With increasing computer-based work burden, prolonged poor sitting posture can result in health issues such as scoliosis. However, current sitting posture recognition systems often require the purchase of additional hardware. The camera-based system can compromise user privacy and be affected by varying lighting conditions. In this paper, we propose a solution to realize a sitting posture recognition system derived from acoustic signals generated by smartphones. Firstly, acoustic signals corresponding to various sitting postures are acquired via the built-in speaker and microphone of the smartphone. Subsequently, an innovative signal segmentation technique based on the adaptive threshold is designed to extract the signals, followed by the creation of a deep learning model for posture recognition. To meet the demands of lightweight deployment, a knowledge distillation compression technique is introduced to compress the model while maintaining its accuracy. The experimental results validate that our sitting posture recognition system has good effectiveness and robustness, making it more universal.

Driven by the demand for high transmission efficiency in 6G networks, semantic communication has attracted significant interest recently. While most existing works focus on optimizing semantic communication system design to enhance end-to-end transmission performance, they often overlook the integration of quality of service (QoS) requirements. To address this gap, we employ the Alpha-Beta-Gamma (ABG) formula to empirically approximate the relationship between end-to-end transmission quality and signal-to-noise ratio (SNR). Based on this model, we first design an optimal beamforming scheme that minimizes transmission power while ensuring real-time QoS guarantees. Furthermore, to account for inevitable channel state information (CSI) estimation errors in practical scenarios, we propose robust beamforming design schemes under QoS constraints for both bounded and unbounded CSI estimation errors. These optimization problems are efficiently solved using semidefinite relaxation (SDR),S-lemma, and Bernstein-type inequalities. Finally, experimental results demonstrate that our proposed optimal beamforming design scheme outperforms conventional beamforming methods, while the robust beamforming schemes achieve superior performance in handling CSI estimation errors compared to existing computational approaches.

Ultrasonic through-metal communication is critical for many applications in industrial IoT (IIoT) environments but suffers from frequency-selective fading, requiring orthogonal frequency-division multiplexing (OFDM). Accurate channel estimation with minimal pilot overhead is essential for optimizing OFDM performance. Existing methods for estimating ultrasonic through-metal communication channel ignore the sparsity of the ultrasonic through-metal channel impulse response (CIR), leading to high pilot overhead, while sparse estimation algorithms fail to exploit the CIR’s structural features. Therefore, a periodic sparsity enhanced channel estimation method via improved block sparse Bayesian learning (BSBL) for ultrasonic through-metal communication is proposed, which further improves the estimation accuracy and reduces the pilot overhead by integrating three key features of CIR—the periodic occurrence of echo blocks, their exponential attenuation, and their intrinsic waveform—into the Bayesian prior. Experiments show that the proposed algorithm significantly outperforms standard BSBL in estimation accuracy while substantially reducing pilot overhead compared to conventional non-sparse techniques. This method not only enhances estimation precision but also improves spectral efficiency and link reliability in resource-constrained and harsh IIoT environments.

Hybrid non-orthogonal multiple access (H-NOMA), which combines the advantages of pure NOMA and conventional OMA, has emerged as a highly promising multiple access technology for future wireless networks. While recent studies have proposed various H-NOMA systems using different successive interference cancellation (SIC) methods, their analyses typically assume a fixed channel gain order between paired users. However, in practice, user pairing is often configured typically based on long-term network deployment requirements or statistical channel characteristics (e.g., geographic layout or average channel gain) rather than instantaneous channel states. This practical pairing strategy leads to random channel gain ordering, where the relative channel gains between paired users are inherently stochastic and time-varying. This aspect is critical and fundamentally affects system performance, yet remains understudied. To address this issue, this paper analyzes the performance of three H-NOMA schemes under such random channel gain ordering: (a) fixed-order SIC (FSIC) aided H-NOMA; (b) hybrid SIC with non-power adaptation (HSIC-NPA) aided H-NOMA; and (c) hybrid SIC with power adaptation (HSIC-PA) aided H-NOMA. For the opportunistic users seeking to maximize data rate, the closed-form expressions for the probability that each H-NOMA scheme underperforms conventional OMA are derived rigorously. Asymptotic analyses in the high SNR regime are also developed. Simulation results validate the theoretical derivations and demonstrate the performance of the H-NOMA schemes across different SNR scenarios, thereby offering foundational insights for deploying robust H-NOMA in next-generation wireless systems.

MicroRNAs (miRNAs) serve as crucial biomarkers in disease diagnosis. Although silicon-based electronic machine learning models provide efficient means for analyzing the massive data associated with miRNAs and for disease diagnosis, they remain constrained by low parallelism and poor biocompatibility. DNA computing provides an ideal interface for the integration of information technology and biotechnology. Here, we employ strand displacement reaction (SDR) to realize the DNA-based support vector machine (SVM) models for disease diagnosis that sequentially integrates four functional modules: weighted summation module, subtraction module, signal restoration module, and reporter module. In contrast to conventional silicon-based computing architectures, the DNA-based disease diagnosis model can directly and specifically identify the expression levels of target miRNAs from biological samples and drive model decisions in real time, enabling precise classification of disease states. We validate the disease diagnosis model using miRNAs expression levels, achieving diagnostic accuracies of 98.54% for cystic, mucinous and serous neoplasms (CMSN), 99.46% for Glioma, and 97.81% for clear cell carcinoma (CCC). The DNA-based disease diagnosis model simultaneously classifies the three disease states in parallel, and the results show a strong agreement with the actual disease states labeled in The Cancer Genome Atlas (TCGA). This work provides a new paradigm for engineering precise, intelligent and integrated disease diagnosis schemes at the molecular level.

With the widespread adoption of location-based services (LBS), user-generated trajectory data shows strong spatiotemporal correlation, rendering it highly vulnerable to inference attacks that expose sensitive information. In particular, once semantic locations like “hospital” and “bank” are identified, the risk of trajectory leakage increases substantially. To address this issue, this paper formulates a personalized spatiotemporal trajectory privacy protection framework, which is designed to protect locations with varying semantic sensitivities on the trajectory from the attacker with spatiotemporal correlation information. We model the trajectory privacy protection problem as a Markov Decision Process (MDP) and introduce the reinforcement learning (RL) technique to adjust the privacy parameters dynamically. Specifically, we leverage the twin delayed deep deterministic policy gradient (TD3) algorithm to enhance the stability and accuracy of policy evaluation, enabling efficient learning of optimal policies in continuous action spaces. Furthermore, a safe exploration strategy is incorporated to continuously evaluate and avoid high-risk state-action pairs, thereby enhancing privacy protection. Simulation results demonstrate that the proposed mechanism significantly improves privacy protection while effectively reducing Quality of Service (QoS) loss, exhibiting better convergence and overall system utility.

