This work is concerned with the coordination gain in integrated sensing and communication (ISAC) systems under a compress-and-estimate (CE) framework, wherein inference performance is leveraged as the key metric. To enable tractable transceiver design and resource optimization, we characterize inference performance via an error probability bound as a monotonic function of the discriminant gain (DG). This raises the natural question of whether maximizing DG, rather than minimizing mean squared error (MSE), can yield better inference performance. Closed-form solutions for DG-optimal and MSE-optimal transceiver designs are derived, revealing water-filling-type structures and explicit sensing and communication (S\&C) tradeoff. Numerical experiments confirm that DG-optimal design achieves more power-efficient transmission, especially in the low signal-to-noise ratio (SNR) regime, by selectively allocating power to informative features and thus saving transmit power for sensing.

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.

The Internet of Thing (IoT) devices play a vital role in collecting mission-critical and time-sensitive sensing data from remote areas, where traditional terrestrial networks are constrained by sparse infrastructures. However, resource-limited ground devices (GDs) in such scenarios often lack the ability to directly transmit essential information to distant data centers. To overcome this challenge, this paper proposes a Satellite-unmanned aerial vehicle (UAV)-assisted data collection framework, where the UAV is controlled by a remote control center via satellite relays. Aiming to maximize the energy efficiency (EE) of the UAV, we first design a reference trajectory to the UAV with given hovering positions. Subsequently, we optimize the power allocation for communication and state sensing strategies for trajectory tracking control, while guaranteeing control stability and communication reliability. These challenging problems are addressed using sequently an efficient algorithm, incorporating Deep Q-Network (DQN), closed-form derivations, and one-dimensional search method. Extensive numerical simulations and experimental validations are conducted to demonstrate the effectiveness of the proposed approach. Key findings point that the data size of collection has greater impacts than transmission power. Moreover, the results reveal the relationships among the communication, control and state sensing in terms of the EE.

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.

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

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.

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

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

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

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

In the upcoming global-coverage 6G networks, high packet loss and long latency in long-distance transmissions exacerbate the trade-off between data timeliness and integrity, particularly in time-sensitive applications involving time-series data with stringent integrity requirements. This challenge exposes the limitations of existing transmission systems, such as source-channel coding and semantic communication, which fail to jointly address both dimensions. In this paper, we propose a deep learning (DL)-enabled generalized deduplication (GD)-based semantic transmission (DLGD-ST) mechanism for time-series data. By leveraging GD to address the impact of semantic ambiguity on data integrity, DLGD-ST exploits the semantic recovery and temporal discreteness of the data to effectively mitigate the conflict between integrity and timeliness. In particular, a well-designed long-short-term memory (LSTM)-based GD algorithm is developed to separate shallow semantic components and supplementary components, ensuring the integrity of semantic transmission. A deep semantic encoding process is then performed using a double-layer progressive dimension reduction (DPDR) and adaptive quantization (AQ) scheme, which capitalizes on the channel robustness of semantics to reduce transmission rounds and improve timeliness. Furthermore, an incremental dimension hybrid automatic repeat request (ID-HARQ) mechanism is introduced to improve semantic reliability by retransmitting high-dimensional semantics, thereby further minimizing end-to-end transmission rounds. To accurately evaluate performance, we introduce the Age of Semantic Information (AoSI), which incorporates integrity constraints into the generalized Age of Information (AoI) to jointly assess integrity and timeliness. Simulation results demonstrate that the proposed DLGD-ST mechanism, enabled by accurate data recovery and reduced transmission rounds, achieves better AoSI performance compared to existing communication systems under both high and low signal-to-noise ratio (SNR) conditions.

This work is concerned with robust distributed multi-view image transmission over a severe fading channel with imperfect channel state information (CSI), wherein the sources are slightly correlated. In contrast to point-to-point deep joint source-channel coding (DJSCC), the distributed setting introduces the key challenge of exploiting inter-source correlations without direct communication, especially under imperfect CSI. To tackle this problem, we leverage the complementarity and consistency characteristics among the distributed, yet correlated sources, and propose an robust distributed DJSCC, namely RDJSCC. In RDJSCC, we design a novel cross-view information extraction (CVIE) mechanism to capture more nuanced cross-view patterns and dependencies. In addition, a complementarity-consistency fusion (CCF) mechanism is utilized to fuse the complementarity and consistency from multi-view information in a symmetric and compact manner. Theoretical analysis and simulation results show that our proposed RDJSCC can effectively leverage the advantages of correlated sources even under severe fading conditions, leading to an improved reconstruction performance.

To facilitate the development of industrial Internet of Things applications, thewireless networked control system(WNCS) is envisioned to support real-time control and communication interactions performed in finite-time manner. A WNCS comprising multiple wirelessly interconnectedsub-systems(SSs) is considered, wherein the sensed state information in each SS is transmitted to the controller via wireless links, thereby enabling timely decision-making processes. Following multiple operation periods of state sensing and transmission, thesystem identification(SI) is performed, leading to the formulation of optimal control policy. To improve the energy efficiency while guaranteeing the SI performance requirement within the allowed decision-making time, the communication and control co-design for WNCS is investigated, where the transmit power, transmission interval length, number of operation periods, coding block-length, and required transmission reliability are jointly optimized. Our investigation demonstrates the interrelationships among effective capacity, energy consumption, and communication parameters. Furthermore, it is found that the optimal communication parameters, such as transmit power and transmission interval length, should be determined by both communication and control requirements. Consequently, it is found that minimizing energy consumption is equivalent to minimize the number of operation periods while guaranteeing the SI performance with defined confidence, which can be effectively addressed by leveraging the non-decreasing property of controllability Gramian. Moreover, the co-design framework is extended to accommodate the scenarios involving link interruptions and overlapping time slots. Simulation results validate the necessity and effectiveness of exploring optimal system operational configurations from the perspective of the proposed co-design. It is also observed that although a 44.2% surge in energy consumption is associated with the proposed relay scheme in the link interruption case, the proposed time scheduling scheme brings a 23.4% reduction in the extra energy expenditure (from 44.2% to 20.8%).

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

Mega-constellation networks (MCNs) of low Earth orbit (LEO) satellites are poised to serve as critical enablers for next-generation 6G wireless systems. These satellite infrastructures not only provide ubiquitous Internet access to terrestrial users but also facilitate relay-assisted data transmission for space-based remote sensing and positioning services. However, the inherent challenges of ubiquitous coverage and overlapping service regions in dense LEO constellations necessitate rigorous optimization of user-satellite association strategies, especially when the serving satellites are from multiple constellations. This paper provides insights into user access selection and resource optimization for mega-constellations that cover extensive terrestrial areas. The selection of multiple satellites from various constellations is predicated on the calculation of their coverage areas and the geolocation of urban areas. By explicitly modeling the transmission traffic requests and data collection process, the access strategy is investigated to maximize network throughput while maintaining a balance in quality-of-service (QoS). Thus, an optimization algorithm is proposed for user access selection that synergistically combines graph convolutional attention networks (GCAN) and deep reinforcement learning (DRL). Simulation results based on the Starlink Phase I and Phase IV models show that the proposed algorithm achieves performance improvements in both throughput and access quality compared to other benchmark algorithms.

Satellite clustering has the potential to enhance inter-satellite cooperation, resist satellite malfunction, and enable more agile space-air-ground integrated applications. This paper investigates a promising model for clustered low Earth orbit (LEO) systems, in which one typical unmanned aerial vehicle (UAV) can assist one satellite cluster to serve one random terrestrial user. Particularly, intra-cluster satellites can communicate user, while inter-cluster satellites are regarded as interference. Two types of satellites and users are randomly deployed at three visible spherical spaces by adopting three independent spherical Poisson point processes. In the modeling, an adaptive selecting mechanism is proposed to pick the strongest received signal between direct and cooperative transmissions. To facilitate a simpler analysis, we firstly transform the three spaces into the three planes through modifying their respective density. Next, assuming that the shadowed-Rician fading is employed in the satellite channel, two Gamma random variables are utilized to approximately express the aggregated power of interference and noise received by the UAV and user, respectively. Subsequently, the exact conditional user association and approximate Laplace transform of the accumulated signal power are derived to further investigate the conditional coverage probability. Finally, simulation results illustrate that: 1) Moderate satellite cluster sizes combined with a UAV altitude of about 200m are beneficial for achieving higher coverage probability; and 2) The adaptive selection mechanism generally achieves comparable or better performance than traditional transmissions by leveraging spatial diversity.

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

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

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

Automatic Modulation Recognition (AMR) is essential for efficient spectrum utilization, cognitive radio, and secure wireless communications. However, deploying accurate AMR models on resource-constrained devices remains challenging due to substantial computational overhead. Importantly, minimizing inference computational cost—distinct from conventional neural network lightweighting—is critical for meeting strict runtime constraints on resource-limited platforms. This paper proposes the Adaptive Modulation Inference (AMI) framework, a dynamic inference solution for efficient and adaptive AMR on limitedresource platforms such as satellites and unmanned aerial vehicles (UAVs). AMI integrates Adaptive Input Skipping (AIS) and Budget-Efficient Exiting (BEE) to dynamically tailor computation based on signal difficulty and real-time resource budgets. AIS employs Layer and Channel Gates for coarse-grained skipping and fine-grained pruning, while BEE adjusts early-exit thresholds based on entropy and Top-1 confidence. Implemented on a lightweight 1D MobileNetV2 backbone, AMI achieves up to 56% reduction in average computational cost with less than 1% accuracy loss on both RML22 and HisarMod2019.1 datasets, outperforming existing dynamic inference strategies applied in the AMR domain.

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.

With lunar exploration attracting global attention, there is a growing interest in the design of relay satellite constellations for future lunar communication systems which is challenged by the huge distance between earth and moon. In this paper, we propose a novel hybrid combined Earth-Moon constellation network structure with Earth-Moon Libration 1/2 (EML1/L2) points Halo orbits, ordinary lunar orbits, and Geostationary Earth Orbit (GEO) to minimize both the total number of satellites and the average per-device Age of Information (AoI) as well as maximizing the coverage ratio of specific lunar surface regions to improve information freshness. This is formulated as a multiobjective optimization problem solved by the Nondominated Sorting Genetic Algorithm-II (NSGA-II). By simulating various constellation configurations, we can obtain the optimal configuration parameters for different total numbers of satellites. The simulation results show that our proposed combined constellation significantly outperforms traditional Walker Star and Delta constellations in both AoI and coverage performance.

LEO cloud storage constellation (LCSC) has gained significant popularity thanks to its on-board data storage capability and extensive inter-satellite connectivity. Unfortunately, the satellite disks will fail occasionally due to cosmic radiation and energy depletion, which leads to data loss and network unavailable. However, multi-node repair in the LCSC leads to the larger repair delay and the higher energy cost. To address this, we introduced the aggregation and multicast coded repair (AMCR) to fast recover the stored data using Reed-Solomon (RS) codes. We first propose the multi-weight multinode repair tree (MWNRT) model, i.e., a staged tree graph containing edge weights, to measure the multiple factors affecting repair performance. Next, we analyze the repair delay and the energy cost associated with multi-node repair leveraging AMCR. Then, to minimize repair delay while reducing energy cost, the aggregation-based multiple single-node repair trees construction (A-MSRT) algorithm is designed to construct multiple single-node repair trees based on the shortest-path principles. While the multicast-based aggregation of multiple repair trees (M-AMRT) algorithm is designed to select the repair tree with the longest delay from the output of A-MSRT as the initial repair tree, then adds the remaining replacement nodes. And the complexity of the two algorithms is elaborated and proven to be reasonable. Simulations show that AMCR scheme outperforms other schemes under different network conditions in LCSC.

In this paper, we focus on the application of coded distributed computing (CDC) in a multi-server clustered network (MSCN), which is designed to accelerate the gradient update process in federated learning (FL) by considering both communication and computational heterogeneity. As the number of participating devices increases and resource heterogeneity becomes more pronounced, reducing total execution latency (TEL) has become a critical challenge. To address this issue, we focus on optimizing the matching between heterogeneous devices and server task loads to improve resource utilization while enhancing the robustness and fault tolerance of the FL system. To minimize TEL, we propose a greedy algorithm and an iter-genetic algorithm for device assignment, named GADA and IGADA, based on the task allocation for the single server (TASS) algorithm, respectively. Based on simulation results and theoretical analysis, we confirm that our proposed algorithms substantially reduce the TEL in various scenarios compared to existing CDC methods, with complexity markedly lower than that of the exhaustive scheme.

Low Earth Orbit (LEO) satellite networks have become a promising solution to support Internet of Things (IoT) services, especially in remote or infrastructure-limited areas. In LEO-enabled IoT scenarios, timely data delivery is essential for situational awareness and real-time decision-making. To measure the freshness of the data, Age of Information (AoI) has been widely adopted as a key performance metric, particularly in applications with frequent status updates. However, minimizing AoI in LEO systems is challenging due to limited onboard resources and dynamic traffic demands. This paper focuses on the joint optimization of beam hopping (BH) scheduling and resource block (RB) allocation to reduce the average AoI across the network. To handle the complex problem, we propose a hierarchical deep reinforcement learning (DRL) framework. At the high level, a centralized satellite agent leverages the Proximal Policy Optimization (PPO) algorithm to determine the beam illumination pattern over the service area. At the low level, a decentralized multi-agent PPO framework is employed, where each beam-level agent independently allocates resource block (RB) to users, aiming to improve data freshness and transmission efficiency. Simulation results show that the proposed method outperforms three benchmark strategies in reducing average AoI.

