Remote state estimation plays a critical role in internet of things (IoT) systems, where distributed sensors report dynamic processes to remote estimators for control and monitoring. Traditional freshness metrics, such as age of information (AoI), do not necessarily capture the information value degradation caused by random transmission latency and content dynamics. In this paper, we evaluate the value of information through the lens of Shannon entropy by adopting theuncertainty of information(UoI), which quantifies the receiver’s uncertainty in estimating the source state caused by stale information delivery. We study a remote estimation system where a binary Markov source is sampled and transmitted over a randomly delayed channel with a long-term sampling frequency constraint. This problem is formulated as a constrained partially observable semi-Markov decision process (C-POSMDP), solved through Dinkelbach’s transformation and Lagrangian relaxation combined with the relative value iteration (RVI) algorithm. To mitigate the complexity of RVI, we further develop a low-complexity index-based policy by approximating the Bellman equation under relatively high delay. Numerical results show that both proposed policies outperform zero-wait, uniform, and AoI-optimal baselines, with the sub-optimal policy achieving near-optimal performance in relatively high delay regimes.

Robust perception for safety-critical Internet of Things (IoT) applications in dense jamming environments demands advanced radar sensing capabilities with enhanced interference mitigation and array-beampattern optimization. When the target direction overlaps with the jamming region, both a narrower mainlobe and a deeper sidelobe are desirable but hard to meet simultaneously since they both consume array degrees of freedom. This paper investigates the application of Movable Antenna Array (MAA) in aiding radar beampattern synthesis under dense jamming scenarios. Using the extra spatial degrees of freedom provided by MAA, this work jointly optimizes both weighting vectors and antenna element positions to achieve superior beampattern without adding array element number. The integrated sidelobe level is chosen as the optimization objective with practical constraints, which leads to a highly non-convex optimization. To address this, we propose an Alternating Optimization-based Sequential Approximation (AOSA) algorithm. In each iteration, the weighting vector subproblem is solved through convex approximations of the primary nonconvex constraints, while the antenna position vector subproblem is tackled indirectly with a specifically derived proposition. Simulation results verify that the joint optimization framework effectively improves target separability and jamming rejection in complex electromagnetic environments, demonstrating its promising potential for advancing radar detection capabilities.

In this paper, we propose a semantic-twin-enabled bifurcated control architecture for multi-unmanned aerial vehicle (UAV) tasks in remote areas. To address the communication and computing burdens caused by high-fidelity reproduction of traditional digital twin (DT) in the remote interference environment, we propose the concept of semantic twin (ST). ST is a task-oriented system, which uses semantics for transmission, computing and decision-making, enhancing communication and computing efficiency. To achieve efficient remote control, we develop a bifurcated control architecture based on ST, in which satellites and the ground control station (GCS) function as edge controllers and the remote controller, respectively. For the satellite edge control, we employ the proximal policy optimization (PPO) algorithm to train a decision-making agent that generates action commands based on semantics from UAVs. Within the ST system of the GCS, we utilize the generative adversarial imitation learning (GAIL) algorithm to train an intelligent and interactive virtual target, creating a parallel environment for agent training. On this basis, we design a ST-enabled model-based offline reinforcement learning algorithm for lifelong learning. Compared to traditional reinforcement learning (RL) algorithms, we refine the weighted sample, model ensemble, and regularization methods, ensuring the reliability of the virtual environment and the efficacy of offline model training. Finally, we validate this framework by designing a multi-UAV tracking task and verify the significant advantages of the proposed control architecture in scenario reconstruction, model training and decision performance. Simulation results show that the ST-enabled bifurcated control architecture can counter the interference environment and accurately capture the motion features of the target, significantly improving the performance of remote UAV tasks.

To address the challenges of reliable and efficient short-packet communication in the Internet of Things (IoT), especially under ultra-reliable low-latency communication (URLLC) constraints, this work optimizes the design of short polar codes tailored for low-power IoT devices. To improve the reliability of short polar codes under successive cancellation list (SCL) decoders, we introduce a novel heuristic optimization algorithm guided by a unified metric. This algorithm carefully balances the tradeoff between the number of minimum-weight codewords (a.k.aerror coefficient) and the reliability of selected information subchannels. Through a guided and deliberate disruption of the partial order property of polar codes, our algorithm reduces the error coefficient to enhance maximum likelihood (ML) decoding performance, while managing the impact on subchannel reliability. Numerical results demonstrate a consistent and significant improvement over the baseline RM-Polar and Gaussian Approximation (GA) constructions across various code parameters. Furthermore, our approach features low offline design complexity, achieving state-of-the-art or highly competitive performance against other advanced schemes, particularly at low code rates. This makes our method highly suitable for URLLC, as the resulting optimized codes can be deployed on existing 5G hardware with zero additional on-device decoding complexity, while the achievable coding gain directly translates into transmission energy savings.

Data freshness, measured by Age of Information (AoI), is highly relevant in networked applications such as Vehicle to Everything (V2X), smart health systems, and Industrial Internet of Things (IIoT). However, freshness alone does not always equate to utility in decision-making. In decision-critical settings, somestaledata may be more valuable thanfreshupdates. Motivated by this, we move beyond AoI-centric policies and investigate how datastalenessaffects remote decision-making effectiveness under random delay and limited communication resources. To this end, we propose AR-MDP, an Age-aware Remote Markov Decision Process framework, which co-designs optimal sampling and remote decision-making under a sampling frequency constraint and random delay. To efficiently solve this problem, we design a newtwo-stagehierarchical algorithm, namely Quick Bellman-Linear-Program (QUICKBLP), where the first stage involves solving the Dinkelbach root of a Bellman variant and the second stage involves solving a streamlined linear program (LP). For the tricky first stage, we propose a new One-layer Primal-Dinkelbach Synchronous Iteration (ONEPDSI) method, which overcomes there-convergenceandnon-expansive divergencepresent in existingper-samplemulti-layer algorithms. Through rigorous convergence analysis of our proposed algorithms, we establish that the worst-case optimality gap in ONEPDSI exhibits exponential decay with respect to iterationKat a rate ofO( 1/RK). Throughsensitivity analysis, we derive a threshold for the sampling frequency, beyond which additional sampling does not yield further gains in decision-making. Simulation results validate our analyses.

Performance evaluation of particular channel coding has been a significant topic in coding theory, often involving the use of bounding techniques. This paper focuses on the new family ofcapacity-achievingcodes, Spinal codes, to provide a comprehensive analysis framework to tightly upper bound the block error rate (BLER) of Spinal codes in the finite block length (FBL) regime. First, we resort to a variant of theGallager random coding boundto upper bound the BLER of Spinal codes over the fading channel. Then, this paper derives a new bound without resorting to the use ofGallager random coding bound, achieving provable tightness over the wide range of signal-to-noise ratios (SNR). The derived BLER upper bounds in this paper are generalized, facilitating the performance evaluations of Spinal codes over different types of fast fading channels. Over the Rayleigh, Nakagami-m, and Rician fading channels, this paper explicitly derived the BLER upper bounds on Spinal codes as case studies. Based on the bounds, we theoretically reveal that thetail transmission pattern(TTP) for ML-decoded Spinal codes keeps optimal in terms of reliability performance. Simulations verify the tightness of the bounds and the insights obtained.

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.

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.

Goal-oriented communication shifts the focus from merely delivering timely information to maximizing decisionmaking effectiveness by prioritizing the transmission of highvalue information. In this context, we introduce the Goal-oriented Tensor (GoT), a novel closed-loop metric designed to directly quantify the ultimate utility in Goal-oriented systems, capturing how effectively the transmitted information meets the underlying application's objectives. Leveraging the GoT, we model a Goaloriented Non-Orthogonal Multiple Access (NOMA) network comprising multiple transmission-control loops. Operating under a pull-based framework, we formulate the joint optimization of transmission and control as a Partially Observable Markov Decision Process (POMDP), which we solve by deriving the belief state and training a Double-Dueling Deep Q-Network (D3QN). This framework enables adaptive decision-making for power allocation and control actions. Simulation results reveal a fundamental trade-off between transmission efficiency and control fidelity. Additionally, the superior utility of NOMA over Orthogonal Multiple Access (OMA) in multi-loop remote control scenarios is demonstrated.

The Space-Air-Ground-Sea Integrated Network (SAGSIN) offers broad communication coverage, enabling operations in remote regions. However, conventional remote UAV communication solutions require satellite relays, resulting in significant latency that hinders real-time response and decision accuracy for time-sensitive tasks like UAV target tracking and attacking. To address this challenge, we establish a direct communication loop between observation UAVs, the satellite control center, and Reconnaissance-Strike UAVs, reducing reliance on satellite-ground relays. This closed loop ensures continuous control and feedback, making the system highly task-oriented by enabling dynamic adjustments to meet real-time mission demands. Equipped with onboard processing and decision-making capabilities, Reconnaissance-Strike UAVs respond more rapidly and autonomously, enabling quicker, decentralized responses. To ensure data timeliness, we introduce the Age of Incorrect Information (AoII) as a metric to quantify transmission delays, optimizing Observation UAVs' transmission strategies through a Deep Q-Network (DQN). Additionally, Proximal Policy Optimization (PPO) with a task-oriented reward function enhances UAV scheduling. Simulation results demonstrate these strategies significantly improve UAV performance in target tracking and attack, offering a robust solution for complex missions.

Deploying and executing large model inference on edge devices is challenging due to their limited computational power and memory resources. To address this challenge, we present a novel Split-and-Pipeline, a collaborative inference scheme that partitions a large model into multiple submodels and executes them across distributed edge devices in a pipelined manner. The scheme parallelizes data transfer across multiple CPU cores to avoid transmission bottlenecks. We build a real-world testbed using NVIDIA Jetson series edge devices to demonstrate the proposed scheme, achieving 1.2×–3.0× throughput improvement over state-of-the-art baselines.

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.

We investigate a time-sensitive wireless networked control system (WNCS) where multiple Internet of Things (IoT) sensors embedded with their respective local controllers send their observations to a remote controller over shared wireless channels. From an infinite-time horizon perspective, each process should be stabilized essentially to prevent the process’s states from divergence. Nevertheless, limited channel resources may not fulfill users’ stability requirements due to possibly insufficient transmission attempts. Regarding the tradeoff between stability property and channel resources, we aim to design a transmission scheduling policy that minimizes the infinite-time control cost under channel constraints. Starting with the stability condition analysis under varying scheduling policies, the applied decentralized networked control architecture shows its superiority in extending the WNCS’s scale. By approximately expressing control cost as a function of the Age of Information (AoI), the considered scheduling issue is transformed into an AoI-dependent optimization problem under channel and stability constraints. Then, we develop a control-oriented Whittle index policy where AoI, system parameters, and stability incentives construct the Whittle indexes. Numerical results demonstrate that our proposed policy outperforms the baseline policies in terms of control cost, especially in heterogeneous WNCSs. Furthermore, results show that the proposed policy containing stability factor can support more users with respective stability guarantees.

For 6G inherent intelligent capability, deep-learning-based wireless localization will become a promising technology for offering commercial location-based services (LBSs). Classical deep neural networks (DNNs) have been designed to learn feature representation for localization tasks. However, due to the uncertainty of radio measurements in complicated wireless propagation, the existing solution has achieved unsatisfactory performance with environmental dynamics. To address this issue, we propose GraphLoc, a novel approach to enhancing fingerprint-based localization with graph representation learning which can encode the structural information underlying radio fingerprints for robust localization. We first adopt graph signal processing of CSI fingerprints to create an unweighted graph. GraphLoc transforms the tasks of location estimations into node classification in a constructed graph. Then, we develop multilayer graph attention networks (GATs) with the residual structure (Res-GAT) to learn graph representation by collecting the neighboring node features and aggregating their neighboring embeddings. Furthermore, in order to guarantee and speed up our Res-GAT convergence, we propose a training strategy to overcome training difficulty and overfitting for improving the quality of graph representation. Finally, extensive experimental results in many typical indoor scenarios demonstrate that the GraphLoc system can achieve better accuracy than other comparative schemes, even with the robustness of environmental dynamics, effectively facilitating fingerprint-based localization for fully practical LBS.

Deep joint source-channel coding (DeepJSCC) has emerged as a novel technology in semantic communication, coinciding with the increasing demand for the edge devices in the Internet of Things (IoT). Consequently, the deployment of DeepJSCC on edge devices has become a crucial research direction. However, DeepJSCC faces challenges related to channel fading. Moreover, implementing DeepJSCC on the edge devices poses challenges due to the constrained computational resources as well as the compatibility issue between DeepJSCC and digital systems. In this article, we devote to engineering the DeepJSCC system deployed on the edge devices. First, we propose a method named DeepJSCC with Ensemble learning (DeepJSCC-ES) to resist the channel fading. Then, we present a pruning algorithm called the DeepJSCC signal-to-noise ratio (SNR)-adaptive pruning method (DJSAP) to make the DeepJSCC network lightweight, reducing the computational demands on the edge nodes. Further, we propose a method called the simulated fixed-point quantization training based on soft quantization function (SFPQSQ) to tackle the compatibility issue between DeepJSCC and digital systems. Finally, we deploy the whole DeepJSCC system on the edge devices and conduct experiments to test the DeepJSCC system. The results of simulations show that the proposed DeepJSCC-ES system outperforms the baseline DeepJSCC, particularly excelling in low SNR conditions. Furthermore, the parameter size of the pruned model using DJSAP is compressed by 93.37% while the average structural similarity index metric (SSIM) decreases only by 0.92% compared with the baseline DeepJSCC. Additionally, the SFPQSQ works better than the ordinary quantization methods in tackling the compatibility issue between DeepJSCC and digital systems. The experiment results also show that our proposed system can serve as a feasible solution for practical deployment on the edge devices.

