This paper investigates the uplink massive grant-free random access in low-earth-orbit (LEO) satellite-based Internet-of-Things systems, where accurate joint activity detection and channel estimation (JADCE) is essential for reliable data recovery. However, the resource constraint and high-dynamic characteristic of LEO scenarios pose significant challenges to traditional JADCE schemes in terms of both estimation accuracy and computational complexity. To overcome these limitations, we propose a novel deep-unfolding JADCE framework based on the synergistic multi-stage optimization iterative shrinkage thresholding algorithm network, referred to as SMO-ISTA-Net, to facilitate efficient massive device access. Specifically, we first develop a synergistic attention module, where an inertia-guided optimization strategy is introduced into the gradient descent process to improve convergence stability and adaptability to fast-varying satellite channels. In order to mitigate the temporal feature inconsistency caused by asynchronous access and multi-path propagation, we further design a lightweight cross-attention mechanism that enables efficient channel feature fusion and facilitates multi-stage information interaction. Moreover, we propose a memory-enhanced proximal-mapping module that incorporates a high-throughput short-term memory mechanism into the unfolded structure, so as to significantly reduce information loss and maximize memory retention of the network. Extensive simulations under diverse LEO scenarios demonstrated that our scheme can achieve superior convergence speed, estimation accuracy, and preamble efficiency, while maintaining low computational complexity and short runtime, compared to the state-of-the-art model-driven JADCE schemes.

Massive machine-type communication in satellite networks presents significant challenges for random access systems, particularly due to the limited number of terminals that can successfully access the network concurrently. To address this issue, this paper proposes a novel binary circular polarization modulation based non-orthogonal multiple access slotted ALOHA (BCPM-NOMA-SA) scheme that fully exploits the polarization characteristics of satellite transmission. The proposed approach first achieves orthogonal signal separation in the polarization domain through BCPM. Subsequently, an equivalent noise model is established by analyzing the Cramér-Rao lower bound (CRLB) of channel estimation errors. Finally, the upper bound of system throughput is derived, providing a theoretical foundation for the synergistic gains of polarization and power domain multiplexing. Simulation results demonstrate that the proposed scheme achieves 20-35% throughput improvement compared to conventional NOMA-SA and BCPM-SA schemes, showing significant performance advantages.

Integrating orthogonal time frequency space (OTFS) modulation into low-Earth-orbit (LEO) satellite communication systems can significantly enhance the robustness against severe Doppler effects caused by fast time-varying channels. However, since the high-dynamic characteristic of terrestrial-to-satellite links considerably constrains the OTFS frame duration, the inevitable fractional Doppler shifts will result in the emergence of inter-Doppler interference (IDI), thereby degrading channel estimation performance. To tackle this challenge, we develop an efficient correlation information-aided channel estimation scheme based on Zadoff-Chu (ZC) sequences in the presence of fractional Doppler shifts. The proposed scheme enables a fast coarse estimation of channel parameters by leveraging the magnitude distribution regularity of the periodical correlation results of ZC pilot under IDI, and remodels channel estimation as a non-convex constrained least squares optimization problem to obtain the fine channel parameters for further resistance of the inter-path interference. Complexity analysis and simulation results validate that our scheme can attain a high estimation accuracy while achieving substantial reductions in both the peak-to-average power ratio and computational complexity, in comparison to the state-of-the-art ones.

In this paper, a multi-replicas independent Q-learning algorithm (MRIQL) assisted enhanced-energy-harvesting-based contention resolution diversity slotted ALOHA (EEH-CRDSA) random access scheme with transmission energy diversity is proposed for sixth-generation (6 G) ultra-massive machine type communications (umMTC) networks, which the RA scheme can be termed as MRIQL-EEH-CRDSA. The case is considered in which every MTC device (MTD) has a finite-sized battery that can be recharged from the Hybrid Access Point (HAP) in the charging subframe and with harvested energy from the circumambient surroundings probabilistically in the transmission subframe. Firstly, the proposed scheme takes full advantage of the difference in the harvested energy level of different replicas from a MTD to result in inter-slot received energy diversity. Subsequently, to overcome the RA congestion issue and to further improve system throughput, a joint optimization problem of the slot and energy levels pairing indexes of replicas is formulated and solved by using the MRIQL algorithm. Numerical simulations demonstrate that the MRIQL-EEH-CRDSA scheme achieves a throughput of 2.0 packets/slot, representing a 284.6 % improvement over conventional CRDSA.