Satellite Federated Learning (SFL) has emerged as a transformative paradigm for distributed machine learning in Low Earth Orbit (LEO) mega-constellations, enabling real-time processing of space-acquired data and enhancing remote sensing missions. The integration of Inter-Satellite Links (ISLs) into SFL alleviates synchronization delays due to intermittent connectivity between LEO satellites and ground-based parameter server (PS). However, the traffic generated from satellites creates communication bandwidth bottlenecks at the PS in the global model aggregation procedure. To address this issue, this paper proposes a novel SFL framework that leverages in-network model aggregation through ISLs to improve communication efficiency. Furthermore, two aggregation strategies, i.e., Chain Aggregation (CA) and Ring Aggregation (RA), are discussed in detail. Through system latency analysis and comprehensive simulations across constellation scales and data distributions, we demonstrate that: (1) in-network model aggregation fundamentally transforms communication load growth from ${\mathcal{O}}\left({{N^2}}\right)$ to ${\mathcal{O}}\left(N\right)$, and (2) the convergence time improves with increasing number of satellites, but degrades beyond a threshold.

Mega-constellation networks of low Earth orbit (LEO) satellites are poised to become an integral part of future 6G networks. Satellite infrastructures can provide Internet access services to terrestrial users and act as relay satellites for on-board remote sensing and positioning services. Given the extensive coverage of LEO satellite constellations and the overlapping coverage areas between satellites, the selection of appropriate access satellites for terrestrial users is critical. This paper provides insights into user access selection and resource optimization for large LEO constellations covering large terrestrial areas. By explicitly modeling the transmission traffic requests and data collection process, the access strategy is investigated to maximize network throughput while maintaining a balance on quality of service. Thus, an optimization algorithm is proposed for user access selection that synergistically combines graph convolutional attention networks and deep reinforcement learning (DRL). Simulation results based on the Starlink Phase I model show that our algorithm achieves performance improvements in both throughput and access quality compared to random access and standalone DRL frameworks.

Driven by the rapid development of industrial Internet of Things applications, the wireless networked control system (WNCS) is expected to support real-time control-communication interaction performed in finite-time, which is task-oriented. A WNCS composed of multiple wirelessly inter-connected subsystems (SSs) is considered in this paper. The sensed state information in each SS is transmitted to the controller via wireless links for decision-and-control tasks. After multiple operation periods of state sensing and trans-mission, the system identification (SI) is executed and the optimal control (OC) policy is made. The SI requirement for OC is analyzed via system-level synthesis (SLS) based on robust control theory. A communication and control co-design is investigated, aiming to improve the energy efficiency while guaranteeing the SI performance requirement within the allowed decision-making time. The transmit powers at each sensor and controller, transmission interval length as well as the number of operation periods are jointly optimized. Simulations are conducted to validate the performance of the proposed co-design.

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

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

Abstract Artificial intelligence (AI) is revolutionizing both industries and reshaping the global economy. However, the rapid advancement of AI technologies brings significant security and privacy challenges. Recent incidents highlight vulnerabilities in AI systems, such as data leakage and malicious code injection, leading to severe financial losses and privacy breaches. Although existing studies have discussed specific security threats, they often lack detailed granularity and cover a limited scope. In this survey, we fill this gap by systematically categorizing and analyzing the threats and countermeasures in AI systems, which span both the training and inference stages, encompass centralized and distributed settings, and address both conventional and foundation AI models. By reviewing existing literature, we aim to provide AI researchers and practitioners with a thorough understanding of system vulnerabilities and current countermeasures. We hope to inspire further research into robust solutions, ultimately contributing to the development of resilient AI technologies.

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

In the Large-Scale Heterogeneous Satellite Networks (LSHSNs) integrating Low Earth Orbit (LEO) and Medium Earth Orbit (MEO) satellites, data delivery faces complex topology and dynamic connectivity, which poses a significant challenge to current graph-dependable transmission strategies requiring global topological information, incurring huge computational cost and interactive overhead. To address this issue, in this paper, we propose a Local Information-dependable Semantic Delivery Mechanism (LISDM) by exploiting topological features at the semantic level, in which we develop a Task-oriented Semantic-aware Topology Compression Network (TSTCN) to condense the global topology according to specific task demands. Besides, we develop a Deep Q-Network enabled Semantic Coded Routing (DQNSCR) algorithm for the semantic delivery in the LISDM by designing a novel metric called Semantic Delivery Efficiency (SDE). The simulation results indicate that the proposed mechanism performs better in improving the required topology scale and throughput compared with typical data delivery mechanisms such as the conventional Open Shortest Path First (OSPF) routing algorithm, the DQN-based Intelligent Routing (DQN-IR) algorithm, and Real-Time Hop-by-Hop Routing (RTHop) algorithm with Space-Time Graph (STG) model, respectively.

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

Marine ecological monitoring, underwater unmanned device surveillance, and resource exploration are increasingly dependent on underwater autonomous systems. Consequently, efficient information transmission to onshore command centers is imperative. Converting underwater acoustic (UWA) signals to radio signals through surface relaying is considered the most efficient method for long-range cross-medium (water and air) transmission. Traditionally, static floating nodes serve as relays in such systems. In this study, we propose a mobile cross-medium relay scheme utilizing autonomous underwater vehicles (AUVs). This approach capitalizes on two key factors: the physical differences between the two mediums (water and air) and the mobility of the AUV. By leveraging these, it improves the UWA links, thereby improving cross-medium transmission performance. Our scheme integrates a spatially varying UWA channel model, commonly employed in physical-layer studies, into network performance analysis, considering the inhomogeneous underwater medium. Within this framework, we optimize the cross-medium transmission performance of dual-hop links by determining the optimal mobile relay path. The AUV can operate on the surface, acting as an acoustic-to-radio signal relay, or navigate underwater, adapting its path to the spatial variations in UWA channels. This adaptability enables more efficient underwater data collection. To identify the most effective mobile relaying paths, we propose a cross-medium relay path planning based on swarm intelligence and reinforcement learning (RL) algorithms. Simulations demonstrate that our proposed mobile relay transmission scheme outperforms static relay systems, achieving higher cross-medium transmission data length, improved end-to-end data rates, and better balance between the two links. Furthermore, RL-based path planning yields superior performance compared to ant colony optimization (ACO)-based planning.

The collaborative cluster satellite system (CCS) within the 6G network establishes the foundation for robust services in the future Star-Earth integrated network by coordinating multiple low-Earth orbit (LEO) satellites for collaborative observation missions and efficient space mission processing. This paper proposes a model-based soft actor-critic (SAC) algorithm, CCS-MASAC, for optimizing throughput in clustered satellite systems within 6G networks. The algorithm integrates the clustering degree of CCS with the entropy regularization term in SAC, proposing an adaptive adjustment method. Unlike existing studies, in this work, we adopt an environment model-based policy optimization approach for the first time. Model-based policy optimization focuses on improving the sample efficiency of reinforcement learning algorithms. It allows agents to learn iteratively in both real and simulated environments, which improves sample efficiency, convergence, and algorithm robustness. To address the dimensionality explosion in single-agent reinforcement learning (RL) algorithms, we extend this approach to a multi-agent RL algorithm by defining observable neighborhoods for each agent, further enhancing performance. Simulation results indicate that the CCS-MASAC algorithm proposed in this paper enhances throughput by 15–20% and accelerates convergence by 30% compared to existing algorithms, including the multi-agent deep Q-network (MADQN), multi-agent proximal policy optimization (MAPPO), multi-agent deep deterministic policy gradient (MADDPG) and multi-agent double and dueling deep Q-learning (MAD3QL). The scalability and robustness of the algorithms are verified by scalability experiments and experiments under dynamic channel conditions. This research provides new solutions for throughput optimization and resource management in CCS systems.

The absence of established communication infrastructures at sea presents a significant hurdle for the development of the Internet of Things (IoT) in maritime environments. In this article, we propose a novel solution: a nonorthogonal multiple access (NOMA) maritime network employing power allocation facilitated by federated multiagent deep reinforcement learning (DRL). Conventional single-agent DRL algorithms encounter challenges, such as dimensionality explosion in real-world scenarios. We address this issue by extending these algorithms to incorporate multiple agents. Furthermore, to mitigate risks associated with centralized training, such as data leakage and network attacks, we adopt a federated learning (FL) framework for distributed training across multiple agents. By uploading only select parameters during training and keeping data locally, FL not only enhances algorithm convergence speed but also bolsters data privacy. Specifically, we introduce the federated multiagent deep Q-network (FLMADQN) algorithm tailored for power allocation in NOMA maritime networks. Our algorithm aims to maximize system throughput, optimize data transfer rates, and expedite convergence. Through extensive computer simulations, we validate the efficacy of our proposed approach. Results demonstrate that the FLMADQN algorithm significantly outperforms the traditional DRL algorithm, DQN, in NOMA maritime environments, improving average system throughput by 20.12%, peak system throughput by 45.10%, and system spectral efficiency by 48.33%. Moreover, FLMADQN exhibits a twofold increase in convergence speed compared to MADQN without FL and is 2.5 times faster than DQN. Our findings underscore the potential of federated multiagent DRL in advancing communication systems for maritime IoT applications.

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.

This paper investigates a bi-static integrated sensing and communication (ISAC) system for multi-target scenarios using impulse radio ultra-wideband (IR-UWB) signals, which offer fine temporal resolution, low power consumption, and strong resistance to multipath interference. Two typical modulation schemes, namely pulse position modulation (PPM) and binary phase shift keying (BPSK), are considered for communication over the delay and phase domains, respectively. An innovative differential decoupling strategy is proposed, which eliminates the need for pilot symbols by leveraging the known starting symbol position. The sensing performance under various modulation and demodulation schemes is analyzed and compared with the conventional pilot-based (time-delay) decoupling strategy under current UWB standards. A key contribution of this work is the development of a unified analytical framework based on the Fisher information matrix (FIM), which characterizes the fundamental coupling between communication and sensing in both delay and Doppler domains. This coupling is examined through the singularity structure of the FIM, providing new insights into the joint performance limits of UWB-ISAC systems. Performance evaluation is conducted using the Cramer-Rao Lower Bound (CRLB) for sensing and the data transmission rate for communication, offering theoretical insights into choosing suitable data signal processing methods in real-world 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.

Reliable image transmission is pivotal for lunar IoT networks, providing visual information for scientific exploration and real-time navigation. However, the moon’s harsh environment, i.e., lack of atmosphere, low surface conductivity, and rugged terrain, induces abnormal signal attenuation and packet losses, significantly degrading image transmission quality. This paper presents the Lunar Adaptive Image Transmission System (LAITS), which integrates the Terrain-Aware Field Strength Prediction (TAFSP) framework with the Hierarchical RaptorQ Redundancy Optimization (HRRO) coding algorithm. The TAFSP framework provides field strength predictions for channel packet loss rate estimation, by leveraging high-resolution lunar elevation data to formulate a radio propagation loss model. The HRRO algorithm optimizes coding efficiency and enables low-overhead transmission, by dynamically adjusting RaptorQ’s data segmentation based on the predictions of channel packet loss rate and priorities of image data. Simulations demonstrate that while maintaining image transmission quality above the 25 dB PSNR threshold, LAITS achieves a coverage rate of 85% in flat terrain with a 4 km radius at 440 MHz, 915 MHz, and 2400 MHz, and achieves coverage rates of 85% at 440 MHz, 70% at 915 MHz, and 56% at 2400 MHz in rugged terrain with the same radius.

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.

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

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

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

Coded caching is an effective technique to reduce the downlink traffic on the network. While coded caching has been extended to many scenarios, coded caching in satellite networks has not been well investigated in the literature. In this paper, we first introduce a novel model of coded caching in satellite networks, which consists of P satellites periodically moving in a given orbit and K users on Earth. In this model, at each timeslot, every satellite (regarded as a server) serves Q consecutive users in a regime, while each user could access one or more satellites at the same time. Due to the cyclic mobility of satellites, the connections between satellites and users could be predictable but also dynamically change in a cyclic wrap-around fashion. Thus, the connections between different satellites and different users at different timeslots could be highly coupled. Taking advantage of the predictable connections given the satellite constellation, we propose a centralized achievable scheme such that different satellites can serve the users jointly. For the converse bound, we introduce a novel method to select user groups and construct request patterns, such that the connections between users and satellites involved could be decoupled. Moreover, the gap between the achievable rate and the converse bound is shown to be at most a constant. Numerical results show the superior performance of the proposed scheme and converse bound.

With the ever-increasing lunar missions, a growing interest develops in designing data relay satellite constellations for cislunar communications, which is challenged by the constrained visibility and huge distance between the earth and moon in pursuit of establishing real-time communication links. In this work, therefore, we propose an age and coverage optimal relay satellite constellation for cislunar communication by considering the self-rotation of the earth as well as the orbital motion of the moon, which consists of hybrid Earth-Moon Libration 1/2 (EML1/L2) points Halo orbits, ordinary lunar orbits, and Geostationary Earth Orbit (GEO) satellites. In particular, by minimizing both the number of satellites and the average per-device Age of Information (AoI) while maximizing the coverage ratio of specific lunar surface regions, a multi-objective optimization problem is formulated and solved by using a well-designed Nondominated Sorting Genetic Algorithm-II (NSGA-II). The simulation results demonstrate that our proposed hybrid constellation significantly outperforms traditional Walker Star and Delta constellations in terms of both AoI and the coverage of communication.

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

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.

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

In Large-Scale Heterogeneous Satellite Networks (LSHSNs) integrating Low Earth Orbit (LEO) and Medium Earth Orbit (MEO) satellites, high-timeliness data delivery confronts dynamical connectivity and obvious latency, which heavily challenges existing graph-dependable transmission strategies requiring to obtain global topological information with huge computational cost and signaling overhead. To address this issue, in this paper, we propose an Age-predicting Local Information Dependable Transmission (ALIDT) mechanism for the LSHSN by considering the impact of time-varying topology on the timeliness of data, in which a novel metric of data freshness called Forwarding-aware Age of Information (FAoI) is well-designed to evaluate the timeliness in data forwarding at node. In particular, we develop a satellite Coverage-based Local Information Sharing (CLIS)-assisted Spatial-Temporal Graph Neural Network (STGNN) to extract the topological features in both temporal and spatial dimensions and a Graph Matching Network (GMN)-based topology compression algorithm to improve computation efficiency. The simulation results indicate that the proposed mechanism performs better in improving the storage overhead, throughput and average FAoI compared with the conventional Open Shortest Path First (OSPF) routing algorithm with Time-Varying Graph (TVG) model, GNN-based Multipath Routing (GMR) algorithm, and Gated Recurrent Units (GRU) based metric prediction algorithm in hybrid satellite networks, respectively.

