Ultra-Wideband (UWB) technology enables high-precision positioning in underground subway environments with poor GNSS signal coverage, but ranging measurements often suffer from data loss due to occlusions, multipath effects, and ranging failures. While recent studies have focused on imputing missing measurements using temporal modeling, they often overlook spatial factors such as anchor group vary and train motion patterns. To address this limitation, this paper proposes a novel approach for imputing missing UWB ranging data by jointly exploiting temporal dynamics and spatial constraints. A mask matrix is introduced to model data availability, and a spatio-temporal optimization framework is established to guide the imputation process. Based on this framework, a Dynamic Spatio-Temporal Missing Data Imputation Network (DSTMIN) is developed for robust localization in complex subway environments. In particular, DSTMIN leverages missing-aware attention and adaptive graph fusion, significantly enhancing its robustness to large missing blocks and non-random data loss. Simulation results demonstrate that DSTMIN outperforms state-of-the-art models, such as STGCN and GRIN, under various missing rates. Under a 40% ranging data missing ratio, DSTMIN reduces the root mean square error (RMSE) by 18.6% and 16.3% compared with STGCN and GRIN, respectively.

Accurate location information is an essential prerequisite for location-based service and operation safety in railways. Ultra wide band (UWB) fused with inertial measurement unit (IMU) has been thought as a promising localization technique for trains. However, due to trains’ high speed, ranging results from UWBs are seriously influenced by Doppler effect. Most existing work focused on Doppler effect mitigation in single round of communication, but ignored its negative impact on two-way time of flight (ToF) and fusion performance. In this paper, we propose an adaptive fusion model with ranging rectifications for high-speed trains. Firstly, two-way ToF ranging errors caused by Doppler effect are studied. Secondly, Doppler effect is mitigated effectively, with prior information of trains’ states in time series. Finally, an adaptive UWB/IMU fusion scheme is proposed, where residual errors of UWB observations after Doppler effect mitigation are modeled by a closed-form expression. Simulation results show that, regarding single time-slot localization, the proposed ranging rectification algorithm outperforms four typical algorithms, with gains of 78.42%, 60.22%, 58.09%, and 28.38%, in terms of RMSE. Regarding continuous localization with temporal correlation, the proposed adaptive fusion model achieves gains of 75.07%, 73.27%, 53.52% and 70.02%. Furthermore, its effectiveness is verified in practical platform.

Integrated sensing and communication (ISAC) has been esteemed as a pivotal driver for the next-generation wireless networks in achieving dual-function spectrum efficiency. In typical ISAC systems, user equipment and targets are treated as distinct entities, operating without interaction between the communication and sensing functionalities. However, in scenarios where the user and target align as a unified entity, current solutions underperform due to the coupled performance metrics involving the achievable rate and the sensing Cramér–Rao Bound (CRB). To address this issue, focusing on this particular scenario, this paper delves into the design of optimal transmission precoder and conducts a fundamental trade-off analysis between the achievable rate and the target sensing CRB. Firstly, we derive a compact expression of the CRB, characterized by the angle and delay parameters of the sensing channel, thereby obtaining the subsequent position error bound (PEB) by exploiting the inherent connection between the PEB and CRB. Next, we aim to design the optimal precoder matrix to minimize the PEB, subject to constrains on the minimum communication rate and total transmit power budget. By leveraging the structural properties of the precoder covariance matrix, we develop an efficient algorithm to devise a closed-form optimal precoder design. Numerical results demonstrate that our proposal yields a favorable CRB-Rate trade-off across various scenarios, closely aligning with the performance of semi-definite relaxation (SDR)-based optimization scheme.