Traditional signal recognition requires the design of multiple different deep neural networks to handle different signal recognition tasks, which not only fails to take into account the correlation among different subtasks, but also leads to large model size and higher computational complexity. In this work, we propose a multitask-learning-based multiband signal recognition network$(\text {M}^{2}\text {-Net})$to simultaneously recognize the location of occupied frequency bands, modulation types, and signal types. The proposed$\text {M}^{2}\text {-Net}$consists of two main parts: 1) shared feature extraction network (SFEN) and 2) multitask classification header (MCH). In SFEN, a plug-and-play multitask feature extraction convolution and an adaptive threshold denoising module are introduced to provide better shared feature extraction and denoising performance. In MCH, the shared features obtained from SFEN are further processed for different recognition tasks. Furthermore, during the multitask model training, homoscedastic uncertainty is introduced as a task-dependent weight to adaptively balance the training loss of different tasks. To evaluate the recognition performance of the proposed method, we construct a multiband signal dataset and compare$\text {M}^{2}\text {-Net}$with several state-of-the-art models in signal recognition field. Experiment results show that the proposed$\text {M}^{2}\text {-Net}$has significant performance improvements in terms of recognition accuracy and model complexity, especially under low signal-to-noise ratio conditions.

This paper establishes an upper bound on the block error rate (BLER) of Spinal codes, the first rateless codes proven to achieve Shannon capacity in additive white Gaussian noise (AWGN) and binary symmetric channels (BSC). Unlike the conventional reliance on the 1965 Gallager random coding bound for deriving upper bounds, as illustrated in 2016 by Yu et al., this study deviates by noting that Gallager’s bound may not adequately represent the distinct properties of specific random codes like Spinal codes and may result in loose bounding performance. We thus introduce novel techniques to refine existing results and enhance the bounding tightness. Our main results are two explicit upper bounds on the BLER of Spinal codes over the AWGN channel, accompanied by theoretical proofs that validate their tightness. Potential applications of the bounds and insights for the coding design are explored in this work.

Recently, deep-learning-based wireless localization has become one of the most promising technologies for intelligent location-based services. However, classical schemes have extracted the appropriate features to construct a static radio map without environmental adaptability, resulting in severe accuracy degradation. To address this issue, we propose a novel approach of robust indoor localization with dynamic adversarial learning, known as DadLoc, which realizes automatic radio map adaptation for accuracy improvement. DadLoc can incorporate multilevel robust factors underlying different fingerprint databases to develop a dynamic adversarial adaptation network, which can learn the evolving feature representation with the complicated environmental dynamics. Furthermore, we adopt the training strategy of prediction uncertainty suppression with source–target dynamic adversarial adaptation, which can enhance the location discriminability of the transferable feature representation. With extensive experimental results, the satisfactory accuracy over other comparative schemes demonstrates that the proposed DadLoc can achieve an average accuracy of$1.78\,\mathrm{m}$with the robustness of indoor environmental dynamics.

Orbital edge computing (OEC) is crucial for supporting space intelligence applications within satellite networks. However, individual satellites face resource constraints, and implementing distributed processing techniques, such as federated learning (FL), across multiple satellites introduces significant scheduling complexity. To address these challenges, we first model the key factors influencing complex satellite networks, including satellite constellations, regional resource demands, inter-satellite communication and routing, energy consumption, and battery aging—a novel aspect invoked by OEC operations. We propose an adaptive aggregation method to fundamentally improve communication efficiency in OEC-based FL. To enhance scheduling performance, we formulate a unified optimization problem that jointly considers data partitioning, resource allocation, and aggregation transmission tasks within a decentralized partially observable Markov decision process (Dec-POMDP) framework. Furthermore, we introduce an episodic-phase-recalling reward shaping (EPRS) method to correlate the influences across these phases. Inspired by multi-task learning, we propose an efficient multi-agent reinforcement learning (MARL) algorithm featuring a multi-head actor-critic (MH-AC) network structure and task-equalized adaptation (TEA) technology, designed to optimize latency, energy consumption, network traffic, and battery aging. Extensive experiments validate the effectiveness of the proposed method, showing a 29.9% reduction in total training time, an 11.5% reduction in network traffic, and superior overall performance compared to rule-based methods.

Task-oriented semantic communications introduce a novel paradigm specifically designed to enhance task-specific performance. However, this paradigm may face limitations as it requires frequent updating with task changes or necessitates storing multiple distinct models for various tasks. To address these challenges, we propose a task-adaptive semantic communication system with feedback (TASC-f), which utilizes a single model capable of adapting to variable tasks. In particular, we formulate a conditional rate-distortion optimization problem, where task-specific prompts serve as dynamic side information to guide coding strategies and enhance task performance. Inspired by visual prompt tuning, we present a learnable semantic prompt model (SPM) coupled with a dynamic parameters network, aimed at effectively extracting task-specific features. A feedback mechanism is also integrated to capture real-time task information, facilitating timely adjustments of the coding policy. In our experiments, we employ the TASC-f system to evaluate its effectiveness across three AI tasks within two distinct scenarios: tasks that are newly introduced and those previously encountered during the training phase. Simulation results show that our proposed TASC-f surpasses all data-oriented communication schemes in both scenarios and achieves performance comparable to single-task-oriented semantic systems with reduced communication overhead and fewer model parameters.

The recent advent of artificial intelligence and 6G technologies has catalyzed a novel research trend towards goal-oriented design for multi-user remote control systems. Existing works have rarely simultaneously focused on the direct characterization for effective control, and targeted schemes for multiuser scenarios, e.g., non-orthogonal multiple access (NOMA). In this paper, we propose a control-oriented NOMA system capable of intelligent transmission power control. Through theoretical derivation, we build up an age of information (AoI)-dependent function of control cost within the NOMA system framework. In pursuit of the optimal trade-off between control cost and power consumption, we formulate an optimization problem as a Markov decision process and employ the Dueling-Double-Deep Q Network (D3QN) to intelligently decide the transmission power. Numerical results demonstrate the superiority of the control-oriented NOMA system over orthogonal multiple access (OMA) in achieving high-quality multi-loop control while considering energy expenditure.

Age of Information (AoI) has been recognized as an important metric to measure the freshness of information. Central to this consensus is that minimizing AoI can enhance the freshness of information, thereby facilitating the accuracy of subsequent decision-making processes. However, to date the direct causal relationship that links AoI to the utility of the decision-making process is unexplored. To fill this gap, this paper proposes a sampling-control co-design problem, referred to as an age-aware remote Markov Decision Process (MDP) problem, to explore this unexplored relationship. Our framework revisits the sampling problem in [1] with a refined focus: moving from AoI penalty minimization to directly optimizing goal-oriented remote decision-making process under random delay. We derive that the age-aware remote MDP problem can be reduced to a standard MDP problem without delays, and reveal that treating AoI solely as a metric for optimization is not optimal in achieving remote decision making. Instead, AoI can serve as important side information to facilitate remote decision making.

Motivated by the vision of establishing the moon as a deep space internet gateway for human exploration of the universe, we focus on the design of a lunar communication and navigation integrated (CNI) satellite constellation. We first introduce the fibonacci lattice virtual observation point (VOP) model with better uniform and stochastic distribution properties. Based on this, we derive objectives related to coverage, power, quadruple overlap, and geometric dilution of precision combined with earth-moon difference analysis, along with multiple multi-objective optimization problems (MOPs). The solution incorporates the pareto model, non-dominated sorting genetic algorithm-II (NSGA-II), and constellation system construction. Inspired by the lunar frozen orbit, a constellation configuration called hybrid inclination (HyInc) Walker is proposed and theoretically analyzed. Extensive simulation results are shown, including the revelation of the advantages of the HyInc Walker configuration and its coverage equalization capability. The design of this first lunar integrated constellation and the proposed HyInc Walker configuration is of founding significance and highly migratory.

The intricate interplay of source dynamics, unreliable channels, and staleness of information has long been recognized as a significant impediment for the receiver to achieve accurate, timely, and goal-oriented decision making. Thus, a plethora of promising metrics, such as Age of Information and Value of Information have emerged to quantify these adverse factors. Optimizing these metrics indirectly improves the goal-oriented utility of decision making. Nevertheless, no metric has been devised to directly evaluate the utility. To this end, this paper investigates a novel tensor-based metric, named Goal-oriented Tensor (GoT), to directly quantify the impact of semantic mismatches on decision making. Leveraging the GoT, we design a sampler-decision maker pair that works collaboratively to achieve a shared goal. This sampling-decision making co-design is challenging since the sampler and the decision maker are strongly coupled. To decouple these processes, we formulate the problem as an infinite-horizon Decentralized Partially Observable Markov Decision Process (Dec-POMDP) to conjointly deduce the optimal joint policy. We tested the sampler-decision maker co-design in terms of goal achievement utility and sampling rate, achieving significant performance advancements over conventional state-of-the-art sampling methodologies.

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.

The wireless networked control system (WNCS) is a closed-loop hierarchical network that enables interaction among wireless communication, computation, and control components to support various services ranging from information exchange to intelligent decision making. Different from a single communication system aiming for reliable or efficient delivery, WNCS is goal-oriented, highlighting the ultimate control performance requirement guaranteed under various limits of communication, computation, and control resources. In this article, we present a comprehensive survey of WNCS from the communication perspective. We discuss appropriate WNCS architecture, topics, such as sensing strategy design, under energy and bandwidth constraints, state estimation problems in the presence of imperfect channels, and control approaches for WNCS performance. We further pay attention to the joint design within WNCS to achieve well-performing WNCS improvements. Moreover, considering the fact that timely transmission of the measurements is of significance to precise control, we provide a review of WNCS design involving the Age of Information (AoI) that copes with the goal-oriented requirements. The challenges and new research directions are discussed at the end of this survey.

Age of information (AoI) defines the freshness of status update in real-time systems, such as the Industrial Internet of Things (IIoT), and can be affected by delays and transmission error probability. To improve the reliability of data transmissions, the recent AoI works on physical layer considered applying practical coding schemes. Since polar codes can be strictly proved to achieve the channel capacity, this article makes an effort to comprehensively investigate and optimize the AoI performance in a polar-coded status update system. First, we propose a practical code-based status update system that takes full consideration of encoding, transmission, propagation, decoding, and feedback delays in AoI analysis. Then, we analyze and derive the average AoI of the proposed system with various transmission protocols. The simulation results of a polar-coded system validate the theoretical analysis and show that hybrid automatic repeat request (HARQ) achieves better AoI performance than non-HARQ. To optimize AoI in polar-coded status update system, we further improve the designs for HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR), respectively. The design signal-to-noise ratio (SNR), puncturing length of HARQ-CC are optimized by traversal, while the code lengths for each transmission and maximum transmission times of HARQ-IR are optimized by the greedy algorithm. Simulation results show that the proposed HARQ can achieve better average AoI performance than traditional HARQ.

Recently, deep-learning-based wireless localization with fingerprinting has gained significant accuracy improvement. However, mainstream schemes cannot overcome the vulnerability of RF signals with complicated environmental dynamics which extremely exacerbates localization accuracy and severely limits widespread practical applications. To address this issue, we propose RobLoc, a novel approach of robust localization which can acquire environmental adaptability to achieve accurate location estimations. RobLoc first exploits dimensionality reduction to explore the geometric structure underlying RF fingerprints in the Grassmann manifold which possesses a stable spatial correlation with the environmental dynamics. Furthermore, we design the manifold embedded dynamic adaptation network to perform both global and local distribution matching and further quantitatively self-evaluate their respective contributions for automatic radio map adaptation. According to such dynamic self-adaptive learning, RobLoc can learn finer environment-independent representation by joint geometrical and statistical alignment to attain the effective enhancement of channel knowledge transfer across different environments. Finally, extensive experimental results in many typical indoor scenarios demonstrate that the proposed RobLoc system can achieve better localization performance than advanced works, facilitating fingerprint-based localization for fully practical LBS with a wide range of deployments.

Deep space communication systems play a key role in human endeavors for lunar basing, Mars, and further cosmic exploration. In pursuit of establishing the Moon as a deep space Internet portal for future human exploration, a lunar communication and navigation integrated (CNI) satellite constellation design is intended into consideration. Based on Fibonacci lattice virtual observation point model, featuring better uniformity and stochasticity, we derive objectives related to access coverage, power, quadruple coverage, and geometric dilution of precision (GDOP), together with multiple multi-objective optimization problems (MOPs) combined with Earth-Moon difference analysis to explore better utilization of overall resources in the lunar integrated constellation. The pareto model, non-dominated sorting genetic algorithm-II (NSGA-II), and the construction of the constellation system are incorporated into the solution to pursue a higher guiding value. In particular, inspired by the lunar frozen orbit (LFO), the hybrid inclination (HyInc) Walker configuration is proposed with theoretical validation and simulation evaluation, showing some superiority over traditional Walker and remains generalizable. Extensive simulation and comprehensive analysis are performed, including the pareto-optimal integrated constellations and the revelation of HyInc Walker’s coverage equalization capability, with the latter being less studied. The entire constellation design process of this work is highly migratory and the proposed perspective of the configuration is enlightening.