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.

The integrated communication and localization (ICAL) has become as a pivotal technology in the evolution towards B5G and 6G networks, particularly for a variety of emerging wireless applications. In ICAL networks, resource allocation and beamforming design are critical components that significantly influence both the precision of localization and the efficiency of communication. Moreover, high accuracy synchronization is extremely challenging in wireless networks. In this paper, we investigate the tradeoff between spectral efficiency (SE) and position error bound (PEB) by formulating a robust power and time-slot allocation and beamforming design problem for asynchronous ICAL networks with the imperfect initial position. We first illustrate the coupling between SE and position error through channel estimation error. Then, we derive a lower bound on the position error in terms of the Fisher information matrix (FIM). The alternating optimization, convex approximate and Bernstein-type inequality algorithms are proposed to solve the non-convex problems. Finally, the simulation results reveal the tradeoff between SE and PEB, and validate the robustness and effectiveness of the proposed algorithms.

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.

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

Wideband signal recognition is a crucial task in the cognitive wireless communication, involving accurate classification of different signal types, modulation types, center frequencies, etc. However, most conventional approaches treat the recognition of different parameters as multiple independent tasks, and often face performance bottlenecks due to the complexity and diversity of wideband signals. To overcome these challenges, we propose a multi-task learning (MTL) network that integrates multiple tasks of signal recognition into an end-to-end model to accomplish spectrum sensing, modulation recognition, and signal classification simultaneously. By employing a shared feature extraction network and a multi-task classification header, the proposed framework effectively captures the correlations and shared information among different tasks, thereby enhancing overall recognition performance. To validate the effectiveness of the proposed scheme, we compare its performance with other state-of-the-art recognition and classification networks. Experimental results demonstrate the significant performance of the proposed MTL network in spectrum sensing, modulation recognition, and signal classification tasks.

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

On-board distributed computing can provide more powerful computation capabilities for future low-earth-orbit (LEO) satellite constellations, serving intelligent information sensing and spatial large model through multi-satellite cooperation. On-board distributed computing depends on the efficient exchanging data flows between satellites termed coflow. The application of laser inter-satellite links (LISLs) will drastically improve the transmission capacity among the satellite distributed computing network (SDCN). However, due to the temporary interruptions of LISLs and the characteristics of optical circuit switching (OCS), the flow interruptions and conflicts significantly affect the coflow completion time (CCT). In this paper, we propose a conflict-aware coflow scheduling scheme to reduce the CCT in the OCS-based SDCN. Firstly, the time-varying LISLs and OCS-based coflow transmission are modeled and the problem of minimizing CCT is formulated. After that, we characterize the routing paths of coflow as the conflict graph and transform the coflow concurrent matching problem into the maximum independent set (MIS) problem in conflict graph. Based on this, we design the coflow polling greedy scheduling (CPGS) algorithm, which not only considers the sequence of coflow scheduling, but more importantly maximizes concurrent flows by MIS search. We deploy three different simulation scenarios to evaluate the algorithm performance. Simulation results show that our algorithm can significantly reduce the CCT by about 28.9% to 42.1% compared with existing works.

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.

Hybrid automatic repeat request (HARQ) is a potential technique for communication systems in non-terrestrial networks (NTN) to ensure reliability. However, the long round-trip time (RTT) within NTN diminishes the information timeliness when applying HARQ. Moreover, the absence of perfect channel state information (CSI) poses challenges to the precise design of HARQ strategies. In this paper, we introduce a novel coded HARQ scheme for NTN, in which the age of coded information (C-AoI) is proposed as a metric of information timeliness to guide the search for the optimal coded HARQ strategy. The rate-compatible inherited bits codeword construction (RCIC) is introduced for HARQ process to enhance the transmission efficiency. By applying Markov decision process (MDP) and Q-learning algorithm in the optimization, the obtained coded HARQ scheme significantly outperforms the existing schemes in terms of information timeliness and number of transmissions. The throughput of satellite-terrestrial links is also improved.

With the advantage of robust resilience, large capacity, and strong adaptability, space-air-ground integrated network (SAGIN) can simultaneously support various task scenarios involving heterogeneous networks and diverse task demands. In this integrated network, the constraints from limited resources and dynamic environment pose challenges in fulfilling concurrent demands, and improper task scheduling strategy can lead to network resource wastage and task dissatisfaction. In this paper, we propose an adaptive solution for multiscenario joint scheduling in SAGIN. We construct a comprehensive task scheduling frame-work and propose the task relevance matrix for in-depth analysis. To achieve the goal of improving network resource utilization and task satisfaction, we formulate the joint optimization problem as a cooperative Markov game and propose a novel multi-scenario task scheduling algorithm based on heterogeneous-agent proximal policy optimization (HAPPO). Simulation results show that the proposed algorithm can achieve better performance by effectively improving resource utilization and reducing task delay, compared with two state-of-the-art multi-agent reinforcement learning algorithms and the random baseline.

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

Low earth orbit (LEO) constellation network is a crucial component and paradigm for big data applications in the future satellite Internet. However, the characteristic of multi-hop transmission significantly increases the data retrieval delay and energy consumption depending on the inter-satellite communications. To mitigate this issue, this paper explores encoding redundancy by reed-solomon (RS) codes to generate the data and parity blocks, and store them in the satellite nodes of LEO constellation. We propose a fast data retrieval strategy, involving three stages: data collection, parity block placement, and data retrieval. Through the derivations of delay and energy consumption, we find that both are intensively related to the placement of parity blocks. To reduce energy consumption during the data retrieval process, we formulate a minimum problem under the delay constraint, which is an integer nonlinear programming problem. Then, we design an energy-efficient parity block placement based on genetic algorithm (PBP-GA), which is heuristic with fast convergence property. Simulation results show that, PBP-GA achieves a comprehensive performance improvement in average data retrieval delay and total energy consumption, compared to other placement schemes, i.e., random placement, retrieval cluster priority placement, nearby placement and non-coding. Specifically, PBP-GA can find the optimal number of parity blocks in a practical scenario of data retrieval in LEO constellation.

Non-geostationary orbit (NGSO) satellites, combined with beam hopping (BH) technology, play a vital role in wide-area communications, sensing, and positioning. However, the uneven distribution of ground users and the high mobility of NGSO satellites pose more significant challenges to multi-beam scheduling. This paper proposes a multi-NGSO BH scheduling framework considering the traffic demand of ground users and inter-satellite interference. We decompose the complex optimization problem into two sub-problems. First, we reallocate the satellite service cells to achieve a balanced distribution of satellite service coverage. Then, we propose a multi-NGSO BH algorithm based on soft actor-critic (SAC) to achieve real-time and traffic-driven beam scheduling. Simulation results show that the proposed algorithm outperforms other benchmarks in throughput and service failure rate (SFR).

Low earth orbit (LEO) computing-aware networks (LCANs) are proposed as an intelligent information infrastructure providing a solution for delay-sensitive computing tasks worldwide. The utilization of distributed computing architecture in an LCAN is emerging as a prospective resolution to cope with the limited computational resources of single satellite. Distributed computing depends on the exchange of information between worker nodes, as a type of concurrent and interrelated data flows called coflow. However, huge delay of coflow transmission is caused by the time-varying network topology and dynamic ISL conditions in an LCAN. To solve this problem, we establish an LCAN topology model, elaborating the orbit movement and ISL connectivity. Then we propose a novel time-varying graph to depict coflow transmission, which can improve the adaptability of coflow routing. Based on the proposed time-varying graph, we formulate coflow routing problem as a path combinatorial optimization and present an iterative heuristic algorithm named dynamic priority coflow routing (DPCoR). The DPCoR can dynamically adjust the priorities of coflow according to their increments to CCT, and thereby ensure that flows with high priorities for better routing paths. Furthermore, we compare DP-CoR with traditional flow routing schemes, i.e., equal-cost multi-path routing (ECMP) and software defined routing algorithm (SDRA) in various LCAN scenarios with different numbers of worker nodes, workloads and link conditions. The simulation results demonstrated that DPCoR algorithm can reduce the coflow completion time (CCT) effectively.

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

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 LEO storage constellation (LSC) has gained significant popularity thanks to its inter-satellite massive con-nectivity and space-terrestrial integrated data storage capability. However, the satellite disks will fail occasionally due to exhausting energy or space debris, which leads to data loss and network unavailable. In terrestrial data centers, aggregation coded repair (ACR) is an emerging data repair technique, which can reduce repair traffic by aggregating source data flows on intermediate nodes. However, existing ACR research does not focus on the problems of large propagation distance and energy consumption constraints in LSC. In this paper, we propose the coding path tree (CPT) model, which is a staged tree graph containing aggregation vectors and distance-related edge weights. For reducing repair delay and energy cost, we further propose the Delay-Sensitive Energy-Efficient ACR (DE-ACR) scheme, which is based on a CPT construction algorithm that combines the ideas of the shortest path and minimum Steiner tree. System-level experiment demonstrates that DE-ACR obtains a 7% reduction in repair delay compared to the single repair pattern tree (SRPT), and achieves a 55 % decrease in energy consumption compared to the shortest path ACR (SP-ACR) in LSC.

Coding techniques, known as coded distributed computing (CDC), have been investigated to alleviate the impact of heterogeneous straggler effects and reduce computation latency in distributed computing systems. However, in the multi-hop complicated network topology, the execution latency of the master's task includes both the computation latency and the communication latency, in which the imbalance computation loads and inadequate path selection will lead to the greater straggler effects extremely. In this paper, we focus on the issues of CDC application in a Two-Hop Heterogeneous Computation Network (THHCN). To make full use of the completed computing results of workers, we deploy Luby transform (LT) code to derive a total execution latency expression. Then, we formulate an optimization problem to minimize the total execution latency by selecting the relays and allocating the computation loads for different workers. Furthermore, a greedy minimum penalty relay selection and load allocation (GMPRS-LA) algorithm is proposed with lower complexity compared to the exhaustive searching to solve the integer nonlinear programming problem. Simulation results demonstrate GMPRS-LA achieves a significant reduction in the total execution latency and leads to a better performance than traditional CDC load allocation algorithms.

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.

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

With its attractive controllable mobility and flexible deployment advantages, the Unmanned Aerial Vehicle (UAV) has emerged as a promising solution to support temporary caching services by pre-fetching popular content. However, it still exists an obvious challenge due to the limited storage and energy of UAVs with stochastic arrival requests, which is not well addressed by present works resulting in low Quality-of-Service (QoS) for users. In this paper, therefore, we propose a queue-aware cooperatively caching mechanism in the multi-UAV assisted system by considering the random user requests, in which a well-designed Connected Dominating Set (CDS) is developed to make collaborative caching schedule. In particular, we formulate the issue as a long-term queue stability constrained energy efficiency maximization problem by a well-tailored Lyapunov optimization framework. As a non-linear mixed-integer optimization with a nonconvex objective function and coupled variables, we solve it by designing a decentralized Cooperative Reinforcement Learning (CRL) algorithm with the developed CDS. The numerical results demonstrate that our proposed joint algorithm outperforms other benchmark algorithms in terms of caching latency, cache hit ratio, and energy efficiency.

Introducing free-space optical (FSO) communication into space-air-ground integrated networks (SAGINs) can enable the realization of high data rates to achieve next-generation wireless communication. However, investigating the performance of the unmanned-aerial-vehicle (UAV)-assisted dual-hop radio-frequency (RF)/FSO systems for SAGINs remains challenging owing to severe channel fading. This article proposes a UAV-assisted RF/FSO relay system with a decode-and-forward relay protocol. A unified statistical channel model that considers the influences of attenuation loss, atmospheric turbulence, pointing errors, and angle-of-arrival fluctuations on the FSO link is developed, and the Málaga distribution is employed to characterize atmospheric turbulence. Closed-form expressions of the outage probability and the average bit error rate are derived for the pure FSO link and overall RF/FSO relay system. We also derive expressions of the system metrics under atmospheric turbulence characterized by Gamma-Gamma and Log-normal distributions, leveraging the broader coverage of our proposed channel model. The effects of the system and channel parameters on the performance of the pure FSO link and the overall RF/FSO relay system are investigated. Finally, our analytical expressions agree well with the Monte Carlo simulation results, demonstrating the validity of the expressions.

Mega-constellation networks (MCNs), comprising an expansive array of orbits and satellites, will be a pivotal component of the prospective nonterrestrial network (NTN). Laser intersatellite links (LISLs) represent a promising technology for the establishment of satellite networks, offering high capacity and highly reliable communication links. Nevertheless, LISL still has some technical challenges, such as link establishment instability, satellite payload capacity, and topology design. For a considerable number of satellites and laser communication constraints, LISLs have elevated the complexity and difficulty of routing and topology construction. In this article, we focus on the LISL connecting stability and propose a novel method to evaluate the intersatellite link (ISL) selection based on the acquisition probability of the laser terminal system. Subsequently, a nonlinear optimization model is formulated for the laser link selection problem, where the terminal acquisition probability is maximized. Finally, an MCN topology design algorithm (MTDA) is proposed to establish greater stability and higher channel gain within the different access distances of the laser terminal. Using the satellite constellation for Phase I of Starlink, three different laser MCN topologies were constructed by MTDA, adapting to the maximum access distance of the laser terminal within 5000 km. The impact of these differing topologies on network latency and hop count was then analyzed in a full satellite period. Compared with the baseline, MTDA has a 15.7% latency advantage and 14.0% hop advantage under the same access distance of laser terminal. The numerical results show that the proposed algorithm has a positive impact on the average network latency, hop count and their respective variances.

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.

