabilistic services scheduled by an EDF scheduler. In deriving the admission condition, we consider all the connections with similar QoS constraints as an aggregate traffic class. A statistical framework is also developed to analyze the per-class QoS metrics.

In this paper, the provision of quality-of-service (QoS) for real-time traffic over a wireless channel deploying automatic repeat request (ARQ) error control is investigated. By introducing the concepts of ARQ capacity and effective capacity, an analytic model has been derived to evaluate the loss probabilities in both the network layer and the physical layer. In contrast to the previous results, this model quantifies the interaction between the network and physical layers. As shown by the simulation experiments, our analysis can predict the real metrics under a wide range of conditions. This enables the call admission controller in wireless networks to control and optimize traffic QoS using instantaneous channel status information.

Differentiated services (DiffServ) networking technologies are under development with the objective to support diverse classes of traffic that require different quality of service (QoS) guarantees. Recent studies have shown that real network traffic exhibits self-similarity or long-range dependence (LRD) in high-speed communication networks, which has a deteriorating impact on the network performance. To assist the development of admission control mechanisms, which can accommodate heterogeneous traffic including short-range independence and long-range dependence, this paper proposes a measurement-based approach to estimate the buffer overflow probability for each priority queue in a multiplexer deploying the head-of-the-line (HOL) priority discipline. The accuracy and effectiveness of this model has been verified by simulations. The results of this paper will provide a practical insight into the buffer dimensioning and admission control design of a HOL multiplexer.