he emergence of unauthorized unmanned aerial vehicles (UAVs) has raised widespread safety threats, making the blind signal recognition of unauthorized multiple UAVs (multi-UAV) critically important.he emergence of unauthorized unmanned aerial vehicles (UAVs) has raised widespread safety threats, making the blind signal recognition of unauthorized multiple UAVs (multi-UAV) critically important.T Meanwhile, the frequency hopping (FH) control signals with the start-end identical preamble (SIP) structure, have three major characteristics: non-stationarity, short dwell time, and scarcity of known labels. These characteristics pose significant challenges to the recognition of unauthorized SIP signals in spectrograms. In this paper, we propose a cross-domain segmenter self-learning classifier (CS-SC) scheme for SIP signals, which can segment each class of UAV in-phase/quadrature (I/Q) signals in multi-UAV environments, and detects the features of unauthorized and unknown SIP signal via self-learning. First, the CS scheme performs time-frequency analysis on received SIP signals, locates signals via an adaptive statistical feature detector on spectrograms, then combines with time-frequency segmentation to obtain I/Q representations of each class of signals. Second, we design a cyclic self-search algorithm in the SC scheme, and the SC scheme can learn discriminative features via the preamble structures, and reduces the interference from payload of unknown UAV signals. Then, these learned features are utilized in template matching for blind SIP signal recognition, which is more efficient than the related learning algorithms. Simulation results validate that, our CS-SC scheme achieves 40% higher clustering accuracy compared with the existing clustering algorithms, and improves the recognition accuracy about 40% than related deep learning algorithms in a wide signal-to-noise ratio (SNR) region.

Network coding (NC) has proven to offer significant benefits in long-distance and broadcast transmissions, enhancing both throughput and energy efficiency. Recent studies have incorporated partial packet recovery (PPR) into packet-level NC, using syndromes from coded packets to correct bit errors and thereby reduce completion delay. Motivated by recent breakthroughs in deep learning, this paper introduces a novel neural networkbased decoding framework for packet-level NC, referred to as Learned-PPR. The proposed framework incorporates a Bilateral Efficient Self-Attention Network (Bi-ESANet) architecture, which leverages a bilateral network structure to effectively capture both inter- and intra-packet information. Furthermore, we introduce an ESA module to mitigate the GPU memory overhead compared with traditional Transformer attention modules. To handle rateless NC, we propose a “rateless masking” training strategy that enables efficient decoding of rateless codes within the Bi-ESANet framework. Simulation results across various transmission scenarios demonstrate that the proposed approach significantly outperforms existing PPR schemes, achieving lower completion delay. Specifically, compared to existing methods, the proposed approach reduces completion delay by more than 25%. However, the introduced framework incurs higher computational complexity due to the integration of the Bi-ESANet architecture.

This paper proposes an iterative detection and decoding (IDD) scheme for asynchronous sparse code multiple access (aSCMA), referred to as aIDD, in satellite-terrestrial uplink communication scenario with the low earth orbit (LEO) satellite equipped with uniform planar array (UPA) antenna. In detector design, we first develop the extended factor graph for aSCMA by considering the memory induced by asynchronous transmission, and an asynchronous message passing algorithm (A-MPA) is proposed. In A-MPA, the noise whitening on the sampled symbols is performed to mitigate the correlation among the noise samples due to the matched filtering, and the updating rules are then designed to achieve superior performance. Furthermore, we propose an asynchronous expectation propagation algorithm (A-EPA) by exploiting the diversity gains induced by UPA, where the means and variances of the transmitted SCMA codewords are updated with high reliability. Simulation results show that the proposed A-EPA can achieve the same performance as that of A-MPA but with lower complexity at a high number of receive antennas. In decoder design, a soft-output ordered likelihood decoder (S-OLD) is proposed to generate the soft information with high reliability compared with the belief propagation (BP) decoder under low-density parity check (LDPC) code. By combining the proposed A-EPA/A-MPA and S-OLD, the proposed aIDD scheme iteratively exchanges the messages between the detector and the decoder until the maximum number of iterations of the outer loop is achieved or the decoding results of all the users are converged. Simulation results show that the proposed aIDD/A-EPA and aIDD/A-MPA have the same performance and are better than that of the synchronous IDD and joint detection and decoding (JDD) schemes.

A design methodology is proposed to optimize the minimum weight and error coefficient of polarization-adjusted convolutional (PAC) codes, enhancing their maximum likelihood (ML) performance based on a theoretical analysis of code asymptotic behavior. Employing an adapted multilevel list search algorithm to identify minimum-weight codewords, the methodology comprises three deterministic optimization algorithms. First, an iterative rate-profiling optimization algorithm substantially reduces the number of minimum-weight codewords through efficient pairwise exchanges of information and frozen indices. Second, a tree search optimization algorithm progressively extends the convolutional impulse response, exploring superior solutions within a theoretically constrained search space. Third, a joint optimization algorithm synthesizes the two algorithms, alternately refining the rate-profiling and convolutional pre-transform. Complexity analysis underscores the computational efficiency of these algorithms for short PAC codes, while optimization results confirm the strong capability of the proposed methodology in improving the minimum weight and error coefficient. With moderate-to-large list decoding for code lengths of 64 to 256, the proposed PAC codes consistently outperform state-of-the-art polar code variants, attaining or approaching the random coding union (RCU) bound. Additionally, the proposed PAC codes demonstrate the capability to exceed the normal approximation (NA) bound at low-to-moderate code rates.

Satellite-integrated Internet is capable of providing extensive coverage for massive terrestrial sensing user equipment (UE), facilitating access that satisfies the diverse requirements of multi-type services. However, existing proactive multiple access schemes often induce severe UE collisions, particularly hindering the performance of services with stringent requirements, and thus degrading overall system utility. In this paper, we investigate a utility-oriented satellite-queried system, where rate-splitting multiple access (RSMA) is adopted to support multi-type services with utility guarantees. The utility characteristic is captured by a semantic-empowered metric, termed utility loss of information (UoI), which comprehensively integrates timeliness, service priority, transceiver matching status, and energy consumption. To minimize the average UoI, we propose an adaptive RSMA (A-RSMA) scheme that dynamically adjusts the number of sub-data and power allocation according to the number of accessing UEs. To further improve UoI, we propose an adaptive grouped RSMA (Ag-RSMA) scheme, where the covered UEs are grouped according to their diverse utility requirements. We also introduce a reinforcement learning approach to optimize the dynamic resource scheduling. Simulation results demonstrate that our A-RSMA scheme achieves a lower UoI compared to the state-of-the-art schemes, and the Ag-RSMA scheme satisfies diverse UoI requirements than its non-grouped counterpart.

In this paper, we propose a rate-compatible partially-coupled staircase low density parity check (PS-LDPC) coding scheme for high speed inter-satellite communications. First, we introduce the encoding process and sliding window decoding (SWD) algorithm of PS-LDPC codes, and we investigate the error floor of component codes, which validate that the PS-LDPC codes with short block-length component code can maintain the reliability, and significantly reduce the decoding latency. Then, we analyze the density evolution (DE) of PS-LDPC codes based on the multi-edge type (MET)- LDPC framework under the Gaussian approximation, and derive its decoding thresholds of SWD. Further, we propose an optimized coupling pattern (OCP) encoding algorithm that achieves the optimal coupling patterns with the minimized threshold by introducing two-stage column permutations, and modify the message exchanges in SWD algorithm according to this encoding algorithm. Moreover, we design a new decoding algorithm, named cascaded SWD (C-SWD) algorithm, which reduces the error floor and enhances decoding performance by pre-decoding, reliability enhancement, and cascading belief propagation (BP) decoder or ordered likelihood decoder (OLD) due to the error floor. Simulation results demonstrate that our PS-LDPC coding scheme outperforms the existing rate-compatible spatially coupled LDPC (SC-LDPC) coding schemes in terms of bit error rate and complexity.

Satellite-integrated Internet can provide multi-type intelligent services for ubiquitous user equipments (UEs) in the next-generation networks. Considering that most existing multicast systems in satellite-integrated Internet cannot accommodate the diverse requirements of heterogeneous services with limited resources, we propose a utility-optimal multi-type services multicast system based on multiple-input multiple-output rate-splitting multiple access (MIMO-RSMA). Specifically, we consider three types of semantic services classified based on their timeliness, reliability, and semantic characteristics. To support these heterogeneous services coexistences under constrained resources, we design three tiered priority scheduling (TPS) policies with progressively increasing inter-service resource coupling, and demonstrate their advantages under different operating conditions. Considering diverse demands of semantic services, we propose the utility loss of information (UoI) to capture the requirements of each service, and formulate a multi-constrained UoI-minimization problem within each transmission stage of the TPS policies, and transform it via Lyapunov framework with the proposed exponentially-weighted virtual queue (EWVQ). Further, we design a soft actor-critic (SAC)-based power allocation and rate control (PARC) scheme, and propose an adaptive weighted priority scheduling (AWPS) function to solve the non-convex UoI-minimization problem under differentiated reliability requirements. Simulation results validate the effectiveness of the proposed TPS policies and demonstrate that our SAC-PARC scheme outperforms state-of-the-art schemes in minimizing UoI.

In the Large-scale Satellite Network (LSN), inter-beam interference significantly hinders the transmission performance of Low Earth Orbit (LEO) satellite downlinks. This interference exhibits notable time-varying characteristics due to the relatively rapid movements between LEO satellites and ground users, posing a substantial challenge to existing transmission resource allocation techniques. To tackle this issue, we introduce the Hypergraph Neural Network (HGNN)-enabled Resource Allocation (HGNNRA) algorithm for the downlinks of LSN. This algorithm aims to solve a transmission-rate maximization problem, effectively handling the time-varying interference among multiple beams in the downlink through a well-tailored Dynamic Hypergraph Neural Network (DynHGNN). Specifically, considering the coupling complexity of satellite beam coverage and user participation, we have developed a Dynamic Hypergraph-based interference model, along with a customized construction algorithm, to describe their time-varying relationships precisely. Simulation results indicate that our proposed HGNNRA outperforms both Graph Convolutional Network (GCN) [14], HGNN [15], GNN-DDQN [48], and GCNRA in terms of transmission rate and the satisfaction degree of user transmission requirement metrics.