Optimizations premised on open-loop metrics such as Age of Information (AoI) indirectly enhance the system’s decision-makingutility. We therefore propose a novel closed-loop metric named Goal-oriented Tensor (GoT) to directly quantify the impact of semantic mismatches on goal-oriented decision-makingutility. Leveraging the GoT, we consider asampler & decision-makerpair that works collaboratively and distributively to achieve a shared goal of communications. We formulate a two-agent infinite-horizon Decentralized Partially Observable Markov Decision Process (Dec-POMDP) to conjointly deduce the optimal deterministic sampling policy and decision-making policy. To circumvent thecurse of dimensionalityin obtaining an optimal deterministic joint policy through Brute-Force-Search, a sub-optimal yet computationally efficient algorithm is developed. This algorithm is predicated on the search for a Nash Equilibrium between the sampler and the decision-maker. Simulation results reveal that the proposedsampler & decision-makerco-design surpasses the current literature on AoI and its variants in terms of both goal achievementutilityand sparse sampling rate, signifying progress in the semantics-conscious, goal-driven sparse sampling design.

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, the Space-Air-Ground-Sea Integrated Network (SAGSIN) attracts great attention due to its ability to provide high-speed communication services to aviation users (AUs) and marine users (MUs), with Low Earth Orbit (LEO) satellites play an essential role. However, the available space in LEO is nearly saturated and full of massive space junks, which, combined with the ultra-low latency requirements for future 6G, presents a significant challenge. To address this issue, we propose designing a Very Low Earth Orbit (VLEO)-based satellite network that efficiently serves AUs and MUs. We first create and analyze the heat maps based on actual collected data of Chinese aviation and marine communication traffic, and generate a benchmark observation point model with grid point method. Then we propose an implicit multi-objective continuous multi-variate optimization problem to achieve the maximum average coverage with minimum VLEO satellites. To solve this problem, we build a satellite constellation simulation system, using the idea of decomposition and polymerization combined with the elite strategic genetic algorithm (ESGA) of swarm intelligence optimization algorithm. Many simulation results are obtained, including the indication that the optimal VLEO constellation has the deployment features of large altitude and low inclination, and has better coverage performance for longitudinal distributed business. The design process in this work is highly migratory,

Energy efficiency and information freshness are two fundamentally critical performance metrics in real-time status update systems which can be measured by energy cost (EC) and age of information (AoI), respectively. This paper examines the AoI and EC performance of the hybrid automatic repeat request with incremental redundancy (HARQ-IR) scheme in code-based status update systems and presents unified results that can generally depict the average AoI and EC over block fading channels. First, we propose a practical code-based status update system that fully takes into account the impact of information processing and long-distance transmission in performance analysis. Then, we analyze and derive the average AoI/EC expressions for HARQ-IR scheme, which are unified results over block fading channels. The simulations of different transmission protocols validate our explicit results and show that there is a distance threshold on whether to retransmit the failed updates. Based on the simulation results, it appears that system AoI/EC demand will affect distance threshold values, which provide guidance for future designs of age-energy tradeoff transmission schemes.

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.

In this article, we consider a networked control system (NCS) with network-induced delay, in which the control center needs to control the remote unmanned aerial vehicle (UAV) to complete the trajectory tracking task. The sensor of the controlled UAV adopts the event-triggered mechanism, while the control center uses the adaptive dynamic programming (ADP)-based tracking control method to generate control actions. The application of the ADP method brings new transmission options, i.e., the control center can choose to transmit control action or neural network model. Considering the fundamental tradeoff between these two transmission options with different transmission energy consumption and tracking cost, we formulate the joint optimization problem as a Markov decision process (MDP). Due to the continuous value of state in MDP, we propose the deep$Q$-network (DQN)-based strategy, which uses the reinforcement learning (RL) algorithm, specifically DQN. Besides, we further propose a greedy strategy by calculating the instantaneous expected cost. Simulation results show that DQN-based strategy has better performance but depends on the training process, while greedy strategy is suboptimal but easy to compute. Besides, compared with the benchmark strategies, the proposed strategies can achieve a better compromise in the long-term average energy consumption and tracking cost by adjusting the value of the weighted factor. Furthermore, by comparing the difference of transmission decisions in the proposed strategies, we show that the proper transmission sequence in DQN-based strategy can reduce the tracking cost and transmission energy at the same time.

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.

This article explores the model-free remote control problem in a wireless networked cyber-physical system (CPS) composed of spatially distributed sensors, controllers, and actuators. The sensors sample the states of the controlled system to generate control instructions at the remote controller, while the actuators maintain the system's stability by executing control commands. To realize the control under a model-free system, the deep deterministic policy gradient (DDPG) algorithm is adopted in the controller to enable model-free control. Unlike the traditional DDPG algorithm, which only takes the system state as input, this article incorporates historical action information as input to extract more information and achieve precise control in the case of communication latency. Additionally, in the experience replay mechanism of the DDPG algorithm, we incorporate the reward into the prioritized experience replay (PER) approach. According to the simulation results, the proposed sampling policy improves the convergence rate by determining the sampling probability of transitions based on the joint consideration of temporal difference (TD) error and reward.

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.

In this paper, we investigate the timeliness performance of a downlink wireless communication system with non-orthogonal multiple access (NOMA). The timeliness of the system is characterized by Age of Information (AoI). To efficiently utilize the time-frequency resource and achieve a tradeoff between timeliness and reliability, we propose an adaptive transmission policy under hybrid automatic repeat request with chase combining (HARQ-CC) aided NOMA systems. In particular, the BS can adaptively adjust the power allocation and decide whether to transmit old or new packets to users in the NOMA system, based on the current AoI status and the positive/negative acknowledgement (ACK/NACK) feedback signal. We first analyze the BLER under such adaptive systems, and then formulate an AoI minimization problem based on the derived BLER. By transforming the objective function to a Markov Decision Process (MDP) problem, an optimal policy is obtained to minimize the average AoI of the system. Considering the high complexity of the MDP, we further divise an alternative near-optimal policy based on Lyapunov Drift function. Furthermore, we consider the fairness of users and propose a greedy policy to minimize the maximal expected AoI of users. Based on extensive simulations, it has been found that NOMA can outperform OMA on both an overall and a user-level basis when operating with adaptive retransmission and power allocation strategies.

WiFi fingerprint-based localization has been intensive studies as a promising technology of ubiquitous location-based services. Two main concerns for its wide spread applications are to tackle with the cumbersome efforts of site survey and to combat vulnerable environment changes. To address these issues comprehensively, we propose a novel approach on adaptive fingerprint-based localization with less site survey, named as LESS, by exploring a new paradigm of radio map construction and adaptation with few-shot relation learning. Firstly, we extend sparsely collected fingerprints with the fingerprint augmentation method which produces new related data and derives their location information based on local proximity property in a low-dimensional manifold space. Then, LESS designs deep relation networks to learn not only the appropriate features but also a transferable deep-distance metric for modeling the fundamental relationships of the neighborhood fingerprints. Finally, once trained, LESS can quickly establish the neighborhood relationships among new fingerprints in the changed surroundings to realize adaptive location estimations, even without the network updating. The extensive experimental results demonstrate that LESS can achieve an attractive trade-off between the system overhead and the location performance with the superiorities over others in dynamic indoor environments.

Satellite communication is regarded as a promising technique for providing connectivity in remote areas, which creates opportunities for data collection and transmission in marine Internet-of-Remote-Things (IoRT) networks. Most existing investigations in the field of satellite access control focus on communication throughput and transmission delay. However, the freshness of information and the heterogeneous satellite networks are rarely considered. To this end, we first present a satellite-based marine IoRT system, where a GEO/LEO heterogeneous network is considered to harness the full potential of existing satellite systems, and the age-of-information (AoI) is introduced to characterize the freshness of the status update information generated by IoRT devices. Then, an optimal age-oriented access control problem is formulated to maintain the freshness of information in the long term. We transform this non-convex sequential decision problem into a model-free Markov Decision Process (MDP) problem and solve it by leveraging the deep reinforcement learning (DRL) framework. Simulation results show that the proposed strategy significantly outperforms the state-of-the-art ones in terms of long-term AoI performance. Moreover, the proposed strategy could make cooperative access decisions and obtain an excellent trade-off between satellites on different layers.

Mega low earth orbit (LEO) high throughput satellite (HTS) constellations are regarded as one of the most important development shifts in the next generation of mobile communication systems in both industry and academia. Consider the short duration of line-of-sight (LoS) link and high dynamic topology of LEO HTSs, we propose a handover uplink non-orthogonal multiple access (Hu-NOMA) transmission scheme for a multi-layer LEO HTS constellation. First, we formulate a practical two-layer LEO HTS constellation, where the higher-layer LEO HTS has a longer LoS link duration but not always visible, and the lower-layer LEO HTS has a shorter LoS link duration and can continuous support the uplink transmission via frequent handovers. Then, we derive the closed-form expressions of ergodic capacity (EC) and outage probability (OP) for both NOMA and orthogonal multiple access (OMA) schemes. Further, we propose an improved ergodic capacity (IEC) NOMA algorithm, and terrestrial user equipments (UEs) can perform our IEC Hu-NOMA transmission according to their exponential distributed random service time, which can achieve higher EC, and have similar OP compared to the conventional OMA scheme but reduce half of the transmission time slot. Simulation results validate the accuracy of our theoretical derivations, and show that our IEC Hu-NOMA can outperform the state-of-art schemes.

In this paper, we consider a task scheduling problem for the freshness-critical services in the Internet of Remote Things scenario (IoRT). In the IoRT scenario, a gateway collects status updates from the surrounding devices and then makes a scheduling decision, in which the status updates would be offloaded to a specific satellite for on-orbit processing. Our objective is to propose a task scheduling scheme which can minimize the age of information of the system. To this end, we use the promising hybrid geosynchronous earth orbit and low earth orbit (hybrid GEO-LEO) satellite networks and design an age-aware task scheduling scheme to utilize heterogeneous communication and processing resources. The issue of task scheduling is considered as cooperation between gateway association and resource management problem. To cope with this complicated problem, we formulate it as a Markov Decision Process with minimum peak age and decompose it into two sub-problems, which are resource management with fixed gateway association indexes and scheduling decisions for gateway association. The convex optimization algorithm is utilized to obtain optimal resource management results, and the deep reinforcement learning network is used to achieve the optimal gateway association indexes. Extensive simulation results demonstrate that the peak age of the designed strategy has an advantage over other referred strategies.

In this paper, we offer an explicit, unified result that can generally depict the age performance of error-correcting techniques at the physical layer. We first propose a more realistic code-based status update system, wherein different types of delay elements, e.g., the coding delay, transmission delay, propagation delay, decoding delay and feedback delay are comprehensively considered. Under this system, we derive closed-form average Age of Information (AoI) expressions for reactive HARQ and proactive HARQ, respectively. On the basis of these explicit expressions, and utilizing the existing results for finite-length codes, we formulate an AoI minimization problem to investigate the age-optimal codeblock assignment strategy in the finite block-length (FBL) regime. Through case studies and analytical results, we provide comparative insights between reactive HARQ and proactive HARQ from the perspective of freshness of information. The numerical results and optimization solutions reveal that proactive HARQ draws its strength from both superior age performance and system robustness, thus enabling the potential to provide new system advancement for a freshness-critical status update system. The full paper version of this work is available on the arXiv at https://arxiv.org/abs/2204.01257.

In this paper, we focus on the remote real-time close-loop control scenarios, where the state of observation process is collected by the sensor and timely transmitted to the remote control center (RCC) over an unreliable channel or network, followed by a control command generated from RCC fed back to the actuator, which is called remote control. The age of information (AoI) is widely used to capture the timeliness. We pay attention to the age outage, which is defined as the probability that the peak age exceeds a certain threshold. Due to the existence of long link delay and channel unreliability, the observation process is likely to be uncontrolled, which may degrade the AoI. We first adopt always remote control (always-RC) and analyze the impact of long delay and channel unreliability on age outage. An interesting result is that long delay and channel unreliability has the potential to decrease the age outage probability. To improve age outage, we further propose a local-assisted joint control policy by introducing the smart sensor that is capable of processing and controlling. Age outage probability under local-assisted joint control is then analyzed. Results show that local-assisted joint control policy has the effectiveness in improving the performance of age outage by setting suitable local control times.