Given the heterogeneous access requirements of massive devices in satellite Internet of Things (IoT), we propose a deep learning-assisted collaborative device activity detection scheme for grant-free random access (GF-RA). In the proposed scheme, the device activation probability is not required to be known in advance, since it can be accurately predicted by means of the designed sparsity estimation module. By leveraging the current activity information to optimize the network structure, we further develop a dual-sparse transformer architecture for the efficient identification of active devices, which can significantly improve the model generalization capability while reducing the computational redundancy. Simulation results reveal the feasibility of our scheme in large-scale GF-RA scenarios, and demonstrate its remarkable performance superiority in terms of successful detection probability over the state-of-the-art ones.

This work studies information freshness of a V2I status updating link in IoV. The status updating link is modeled as a multi-source Ber/Geo/1/1 non-preemptive or preemptive queue. We focus on statistical characteristics of the age of information (AoI) and peak AoI (PAoI). To fully track the AoI evolutions under non-preemptive and preemptive policies, Markov three-dimensional age process (3DAP) and two-dimensional age process (2DAP) are respectively introduced. Their first element is the AoI process; The second one stands for if an update of the concerned source is in transmission and its current age; The third element of 3DAP denotes if an update of another source is in transmission. An analytical approach for studying the AoIs and PAoIs in discrete-time multi-source systems is presented. By studying the state transitions, balance equations, and stationary distributions of 3DAP and 2DAP, analytical expressions of the distributions and averages of AoIs and PAoIs under both queueing policies are derived. Moreover, the optimal probabilistic update selection mechanism (PUSM) that maximizes overall freshness is derived in closed-form for the two-source case. Numerical results validate effectiveness of the theoretical analyses and reveal usefulness of the retransmission. It is found that in terms of improving the overall freshness, the PUSM should be designed to make effective update generation probabilities of sources as close as possible.

As an indispensable component of the 6G-enabled intelligent transportation systems, the satellite-ground integrated vehicular networks (SGIVN) have attracted widespread attention in recent years for its ability to provide continuous and ubiquitous connectivity services. However, in view of a huge number of access requirements from vehicle terminals and the restricted contention resources, the conventional random access (RA) schemes will suffer from severe overload issues when applied to the emerging SGIVN. To address this challenge, we propose a novel deep learning (DL) assisted collision detection and load estimation scheme to efficiently support massive access in the SGIVN. Specifically, a reliable RA preamble based on cyclically shifted Zadoff-Chu sequences is first designed as the precondition of collision detection, which can achieve an optimal performance trade-off between interference mitigation and user identification. By making full use of the intrinsic properties of preamble correlation results and the relevance analysis capability of attention mechanism, we further present a correlation feature extraction based deep RA collision detection framework embedded with a lightweight transformer network, thereby enabling the global dependencies of the few and important features associated with collided loads to be thoroughly acquired from the local correlation results with low overhead. Extensive simulation results validate the feasibility of our scheme in high-dynamic non-terrestrial network scenarios involving large-scale RA collisions, and demonstrate that it can obtain remarkably enhanced detection performance with short computational time, in comparison with state-of-the-art DL-based schemes.