Extremely large-scale multiple-input multiple-output (XL-MIMO) enables the formation of narrow beams, effectively mitigating path loss in high-frequency communications. This capability makes the integration of wideband high-frequency communications and XL-MIMO a key enabler for future 6G networks. Realizing the full potential of such wideband XL-MIMO systems depends critically on acquiring accurate channel state information. However, channel estimation is significantly challenging due to inherent wideband XL-MIMO channel characteristics, including near-field propagation, beam split, and spatial non-stationarity. To effectively capture these channel characteristics, we formulate channel estimation as a maximum a posteriori problem, which facilitates the use of prior channel knowledge. We then propose an unrolled proximal gradient descent algorithm with learnable step sizes, which employs a dedicated neural network for proximal mapping. This design empowers the proposed algorithm to implicitly learn prior channel knowledge directly from data, thereby eliminating the need for explicit regularization functions. To improve the convergence, we introduce a monotonic descent constraint on the layer-wise estimation error and provide theoretical analyses to characterize the algorithm’s convergence behavior. Simulation results show that the proposed unrolling-based algorithm outperforms the traditional and deep learning-based methods.

With tens of thousands of low earth orbit (LEO) satellites to be launched in the near future, phased array antennas are envisioned as attractive candidates for future satellite ground stations due to their ability to generate multiple beams via beamforming network, thus supporting multiple satellites simultaneously. Multi-satellite tracking is of great importance for ensuring link quality in satellite communications. However, it is challenging to simultaneously tracking multiple satellites due to orbital perturbations and interference from other satellite signals. In this paper, we propose a multi-satellite tracking scheme for multibeam phased array ground station communication with LEO satellites, which employs direction of arrival (DOA) measurements of satellite signals to aid the satellite dynamics. First, we establish a tracking model that incorporates the relationship between satellite dynamics and measurement angles. Then, we develop a data fusion-based method for multiple LEO satellites by exploiting the DOA measurements of satellite signals using a phased array antenna. The measured DOA data are associated with the target satellite state and processed using an extended Kalman filter (EKF) to enhance tracking accuracy. The updated satellite position is further integrated into a dynamics model to predict angular information, leading to accurate satellite tracking during measurement gaps. Simulation results demonstrate that the proposed tracking scheme achieves a tracking accuracy of within 0.1 degrees in multi-satellite scenarios, significantly improving the tracking accuracy compared to other methods.

The thriving of satellite communication (SatCom), particularly the expansion of constellation size, offers significant opportunities for cooperative multi-satellite transmission (MST). MST leverages the diversity of fading channels arising from the spatial separation of satellites through novel waveform schemes, such as delay-Doppler (DD) domain-based orthogonal time frequency space (OTFS) and chirp domain-based affine Fourier division multiplexing (AFDM). This paper demonstrates that MST can achieve substantial synergistic gain by combining signal-to-noise ratio (SNR) gain and diversity gain with proper waveform design. We prove that OTFS/AFDM avoids the loss of diversity distinguishability compared to current orthogonal frequency division multiplexing (OFDM) and single-carrier (SC) systems, thereby maximizing the synergistic gain of MST. Furthermore, our simulation results indicate that, although the maximum likelihood (ML) receiver can theoretically achieve the SNR gain of MST under weak small-scale fading channels, the MMSE equalizer fails to do so. Overall, the results suggest that MST is more suitable for severe fading channels and highlight a challenge for future receiver designs to achieve SNR gain under weak fading channels.

This paper proposes an asynchronization-aided iterative detection and decoding (AIDD) scheme for sparse code multiple access (SCMA) in satellite-terrestrial communication scenario, where the messages between the detector and decoder are iteratively exchanged with an additional degrees- of-freedom (DoF) in terms of delay. We first propose a parallel expectation propagation algorithm (P-EPA) for asynchronous multiuser detection, where a new initialization method is introduced by efficiently utilizing the prior information to enhance the performance of the detector. Furthermore, a universal soft-output decoder (S-OLD) is proposed based on the ordered likelihood decoder (OLD), which can generate the soft information with high reliability and serve as the input of P-EPA in the proposed AIDD. The iteration between the P-EPA and S-OLD is terminated when the maximum iteration number of the outer loop is achieved or the decoding results of all the users are converged. Simulation results show that the proposed P-EPA has better performance and lower latency compared to its counterparts, and the proposed AIDD also has better performance and fewer iterations than the synchronous IDD and joint detection and decoding (JDD) schemes.

A fundamental prerequisite for implementing distributed satellite cooperative beamforming (DSCBF) is achieving accurate time, phase, and frequency synchronization. However, existing synchronization techniques often fall short of the accuracy required for DSCBF applications. Moreover, many of these techniques rely on external references, such as GPS, to coordinate electrical states, thereby limiting their applicability in environments where external references are unavailable. Furthermore, many synchronization techniques fail to rigorously account for the impacts of platform motion, thereby constraining their applicability in distributed satellite systems (DSS). In this paper, we first analyze the impacts of timing offset, frequency offset, and phase shift on cooperative beamforming gain, thereby establishing the synchronization requirements for DSS. Subsequently, the waveform-based synchronization algorithm is proposed in this paper that enables high-precision frequency offset estimation without additional hardware or external references while effectively compensating for Doppler frequency shifts induced by relative motion. Simulation results demonstrate that the proposed algorithm significantly enhances the performance of beamforming in mobile DSS.

Forward-looking sonar (FLS) image segmentation can help reduce the amount of raw data that needs to be transmitted in underwater communication systems, making it a crucial technique for next-generation communication systems and the Internet of Things (IoT). However, its effectiveness is often hindered by weak semantic information, blurry edges and low resolution, which pose challenges for current segmentation algorithms. In this study, we propose a multimodal feature-enhanced Unet for FLS image segmentation (MFEUnet), built upon the Unet framework. The multimodal features considered primarily include spatial and frequency features. For spatial features, recognizing Unet’s strength in local feature extraction, we integrate a transformer to enhance its ability to capture global features. Frequency features are utilized to capture different details of FLS images, with a dual-branch wavelet transformation employed to decompose images into low-frequency and high-frequency components, facilitating the enhancement of these features. And a preprocessing reconstruction module is integrated to reduce the noise of FLS images. Furthermore, to address class imbalance in FLS datasets, we design a specialized segmentation loss function. Experimental results show that MFEUnet significantly outperforms state-of-the-art segmentation methods, demonstrating its effectiveness in overcoming the unique challenges of underwater sonar imaging.

To assist the design and optimization of low-density parity-check (LDPC) codes via density evolution (DE) on binary input additive white Gaussian noise (BIAWGN) channels, we propose a novel mixed Gamma approximation (MGA) scheme to obtain more accurate distribution of messages updated and output by the check nodes during DE iterations. Firstly, we highlight the inaccuracy of existing Gaussian approximation (GA) methods in approximating the distribution of check node output messages, especially when the messages from variable nodes are small with high probability (i.e. low signal-to-noise ratio), and the check nodes have a large degree, which leads to inexact results in GA methods. Then, we establish the MGA scheme by utilizing the statistical properties of Gamma distribution and combine it with GA, which outperforms the existing GA methods in the metrics of error of output mean and Kullback-Leibler (KL) divergence of output distribution for a wide range of parameters. Simulation and analysis validate that our MGA scheme has the potential for the design and optimization of LDPC codes, which can provide adequately accurate estimation of check node outputs with moderate complexity for a variety of approximation methods, such as Gaussian capacity approximation, and significantly reduce the computational complexity by sacrificing minor accuracy.

The pull-based transmission initiates the generation and updating of status to the destination as needed, potentially reducing unnecessary energy costs and maintaining data freshness for satellite-integrated Internet with limited resources. In this paper, we introduce a semantic-empowered metric called utility loss of information (UoI) for a multi-state Markov source to assess the freshness and value of information, and the synchronization of transceivers, which can simultaneously quantify the age of information (AoI), value of diversity states, and the mismatch of transceivers. Then, we propose a utility-critical prompt (UP) transmission scheme for terrestrial Internet of Things (IoT) sensors with multi-state Markov source to transmit status update to the satellite efficiently, and derive the average UoI (AUoI) in both periodic and stochastic queries. Simulation results demonstrate that the UP scheme can achieve an optimal tradeoff between freshness, value, and synchronization of transceivers in both periodic and stochastic queries, and outperforms than state-of-the-art schemes.

Abstract Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites (CCS) system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control, fundamentally enhancing network resilience. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, new waveform design, grant-free massive access, nonorthogonal multicast, distributed phased array antennas, high-speed inter-satellite communication, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.

Cohesive clustered satellite (CCS) system can utilize multi-satellite coordinated transmission (MSCT) to enhance the sum rate and provide direct satellite-to-device connectivity, which is regarded as a key component for low Earth orbit (LEO) satellite-integrated Internet. Considering the high-mobility LEO satellites and fractional Doppler interference (FDI) due to the non-integer Doppler tap, we utilize orthogonal time frequency space (OTFS) modulation to mitigate the complex delay-Doppler effects on a linear time-varying (LTV) channel. Then, we analyze the impact of FDI and the block circulant matrix with circulant block (BCCB) structure on the OTFS channel matrix, and derive the approximate super resolution (SR) relationships between the integer and fractional Doppler channel matrices. Furthermore, to improve communication efficiency and reliability of MSCT, we propose an OTFS-based super resolution-fractional Doppler channel estimation (SR-FCE) scheme, and introduce an enhanced low-correlation-zone periodic sequence (ELPS) superimposed on the OTFS frame to lower the peak-to-average power ratio. In the coarse estimate stage of SR-FCE scheme, a low-resolution channel matrix is obtained via the threshold method, followed by the fractional Doppler network (FracNet) to extract FDI parameters for high-resolution channel matrix reconstruction. Simulation results validate the feasibility of the SR-FCE scheme in MSCT, and outperforms the state-of-the-art schemes in terms of normalized mean squared error and bit error rate.

This article presents a novel under-sampling method for ECG signals, aimed at reducing the sampling rate and power consumption in IoT-based ECG wearable devices. The key contribution addresses the common issue of model mismatch in existing methods, which negatively impacts signal reconstruction accuracy. Initially, the ECG signal is modeled as a linear combination of several Gaussian second-order derivative functions, which can be efficiently represented with only a few parameters, thus mitigating the problem of large model matching errors. To further enhance reconstruction accuracy, an improved two-channel finite rate of innovation sampling framework is introduced, effectively addressing the nonideal effects caused by the low-pass filter during sampling. Additionally, a modified annihilating filter reconstruction algorithm is proposed, allowing high-precision signal reconstruction using a small number of sampling points to estimate parameters. The validity of the proposed method is confirmed through simulations with real ECG signals from the MIT-BIH arrhythmia database, and a hardware platform is developed to verify its feasibility in a practical system. Experimental results demonstrate that, compared to the existing methods, the proposed approach significantly reduces reconstruction error (achieving a PRD as low as 2.29% and an SRR of 11.77 dB), and exhibits better robustness in noisy environments.