Adaptive compressive spectrum sensing (ACSS) plays a vital role in cognitive radio networks due to its reduced sampling rate and power consumption. Most of the existing ACSS schemes focus on the improvement of spectrum sensing performance, but do not provide the deterministic assurance of sensing results. Hence, we propose an ACSS based on deterministic estimation model (ACSS-DEM) to provide the confidence level of the reconstructed signals. This is realized by deriving the closed-form expression of the cumulative distribution function (CDF) of reconstructed errors. Firstly, in each sensing interval of ACSS, we propose a novel signal reconstruction algorithm that incorporates the prior knowledge into ℓ2,1-norm minimization of block-sparse signals. Secondly, a prior knowledge refining strategy is designed through convex geometry theory to further improve the reconstruction accuracy. Finally, the CDF of reconstruction error is derived and regarded as a stopping criteria for observation sample collection. The experiment results demonstrate that the proposed ACSS-DEM delivers optimal spectrum-sensing performance, offering over a 12.2% improvement in detection performance at a false alarm probability of 0.1, compared with other typical ACSS methods, e.g., ACSS-JL, ACSS-SOC, and ACSS-CV.

Since the waveforms of the signals received at adjacent times are different due to varying modulated signals, it is still a key challenge on how to describe temporal correlations precisely in compressive spectrum sensing (CSS). In this paper, we propose a higher-order cumulant-assisted CSS (HOC-CSS) algorithm, where the semantic correlation of adjacent received signals in higher-order domain is exploited. Firstly, the fourth-order cumulants of the reconstructed signals in the previous time are calculated. Secondly, the fourth-order cumulants are integrated with$\ell _{p}$-norm minimization to estimate the current spectrum signals. Furthermore, a two-branch CSS scheme is proposed to eliminate the effect causing by adjacent signals under different modulations. The HOC-CSS is performed in one branch and a noninformative CSS algorithm is performed in another branch, thereby two different reconstructed signals are obtained. The Euclidean distances between the measurements and the projection of reconstructed signals are calculated. The final output is the estimated signal with smaller Euclidean distance. The experimental results demonstrate that the proposed algorithm has more than 8% and 10% detection performance improvement at false alarm probability 0.1, under compressive ratio of 0.4 and 0.5 respectively, compared with the state-of-the-art CSS schemes, e.g., HMCPI-CSS, Weighted$\ell _{1}$-CSS, AR-IRLS, and IP-IRLS.

Recently, with the development of automatic driving, high-precision localization has become indispensable for safety. The inertial measurement unit (IMU) integrated with ultra-wideband (UWB) technology has been thought as a promising scheme by the company Humatics for trains. Generally, the spaces of transportation are extended along a long path with flexural tunnels and sideway obstacles around, where non-line-of-sight (NLoS) propagation happens frequently. However, a large interval deployment of UWB anchors along such narrow path with NLoS interference will dramatically decrease localization performance. Hence, we propose a robust algorithm to mitigate the NLoS interference. Firstly, a joint LoS/NLoS detection and mitigation algorithm is proposed to improve the ranging accuracy of mobile target with UWB tags under mixed LoS/NLoS interference. Secondly, we improve the conventional Kalman Filter (KF) algorithm with forward-backward propagation to integrate temporal correlations further. Finally, a comprehensive fusion localization scheme with ranging error mitigation and improved KF is proposed. The simulation results show that, regarding the static localization, the proposed joint LoS/NLoS detection and mitigation algorithm outperforms five other typical NLoS elimination algorithms, and achieves 48.2%, 62.4%, 45.5%, 69.9% and 50.2% gains in terms of root mean square error (RMSE), under NLoS scenarios with a mean ranging error of 0.5 m. Regarding the dynamic localization, compared with two other typical KF localization algorithms, the proposed localization scheme achieves 20.9% and 14.5% localization accuracy improvements in LoS scenarios, 45.7% and 47.2% improvements in mixed LoS/NLoS scenarios, respectively. Furthermore, the effectiveness of the proposed scheme is also verified in our testing platform.