Multi-access Edge Computing (MEC) enables users to handle resource-intensive and latency-sensitive tasks. However, the offloading behaviors, which are closely correlated with wireless channel conditions, can inadvertently reveal users' location information to untrustworthy MEC servers. Existing location privacy-aware task offloading (LPTO) mechanisms have not fully considered and comprehensively analyzed personalized location privacy protection requirements. To address this gap, this paper proposes a differential privacy (DP)-based personalized LPTO mechanism for MEC environments that jointly optimizes the perturbation region, privacy budget, and offloading rate while maximizing the offloading utility. We quantify personalized privacy requirements by incorporating task sensitivity, user privacy preference, and task priority. Then, we propose a two-timescale (2Ts) optimization framework to solve the complex personalized location privacy-aware task offloading optimization problem. Specifically, we optimize the perturbation region on a long timescale to align with long-term privacy requirements. In contrast, the offloading ratio and privacy budget are dynamically optimized on a short timescale based on instantaneous channel states and offloading workloads. Furthermore, we model the privacy-aware offloading problem as a Markov decision process (MDP) and develop a dual-agent deep reinforcement learning (DRL)-based personalized LPTO mechanism (DDPLM) to optimize strategies under dynamic MEC systems. Simulation results validate that the proposed DDPLM achieves personalized location privacy protection while reducing computational costs.

Choroidal thickness variations serve as critical biomarkers for numerous ophthalmic diseases. Accurate segmentation and quantification of the choroid in optical coherence tomography (OCT) images is essential for clinical diagnosis and disease progression monitoring. Due to the small number of disease types in the public OCT dataset involving changes in choroidal thickness and the lack of a publicly available labeled dataset, we constructed the Xuzhou Municipal Hospital (XZMH)-Choroid dataset. This dataset contains annotated OCT images of normal and eight choroid-related diseases. However, segmentation of the choroid in OCT images remains a formidable challenge due to the confounding factors of blurred boundaries, non-uniform texture, and lesions. To overcome these challenges, we proposed a mixed attention-guided multiscale feature fusion network (MAMFF-Net). This network integrates a Mixed Attention Encoder (MAE) for enhanced fine-grained feature extraction, a deformable multiscale feature fusion path (DMFFP) for adaptive feature integration across lesion deformations, and a multiscale pyramid layer aggregation (MPLA) module for improved contextual representation learning. Through comparative experiments with other deep learning methods, we found that the MAMFF-Net model has better segmentation performance than other deep learning methods (mDice: 97.44, mIoU: 95.11, mAcc: 97.71). Based on the choroidal segmentation implemented in MAMFF-Net, an algorithm for automated choroidal thickness measurement was developed, and the automated measurement results approached the level of senior specialists.

Hybrid non-orthogonal multiple access (H-NOMA) technology, which combines the benefits of non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) through flexible resource allocation in a single transmission, has shown great potential for enhancing the performance of wireless communication systems. To further exploit the potential of H-NOMA, this paper proposes a novel design of H-NOMA which jointly incorporates hybrid successive interference cancellation (HSIC) and power adaptation (PA) in the NOMA transmission phase, by introducing a power adaptation factor . For a given power reducing coefficient β, which ensures that the energy consumption of the proposed scheme is lower than that of conventional OMA, the probability that the achievable rate of the proposed HSIC-PA aided H-NOMA scheme fails to outperform its OMA counterpart is derived in closed form. Besides, the impact of user pairing is considered. Furthermore, the asymptotic analysis shows that the aforementioned probability of the proposed H-NOMA scheme can approach zero in the high signal-to-noise ratio (SNR) regime without constraints on either users’ target rates or transmit power. By dynamically adjusting the transmission power of the opportunistic user and the decoding order of HSIC, signal interference between the legacy user and the opportunistic user can be effectively controlled, thereby improving the achievable rate and energy efficiency of the opportunistic user. This represents a significant improvement over conventional H-NOMA schemes, which require specific restrictive conditions to make the probability that their achievable rate underperforms OMA approach zero at high SNR, as shown in existing work. The above observation indicates that, with lower energy consumption, the proposed HSIC-PA aided H-NOMA can achieve a higher data rate than pure OMA with probability 1 at high SNR, leading to improved energy efficiency. Finally, numerical results are provided to verify the accuracy of the analysis and also to demonstrate the superior performance of the proposed H-NOMA scheme.

Human Action Quality Assessment (AQA), which aims to automatically evaluate the performance of actions executed by humans, is an emerging field of human action analysis. Although many review articles have been conducted for human action analysis fields such as action recognition and action prediction, there is a lack of up-to-date and systematic reviews related to AQA. This paper aims to provide a systematic literature review of existing papers on vision-based human AQA. This systematic review was rigorously conducted following the PRISMA guideline through the databases of Scopus , IEEE Xplore, and Web of Science in July 2024. Ninety-six research articles were selected for the final analysis after applying inclusion and exclusion criteria. This review presents an overview of various aspects of AQA, including existing applications, data acquisition methods, public datasets, state-of-the-art methods and evaluation metrics . We observe an increase in the number of studies in AQA since 2019, primarily due to the advent of deep learning methods and motion capture devices. We categorize these AQA methods into skeleton-based and video-based methods based on the data modality used. There are different evaluation metrics for various AQA tasks. SRC is the most commonly used evaluation metric, with fifty-six out of ninety-six selected papers using it to evaluate their models. Sports event scoring, surgical skill evaluation and rehabilitation assessment are the most popular three scenarios in this direction based on existing papers and there are more new scenarios being explored such as piano skill assessment. Furthermore, the existing challenges and future research directions are provided, which can be a helpful guide for researchers to explore AQA.