To accommodate the proliferation of Internet-of-Things (IoT) applications, next-generation wireless communication networks, particularly the sixth-generation (6G), are expected to offer excellent support for the massive access of machine-type communication (MTC). In this paper, we investigate the grant-free random access (GFRA) employing orthogonal preambles in cell-free massive multiple-input multiple-output (mMIMO), which shows immense potential for enabling massive connectivity. In particular, we take into account the capture effect, defined as successful decoding despite preamble collisions, when the received signal-to-interference-plus-noise ratio (SINR) exceeds a predefined threshold. To this end, we develop an analytical framework to model GFRA with the capture effect adopting stochastic geometry. Subsequently, approximate analytical expressions for the received SINR and the access success probability for the typical GFRA frame structure are derived. Furthermore, leveraging these theoretical expressions, we formulate an optimization problem to determine the optimal preamble length that maximizes effective throughput. Simulation results validate the accuracy of our theoretical analyses and demonstrate the superior access performance of the optimized frame structure, whereas a frame structure with a constant preamble length does not consistently attain maximum effective throughput across varying user densities.

Satellite communications have been regarded as a promising solution to be incorporated in future Internet of Things (IoT) to support continuous and ubiquitous connectivity services. Constrained by limited contention resources, the conventional random access (RA) scheme will suffer from severe overload problem when applied to the emerging satellite-based IoT. Focusing on improving access efficiency in the massive and concurrent access scenarios, we propose a sample feature enhancement based collision detection and load estimation scheme with the aid of deep learning. Specifically, by leveraging the inherent characteristics of correlation results in case of preamble collision, a feature extraction method is first designed to precisely screen important sample features related to the current load information. Then, a multi-feature enhancement network with an adaptive neuron optimization strategy is further proposed to enable original 1D features mapped to a 2D domain, so as to improve the representation capability of the model while preventing overfitting. Simulation results validate the feasibility of our scheme in large-scale RA collision scenarios and demonstrate its remarkable performance superiority in terms of load estimation accuracy and collision detection probability over the state-of-the-art schemes.

Combining half-duplex (HD) and full-duplex (FD) is promising in improving the information transmission rate of relay channels. This work proposes a novel two-phase hybrid duplex scheme for Gaussian relay channel where the relay operates in FD mode for a fraction of time and only transmits information for the rest of time. The achievable rate of the proposed hybrid duplex scheme is characterized in detail. Based on the obtained result, a joint time division and power allocation problem is formulated to maximize the achievable rate. In particular, the formulated problem is solved through a two-step optimization method. Firstly, the optimal relay power allocation is obtained for given time division factors. Then, the achievable rate maximization problem is addressed by finding the optimal time division factors. The closed-form expression for the maximal achievable rate is derived for some specific cases. Numerical results show that the proposed two-phase hybrid duplex scheme significantly improves the achievable rate of Gaussian relay channel compared with existing benchmark schemes.

In this paper, we present a double‐intelligent reflecting surfaces (IRS)‐assisted multiuser secure system where the inter‐IRS channel is considered. In particular, we maximize the weighted sum secrecy rate of the system by jointly optimizing the beamforming vector for transmitted signal and artificial noise at the base station (BS) and the cooperative phase shifts of two IRSs, under the constraints of transmission power at the BS and the unit‐modulus phase shift of IRSs. To tackle the nonconvexity of the optimization problem, we first convert the objective function to its concave lower bound by utilizing a novel successive convex approximation technique, then solve the transformed problem iteratively by applying an alternating optimization method. The Lagrange dual method, Karush–Kuhn–Tucker conditions, and alternating direction method of multipliers are applied to develop a low‐complexity solution for each subproblem. Finally, simulation results are provided to verify the advantages of the cooperative double‐IRS scheme in comparison with the benchmark schemes.