In long-span space communication, the current Licklider Transmission Protocol (LTP) confronts apparent challenges such as high packet loss rate and huge latency when carrying the bundles in the Disruption Tolerant Networks (DTN). These challenges incur obviously low freshness of satellite telemetry and instruction data with high timeliness requirements since the typical Automatic Repeat reQuest (ARQ) mechanism is exploited in the LTP for reliable transfer. To address this issue, in this paper, we propose an age-driven bundle delivery mechanism called as Semantic LTP (S-LTP) by considering the semantic correlations in the context-dependent data, which has excellent error-tolerant capability by a well-designed Semantic Supplement Hybrid Automatic Repeat reQuest (SS-HARQ), making it with high timeliness. In particular, a novel metric of semantic freshness of data called Age of Semantic Information (AoSI) is proposed to evaluate the timeliness contribution of information at the semantic level. The simulation results indicate that the proposed SS-HARQ scheme performs better in reducing the average AoSI and AoI by 62.24% and 64.52% respectively compared to the conventional LTP-ARQ with Cyclic Redundancy Check (CRC), 6.39% and 27.09% respectively compared to the Semantic Coding HARQ (SCHARQ) with a similarity detection network called Sim32.

With the proliferation of massive explorations on Lunar Far-side Surface (LFS), deployments of scientific infrastructures have drawn substantial attentions with the aids of relay satellites for the moon, i.e., China’s "Queqiao", which allows real-time reporting for current status information from the landing equipment and space vehicles. Without suitable constellation with well designed scheduling scheme, it would be very difficult to get timely information if depending only on single Halo relay satellite due to large coverage gap and limited energy budget for the communication links. In this paper, we design a hybrid circular-Halo orbital multi-relay constellation system for the LFS communication by minimizing the average per-device Age of Information (AoI) of users in the earth. In particular, we develop an age-optimal scheduling strategy with constellation design for accessing different relay satellite of constellation by solving a Constrained Markov Decision Process (CMDP) optimization problem to significantly reduce the average coverage gap in LFS area. Simulation results show that the average per-device AoI of the proposed scheduling algorithm with the well-designed constellation could achieve 16.20% less in time than that of single Halo satellite relay system compared with typical algorithms.

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

Since the traffic administration at road intersections determines the capacity bottleneck of modern transportation systems, intelligent cooperative coordination for connected autonomous vehicles (CAVs) has shown to be an effective solution. In this paper, we try to formulate a Bi-Level CAVs intersection coordination framework, where coordinators from High and Low levels are tightly coupled. In the High-Level coordinator where vehicles from multiple roads are involved, we take various metrics including throughput, safety, fairness and comfort into consideration. Motivated by the time consuming space-time resource allocation framework, we try to give a low complexity solution by transforming the complicated original problem into a sequential linear programming one. Based on the “feasible tunnels” (FT) generated from the high-Level coordinator, we then propose a rapid gradient-based trajectory optimization strategy in the low-level planner, to effectively avoid collisions beyond high-level considerations, such as the unexpected pedestrian or bicycles. Simulation results and laboratory experiments show that our proposed method outperforms existing strategies. Moreover, the most impressive advantage is that the proposed strategy can plan vehicle trajectory in milliseconds, which is promising in real-world deployments. A detailed description include the coordination framework and experiment demo could be found at the supplement materials, or online at https://youtu.be/MuhjhKfNIOg.

In large-span space communication, the current bundle delivery mechanism using the Disruption-Tolerant Networks (DTN) technique confronts huge challenges such as high packet loss rate and huge latency. These challenges incur obviously low goodput when delivering scientific and engineering data for the target missions, such as instructions, text, and images. However, the context correlations in these data are not yet excavated to resist the above challenges by current works. To address this issue, therefore, we propose a semantic-aware bundle delivery mechanism for context-dependent data via a cross-layer design on Bundle Protocol (BP) and Licklider Transmission Protocol (LTP), which has the excellent error-tolerance capability by the well-designed semantic-oriented Automatic Repeat reQuest (ARQ) scheme. In particular, the jointed cross-layer design consists of a Semantic Blocking (SB) and Semantic Coding (SC)-based Bundle Updating (BU) mechanism and a dynamic Red/Green-part Allocation method based on Semantic Importance (RGA-SI) for bundles and segments in the two layers. Simulation results show that the proposed mechanism can reduce data latency and improve goodput from about 50% to 70% compared with the current bundle delivery mechanism in DTN with optimal segment size, especially under bad channel conditions.

In large-scale satellite networks, data delivery confronts obvious challenges such as high loss rate and long propagation delay leading to low Packet Delivery Ratio (PDR) and huge delivery latency over intermittent Inter-Satellite Links (ISLs), making the current routing algorithms exploiting typical Automatic Repeat reQuest (ARQ) mechanisms extremely inefficient and even incapable. To address this issue, in this paper, we propose a semantic-aware coding and routing joint mechanism called Semantic Adaptive Coding and Routing (SACR) by considering both the semantic correlations in the context-dependent data and the link status knowledge. In particular, the proposed SACR achieves excellent error-tolerant and routing-agile capabilities by an elaborately interactive design consisting of a customized routing-aware Semantic Adaptive Coding Hybrid ARQ (SAC-HARQ) mechanism and a Semantic Coding-based Routing Mechanism (SCRM). The simulation results indicate that the proposed SACR mechanism performs better in reducing the average delivery latency and improving the effective throughput compared with typical routing mechanisms such as Open Shortest Path First (OSPF) routing, Deep Q-Networks based Intelligent Routing (DQN-IR), and Real-Time Hop-by-hop routing (RTHop), integrating with typical semantic coding methods, i.e., Deep Learning-based Joint Channel-Source Coding (DL-JSCC), Deep learning-based Semantic Communication system (DeepSC), and Semantic Coding HARQ (SCHARQ), respectively.

Space-air-ground integrated network (SAGIN) can support a wide variety of task scenarios as a next-generation network. In this dynamic network, tasks with different characteristics and demands are usually simultaneously generated, which requires an efficient task scheduling method to meet diverse task demands, reduce system cost, and improve resource utilization. In this paper, we propose a scalable task scheduling and resource allocation solution suitable for various task scenarios in SAGIN. Specifically, we construct a generic task scheduling framework that considers diverse task types and demands in SAGIN. With the objective of minimizing the total system cost, we propose a novel joint task scheduling and resource allocation algorithm based on deep reinforcement learning to obtain the optimal solution. Extensive simulations are conducted in a civil aviation scenario to evaluate the performance of the proposed algorithm. Numerical results demonstrate that our proposed algorithm can efficiently reduce system cost and task drop rate compared with three benchmark schemes, and the improvement of resource allocation to task scheduling is verified through analysis.

The unprecedented growth of industrial Internet of Things applications requires the evolution of wireless networked control system (WNCS). WNCSs are becoming the fundamental infrastructure technologies for critical wireless control applications due to the main benefits of the reduced deployment and maintenance cost, as well as the enhanced flexibility and safety. However, independent designs between communication and control without considering their tight interaction in conventional WNCS lead to poor overall system performance and efficiency. Co-designs are expected to achieve the target control performance while improving the wireless resource efficiency. In this paper, by considering how to allocate wireless resource while guaranteeing control performance, a co-design framework is established based on the finite-time wireless system identification (WSI) - a fundamental problem in systems theory and intelligent control. To this end, two design problems are investigated aiming at maximizing the communication throughput or minimizing the power consumption while guaranteeing the WSI performance. In the former design, the joint optimization of power and channel allocations leads to a non-convex integer combinatorial problem, which is iteratively solved by optimizing the power allocation via Lagrangian method and obtaining the optimal channel allocation via Hungarian algorithm. The minimum number of data samples for guaranteeing the WSI accuracy under confidence level is further derived by exploiting the relationship between WSI accuracy and the number of state sampling processes, which leads to the maximum throughput with respect to both the communication and control processes. In the latter design for energy-efficient WSI, by exploiting the relationship between the power consumption and channel allocation given the WSI performance requirement, the optimization problem can be simplified and solved by Hungarian algorithm. Simulations are conducted to verify the performance of the proposed solutions.

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

In this paper, we propose a novel UAV aided ground nodes (GNs) localization and communication integrated framework, where the age of information (AoI) is introduced to evaluate the system timeliness. We aim to jointly optimize the UAV trajectory, localization accuracy, bandwidth and beamwidth, to guarantee the information freshness. Specifically, we give a two-stage method, where a low complexity initial UAV trajectory searching algorithm is firstly proposed, based on theroughposition information of GNs. Afterwards, we formulate a joint UAV location and resource optimization problem. This essential mixed integer problem can be solved by efficient successive convex approximation based iterative algorithm. Simulations show that the localization and communication integrated framework can obtain about 50% performance gain compared with the UAV communication aid only solution, and over 37% performance gain via proper resource allocation. Moreover, the analysis reveals that our proposed scheme strikes a balance among the time durations of localization, data transmission and UAV movement. Additionally, we conduct practical experiments to draw valuable insights into the system design and implementations (An experimental can be found on the supplementary materials, or online at https://youtu.be/OX6Bgz6naUA).

Ultra wideband (UWB) systems have received great interest again due to the high range resolution, flexible data transmission capability, and low power consumption. In this paper, we develop a practical asynchronous integrated sensing and communication (ISAC) system using impulse radio UWB signals. This system operates within a joint mono-bistatic sensing network, accommodating multiple static and dynamic targets. To achieve simultaneous communication and target sensing, a reliable soft information based decouple solution is proposed to perform data demodulation in the typical UWB modulation waveforms. The data transmission can benefit from proper channel sensing, to about 2-3 dB gain, by exploiting the multipath components for demodulation. In addition to the demodulated data bits at the bi-static receiver, the environmental target distance and Doppler shift can also be achieved at both mono- and bi-static receivers, respectively. We then evaluate the sensing capability of the ISAC UWB system, by extensive simulations and practical experiments. The target tracking accuracy can be achieved within 20 cm at over 80% confidence, with commercial UWB devices according to practical measurements.

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.

To improve the performance of the cooperative free space optical/radio frequency (FSO/RF) space-air-ground integrated network (SAGIN) system, a SAGIN system with an adaptive combining scheme is proposed. Specifically, when the instantaneous signal-to-noise ratio of the FSO relay link exceeds the threshold, only the FSO relay link with the amplify-and-forward protocol exists. Otherwise, the RF and FSO links are in operation, and two signals are combined at the receiver using the maximal ratio combining approach. Moreover, the$\alpha -\eta -\kappa -\mu $distribution is employed to characterize the RF channel, and the effects of atmospheric absorption, atmospheric turbulence with the Málaga fading model, pointing errors, and angle-of-arrival fluctuations on the optical signal are considered. The closed-form expressions of the outage probability, symbol error rate (SER), and ergodic capacity were derived. Furthermore, asymptotic expressions for the outage probability and SER are provided. The findings indicate that the performance of the considered SAGIN system with an adaptive combining scheme outperforms that of other schemes.

As a promising approach to increase spectrum efficiency and user fairness, non-orthogonal multiple access (NOMA) technique, with strong potential for applications in integrated satellite-terrestrial relay networks (ISTRNs), has been considered as a vital part of the future wireless network architecture. However, studies on the symbol error performance of NOMA-based ISTRNs with multiple relays and multiple users are still in their infancy. In this study, we propose a dual-user NOMA-based ISTRN architecture with hardware impairments to all nodes. This study uses the opportunistic scheduling scheme to select the optimal relay for the relaying system with a decode-and-forward protocol and maximum ratio combination technique to improve the signal quality. We also use a shadowed Rician distribution to model fading in the satellite channels, while the terrestrial channels are assumed to follow a Nakagami-mfading distribution. In addition, the impacts of the path loss and beam pattern on the system are considered. Closed-form expressions are derived for the average symbol error rate (SER) for near and far users. We verify that the numerical results agree with the theoretical calculations and demonstrate the superiority of the proposed architecture with decode-and-forward protocol compared with the case where line-of-sight links are used to transmit signals alone and with the case using amplify-and-forward protocol. Finally, we analyze the effect of some critical parameters on the average SER of the considered system and present some helpful insights in relation to engineering design.

Although pre-trained language models (PLMs) have shown impressive performance by textonly self-supervised training, they are found lack of visual semantics or commonsense.Existing solutions often rely on explicit images for visual knowledge augmentation (requiring time-consuming retrieval or generation), and they also conduct the augmentation for the whole input text, without considering whether it is actually needed in specific inputs or tasks.To address these issues, we propose a novel Visually-Augmented fine-tuning approach that can be generally applied to various PLMs or NLP tasks, Without using any retrieved or generated Images, namely VAWI.Experimental results show that our approach can consistently improve the performance of BERT, RoBERTa, BART, and T5 at different scales, and outperform several competitive baselines on ten tasks.Our codes and data are publicly available at https://github.com/RUCAIBox/VAWI.

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,

Recently, coding techniques have been introduced in distributed computing systems, i.e., coded distributed computing (CDC), to alleviate the heterogeneous straggler effect. However, these techniques bring about additional decoding latency impacting on the task completion time. In this paper, we study the issues of load allocation and latency analysis of systematic CDC in heterogeneous computation and communication straggling networks (HCCSNs). In order to exploit the partial works completed by straggling workers, we use the method of block division to accelerate the sub-tasks' results returning from all workers. Moreover, we attempt to leverage the systematic MDS code, which needs fewer decoding operations, to reduce the decoding latency, but it requires prior determining of the systematic blocks and the parity blocks on the master not on the workers. Therefore, in order to minimize both of the execution (communication and computing) latency and decoding latency, we propose two algorithms, i.e., greedy-based binary search algorithm (GBSA) and proportional systematic block allocation (PSBA), to obtain the optimal numbers of blocks and systematic blocks assigned to each worker, respectively. Simulation results are presented to show that GBSA and PSBA outperforms other conventional block allocation schemes in both execution latency and decoding latency with various straggling parameters.

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.