In this article, we exploit the positioning results for communication, and propose an integrated positioning and communication (IPAC) framework for terahertz (THz) massive multi-input-multioutput (MIMO) networks. Specifically, we derive an explicit expression for the Cramér-Rao bound (CRB), which is used to evaluate the positioning performance. Furthermore, based on the established relationship between positioning and communication, we propose a joint beam tracking and power allocation scheme for mobile users in THz massive MIMO networks, which minimizes the positioning error under both the data transmission outage constraint and total power constraints. Unfortunately, the joint beam tracking and power allocation optimization problem is nonconvex, and intractable due to the outage constraint. To address this challenge, we decompose the nonconvex problem into a beam tracking subproblem and a power allocation subproblem, and propose a proximal policy optimization beam tracking (PPO-BT) algorithm for the beam tracking subproblem and a robust power allocation (RPA) algorithm for the power allocation subproblem. Furthermore, we extend the proposed THz IPAC scheme to more practical 3-D scenarios. Simulation results demonstrate that our proposed THz IPAC framework can satisfy positioning and communication requirements at the same time, and our proposed methods outperform existing methods.

In this article, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multiuser broadcast (broadcast communication (BC)) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multiuser semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in the same time-frequency resource. Specifically, for inference tasks, we design a SFDMA digital BC network based on robust information bottleneck (RIB), which can achieve a tradeoff between inference performance, data compression and multiuser interference. Moreover, for image reconstruction tasks, we develop a SFDMA digital BC network by utilizing a Swin Transformer, which significantly reduces multiuser interference. More importantly, SFDMA can protect the privacy of users’ semantic information, in which each receiver can only decode its own semantic information. Furthermore, we establish a relationship between performance and signal to interference plus noise ratio (SINR), which is fitted by an Alpha-Beta–Gamma (ABG) function. Furthermore, an optimal power allocation method is developed for the inference and reconstruction tasks. Extensive simulations verify the effectiveness and superiority of our proposed SFDMA scheme.

Due to the black-box characteristics of deep learning based semantic encoders and decoders, finding a tractable method for the performance analysis of semantic communications is a challenging problem. In this paper, we propose an Alpha-Beta-Gamma (ABG) formula to model the relationship between the end-to-end measurement and SNR, which can be applied for both image reconstruction tasks and inference tasks. Specifically, for image reconstruction tasks, the proposed ABG formula can well fit the commonly used DL networks, such as SCUNet, and Vision Transformer, for semantic encoding with the multi scale-structural similarity index measure (MS-SSIM) measurement. Furthermore, we find that the upper bound of the MS-SSIM depends on the number of quantized output bits of semantic encoders, and we also propose a closed-form expression to fit the relationship between the MS-SSIM and quantized output bits. To the best of our knowledge, this is the first theoretical expression between end-to-end performance metrics and SNR for semantic communications. Based on the proposed ABG formula, we investigate an adaptive power control scheme for semantic communications over random fading channels, which can effectively guarantee quality of service (QoS) for semantic communications, and then design the optimal power allocation scheme to maximize the energy efficiency of the semantic communication system. Furthermore, by exploiting the bisection algorithm, we develop the power allocation scheme to maximize the minimum QoS of multiple users for OFDMA downlink semantic communication Extensive simulations verify the effectiveness and superiority of the proposed ABG formula and power allocation schemes.

Location-based services (LBSs) in vehicular ad hoc networks (VANETs) offer users numerous conveniences. However, the extensive use of LBSs raises concerns about the privacy of users' trajectories, as adversaries can exploit temporal correlations between different locations to extract personal information. Additionally, users have varying privacy requirements depending on the time and location. To address these issues, this paper proposes a personalized trajectory privacy protection mechanism (PTPPM). This mechanism first uses the temporal correlation between trajectory locations to determine the possible location set for each time instant. We identify a protection location set (PLS) for each location by employing the Hilbert curve-based minimum distance search algorithm. This approach incor-porates the complementary features of geo-indistinguishability and distortion privacy. We put forth a novel Permute-and-Flip mechanism for location perturbation, which maps its initial application in data publishing privacy protection to a location perturbation mechanism. This mechanism generates fake locations with smaller perturbation distances while improving the balance between privacy and quality of service (QoS). Simulation results show that our mechanism outperforms the benchmark by providing enhanced privacy protection while meeting user's QoS requirements.

In this article, we consider a practical indoor 3-D mobile online visible light positioning (VLP) system, where the orientation of the user equipment (UE) is arbitrary. Based on the received signal strength (RSS) of multiple photodetectors (PDs), we formulate the 3-D VLP problem as a nonlinear least squares (NLSs) optimization problem, and then propose a sequential quadratic programming (SQP) positioning algorithm to efficiently calculate UE’s location. To obtain more accurate positioning solutions, we further leverage the advantages of deep learning and develop a stochastic gradient descent (SGD)-based VLP algorithm, and achieve an average positioning error of 1.77 cm, which significantly outperforms existing RSS VLP localization methods. Moreover, we design a 3-D mobile online VLP system prototype by using a portable RaspberryPi 4 Model B as the positioning signal processor and data memory, and establish the first publicly available 3-D VLP measured data set, including both RSS and orientation. The proposed positioning schemes are implemented and evaluated via the designed prototype system, which can achieve centimeter-level positioning accuracy (below 1 cm in certain condition).

Location privacy protection (LPP) has become a key concern during mobile crowdsourcing (MCS) task allocation. Existing LPP mechanisms for MCS applications mainly focus on two-dimensional (2D) plane scenarios or directly apply 2D techniques into three-dimensional (3D) space scenarios, leaving the height dimension of 3D geolocation vulnerable to privacy breaches. To facilitate the LPP in 3D MCS, we propose a learning-based geo-perturbation mechanism using 3D geo-indistinguishability (3D-GI). In this mechanism, we first define an optimization objective to balance location privacy and MCS server profit, making it adaptable to different types of MCS applications. Then, we adopt the Asynchronous Advantage Actor-Critic (A3C) algorithm to design a reinforcement learning (RL)-based approach without knowing the accurate system and attack models. This approach enables us to derive the optimal perturbation policy in continuous policy space and accelerates the learning speed using asynchronous multi-thread training. Simulation results demonstrate that the proposed mechanism can better balance location privacy and server profit in 3D MCS applications compared to existing benchmarks.

Ultra-wideband (UWB) positioning system offers high-precision location capabilities. However, it introduces positive biases in complex environments. Pedestrian Dead Reckoning (PDR) algorithm based on Inertial Measurement Unit (IMU) can maintain robust tracking even in cases of abrupt changes in pedestrian trajectories but suffers from cumulative errors. Therefore, in this study, the strengths of both systems are combined. Hence, a factor graph model is established to enhance the multi-system fusion localization method based on factor graphs. Experimental verification in both straight-line trajectories and scenarios involving state mutations demonstrates an integrated average positioning accuracy within 0.1m. When compared to traditional system fusion localization methods, the accuracy is enhanced by more than 50%.