With the advancement of low earth orbit (LEO) satellite constellation, the LEO satellite-based Internet of Things (S-IoT) has attracted extensive attentions due to its wide coverage and broadband access capability. Considering the mission critical communications (MCC) and massive machine-type communications (mMTC) requirements of terrestrial user equipments (UEs), we first propose a quality of service (QoS)-aware uplink non-orthogonal multiple access (NOMA) transmission scheme for LEO high-throughput satellite (HTS) constellation, where MCC and mMTC services can coexist. Then, we derive the closed-form expressions of ergodic capacity (EC) and outage probability (OP), and obtain the expression of system throughput (ST) for NOMA and OMA schemes. Further, we propose a reduced system outage probability (RSOP) algorithm to minimize the OP of MCC services, and an improved system throughput (IST) algorithm to enhance the ST of mMTC services, while guaranteeing the OP requirements for each UE. Finally, simulation results validate the accuracy of our theoretical derivations and show that both RSOP-NOMA scheme and IST-NOMA scheme can outperform the state-of-art ones.

In this paper, we focus on an age-critical grant-free massive access setup and analyze the freshness of information via a new metric named age of information (AoI), where a large number of user equipments (UEs) are randomly activated and attempt transmitting packets of status update to a base station (BS) over a common shared channel. We propose a graph-based spatially coupled irregular repetition slotted ALOHA (G-SC-IRSA) random access protocol combined with the pseudo-random access method and coupled frames to support massive access. In order to analyze the packet loss rate (PLR) performance of the G-SC-IRSA protocol, we first utilize the density evolution (DE) with a bipartite graph to evaluate the system load threshold of G-SC-IRSA. Then, we derive an analytical expression of average AoI as a function of the active probability of UEs, frame length and PLR. Simulation results validate the accuracy of our theoretical analysis and show the great advantages of G-SC-IRSA in better PLR and AAoI performance compared to the existing benchmark schemes.

In this paper, we consider a satellite-based Internet of Things (S-IoT) network under shadowed-Rician fading channels, where a satellite transmits timely status updates to multiple user equipments (UEs) with non-orthogonal multiple access (NOMA). In each transmission, the satellite needs to allocate limited power to the status updates for UEs in an appropriate way to guarantee the freshness of updates, characterized by age of information (AoI). To minimize the average AoI of S-IoT network, we formulate a power-constrained optimization problem and then reformulate it as a Markov decision process (MDP). Considering the non-convexity of the optimization problem and the high dimensionality of the multiuser MDP with large state and action spaces, we propose a deep reinforcement learning-assisted age-optimal power allocation (DRAP) scheme to solve the problem and obtain an optimal power allocation policy. Furthermore, a double-network deep reinforcement learning structure is designed to enhance the training effectiveness for our optimization problem. Finally, simulation results show that our proposed DRAP scheme outperforms the benchmark schemes.

In this paper, we propose a T -order collision resolution grant-free random access (T -GFRA) protocol for massive access in Internet of Things (IoT), where each activated user equipment (UE) can randomly choose one of L pilot sequences and performs random access, and each pilot sequence is corresponding to a unique T -order codebook. A collision occurs when two or more UEs choose the same codebook, and we assume that the base station (BS) can decode at most T UEs who have selected the same T -order codebook. Then, we analyze the decoding error probability of our T -GFRA protocol in Rayleigh fading channel, and derive the access failure probability (AFP) for the singleton pilot, decodable collision pilot, and undecodable collision pilot. Furthermore, we derive a lower bound of AFP for the T -GFRA protocol in Rayleigh fading channel. Finally, simulation results validate the accuracy of our theoretical analysis, and show that our scheme can significantly lower the AFP and support massive access.

This paper considers the autonomous detecting and tracking task of the unmanned aerial vehicle (UAV) in the maritime environment. In the maritime UAV tracking system, due to the large size of the image computing-task and the shortage of UAV batteries and computational capability, the UAV needs to offload the computing-intensive task to the edge computing server (ECS) to reduce energy consumption and task latency. However, the task latency is still too long for the UAV tracking algorithm due to the large image size. We research the impact of image resolution on the computing task size and detection accuracy, and formulate an edge-aided UAV system with dynamic image resolution. With the constraint on task latency, we jointly optimize the image resolution, offloading rate, transmission power and local central processing unit (CPU) frequency to minimize energy consumption. Although the proposed problem is non-convex, we transform it into a convex optimization problem through decoupling and problem decomposition, and obtain an optimal offloading strategy. The numerical results show the energy efficiency of the proposed strategy by comparing it with the local first offloading strategy and the edge first offloading strategy.

In this paper, we consider a networked control system (NCS) with bidirectional network-induced delay, in which the control center needs to control the remote unmanned aerial vehicle (UAV) to complete the trajectory tracking task. The sensor of the remote controlled UAV adopts the event-triggered mechanism, and the control center uses the adaptive dynamic programming (ADP) method to generate control actions. The application of ADP method to NCS brings new transmission options, that is, transmitting control action or neural network (NN) model. There exists a fundamental tradeoff between different transmission options with different transmission energy consumption and tracking cost, which still receives little attention in the NCS design. To fill this gap, we propose a cost-based transmission strategy that can balance the average energy consumption and the average tracking cost. By deliberately making decisions on whether to transmit the control action or the NN model, the weighted sum of the average energy consumption and the tracking cost is minimized. Simulation results show that compared with the benchmark strategies, the proposed strategy can achieve a better compromise in the long-term average energy consumption and long-term average tracking cost, and can obtain better performance in a specific weight range.

The recent development of the low Earth orbit (LEO) satellite constellation construction has accelerated the research on applications associated with LEO satellites. One such typical application is to transmit high-resolution remote-sensing images from LEO satellites to ground stations (GS). However, the stringent visible time and the complicated antenna manipulation between LEO satellites and GS makes it challenging for a LEO satellite to complete its full transmission mission within a specified stringent deadline. As such, this paper introduces a geosynchronous equatorial orbit (GEO) satellite as a relay and explores the distributed coding-decoding schemes to assist reliable and high-speed transmission. Specifically, four types of GEO-satellite-relay coded schemes are proposed and analyzed, including three PHY-only coding systems with GEO-full-decoding on board, decoding on ground only, and GEO-partial-decoding on board and one layered coding system. Through simulations, the comparative insights among the four schemes are provided from three dimensions: effectiveness, reliability, and relay complexity. The trade-offs concerning the four schemes in terms of the three indexes are also revealed.

This paper presents a Hybrid Automatic Repeat Request (HARQ) based scheduling scheme for a multi-loop Wireless Networked Control System (WNCS). For each single-loop system in the multi-loop system, it includes uplink transmission and downlink transmission. By considering a practical application scenario, we formulate a mathematical model wherein the downlink transmission can be assumed ideal, and the uplink transmission updates the new system status which is used to generate control commands. Due to the resource constraints, not all single-loop systems can update their status information in the same time slot. Meanwhile, using the HARQ mechanism can ensure a higher probability of successful transmission. To achieve the stability of the system, we propose a scheduling strategy to minimize the long-term average Mean Square Error (MSE) of the plant state. And we model the optimization problem as a Markov Decision Process (MDP) problem to obtain the optimal strategy. For the case that the channel error rates change rapidly, we propose the Lyapunov optimization strategy. And through further analysis, the Lyapunov optimization strategy is a suboptimal strategy, it can achieve the performance approach to the optimal strategy.

With the rapid development of low Earth orbit (LEO) satellite constellation, the LEO satellite-based Internet-of-Things (S-IoT) has attracted extensive attentions due to its advantages, such as broadband access capability, seamless coverage and low propagation delay. Considering the periodic activated sporadic transmission of massive user equipments (UEs) under satellite coverage and the short packet communication in uplink S-IoT, the length and available pilot sequences are limited, which leads the pilot collision to a challenging problem. To alleviate pilot collision, we propose a non-orthogonal superimposed pilot grant-free random access (NSP-GFRA) scheme in this paper. First, considering the substantial deterioration of non-orthogonal Zadoff-Chu sequences (ZCS) in noise channel, we adopt zero-correlation-zone periodic complementary sequences (ZPCS) in our NSP-GFRA scheme, which can maintain a low cross-correlation with random non-orthogonal shifts. Then, we utilize the scheme to improve the performance of the random access uplink LEO S-IoT system s under the shadowed-Rician fading channel and derive the theoretical expressions of access failure probability (AFP) for our NSP-GFRA scheme. Monte Carlo simulation results validate it and demonstrate that our NSP-GFRA scheme with ZPCS pilot can achieve lower AFP than that with ZCS pilot.

In this paper, we consider a cyber-physical system (CPS) with multiple Internet of Things (IoT) devices. There are multiple independent linear time-invariant processes in the system, which are sampled by sensors, scheduled by controllers and controlled by actuators. In the literature of wireless control CPS, commonly assume that the system just have one controller and ignore the processing time on server. In this work we employ the edge computing, the controllers are facilitated by edge server and cloud server. The processing time of status update depends on the characteristic of different processes and servers. By taking into account such conditions, we mainly investigate how to choose the destination of status updates (i.e., edge server or cloud server) to minimize the average Mean Square Error (MSE) of the entire system. To address this issue, we formulate a Markov Decision Process (MDP) problem and obtain the optimal scheduling policy. The threshold property of the optimal scheduling policy is proved, and a suboptimal policy is proposed to overcome the curse of dimensionality. The simulation results illustrate that the selection of controller is related to the timeliness of process and show the superiority of the proposed policies.

With the growing interest in the smart ocean, the satellite-based marine Internet of Remote Things (IoRT) network has been regarded as a promising architecture for sensory data collection and transmission in infrastructure-limited offshore areas. In this article, we investigate the access control problem in the context of GEO/LEO heterogeneous IoRT networks, where multiple gateways are deployed to collect data generated by IoRT devices and then forward them to the terrestrial data center via satellite links. However, most existing access control strategies shed light on the traditional network performance (i.e., transmission delay and communication throughput) in single-layer satellite networks (i.e., low-Earth orbit (LEO) layer or geosynchronous orbit (GEO) layer), whereas the interplay between LEO and GEO layers and the freshness of information are rarely considered. To this end, we first formulate an age-oriented access control problem to minimize the long-term peak Age of Information (AoI) and transform it into a model-free Markov decision process (MDP). Then, a Deep-Double-Dueling-$Q$-Learning (D3QN) policy is trained offline and can be deployed online to make decisions according to dynamic data arrivals and time-varying channels. Simulation results show that the proposed strategy significantly outperforms the state-of-the-art ones in terms of the long-term AoI performance. Furthermore, our strategy could make cooperative decisions for gateways and obtain a proper tradeoff between satellites on different layers.

Satellite-based Internet of Things (S-IoT) is viewed as an efficient solution to provide timely status updates to the terrestrial user equipment (UE), due to its ubiquitous coverage and broadband access capability inherited from high throughput satellite (HTS). However, the conventional hybrid automatic repeat request (HARQ) cannot guarantee the freshness of status update transmission, because the reliable transmission needs the retransmission of the lost packets, which deteriorates the freshness due to the nontrivial propagation delay and high bit error rate (BER) of the satellite–territory link (STL). In this article, we propose an age-optimal network coding HARQ (NC HARQ) scheme with the metric of information timeliness, i.e., Age of Information (AoI) to realize timely status updates in S-IoT. First, we model the STL as a shadowed Rician (SR) fading channel and derive the closed-form expressions of BER. Then, we propose a fixed interval NC inserted HARQ (f-NC HARQ) scheme, where the NC packets are inserted in the information packets with fixed interval to accelerate the recovery of lost information packets and derive the expressions of Peak AoI (PAoI) and average end-to-end delay. Furthermore, we propose an adaptive NC inserted HARQ (A-NC HARQ) scheme for the drastic variations in the SR fading channel, where the transmission of the status update is modeled as a partially observable Markov decision process (POMDP) problem and solved by a low complexity improved fast informed bound (iFIB) algorithm. Simulation results validate the accuracy of our theoretical derivations and show that the A-NC HARQ scheme can achieve the lowest PAoI and average end-to-end delay.

In this article, we consider a cyber–physical system (CPS) with multiple Internet of Things (IoT) devices. There are multiple independent linear time-invariant processes in the system, which are sampled by sensors, scheduled by controllers, and controlled by actuators. In the literature of wireless control CPS, commonly assume that the system just have one controller and ignore the processing time on server. In this work we employ the edge computing, the controllers are facilitated by edge server and cloud server. The processing time of status update depends on the characteristic of different servers and processes. By taking into account such conditions, we mainly investigate how to choose the destination of status updates (i.e., edge server or cloud server) to minimize the average mean square error (MSE) of the entire system. To address this issue, we formulate a Markov decision process (MDP) problem and obtain the optimal scheduling policy. The threshold property of the optimal scheduling policy is proved, and a suboptimal policy is proposed to overcome the curse of dimensionality. Furthermore, the processing preemption mechanism is considered to schedule the status updates more flexibly, and its consistency property is proved. The simulation results illustrate that the selection of controller is related to the timeliness of process and show the superiority of the proposed policies.

Rateless codes can adapt to the wireless channel conditions without accurate channel state information (CSI) at the transmitter side, avoiding CSI feedback and retransmission, and thus are a promising channel coding approach to meet the stringent requirements of ultrareliable low-latency communications (uRLLCs). This article investigates a self-adaptive ordered statistics decoder (S-OSD) scheme for finite block length nonbinary Raptor code (NBRC). Aiming at minimizing the decoding complexity, some strategies for our S-OSD scheme are designed, including the segmentation rules of most reliable basis, the generating and discarding rules of test error patterns, and the stop criteria, respectively. In addition, an upper bound of block error rates (BLERs) for the NBRC under OSD is derived, which can be used to estimate the number of NBRC symbols required to successfully decode the input information via the S-OSD. Simulation results show that the complexity of our S-OSD scheme is greatly reduced comparing to the existing OSD schemes, while achieving very low BLER in the short block length regime.