Coded distributed computing (CDC) has been recently proposed as a promising technique to mitigate the straggler effect in the distributed computing cluster which consists of workers with different computing capabilities, and to reduce the end-to-end task execution latency. However, the heterogeneity of computing and transmission will critically impact the latency performance, especially in the wireless multiple-access network. In this paper, we use CDC over the heterogeneous wireless multipleaccess network (HWMAN) including both computation stragglers and transmission stragglers with various capabilities. In order to reduce the computing task completion latency (computing latency and transmission latency), the optimal stop computing time of workers and the sorting order of result transmission back are obtained via two designed algorithms, namely straggler detection and ordered transmission (SDOT) and worker sorting and ordered transmission (WSOT), respectively, which not only fully utilize the computing results of stragglers, but also improve the total latency performance compared with other existing state-of-theart algorithms.

This paper aims to minimize the age of information (AoI) of downlink rate-splitting multiple access (RSMA) in satellite-based Internet of Things (S-IoT) network over shadowed-Rician fading channels, where a satellite multicasts with multiple user equipments (UEs) by timely transmitting short-packet status updates. First, the expressions for block error rate (BLER) and average AoI (AAoI) are derived in a closed-form for short-packet communications with finite blocklength bound. Then, we formulate an AAoI minimization problem based on the theoretical derivations for the downlink RSMA S-IoT network, and design an age-optimal stationary power allocation (ASPA) scheme to solve the problem by utilizing the particle swarm optimization (PSO) algorithm. We further propose an age-optimal dynamic power allocation (ADPA) scheme based on the Markov decision process (MDP), and solve it by two deep reinforcement learning (DRL) algorithms. Monte Carlo simulations verify the accuracy of our derivations of BLER and AAoI, and also show that our ADPA scheme outperforms the related schemes.

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.

Low orbit satellite constellation as a relay network provides a possible solution for remote real-time data gathering applications, in which freshness information updates will be forwarded via dynamical intersatellite links (ISLs). Modeling by a time-varying network, this article studies minimizing Age of Information (AoI) of delivering the data through a multihop path, in particular, focusing on the effect of frequent interruptions of ISLs. Subjected to two constraints of path and effective arrival rate, the minimizing AoI problem is formulated to find a pair of optimal transmission delay and arrival rate. In particular, the$\mathcal {H}$-approximate optimal algorithm, called a latest update routing (LUR) algorithm, is proposed with a well-designed continuous time-varying graph. Using LUR, a set of paths can be obtained with degraded transmission delay that satisfies a given arrival rate. By screening all the arrival rates satisfying the effective constraint, the maximum rate and a corresponding path set that minimizes age can be found. The simulation results verified that a degraded average AoI can be obtained by the proposed path selection mechanism compared with the typical shortest delay path (SDP) strategy, minimum spanning tree (MST) strategy, and MAoIG. In particular, the numerical findings show that the proposed LUR reduces average AoI by a maximum of 12.66% compared with SDP, 75.28% compared with MST, and 69.3% compared with MAoIG, respectively, under different scenarios.

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.

Cooperative coordination at unsignalized road intersections, which aims to improve the driving safety and traffic throughput for connected and automated vehicles (CAVs), has attracted increasing interests in recent years. However, most existing investigations either suffer from computational complexity or cannot harness the full potential of the road infrastructure. To this end, we first present a dedicated intersection coordination framework, where the involved vehicles hand over their control authorities and follow instructions from a centralized coordinator. Then a unified cooperative trajectory planning problem will be formulated to maximize the traffic throughput while ensuring driving safety. To address the key computational challenges in the real-world deployment, we reformulate this non-convex sequential decision-making problem into a model-free Markov Decision Process (MDP) and tackle it by devising a Twin Delayed Deep Deterministic Policy Gradient (TD3)-based strategy in the deep reinforcement learning (DRL) framework. Simulation and practical experiments show that the proposed strategy could achieve near-optimal performance in sub-static coordination scenarios and significantly improve the traffic throughput in the realistic continuous traffic flow. The most remarkable advantage is that our strategy could reduce the time complexity of computation to milliseconds, and is shown scalable when the road lanes increase.

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.

Coded caching is an effective technique to reduce the downlink traffic on the network. While coded caching has been extended to many scenarios, coded caching in satellite networks has not been well investigated in the literature. In this paper, we introduce a novel model of coded caching in satellite networks, which consists of P satellites periodically moving in a given orbit and K users on the earth. In this model, at each timeslot, every satellite (regarded as a server) serves Q consecutive users in a regime, while each user can access one satellite. Due to the cyclic mobility of satellites, the connections between satellites and users could be predictable but also dynamically change in a cyclic shift pattern. Thus, the connections between different satellites and different users at different timeslots could be highly coupled. Taking advantage of the predictable connections, we propose a centralized achievable scheme such that different satellites can serve the users jointly. For the converse bound, we introduce a novel method to construct request patterns such that the connections between users and satellites involved could be decoupled. The gap between the achievable rate and the converse bound is shown to be at most a constant. Numerical results for the performance of our scheme are also demonstrated.

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.

The low earth satellite networks are envisioned to be an indispensable part of next-generation network due to the seamless Internet access. Deploying distributed computation into LEO satellite networks can decrease the latency of transmitting satellite-terrestrial computing jobs, which is crucial to expanding the service capability. Distributed computation depends on the exchange of data flows between worker nodes, as a type of concurrent and interrelated flows called coflow. However, the mesh-shaped topologies of LEO satellite networks make coflows prone to bandwidth competition on multi-hop links, which impedes the efficiency of distributed computation. In this paper, we formulated the multi-hop coflow scheduling process in LEO satellite networks as a routing and bandwidth allocation problem. Then, we simplified the problem to a coflow routing problem, and proposed the coflow routing greedy scheduling (CRGS) algorithm on the basis of the characteristics of multi-hop networks. Finally, we simulated in an SDN environment, where CRGS was deployed in an SDN controller. Compared with several existing algorithms, the CRGS algorithm is proved to reduce the coflow completion time (CCT) more effectively.

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 the data updating problem in the Satellite-integrated Internet of Things network for the time-critical scenarios, i.e., animal tracking and environmental monitoring. Due to the limited channel rate during contact of transmission, however, constantly updating data with huge volume over the uplink will incur obvious waiting and transmission delay bringing stale information to the satellite node. To address this issue, we propose a novel metric, spatially temporally correlative mutual information (STI), to characterize the information timeliness from perspective of information entropy by considering the correlations between the last update message and the status of the information source. By maximizing the averaged STI, we find the optimal allocation policy of channel slots with a fixed updating period by formulating the problem as a Markov decision process (MDP) with possibly infinite state space. Furthermore, we derive the optimal amounts of allocated time slots in a unit frame by solving a constrained range integer optimization problem with respect to the average STI. The simulation results show that the proposed periodically updating policy can significantly improve the information freshness compared with the original slot allocation strategy and current commonly used scheduled access strategies, i.e., slotted ALOHA and Threshold-ALOHA.

High-precision location information formulates the basis of the modern Internet of Things (IoT). However, since the navigation signals from the global navigation satellite systems (GNSSs) are frequently attenuated or blocked in urban areas, reliable and high accuracy positioning alternatives are thus required for ground devices (GDs). Due to the advantages of their flexible deployment and extensive coverage, unmanned aerial vehicles (UAVs) show significant potential in this ground localization enhancement system. In this article, we propose a UAV aided positioning (UAP) system for GDs, where the UAVs provide valuable flying Line of Sight (LoS) observations. Specifically, we first give the fundamental limits of the proposed UAP system in terms of the Cramer–Rao low bound (CRLB), where the UAVs are treated as “agents” with unknown positions instead of anchors. Then, we formulate a general UAP method using the nonparametric belief propagation (NBP)-based probabilistic framework, to jointly positioning UAVs and GDs simultaneously. Moreover, a two-step clustering-based solution is given to tackle the data association challenge in the multi-UAV scenarios. We also show that proper data feedback could achieve additional performance advantages without any extra measurements. The optimal multi-UAV deployment strategy is then proposed, by which the potential of the UAP system could be fully characterized. Last but not least, we verify our solutions via numerical simulations and practical experiments, which provide meaningful insights and performance evaluations to the system design and implementations.

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.

In a typical Satellite-integrated Internet of Things (SIoT), the limited transmission rate of a sensor causes a stale in data freshness due to unavoidable time waiting for transmission. Moreover, due to the high bit error rate (BER) of the satellite-to-ground link, data freshness will be further exacerbated by frequent retransmissions. Generally, this issue is partially solved using a powerful compression scheme that can reduce the data volume. However, conventional compression schemes will necessitate a significant amount of time to accumulate constant data to a certain quantity, posing a difficult challenge. Therefore, this study proposes an age-optimal hybrid temporal-spatial generalized deduplication and automatic repeat request (HARQ-GD) protocol for the high-sampling data collection in SIoT, considering data compression, and transmission collaboratively. A novel Age of Information (AoI) metric is developed for timeliness evaluation over a two-hop end-to-end link of SIoT, which is optimized to design the proposed HARQ-GD protocol by considering the temporal and spatial correlations of sampled data with specific encoding/decoding algorithms and packet formats. The simulation results indicate that the proposed HARQ-GD protocol performs better performance than typical generalized deduplication (GD) and hybrid automatic repeat request with chase combing (HARQ-CC) schemes in reducing AoI, because of its fewer transmission times and higher compression rate.

High accuracy and seamless position information formulates the basis of many modern wireless applications, such as the Internet of Things (IoT) and intelligent transportation systems (ITSs). In this article, aiming at the ground user equipment (UE) those in the “blind spots,” where only limited navigation signals are provided, the temporary aerial-aided “anchors” such as the unmanned aerial vehicles (UAVs) are introduced as alternating solutions. We first give the general fundamental limits of the three-dimensional space–air–ground-integrated localization networks (SAGILNs) using both time and angle measurements. Unlike most existing investigations, we treat aerial nodes as “agents” whose positions are not known beforehand. We then try to formulate an efficient scheduling strategy, where proper networkbehaviors, including the resource optimization and UAV deployment, are provided. We find that the proposed scheduling problems could be formulated as standard semidefinite programming (SDP) problems and solved by off-the-shelf solvers. Numerical results are provided to validate our analysis. The proposed methods and analyses provide meaningful insights for performance benchmarks for the implementation of SAGILN.

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.

Since vehicles from multiple roads frequently merge at intersections, it formulates a typical traffic bottleneck of modern transportation systems. Proper vehicle coordination and motion plan at road intersections are of importance to guarantee safety as well as improving the traffic throughput, fuel efficiency and so on. In this paper, we try to present a general dedicated intersection coordination framework for autonomous vehicles, where both high- and low-level planners are appropriately designed and integrated. In the high-level planner, two different strategies are formulated to coordinate the autonomous vehicles to generatereference trajectoriesandfeasible “tunnels”, respectively. Especially, a novel space-time-block based resource allocation scheme is presented to describe the feasible tunnels. Furthermore, to avoid collisions with unexpected obstacles such as pedestrians, bicycles or other vehicles with human drivers, a low-level planner is designed to generate practical trajectories based on the solutions from the high-level planner, according to their local on-board observations. Simulations and practical experiments are carried out, to show that our proposed coordination framework can achieve obvious performance advantages in various traffic metrics, including the throughput, fairness in driving maneuvers and driving comfort, etc. We also find that the high-level planner is effective in eliminating possibledeadlocksamong autonomous vehicles, which is rarely discussed in existing investigations.

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.

In cooperative communication, due to the difference of channel qualities between nodes, the effective combining and processing for the signals from multiple nodes is extremely important to improve the system performance. In this paper, we propose an approach named joint multi-branch weighted combining and equalization detector (JMWC-ED) where all inter-node links are independent frequency selective fading channels, and present the corresponding performance analysis. Compared with the existing approaches, in the JMWC-ED, the adjustment of weighted combining for all branches and the equalization of each branch are synthesized into an integrated detector. The weighted combining of multiple branches and the adjustment of the tap coefficient vector of each branch are not independent of each other, but are implemented jointly. Moreover, the weighted coefficients of all branches are adaptively adjusted in accordance with the corresponding channel qualities, and there is no need to know the channel state informations between nodes. Simulation results validate the adjustment ability of the JMWC-ED to weighted coefficients and the performance analysis for the JMWC-ED. We also show the superior performance of the JMWC-ED over existent counterparts.

Space-air-ground integrated networks (SAGIN) within UAV-assisted free-space optical (FSO) communication systems can efficiently accommodate massive connections and provide highly reliable and seamless communications. In this work, we investigate the performance of a UAV-assisted, asymmetric, dual-hop radio frequency (RF)/FSO system with the amplified-and-forward relay protocol for the SAGIN. Specifically, the shadowed Rician fading is utilized in this study to characterize the shadowing effect on the RF signal for the satellite-to-UAV link. Meanwhile, the atmospheric turbulence effect on the optical signal for the UAV-to-terrestrial user link is modeled by the Málaga distribution fading, in view of the pointing error impairments. For comparison, the heterodyne detection technique is employed, as well as the intensity modulation with direct detection technique, in improving performance of the relay system. Thus, we derive the closed-form expressions for the cumulative distribution function, probability density function, the moment generating function, and particularly, the moments of the end-to-end RF/FSO system, in terms of Meijer’s G-function. Utilizing these derived formulae, the precise closed-form expressions for the outage probability, the average bit error rate (BER) with various modulation schemes, and the ergodic capacity are given. In specific, the tight asymptotic results for the outage probability and the average BER at the high SNR regions are derived using the asymptotic expansion of Meijer’s G-function. Furthermore, closed-form expressions are presented for the case that the FSO link experiences Gamma-Gamma distribution by changing some specific parameters. Finally, extensive numerical results validate the theoretical results with the Monte-Carlo simulation.

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.