The rapid development of indoor location-based services (LBS) has raised concerns about location privacy protection in the 3-dimensional (3D) space. The existing 2-dimensional (2D) location privacy protection mechanisms (LPPMs) cannot effectively resist attacks in 3D environments. Furthermore, users may have various sensitive attributes at different locations and times. In this paper, we first formally study the relationship between two complementary notions of geo-indistinguishability and distortion privacy (i.e., expected inference error) in the 3D space and develop a two-phase personalized 3D LPPM (P3DLPPM). In Phase I, we search for neighboring locations to formulate a protection location set (PLS) for hiding the actual location based on the above-mentioned relationship. To realize this, we develop a 3D Hilbert curve-based minimum distance searching algorithm to find the PLS with minimum diameter for each location while guaranteeing differential privacy. In Phase II, we put forth a novel Permute-and-Flip mechanism for location perturbation, which maps its initial application in data publishing privacy protection to a location perturbation mechanism. It generates fake locations with smaller perturbation distances while improving the balance between privacy and quality of service (QoS). Simulation results show that the proposed P3DLPPM can significantly improve personalized privacy protection while meeting the user's QoS needs.

Light fidelity (LiFi) is a potential key technology for future 6G networks. However, its feasibility of supporting mobile communications has not been fundamentally discussed. In this paper, we investigate the time-varying channel characteristics of mobile LiFi based on measured mobile phone rotation and movement data. Specifically, we define LiFi channel coherence time to evaluate the correlation of the channel timing sequence. Then, we derive the expression of LiFi transmission rate based on the m-pulse-amplitude-modulation (M-PAM). The derived rate expression indicates that mobile LiFi communications is feasible by using at least two photodiodes (PDs) with different orientations. Further, we propose two channel estimation schemes, and propose a LiFi channel tracking scheme to improve the communication performance. Finally, our experimental results show that the channel coherence time is on the order of tens of milliseconds, which indicates a relatively stable channel. In addition, based on the measured data, better communication performance can be realized in the multiple-input multiple-output (MIMO) scenario with a rate of 36Mbit/s, compared to other scenarios. The results also show that the proposed channel estimation and tracking schemes are effective in designing mobile LiFi systems.

With the ever-increasing user density and quality of service (QoS) demand, 5G networks with limited spectrum resources are facing massive access challenges. To address these challenges, in this paper, we propose a novel discrete semantic feature division multiple access (SFDMA) paradigm for multi-user digital interference networks. Specifically, by utilizing deep learning technology, SFDMA extracts multi-user semantic information into discrete representations in distinguishable semantic subspaces, which enables multiple users to transmit simultaneously over the same time-frequency resources. Furthermore, based on a robust information bottleneck, we design a SFDMA based multi-user digital semantic interference network for inference tasks, which can achieve approximate orthogonal transmission. Moreover, we propose a SFDMA based multi-user digital semantic interference network for image reconstruction tasks, where the discrete outputs of the semantic encoders of the users are approximately orthogonal, which significantly reduces multi-user interference. Furthermore, we propose an Alpha-Beta-Gamma (ABG) formula for semantic communications, which is the first theoretical relationship between inference accuracy and transmission power. Then, we derive adaptive power control methods with closed-form expressions for inference tasks. Extensive simulations verify the effectiveness and superiority of the proposed SFDMA.

Single-user semantic communications have attracted extensive research recently, but multi-user semantic broadcast communication (BC) is still in its infancy. In this paper, we propose a practical robust features-disentangled multi-user semantic BC framework, where the transmitter includes a feature selection module and each user has a feature completion module. Instead of broadcasting all extracted features, the semantic encoder extracts the disentangled semantic features, and then only the users’ intended semantic features are selected for broadcasting, which can further improve the transmission efficiency. Within this framework, we further investigate two information-theoretic metrics, including the ultimate compression rate under both the distortion and perception constraints, and the achievable rate region of the semantic BC. Furthermore, to realize the proposed semantic BC framework, we design a lightweight robust semantic BC network by exploiting a supervised autoencoder (AE), which can controllably disentangle sematic features. Moreover, we design the first hardware proof-of-concept prototype of the semantic BC network, where the proposed semantic BC network can be implemented in real time. Simulations and experiments demonstrate that the proposed robust semantic BC network can significantly improve transmission efficiency.

In this study, we develop multi-intensity planes color-shift keying (MIP-CSK) constellations to determine the effect of the MIP-based color-shift keying design scheme on the reliability of visible light communication systems under the constraint of target color constraint. Based on a 2-D triangle partition (TP) constellation plane, we introduce algorithms to design the nonintegral powers of two MIP-CSK constellations in 3-D symbol space by using set partition (SP) and intensity plane levels, which improves the minimum Euclidean distance. We then develop a coding scheme by designing a finite-state machine from the nonintegral powers of two constellations to further improve the reliability under the white color constraint. Subsequently, by considering the spatial symmetric nature of the TP constellation distribution, we study the design strategy of powers of two constellation symbols and further propose the powers of two MIP-CSK design scheme in a 3-D symbol space. Moreover, we investigate the reliability benefit obtained by SP and optimal intensity levels for powers of two MIP-CSK constellations by improving the normalized minimum squared distance. The simulation results indicate that the error performance of the proposed new family of MIP-based design schemes outperforms its counterparts while complying with the white color constraint at the high signal-to-noise ratios.

In this paper, we investigate an energy efficient waveform design for integrated visible light positioning and communication (VLPC) systems by exploiting the relationship between visible light positioning (VLP) and visible light communication (VLC). We propose that the direct current component and the alternating current component of the VLPC signals are utilized for positioning and communication, respectively. With a single LED-lamp, we propose a received-signal-strength based 3D VLP scheme, and further derive the Cramer-Rao lower bound (CRLB). Then, by exploiting the inherent coupling relationship between VLP and VLC, the positioning results are utilized for channel estimation of VLC, which can significantly reduce the channel estimation pilot overhead. Furthermore, we optimize the waveform design by minimizing the CRLB, while satisfying both the outage probability of communication rate and total transmit power constraints. However, this problem turns to be non-convex and intractable. To address this challenging problem, we utilize the Conditional Value-at-Risk to conservatively transform the outage probability constraint into a deterministic form. By exploiting the block coordinate descent algorithm, the waveform design problem can be efficiently solved by alternately optimizing VLP and VLC convex sub-problems and dual problem. Finally, simulation results verify both the effectiveness and robustness of the proposed waveform design.