The objective of this article is to investigate an update scheduling issue in the satellite-based network for time-sensitive marine Internet of Things (marine IoT) applications. In this particular scenario, multiple gateways capture updates from surrounding marine IoT sensors and make online decisions regarding task scheduling for orbital processing by a specific satellite. A hybrid low earth orbit and geosynchronous earth orbit (hybrid GEO-LEO) satellite architecture shows promise in achieving timely update delivery. However, the limited communication and orbital processing resources create significant challenges for ensuring timely task scheduling in the hybrid network. To address this challenge, we model the age-optimal scheduling issue as a collaborative gateway association and resource management problem. We first transform it into two corresponding subproblems: 1) resource management and 2) scheduling decision making. Subsequently, we employ the Lagrange multiplier algorithm to achieve optimal resource allocation results while utilizing deep reinforcement learning techniques to determine the scheduling decisions intelligently. Extensive simulation results demonstrate that our designed intelligent task scheduling scheme with optimal resource management outperforms state-of-the-art schemes in terms of peak-age, thereby highlighting the effectiveness of hybrid GEO-LEO networks for time-sensitive marine IoT applications.

Accurate traffic flow measurement is essential for the development of smart cities, yet the deployment of ubiquitous monitoring sensors using traditional methods is often cost-prohibitive. This paper proposes an innovative entropy-aware masked autoencoder framework, namely TMAE, for low-cost traffic flow inference. TMAE leverages a small number of selectively measured regions with few deployed sensors to infer traffic flow across entire urban areas, incorporating prior knowledge from road distribution maps. Specifically, TMAE employs a shared encoder to process traffic flow context, using self-attention scores to identify the importance of each region and guide a masking policy that retains regions rich in traffic flow information. The road distribution map, reflecting inherent traffic flow patterns, is incorporated as prior knowledge by substituting masked tokens during training. A cross-attention mechanism in the decoder further refines inference, where embeddings from the road distribution map serve as queries, and retained visible patches act as keys and values. Additionally, regional traffic entropy is introduced to quantify the information richness of each region, enabling the selection of minimal measurement regions to optimize inference for other areas. Extensive experiments conducted on datasets from various cities demonstrate the effectiveness and efficiency of TMAE, highlighting its potential as a scalable solution for low-cost traffic flow inference in urban environments. The source code of this work is released at https://github.com/TextGraph/TMAE.

We present a 2-bit coding array that features high beam-pointing accuracy and side-lobe-level (SLL) performance for wide-angle beam scanning. By symmetrically exciting a circular ring patch antenna and integrating a 90° phase shifter into the feeding line, a 2-bit characteristic is obtained. Four positive-intrinsic-negative (PIN) diodes are deployed in each antenna element and are controlled by a field-programmable gate array (FPGA) for switching the states of PINs. The 2-bit circular ring patch element is subsequently used for building a$1\times 12$array. Apart from introducing a set of initial phases, we add initial amplitudes to the array for the first time to further improve the array performance. Both initial phases and amplitudes are obtained from the invasive weed optimization (IWO) algorithm by setting the goals of desired beam pointing accuracy and SLL performance. In addition, the active element pattern (AEP) that accounts for the mutual coupling between elements is used in the optimization process to provide an accurate array response. The simulated and measured results agree well, and both show that the proposed array can achieve a scanning range of ±50° with a beam pointing error (BPE) within ±1°, yet the simulated maximum SLL (MSLL), 9.2 dB, deteriorates to 8.3 dB in the measurement. The measured peak aperture efficiency is 43.2% with a peak gain as 13.2 dBi. The proposed array, which features low cost, low profile, and low power consumption, is well suited for intelligent Internet of Things (IoT) applications where space and power are limited.

The large-scale low-Earth orbit (LEO) satellite network presents an obvious challenge for user access with obviously dynamical coverage resulting from the fast-changing locations of LEO satellites, i.e., time-varying overlapped range in spatial and temporal coverage by multiple overhead satellites, making existing studies low throughput for supporting various users with fluctuating access demands. In this article, we propose a multiagent deep deterministic policy gradient-based access (MADDPGA) mechanism for the large-scale satellite network, which allows each user to adjust its strategy autonomously by learning the changing network conditions in the long term. By solving a throughput-maximizing optimization problem, we develop a fully decentralized multiagent deep reinforcement learning (MADRL) algorithm by exploiting optimal dormancy probability (DP) and a well-designed weighted allocation strategy. The simulation results show that the proposed method can effectively improve the throughput performance compared with the Random algorithm and fixed DP algorithm.

The existing task-agnostic and resource-constrained satellite communication fails to meet diverse task demands in the upcoming sixth-generation (6G) network. In this paper, to enable the ubiquitous intelligent services with massive traffic for global users through satellite-Integrated Internet, we first propose a novel semantic metric named utility loss of information (UoI), which can capture the task-oriented aspects by quantifying both value loss of semantic mismatch, and energy loss of unnecessary transmissions. Then, we design a UoI minimization data generation and transmission (UMGT) scheme for task-adaptive communications in satellite-Integrated Internet with energy constraint and reliability requirement. For the time-varying satellite-terrestrial link with high bit error rate (BER) and delayed feedback, we derive the closed-form expressions of BER, and apply the long erasure coding (LEC) to combat the deep fading. Subsequently, we transform the optimization problem to minimize the upper bound of an unconstrained Lyapunov drift-plus-penalty (DPP). Further, we propose two deep reinforcement learning (DRL) algorithms to intelligently choose when to generate data, how to adjust the number of LEC packets and whether to retransmit, thereby minimizing the average UoI. Simulation results validate that our UMGT scheme can achieve the lowest UoI than several state-of-the-art schemes, and demonstrate its adaptability to various task demands.

Image deblurring is a technique employed to reduce or eliminate degradation resulting from the effects of impulse response and atmospheric turbulence during remote sensing image acquisition. However, the issue of low recovery accuracy due to inadequate point spread function (PSF) matching is a significant challenge in current deblurring methods. In this letter, a remote sensing image deblurring method based on the differential Lorentzian PSF model is proposed. First, a differential Lorentzian PSF model is proposed, which is able to model the line spread function (LSF) of a real image as a series of pulsewidth variable Lorentzian functions and their differential function combinations. Subsequently, a model parameter estimation algorithm based on the improved zeroing filter is proposed. This algorithm is able to reconstruct the target parameters by utilizing the edge information of the actual image, with the objective of eliminating the matching error of the PSF model in the actual complex scene and improving the parameter estimation accuracy. Finally, a remote sensing image deblurring method is proposed by using the differential Lorenz point diffusion function model combined with the L-R algorithm. The results of the simulation experiments demonstrate that the method proposed in this letter is more effective than existing remote sensing image deblurring methods in terms of image recovery accuracy.

In response to the requirements of precise information conveying and goal-oriented transmitting with minimal cost for the upcoming satellite-integrated Internet, we focus on a semantic communication metric named utility loss of information (UoI), which is generalized to capture the tradeoff of the value and energy loss of information. The former is quantified by the duration and severity of mismatch transceivers, and the latter is evaluated by unnecessary data generation and transmissions. To achieve the optimal tradeoff between value and energy loss of information for status update, we formulate a joint optimization problem to design a UoI-optimal policy to generate and transmit data for a multi-state Markov source. By regarding the limited energy, we transform the above problem to a constrained Markov decision process (CMDP), and rigorously prove the UoI-optimal policy has a dual-threshold structure. Then, we derive the closed-form expressions for average UoI and generation and transmission ratio. Moreover, we propose a simplified relative value iteration (SRVI) algorithm based on the theoretical derivations, combined with the bisection search to find two optimal thresholds for the UoI-optimal policy. Simulation results verify that our UoI-optimal policy achieves the optimal tradeoff among timeliness, reliability, and energy efficiency, and outperforms several state-of-the-art semantic-aware policies.

A novel rate-profiling design is proposed for polarization-adjusted convolutional (PAC) codes under list decoding, adopting a path probability perspective. Inspired by the concepts of Hamming distance and Hamming weight, the concepts of path probability distance (PPD) and path probability weight (PPW) are innovatively introduced to measure the disparity between the erroneous paths and the correct path for PAC codes under probabilistic decoding. An in-depth analysis of the recursion of node log-likelihood ratios (LLRs) in the factor graph is conducted, elucidating the derivation of a lower bound on the PPW as a consequential outcome. Utilizing the concept of the PPW, a rate-profiling design algorithm is proposed to establish the reliability ranking for the bit-channels of PAC codes. Simulation results demonstrate that PAC codes employing the proposed rate-profiling exhibit advantages over alternative rate-profiling and other state-of-the-art polar coding schemes across various block lengths and code rates under short decoding list sizes.

The upcoming satellite-integrated Internet can provide onboard remote sensing image processing and efficient communication to ensure ubiquitous intelligent services. Given the massive volume of remote sensing images, the efficient extraction and transmission of task-oriented information to the corresponding user equipment (UE) remains a critical challenge. To address this challenge, we propose a task-oriented semantic coding and utility-optimal transmission (TUT) framework for satellite-integrated Internet. Specifically, we propose a metric named utility loss of information (UoI) to simultaneously capture the freshness, task updates, and task completion of UEs. Building upon this metric, our TUT framework leverages perceptual-weight maps (PM) generated from the remote sensing images which allowing for variable code rates specific to the tasks of UEs. Besides, the TUT framework can dynamically adjust the numerical distribution of PM to optimize semantic coding tailored to UoI. Considering limited onboard resources, we further model a long-term UoI minimization problem by utilizing the Lyapunov optimization framework and decompose it into a set of single-slot problems, and employ a proximal policy optimization (PPO) algorithm to solve this non-convex UoI minimization problem. Simulation results demonstrate that our TUT framework can achieve minimum long-term average UoI and power consumption compared to the state-of-the-art schemes.

Low earth orbit (LEO) satellite mega constellations are regarded to provide pervasive intelligent services in the upcoming sixth generation network via the inter-satellite links (ISL). However, the inherent challenges of LEO satellites including limited onboard resources and failure-prone topology, create substantial hurdles for multi-attribute services routing in mega constellations. In this paper, we propose a multi-attribute consistency segment resilient (MCSR) routing algorithm, and a segmentation approach is designed to partition the mega constellation into non-intersecting segment routing domains (SRDs) through joint optimization of intra- and inter-SDRs update time, which leads to the potential of balancing network load and minimizing routing convergence time. Then, we utilize the multi-attribute consistency to determine the dominant paths of ISLs within and between SRDs for multi-attribute services. Furthermore, we develop a resilient rerouting strategy that utilizes the ephemeris to manage periodic ISL handovers, and selects a reserved/recalculated candidate path from the dominant paths for ISL random failures. Thus, our MCSR routing can converge to an optimal path for multi-attribute services from the dominant paths under ISL failures in mega constellations. Finally, we develop a testbed and simulation results validate the advantages of MCSR routing in handling multi-attribute services and rerouting capability in response to failures.