Accurate and reliable multi-target detection and localization in complicated environments are challenging due to the existence of multiple reflectors that may distract the observations. In this paper, we try to adopt the linear frequency modulated continuous wave (LFMCW) signals, in addition to the antenna array, to perform target detection and positioning. Aiming at the ghost observations which may be generated during the measurement and data fusion phases, we try to properly combine the observations from multiple radar nodes using a probabilistic data fusion framework. The positions of targets could be obtained straightforwardly using the minimum mean squared error (MMSE) estimator. The fundamental limits of target positioning are also provided in terms of the Cramer Rao Lower Bound (CRLB). Furthermore, we try to accurately identify the target of interest by characterizing its micro-Doppler effects. Both simulations and experiments are carried out to show that the ghosts could be effectively eliminated by the proposed multi-radar fusion framework. Meanwhile, the target positioning accuracy can also be obviously improved.

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.

Ultra-dense computation services are widely distributed in various application scenarios with the rapid development of artificial intelligence and machine learning. Relying on the existing ground cellular networks, it is challenging to satisfy the 6G vision of full coverage and massive machine connectivity. Satellite-terrestrial integrated network (STIN) has abundant computation resources and seamless coverage ability, which can be served as an effective supplementary for the task allocating in cellular networks. Nevertheless, STIN has the characteristic of architecture complexity, unavoidable stragglers and high economic costs. The rational computation resource allocation among distributed on-orbit satellites becomes an urge problem, synthesizing these drawbacks in STINs. In this paper, to address these issues, we attempt to design a coded computation offloading strategy (CCOS) to migrate ground ultra-dense computing tasks to distributed satellite constellations in space. Considering the effect of unpredictable computation resource occupation on satellites, we investigate two coded computation methods, i.e., maximum distance separable (MDS) code and rateless code, to resist the random stragglers occurring on satellite nodes. Then, we formulate the optimization problem about minimizing the delay-energy tradeoff cost with different CCOSs under the tolerant time constraints, and obtain the optimal task offloading decisions (i.e., executing locations and coding parameters) using a proposed low-cost offloading decision searching algorithm (LODSA). Numerical simulation results show that, our coded computation strategies can significantly eliminate the effect of stragglers, and improve the cost performance obviously compared with the un-coded strategies in typical application cases.

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.

Unmanned aerial vehicles (UAVs) have been widely used in wireless edge networks for task offloading, with the advantages of their agile management and high-flexibility deployment. However, due to limited computation capability and restricted battery life, processing computation-intensive tasks on board may cause the excessive cost of latency and energy. In this paper, we propose a novel framework with coded distributed computing (CDC) for the task offloading from multi-UAV to ground edge servers, which can save transmitting and flying energy consumption in the air, and reduce computation latency in the terrestrial distributed server networks with stragglers. Specifically, we formulate a latency-energy cost minimization problem, to obtain the optimal the UAVs' trajectory schedule and the appropriate CDC's parameters. Moreover, we divide this problem into two sub-optimization problems, which are solved by a cost optimal trajectory schedule (COTS) algorithm and a cost optimal code parameter design (COCPD) algorithm, respectively. Finally, numerical results indicate the feasibility and the effectiveness of our proposed framework, which also validate that CDC can significantly reduce the cost in the UAV edge computing network.

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.

Abstract This work introduces an efficient neuron design for fixed‐point artificial neural networks with the rectified linear unit (ReLU) activation function for energy‐constrained wireless applications. The fixed‐point binary numbers and ReLU activation function are used in most application‐specific integrated circuit designs and artificial neural networks (ANN), respectively. It is well known that, owing to involved computation intensive tasks, the computational burden of ANNs is ultra heavy. Consequently, many practitioners and researchers are discovering the ways to reduce implementation complexity of ANNs, particularly for battery‐powered wireless applications. For this, a low‐complexity neuron to predict the sign bit of the input of the non‐linear activation function, ReLU, by employing the saturation characteristics of the activation function is proposed. According to our simulation results based on random data, computation overhead of a neuron using the proposed technique can be saved by a ratio of 29.6% compared to the conventional neuron using a word length of 8 bits without apparently increasing the prediction error. A comparison of the proposed algorithm with the popular 16‐bit fixed‐point format of the convolutional network, AlexNet, indicates that the computation can be saved by 48.58% as well.

Abstract In clustered distributed storage systems (CDSSs), one of the main design goals is minimizing the transmission cost during the failed storage nodes repairing. Generalized regenerating codes (GRCs) are proposed to balance the intra‐cluster repair bandwidth and the inter‐cluster repair bandwidth for guaranteeing data availability. The trade‐off performance of GRCs illustrates that, it can reduce storage overhead and inter‐cluster repair bandwidths simultaneously. However, in practical big data storage scenarios, GRCs cannot give an effective solution to handle the heterogeneity of bandwidth costs among different clusters for node failures recovery. This paper proposes an asymmetric bandwidth allocation strategy (ABAS) of GRCs for the inter‐cluster repair in heterogeneous CDSSs. Furthermore, an upper bound of the achievable capacity of ABAS is derived based on the information flow graph (IFG), and the constraints of storage capacity and intra‐cluster repair bandwidth are also elaborated. Then, a metric termed global repair bandwidth cost (GRBC), which can be minimized regarding of the inter‐cluster repair bandwidths by solving a linear programming problem, is defined. The numerical results demonstrate that, maintaining the same data availability and storage overhead, the proposed ABAS of GRCs can effectively reduce the GRBC compared to the traditional symmetric bandwidth allocation schemes.

In order to better explore the ocean, autonomous underwater vehicles (AUVs) have been widely applied to facilitate the information collection. However, considering the extremely large-scale deployment of sensor nodes in the Internet of Underwater Things (IoUT), a homogeneous AUV-enabled information collection system cannot support timely and reliable information collection considering the time-varying underwater environment as well as AUV’s energy and mobility constraints. In this article, we propose a multi-AUV-assisted heterogeneous underwater information collection scheme for the sake of optimizing the peak Age of Information (AoI). Moreover, the limited service M/G/1 vacation queueing model is utilized to model the process of information exchange, where the optimal upper limit of the number of AUVs served in the queueing system as well the steady-state distribution of the queue length are derived. A low-complexity adaptive algorithm for adjusting the upper limit of the queuing length is also proposed. Finally, simulation results validate the effectiveness of our proposed scheme and algorithm, which outperform traditional methods in terms of the peak AoI.

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.

Industrial Internet of things (IIoT) is expected to improve efficiency and productivity by connecting massive devices, but it will cause potential congestions in backhual link and high energy consumptions. Caching with coded transmission is an effective method to reduce backhual load for content delivery. However, due to the hierarchy and heterogeneity in IIoT, it is very challenging to perform content placement with lower energy consumption. In this article, we propose an energy-efficient content placement strategy in cache-enabled hierarchical IIoT network with coded transmission. We derive a closed-form expression including the energy consumption for content placement and transmission by the macro base station and the small base stations. In addition, we establish an optimization problem to minimize the total energy consumption, whereby we find the optimal content placement matrix and optimal cache size allocation, respectively. Simulation results show that, the proposed content placement strategy can greatly improve energy efficiency in IIoT.

This special section aims at soliciting original research and practical contributions from both industry and academia to advance the IIoT, including network modeling and architecture, AI algorithms for various layers, intelligent resource management, big data driven edge systems, orchestration of edge, and cloud servers. Through a rigorous peer-review process, nine articles have been accepted. In the following, we summarize the accepted articles in this editorial.

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.

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

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

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.

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.

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

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

In this paper, we study the joint resource allocation and the corresponding performance of non- orthogonal multiple access (NOMA) systems with imperfect successive interference cancellation (SIC), wherein we consider the precoding, user clustering and power allocation design in a single cell with one base station and multiple users. Specifically, we propose a precoding scheme based on zero-forcing beamforming, and a user clustering algorithm based on channel correlation to reduce inter-cluster interference. In order to maximize the sum capacity, the power allocation optimization problem is formulated and solved via interior point methods. Numerical and simulation results demonstrate that our proposed design has better sum capacity performance when SIC is imperfect.

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.

With the increasing usage of UAV in reconnaissance, agriculture, logistics and entertainment, it's necessary for multi-UAV to automatically avoid the dynamic obstacles in order to ensure the safety of drones and livings in environment. The automatic obstacle avoidance is a classic multiple agent decision-making problem. Traditional algorithms, limited in the method of state classification and policy selection, are not applicable in such a complex scene including randomly dynamic scene and cooperative decision-making. In this paper, Advantaged Actor-Critic Algorithm is introduced to train multi-UAVs to automatically avoid obstacles and optimize avoidance decision-making model. Deep Q Learning, Actor-Critic (AC) and Advantaged Actor-Critic (A2C) algorithm are compared. And to further maximize the performance, we specifically improved A2C algorithm towards the multi-UAV scene by sharing experiences between UAVs to expedite the training process. Our experimental result shows our Experience-shared A2C (ES-A2C) algorithm leads to a higher performance and a shorter training period.

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

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

Accurate position information is the basis of many modern wireless applications. Hybrid time of arrival/angle of arrival (TOA/AOA) measurements are attractive in location‐aware networks, especially when the number of anchors is limited. Recent investigations show that proper resource allocation is of great importance in cost and complexity restricted localisation networks. In this study, the authors focus on the energy efficient localisation strategy using hybrid TOA/AOA measurements. A joint resource allocation and antenna selection framework is presented to minimise the localisation error. Due to the essential non‐convexity of the original formulated integer programming problem, a low complexity and high accuracy two‐phase solution is presented to perform proper convex relaxations. Numeric results validate the authors' analysis, and provide meaningful insights to practical TOA/AOA localisation networks.

The aim of this work is to improve the secrecy capacity of primary users (PUs), meanwhile, spectrum utilisation and energy efficiency are considered. the authors present a communication system model with secondary users (SUs). The SUs are provided access to the spectrum. Also, by means of beamforming, their signals will not interfere the PUs but eavesdropper, and the PUs' transmissions are protected. By leveraging the SUs instead of traditional jamming nodes can also make the energy efficiency higher. They formulate the system model, signalling plan, and key enabling techniques to enhance the spectrum efficiency and PUs' physical‐layer security with SUs' participation. They provide theoretic analysis of a sum capacity maximisation under a certain power constraint to evaluate the performance of this system. Numerical results show that the proposed scheme not only improves PU's secrecy capacity but also enhances the spectrum utilisation.

High-precision location information formulates the basis of many modern wireless location-based services (LBSs). However, location privacy becomes an important issue along with the advantages that LBS offer. In this paper, we first show that a novel eavesdropper is able to perform position estimation of the agent by purely overhearing the measurement signals between anchors and the agent, using a time difference of arrival way. Optimal power allocation-based approaches are presented to protect the location privacy against the eavesdropper. Then, high-accuracy approximation algorithms are also presented to solve the essential non-convex optimization problems. According to the numeric results, we can see that the presented power allocation strategies are able to prevent the eavesdropper from performing accurate position estimation of the agent. We can also see that there exists an optimal tradeoff between the system localization and privacy protection, which can be achieved by the presented frameworks.

Due to its advantages in target sensing and detection, the widely adopted orthogonal frequency division multiplexing (OFDM) signals have recently been attractive in the integrated radar and communications (RadCom) systems. An improved tone reservation scheme combing the phase coding technique to reduce the peak-to-average power ratio (PAPR) of the OFDM integrated signals is given. A novel signal processing scheme that is able to retrieve a joint estimation on the scatter type in addition to the range and velocity, is propose for the complicated target that consists of multiple scatterers. Moreover, a least square (LS) based method is given to estimate the scatterer type from the achieved high range resolution profile (HRRP) spectrum. Numerical results are provided to validate the performance advantages of the proposed schemes. Furthermore, we see that a large symbol number can help to improve the recognition accuracy of the scatterer, especially under low SNR conditions.

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.

In this paper, we address the optimal autonomous vehicle (AV) coordination problem at road intersections, which is of great importance in modern intelligent transportation systems (ITS). We first formulate it as a general collision-free traffic scheduling framework. Aiming at the dynamic characteristics of vehicles' arrival, a corresponding dynamic coordination strategy is thus proposed to achieve better quality of service (QoS), i.e., the traffic throughput and delay. The road stability is also guaranteed using the Rate Stability Theorem and Lyapunov Theorem. Provided numerical results validate our analysis and show the performance improvements achieved by the proposed framework.

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.

Reliable and efficient overtaking maneuvers are important and challenging for autonomous vehicles. Position information of both involved vehicles during overtaking is important, to avoid collisions. In this paper, we try to perform overtaking based on relative position information, such as the distance, angle and velocity between vehicles, in a non-collaborative scenario. To reduce the complexity of maneuvers, a fuzzy inference system (FIS) is applied to analyze the driving behavior of the preceding vehicle based on the relative position information. An output of “safe” or “dangerous” will be sent to the decision part based on reinforcement learning frameworks. Various overtaking maneuvers including “conservative” and “aggressive” can be obtained accordingly. Numeric results validate our analysis, and show that our proposed strategies can be easily extended to the multiple-vehicle-scenario.

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.

In this paper, we address the interference problem caused by the cross-polarization components in a massive dualpolarized MIMO (DP-MIMO) system over Ricean-K fading Channel. To effectively suppress the interference, a novel precoding design based on oblique projection is proposed. Furthermore, compared with an Nt × Nr uni-polarized MIMO (UP- MIMO), Nt×Nr DP-MIMO can maintain the same diversity order while achieve twice the multiplexing gain of UP-MIMO in symbol error rate (SER) performance by using the proposed precoding design. The expression of the moment generation function (MGF) of signal noise ratio (SNR) for the proposed scheme is derived, and an analytical expression of SER with M-ary phase-shift keying (M-PSK) modulation is obtained. The effectiveness of the proposed scheme is demonstrated through extensive numerical results.

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.