Reconfigurable intelligent surface (RIS) is playing a growing and ever-more significant role in constructing six-generation (6G) wireless networks due to its properties of low-cost and easy-integration. Current studies about RIS-assisted communication generally assume the RISs are deployed at fixed position or devices, however with the rapid development of unmanned aerial vehicle (UAV) technology, this readily flying wireless access platform is increasingly used to realize reliable communication. If the RISs are mounted on random 3-D (three-dimension) mobile UAVs, how to investigate the uplink transmission of RIS-assisted UAV communication systems would be a great challenge. To resolve this open issue, we establish a novel theoretical model to analyze the uplink performance of RIS-assisted UAV communication systems with random 3-D mobile pattern. In the modeling process, we firstly provide a random 3-D mobile model for UAVs, where the random waypoint and uniform mobility models are simultaneously used. Next we build an end-to-end (E2E) transmission model for RIS-assisted UAV communication system, where the impacts of channel fading type, RIS configuration, UAV’s mobility and association policies are comprehensively considered. Combining the above two models, we derive the analytical expressions of uplink transmission metrics, and make a bound performance analysis on this base. Finally, we evaluate the uplink performance of RIS-assisted UAV communication system with random 3-D mobile pattern, and verify the proposed theoretical model.

In industrial cyber–physical systems (ICPS), real-time condition monitoring of wear-prone components of steel rolling production equipment is a key scenario for predictive maintenance. Machine vision-based crack detection can quickly identify critical damage and prevent unplanned downtime. However, the harsh working environment poses difficulties for data collection, a large amount of noise tends to contaminate surface crack images, and complex surface crack morphology affects the recognition accuracy. The real-time and accuracy performance of traditional crack detection algorithms are hard to meet the requirement of industrial applications. To tackle this challenge, a high-precision surface crack detection architecture for rolling steel production equipment based on image semantic segmentation is proposed. First, a coordinate attention-deep convolution generative adversarial networks (CA-DCGANs)-based data augmentation method is proposed to augment the original data set with high quality. Second, a crack detection model based on multiscale learning efficient spatial pyramid network (MLESPNetV2) is proposed. It effectively improves detection accuracy to obtain semantic information strongly correlated with crack using multiscale modeling and attention mechanism. Third, A semi-supervised learning method based on multiscale learning efficient spatial pyramid-generative adversarial network (MLESP-GAN) is proposed to solve the problem of insufficient labeled data and unstable training process. Finally, extensive experimental results on KolektorSDD and CAS-Crack data sets demonstrate that the proposed MLESPNetV2 significantly improves accuracy and real-time performance compared with the benchmark model. It is therefore suitable for deployment in industrial sites for real-time health monitoring of industrial equipment.

In practical status updating systems, most decisions made at monitors are based on diverse data streams. Due to the cask effect, it can be cognised that the effectiveness of decisions is often constrained by the stalest one, i.e., the straggler among all the streams. This work studies the statistical characteristics of age of the stalest information (AoSI) which describes the timeliness of the stalest stream. The AoSI is defined as the time elapsed since the latest successfully received update of the currently stalest stream among all different streams at the monitor was generated. Peak age of the stalest information (PAoSI) is also studied for evaluating the worst cases, i.e., the peaks of AoSI process. We develop an analytical approach to derive the AoSI and PAoSI based on the per-stream age of information (AoI) and peak age of information (PAoI), for the multi-stream single-monitor system with separate status updating. In particular, to comprehensively characterize the timeliness of the stalest stream, the distributions of AoSI and PAoSI are derived in closed-form for the general multi-stream system. Moreover, we concisely derive the explicit expressions of the distributions and averages of AoSI and PAoSI, upon a typical two-stream case with the classical automatic repeat-request protocol. Finally, the accuracy of the theoretical analyses is validated by the numerical results. Appropriateness and advantages of the AoSI (PAoSI) are elaborated by comparing with the maximum average AoI (PAoI), i.e., the maximal one among the averages of all the per-stream AoIs (PAoIs).

The proposed study introduces a reconfigurable intelligent surface (RIS)-aided hybrid radio frequency (RF)/free space optical (FSO) system with an unmanned aerial vehicle (UAV) relay to enable an ultra-dense sixth-generation (6G) network. The channels for RF and FSO are represented by Rayleigh and Gamma-Gamma probability distributions, correspondingly. Additionally, the network includes a terrestrial eavesdropper that follows the Nakagami-m distribution, attempting to breach confidential information. To counter this threat, RIS technology is used to enhance the hybrid system's secrecy. The study conducts a closed-form analysis of the secrecy outage probability (SOP) and obtains its asymptotic expression for determining the diversity order and coding gain. Theoretical findings have been confirmed through thorough numerical simulations implemented with the Monte-Carlo approach. The findings demonstrate the RIS technology's effectiveness in enhancing the network's secrecy performance.