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.

Delivering timely status updates in a timeliness-critical communication system is of paramount importance to assist accurate and efficient decision making. Therefore, the topic of analyzing Age of Information (AoI) has aroused new research interest. This paper contributes to new results in this area by systematically analyzing the AoI of two types of Hybrid Automatic Repeat reQuest (HARQ) techniques that have been newly standardized in the Release-16 5G New Radio (NR) specifications, namely reactive HARQ and proactive HARQ. Under a code-based status update system with non-trivial coding delay, transmission delay, propagation delay, decoding delay, and feedback delay, we derive unified closed-form average AoI and average Peak AoI expressions for reactive HARQ and proactive HARQ, respectively. Based on the obtained explicit expressions, we formulate an AoI minimization problem to investigate the age-optimal codeblock assignment strategy in the finite block-length (FBL) regime. Through case studies and analytical results, we provide comparative insights between reactive HARQ and proactive HARQ from a perspective of freshness of information. The numerical results and optimization solutions show that proactive HARQ draws its strength from both age performance and system robustness, thus enabling the potential to provide new system advancement of a freshness-critical status update system.

The age of information (AoI) is a new metric to measure the timeliness of various status update systems, and hybrid automatic repeat request (HARQ) transmission scheme is usually applied to ensure higher timeliness. However, little research considers encoding delay, propagation delay, decoding delay and feedback delay in the HARQ-based coded status update system. To the best of our knowledge, in this paper, the HARQ-based Spinal coded timely status update system with all the practical delay elements is considered for the first time. We derive the average AoI expression of the system and analyze the monotony of the AoI expression to give an average AoI upper bound. Then we optimize the HARQ transmission scheme to minimize the AoI. To decrease the complexity of the optimization algorithm, we separate it into two steps. First, we optimize the puncturing pattern of Spinal codes and propose a transmission scheme under incremental tail transmission puncturing (ITTP) pattern. Second, we optimize the number of symbols in each round under the ITTP transmission scheme, and propose the optimal transmission scheme under the coarse-grained ITTP pattern. Simulation results show that the proposed transmission scheme can significantly decrease the AoI compared to the baseline transmission schemes.

In satellite-based Internet of Things (S-IoT) system, the timely status updating of terrestrial sensing user equipments (UEs) to satellite could be hampered by the long propagation delay, especially in massive machine type communications (mMTC). To guarantee the information freshness in S-IoT, a new performance indicator called age of information (AoI) is exploited to analyze the average AoI (AAoI) in the overload case of mMTC, and a grant free age-optimal (GFAO) random access protocol is proposed to lower the AAoI. Specifically, the closed-form expression of AAoI is derived by tracing the instantaneous AoI evolution of each UE through Markov analysis. Then, the proposed GFAO random access protocol is proved to achieve a minimum AAoI and a maximum throughput in S-IoT, by adjusting the number of access time slots in each transmission frame in the overload case of mMTC. Extensive simulations are conducted to validate the theoretical analysis, and show that there exists different optimal value of access time slots in system load region from 0.2 to 3, which can minimize AAoI and maximize throughput in the proposed GFAO random access protocol.

In this paper, we investigate the timeliness of the vehicular status updates in space-air-ground integrated networks (SAGIN) for intelligent transportation systems (ITS). The Age of Information (AoI) is introduced to capture the timeliness of the vehicular status updates. To overcome the inherent end-to-end latency taken by the long-distance communications in SAGIN for ITS, prediction has attracted extensive attention in the existing literature and shown its superiority. Nevertheless, it is not clear whether prediction is beneficial to the AoI. Inspired by the motivation, we first formulate a model of a real-time vehicular communication link with prediction, where the generated update can be predicted and transmitted to the receiver in advance. Then, we derive the explicit expression of the average age and show that the prediction is not always beneficial to the AoI. Instead, prediction is more applicable for the short-distance communications than long-distance communications. Further, to improve the AoI performance, a MDP framework is presented to obtain a switching structure of the optimal prediction policy. The results show the advantage of the optimal prediction policy over the policy of predicting all the time or with no predicting.

In this paper, we study the age-oriented hybrid automatic repeat request (HARQ) protocol design in space-air-ground integrated networks (SAGINs) scenarios. A real-time communication system, where the updates are delivered from the remote nodes to terrestrial devices, is formulated. As the end-to-end latency$D$is nontrivial, the traditional HARQ with frequent feedbacks is not always beneficial to timely transmission. Intuitively, there is a threshold$D^{*}$of$D$, only within which retransmission is advantageous to age. Inspired by this, we formulate an age-optimal redundancy allocation problem and derive the explicit expression of$D^{*}$for advantageous retransmissions. Besides, to further increase the timeliness of the system, we propose a fast incremental redundancy hybrid ARQ protocol (fast IR-HARQ), where successive decoding and feedback operations are omitted based on channel estimation. Considering the shadowed Rician fading channel and finite blocklength regime, we derive expressions of the average age for the standard IR-HARQ and fast IR-HARQ setups. As expected, the proposed fast IR-HARQ scheme reduces the average age significantly compared with the IR-HARQ strategy. Further, we evaluate the influence of different parameters on the age performance of the fast IR-HARQ scheme. The results demonstrate the superiority of the proposed fast IR-HARQ protocol without loss of reliability.

It is witnessed that blockchain technology has been widely studied in Internet of Things (IoT) applications due to its decentralized tamper-resistance. Meanwhile, satellite-based IoT (S-IoT) becomes popular and has been regarded as a potential solution of the scalability due to its ubiquitous coverage inherited from satellites. Nevertheless, the large-scale blockchain network enabled S-IoT (BNS-IoT) would be limited by timely performing consensus. In this paper, we propose an age-critical blockchain sharding (ABS) scheme with the metric of information timeliness, i.e., age of information (AoI) to realize timely consensus in BNS-IoT. Specifically, we propose a forking-waiting-retransmission (FR) mechanism for the ABS scheme to deal with forking events, and realize a secure consensus. Then, we derive the closed-form expressions of average AoI (AAoI), throughput and security performance of the FR mechanism in ABS scheme, respectively, and compare with the$n$-block confirmation and select the longest-chain ($n$-LC) mechanism. Simulation results show that our ABS scheme can realize the linear expansion of throughput with the increasing number of shards, and our FR mechanism can greatly improve the security by sacrificing minor AAoI compared with the$n$-LC mechanism. Furthermore, our ABS scheme can outperform the conventional random sharding (RS) scheme in terms of AAoI and throughout.

In this paper, we study the information freshness of short-packet communication in wireless networks, where a base station (BS) sends time-sensitive status updates to users via adaptive multiple access technology. To improve the Age of Information (AoI) performance of the network, the BS adaptively switches among non-orthogonal multiple access (NOMA), orthogonal multiple access (OMA) and cooperative NOMA with simultaneous wireless information and power transfer (SWIPT). Specifically, the BS carefully decides the appropriate multiple access technology and the corresponding power allocation according to the state of the network to optimize the expected weighted sum of AoI (EWSAoI) of the system. To this end, we first analyze the EWSAoI of these three multiple access technologies and propose an adaptive NOMA/OMA/cooperative-SWIPT-NOMA transmission scheme. In specific, we formulate a Markov Decision Process (MDP) problem and develop an optimal policy for the BS to decide whether to use NOMA, OMA or cooperative-SWIPT-NOMA for downlink transmission based on the current state of the network. We further prove the existence of optimal stationary and deterministic policy. Furthermore, to reduce the computation complexity, a suboptimal adaptive policy based on Lyapunov Optimization is also devised, which can achieve near optimal performance according to our simulation results. The extensive simulation results demonstrate the advantages of the proposed policies, which provide useful insights for practical system designs.

In this paper, we analyze the freshness of information in grant-free massive access via a new metrics named age of information (AoI), and propose an age-critical frameless ALOHA (ACFA) random access protocol, where the average AoI (AAoI) is implicitly reduced by banning the transmission of activated user equipments (UEs) recovery successful in the last frame. In particular, in order to analyze the AAoI performance of the ACFA random access protocol, we define two metrics named the average channel load and packets recovery rate (PRR) of ACFA protocol, and tracking the evolution of the number of access-allowed UEs in each frame. Then we derive an analytical expression of AAoI as a function of the frame length and the PRR in the ACFA random access protocol. Simulation results validate the accuracy of our theoretical analysis and show the great potential of ACFA random access protocol in minimizing AAoI.

Due to ubiquitous coverage inherited from the satellites, space-air-ground integrated networks (SAGIN) has been viewed as a promising enabler to provide “anywhere and anytime” broadband access for the next generation of mobile network. Nevertheless, the status updating to the satellite of terrestrial sensing devices could be hindered by the propagation delay. As a result, it becomes crucial to investigate the timeliness of information for massive machine type communications (mMTC) random access in SAGIN. In this paper, we analyse the timeliness of information for the mMTC random access scenario via a new performance metric named age of information (AoI), and propose an age-critical pilot allocation (ACPA) random access protocol aiming to lower the system average AoI (AAoI). By tracking the AoI evolution of each device via Markovian analysis, the closed-form expression of the system AAoI is derived, then we conduct an optimal number of slots to achieve the lowest system AAoI with the increasing of the system load. Simulation results validate the accuracy of our theoretical analysis, and also show that our ACPA protocol can significantly outperform other relevant random access protocols in terms of reducing the AAoI in overload cases.

This paper investigates the timeliness performance of a downlink wireless communication system with a base station (BS) serving two users under the non-orthogonal multiple access (NOMA) system. The hybrid automatic repeat request with chase combining (HARQ-CC) in finite blocklength is considered. For minimizing the information freshness which is characterized by Age of Information (AoI), an intelligent system is adopted. The BS can adjust the power allocation to each user in NOMA, and decide to transmit old or new packets to each user, according to the users’ current AoI status and the positive/negative acknowledgement (ACK/NACK) feedback signal. First, the closed-form of the individual user’s outage probability with arbitrary power allocation combination of HARQ-CC aided NOMA system is derived. Based on the outage probability, the optimization of the system average AoI is achieved by minimizing the Lyapunov Drift function of each time slot. By introducing the HARQ-CC mechanism into the NOMA scheme, a trade-off between reliability and timeliness can be achieved, which is found that this policy can improve the AoI performance of NOMA scheme in low signal-noise ratio (SNR) and outperform existing works on NOMA and orthogonal multiple access (OMA).

In this paper, we study downlink short-packet communication in wireless networks, where a base station (BS) sends time-sensitive status updates to users via non-orthogonal multiple access (NOMA) or Adaptive NOMA/orthogonal multiple access (OMA). The Age of Information (AoI), namely the amount of time that elapsed since the most recently delivered packet was generated, captures the freshness of the information. We aim to minimize the Expected Weighted Sum AoI (EWSAoI) by optimizing the power allocation. First, a low-complexity power allocation policy, namely Stationary Power Allocation policy is proposed in NOMA. In this policy, the BS allocates fixed power to each user and we obtain the closed-form expression of the optimal allocation factor to minimize the EWSAoI. Then, we propose an Adaptive NOMA/OMA policy based on Lyapunov Optimization in which the BS can adaptively switch between NOMA and OMA and dynamically allocate power for users to keep the EWSAoI low. Numerical results demonstrate the advantages of the proposed policies, which provide useful insights for practical system designs.

In this paper, we consider a non-orthogonal multiple access (NOMA)-based satellite-integrated internet of things (S-IoT) network, where a satellite transmits timely status updates to multiple user equipments (UEs). To keep the freshness of status updates in this network, we formulate an age of information (AoI) optimization problem subject to long/short-term power and throughput constraints. We leverage tools from Lyapunov optimization to transform the optimization problem into a sequence of online power allocation problems. Since the original optimization problem is non-convex and hard to find the optimal solution, we utilize the particle swarm optimization (PSO) algorithm to obtain an optimal solution within a linear computational complexity. Simulation results show that our proposed NOMA-AoI scheme outperforms the benchmark schemes with regard to AoI performance. Furthermore, we also discuss the impact of importance weight V on the AoI and power consumption and validate the tradeoff.

Short length channel coding and low complexity decoding is essential for 5G ultra-reliable low latency communications (uRLLC). In this paper, an efficient ordered statistics decoder (E-OSD) scheme is proposed for finite length non-binary Raptor code (NBRC) towards uRLLC. The segmentation and discarding rules of test error patterns, and the stop criteria are designed for the proposed E-OSD scheme to reduce the decoding complexity. A block error rate (BLER) upper bound of the NBRC under OSD is derived to estimate the number of NBRC symbols required for achieving the desired BLER performance. Simulation results show that the complexity of the proposed E-OSD scheme is greatly reduced compared to the existing OSD schemes, and it can achieve the BLER lower than 10−5in the finite length regime (<256 bits), satisfying the requirements of uRLLC.