Integrated visible light positioning and communication (VLPC), capable of combining advantages of visible light communications (VLC) and visible light positioning (VLP), is a promising key technology for the future Internet of Things. In VLPC networks, positioning and communications are inherently coupled, which has not been sufficiently explored in the literature. We propose a robust power allocation scheme for integrated VLPC Networks by exploiting the intrinsic relationship between positioning and communications. Specifically, we derive explicit relationships between random positioning errors, following both a Gaussian distribution and an arbitrary distribution, and channel state information errors. Then, we minimize the Cramer-Rao lower bound (CRLB) of positioning errors, subject to the rate outage constraint and the power constraints, which is a chance-constrained optimization problem and generally computationally intractable. To circumvent the nonconvex challenge, we conservatively transform the chance constraints to deterministic forms by using the Bernstein-type inequality and the conditional value-at-risk for the Gaussian and arbitrary distributed positioning errors, respectively, and then approximate them as convex semidefinite programs. Finally, simulation results verify the robustness and effectiveness of our proposed integrated VLPC design schemes.

In this paper, we investigate the trade-off between positioning and communication for an integrated positioning and communication (IPAC) millimeter wave system. First, in terms of the positioning in the IPAC system, the Cramér-Rao bound (CRB) is commonly used as a performance metric. Unfortunately, the CRB is only tight in a certain region for the high signal-to-noise ratio (SNR). To compensate for this deficiency, we derive the Ziv-Zakai bound (ZZB) for the IPAC system by exploiting the a priori delay information extracted from both the time delay parameter and the channel amplitude attenuation. Further, we derive the expected CRB (ECRB) and the weighted CRB (WCRB) of the system for comparisons. Second, we analyze the trade-off between positioning and communication for this IPAC system based on the derived ZZB. Specifically, we aim to optimize the power allocation to maximize the achievable data rate subject to the ZZB and total transmit power constraints. Numerical results show that the ZZB provides a tighter and more reasonable bound for the minimum mean square error (MMSE) estimator over the wide range of SNRs compared to the ECRB and WCRB. Moreover, the trade-off between positioning and communication is revealed via changing critical parameters by simulations.

Semantic communications utilize the transceiver computing resources to alleviate scarce transmission resources, such as bandwidth and energy. Although the conventional deep learning (DL) based designs may achieve certain transmission efficiency, the uninterpretability issue of extracted features is the major challenge in the development of semantic communications. In this paper, we propose an explainable and robust semantic communication framework by incorporating the well-established bit-level communication system, which not only extracts and disentangles features into independent and semantically interpretable features, but also only selects task-relevant features for transmission, instead of all extracted features. Based on this framework, we derive the optimal input for rate-distortion-perception theory, and derive both lower and upper bounds on the semantic channel capacity. Furthermore, based on the$\beta $-variational autoencoder ($\beta $-VAE), we propose a practical explainable semantic communication system design, which simultaneously achieves semantic features selection and is robust against semantic channel noise. We further design a real-time wireless mobile semantic communication proof-of-concept prototype. Our simulations and experiments demonstrate that our proposed explainable semantic communications system can significantly improve transmission efficiency, and also verify the effectiveness of our proposed robust semantic transmission scheme.

We propose covert beamforming design frameworks for integrated radar sensing and communication (IRSC) systems, where the radar can covertly communicate with legitimate users under the cover of the probing waveforms without being detected by the eavesdropper. Specifically, by jointly designing the target detection beamformer and communication beamformer, we aim to maximize the radar detection mutual information (MI) (or the communication rate) subject to the covert constraint, the communication rate constraint (or the radar detection MI constraint), and the total power constraint. For the perfect eavesdropper’s channel state information (CSI) scenario, we transform the covert beamforming design problems into a series of convex subproblems, by exploiting semidefinite relaxation, which can be solved via the bisection search method. Considering the high complexity of iterative optimization, we further propose a single-iterative covert beamformer design scheme based on the zero-forcing criterion. For the imperfect eavesdropper’s CSI scenario, we develop a relaxation and restriction method to tackle the robust covert beamforming design problems. Simulation results demonstrate the effectiveness of the proposed covert beamforming schemes for perfect and imperfect CSI scenarios.

In this paper, we propose joint beamforming and photo-detector (PD) orientation (BO) optimization schemes for mobile visible light communication (VLC) with the orientation adjustable receiver (OAR). Since VLC is sensitive to line-of-sight propagation, we first establish the OAR model and the human body blockage model for mobile VLC user equipment (UE). To guarantee the quality of service (QoS) of mobile VLC, we jointly optimize BO with minimal UE the power consumption for both fixed and random UE orientation cases. For the fixed UE orientation case, since the transmit beamforming and the PD orientation are mutually coupled, the joint BO optimization problem is nonconvex and intractable. To address this challenge, we propose an alternating optimization algorithm to obtain the transmit beamforming and the PD orientation. For the random UE orientation case, we further propose a robust alternating BO optimization algorithm to ensure the worst-case QoS requirement of the mobile UE. Finally, the performance of joint BO optimization design schemes are evaluated for mobile VLC through numerical experiments.

In this study, a new class of median partition color-shift keying (MPCSK) constellation is developed for visible light communication that is based on the median partition triangle and allocates the symmetrical symbol on the medians. We study the MPCSK constellation expanding from a two-dimensional (2D) triangle intensity plane to three-dimensional (3D) multiple intensity planes under the target color constraint. Furthermore, we design the optimal problem of the 3D MPCSK constellation to improve the error performance. Finally, a joint coded-modulation (JCM) scheme, which is based on the MPCSK constellation in 3D intensity space and flnite-state machines to obtain a high coding gain, is proposed. Our simulation results indicate that the proposed JCM scheme performs better than the standard CSK and other existing methods under the constraint of the target color.