Compressive spectrum sensing (CSS) is regarded as one of the promising techniques to detect wideband spectrum holes. How to formulate an accurate prior knowledge in CSS is still an important and open topic. In this paper, we propose a novel CSS algorithm with cascade forward-backward propagation and prior knowledge refining. Firstly, the wideband signal reconstruction model is formulated as a$\ell _{p}$-norm ($0 < p < 1$) nonconvex optimization problem where the temporal correlation between continuous observed spectrum signals is exploited to improve the reconstruction accuracy of the current spectrum signal, and such nonconvex optimization problem is solved by iteratively reweighted least squares algorithm. Then, the multiple reconstructed versions of the current spectrum signal obtained by forward-backward propagation are fused into a prior. Finally, such fused prior is modified adaptively in each iteration, and thus significantly improves the accuracy of spectrum decision and speeds up the convergence. The simulation results show that compared with other CSS schemes based on weighted$\ell _{1}$minimization,$\ell _{1}$-$\ell _{1}$minimization,$\ell _{1}$-$\ell _{2}$minimization, and maximizing correlation, the proposed scheme has more than 7% and 10% detection performance improvement when the probability of false alarm is 10%, under received signal-to-noise ratio as −10 dB and −5 dB, respectively.

Nowadays, unmanned aerial vehicles (UAVs), empowered with the capability of high-definition image transmission, are used to capture the rapidly changing physical environment by leveraging its high flexibility to reconstruct an immersive realistic virtual environment for metaverse users. In this paper, we consider a novel UAV-assisted image acquisition system where a UAV is dispatched to take off from an initial location to capture real-time images of multiple ground targets and then transfer the captured images back to the ground user for virtual environment reconstruction. We aim to minimize the time for the UAV to complete the image acquisition task by optimizing the three-dimensional UAV trajectory under the constraints of image quality, information causality and energy consumption. To this end, we first formulate the investigated scenario into a mixed integer optimization problem, which, however, is difficult to solve due to the infinite time-varying variables closely coupled with each other. Then, a three-stage progressive algorithm is proposed to obtain an efficient solution to the formulated mixed integer optimization problem, where the constraints of image quality, information causality and energy consumption can be sequentially satisfied. Finally, comprehensive performance evaluation is conducted to verify the effectiveness of the proposed three-stage progressive trajectory design algorithm, and the results show that the proposed algorithm significantly outperforms the benchmark schemes.

In dense traffic cases, the channels among vehicles and infrastructures in cellular vehicle-to-everything (C-V2X) network are likely to be congested. Vehicles can select spectrum resources autonomously by adopting sensing-based semipersistent scheduling scheme, which may result in frequent packet collisions due to the random selection process, especially when spectrum resources are limited. So far, there still lacks a definite stochastic expression to characterize the channel selection process for vehicles in C-V2X. In this article, we propose a novel deep reinforcement learning (DRL)-based dynamic spectrum access (DSA) algorithm for C-V2X via imitating Indian buffet process (IBP), aiming to meet the vehicles’ strict communication requirements on high-transmission rate and low-collision probability. First, we explore the correlations among historical spectrum access data to acquire the channel availability list. Then, we exploit DRL to achieve distributive DSA among vehicles. Specifically, the channel state prediction for the distributive DSA is facilitated via imitating the classic IBP, and the spectrum access decisions are made by leveraging the deep$Q$-learning network combined with long short-term memory technique. Finally, comprehensive simulation results are presented to show that the proposed algorithm can improve the transmission rate by 15% and reduce the collision probability by 12% with a false alarm probability of 0.1 compared with other spectrum access methods.

In this paper, we consider a new unmanned aerial vehicle (UAV)-assisted oblique image acquisition system where a UAV is dispatched to take images of multiple ground targets (GTs). To study the three-dimensional (3D) UAV trajectory design for image acquisition, we first propose a novel UAV-assisted oblique photography model, which characterizes the image resolution with respect to the UAV’s 3D image-taking location. Then, we formulate a 3D UAV trajectory optimization problem to minimize the UAV’s traveling distance subject to the image resolution constraints. The formulated problem is shown to be equivalent to a modified 3D traveling salesman problem with neighbourhoods, which is NP-hard in general. To tackle this difficult problem, we propose an iterative algorithm to obtain a high-quality suboptimal solution efficiently, by alternately optimizing the UAV’s 3D image-taking waypoints and its visiting order for the GTs. Numerical results show that the proposed algorithm significantly reduces the UAV’s traveling distance as compared to various benchmark schemes, while meeting the image resolution requirement.