Extremely large reconfigurable intelligent surface (XL-RIS) is emerging as a promising key technology for 6G systems. To exploit XL-RIS’s full potential, accurate channel estimation is essential. This paper investigates channel estimation in XL-RIS-aided massive MIMO systems under hybrid-field scenarios where far-field and near-field channels coexist. To handle the high-dimensional nature of XL-RIS channels, a convolutional dictionary learning (CDL) problem is formulated, which is cast as a bilevel optimization problem. To compute the gradient of the upper-level objective, we introduce an unrolled optimization method based on proximal gradient descent (PGD) and its special case, the iterative soft-thresholding algorithm (ISTA). We propose two neural network architectures, Convolutional ISTA-Net (CISTA-Net) and its enhanced version CISTA-Net+, for end-to-end optimization of the CDL. To overcome the limitations of linear convolutional dictionary in capturing complex hybrid-field channel structures, we further replace linear convolution dictionary with convolutional neural network blocks in the gradient descent step, while employing a learnable proximal mapping module and incorporating cross-layer feature integration. Simulation results demonstrate the effectiveness of the proposed channel estimation algorithms for hybrid-field XL-RIS massive MIMO systems.

This paper proposes a concatenated multi-belief ordered likelihood decoding (MB-OLD) algorithm for rate-compatible (RC) short low-density parity check (LDPC) codes, where the output log-likelihood ratios (LLRs) of belief propagation (BP) are sent to a well-designed bit-flipping decoder, which we called ordered likelihood decoder (OLD). In contrast to conventional ordered statistic decoder (OSD), the test error patterns (TEPs) sequence of OLD is generated from most likely to least likely, where the ordered reliability sequence associated with the most reliable basis (MRB) is approximated as multiple lines, and a stopping criterion (SC) is taken to reduce the decoding complexity. Furthermore, we analyze the LLR behavior of BP decoder in short block-length regimes, and propose an optimal iteration number. Based on these analyses, the output LLRs of BP within the optimal number of iterations are well combined and sent to OLD. Simulation results show that the proposed MB-OLD has the superior decoding performances in terms of error-rate and decoding complexity than its counterparts.

Semantic status update (SSU) communication is envisioned to provide semantic-aware and energy efficient semantic information (SI) delivery in future intelligent Internet of Things (IoT) applications. In this paper, we integrate the knowledge base (KB)-enabled semantic network into a discrete time Markov chain, and introduce a new metric in the SSU communication system, named semantic utility loss (SUL), which captures the timeliness and estimation accuracy of SI. The transmitter samples and extracts SI from the physical process, and sends the SSU. To combat semantic noise, the receiver can update KB at the cost of energy consumption to keep semantic match with the transmitter, i.e., inferring informative SI from received SSU. To minimize the weighted sum of SUL and overall energy cost incurred by transmitting SSU and updating KB, we formulate an infinite horizon average cost Markov decision process. We prove that the optimal joint transmission and updating (JTU) policy has a double threshold structure concerning SUL. Simulation results show the superiority of the proposed JTU policy over the zero-wait and sample-at-change baseline policies. In addition, we reveal that under the optimal JTU policy, the SSU communication framework outperforms the non-SSU framework in providing informative and energy efficient SI delivery.

Deep learning-based classifiers have been widely used in the field of financial fraud transaction detection. However, training a high-performance classifier for fraud detection is challenging due to the lack of sufficient labeled fraud data. Particularly, it is difficult to detect stealthy fraud transactions that closely mimic benign user behaviors. We observe that the suspicious transactions identified by the online detection system can augment the feature space to improve the detection performance of machine learning-based models. In this paper, we propose a new framework GIANTESS to leverage suspicious transactions to augment the feature space and thus enhance the detection of stealthy fraud transactions. Our semi-supervised approach combines both labeled transactions and unlabeled suspicious transactions to train a detection model. Specifically, it first estimates pseudo labels of suspicious transactions and then combines the pseudo labels with ground truth labels to train the detection model. We conduct experiments on two real-world datasets to demonstrate the effectiveness of our proposed method on detecting stealthy fraud transactions. The experimental results show that GIANTESS successfully improves the recall by up to 6.3% at the fixed low false positive rate of 1%. We also perform a 9-week deployment test of our system in a real-world online payment platform to demonstrate the performance of GIANTESS.

In the upcoming sixth-generation (6G) networks, semantic communication has made remarkable strides, where the transceivers utilizing local knowledge bases (KBs) to encode and recover semantic information. In this paper, we propose a universal weighted-KB (UW-KB) endowed with a sample confidence function for an end-to-end (E2E) task-unaware semantic communication system, where both the KB and semantic coding networks at the transceivers are incomplete in the initial stages. This intelligent UW-KB is shaped by receiver feedback during training, autonomously assigning weights to samples to mitigate biases in KB data, which significantly improves the efficiency of semantic coding networks. Simulation results demonstrate the effectiveness of our UW-KB in addressing KB data bias, providing valuable insights to bolster the robustness of task-unaware semantic communication systems.

In this paper, towards the ultra-reliable low-latency requirements of industrial Internet of Things (IIoT), we design a low decoding complexity ordered statistic decoder (OSD) for short analog fountain codes (S-AFCs). We first propose a concatenated decoder named soft-OSD (S-OSD) for S-AFCs, where the S-AFCs are concatenated with LDPC codes. Then, we analyze the log-likelihood ratio (LLR) output of inner decoder via the density evolution (DE), the DE results provide the theoretical guidelines to design the discarding criterion (DC) of test error patterns (TEPs) and stopping criterion (SC) to lower the complexity of S-OSD. Simulation results show that the S-OSD can achieve the same error performance with existing decoding algorithms for S-AFCs, and the complexity of S-OSD is greatly decreased, in terms of the average re-encoding number of OSD and operations number per information bit.

In this paper, we first introduce an efficient method for estimating weight distributions of polar codes and polarization-adjusted convolutional (PAC) codes. Based on a recursive algorithm of computing the weight enumerating functions of polar cosets, this method focuses on two key objectives: accurately determining the number of low-weight codewords and quickly approximating the distribution of high-weight codewords. Then we optimize the Reed Muller-Gaussian Approximation (RM-GA) rate profiling scheme with the help of the proposed method aiming at reducing the truncated union bound (TUB). Simulation results demonstrate that the proposed hybrid method maintains competitively low complexity while effectively achieving the objectives. The TUB-improved RM-GA rate profiling scheme for polar codes exhibits a performance improvement of nearly 1 dB at 10–4compared to GA and around 0.3 dB improvement compared to RM-GA. The proposed scheme for PAC codes also achieves an enhancement of approximately 0.52 dB at 10–5compared to the commonly used RM-GA scheme.

In the Satellite-integrated Internet of Things (SIoT), typical multi-access schemes, i.e., Contention Resolution Diversity Slotted ALOHA scheme (CRDSA), face with the obvious challenge of heavily conflicting packets regarding high channel traffic, which is not well addressed by the methods of Successive Interference Cancellation (SIC) due to the stubborn loop issues. In this work, therefore, we develop a Generalized Deduplication (GD) based Contention Resolution Diversity Slotted ALOHA scheme with a Compulsory Divorce mechanism (CD-CRDSA) by considering the correlative properties among the accessing data from the users. In particular, the proposed mechanism could effectively separate individual packets from conflicting slots to maintain the sustainability of SIC process, thus achieve better throughput. Moreover, we make the theoretical analysis on the throughput performance with the compression gain of the proposed mechanism. The simulation results indicate that compared to the typical CRDSA protocol and the Non-Orthogonal Multiple Access (NOMA) scheme, the proposed CDCRDSA significantly reduces the amounts of un-resolved slots and improves throughput performance, especially in high-load areas.

Satellite-integrated Internet can provide pervasive intelligent services for ubiquitous terrestrial equipments (TEs) in the forthcoming sixth generation network. Consider that the most of existing multicast systems in satellite-integrated Internet are content independent, which may result redundant data transmission in satellites with limited resources, we propose a semantic empowered rate splitting multiple access (RSMA) downlink system. First, we propose a semantic empowered metric, named Utility Loss of Information (UoI), which can simultaneously capture freshness, mismatch of transceivers, and environment ingredient for the RSMA downlink system. Then, we design a joint content- and environment-aware sampling policy for discrete multistate Markov sources to achieve minimum UoI, and provide rigorous proof to show the policy has a threshold structure and derive the closed-form transmission ratio. Further, we formulate a joint optimization of power allocation and rate control for the RSMA downlink system to minimize long-term average UoI with limited onboard resources, and utilize the Lyapunov optimization framework to transform the above problem to minimize the upper bound of corresponding drift-plus-penalty expression, and solve via a deep reinforcement learning-based algorithm. Simulation results validate that our scheme achieves the minimum long-term average UoI, under optimal tradeoff among timeliness, reliability, and environment-aware importance, and outperforms the state-of-the-art schemes.

Recently, the Satellite-Integrated Internet of Things (S-IoT) has attracted wide interest in remote data-gathering scenarios supporting requirements of user access within a wide area. Nevertheless, considering various priorities of timeliness, huge challenges in the diversified access scenario still exist, which could not be addressed efficiently by the current Irregular Repetition Slotted ALOHA (IRSA) access protocol. Therefore, in this paper, we propose an Value-Optimal Irregular Repetition Slotted ALOHA (V-IRSA) mechanism for the S-IoT via a distributed non-cooperative game theoretic approach, in which a utility function of the Cost of Information Value (CoIV) is developed for capturing the loss of information value during the packet transmission. By deriving the closed-form of the CoIV function, we make optimization on the proposed mechanism parameters during which various priorities of IoT devices could learn autonomously with their respective utilities in a selfish way. Numerical results show that the proposed mechanism could efficiently achieve a higher access probability for more emergent nodes in the system compared with the present algorithm.