Wireless localization systems based on determination of signal runtime (TOA/TDOA) are of great importance for a variety of applications. In many cases, synchronization of the clocks of the agent nodes to those of the network nodes (anchors) has to be performed together with the localization. The current paper investigates the fundamental accuracy limits of such a joint localization/synchronization. In particular we analyze the impact of allocating power to the different anchor nodes, and optimize this power allocation to maximize accuracy. Simulation results confirm the importance of proper power allocation; known special cases (TDOA localization, localization with already-synchronized clocks) are recovered from our general solution.

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.

In Mars-to-Earth communications, data transmission suffered severe losses due to the huge path-loss, extremely long propagation delay and lack of line-of-sight link in rovers-to-Earth. Based on delay/disruption tolerant networks (DTN), we proposed a systematic Raptor Network Coding (RNC) scheme for the multi-rovers transform data through an orbiter to Earth station communication scenarios. To enhance the reliability of rover-to-Earth file delivery, and considering the limited capacity of the relaying orbiter, a simplified network coding scheme is designed for the orbiter. We analyzed the asymptotic performance of RNC scheme. Moreover, an improved RNC (IRNC) scheme is optimized in a finite code-length and limited coding complexity. Simulation results show that, our RNC and IRNC schemes can achieve better performance in comparison with existing distributed rateless erasure codes.

To improve the reliability of file transfer and shorten file transfer time in space communication, this study aims to provide an improved strategy for deferred negative acknowledgement (NAK) in Consultative Committee for Space Data Systems File Delivery Protocol (CFDP). Based on a theoretical analysis of the recommended deferred NAK, the authors propose a double retransmission deferred NAK strategy instead to guarantee the reliability of file transfer; the file transfer time is reduced significantly using fewer retransmission spurts. They make the performance comparisons of the recommended deferred NAK in CFDP with the authors’ proposed strategy under several typical scenarios. Numerical and simulation results show the effectiveness of the proposed strategy.

We employ nondestructive evaluation involving AC field measurement in detecting and identifying metal barcode labels, providing a reference for design. Using the magnetic scalar potential boundary condition at notches in thin-skin field theory and 2D Fourier transform, we introduce an analytical model for the magnetic scalar potential induced by the interaction of a high-frequency inducer with a metal barcode label containing multiple narrow saw-cut notches, and then calculate the magnetic field in the free space above the metal barcode label. With the simulations of the magnetic field, qualitative analysis is given for the effects on detecting and identifying metal barcode labels, which are caused by metal material, notch characteristics, exciting inducer properties, and other factors that can be used in metal barcode label design as reference. Simulation results are in good accordance with experiment results.

Network localization is a key feature in many wireless services and applications. In typical range-based non-cooperative localization techniques, agents try to perform position estimation through ranging with respect to anchors with known positions. Based on the definition of squared positional error bound, the localization accuracy can be determined by the transmit power, carrier frequency, and signal bandwidth. This paper analyzes the joint power and spectrum allocation (JPSA) optimization problems in resource restricted wireless localization systems. We first formulate both optimal interference-free and interference affected JPSA problems. We then formulate the robust counterparts of the problems in the presence of uncertainty of the agents' positions. Since all JPSA problems are non-convex, we show that they can be modeled and solved as geometric programming (GP) by proper approximations. Numeric results validate our analysis and show that the developed algorithms are able to find solutions close to the global optimum in the investigated cases. We can find the optimal/robust resource deployment in non-cooperative wireless localization networks based on the proposed frameworks.

Cooperative localization can enhance the accuracy of wireless network localization by incorporating range information among agent nodes in addition to those between agents and anchors. In this paper, we investigate the optimal allocation of the restricted resources, namely, power and bandwidth, to different nodes. We formulate the optimization problems for both synchronous networks and asynchronous networks, where one way and round trip measurements are applied for range estimation, respectively. Since the optimization problems are nonconvex, we develop an iterative linearization-based technique, and show by comparison with brute-force search that it provides near-optimal performance in the investigated cases. We also show that especially in the case of inefficient anchor placement and/or severe shadowing, cooperation among agents is important and more resources should be allocated to the agents correspondingly.

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.

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

Disruption-Tolerant Networks provides store-and-forward enabled routing strategies for the satellite networks with frequently intermittent links. However, current dynamic route selection algorithm (DRSA), including Contact Graph Routing (CGR) algorithm, could not find an end-to-end route over a serial of link segments with time-disjointed contacts. In this paper, we proposed a novel routing algorithm as expanding range route selection (ERRS), which could find the EDT-optimal route by searching at each snapshot of time-varying topology. The proposed algorithm is compared with DRSA on the Linux-based experimental platform with built-in ION (Interplanetary Overlay Networks) software. The results show our algorithm has less delivery time and the obviously improved throughput of the network.

In a sensing‐based hybrid spectrum sharing paradigm, cognitive radio first performs spectrum sensing to identify primary users’ states (idle/busy) and then adapts its transmit power according to sensing outcomes and channel conditions. To investigate the capacity of such systems in fading channels, existing works modelled fading channel in transmission phase while additive white Gaussian noise channel in spectrum sensing; however, sensing channels also exhibit fading characteristics in practice. Therefore a more realistic system model with channel fading in both sensing and transmission is considered in this study. Under the new system model, spectrum sensing and power allocation are coupled in the ergodic capacity and an equivalent decoupling processing is proposed via mathematical manipulations. Further, joint sensing and power allocation over Rayleigh fading is studied under average interference and transmit power constraints. The optimal and suboptimal schemes are obtained by alternating optimisation and Lagrangian dual method. Finally, system performance is evaluated via extensive numerical simulations.

Heterogeneous Networks (HetNets) are an attractive way of increasing network throughput, expanding network coverage, and reducing energy consumption, but it may lead to the problems of high cost infrastructure investment and high computational complexity to mobile operators. In this paper, we propose a new paradigm for the deployment of HetNets in LTE-Advanced system, where the Donor evolved nodeB (DeNB) cooperates with multiple relay nodes (RNs) in a crowdsourcing way. In order to stimulate these RNs, we propose an incentive mechanism by exploiting the game theory. In our incentive mechanism, the DeNB announces rewards to the participating RNs, and the RNs adjusts their transmission strategies, i.e., transmission power. This incentive mechanism can bring about a win-win situation, in which the DeNB can increase its income and the RNs can receive satisfying reward from the cooperative transmission. Extensive numerical simulations are conducted to validate the effectiveness and efficiency of our proposed incentive mechanism.

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

Localization of wireless node is a key feature in many applications. Traditional localization has exploited the signal runtime between “agent” nodes that are to be localized and a set of “anchor” nodes with known position. Recently, cooperative localization that also uses runtime measurement between agent nodes has been shown to provide superior performance. This paper analyzes the optimum power and bandwidth allocation in such systems. We first formulate the general optimization problem and show that it is non-convex. We then develop an approximate algorithm based on Taylor expansion and iterative optimization of power and bandwidth separately to find an approximate solution; simulations show that results are close to the optimum solution (which is NP-hard). We also find that the importance of cooperative localization increases (and agents get assigned more resources) if the anchor deployment is bad in the sense that it provides high geometric dilution of precision and/or suffers from significant blockage between anchors and agents.

In wireless body area networks (WBAN), energy efficiency is one of the most important issues to be addressed. In this paper, researches on efficiency optimization in WBAN are carried out. Based on the quality of service (QoS) required from each sensor node, intelligent time and power resource allocation is performed for energy saving. First, global energy minimization (GEM) model is proposed as a general target for optimization. Due to the special requirements of typical WBAN applications (e.g. health monitoring), network lifetime is defined and then handled as the objective function to be maximized. Both problems are proved to be geometric programming, which can be solved by many off the shelf solvers efficiently. Numeric results show that, compared to the sub-optimal resource allocation schemes, the proposed methods are able to improve the energy efficiency obviously. Furthermore, they also provide a performance benchmark for developing low complexity distributed algorithms in the future.

This study proposes a novel adaptive network‐coded rateless coding scheme for an erasure multiple‐access relay system with two distributed sources and an asymmetric network topology. To increase transmission efficiency, a two‐dimensional degree distribution, as part of network‐coded relay protocol, is designed based on the AND–OR tree analysis technique. The degree distributions of rateless coding at the sources and network coding at the relay are optimised by the linear programming approach under asymmetric channel conditions. Simulation results demonstrate that the proposed scheme outperforms existing classical relay protocols under time‐varying channel conditions, and achieves a significantly better performance.

The Feher quadrature phase shift keying (FQPSK) modulation is significantly susceptible to frequency and phase offsets under low signal‐to‐noise ratios. In this study, the authors proposed a serially concatenated signalling scheme with FQPSK modulation and low‐density parity‐check coding, which could efficiently resist residual frequency offset by employing an intended compensation algorithm. The designed maximum‐likelihood estimation‐enabled compensation algorithm is incorporated into the iterative concatenated demodulation‐decoding process by using soft‐input–soft‐output‐based maximum‐a‐posteriori‐probability criterion. On the other side, the codeword sequence to be transmitted at the sender is re‐arranged in a pre‐configured order different from original codeword, in order to help the compensation algorithm diminish the impacts of frequency offsets. Simulation results show that the bit error rate of the proposed scheme can be improved efficiently up to three orders of magnitude with the frequency offsets from 100 to 700 ppm.

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

This paper considers the hybrid spectrum sharing paradigm where a cognitive radio system first performs spectrum sensing to identify primary users' (PU) status (idle/busy) and then adapts its transmit power according to sensing outcomes. To maximize ergodic throughput in fading environments, joint sensing and power allocation has to be considered. However, existing studies determine the optimal sensing time based on instantaneous channel state information (CSI) at each time slot, which imposes a stringent requirement in practice. In this paper, we obtained a statistical CSI-based optimal sensing time by exploring the ergodic rates of both overlay and underlay access in Rayleigh fading environments. Simulation results validate the derived analytic expressions, showing that significantly higher maximum throughput can be achieved by hybrid access compared with conventional overlay access.

An orthogonal modulation enabled two-phase energy-efficient framework is presented for active cooperation between secondary users (SUs) and primary users (PUs) in a cognitive radio network. Since the PU has higher priority, and SUs compete for spectrum accessing, we model the power control problem as a Stackelberg game which incorporates throughput and energy consumptions into utility design. Due to the two-phase feature and SUs' power constraint, this game is played in an additive coupled sum constrained type. Unique Nash Equilibrium is achieved in analytical format, and simulations demonstrate the effectiveness of the proposed cooperation framework.

Delay/Disruption Tolerant Networking (DTN) architecture is expected to play a promising role in future deep space missions. Scientific data interactions over space DTN involve several hops inevitable, since simultaneous and direct connectivity among all intermediate nodes are becoming more difficult in space scenarios. Therefore, the characteristics and capabilities of the node storage are vital factors for the quality of data delivery over space DTN. This paper proposes an analytical framework based on multi-dimension Markov chain to evaluate the dynamic on storage of intermediate nodes in space DTN. According to the proposed framework, we develop a delay model and consequently a success probability model for bundles delivery over space DTN, both of which are dependent closely on the sojourn time in node storages. The numerical results show that: a) dividing source-file data into bigger bundles can bring longer high-storage-occupancy time on intermediary nodes; b) the shorter storage occupation time of node is more susceptible to the bundle sizes than to LTP segment sizes. c) the delivery success probability of the bundles is more dependent on smaller DTN bundles than on LTP segment sizes given the constrains on Time-to-live of bundles in space missions.

This work is concerned with enhancement of spectrum-energy efficiency whereby a primary user (PU) engages secondary users (SUs) to relay its transmission in an energy-aware cognitive radio network, i.e., forming a cooperative cognitive radio network (CCRN). The cooperation framework in CCRN can be multiple two-hop relaying with or without PU's direct link transmission using an amplify-and-forward or decode-and-forward mode. In the energy-aware CCRN, an individual cooperating partner attempts to maximize its own utility. The partner selection and parameter optimization, led by the PU, are formulated as two Stackelberg games, namely a sum-constrained power allocation game for two-phase cooperation and a power control game for three-phase cooperation, respectively. Unique Nash Equilibrium is proved and achieved in analytical format for each game. The optimal communication strategy is chosen which achieves the maximum PU utility among different optimal communication strategies. Moreover, an implementation scheme is presented to perform the partner selection and parameter optimization based on the analytical results. Theoretical analysis and performance evaluation show that the proposed CCRN model is a promising framework under which the PU's utility is maximized, while the relaying SUs can attain acceptable utilities.

Practical low complexity time of arrival (TOA) estimation method with high accuracy is attractive in ultra wideband (UWB) ranging and localization. In this paper, a generalized maximum likelihood energy detection (GML-ED) ranging method is proposed and implemented. It offers low complexity and can be applied in various environments. An error model is firstly introduced for TOA accuracy evaluation, by which the optimal integration interval can be determined. Aiming to suppress the significant error introduced by the false alarm events, multiple pulses are utilized for accuracy promotion at the cost of extra energy consumption. For this reason, an energy efficiency model is also proposed based on the transmitted pulse number. The tradeoff between accuracy and efficiency is discussed. The performance is evaluated and verified through practical experiments in a typical indoor environment.

The Feher quadrature phase shift keying (FQPSK) modulation based Physical-layer Network Coding (PNC) is investigated in this paper, by which the nonlinear distortion effects resulted from the high power amplifier (HPA) in the system can be avoided. In our presented framework, a novel remapping rule for the FQPSK modulation in the PNC system is proposed to make a better bit error rate (BER) performance. Moreover, a joint demapping-and-demodulation scheme based on Low Density Parity Check (LDPC) is employed to recover the data bits with a low computational burden. Numerical results demonstrate the efficiency of the proposed method.

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

This work is concerned with enhancement of spectrum utilization by using polarization enabled two-phase cooperation between primary users (PUs) and secondary users (SUs) in cooperative cognitive radio networking (CCRN). The use of orthogonally dual-polarized antennas (ODPAs) enables concurrent transmissions of multiple independent signals of PUs and SUs, and interference suppression via polarization zero-forcing and polarization filtering to obtain significant performance improvement. To maximize a weighted sum throughput of PUs and SUs under energy/power constraints, the problem is formulated and solved based on a multi-timescale Markov decision process, and two modified backward iteration algorithms are devised to attain the optimal policies. Numerical results validate the effectiveness of the proposed CCRN framework, showing that the obtained policy outperforms both greedy and random ones.