The explosive demands for high quality mobile video services have caused heavy overload to the existing cellular networks. Although the small cell has been proposed to alleviate such a problem, the network operators may not be interested in deploying numerous base stations (BSs) due to expensive infrastructure construction and maintenance. The unmanned aerial vehicles (UAVs) can provide the low-cost and quick deployment, which can support high-quality line-of-sight communications and have become promising mobile BSs. In this paper, we propose a quality-of-experience (QoE)-driven UAV-enabled pseudo-analog wireless video broadcast scheme, which provides mobile video broadcast services for ground users (GUs). Due to limited energy available in UAV, the aim of the proposed scheme is to maximize the minimum peak signal-to-noise ratio (PSNR) of GUs’ video reconstruction quality by jointly optimizing the transmission power allocation strategy and the UAV trajectory. Firstly, the reconstructed video quality at GUs is defined under the constraints of the UAV's total energy and motion mechanism, and the proposed scheme is formulated as a complex non-convex optimization problem. Then, the optimization problem is simplified to obtain a tractable suboptimal solution with the help of the block coordinate descent model and the successive convex approximation model. Finally, the experimental results are presented to show the effectiveness of the proposed scheme. Specifically, the proposed scheme can achieve over 1.6 dB PSNR gains in terms of GUs’ minimum PSNR, compared with the state-of-the-art schemes, e.g., DVB, SoftCast, and SharpCast.

Cognitive radio (CR) has been proposed to mitigate the spectrum scarcity issue to support heavy wireless services on sub‐3GHz. Recently, broadband spectrum sensing becomes a hot topic with the help of compressive sensing technology, which will reduce the high‐speed sampling rate requirement of analog‐to‐digital converter. This paper considers sequential compressive spectrum sensing, where the temporal correlation information between neighboring compressive sensing data will be exploited. Different from conventional compressive sensing, the previous compressive sensing data will be fused into prior knowledge in current spectrum estimation. The simulation results show that the proposed scheme can achieve 98.7% detection probability under 3.5% false alarm probability and performs the best compared with the typical BPDN and OMP schemes.

The optimal spectrum access strategy is investigated for multi-user multi-channel scenario in cognitive radio networks. At first, an online learning method based on Dirichlet Process is adopted to predict the channel usage based on ACK/NACK feedbacks, which can avoid frequent information exchange among users. Based on the prediction result, the delay performance can be computed when a user transmits certain percentage of multimedia packets on a specific channel. Second, the packet delivery ratio (PDR) is derived from the prediction result of channel usage to reflect the accessing competition among multiple users. Finally, the quality of service (QoS) of multimedia applications is defined as the joint delay and throughput performances. Moreover, a dynamic spectrum access scheme is proposed to optimize the QoS metrics. The simulation results demonstrate that the QoS and the peak-signal-to-noise ratio (PSNR) of the proposed spectrum access algorithm outperform the three existing spectrum access algorithms, i.e., cognitive cross-layer algorithm, dynamic learning algorithm, and dynamic least interference algorithm. The proposed algorithm achieves more than 21.8%, 5.4%, and 3.9% PDR enhancement and over 3.23 dB, 0.82 dB, and 0.50 dB PSNR gains, compared with those three algorithms, given the transmission power as 10, 20, and 30 units, respectively.