In this paper, we propose three unequal timeliness (UT) protection massive access (UTMA) schemes in satellite-based Internet of Things (S-IoT) for mission critical communications (MCC) user equipments (UEs) with three types of timeliness requirements: independent successive UTMA (IS-UTMA), extended cognitive offloading UTMA (ECO-UTMA), and independent cognitive offloading UTMA (ICO-UTMA). First, MCC UEs are grouped according to their timeliness requirements, and a multi-dimensional codebook is introduced to resolve the UE collisions in massive access. Then, the IS-UTMA exclusively allocates time slots and pilots to different MCC UE groups to perform massive access, while the ECO- and ICO-UTMA allow timeliness critical group to share resources with timeliness tolerant group to improve the system timeliness. To capture the timeliness evaluation of each MCC UE group, we utilize age of information (AoI) to model the information freshness and derive closed-form expressions of average AoI (AAoI) by tracing the access failure probability (AFP) and instantaneous AoI. Furthermore, we establish the parameter optimization problems to minimize AAoI under desired AFP requirements. Extensive simulations validate the accurate of theoretical derivations, and demonstrate the effectiveness of the proposed UTMA scheme with joint optimized parameters, which can achieve minimum AAoI under desired AFP than the state-of-the-art schemes.

Satellites can provide timely status updates to massive terrestrial user equipments (UEs) via non-orthogonal multiple access technology (NOMA) in satellite-based Internet of Things (S-IoT) network. However, most of the existing downlink NOMA system are content-independent, which may result redundant transmission in S-IoT with limited resources. In this paper, we design a content-aware sampling policy via a semantic-empowered metric, named Age of Incorrect Information (AoII) to evaluate the freshness and value of status updates simultaneously, and formulate a long-term average AoII minimization problem with three constraints, including average/peak power constraint, network stability and freshness requirement. By regarding the long-term average AoII and three constraints as Lyapunov penalty and Lyapunov drift, respectively, we transform the long-term average AoII minimization problem to minimize the upper bound of Lyapunov drift-plus-penalty (DPP). Then, we utilize the deep reinforcement learning (DRL) algorithm Proximal Policy Optimization (PPO) to design our AoII minimization resource allocation scheme, and solve the non-convex Lyapunov optimization problem to enable the semantic-empowered downlink NOMA system. Simulation results show that our proposed SAC-AMPA scheme can achieve the optimal long-term average AoII performance under less power and bandwidth consumption than state-of-the-art schemes.

This paper investigates a wireless remote control problem in cyber-physical system (CPS) with multiple processes. In the system, sensors collect the state information of each process and transmit it to the controller through wireless channels. The communication constraints, including transmission delays, packet loss, and bandwidth limitation, are taken into account. To evaluate control timeliness for each process, this paper adopts the concept of age of information (AoI) in the context of closed-loop control. Meanwhile, to strike a tradeoff between transmission delay and outage, this paper introduces an innovative cross-slot scheduling policy not covered in existing literature, which can freely allocate transmission time and occupancy bandwidth. We prove that the scheduling problem in bandwidth limited remote control is an NP-hard problem, and establish the optimization problem as a Markov decision process (MDP) problem. The Deep-Double-Dueling-Q-Learning (D3QN) algorithm is employed to approximate the optimal scheduling policy for the scenario where the channel information is unknown and the system is model-free. By extensive simulations, the proposed cross-time slot scheduling policy demonstrates superior effectiveness in allocating time-frequency resources and achieving outstanding results.

Recently unsupervised machine learning based systems have been developed to detect zero-day Web attacks, which can effectively enhance existing Web Application Firewalls (WAFs). However, prior arts only consider detecting attacks on specific domains by training particular detection models for the domains. These systems require a large amount of training data, which causes a long period of time for model training and deployment. In this paper, we propose RETSINA, a novel meta-learning based framework that enables zero-day Web attack detection across different domains in an organization with limited training data. Specifically, it utilizes meta-learning to share knowledge across these domains, e.g., the relationship between HTTP requests in heterogeneous domains, to efficiently train detection models. Moreover, we develop an adaptive preprocessing module to facilitate semantic analysis of Web requests across different domains and design a multi-domain representation method to capture semantic correlations between different domains for cross-domain model training. We conduct experiments using four real-world datasets on different domains with a total of 293M Web requests. The experimental results demonstrate that RETSINA outperforms the existing unsupervised Web attack detection methods with limited training data, e.g., RETSINA needs only 5-minute training data to achieve comparable detection performance to the existing methods that train separate models for different domains using 1-day training data. We also conduct real-world deployment in an Internet company. RETSINA captures on average 126 and 218 zero-day attack requests per day in two domains, respectively, in one month.

The upcoming space-air-ground integrated network (SAGIN) can provide status updates relaying for ground user equipment (UEs). However, the SAGIN cannot utilize traditional hybrid automatic retransmission request (HARQ) for reliable transmission due to the high bit error rate (BER) and long propagation latency. In this paper, we propose the age-critical long erasure code-CCSDS file delivery protocol (LEC-CFDP) schemes with the metric of age of information (AoI) to realize timely status updates in dual-hop SAGIN. We first propose the uniform LEC-CFDP (U-LEC-CFDP), where the UE and satellite can uniformly insert one LEC packet in every$(L-1)$information packets, and the receiver can utilize the LEC packet to recover the lost packets and avoid retransmission. Moreover, the satellite can immediately forward the successively recovered information packets to the destination, named U-LEC-i CFDP, and a close-form expression of peak AoI (PAoI) for the U-LEC-i CFDP is derived. To further improve PAoI, we model a partially observable Markov decision process (POMDP) problem to analyse optimal$L$for our dynamic LEC-i CFDP (D-LEC-i CFDP), and design an effective Point-based Informed Bound (PIB) algorithm to update optimal$L$. Simulation results show that the D-LEC-i CFDP scheme can lower the expected end-to-end delay and PAoI in comparison with U-LEC-CFDP schemes.

To satisfy the diversified timeliness requirements in massive machine-type communications (mMTC) for satellite Internet of Things (S-IoT), we propose two unequal timeliness protection (UT) schemes based on the grant free age-optimal (GFAO) random access protocol, where the number of access slots in a frame can be adjusted according to the system load to achieve the required age of information (AoI) performance. We first propose the independent UT protection (IUT) scheme, where the different groups of user equipments (UEs) are successively access according to their AoI priority. Then, we propose the expanded UT protection (EUT) scheme, where the lower priority groups are allowed to offloading access with the higher priority groups. By exploiting Markov analysis through tracing the instantaneous AoI evolution of UE from each priority group, we derive the closed-form expressions to the average AoI (AAoI) of different priority groups and the system AAoI for multitype services coexistence mMTC in practical S-IoT. Simulation results show that both of IUT and EUT schemes can satisfy the AAoI of the higher priority groups, and the EUT scheme can improve the AAoI of the lower priority group, thus improve the system AAoI.

The upcoming mega low-earth orbit (LEO) high-throughput satellite constellation is regarded as an emerging paradigm shift in the fifth-generation-advance (5GA) networks. In this article, we propose a multitype services coexistence handover (MSCH) nonorthogonal multiple access (NOMA) scheme for a dual-layer mega LEO satellite constellation, which can simultaneously and efficiently provide uplink NOMA for three types of fifth-generation user equipments (UEs): 1) mission-critical communications (MCCs) UEs (CUs); 2) massive machine-type communications (mMTCs) UEs (MUs); and 3) enhanced mobile broadband (eMBB) UEs (EUs). The EUs are mainly served in the higher layer satellites for longer service duration and may handover to the lower layer satellites to coexist with CUs or MUs. Moreover, the CUs and MUs perform grant-based (GB) and grant-free (GF) NOMA on resource blocks (RBs) in the lower layer satellites, respectively. Then, we derive the closed-form expressions of three specific key performance indicators (KPIs), i.e., outage probability (OP), system throughput (ST), and ergodic capacity (EC) in the MSCH NOMA scheme, and design five corresponding NOMA algorithms. Simulation results verify the accuracy of our theoretical derivations and show that the proposed NOMA schemes can achieve a better KPI performance than the state-of-the-art ones.

In this article, we focus on a grant-free massive access setup and analyze its Age of Information (AoI), where a large number of user equipments (UEs) are randomly activated and attempt to transmit status update packets to a base station (BS) over a common shared channel. To support this age-critical grant-free massive access, we propose a graph-based spatially coupled irregular repetition slotted ALOHA (G-SC-IRSA) random access protocol, which utilizes the pseudo-random access pattern (PRAP), coupled frames, and sliding window decoder (SWD) to improve the packet loss rate (PLR) and AoI performance. Specifically, we derive the approximate expressions to the normalized Average AoI (AAoI) as a function of the PRAP and system load. Then, we establish the problem of minimizing the AAoI under the G-SC-IRSA protocol. Furthermore, we utilize the density evolution (DE) with a bipartite graph to evaluate the system load threshold of G-SC-IRSA in asymptotic regime, achieve an optimal degree distribution via the differential evolution algorithm, and finally obtain the optimal PRAP with progressive edge-growth algorithm. Simulation results validate the accuracy of our theoretical derivations and show that the G-SC-IRSA can achieve the minimum AAoI with the optimal PRAP and outperforms the existing benchmark schemes in terms of PLR and AAoI.

The upcoming satellite Internet of Things (S-IoT) can provide status updates relaying for ground user equipment (UE). However, the S-IoT cannot utilize conventional hybrid automatic retransmission request (HARQ) for reliable transmission due to the high bit error rate (BER) and long propagation latency. The consultative committee for space data systems (CCSDS) has proposed the CCSDS file delivery protocol (CFDP) to relieve the long propagation latency, and the CFDP utilizes retransmission to guarantee the reliability. In this paper, we propose two age-critical long erasure coding-CFDP (LEC-CFDP) schemes to realize dual-hop timely status updates in S-IoT via a relay satellite over shadowed Rician (SR) fading channel, where the satellite and destination can select the deferred or asynchronous mode to adjust the number of inserted LEC packets, called D-LEC CFDP and A-LEC CFDP, respectively. Further, the satellite can select half-duplex or full-duplex relay mode, i.e., LEC-h CFDP or LEC-f CFDP to forward packets to the destination. We derive a close-form expression for the peak age of information (PAoI) and an approximation expression for the expected end-to-end delay for the D-LEC-f CFDP scheme. Moreover, we propose an A-LEC-f CFDP scheme to further improve the PAoI, and model it as a partially observable Markov decision process (POMDP) problem, which can be solved by a low complexity Point-based Informed Bound (PIB) algorithm. Simulation results verify the accuracy of the theoretical derivations, and illustrate that the A-LEC-f CFDP scheme can achieve lower end-to-end delay and PAoI in comparison with the existing schemes.