Resilient and secure ultrareliable low-latency communications (URLLCs) over radio interface is expected to play a crucial role in next-generation Industrial Internet of Things scenarios. However, attacking wireless pilot signals has been a potential easy way to interrupt URLLC services. In this work, we propose a random structured code to encode and decode pilot signals on multidimensional physical resources, and also design a quantum learning framework to make this code secure and reliable. Specifically, the code suggests using random encoding with little structures to disperse the effect of attacks. We find that the decoding process can be modeled as a computing trap if the group spatial channel features are employed. The security problem is, therefore, transformed as random computing with redundancy. We employ a quantum algorithm to learn the computing trap model such that the computing redundancy can be removed quickly while the dispersed attack can be eliminated. In this respect, we can prove the existence of the quantum black-box model corresponding to the computing trap, and derive a precise expression of computing performance. Based on the result, we can formulate novel analytical closed-form expressions of system failure probability to characterize the reliability of the URLLC. Numerical results show that the proposed system can maintain ultrahigh reliability and low latency against attacks on wireless pilots.

Satellite-ground integrated vehicular network can provision ubiquitous and unlimited network connectivity for massive vehicles, and is expected to play a vital role in 6G-supported intelligent transportation systems (ITS). However, due to its high-dynamic channel environments and limited satellite payload, the uplink synchronization has become a major bottleneck to restrict vehicular communication performance. Focusing on maintaining reliable uplink synchronization, we propose an efficient timing advance (TA) update approach in this paper. Specifically, an enhanced preamble format is first presented based on the periodical pairing sounding reference signals (SRSs), which enables the satellite to continuously track uplink timing variation with a low signaling overhead. By taking full advantage of all the fourth-order autocorrelation produces from the received preamble, we further design a novel timing metric consisted of the correlation and differential normalization functions, which is capable of having a considerably increased correlation length and shaper mainlobe, as compared to the existing ones. Through theoretical performance analysis, it is indicated that the proposed approach not only notably promotes class distance between the correct and wrong timing indexes, but also can achieve the immunity to multi-path effect and large carrier frequency offset (CFO), while having a reduced computational complexity. Simulation results in a typical low-earth-orbit (LEO) scenario reveal the superiority of our approach in terms of the false alarm probability, the missed detection probability, as well as the timing mean square error.

Accurate traffic flow forecasting is crucial to improving traffic safety and alleviating road congestion for intelligent transportation network management. Recently, spatial-temporal graph-based deep learning methods have achieving significant performance improvements in traffic flow forecasting. However, they only consider spatial-temporal correlation of traffic network but ignore a mass of semantic correlation. In addition, they need to centralize data for training models, leading to privacy leakage concern. To tackle these problems, we introduce a federated learning-based intelligent traffic flow forecasting model that integrates our proposed spatial-temporal graph-based deep learning model into the devised Multilevel Federated Learning framework(MFL), named MFVSTGNN. This MFL is used to allow data collaboration among different data owners to train an efficient model without sharing their private data, while achieving the trade-off between communication overhead and computation performance. The proposed spatial-temporal graph-based deep learning model is composed of two phases. The first phase utilizes Variational Graph Autoencoder (VGAE) to dynamically generate adjacency matrix that contains both the spatial and semantic dependencies, contributing to preserving valuable information for improving prediction accuracy, and the second phase employs general spatial-temporal graph neural network to conduct prediction. We evaluate the performance of MFVSTGNN with two large-scale traffic datasets from California and Los Angeles County. The experimental results demonstrate the superior performance of MFVSTGNN in reducing communication overhead, and improving prediction accuracy, validating the effectiveness of our proposed model.