The convergence of mobile communications and cloud computing facilitates the cross-layer network design and content-assisted communication. Mobile video broadcasting can benefit from this trend by utilizing joint source-channel coding and strong information correlation in clouds. In this paper, a knowledge-enhanced mobile video broadcasting (KMV-Cast) is proposed. The KMV-Cast is built on a linear video transmission instead of a traditional digital video system, and exploits the hierarchical Bayesian model to integrate the correlated information into the video reconstruction at the receiver. The correlated information is distilled to obtain its intrinsic features, and the Bayesian estimation algorithm is used to maximize the video quality. The KMV-Cast system consists of both likelihood broadcasting and prior knowledge broadcasting. The simulation results show that the proposed KMV-Cast scheme outperforms the typical linear video transmission scheme called Softcast, and achieves 8 dB more of the peak signal-to-noise ratio (PSNR) gain at low-SNR channels (i.e., -10 dB), and 5 dB more of PSNR gain at high-SNR channels (i.e., 25 dB). Compared with the traditional digital video system, the proposed scheme has 7 dB more of PSNR gain than the JPEG2000 + 802.11a scheme at a 10-dB channel SNR.

It is predicted that the global mobile data traffic will increase nearly eightfold in the next five years, and the cloud applications will consist of 90 percent of total mobile data traffic by the end of 2019. There is no doubt that the increasing high quality video requirements and huge similar information stored in cloud servers enforce to setup a new communication paradigm. In this paper, we will take a brief review over the knowledge-enhanced mobile video broadcasting (KMV-Cast) framework, and analyze its performance with imperfect prior knowledge, which means that the prior knowledge will be transmitted through Gaussian channel. The simulation results have shown that KMV-Cast with imperfect prior knowledge performs the worst compared with the other two schemes in terms of PSNR. It indicates that the prior knowledge is very important for the video reconstruction in KMV-Cast. Meanwhile, with the SNR increasing for prior knowledge transmission, the performance still performs poor and has an optimum point.

In cognitive radio networks (CRNs), the spectrum utilization can be dramatically improved with the secondary users (SUs) accessing the unoccupied licensed channels opportunistically. However, how to fully utilize the spectrum holes to meet the quality of service requirements of SUs is still an open issue. In this paper, we focus on multi-user, multi-channel case, and analyze the stable channel state after allocating packets over a licensed channel. We first assume that the SUs' packet arrival rate obeys Poisson distribution, and the lost packets will be retransmitted with exponential backoff delay. Then, we analyze the stable channel state when a SU selects and allocates certain percentage of its packets over one channel. The theoretic analysis shows that such stable channel state can be solved by the steady-state equations. Based on such stable channel state analysis, we propose a novel greedy packet allocation scheme in multi-user and multi-channel S-ALOHA system. The proposed packet allocation scheme will obtain a maximal spectrum utilization, which will support more SUs to share the spectrum holes or cause less access conflict with PUs. In the simulation part, we assume the PUs' channel access pattern follows Markov model, and study the spectrum efficiency after packet loading over licensed channels and make comparisons with different packet allocation schemes. The simulation results show that the proposed packet allocation scheme outperforms other related works in terms of successful packet delivery ratio, the number of collision packets, and spectrum efficiency.

In vehicular ad-hoc network (VANET), the reliability of communication is associated with driving safety. However, research shows that the safety-message transmission in VANET may be congested under some urgent communication cases. More spectrum resource is an effective way to solve transmission congestion. Hence, we introduce cognitive radio (CR) enabled VANET (CR-VANET), where CR device can detect possible idle spectrum for VANET communications and assist to timely broadcast safety-message. Given high-speed mobility of vehicles and dynamically-changing availability of channels, a novel prediction algorithm is proposed to pick out the channel with the greatest probability of availability, which can meet the quality of service (QoS) requirement of urgent communications and effectively avoid conflict with licensed users. Specifically, the spatiotemporal correlations among historical spectrum sensing data are exploited to form prior knowledge of channel availability probability, and Bayesian inference is used to derive posterior probability of channel availability. Comparing with other spectrum detection methods, the proposed algorithm has more than 8 percent detection performance improvement at false alarm probability 0.2, and thus can avoid access conflict with licensed users dramatically. Furthermore, the proposed algorithm always has larger packet reception probability (PRP) and lower transmission delay compared with conventional VANET broadcasting. Hence, the proposed algorithm can improve reliability of safety-message transmission and enhance driving safety significantly.