This paper investigates the resource allocation and computation offloading problem for multi-access edge computing (MEC) systems, where multiple mobile users (MUs) equipped with multiple antennas access the base station in a non-orthogonal multiple access manner. We jointly optimize the offloading ratio, computational frequency and transmit precoding matrix of each MU to minimize the total energy consumption of all MUs while satisfying the latency constraints. The problem is formulated as a non-convex optimization problem and a two-layer iterative method is proposed to solve the problem efficiently with low complexity. Specifically, we first decompose the original problem into several subproblems, and then sequentially solve these subproblems in an alternative fashion. Furthermore, we also discuss the optimal decoding order of MUs under two different scenarios. Firstly, when the MUs’ channel conditions are similar, by deriving closed-form expressions for energy consumptions of all MUs, we prove that the optimal decoding order is only determined by the latency requirements. On the other hand, when the MUs’ channel conditions are different, we show that the optimal decoding order is determined by both the channel conditions and the latency requirements. As such, we propose a metric aiming to balance the effects of channel conditions and latency requirements on the MUs’ decoding order. Simulation results validate the convergence of the proposed method and demonstrate its superiority over benchmark algorithms.

Satellite Internet of Things (S-IoT) with low Earth orbit satellites has become an effective solution for providing global coverage for massive machine type communications (mMTC). Considering that the massive user equipments covered by the S-IoT are periodically activated and dominated by short packet communications, the pilot collision has become a challenging problem due to the limited length and number of pilot sequences. In this paper, we propose a low-correlation superimposed pilot grant-free massive access (LSP-GFMA) scheme, where a low-correlation-zone periodic sequence (LPS) is designed for the superimposed pilot (SP) structure. Our LPS can maintain low cross-correlation with random non-orthogonal shifts compared with the conventional Zadoff-Chu sequence (ZCS), which can alleviate pilot collision while ensuring high spectral efficiency. In addition, we propose an iterative channel estimation based on Kaczmarz algorithm to attain accurate channel state information for the SP structure with low complexity. Then, we derive the theoretical expressions of access failure probability (AFP) and achievable throughput for our LSP-GFMA scheme under the shadowed-Rician fading channel. Simulation results validate the accuracy of our theoretical derivations, and demonstrate that our LSP-GFMA scheme with LPS can achieve lower AFP and higher achievable throughput than that with ZCS, and also outperforms the state-of-the-art schemes.

In this paper, we focus on the optimal problem of average age of information (AAoI) in grant-free massive access, and propose an age-critical frameless ALOHA (ACFA) random access protocol, where the AAoI is implicitly reduced by banning the transmission of activated user equipments (UEs) recovered successfully in the last frame. In particular, we analyze the dense and sparse access models according to the activation probability, and present these scenarios with time-stamped sampling either at the beginning of the frame or in the first slot transmitting the packet. In order to qualify the AAoI of proposed protocol, we define two virtual rates and establish an iterative framework to analyze the access successful probability (ASP) of the protocol in asymptotic regime, and derive the closed-form expressions of AAoI as a function of ASP and virtual rate in all cases. Further, we formulate the optimal problems of normalized AAoI in all cases, and obtain the selection of access parameters by asymptotic analysis and simulations, respectively. Finally, we compare our protocol with state-of-the-art schemes, and the simulation results show that the ACFA random access protocol outperforms these benchmark schemes, and has great potential of access-banned policy in minimizing AAoI for frame-based protocols.

Code domain grant-free random access (GFRA) is regarded as a potential framework to serve massive access in Internet of Things (IoT). In this paper, we propose an$LT$-collision resolution GFRA ($LT$-GFRA) scheme by combining a pilot set containing$L$orthogonal pilots and a$T$-order codebook, where each activated user equipment (UE) randomly selects one of$L$pilots, and directly sends to the base station (BS) followed with data encoded by the$T$-order codebook together. Thus, the BS can receive$L$different frames and a conventional collision occurs when more than one UE select the same pilot. Moreover, we design a successive cancellation then joint decoding (SCJD) decoder, and prove the BS can decode at most$T$UEs from the frame on the same pilot and can recover up to$LT$UEs. Then, we derive the decoding error probability of our$LT$-GFRA scheme in Rayleigh fading channel, and further derive the access failure probability (AFP) and the system throughput in the single- and multiple-antenna systems. We also extend and evaluate our$LT$-GFRA scheme in the shadowed-Rician fading channel. Finally, simulations validate our analytical results, and indicate that our$LT$-GFRA scheme can greatly outperform the state-of-art schemes for massive access in IoT.

Uplink broadband communication is very popular for V2X high date rate sensing data uploading. A joint design of UL coherent transmission and coherent receiving (JCTCR) mechanism is proposed to significantly increase the uplink capability of 5G-Advanced networks. In the transmission side, a physical layer cooperation mechanism is introduced, the data is transmitted as a single transport block over two 2Tx UEs’ physical layer. The tight cooperation in physical layer forms a more powerful 4Tx virtual UE (VUE), which significantly improves the uplink edge user perceived throughput by about 143%. The coherent transmission performance sensitivity to the synchronization error is also evaluated and analyzed in the paper. In the receiving side, a broadly applicable UE assisted non-ideal backhaul multi-BS coherent receiving solution is introduced. In order to enable the coherent joint receiving in non-ideal backhaul scenario, a novel mechanism to make use of the UE OTA signaling assistance is proposed to enable the multi-BS channel information acquisition and multi-BS joint receiving, which makes the JCTCR to be widely applicable for general deployment scenarios and no need for very high quality backhaul assumption in traditional way. Evaluation results show, although the pre resource allocation and the channel information acquisition have singling overhead, the CJTJR still can achieve the uplink capacity gain of 42%, only slight 4% decrease comparing with the ideal coherent receiving with ideal backhaul.

In this article, we investigate the freshness of the vehicular status updates in space–air–ground-integrated networks (SAGINs), where the status updates are generated by sampling a fixed-rate dynamic Markov process and delivered to the monitor over an unreliable channel instantaneously. The Age of Information (AoI) is adopted to capture the timeliness of the status updates. Two hybrid automatic repeat request (HARQ) schemes, namely, classical HARQ scheme and incremental redundancy HARQ (IR-HARQ) scheme, are taken into consideration to combat the errors occurred in the transmission. In this setting, once an update is not decoded successfully, one should carefully decide how to schedule the updates for optimizing the AoI. Especially, differential encoding scheme is introduced in the considered system to exploit the temporal correlations of the source. By differential encoding, each update can be actual or differential, based on the differential encoding level. To minimize the long-term average age, we formulate a Markov decision process (MDP), and prove that the optimal transmission policies for classical HARQ scheme and IR-HARQ scheme behave differently in threshold structures. Furthermore, we jointly optimize the codeword length, differential encoding level, and retransmission times to minimize the AoI. The performance comparison shows the advantages of the IR-HARQ scheme over the classical HARQ scheme from the age perspective.

Network slicing enables multiple virtual networks run on the same physical infrastructure to support various use cases in 5G and beyond. These use cases, however, have very diverse network resource demands, e.g., communication and computation, and various performance metrics such as latency and throughput. To effectively allocate network resources to slices, we propose DeepSlicing that integrates the alternating direction method of multipliers (ADMM) and deep reinforcement learning (DRL). DeepSlicing decomposes the network slicing problem into a master problem and several slave problems. The master problem is solved based on convex optimization and the slave problem is handled by DRL method which learns the optimal resource allocation policy. The performance of the proposed algorithm is validated through network simulations.

In this paper, we consider a status update system, in which the source monitors a dynamic Markov process. The status updates are generated with a fixed rate, and delivered to the receiver over an unreliable channel instantaneously. The timeliness of the status updates is characterized by a recent metric, age of information (AoI). In this setting, error would occur in the transmission, deteriorating the reliability of updates. Thus, once an update is not decoded successfully, one should decide whether to retransmit the stale update or switch to transmit the newly generated one. Especially, differential encoding scheme is applied to the considered system to exploit the temporal correlations of the source. By differential encoding, each update can be actual or differential, based on the differential encoding level. To minimize the long-term average age, we formulate a Markov Decision Process (MDP). We prove that the optimal transmission policy has a threshold structure. We also show the existence of the optimal differential encoding level that minimizes the long-term average age under the optimal transmission policy. Numerical results are provided to validate our analytical results. Furthermore, numerical results show that the optimal differential encoding level is decreasing with higher erasure probability of the channel.

We focus on a mobile caching system using erasure codes to cache content in mobile devices, which enter and depart a fixed area according to Poisson process. Due to the high mobility of devices, cached content is lost and to be repaired by device-to-device (D2D) communication. We consider the limited communication range and repair process with multiple contacts among mobile devices. We adopt a coded repair scheme which the repair process runs periodically, and derive analytical expressions of the average repair delay. The derived expressions are then used to evaluate repair delay using different erasure codes and file size. The results show that maximum distance separable codes can yield lower average repair delay compared to regenerating codes for small size of file. We further find that increasing the speed of mobile devices can reduce the average repair delay.

The Internet of Things (IoT) is creating billions of connected devices and generating enormous amounts of data. Data needs to be stored efficiently so that it can be retrieved easily on demand. Cloud storage is an inevitable choice for data management for IoT. Because of application diversity, limited bandwidth of end devices and the demand for real time, it is necessary to decrease the cost of data access in Heterogeneous Distributed Cloud Storage System (HDCSS). According to the point that applications always access the partial data, this paper combining the data access rate, proposes a degraded read scheme based Local Reconstruction Code (LRC) to improve the local max throughput in HDCSS. Simulation results show that our proposed scheme can achieve about a 50% increase in local throughput of hot data blocks without adding additional access load compared with commonly used LRC.

High-throughput satellite (HTS) is viewed as a promising solution for the next generation of satellite-based Internet of Things (S-IoT). Considering that the onboard communication resources, such as power and storage, are limited, we formulate a joint network stability and resource allocation optimization problem to maximize the long-term network utility of a nonorthogonal multiple access (NOMA) S-IoT downlink system. First, we establish two virtual queues for both the data queueing and power expenditure. Then, a joint optimal problem can be formulated as a problem that optimizes the time average of network utility, which perfectly matches the Lyapunov optimization framework. Therefore, by taking into account the condition of successive interference cancellation decoding, we propose a practical solution under the Karush-Kuhn-Tucker (KKT) conditions, and further introduce an optimal solution by using the particle swarm optimization (PSO) algorithm for the joint resource allocation problem. The simulation results demonstrate that our joint optimization allocation schemes outperform the existing benchmark schemes.