Abstract The working face is the most dangerous place of coal mines, and it is difficult (even impossible) to replenish energy by replacing batteries of sensor nodes in the working faces. This paper presents a wireless charging method for this scenario that is composed of two sub methods called mining charging and maintaining charging, respectively. Mining charging provides opportunistic charging services during the process of coal cutting through two airborne Mobile Chargers (MCs) installed on the two ends of the shearer and two portable MCs carried by shearer drivers. Maintaining charging provides opportunistic charging services for nodes in the charging radius when scraper conveyor repairmen and hydraulic support repairmen check or repair equipment, with each repairman carrying one portable MC. Simulation results show that both mining charging and maintaining charging can give energy replenishment for nodes in coal faces. When charging power of MCs is greater than or equal to 5.2 W, the first row of nodes can work sustainably. If the energy requirements of the second row of nodes are also met, the charging power of MCs cannot be less than 12 W.

In this article, we consider covert beamforming design for intelligent reflecting surface (IRS)-assisted Internet-of-Things (IoT) networks, where Alice utilizes IRS to covertly transmit a message to Bob without being recognized by Willie. We investigate the joint beamformer design of Alice and IRS to maximize the covert rate of Bob when the knowledge about Willie’s channel state information (WCSI) is perfect and imperfect at Alice, respectively. For the former case, we develop a covert beamformer under the perfect covert constraint by applying semidefinite relaxation. For the latter case, the optimal decision threshold of Willie is derived, and we analyze the false alarm and the missed detection probabilities. Furthermore, we utilize the property of the Kullback–Leibler divergence to develop the robust beamformer based on a relaxation,$S$-Lemma, and alternate iteration approach. Finally, the numerical experiments evaluate the performance of the proposed covert beamformer design and robust beamformer design.

In this paper, we investigate an integrated visible light positioning and communication (VLPC) system with a single LED-lamp. First, by leveraging the fact that the VLC channel model is a function of the receiver’s location, we propose a system model that estimates the channel state information (CSI) based on the positioning information without transmitting pilot sequences. Second, we derive the Cramer-Rao lower bound (CRLB) on the positioning error variance and a lower bound on the achievable rate with on-off keying modulation. Third, based on the derived performance metrics, we optimize the power allocation to minimize the CRLB, while satisfying the rate outage probability constraint. To tackle this non-convex optimization problem, we apply the worst-case distribution of the Conditional Value-at-Risk (CVaR) and the block coordinate descent (BCD) methods to obtain the feasible solutions. Finally, the effects of critical system parameters, such as outage probability, rate threshold, total power threshold, are revealed by numerical results.

In this paper, we investigate the optimal probabilistic constellation shaping design for covert communication systems from a practical view. Different from conventional covert communications with equiprobable constellations modulation, we propose non-equiprobable constellations modulation schemes to further enhance the covert rate. Specifically, we derive covert rate expressions for practical discrete constellation inputs for the first time. Then, we study the covert rate maximization problem by jointly optimizing the constellation distribution and power allocation. In particular, an approximate gradient descent method is proposed for obtaining the optimal probabilistic constellation shaping. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes a lower bound on the achievable rate where the optimal probabilistic constellation shaping problem can be solved efficiently using the Frank-Wolfe method. Extensive numerical results show that the optimized probabilistic constellation shaping strategies provide significant gains in the achievable covert rate over the state-of-the-art schemes.

In this paper, we investigate the performance of a practical aggregated LiFi-WiFi system with the discrete constellation inputs from a practical view. We derive the achievable rate expressions of the aggregated LiFi-WiFi system for the first time. Then, we study the rate maximization problem via optimizing the constellation distribution and power allocation jointly. Specifically, a multilevel mercy-filling power allocation scheme is proposed by exploiting the relationship between the mutual information and minimum mean-squared error (MMSE) of discrete inputs. Meanwhile, an inexact gradient descent method is proposed for obtaining the optimal probability distributions. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes the lower bound of the achievable rate where the optimal power allocation can be obtained in closed forms and the constellation distributions problem can be solved efficiently by Frank-Wolfe method. Extensive numerical results show that the optimized strategies are able to provide significant gains over the state-of-the-art schemes in terms of the achievable rate.

Indoor location-based services (LBS) are widely used in large-scale indoor buildings, such as high-rise hospitals and multi-story shopping malls. At the same time, location privacy protection in such three-dimensional (3D) space has recently attracted considerable attention. Currently, most existing location privacy protection schemes focus on two-dimensional (2D) location protection and fail to prevent location inference attacks when the user’s location data include height dimension, i.e., 3D geolocation. Enlightened by the concept of differential privacy, in this paper we first study the impact factors of the degree of indistinguishability of 3D geolocations. Then, we quantify location privacy for LBS applications in the 3D space with geo-indistinguishability (3D-GI) rigorously and provably. We develop a mechanism of three-variates Laplacian to generate perturbed locations considering the locations’ X, Y, and Z-coordinates simultaneously, guaranteeing geo-indistinguishability. Furthermore, the discretization noise-adding mechanism is studied to satisfy geo-indistinguishability in the 3D space under the finite precision of hardware/devices. Considering the discretized mechanism can only satisfy geo-indistinguishability in finite 3D space and users visit the limited regions, we further study the truncation of the Laplacian mechanism to limit the generated perturbed locations within a specific region. Simulation results demonstrate that the proposed 3D-GI outperforms the benchmarks while guaranteeing privacy regardless of the adversary’s prior knowledge.

Visible light communication (VLC) is seen as a potential access option for fifth-generation (5G) wireless communication (Wanget al., 2014) and (Ayyashet al., 2016) and beyond 5G (Strinatiet al., 2019). A reliable VLC system benefits from an accurate estimate of the receiver’s position and orientation. In many cases, the orientation of the receiver is estimated with an external orientation estimation device. However, these devices generally suffer from drift and misalignment, causing an uncertainty in the orientation presented to the receiver. Hence, the external device can only provide a probability distribution of the orientation to the position estimator, which can be used as prior information for the position estimation. Since the orientation of a receiver greatly affects the performance of a visible light system, the orientation uncertainty will degrade the performance of standard positioning algorithms, implying it should be taken into account when designing a robust positioning algorithm. In this paper, we design an received signal strength (RSS)-based hybrid position and orientation estimation algorithm using the hybrid maximum likelihood (ML)/maximuma posteriori(MAP) (HyMM) principle for a multiple LEDs - multiple photodiodes (PDs) (MLMP) system to take into account the presence of prior information on the orientation. The proposed HyMM estimator is compared with three existing estimators, i.e., the simultaneous position and orientation (SPO) estimator, the misspecified maximum likelihood (MML) estimator and the first-order-approximation-based positioning algorithm, subject to the orientation uncertainty. Further, in order to analytically assess the performance of the proposed estimator, the theoretical lower bound on the mean squared error (MSE), i.e. the hybrid Cramér-Rao bound (HCRB) for HyMM is derived. Computer simulations show an asymptotic tightness between the performance of the estimator and its associated theoretical lower bound.