The growing boom in time-critical applications such as remote sensing and monitoring has made low latency of information an important requirement. Age of information (AoI) has been proposed to measure the freshness of information from the receiver side. In this paper, we analyze that multiple sources transmit their status packets to a remote controller for timely updates. Characterized by long transmission distances, satellite networks are commonly using Aloha as a random access protocol by preconceiving channel sensing is low efficient. Yet, for some non-terrestrial networks where the propagation delay is comparable to the transmission time, the performance comparison between Aloha and CSMA requires more detailed consideration. By building the node-centric discrete-time Markov chain, we quantify the performance of Aloha and CSMA on AoI and give the performance break-even point. Only when the ratio of propagation delay to transmission time is larger than this point, Aloha performs better on the timeliness metric. Furthermore, we derive the optimal attempt probability of CSMA to achieve the lowest latency. In the end, simulation results confirmed the validity of the theoretical analysis.

With the development of low earth orbits (LEO) high throughput satellite (HTS), satellite-Internet of Things (S-IoT) has become a crucial direction in beyond 5G (B5G) and future sixth generation (6G) mobile system due to the ubiquitous coverage and broadband access capability inherited from the HTS. Several S-IoT applications for monitoring status updates can be seen as classic cases of real time transmission, where the main performance parameters are the information freshness and fairness of the network, i.e., the age of information (AoI) and Jain's Fairness Index (JFI), respectively. In this paper, we propose a freshness-critical incremental-redundancy hybrid automatic repeat request (FCIR-HARQ) multi-hop transmission scheme to improve the AoI and JFI performance, where each hop adopts the Last Come First Serve with preemption only in waiting (LCFS-W) policy with a buffer only store the latest file. In particular, we derive the closed form expression of average AoI (AAoI) in the above multi-hop S-IoT, and analyze the corresponding JFI in each hop. Simulation results validate the accuracy of our derivations, and show that the proposed FCIR-HARQ multi-hop transmission scheme with LCFS- W policy outperforms the existing First Come First Serve (FCFS) policy in terms of AAoI.

With the development of next generation of mobile communications, the access of massive Internet of Things (IoT) devices need a more intelligent and secure network. Blockchain has become an emerging technology due to its characteristics of decentralization, stability and transparency. Thus, the combination of blockchain and IoT has attracted the focus of researches. However, the conventional blockchain based on terrestrial network are limited to the scalability and latency. With the support of the wide coverage of satellite, satellite-based Internet of Things (S-IoT) can solve the defect of large consensus latency of blockchain in terrestrial networks. In this paper, considering the limitation of system power and to improve the freshness of information, i.e., the age of information (AoI), we propose a power allocation scheme to accelerate the blockchain consensus over the S-IoT network. Then, we derive the closed-form expressions to the latency and power consumption of blockchain consensus over the S-IoT network. Moreover, we formulate an average AoI (AAoI) optimization problem subject to the total power constraints and solve it by genetic algorithm. Simulation results show that the proposed power allocation scheme has a superior performance in terms of AAoI and throughput compared to the conventional blockchain schemes.

Satellite-based Internet of Thing (S-IoT) is generally considered as a potential solution for the ubiquitous coverage broadband access in the next generation of mobile network. Considering the limited onboard resource of satellites and massive machine type communications (mMTC) requirement, we propose a fairness-improved resource allocation scheme in Quality of Service (QoS)-guaranteed S-IoT non-orthogonal multiple access (NOMA) downlink network. To ensure all the downlink NOMA user terminals' (UTs') QoS and approach the maximization energy efficiency simultaneously, we formulate a joint energy efficiency and fairness optimization problem. Then, we construct three virtual queues to record the power consumption, queue backlog and transmission delay, respectively, and utilize the Lyapunov optimization framework for the purpose of coping with the joint optimization problem. Simulation results validate our proposed NOMA-QoS scheme outperforms existing optimization works in terms of fairness performance while approaching the maximum energy efficiency. Furthermore, the NOMA-QoS scheme also has higher satisfaction and lower outage probability than the existing optimization works.

In satellite-based internet of things (S-IoT), a noted limitation is the non-trivial propagation delay due to the long distances. Hence, to support emergent real-time IoT applications, where information must be transmitted with short end-to-end latency, the traditional hybrid automatic repeat request (HARQ) strategies in terrestrial network are not fit anymore because the reliable feedback transmission has low efficiency in S-IoT. In this paper, we propose a network code HARQ (NC-HARQ) transmission protocol combined with the concept of information timeliness, i.e., age of information (AoI) to realize limited/no feedback multi-hop transmission in S-IoT. We consider a two-hop end-to-end transmission scenario in the S-IoT, and derive the closed form expressions for average AoI of our NC-HARQ protocol through establishing a four states Markov chain. Simulation results illustrate that the NC-HARQ protocol achieves lower average AoI compare with several state-of-the-art HARQ schemes.

The timeliness of information is important for massive machine type communications (mMTC) random access in satellite internet of things (S-IoT), where the propagation delay would hinder the terrestrial sensing devices update their timely status to the satellite. In this paper, we analyse the timeliness of information for the mMTC random access scenario via a new performance metric named age of information (AoI), and propose an age-optimal multi-slots pilot allocation random access (AMSPA) protocol, which aims to lower the system average AoI with a certain access failure probability (AFP) requirement. We derive the closed-form expression of the system average AoI by tracking the AoI evolution of each device via Markovian analysis, then we conduct an optimal number of slots to achieve the lowest system average AoI with the increasing of the system overload. Simulation results validate our theoretical analysis and show that we can minimize the system average AoI via choosing an optimal number of slots under diversity system load for our AMSPA protocol.

In this article, we propose an intelligent hybrid nonorthogonal multiple access (NOMA) relaying system for the next generation of millimeter-wave (mmWave) band end-edge-cloud vehicular networks, which mainly comprises the cloud high-throughput satellite (HTS), edge base station (BS), and end vehicle nodes (VNs). Specifically, by taking account of the movement of the end VNs in the edge BS, we investigate three typical scenarios due to the mobility of the end VNs during the downlink transmission, including quasistatic, intracell, and intercell scenarios, and formulate the optimal power allocation problem of the intelligent hybrid NOMA system for the throughput maximization and outage probability (OP) minimization. Concretely, we first present an iteration power allocation (IPA) algorithm to derive the optimal set of power coefficients for the NOMA transmission in the quasistatic scenario, and also design a power reallocation method based on the expectation–maximization (PREM) algorithm for the intracell and intercell scenarios. Simulation results validate that our proposed algorithms can approach to the exhaustive search method and outperform the existing optimal NOMA schemes. Further, we exploit the effects of the number of the moved end VNs, which can offer some useful guidelines for the design of the next-generation vehicular network.

To provide massive connectivity in massive machine-type communications (mMTCs) for the Internet of Things (IoT) system, a novel grant free random access protocol, called multislot pilot allocation (MSPA) is proposed in this article, where the user equipments (UEs) are permitted to jointly transmit randomly chosen pilot sequences along with their data packets over multislot to resolve intracell pilot collision. In addition, by utilizing the belief propagation tool for the MSPA protocol, the closed-form expressions to the access failure probability (AFP) and system throughput in a finite length regime are derived, which are highly desired for practical-interest mMTC network. Further, a guideline for certain mMTC scenarios that target urgent serving requirement UEs is also proposed to minimize the access latency and maximize the system throughput under diverse AFP constraints. Finally, the parametrical analysis of the MSPA protocol is given by theoretical proof and simulation verification, which shed light on the advantages of our MSPA protocol over the existing protocols in terms of achieving high throughput and shortening the access latency.

In order to facilitate the reliability of data transmission of Spinal codes over the fading channel, performance analysis of Spinal codes is conducted, and an improved encoding structure is proposed. First, we derive an approximate frame error rate (FER) upper bound for Spinal codes over the Rayleigh fading channel in the finite block length (FBL) regime. Then, inspired by the FER analysis process, we propose an improved encoding structure, named self-concatenation structure, to reduce the FER of Spinal codes. In addition, a parallel structure is proposed for Spinal codes to improve the decoding throughput. For the self-concatenation structure, simulation results show that it exhibits a significant gain in anti-noise performance compared with the original Spinal codes over the Rayleigh fading channel. For the parallel structure, we find that by combining the parallel structure with the self-concatenation structure, not only is the encoding and decoding throughput of Spinal codes significantly improved but also the FER of Spinal codes is reduced.

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.

In this paper, we focus on the design of low complexity antenna selection algorithm of a millimeter-wave (mmWave) multiple-input multiple-output nonorthogonal multiple access (MIMO-NOMA) for space information network (SIN). First, the angle-domain sparse geometric based mmWave channel model is utilized in the mmWave downlink system. By grouping the NOMA users according to the distance and path loss, the system performance is related to the instantaneous channel gain of users. Hence, we propose a continuous maximum antenna selection (CM-AS) algorithm, which can approach the maximum sum-rate of the high complexity exhaustive search algorithm. Based on this CM-AS algorithm, we propose two algorithms to improve the user fairness, including discrete maximum AS (DM-AS) and ratio maximization AS (RM-AS) algorithms. Simulations are conducted to confirm the performance of the proposed algorithms in sum-rate and user fairness, and shown that the DM-AS and RM-AS algorithms are reasonable compromise using in practice based on the sum-rate and fairness.

In this paper, we investigate an energy efficiency (EE) millimeter-wave (mmWave) band non-orthogonal multiple access (NOMA) downlink multi-relay system for integrated terrestrial-satellite networks (ITSN), where multiple terrestrial relay nodes decode and forward (DF) the NOMA signal from a high throughput satellite (HTS) to multiple destination nodes. We first define a common framework in which the HTS system and different terrestrial networks coexist in millimeter-wave (mmWave) band NOMA system. Then, the EE expressions of the system is obtained and the optimization problem of maximizing EE is proposed. To solve this non-convex problem, we address the user scheduling and power allocation problem and a new iterative algorithm to jointly optimize the user scheduling and power allocation. Eventually, simulation results are carried out to show the benefits of the proposed scheme and discuss the influence of the key system parameters on the EE mmWave NOMA downlink multi-relay system.

The flip-successive cancellation (SCF) decoding algorithm is a decoding scheme to improve the performance of the SC decoding algorithm under short code length by flipping erroneous bits in initial SC decoding. The degraded performance of the SCF decoding algorithm is usually caused by the wrong locating of the first erroneous bit or additional erroneous bits. To address this issue, we propose a machine learning based multi-flips SC decoding scheme (ML-MSCF), which can improve the performance of the SCF decoding algorithm with multiple flips based on the long short-term memory (LSTM) network and reinforcement learning (RL). Specifically, we use a LSTM network to locate the first erroneous bit when initial SC decoding fails, then the outputs of the LSTM network are used as the action space of RL to identify additional erroneous bits in the followed procedure. Simulation results show that the proposed scheme can achieve performance improvement of 0.2-0.3dB over the stateof-art SCF decoding algorithm on both the bit error ratio (BER) and the frame error rate (FER) with less decoding latency.

As a newly invented type of rateless codes, Spinal codes can be capacity-achieving with short message length and thus hold great prospects for the design of Ultra-Reliable Low-Latency Communication (URLLC) systems. However, the error probability of Spinal codes over Binary Symmetric Channel (BSC) in the finite-length regime lacks explicit analysis in the literature, which in turn hinders efforts to the analytical design of high-efficiency associated techniques, such as the puncturing strategy. In this paper, with the bound on the number of erroneous bits in the Maximum Likelihood (ML) decoding result, we derive the asymptotically tight bound on the Bit Error Rate (BER) of Spinal codes over BSC. Based on this result, we then design the optimal puncturing strategy for Spinal codes over BSC by formulating a rate maximization problem under the constraint of low error probability. In addition, we carry out extensive simulations to verify the correctness of the error probability analysis and the effectiveness of the puncturing strategy design.

Raptor codes can approach the capacity of era-sure channel without accurate channel state information at the transmitter side, which is viewed as a potential channel coding approach to meet stringent requirements of ultra-reliable low latency communications (uRLLC) (block error rate (BLER) ≤ 10-5, and end-to-end latency ≤1 ms). This letter investigates a modified ordered statistics decoder (mOSD) algorithm for finite length non-binary Raptor code towards uRLLC. The upper bound of BLER for the non-binary Raptor code under OSD is derived, which can estimate the required block length under certain reliability requirement. Simulation results show that the BLER can be lower than 10-5in the finite length regime (-5, which is reduced by up to 50% of the average decoding complexity than the conventional OSD.

In this paper, we consider a long-distance point-to-point communication system with only a single buffer in which the source generates status updates with rate λ and can only transmit one update at a time to the receiver. The timeliness of the status updates is evaluated by the age of information (AoI). In this setting, two scheduling policies, namely preemption and non-preemption respectively, are adopted to minimize the AoI. Specifically, we investigate the priority of the two scheduling policies in the presence of non-trivial propagation delay, which has received little attention in the existing work. Utilizing the evolution of AoI, explicit expressions of the limiting average age for the two scheduling policies are derived, based on which we theoretically prove that for given λ, there exists a threshold of the propagation delay, within which preemption policy outperforms non-preemption policy from the perspective of the limiting average age. We further formulate an optimization problem minimizing the limitng average age under the constraint of decoding failure probability for the two scheduling policies and determine the optimal codeword length. Numerical results are provided to validate our theoretical analysis.