Internet of Things (IoT) is regarded as the fundamental platform for many emerging services, such as smart city, smart home, and intelligent transportation systems. With ever-increasing penetration of IoT, it becomes of great importance to ensure the IoT security, as the security threats are extended from the cyber world to the physical world. In this article, we investigate physical-layer authentication to help verify the identity of IoT entities for preventing unauthorized access to information or service. Specifically, we propose a Gaussian-tag-embedded physical-layer authentication (GTEA) scheme by using a weighted fractional Fourier transform (WFRFT). Through the superimposition of a low-power Gaussian WFRFT tag onto the message signal, the legitimate receiver can verify the authenticity of the received signal at the physical layer, without being detected by adversaries. Moreover, security analysis shows that with the deliberately designed Gaussian tag, the GTEA scheme is robust against spoofing and replaying attacks. In addition, tradeoff analysis and simulation results are provided to demonstrate the capability of the GTEA scheme in achieving reliability of the message delivery, stealth of the embedded tag signal, and balancing the tradeoff among the robustness of user authentication. Moreover, a prototype is further developed using FPGA and experiments are conducted to demonstrate the effectiveness and performance improvement of the proposed GTEA scheme.

Network selection is one of the important techniques in cognitive radio networks (CRNs). With the development of network convergence technology and the popularity of heterogeneous networks, multiple primary CRNs interacting with multiple authorized networks are becoming possible, which can provide secondary users with more spectrum resources by network selection. Network selection is the key to spectrum sharing between CRNs and multiple primary networks. However, the spectrum sensing results, highly complex system state, and unsystematic research framework make the research of network selection very challenging. Traditional network selection algorithms are offline selection methods that are based on prior knowledge of primary networks. However, in the complex network environment, it is impossible to get prior knowledge from multiple primary networks, because the offline network selection methods lack efficiency. In order to meet these challenges, this article aims at improving the quality of service of cognitive users, and based on reinforcement learning method and the achievements of dynamic spectrum access of cognitive radio in single primary network environment, proposed a deep reinforcement learning based online network selection method of CRNs with multiple primary networks.

In this paper, we propose a novel multi-slot pilot allocation (MSPA) random access scheme with unequal access latency (UAL) protection for user equipments (UEs) in massive machine-type communications (mMTC). In order to provide UAL protection, we allocate the UEs into different groups according to their UAL requirements, where the higher priority groups can access in a stringent latency under the required access failure probability (AFP) requirement; while the lower priority groups are able to access with predetermined AFP by allocated multi-slot. Specifically, our generalized UAL-MSPA random access protocol is incorporated into two considered UAL protection schemes, i.e., the independent UAL scheme and the expanded UAL scheme. We derive the closed-form expressions to the AFP and the sum throughout for the investigated UAL schemes. By utilizing these analytical results, a joint parameter optimization problem is formulated for obtaining the minimum access latency under the desired AFP requirements. Detailed simulations validate our analytical results and confirm the efficiency of our proposed UAL schemes. Finally, the UAL-MSPA random access protocol with joint optimized parameters outperforms the existing protocols in terms of achieving high sum throughput and shortening the access latency of low priority group.

In this paper, we propose a novel power allocation scheme of downlink non-orthogonal multiple access (NOMA) system for space information networks (SINs). Since the communication resources such as power and storage on satellites are limited, we focus on an optimization policy of long-term resource allocation to meet these practical constraints. To address this problem, we convert the long-term optimization problem into a series of online power allocation and rate control problems by leveraging the Lyapunov optimization framework. Then, we employ the particle swarm optimization (PSO) algorithm to derive a globally optimal solution for this joint optimization problem, with a linear computational complexity. Simulation results show that the proposed joint optimization allocation NOMA scheme for SIN can outperform OMA and multiple sub-optimal NOMA benchmark schemes, in terms of long-term network utility, average arriving rate and queuing delay.

The soft-decision decoding of polar codes is a trend that will be extensively applied in modern complex communication systems. However, the existing soft-decision decoding of polar codes is not satisfied due to the poor performance and high complexity. In this paper, a novel adjustable list decoding and its soft-decision type are proposed. Some bounds are given to depict the features of the decoding list with a correct path, which provides a guide to adjust the decoding list. The proposed adjustable list decoding scheme can achieve an equivalent performance to conventional SCL with significant lower complexity. Moreover, the soft adjustable list decoding can also outperform than the conventional soft-decision decoding schemes in concatenated structures.

In this paper, we establish a sparse geometric-based millimeter-wave (mmWave) band multiple-input and multiple-output (MIMO) channel model between a high throughput satellite (HTS) and terrestrial user equipments (UEs) for space information network (SIN). By exploiting the inherent sparsity of mmWave band, we propose an adaptive random-selected multi-beamforming (ARM) estimation scheme for efficient mmWave MIMO channel modeling in SIN. The ARM estimation scheme measures the propagation paths between the HTS and UEs in angle domain, where the HTS can randomly select multiple beamformings to estimate the CSI of multiple UEs simultaneously. Compare to the existing fix number of measurements schemes, the required number of measurements in our ARM estimation scheme can adaptively reduce as well as the signal-to-noise ratio (SNR) increases. Simulation results show that our ARM estimation scheme can reduce the required number of measurements and achieve a better tracking performance over a wide range of SNRs.

Raptor code is the current standard of 4G long term evolution (LTE) evolved multimedia broadcast and multi-cast services (eMBMS), which is viewed as a potential approach in the design of ultra-reliable low latency communications (uRLLC) for 5G. This paper analyzes the performance of finite length non-binary (over finite field of order q, GF(q)) Raptor codes under ordered statistics decoder (OSD) towards uRLLC, where the non-binary Raptor code ensembles by a non-binary low density parity-check (LDPC) code as pre-code and a non-binary inner Luby transform (LT) code. Moreover, by investigating the property of code structure and decoding algorithm, an upper bound of decoding failure probability (DFP) of finite length non-binary Raptor code under OSD is derived. Simulation results validate the accuracy of our derived upper bound, and demonstrate that our non-binary Raptor codes can achieve 10â'5 DFP with block length 128 bits at SNR 3.6 dB.

Sparse code multiple access (SCMA) is one of the key techniques to address the high spectral efficiency and massive connectivity requirements for the fifth generation (5G) wireless systems. Moreover, polar codes are selected as the candidate scheme of control codes in enhanced mobile broadband (eMBB). Note that the joint design of channel coding and SCMA scheme can significantly improve the system overall performances, which essentially shows the potential for 5G massive machine type communications (mMTC). Thus, in this paper, we proposed a polarization weight (PW)-based polar coded SCMA (PC SCMA) system to satisfy the requirements of low complexity implementation and high reliability under a wide range of code length and rate. Our design of PW-based PC SCMA system is mainly including the following three aspects: 1) deploy the polarization weight (PW) algorithm to construct polar code with lower complexity; 2) employ the bit-reverse shortening (BRS) algorithm to achieve rate matching in the encoding part; 3) adopt the cyclic redundancy check (CRC) to set up an early stopping criterion in the decoding part. Simulation results show that the proposed PW-based PC SCMA system can outperform the existing PC SCMA system over AWGN and fading channels.

In this paper, towards the fifth generation (5G) massive machine type communications (mMTC), a theoretical framework of the design and evaluation model for analog fountain codes (AFC) is proposed. Motivated by the capacity analysis of AFC, we propose a weight adaptive (WA) AFC transmission scheme by introducing a limit feedback link, which can realize the optimal AFC in theoretical. Simulation results reveal that our WA-AFC coding scheme can approach the Shannon capacity in a wide range of SNRs over AWGN channel.

As a new type of rateless codes, Spinal codes can be proved in theory that it can achieve capacity over both the additive white Gaussian noise (AWGN) channel and the binary symmetric channel (BSC) with short message length. Due to the good adaptability under different channel conditions, Spinal codes have broad prospects in ultra-reliable low-latency communication (URLLC) scenarios such as self-driving car and factory automation. However, Spinal codes transmitted by short codes need frequent times of feedback, while transmitted by long codes have a high decoding complexity, which limits the practical application of Spinal codes. In this work, a new type of encoding scheme named as segmented CRC-aided scheme is proposed. In this scheme, message is equally divided into λ segments, each of which is concatenated with a cyclic redundancy check (CRC) sequence. At the decoding end, all the segments are decoded in parallel, and the correspondingly CRC check results are collected and transmitted back to the encoder together. The encoder judges the current decoding state through the feedback and then constructs the next encoding pass accordingly. The segmented CRC-aided scheme combines the advantages of long codes transmission and short codes transmission of the Spinal codes, it uses fewer feedbacks, and it can reduce the transmission of redundance bits. Results demonstrate that the proposed scheme has significant performance improvement over the original Spinal encoding scheme by achieving higher code rate with lower encoding complexity.

Space‐terrestrial integrated networks consisting of low earth orbit (LEO) satellites andterrestrial users are widely developed for potentially diversified requirementsof content distribution. With an obviously time‐varying topology, however, designing a distribution strategy faces several explicit challenges, such asprolonged content access latency and significant transmission overheads, due tolack of contact opportunities and limited on‐board storage space. In this study, therefore, a novel back‐tracing partition directed on‐path caching distributionmechanism (BPDM) is proposed for the file distribution in the hybrid LEOconstellation and terrestrial network. In the proposed strategy, a group offeasible on‐path cache nodes is iteratively selected by utilising awell‐designed cross‐timeslot graph, as well as a collaborative cached contentplacement strategy, called as multiple regions cooperative cache algorithm, bycarefully considering diversified popularity of target files. As a result, theproposed BPDM could efficiently reduce redundant transmissions of content accessfor different users by fetching objective file mainly from limited quantities ofintermediate caching nodes. Through the simulation results, the proposed methodcan obviously decrease the holistic overheads and access delay compared with theminimum spanning tree algorithm and Network Central Location (NCL) nodeselection metric.

Recently, a hybrid remote sensing network constituted by satellites in constellation and Unmanned Aerial Vehicles (UAVs) in formation attracts a lot of interests, benefiting from the flexible architecture and excellent rapid responsiveness. Considering frequently intermittent connectivity and limited resource onboard, Disruption‐Tolerant Networking (DTN) develops a feasible solution for the remote sensing scenarios. However, the intrinsic motion models of multifarious nodes lead to deterministic or semi‐deterministic contacts, which makes finding a reliable end‐to‐end routing path for timely data delivery difficult, with typical routing strategies such as Contact Graph Routing (CGR). To cope with such routing challenge in the hybrid network, a Probabilistic Contact Graph (PCG) is designed, taking the diverse node properties into consideration. In particular, a probability prediction model for semi‐deterministic contacts between the UAV nodes is proposed, with a semi‐Markov motion model for the UAV nodes. Besides, a Markov Decision Process based Routing (MDPR) algorithm is designed to search for a feasible data transmission path with a series of hybrid deterministic and semi‐deterministic contacts. Through the numerical and experimental simulations with Interplanetary Overlay Network (ION), the proposed MDPR algorithm shows excellent routing performance concerning delivery delay and delivery ratio, compared with the typical CGR strategy.