The bound of the information transmission rate of direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) for visible light communication (VLC) with finite-alphabet inputs is yet unknown, where the corresponding spectral efficiency (SE) and energy efficiency (EE) stems out as the open research problems. In this paper, we derive the exact achievable rate of the DCO-OFDM system with finite-alphabet inputs for the first time. Furthermore, we investigate SE maximization problems of the DCO-OFDM system subject to both electrical and optical power constraints. By exploiting the relationship between the mutual information and the minimum mean-squared error, we propose a multi-level mercury-water-filling power allocation scheme to achieve the maximum SE. Moreover, the EE maximization problems of the DCO-OFDM system are studied, and the Dinkelbach-type power allocation scheme is developed for the maximum EE. Numerical results verify the effectiveness of the proposed theories and power allocation schemes.

Abstract It is of great importance to power the nodes of the rechargeable wireless sensor network to detect events continuously in the area of interest. This paper proposes a joint data transmission and on‐demand charging algorithm based on the space correlation. The new algorithm optimises the event detection, data forwarding and node charging jointly to improve the charging efficiency. First, the active nodes participating in the event detection are selected using an improved iterative node selection method to reduce the number of nodes working concurrently. Then, the greedy data transmission scheme based on grid partition is proposed to transmit the observed data to the sink node. Finally, the nodes in the networks are charged using the on‐demand charging method based on grid partition, which greatly decreases the charging frequency and energy loss of the mobile charger. The simulation results demonstrate that the proposed method has superior performance in the distance travelled by the mobile charger, the energy utilisation, the average energy consumption of the mobile charger and the node charging latency.

In this paper, we consider a common unicast beamforming network where Alice utilizes the communication to Carol as a cover and covertly transmits a message to Bob without being recognized by Willie. We investigate the beamformer design of Alice to maximize the covert rate to Bob when Alice has either perfect or imperfect knowledge about Willie's channel state information (WCSI). For the perfect WCSI case, the problem is formulated under the perfect covert constraint, and we develop a covert beamformer by applying semidefinite relaxation and the bisection method. Then, to reduce the computational complexity, we further propose a zero-forcing beamformer design with a single iteration processing. For the case of the imperfect WCSI, the robust beamformer is developed based on a relaxation and restriction approach by utilizing the property of Kullback-Leibler divergence. Furthermore, we derive the optimal decision threshold of Willie, and analyze the false alarm and the missed detection probabilities in this case. Finally, the performance of the proposed beamformer designs is evaluated through numerical experiments.

Most nodes of Mine Internet of Things (Mine IoT) are powered by batteries, and wireless charging using mobile chargers (MCs) is an effective way to make nodes work sustainably. A novel hybrid charging method combining the controlled and opportunistic MCs (C&O charging) is proposed in this study. Workers (such as the repairmen and gas inspectors) carrying portable chargers are proposed to be opportunistic MCs to provide an incidental charging service for the surrounding rechargeable Mine IoT nodes while doing its own work to reduce the payload of controlled MCs. The hybrid charging model based on the incidental charging ability of the opportunistic MC is constructed and the scheduling strategy of the controlled MC and the queueing management scheme of the charging request are also proposed. The simulation results indicate that the power demands of the majority of the nodes in the maintenance areas can be met or partially met by opportunistic MCs and the charging time of C&O charging is greatly decreased compared to that of only using controlled MCs.

Visible light communication (VLC) is seen as a supplement for fifth-generation (5G) wireless communication in short-range high data rate communication applications [1]. A reliable VLC system relies on an accurate estimate of the position and orientation of the receiver, which corresponds to the six-dimensional positioning problem mentioned in [2]. In this paper, we investigate the simultaneous position and orientation estimation (SPO) problem using received signal strength (RSS), for a visible light system containing multiple LEDs and multiple photodiodes (PDs) (MLMP). Although in general, the position and orientation of the receiver can be represented by a vector and a rotation matrix, respectively, the constraints imposed by the rotation matrix make the numerical optimization in the estimation process cumbersome, e.g, the commonly used constrained optimization method is often very complex and non-robust. Therefore, in this paper, we design two SPO algorithms using the principle of optimization on manifolds, which alleviates the constraints from the rotation matrix. In addition, we propose an initialization algorithm, based on the direct linear transformation (DLT) principle, to obtain an initial estimate in closed-form for the iterative algorithms. To evaluate the performance of the proposed RSS-based SPO algorithms, we derive the Cramer-Rao bound (CRB). In particular, the orientation error component of the CRB corresponds to the intrinsic CRB or the CRB on manifolds, which measures the error in the estimated rotation matrix in a physically meaningful way. Finally, computer simulations show an asymptotic tightness between the performance of the proposed algorithms and the theoretical lower bound, demonstrating the effectiveness of the proposed solutions.

Run-length limited (RLL) codes can facilitate reliable data transmission and provide flicker-free illumination in visible light communication (VLC) systems. We propose novel high-rate RLL codes, which can improve error performance and mitigate flicker. Two RLL coding schemes are developed by designing the finite-state machine to further enhance the coding gain by improving the minimum Hamming distance and using the state-splitting method to realize small state numbers. In our RLL code design, the construction of the codeword set is critical. This codeword set is designed considering the set-partitioning algorithm criterion. The flicker control and minimum Hamming distance of the various proposed RLL codes are described in detail, and the flicker performances of different codes are compared based on histograms. Simulations are conducted to evaluate the proposed RLL codes in on-off keying modulation VLC systems. Simulation results demonstrate that the proposed RLL codes achieve superior error performance to the existing RLL codes.

The aggregated visible light communication (VLC) and radio frequency (RF) system, which can be viewed as a heterogeneous multi-input-multi-output system, can improve data rate compared to the conventional RF communication systems. In this paper, we first develop optimal power allocation schemes for the aggregated VLC-RF systems for the single and the multi-light-emitting diode scenarios under different dimming control setups. Moreover, we study the energy efficiency maximization problem of the considered system with the minimum rate requirement, transmitted power constraint, and the dimming control consideration which is non-convex. By using the Dinkelbach-type algorithm, we tackle this problem by solving a sequence of convex problems which converges to the global solution. Finally, the effect of critical parameters, such as total power threshold, dimming level, and bandwidths, are revealed by some selected numerical results.