Multiple-input multiple-output (MIMO) is a promising way to increase link capacity and energy efficiency in the next generation communication systems. However, the benefits of such an approach depend on proper channel state information (CSI) availability at the transmitter. The CSI is usually estimated at the receiver and fed back to the transmitter through a band-limited channel. Thus, an efficient feedback scheme is needed. In this paper, a comprehensive Bayesian compressive sensing (BCS) based feedback mechanism is proposed for time-varying spatially and temporally correlated vector autoregression (VAR) wireless channel, and the feedback rate distortion function is derived in closed form in statistics. The proposed BCS feedback scheme utilizes the sparse CSI features and prior knowledge to significantly compress the dimensionality of the feedback CSI. Furthermore, the relationship between the feedback rate and downlink capacity is derived in closed form in statistics to guide rate-adaptive feedback in MIMO system. We find out that the ergodic downlink capacity of a user is determined only by its own feedback rate in the proposed feedback scheme. Theoretical and simulation results all show that the proposed feedback scheme can realize efficient, rate-adaptive feedback based on downlink capacity requirement, and the proposed feedback performance is superior to other related works.

Cognitive radio (CR) is a critical technology for improving spectrum utilization and solving the radio spectrum scarcity problem. In CR devices, spectrum sensing is important to implement opportunistic spectrum access. Many spectrum sensing schemes have been proposed, including uncooperative, cooperative, centralized, and distributed algorithms. However, they aimed to obtain a global consensus sensing result, which may not always be possible in large-scale cognitive radio networks (CRNs) due to heterogeneous spectrum availability in different areas. Hence, some new spectrum sensing schemes should be designed to discover idle heterogeneous spectrum in CRNs. In this paper, we propose an intelligent cooperative spectrum sensing algorithm based on a non-parametric Bayesian learning model, namely the hierarchical Dirichlet process, which groups spectrum sensing data without the need to know the number of hidden spectrum states, and discovers a common sparse spectrum within each group. Furthermore, a concisely distributed information exchange scheme is designed, where intra-cluster and inter-cluster spectrum information is shared for global spectrum cognition. Experimental results show that the proposed algorithm can exploit the spatial relationship among sensed data to achieve a better spectrum sensing performance in terms of detection probability and false alarm probability.

Recently, multimedia transmission over cognitive radio networks (CRNs) becomes an important topic due to the CR's capability of using unoccupied spectrum for data transmission. Conventional work has focused on typical quality-of-service (QoS) factors such as radio link reliability, maximum tolerable communication delay, and spectral efficiency. However, there is no work considering the impact of CR spectrum sensing frequency and packet-loading scheme on multimedia QoS. Here the spectrum sensing frequency means how frequently a CR user detects the free spectrum. Continuous, frequent spectrum sensing could increase the medium access control (MAC) layer processing overhead and delay, and cause some multimedia packets to miss the receiving deadline, and thus decrease the multimedia quality at the receiver side. In this research, we will derive the math model between the spectrum sensing frequency and the number of remaining packets that need to be sent, as well as the relationship between spectrum sensing frequency and the new channel availability time during which the CRN user is allowed to use a new channel (after the current channel is re-occupied by primary users) to continue packet transmission. A smaller number of remaining packets and a larger value of new channel availability time will help to transmit multimedia packets within a delay deadline. Based on the above relationship model, we select appropriate spectrum sensing frequency under single-channel case, and study the trade-offs among the number of selected channels, optimal spectrum sensing frequency, and packet-loading scheme under multi-channel case. The optimal spectrum sensing frequency and packet-loading solutions for multi-channel case are obtained by using the combination of Hughes-Hartogs and discrete particle swarm optimization (DPSO) algorithms. Our experiments of JPEG2000 packet-stream and H.264 video packet-stream transmission over CRN demonstrate the validity of our spectrum sensing frequency selection and packet-loading scheme.