In this paper, we propose a novel pilot allocation with desired reliability (PA-DR) random access protocol for integrated terrestrial-satellite network (ITSN). ITSN is regarded as an effective solution to achieve massive connectivity and ubiquitous coverage in future communication systems. To provide massive machine type communications (mMTC) to a backbone satellite in ITSN, the dense user equipments (UEs) are permitted to jointly transmit randomly chosen pilot sequences along with their data packets over multi-slot in our PA-DR random access protocol, which allows for the potential performance gain in resolving more intra-cell pilot collisions with high probability. By utilizing the finite length analysis of pilot allocation over muti-slot, we derive the closed-form expressions to the access failure probability and system throughout in the finite length regime, which is highly desired for practical-interest ITSN. With the help of the derived expressions, we propose a guideline for mMTC ITSN that target on satisfying desired reliability of UEs, and optimize the number of allocated pilots and minimum access latency under diverse access failure probability requirements. In addition, simulation results show that our PA-DR random access protocol outperforms the existing protocols in achieving high throughput and shortening the access latency.

In this paper, we study the timely transmission of status updates from multiple sources to an interested receiver. The sources generate updates independently at Poisson rate, which are then coded and transmitted through a shared block fading channel. Age of information (AoI) is an effective indicator to describe the timeliness of transmission by depicting the freshness of information the receiver knows about the interested source. An intuitively age-oriented transmission scheme is sequentially independent transmission, i.e., the transmitter submits the updates one-by-one without waiting. However, this scheme does not always minimize the age. Aiming to improve the age performance of the considered system, we propose a waiting-and-batching transmission scheme. Specifically, earlier generated updates are waiting for late ones and then batching into a long packet before being coded and transmitted. We derive the close-formed expression of the average AoI of the proposed scheme, and conduct extensive numerical comparisons with the independent transmission scheme. Results reveal that our waiting-and-batching scheme can be more beneficial to AoI if: (1) the update generation rate is high; (2) the propagation delay is non-trivial.

Integrated terrestrial-satellite networks (ITSN) is regarded as an effective solution to enable ubiquitous connectivity for massive machine type communications (mMTC) in the next generation of mobile system. In this paper, we study an uplink code-domain non-orthogonal multiple access (CD-NOMA) mMTCs system for ITSN. Considering that the conventional orthogonal pilot (OP) scheme is inefficient due to allocate dedicated time slot for pilot sequences, especially when the length of pilot sequences is large and need allocated more time slot for pilot transmission, the residual time slot for data offloading need consume more energy. To address this challenge, the power and time allocation is jointly optimized to reduce the energy consumption. Closed-form expressions for the joint optimal power and time allocation solutions are obtained, and used to establish the conditions for determining whether the OP scheme, superimposed pilot scheme, or hybrid pilot scheme should be used for mMTC. Simulations are provided to confirm the reliability of our analytical results and show the impact of various parameters on the system performance.

Space information network (SIN) is regarded as an effective solution to enable ubiquitous connectivity in a global coverage and a cost-effective manner for massive machine type communications (mMTC) in the future internet of things (IoT). In this paper, we study an uplink code-domain non-orthogonal multiple access (CD-NOMA) mMTCs system for SINs, and introduce an uncoordinated code-domain NOMA protocol. Considering the dominant traffic in uplink mMTC communications is short packet, where the fixed length control overhead becomes inefficient due to the short length of payload. To address this challenge, superimposed pilots (SP) scheme is adopted for synchronization and channel estimation. Moreover, we utilize successive interference cancellation (SIC) and successive joint decoding (SJD) to recover the signals in collisions under the shadowed-Rician fading and path loss satellite-ground channel, and the expressions of the outage probability and maximum system throughput of SP with SIC and SJD decoding methods are derived, respectively. Simulation results validate our analytical results and show that the maximum system throughput of SP with SJD can outperform that of SIC in SIN for a short packet transmission.

Polar codes with short code length under successive cancellation (SC) decoding are inferior to other advanced codes of similar block length. Although more sophisticated algorithms, such as SC list (SCL) decoding and SC stack (SCS) decoding were introduced to address the problem, the complexity of these algorithms has also increased. In this paper, we first propose a novel construction of Polar codes, named index modulated Polar (IM-Polar) codes. This scheme conveys information not only by the information bits in non-frozen channels as conventional Polar codes, but also by the indices of channels, which are activated according to the incoming bit stream. Moreover, we give a specific implementation of IM-Polar codes under cyclic redundancy check (CRC) aided SCL (CA-SCL) decoding. In this implementation, repetition-assisted encoding is employed to improve the accuracy of index detection. It is shown via simulations that the proposed implementation of IM-Polar codes can provide gain of 0.2--0.3 dB over the classical CRC-aided Polar (CA-Polar) codes with code rate 0.357 and code length 128 at the bit error ratio (BER) of $10^{-4}$.

Recently, with the development of the next generation of high throughput satellites, deploying satellite-based Internet of Things (S-IoT) is suggested to solve the increasing demand for ubiquitous broadband access capability terrestrial communications. Under the current situation that the number of communication devices and the hardware capabilities of devices continue to increase, network coding becomes an effective way to further improve the throughput and efficiency in S-IoTs. In this paper, a Network Coded Hybrid Automatic Repeat Request (NCed HARQ) transmission scheme is proposed based on typical bidirectional relaying scenarios of S-IoT, and a general process of the NCed HARQ is presented. The corresponding detailed transmission process is given, and the theoretical performance index is derived and verified by simulations, which emphasizes the benefit of network coding. Besides, we adopt matrix exponential distribution in the calculation to make formulations more concise and unified.

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.

In this article, we consider the satellite Internet of Things (IoT) system, in which the IoT device observes physical processes and transmits the status updates to the monitor node over an error-prone channel with nontrivial propagation delay. The freshness of status updates is characterized by Age of Information (AoI), a novel metric that is defined as the time that elapsed since the freshest received status update was generated. Channel coding is used to combat the burst channel errors and feedback is available through hybrid automatic repeat request (HARQ) protocols. By adopting both the simple-HARQ and incremental redundancy HARQ (IR-HARQ) transmission schemes, we study the age-optimal redundancy allocation problems under the constraint of reliability. As we put special interests on the satellite-IoT scenarios in which the propagation delays are nonnegligible, there exists a threshold of the propagation delay only below which using retransmissions is beneficial to AoI. However, the characterization of such a threshold has received little attention in the literature. By formulating and solving the age-optimal redundancy allocation problems for the adopted HARQ schemes, explicit expressions of the optimal codeword length for each transmission round are derived, and then the threshold of the propagation delay for beneficial retransmissions is obtained. Extensive numerical analysis is conducted to show the effects of propagation delay and channel state on the redundancy allocation results and the optimal AoI. The threshold is also demonstrated by numerical analysis. The results shed important light on the age-optimal HARQ design for freshness-critical satellite-IoT systems in the presence of nontrivial propagation delay.

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.

In this paper, a theoretical framework for the design and evaluation of finite block-length analog fountain codes (AFC) towards ultra-reliable low latency communications (URLLC) is proposed. First, based on the achievable rate analysis and extrinsic information transfer (EXIT) analysis for AFC, we propose a weight adaptive (WA) AFC transmission scheme by introducing a limited feedback link, which can realize the lowest complexity AFC over a wide range SNRs. Further, by combining the conventional EXIT analysis and the dispersion perspective of mutual information, we propose a modified weight selection scheme for short block length WA-AFC (SWA-AFC) scheme. Simulation results show that our SWA-AFC scheme can achieve a superior performance than the existing AFC schemes, and approaching to the Polyansky-Poor-Verdu (PPV) bound.

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.

By allowing a mobile device to offload computation- intensive tasks to a base station, mobile edge computing (MEC) is a promising solution for saving the mobile device's energy. In real applications, the offloading may span multiple fading blocks. In this paper, we investigate energy-efficient offloading over multiple fading blocks with random channel gains. An optimization problem is formulated, to find out how much data should be offloaded such that the mobile device's energy consumption is minimal. Although the formulated optimization problem is non-convex, we prove that the objective function of the problem is piecewise convex, and accordingly develop an optimal solution for the problem. Numerical results verify the correctness of our findings and the effectiveness of our proposed method.

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.

As a type of newly invented rateless codes, Spinal codes can achieve the capacity of both additive white Gaussian noise (AWGN) channel and binary symmetric channel (BSC) with short message length and pseudo-random like codewords. In this paper, a novel puncturing pattern called inverted triangle-shaped puncturing is proposed for Spinal codes. We prove a lemma as theoretical support for the proposed inverted triangle-shaped puncturing. Compared with the uniform puncturing pattern, Spinal codes can be punctured to achieve both high and finer-grained rates by the inverted triangle-shaped puncturing, without increasing the cost of decoding. Extensive simulations are carried out to verify the effectiveness of the proposed pattern. Results show that the inverted triangle-shaped puncturing pattern can increase the code rate significantly without any harming to the bit error rate (BER) performance.

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.

The long-haul communication systems can offer ultra high-speed data transfer rates but suffer from burst errors. The high-rate and high-performance staircase codes provide an efficient way for long-haul transmission. The staircase coding scheme is a concatenation structure, which provides the opportunity to improve the performance of high-rate polar codes. At the same time, the polar codes make the staircase structure more reliable. Thus, a high-rate polar-staircase coding scheme is proposed, where the systematic polar codes are applied as the component codes. The soft cancellation decoding of the systematic polar codes is proposed as a basic ingredient. The encoding of the polar-staircase codes is designed with the help of density evolution, where the unreliable parts of the polar codes are enhanced. The corresponding decoding is proposed with low complexity, and is also optimized for burst error channels. With the well designed encoding and decoding algorithms, the polar-staircase codes perform well on both AWGN channels and burst error channels.

Polar codes are the first provable capacity-achieving channel codes. Despite the splendid performance of long Polar codes, short Polar codes have relatively poor performance compared with other modern channel coding schemes (e.g., Turbo codes and LDPC). In this paper, we explore some practical methods to improve the performance of Polar codes with short to moderate codeword lengths. First, we use Reed Solomon (RS) codes as outer codes. With a specific interleaving strategy, we can concatenate multiple RS codes with one frame of Polar codes. Combining a strategy of allocating unequal RS code rates with the concatenation, different levels of protection are assigned based on the error pattern of successive cancellation list (SCL) decoders. Thus, the finite length performance will certainly be enhanced for this encoding scheme. Meanwhile, the memory size that the original SCL decoding procedure requires is reduced, and the increment of overall decoding complexity is small. Additionally, we propose an intra-frame interleaver to further enhance the performance by dispersing errors. Finally, we designed a list decoding scheme for the proposed multi-RS concatenated Polar codes. Depending on the soft information generated by an SCL decoder, we calculated the reliability of each RS symbol and conducted soft RS decoding. So, the overall performance was enhanced under this joint decoding strategy. Simulation results indicate that the bit error rate (BER) performance of short Polar codes can be well improved.

The integration of high throughput satellite into Internet of Things (IoT) is regarded as an effective strategy to provide ubiquitous broadband access in a seamless, cost-efficient manner. Meanwhile, due to the demand of machine-to-machine (M2M) high throughput services, millimeter-wave (mmWave) IoT networks arouses huge interest. In this paper, we investigate the performance of an amplify-and-forward (AF) mmWave hybrid satellite-terrestrial relay networks (HSTRN) for IoT broadband communications, where we assume source-relay link undergos Shadowed-Rician fading and the relay-destination link undergos Rayleigh fading. Considering rain attenuation is the main factor at mmWave bands, we utilize the multidimensional rain attenuation model to analyze the effect of rain attenuation on system performance. Then we derive the closed-form expression of outage probability and tight approximation of ergodic capacity. Finally, numerical and simulation results are provided to validate our analytical results and show the effect of rain attenuation on the system performance.

Machine-to-Machine (M2M) communications are expected to support extremely harsh requirements on both latency and reliability, which is characterized by the ultra-reliable, low-latency coding (uRLLC) technology in physical layer. In this paper, motivated by the recent development on the finite-blocklength information theory, we propose an ultra-reliable short blocklength analog fountain code (AFC) for M2M communications. First, we use the extrinsic information transfer (EXIT) chart to analyze the AFC compressive sensing belief propagation (CS-BP) decoding algorithm, by tracking the mutual information of AFC CS-BP decoding process, which related to the channel dispersion for the short blocklength AFC. Then, based on the EXIT chart analysis, we propose a Weight-set optimization progressive edge-growth (WO-PEG) encoding algorithm for the short blocklength AFC. Simulation results show that the proposed WO-PEG AFC scheme can effectively improve block error rate (BLER) in the short blocklength regime.

To cope with the tremendous growth of data traffic and obtain a given communication service with minimal energy use, traffic offloading and energy efficiency (EE) improving are two important issues to address for green cellular networks. The authors investigate downlink WiFi offloading in a heterogeneous network consisting of one long term evolution eNodeB (eNB) and multiple overlaid WiFi access points to maximise the user satisfaction of the whole system. In addition, a designed resource reallocation scheme after offloading is jointly considered to improve the EE of the eNB. In the offloading model, two constraints are considered to guarantee the rate promotion of the offloaded users and less impact on WiFi networks. Moreover, the authors transform the model into a combinatorial optimisation problem and adopt the best response (BR) algorithm based on game‐theoretic approach to obtain the optimal offloading user set. Numerical results show that the proposed WiFi‐offloading model can significantly improve the aggregate user satisfaction as well as EE of the eNB. Also, the BR algorithm can converge to the optimal solution same as the exhaustive search algorithm through several iterations.