In this study, we investigated the nonorthogonal multiple access (NOMA) for visible light communication (VLC) Internet of Things (IoT) networks and provided a promising system design for 5G and beyond 5G applications. Specifically, we studied the capacity region of a practical uplink NOMA for multiple IoT devices with discrete and continuous inputs, respectively. For discrete inputs, we proposed an entropy approximation method to approach the channel capacity and obtain the discrete inner and outer bounds. For the continuous inputs, we derived the inner and outer bounds in closed forms. Based on these results, we further investigated the optimal receiver beamforming design for the multiple access channel (MAC) of VLC IoT networks to maximize the minimum uplink rate under receiver power constraints. By exploiting the structure of the achievable rate expressions, we showed that the optimal beamformers are the generalized eigenvectors corresponding to the largest generalized eigenvalues. Numerical results show the tightness of the proposed capacity regions and the superiority of the proposed beamformers for VLC IoT networks.

The nodes of the mine Internet of things (MIoT) are powered by batteries, for which the wireless charging is essential for their continuous and stable operation. This study proposed a method of directional mobile charging for the MIoT based on smart antenna, in which the mobile chargers charge the nodes need power not only when stationary, but also when moving. The theoretical calculation method of the charged energy is studied and established its approximate calculation algorithm, which can greatly reduce the computational complexity, based on the discretised model of effective charging distance of smart antenna. Then, the method for estimating the residual energy of the nodes was designed, and the upper bound of the transmitting power of the mobile charger was determined. The results of the simulation experiments indicated that this method has high charging efficiency and can meet the power demand of MIoT nodes.

In this paper, we focus on the fundamental issues of non-orthogonal multiple access (NOMA) visible light communication (VLC) networks: achievable rates and optimal power allocation schemes for both static and mobile users. First, we derive both a lower bound and an upper bound of the achievable rates with closed-form expressions for static users in NOMA VLC networks. With the derived lower bound, we minimize transmit power under the minimum rate requirements and individual light emitting diodes (LED) power constraints, which turns out to be NP-hard. By exploiting the semidefinite relaxation (SDR) technique, the optimal power allocation scheme can be obtained by solving a convex semidefinite program (SDP). Second, we develop an optimal power allocation scheme for mobile users. Due to users' movement, the estimated channel state information (CSI) may be inaccurate. We first characterize the CSI uncertainties as ellipsoidal regions, and derive a lower bound of the achievable rate expression. Then, we study the transmit power minimization problem for mobile users, which is non-convex. By applying S-lemma and SDR, the transmit power minimization problem can be reformulated as a convex SDP. Simulation results are presented to verify the effectiveness and robustness of the proposed power allocation schemes.

In this paper, we investigate the channel capacity region of interference channel and develop both centralized and distributed interference management schemes for visible light communication (VLC) networks. For a typical multiuser and multi-LED scenario, we derive both discrete inner and outer bounds of the channel capacity region, and such a proposed inner bound is numerically shown to be the highest among the existing inner bounds. Moreover, with continuous input signals, we develop the channel capacity region bounds in a closed form, termed (α, β, γ) (ABG) inner bound and ABG outer bound, which are tight for the large amplitude-to-variance ratio. Then, based on the derived ABG inner bounds, we investigate a centralized beamforming design problem to minimize the total transmit power under three practical constraints: peak optical power, average optical power, and average electrical power. By utilizing semidefinite relaxation technique, we reformulate this NP-hard problem as a convex semidefinite program and obtain the optimal beamformers. Furthermore, to reduce the cost of channel station information exchange, we propose a distributed coordinated interference management scheme by adopting the alternating direction method of multipliers method. Finally, numerical results are presented to evaluate the performance of the proposed interference management schemes in VLC networks.

In this paper, we investigate a novel simultaneous lightwave information and power transfer (SLIPT) in visible light communication (VLC) systems, where a photo diode (PD) and a solar panel are utilized as the information receiver and the energy harvester, respectively. By systematically analyzing both the information receiver and the energy harvester, we obtain the explicit expressions to characterize the illumination-rate-energy region. Based on the derived expressions, we investigate the downlink unicast transmission of multi-LED multi-user SLIPT VLC networks, and study the total transmit power minimization problem under the rate requirements, the minimum energy harvesting requirements, and dimming control constraints. To solve such non-convex problem, we exploit the semidefinite relaxation (SDR) technique and relax the problem into a convex problem, which can be efficiently solved via interior-point methods. Moreover, for the sake of users' fairness, we further investigate the beamformer design to maximize the minimal rate under both minimum energy harvesting and dimming control constraints. Finally, the numerical results are provided to evaluate the proposed SLIPT system.

Abstract Two‐way exclusive OR (XOR) relay can enable hidden nodes to exchange data with low delays and high data rate, while keeping signal processing simple. In this paper, we analyse practical two‐way XOR relaying systems, where finite relay buffer, non‐negligible signalling overhead, and lossy wireless channels are all captured. A two‐layer model is developed to characterise such practical two‐way relay systems, which is then reformulated into a Markov process after we project and combine inter‐layer state transitions of the two‐layer model. Using Markov techniques, we evaluate the steady state probabilities of the Markov process and, in turn, the key performance measures of two‐way XOR relaying, such as throughput, delay, and packet loss. The accuracy of our model is validated by simulations. Our model can also be used as an online tool to configure the buffer resources, adapting to wireless channel conditions and signalling requirements. Copyright © 2016 John Wiley & Sons, Ltd.

The novel coal-gas dangerous-level prediction model established has advantages of the EA and BP neural nets, and overcomes the shortcomings of misreport and missing-report of others. This approach can accurately capture the complicated relationships among feature values of coal-gas outbursts and dangerous circumstances. We considered the characteristic of coal-gas outburst carefully, combining with the raw data of coal-gas monitor system in the Daping colliery and the 10thcolliery of Pingdingshan Company as well as real-time samples of accidents, and selected pattern sets to train the proposed model and generate the corresponding rules for prediction. Results show that the ENN has better performance than the ANN or the traditional method used individually, and enhances the practical techniques for prediction of coal and gas in coal mine to guarantee safety.