In a satellite disruption/delay‐tolerant network, bundle delivery is obviously affected by time‐varying parameters, i.e. bit error rate and propagation latency, due to constantly changing distance and connectivity between paired orbital nodes. The authors proposed a Markov decision‐based optimisation approach for bundle size, which could efficiently improve the expected time of delivery over a dynamic two‐hop inter‐satellite link. In particular, a group of optimal bundle sizes are adaptively selected according to current distance‐dependent channel parameters, leading to a full utilisation on intermediate node's memory. The simulation results verified the proposed method under different conditions with comparison.

In this paper, we propose an interference coordination scheme by an innovative combination of soft frequency reuse (SFR) and cooperative multipoint transmission (CoMP) to manage both co- tier and cross-tier interference in a randomly deployed macro-pico network modeled by Poisson Point Process (PPP). The proposed scheme is performed in three stages. First, the frequency reuse pattern of SFR for macrocells is determined based on the interference graph that describes interference relationship between macrocells. Second, the frequency reuse pattern of SFR for each picocell mainly depends on the measurement exchanged with its surrounding cells. Third, the CoMP scheme is performed on the secondary bands of macrocells to enhance the performance of Picocell-edge users and ensure fairness between various users. A scaling factor is considered to protect the data rates of macro cell-center users. In addition, the cross- component carrier (Cross-CC) proportional fair (PF) scheduling is adopted for the detailed resource block (RB) allocation in the carrier aggregation (CA) supported system to achieve the maximum of system fairness. Numerical results show that the proposed scheme can effectively improve cell-edge user data rate and ensure the fairness between users when compared with the SFR scheme and the full frequency reuse (Reuse 1) scheme.

With the development of secondary spectrum markets, it is anticipated that multiple Primary Networks (PRNs) who own underutilized spectrum resources will be incorporated into Cognitive Radio Networks (CRNs). In this scenario, CRNs will have a greatly enhanced choice of accessible spectrum resources to support large volumes of Secondary Users (SUs), and guarantee the QoS reliability. Network selection problem, i.e. choosing which PRN to access, is essential for CRNs in a multi-PRN environment. However, to the best of our knowledge, there is still lack of a unified method to address the network selection problem. In this paper, we aim to present a uniform framework to investigate and evaluate network selection strategies for CRNs. First, we model the interactive process of SUs and PUs as a Continuous Time Markov Decision Process (CTMDP), and abstract the network selection strategy into the set of decision variables with respect to system states in the CTMDP. Second, under the proposed framework, we discuss multiple existing strategies, such as random, greedy, and statistically-weighted. Third, to achieve a more effective method, we derive the performance gradient of CRNs' utility function with respect to the network selection strategy, and propose a gradient-based optimal network selection strategy by using the theory of Markov performance potential. At last, simulations are conducted to validate the correctness of the proposed analytical framework, and the effectiveness of the proposed network selection scheme.

In TD-LTE-A systems, feedback information of uplink channel is of importance for eNodeB to perform downlink scheduling, beamforming, and adaptive Modulation and Coding Scheme (MCS). Channel quality indicator (CQI) is the most important parameter in all feedback information, and therefore, how to improve the accuracy of CQI feedback is a key issue for system configuration. In this paper, we propose a frequency domain CQI feedback algorithm, using channel capacity invariability criterion and channel reciprocity. Simulations show that periodic feedback with frequency domain CQI feedback algorithm can reduce system first retransmission ratio by about 4% and improve system throughput by about 7%, compared with normal periodic feedback Mode 1-1. In addition, compared with aperiodic feedback Mode 3-1, the proposed CQI feedback algorithm costs less signaling overhead, while keeping similar performance in terms of system first retransmission ratio.

In TD-LTE-A downlink, multi-user beamforming is used to decrease co-channel interference of different users and increase system performance, and Signal-to-Leakage-and-Noise Ratio (SLNR) beamforming algorithm has been proved to have better performance in terms of sum capacity and average BER performance compared with other beamforming algorithms with moderate complexity. In order to further improve the performance, the power allocation algorithm is combined with the framework of SLNR beamforming. Based on the combined algorithm, we can maximize sum capacity and guarantee users' requirements in multiuser downlink scenario with multiple-input-multiple-output (MIMO). The problem is transformed into convex optimization and then solved by geometric programming. Simulation results show that sum capacity of the proposed algorithm is slightly inferior to water-filling algorithm, however, the proposed algorithm can satisfy users' requirement while water-filling algorithm cannot. Then, a sub-optimal algorithm is also proposed to reduce implementation complexity at the cost of 10% sum capacity loss.

Mobile device security has become increasingly important as we become more dependent on mobile devices. One fundamental security problem is user authentication, and if not executed correctly, leaves the mobile user vulnerable to harm like impersonation. Although many user authentication mechanisms have presented in the past, studies have shown mobile users prefer usability over security and, unfortunately, a higher level of security often entails sacrificing usability. Moreover, mobile users often unlock their devices in public spaces, inevitably resulting in a high possibility of user credentials disclosure. Motivated by the above, we introduce a novel user-habit-oriented authentication model, where mobile users can integrate their own habits with user authentication on mobile devices. The user-habit-oriented authentication turns a tedious security action into an enjoyable experience. Also, we propose a rhythm based authentication scheme, providing the first proof of concept toward secure user-habit-oriented authentication for mobile devices. Experimental results show that the proposed scheme has high accuracy in terms of false rejection rate. Also, the proposed scheme is able to protect from attacks caused by credential disclosure, which could be fatal to the traditional schemes.

With the emergence of secondary spectrum markets, it is envisioned that multiple Primary Networks (PRNs) with non-overlapping spectrum pools will be incorporated into Cognitive Radio Networks (CRNs). As a result, CRNs will have a greatly enhanced choice of accessible spectrum resources available to them, which, in turn, brings a significant increase in the diversity of available PRNs; this can greatly increase reliability and stability in the system performance experienced by secondary users (SUs) on the network. However, due to the nature of dynamic network environments, it is hard to meet the requirements of sensing task when the sensing resources, such as the number of participating SUs and A/D sampling capability, are limited. In this paper, we address this issue by studying the problem of cooperative sensing scheduling of CRNs for a dynamic multi-PRN environment. By jointly considering the dynamics of spectrum usage, and the channel conditions of SUs, cooperative spectrum sensing scheduling is formulated as two optimization problems, from the perspectives of primary users (PUs) and SUs, respectively. To solve these problems, two straightforward scheduling schemes are discussed: Random Scheduling and SNR-based Greedy Scheduling. To further improve the sensing performance, a cross entropy (CE) method-based sensing scheduling scheme is proposed. At last, simulation results validate the effectiveness of the proposed CE-based sensing scheduling scheme.

In TD-LTE-A system, the objective of conventional non-codebook beamforming algorithms is to maximize sum capacity to accommodate more users. However, fairness among users is not considered by these algorithms, and the performance of users with poor channel quality will always be bad. To relax the fairness requirement in resource allocation, this paper first introduces two parameters of user satisfaction into TD-LTE-A system for Guaranteed Bit Rate (GBR) and Non-GBR traffics, respectively. Then a user-satisfaction-based beamforming algorithm is proposed. This algorithm employs user satisfaction parameter to adjust the weights for weighted Signal-to-Leakage-plus-Noise Ratio (SLNR) algorithm. Finally, the weights of different traffics are also considered in the proposed beamforming algorithm so that average user satisfaction across different types of traffics can be modified according to the requirement of telecommunication operators. Simulation results show that the proposed algorithm can improve user satisfaction and user fairness, and average user satisfaction of GBR and Non-GBR traffics can be adjusted by changing of GBR priority parameter.

Coordinated multipoint (CoMP) is proposed recently as a promising technique to improve the performance of cellular networks and meet the increasing demand for digital service. However it faces several constraints to perform CoMP scheme in real systems. In this paper, we consider a downlink CoMP system and formulate the resource allocation problem of it in terms of resource block (RB) scheduling and power allocation (PA) under the constraints of both transmit power at each transmit point (TP) and backhaul capacity. Combining with existing scheduling methods, we propose a PA algorithm to solve the formulated problem. The proposed PA method decouples the problem into independent sub-problems in order to reduce the involved variables. Then, to further reduce the computation, suboptimal solutions are approached instead of the optimal ones. Simulation results verify that the proposed algorithm is able to improve the network throughput and save transmit power of TPs with reasonable computational complexity.

This paper investigates the effects of spectrum sensing errors on the performance of cognitive radio based dynamic spectrum access system (CR-DSA). We first analyze the DSA process with imperfect sensing information by a continuous-time Markov chain (CTMC) model, and then derive the performance metrics with respect to the sensing errors. To alleviate effect of errors in the spectrum sensing process on the system performance, we propose a joint optimization of the spectrum sensing and DSA process. The design is based on the observation that there exists the unique optimal false alarm (FA) probability/miss detection (MD) probability such that the achievable throughput of secondary system maximal. To find the optimal FA probability, a gradient information based algorithm is proposed, and simulation results reveal a significant performance improvement by virtue of the proposed algorithm.

To suppress co‐channel interference in polarisation‐enabled wireless communication systems, this work aims to provide an interference suppression scheme by exploiting polarisation domain, besides the state‐of‐the‐art temporal, frequency, spatial and code domains. System models, algorithms, characteristics and applications of polarisation filtering (PF) for co‐channel interference suppressions for polarisation‐enabled (e.g. orthogonal dually polarised antennas) wireless communications are investigated. Specifically, four system models for PF using subspace analysis are established and discussed. The four proposed system models are categorised based on different statistic characteristics of the target signal and that of the interfering signal: both the target signal and interference are temporal deterministic, the target signal is deterministic whereas interference is temporal random, the target signal is random whereas interference is deterministic and both the target signal and interference are random, respectively. Based on the statistic characteristics and subspace theory, the detailed PF implementation for each model is analysed and the closed‐form filtering operator is given. It is also shown that the PF implementation for each model can be attained by using one of the zero‐forcing matched subspace processing, decorrelating matched subspace processing or Wiener subspace processing. Furthermore, relationship among these four models indicates that, under certain conditions, the implementation of the other three models can be fulfilled by using the implementation of the first model. Numerical and simulation results show the effectiveness of the proposed scheme.