Aerial Radio Access Networks (ARANs) are used to connect aerial nodes (such as satellites, aircraft, floating balloons) and ground infrastructures, which enables a global network coverage and provides a wide range of high-quality network services. At present, extensive researches are to integrate it with Mobile Edge Computing (MEC), to achieve more efficient data computing, data storage, and cache. In this paper, we primarily focus on exploring the edge computing architecture integrated with ARANs. Since the existing MEC architecture is not deeply integrated with ARANs, we propose the scenario of a complete four-tier MEC architecture that allows MECs and ARANs to collaborate effectively. Besides, for environmental protection and cost reduction, we propose a Q-learning algorithm based on the improved ϵ – greedy model to complete the MEC server selection and resource allocation. Finally, the simulation results are compared with other benchmark methods, and the effectiveness of the proposed method is proved. The energy consumption of the proposed method is significantly reduced.

In this paper, we investigate the secrecy outage probability (SOP) for a multi-tier unmanned aerial vehicular (UAV) assisted cognitive communication network. Specifically, the low-altitude rotary-wing (LARW) UAV relays harvest radio frequency (RF) energy from the transmission of a high-altitude fixed-wing (HAFW) UAV and forward the information to a ground destination under a decode-and-forward protocol while a ground eavesdropper tries to capture the relay signal. The multi-tier UAV system transmits in an underlay fashion over a licensed spectrum of a primary user. We study the exact statistics of SOP assuming that the number of UAV relays follows a Poisson point process (PPP). The simulation results are presented to illustrate and verify the analytical results.

This article investigates intelligent reflecting surface (IRS)-assisted wireless-powered Internet of Things (IoT) networks. Specifically, multiple IoT devices first collect energy radiated from a power station (PS), then each device uses its harvested energy to support data transmission to an access point (AP) via frequency-division multiple access (FDMA). In addition, an IRS aims to improve wireless energy transfer (WET) and wireless information transfer (WIT) capabilities using passive reflection beamformers. The system sum throughput, as a performance metric, is maximized evaluate the overall performance of the considered model, which is subject to the constraints of IRS phase shifts, transmission time scheduling, and bandwidth allocation. This problem is not convex with respect to multiple coupled variables, and cannot be directly solved. To circumvent this nonconvexity, the transmission time scheduling and the bandwidth allocation are optimally designed in the closed form by the Lagrange dual method and the Karush–Kuhn–Tucker (KKT) conditions. Moreover, an alternating optimization (AO) algorithm is used to optimally design the IRS’s phase shifts during the WET and WIT phases in an alternating fashion. Specifically, we propose elementwise block coordinate decent (EBCD) and complex circle manifold (CCM) algorithms to iteratively derive the optimal phase shifts in the closed form. We also characterize the convergence behavior of the proposed algorithms. Finally, numerical results are presented to validate the performance of the proposed scheme, where the benefits of the IRS are highlighted in terms of sum throughput, transmission time scheduling, and energy harvesting, compared with the benchmark schemes.

Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attractive in intelligent transportation systems. However, it is difficult for the existing DL-OD schemes to realize the responsible, cost-saving, and energy-efficient autonomous vehicle systems due to low their inherent defects of low timeliness and high energy consumption. In this paper, we propose an object detection (OD) system based on edge-cloud cooperation and reconstructive convolutional neural networks, which is called Edge YOLO. This system can effectively avoid the excessive dependence on computing power and uneven distribution of cloud computing resources. Specifically, it is a lightweight OD framework realized by combining pruning feature extraction network and compression feature fusion network to enhance the efficiency of multi-scale prediction to the largest extent. In addition, we developed an autonomous driving platform equipped with NVIDIA Jetson for system-level verification. We experimentally demonstrate the reliability and efficiency of Edge YOLO on COCO2017 and KITTI data sets, respectively. According to COCO2017 standard datasets with a speed of 26.6 frames per second (FPS), the results show that the number of parameters in the entire network is only 25.67 MB, while the accuracy (mAP) is up to 47.3%.