In this paper, an improved random puncturing pattern of polar codes is proposed, where only the frozen bits can be selected to puncture. Compared to the existing random puncturing schemes, our improved random puncturing scheme can achieve 0.2-1dB decoding performance improvement. Then, an optimized rate-compatible hybrid automatic repeat request (HARQ) transmission scheme is proposed based on parallel concatenated punctured (PCP) polar codes. By analyzing the overhead of the previous successful decoded coding block in our rate-compatible HARQ scheme, two methods of determining the optimal initial code-rate of each new PCP polar coding block are proposed over a time-varying channel. Simulation results show that the average number of retransmissions is about 1.5 times in our proposed rate-compatible HARQ schemes with a 2-level PCP polar encoding construct, which reduces half of the average number of retransmissions than the existing rate-compatible polar coding scheme.

As the first ever provably capacity achieving codes, Polar codes have drawn a wide range of research interests in recent years. It is well known that short/finite-length Polar codes have relatively not so good bit error rate (BER) performance as the state-of-the-art channel codes (e.g. Turbo codes, LDPC). One commonly used way to improve the performance of short Polar codes is to concatenate the Polar codes with outer codes, but the amount of improvement is largely constrained by the performance of the outer codes with short codeword length. Motivated by this, in this work, we propose to use the newly invented Spinal codes, which has high performance with short code length, as the outer codes. Specifically, the designed codes, named as Spinal-Polar, is implemented through an interleaved concatenation scheme. In addition, we propose a joint iterative decoding algorithm for SpinalPolar, and the decoding complexity is analyzed theoretically. Extensive simulations are carried out, and results show that the proposed concatenation scheme can significantly improve the BER performance of short Polar codes.

By combining channel coding and virtual MIMO transmission, coded cooperation could achieve coding gain and diversity gain simultaneously, making it a good candidate for the key technologies enabling ultra-high speed 5G communications. As the first ever provably capacity achieving codes, Polar codes naturally sticks out to be one of the most competitive coding technologies for coded cooperation. In this paper, we aim to propose methods that can fully explore the performance potential of Polar coded cooperation under fading channels. Specifically, Polar coded cooperation by adopting the Plotkin construction for sub-codeword generation is used as the basic method. Then, three codeword shaping methods are proposed to improve the performance of the basic method. The first one is to introduce an interleaver at the receiver terminal to help combat the burst errors. On this basis, the idea of information-refreezing is used to improve the sub-codeword decoding performance on the interuser channels, which in turn increases the cooperation probability. And lastly, the codeword generation scheme is extended from non-systematic Polar codes to systematic Polar codes so that a systematic coding gain is further achieved. The proposed three shaping methods can be used either singly or superimposedly. Simulation results show that under slow fading channels, the system performance in terms of bit error rate can be significantly improved over that of existing Polar coded cooperation method.

In this paper, we propose a novel compressed sensing (CS) algorithm by collaborative use of statistical and structural priors of natural images. The statistical priors include two aspects which are the statistical dependencies of wavelet coefficients in transform domain and non-local self- similarity among pixels in spatial domain. And the structural prior refers to the structural dependencies of wavelet coefficients in transform domain. Our algorithm which employs both multi- domain as well as multi-class prior information is realized under the framework of iterative hard thresholding (IHT). The reconstruction process is divided into two stages. In the first stage, the local statistical prior model is used to correct the signal estimation to obtain the preliminary estimation. In the second stage, first the non- local self-similarity model, and then the global structural prior model are employed to further refine the preliminary estimation. The results show that our algorithm outperforms the state of art. Our algorithm can be utilized in efficient communication in multimedia internet of vehicles (IoV). We demonstrate the effectiveness of our algorithm for multimedia IoV devices by showing its capacity in reducing the amount of multimedia data need to be transmitted while improving the recovery quality.

Cross-layer optimization and transmission could bring a significant performance improvement for terrestrial communication systems. However, very limited work has been conducted to address the cross-layer transmission in deep-space communications. To improve the efficiency of downlink image transmission in deep-space communications, this paper proposes a cross-layer image transmission scheme to maximize the throughput. The proposed scheme is designed based on the compressed sensing (CS) for image compression in the application layer, the Spinal codes for error protection in the physical layer and the licklider transmission protocol (LTP) for transmission control in the transport layer. By jointly optimizing across the application, transport, and physical layers, we dynamically adjust the transmission strategies to achieve high image transmission efficiency. In order to evaluate the performance of the proposed scheme, we build a semi-physical simulation platform for the Earth- Mars communication scenarios. Extensive simulations are carried out for performance evaluation. Results show that the proposed cross-layer image transmission scheme can significantly improve the performance of transmission efficiency based on comparisons with the other schemes.

As a type of newly invented rateless codes, Spinal codes are characterized by capacity achieving over both additive white Gaussian noise (AWGN) and binary symmetric channel (BSC) with short message length and pseudo-random like codewords. For the emerging ultra- reliable low-latency communication (URLLC) scenarios such as information exchanging between self-drive cars, Spinal codes hold great prospects. However, the high decoding complexity of Spinal codes remains a bottleneck for its practical applications. In this work, a novel low complexity decoding algorithm named sliding feedback decoding (SFD) for Spinal codes is proposed. By 'sliding', the decoding tree is layered by a sliding window. By 'feedback decoding', the optimal parent node decision for each layer located by the sliding window is made by the feedback from the best leaf node in the located layer. And the final decoding path is composed of all the optimal parent nodes selected layer by layer. The complexity of the proposed algorithm is analyzed theoretically, and the results show that it is lower than the complexity of other algorithm . Extensive simulations are carried out to verify the effectiveness of the proposed algorithm. Compared with the bubble decoder and the forward stack decoding (FSD) proposed in the literature, SFD can significantly reduce the decoding complexity without any harming to the rate performance.

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.

We consider downlink WiFi offloading in a heterogeneous network consisting of one LTE base station (BS) and multiple overlaid WiFi access points (AP) to maximize the user satisfaction of the whole system. Two constraints are considered to guarantee the rate promotion of the offloaded users and less impact on WiFi networks. Furthermore, the resource block (RB) reallocation after offloading is also taken into account. In order to solve the combinatorial optimization problem, we first propose the RB allocation algorithm to reallocate the RBs left by offloaded users. Then, we adopt the best response (BR) algorithm based on game-theoretic approach to obtain the optimal offloading user set. Numerical results show that the proposed WiFi- offloading model can significantly improve the user satisfaction of the whole system, and the BR algorithm can converge to the optimal solution same as the exhaustive search algorithm through several iterations.

Space Information Network (SIN) with backbone satellites relaying for vehicular network (VN) communications is regarded as an effective strategy to provide diverse vehicular services in a seamless, efficient, and cost-effective manner in rural areas and highways. In this paper, we investigate the performance of SIN return channel cooperative communications via an amplify-and-forward (AF) backbone satellite relaying for VN communications, where we assume that both of the source-destination and relay-destination links undergo Shadowed-Rician fading and the source-relay link follows Rician fading, respectively. In this SIN-assisted VN communication scenario, we first obtain the approximate statistical distributions of the equivalent end-to-end signal-to-noise ratio (SNR) of the system. Then, we derive the closed-form expressions to efficiently evaluate the average symbol error rate (ASER) of the system. Furthermore, the ASER expressions are taking into account the effect of satellite perturbation of the backbone relaying satellite, which reveal the accumulated error of the antenna pointing error. Finally, simulation results are provided to verify the accuracy of our theoretical analysis and show the impact of various parameters on the system performance.

To enhance the performance in practical communications, a novel construction of polar code is designed for a rational polar and space-time block coding (Polar-STBC) system. The Polar-STBC system can be equivalent to a single transmission channel for each polar code bit in Rayleigh fading MIMO channels, and the equivalent channel can be regarded as a fading channel, of which the gain coefficient and additive noise are studied. Moreover, the distribution of the additive noise is also derived. Finally, we show that the bit error rate performance of our Polar-STBC system in 2 × 2, 4 ×⌉ 2 and 4 × 4 MIMOs.

Ranging has been regarded as one of the fundamental enabling technologies for a multitude of applications that require high accurate position information, such as automated navigation, vehicle platooning, asset management, etc. Among various ranging techniques, impulse-radio ultra-wideband is one of the most competitive technologies for high-precision ranging, because of its capability of achieving centimeter-level ranging accuracy, even for dense urban, indoor or cave like environments. However, two main challenges arise when fully exploiting the ranging capability of impulse-radio ultra-wideband: (i) the extremely high sampling rate to acquire the received multipath signal, and (ii) the optimal thresholding strategy to differentiate the first path. To efficiently tackle those challenges, in this work, we propose a ranging approach under the compressed sensing framework. Specifically, the received ranging signal is acquired by low-rate compressed sampling through parallel random projections. Then, an algorithm named matching-pursuit search-back is proposed to detect the first arrival path, which integrates a backward iterative search and thresholding process starting from the peak path. The detection threshold is dynamically adjusted in each iteration to asymptotically minimize the averaged detection errors over false alarm and missed detection. Extensive simulations and experiments with field data are provided to demonstrate that the proposed approach can achieve high-precision ranging with far fewer samples compared with the traditional Nyquist-sampling based ones. Copyright © 2016 John Wiley & Sons, Ltd.

Compressed sensing (CS) based image processing is a important branch of CS based signal processing. However, the high complexity involved in CS based large-size image processing is a challenging issue. In this paper, an orthogonal matching pursuit enabled two-stage CS recovery scheme with low complexity yet high performance, termed separate-combine recovery method is proposed for separable image sensing operators. Specifically, at the separate stage, both compressed row and column data is estimated as intermediates. Based on these estimates, two sparse representations of the original image, called row-first-recovery result and column-first-recovery result, are respectively obtained. At the combine stage, the final result is achieved by weighted aggregating these two sparse representations. The complexity and performance of the proposed method are evaluated through extensive numerical results, showing that the presented scheme outperforms existing methods in terms of recovery speed and computational complexity.

Sensitivity to noise uncertainty is a fundamental limitation of current spectrum sensing strategies in detecting the presence/absence of primary users in cognitive radio (CR). Because of noise uncertainty, the performance of traditional detectors such as matched filter, energy detector and even cyclostationary detectors deteriorates rapidly at low signal-to-noise ratio (SNR). Without accurate estimation of noise power, an absolute ‘SNR wall’ exists in traditional detectors below which robust detection is impossible, no matter how long the observations are. To counteract the effect of noise uncertainty in low SNR, the authors propose a blind frequency-domain entropy-based spectrum sensing scheme. The entropy of the sensed signal is estimated in the frequency domain with probability space partitioned into fixed dimensions. The authors prove that the entropy of noise is a constant and the proposed scheme is thus intrinsically robust against noise uncertainty. Monte Carlo experiments are carried out to verify the robustness and further show that the proposed scheme outperforms classical energy detector and cyclostationary detector in low SNR region with 6 and 4 dB performance improvement, respectively. In addition, the sensing time is reduced to about 75% by the proposed scheme compared to energy detector under the same detection performance.

Abstract Localization is essential for wireless sensor networks (WSNs). It is to determine the positions of sensor nodes based on incomplete mutual distance measurements. In this paper, to measure the accuracy of localization algorithms, a ranging error model for time of arrival (TOA) estimation is given, and the Cramer—Rao Bound (CRB) for the model is derived. Then an algorithm is proposed to deal with the case where (1) ranging error accumulation exists, and (2) some anchor nodes broadcast inaccurate/wrong location information. Specifically, we first present a ranging error‐tolerable topology reconstruction method without knowledge of anchor node locations. Then we propose a method to detect anchor nodes whose location information is inaccurate/wrong. Simulations demonstrate the effectiveness of our algorithm. Copyright © 2008 John Wiley & Sons, Ltd.

Localization is essential for wireless sensor networks (WSNs). It is to determine the positions of sensor nodes based on incomplete mutual distance measurements. In this paper, to measure the accuracy of localization algorithms, a ranging error model for time of arrival (TOA) estimation is given, and the Cramer-Rao bound (CRB) for the model is derived. Then an algorithm is proposed to deal with the case where 1) ranging error accumulation exists, and 2) some anchor nodes broadcast inaccurate/wrong location information. Specifically, we first present a ranging error-tolerable topology reconstruction method without knowledge of anchor node locations. Then we propose a method to detect anchor nodes that have inaccurate/wrong position information. Simulations demonstrate the improvement of our algorithm compared to others.

To mitigate the non-line-of-sight (NLOS) error of ultra-wideband (UWB) ranging caused by obstructions in dense multipath environments, this paper proposed a novel NLOS error mitigation method. The principles and characteristics of NLOS error are analyzed. Based on the signal propagation path loss model, the NLOS error estimation expression is deduced and further used to calibrate the ranging results. Low complexity path detection algorithms are proposed for implementation of the method. Test on measured data shows that the method can improve the ranging precision greatly.