A novel polarization enabled two-phase cooperation framework for cognitive radio networking is proposed in this paper. By leveraging the degrees of freedom provided by orthogonally dual-polarized antennas, secondary users can relay the traffic of primary users and transmit their own in the same time slot without interference. To evaluate the performance of the proposed framework, a sum throughput maximization problem is formulated. By using the geometric programming algorithms, the nonlinear and non-convex optimization problem is solved by applying different power constraints for high and medium (or low) signal-to-noise ratio regimes. Simulation results validate the effectiveness of the proposed two-phase framework.

Some work has been done with the DTN convergence layer adapters (CLAs) such as TCP-based CLA (i.e., TCPCL), Licklider transmission protocol CLA (i.e., LTPCL) and user datagram protocol (UDP)-based CLA (i.e., UDPCL) in space communication, running as an individual protocol. Because each DTN CLA is designed for a different type of communication link, using any single CLA under BP is generally inefficient over a heterogeneous end-to-end interplanetary infrastructure. In this paper, we propose to use a hybrid of DTN CLAs for efficient file transmission in interplanetary environment, i.e. using one CLA for one hop of the end-to-end path and a different CLA for another hop of the path. Based on experimental investigation of DTN protocols over a typical relay-type of interplanetary infrastructure, we found that a hybrid of TCPCL and LTPCL has significant goodput advantage over single-CL-protocol configurations at long link delays, especially with a high BER, and a transmission with LTPCL utilized over a long-delay hop is more tolerant of long channel delay and high channel error.

Delay/disruption tolerant networking (DTN) offers a new solution to highly stressed communications in space environments. It is considered one of the most suitable technologies to be employed in space internetworking. To date, little work has been done in investigating how to achieve the best performance of DTN transmission over lossy, long-delay space channels. In this paper, we present an experimental investigation of how the throughput performance of Licklider transmission protocol (LTP)-based DTN is affected by the LTP flow control “window” size, established by the Number of Sessions (NOS) and Number of Bytes per Session (NBS) characterizing the LTP channel. The intent of the work is, in particular, to find a relationship between the window size and throughput performance of DTN in a typical lossy and long delay cislunar communications infrastructure. One major conclusion is that a bigger NOS generally results in higher throughput than a low NOS, and NBS does not affect the throughput significantly.

Delay/disruption tolerant networking (DTN) is considered one of the most suitable technologies to handle challenging space communications. Aggregation of multiple DTN bundles within a data transport block for space communications has been in controversy for years. In this paper, we present an experimental investigation, using a PC-based testbed, of whether aggregation of multiple DTN bundles within a single Licklider transmission protocol (LTP) block has performance advantage over the default approach of “one bundle per block” for channelrate asymmetric cislunar communications.

A quadrature signaling based two-phase cooperation framework for cooperative cognitive radio networking is proposed. By leveraging the degrees of freedom provided by orthogonal modulation, secondary users are able to relay the traffic of primary users and transmit their own in the same time slot without interference. To evaluate the cooperation performance of the proposed framework, a weighted sum throughput maximization problem is formulated, and closed-form solutions of the optimal power setting/allocation are obtained in the amplify-and-forward and decode-and-forward relaying modes. Simulation results validate the efficiency of the proposed framework.

Interplanetary Overlay Network (ION) is an implementation of delay/disruption tolerant networking (DTN) developed as infrastructure for space communications in interplanetary flight mission systems. To date, no work has been done in evaluating the effectiveness of ION when it is applied to an interplanetary Internet involving very long link delay and highly asymmetric channel rates. In this paper, we present an experimental evaluation of ION over a typical three-node interplanetary infrastructure in the presence of a long link delay, highly asymmetric channel rates and varying data loss rate. One major conclusion is that the hybrid of TCP and Licklider transmission protocol (LTP) convergence layer protocols has significant goodput advantage over other protocol options as the ratio of data channel rate to ACK channel rate increases.

In a downlink multiuser multiple-input and multiple-output (MU-MIMO) system, a base station (BS) communicates with multiple mobile stations (MS) simultaneously in a given spectrum band. The performance of a MU-MIMO system depends on the choice of user selection, power allocation and precoding schemes. In this paper, we study user selection in Zero-Forcing (ZF) precoding based MU-MIMO systems to maximize the sum rate for high data rate applications. We derive analytical results for simultaneous transmission to two MSs with zero forcing (ZF) precoding, based on which we propose a extended low-complexity algorithm that jointly considers the noise power, the channel gain of the candidate MSs and orthogonality with respect to the selected MSs' channels. Analysis shows that the proposed scheme requires much lower complexity than current schemes. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms in terms of higher throughput.

To suppress the effects of ultra-wideband receiver caused by non-Gaussian noise and difference of channel profile, an adjustable observation window length equalisation receiver based on H∞ criterion is proposed. In contrast to the existing fixed observation window length (FOWL) equalisation receivers based on the minimum mean square error (MMSE) criterion, the proposed receiver is found on H∞ criterion and can adaptively adjust the observation window length according to the specific channel profile. The proposed receiver so designed is shown to outperform the FOWL equalisation receivers based on the conventional MMSE criterion in a non-Gaussian noise environment.

The radar members are likely to interfere with each other if their waveforms and polarized states are not orthogonal in radar sensor network (RSN). In this paper, we propose the oblique projection polarization filtering (OPPF) based interference suppressions for RSN where each radar member is equipped with the orthogonally dual-polarized antenna (ODPA). In our discussed cooperative environment, under which radar members share their polarized states, members radiate EM waves using the same waveform but different polarized states, however, their polarized states are not needed to be orthogonal. Doppler-Shift and its uncertainty are not involved due to the independence from the polarized state, which makes the proposed method simple and effective. The results demonstrate that, after passing through the proposed OPPF scheme, each radar member can effectively suppress the echoes from the others while keep its own amplitude and phase unchanged, which improves the target detection performance of the RSN. Theoretical analysis is done, and the simulation results are illustrated, both showing the proposed method suitable for suppressing interferences for RSN.

In UMTS cellular networks, Common Pilot Channel (CPICH) signals are broadcast by base stations for channel estimation and cell selection. The strength of CPICH signal determines cell coverage and pilot pollution to neighboring cells; pilot power allocation thus involves a tradeoff between coverage and interference. In this paper, we study the issue of pilot power management in closed-access High-Speed Downlink Packet Access (HSDPA) femtocell network. We minimize the total pilot power in a HSDPA femtocell network subject to instantaneous coverage requirements. We formulate a generalized optimization problem in a femtocell network and propose a suboptimal analytic solution to pilot power allocation implemented in each femtocell. Simulation results show that the proposed algorithm outperforms fixed pilot power schemes in terms of much lower allocated pilot power and interference to macro user equipments.

Polarization filtering has attracted a great interests for it can be used to solve problems of signal separation and interference suppression those are difficult to process in the time, frequency and spatial domains. Polarization information of both target signal and interference are needed to design the polarization filter in the conventional method, while exact estimation of the polarization information is difficult and some estimation errors also render poor performance of polarization filtering. Based on the superior merits of oblique projection in signal processing applications, a novel blind adaptive oblique projection polarization filtering (OPPF) algorithm is proposed in this paper. The pseudo-inverse of the covariance matrix obtained from the received signal and the polarization state of target signal are used to construct the vector of polarization filtering, and the estimation of interference polarization is replaced by the power estimation of AWGN. Detailed analysis and deduction are made, and simulation and numerical results show the effectiveness of the proposed algorithm, which is in-line-with the theory of polarization filtering.

A novel and robust subspace-based blind adaptive detector for synchronous CDMA systems based on oblique projection is proposed in this paper, in respect that the oblique projection can be used to extract desired signal while nulling interferences. The suggested method requires the assumption that the desired user's spreading code and noise variance of AWGN are known to the receiver rather than the assumption that all users' spreading codes are known to the receiver in conventional subspace-based detection, and this assumption can be obtained more realistic. When the noise variance of AWGN is not available, the rank-reduced form is also given. It is shown that this detector performs a perfect rejection of MAI. It is known that the subspace-based approach is robust to the near-far effect, thus the proposed scheme based on this property is immune to the near-far effect.

Recent decades, more and more people both from academic and commercial pay attention to blind signal separation (BSS). As an important part, independent component analysis (ICA) is a valid and effective solution to the problem of BSS, under the assumption conditions of ICA, a BSS algorithm using oblique projection operators is proposed in this paper. The autocorrelation matrix of mixing matrix is used to construct the objective function while the principle of maximum kurtosis is adopted to iterate and extract the component, and the mixing matrix can be obtained in a direct way. The description of the problem is demonstrated, and the detailed flow of the proposed method is listed. Simulation results show the suggested scheme is valid even when the weakest signal is less than -80dB to others.

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.

According to the low cost and complexity demands in most sensor applications, a new ultra wideband (UWB) time of arrival (TOA) estimation method based on weighted energy detection is proposed in this paper. Firstly, the energy block that contains the direct path (DP) component is detected by generalized likelihood ratio test (GLRT). Then the precise position of DP within the detected energy block is obtained by channel statistics. Key parameters of performance are studied and optimized. The results compared with traditional TOA estimation algorithms also show that this method can achieve a relative high performance under low sampling rate conditions.

Abstract In this paper, based on the analysis of the experimental data using a new post‐processing method for time‐domain channel measurements, a new double‐cluster statistical model for UWB systems with a bandwidth lower than 1 GHz in non‐line‐of‐sight (NLOS) indoor propagation environment is proposed. By using the proposed model, both the model itself and the parameter estimation of the corresponding model are simplified. By defining the polarity of a particular model parameter, the model has the flexibility to deal with both ‘soft NLOS’ and ‘hard NLOS’ indoor propagation environments. Therefore, the channel impulse responses (CIRs) generated by the proposed model ‘resemble’ the measured CIR better than the SV (Saleh‐‐Valenzuela)/IEEE 802.15.3a model not only in terms of the average values, but also in terms of the cumulative distribution functions (CDFs) of the small‐scale statistics. Copyright © 2008 John Wiley & Sons, Ltd.

By exploring the deterministic characteristics of the measurement data, a new propagation model with two deterministic clusters and stochastic arriving rays within each cluster is proposed. When considering cumulative distribution function (CDF) of the three key channel statistics, the proposed model fits the measurement data better than SV/IEEE 802.15.3a model which is seen as standard model for UWB indoor propagation channel. That means, with the additional knowledge of the specific environment geometry, the proposed model generating impulse responses "resemble" the measured channel impulse responses better than IEEE model. Moreover, the proposed model's parameters obtaining procedure is simplified by utilizing simple parameters of physical channel.

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.

The end goal of source localization problem is to find the parameters of the brain source. The source number is also the case of these parameters. In this paper we consider two system identification methods (e.g., the information criterion method and the F-test method) to determine the dipole number only with one EEG or MEG topography. Then compare the results from these two methods and find the better one. The following investigations are presented to show that the information criterion method used in this paper is an advanced approach for determining the dipole number just with one EEG or MEG topography.

The electric activity in the human cerebral cortex can be recorded with surface EEG electrodes applied to the scalp. The source of recorded EEG signals can be approximated to one or more equivalent current dipoles within the brain. It is an important problem that how to determine the optimal dipole number. In this paper, we propose a new method combining the Powell algorithm and the information criterion method for determining the optimal dipole number. With the common model, it is shown how to calculate the potential error by the Powell algorithm with the cost function, and how to use this potential error to choose the optimal dipole number by the information criterion method. The new method has the advantages of identification accuracy of dipole number and EEG data number, because in this method: (1) only an EEG topography is used in the computation, (2) the information criterion method can get the high accuracy. In order to prove our method to be efficient, precise and robust to the noise, the 10% white noise inserted to test this method. Results are presented here to show our method is an efficient approach for determining the dipole number.

In some cases, temporary or permanents occlusion of the arteries, which participates the cerebral blood flow, are needed during surgery. The exact and fast EEG monitoring of the brain functions and their dynamic changes is an important way to minimize the neurological deficits during surgery. The goal of this study is to develop the integrated EEG monitoring system with expandability. The system consists of a data acquisition, a waveform memory management, and a data analysis part. At the present stage, it can display waveform, spectrum and topography change of each frequency component on arbitrary electrode groups, and DRT (deviation ratio topography) in real time. In this stage, the system warns the operator by the alarm when the measured EEG changes significantly. This function makes possible to know the patient's condition without seeing the monitoring system.

We have constructed a multilayered neural network system that identifies brain dynamics from electroencephalogram (EEG) data by error backpropagation (BP) learning. EEG data in the rest state with closed eyes and open eyes are measured with electrodes that are placed by the international 10-20 system. The brain dynamics are embedded in the neural networks. The developed system discriminates the dynamics of the brain dynamics of the brain activities associated with open eyes from those with closed eyes.

EEG source localization is well known as an important inverse problem of electrophysiology. Usually, there is no closed-form solution for this problem and it requires iterative techniques such as the Levenberg-Marquardt algorithm. However, the method requires long computing times, huge memory and large number of electrodes to avoid local minima. To overcome these problems, a method combining back propagation neural network (BPNN) with nonlinear least square method (NLS) is therefore proposed in this study. The new method shows how to estimate an approximate solution of the inverse problem by the BPNN method, and how to select the initial value of the NLS method due to the results of BPNN to obtain the optimum solution, where the problem is solved by POWELL iterative algorithm.

Environmental stimulation, e.g., sound, light and temperature, may produce biological signal sources in the brain, which show autonomous activities without the stimuli. The properties of the source are identified by using BP neural networks. Auditory sources and a circadian source are identified as an example from measured data. This method may be very useful for analyzing brain functions and medical diagnoses.