This paper investigates the robust beamforming design in a secrecy multiple-input single-output (MISO) network aided by the intelligent reflecting surface (IRS) with simultaneous wireless information and power transfer (SWIPT). Specifically, by considering that the energy receivers (ERs) are potential eavesdroppers (Eves) and imperfect channel state information (CSI) of the direct and cascaded channels can be obtained, we investigate the max-min fairness robust secrecy design. The objective is to maximize the minimum robust information rate among the legitimate information receivers (IRs). To solve the formulated non-convex design problem in bounded and probabilistic CSI error models, we utilize the alternating optimization (AO) and successive convex approximation (SCA) methods to obtain an approximate problem. Then, an iteration-based algorithm framework was proposed, where the unit modulus constraint (UMC) of the IRS is handled by the penalty dual decomposition (PDD) method. Moreover, a stochastic SCA method is proposed to handle the outage constrained design with statistical CSI. Finally, simulation results validate the promising performance of the proposed design.

Due to concurrent access attempts from massive machine-type devices (MTDs) within the wide beam coverage, the existing contention-based random access (RA) scheme suffers from severe physical random access channel (PRACH) over-load when applied to the emerging satellite-enabled machine-to-machine (M2M) communications. In this paper, we propose an efficient collision detection scheme based on cyclically shifted Zadoff-Chu (ZC) sequences, which are generated by the minimum number of required root indexes and a fixed cyclic shift offset independent of the beam radius. The proposed scheme enables rapid collision detection at the first step of RA procedure by capturing correlation peaks at the timing positions corresponding to the multiples of the cyclic shift offset, thus can reduce the access delay and resource consumptions for the collided MTDs. Simulations are carried out to validate the correctness of mathematical analysis, and to demonstrate the significant detection performance improvement of our scheme with effective non-orthogonal interference (NOI) mitigation by compared to the conventional one.

Satellite communication system is expected to play a vital role for realizing various remote Internet-of-Things (IoT) applications in sixth-generation vision. Due to unique characteristics of satellite environment, one of the main challenges in this system is to accommodate massive random access (RA) requests of IoT devices while minimizing their energy consumptions. In this article, we focus on the reliable design and detection of RA preamble to effectively enhance the access efficiency in high-dynamic low-earth-orbit (LEO) scenarios. To avoid additional signaling overhead and detection process, a long preamble sequence is constructed by concatenating the conjugated and circularly shifted replicas of a single root Zadoff-Chu (ZC) sequence in RA procedure. Moreover, we propose a novel impulse-like timing metric based on length-alterable differential cross-correlation (LDCC), that is immune to carrier frequency offset (CFO) and capable of mitigating the impact of noise on timing estimation. Statistical analysis of the proposed metric reveals that increasing correlation length can obviously promote the output signal-to-noise power ratio, and the first-path detection threshold is independent of noise statistics. Simulation results in different LEO scenarios validate the robustness of the proposed method to severe channel distortion, and show that our method can achieve significant performance enhancement in terms of timing estimation accuracy, success probability of first access, and mean normalized access energy, compared with the existing RA methods.

This paper investigates a secure wireless-powered sensor network (WPSN) with the aid of a cooperative jammer (CJ). A power station (PS) wirelessly charges for a user equipment (UE) and the CJ to securely transmit information to an access point (AP) in the presence of multiple eavesdroppers. Also, the CJ are deployed, which can introduce more interference to degrade the performance of the malicious eavesdroppers. In order to improve the secure performance, we formulate an optimization problem for maximizing the secrecy rate at the AP to jointly design the secure beamformer and the energy time allocation. Since the formulated problem is not convex, we first propose a global optimal solution which employs the semidefinite programming (SDP) relaxation. Also, the tightness of the SDP relaxed solution is evaluated. In addition, we investigate a worst-case scenario, where the energy time allocation is achieved in a closed form. Finally, numerical results are presented to confirm effectiveness of the proposed scheme in comparison to the benchmark scheme.

Codebook design is one of the core technologies in limited feedback multi-input multi-output (MIMO) communication systems. However, the conventional codebook designs usually assume MIMO vectors are uniformly distributed or isotropic. Motivated by the excellent classfication and analysis ability of clustering algorithms, we propose a K-means clustering based codebook design. First, large amounts of channel state information (CSI) is stored as the input data of the clustering, and finally divided into N clusters according to the minimal distance. The clustering centroids are used as the statistic channel information of the codebook construction which the sum distance is minimal to the real channel information. Simulation results consist with theoretical analysis in terms of the achievable rate, and demonstrate that the proposed codebook design outperforms conventional schemes, especially in the non-uniform distribution of channel scenarios.

In vehicle‐to‐vehicle (V2V) communications, achieving reliable physical layer performance is a challenging task due to the highly dynamic nature of V2V propagation channels. Frame timing estimation, as one of the most critical signal processing procedures that rely on channel statistics, has to be appropriately enhanced to tackle this challenge. This study presents a novel frame timing estimation scheme based on both the available periodical preambles in IEEE 802.11p standard. By designing the fourth‐order statistics‐based correlation and differential normalisation functions, the proposed timing metric not only is capable of possessing an extensible correlation length, but also achieves the robustness to multipath effect and large carrier frequency offset. From the standpoints of hypothesis testing and classification, the proposed approach can effectively increase the distinction between correct and wrong timing indexes in terms of the class‐separability criteria, and consequently has a significantly improved timing estimation performance compared with the existing methods. Simulation results consist with theoretical analysis under the typical V2V channel model, and demonstrate that the proposed method can significantly reduce both the probabilities of false alarm and missed detection, and make the selection of a suitable threshold for frame detection much easier.

Reasonable design and effective detection of the random access preamble has become a challenging task due to the unique characteristics of mobile satellite communications. To tackle this challenge, we first design a universal long sequence structure by concatenating multiple short Zadoff-Chu sequences that are insensitive to carrier frequency offset (CFO), and then propose the new principles of parameter selection for short sequences to ensure the minimum utilization of root sequence and the independence of the cyclic shift offset on the beam radius. To further reduce the detection complexity and improve the multi-user access performance, a fast timing detection approach is also presented by leveraging the piecewise cumulative detection and the multi-peaks joint estimation to obtain an accurate timing advance for each access user. Simulation results and complexity analysis validate the effectiveness of the new preamble in a typical satellite communication environment, and reveal that the proposed timing detection can achieve the robustness to CFO and offer outstanding performance improvements especially in multi-user scenarios while having a notably reduced computational complexity.

This study investigates the problem of frame timing estimation in orthogonal frequency division multiplexing systems. Conventional timing estimation methods, which take advantage of the correlation property of a given preamble, always experience performance degradation in multipath fading channels with severe channel dispersion. To achieve accurate timing estimation, the authors propose a robust threshold‐based timing detection method independent of the preamble structure. Based on the autocorrelation and cross‐correlation, a novel timing metric with an extended correlation length is proposed to mitigate noise and resist large carrier frequency offsets. Due to the superior statistical property of the proposed timing metric, the threshold can be easily determined with no need for the process of noise variance estimation. Simulation results under different multipath fading channels demonstrate that the proposed method achieves a remarkably improved timing accuracy compared to the existing methods.

The research has been done to derive the soil moisture information at local scale by using single polarization, single frequency sensors such as ERS-1/2, Radarsat, and JERS-1. There is a need to develop a technique to estimate soil moisture information from these currently available data sources at both regional and local scales. In this study, a soil moisture change detection algorithm was developed for using the multi-temporal 50m resolution Radarsat SCANSAR image data. It was based on the theory model analysis results, with the correction of vegetation and incident angle effects. The relative soil moisture change value can be derived. The results were compared with in-situ measured soil moisture data from 3 different sampling sites at study area. The validation indicated our algorithm with RMSE error of 0.44 in estimating soil moisture change ratio.