With the rapid development of satellite-enabled Internet of Things (IoT), malicious interference has become an inevitable issue, especially in the uplink due to the exposed satellite location. To mitigate uplink interference, this paper investigates the beam nulling technique for multi-beam satellite communication systems equipped with hybrid analog and digital (HAD) arrays. Unlike fully digital arrays, the beamforming for HAD architectures consists of a high-dimensional analog part implemented exclusively with phase shifters and a low-dimensional digital part with adjustable amplitude and phase, introducing the challenge of limited degrees of freedom. Despite extensive research on HAD beamforming, there is no consensus on the efficient paradigm to implement beam nulling against extremely strong malicious interference. In this paper, we first verify that phase-only beam nulling is an achievable paradigm that fully exploits the beam utilization for multi-beam satellites while effectively mitigating interference. Additionally, we design two normalized phase-only beam nulling approaches based on majorization minimization and per-antenna optimization respectively. Simulation results substantiate our analysis concerning the beam nulling paradigm for HAD arrays, and validate the superiority of our proposed approaches which require significantly lower computational complexity. Furthermore, a prototype is developed to demonstrate the practical effectiveness of the proposed phase-only beam nulling approaches.

Multi-beam satellites (MBS) exploit multiple spot beams and frequency reuse to achieve high spectral efficiency and flexible coverage, which are key characteristics of modern satellite communication systems. Among them, beam hopping (BH) leverages phased-array antennas to steer onboard beams and employs time-division multiplexing (TDM) to dynamically schedule illumination patterns in the time domain, with electronic beam position switching enabling rapid reconfiguration. However, existing works exhibit two critical limitations: (1) they rely on global decision strategies that incur high inference complexity, which further increases with the number of beams and ground cells, making it difficult to balance performance and computational complexity; (2) in pursuing high spectral reuse, they overlook beam overlap and co-frequency interference (CFI), and thus cannot effectively mitigate interference without sacrificing spectral efficiency. To address these challenges, this paper proposes a cell clustering-based BH (CCBH) algorithm with low complexity and interference avoidance. Specifically, we propose a region-growing cell-clustering method based on user demand load balancing in which each beam independently serves a cell cluster, and full-frequency reuse across beams maximizes spectral efficiency. In addition, based on the centralized training and decentralized execution (CTDE) paradigm, we propose a cooperative multi-agent soft actor–critic (SAC) framework with parameter sharing and centralized training, called CoopMASAC-PSCT. Among them, an SAC agent is deployed for each beam; Specifically, all actor networks share the same architecture and parameters, and the execution phase remains decentralized, with each actor making decisions solely on its local observations; To prevent inter-cluster interference, the shared global reward integrates system throughput, queueing delay fairness, and an interference-penalty term; Moreover, only a single global critic network is employed, which accesses the joint observations and actions of all agents during training, thus balancing individual beam performance with influence between all beams. Simulation and comparative analyses demonstrate that CCBH delivers better performance while significantly reducing inference complexity.

Satellite communication networks operate under stringent computational constraints and are susceptible to sophisticated cyberattacks. This paper introduces a novel defense framework that decouples security optimization into ground-based analysis and onboard real-time execution. In the long-term loop, the ground segment processes historical data to estimate key statistical parameters of the task environment. Additionally, we incorporate the time-varying characteristics of satellite wireless links to account for the dynamic communication context. In the short-term loop, the satellite employs a receding horizon optimization that models dynamic task arrivals and maximizes a utility function considering detection rates and resource costs. To counter intelligent adversaries interception, we introduce a deception mechanism using Bayesian persuasion theory. By strategically manipulating the short-term action sequences in the telemetry downlink, we mislead an external attacker’s beliefs. We mathematically model the attacker’s optimal response under channel uncertainty and demonstrate that our framework significantly reduces attacker utility. The approach’s effectiveness is formally proven using Lyapunov theory.

Orthogonal time frequency space (OTFS) waveform has emerged as a promising technology for low-earth orbit satellite (LEO) communications owing to its capacity in mitigating Doppler effects. However, the OTFS modulation in LEO communications with massive multiple-input multiple-output (MIMO) techniques suffer from enormous training overhead due to the large number of satellite antennas. In this paper, we propose a hybrid model-data driven channel estimation scheme for massive MIMO-OTFS LEO satellite communications. We first derive the input-output relationship for massive MIMO-OTFS and formulate the channel estimation as a sparse signal recovery problem. Subsequently, we unroll the orthogonal approximate message passing (OAMP) algorithm into a model-driven deep unrolled network (DUN) to recover the sparse channel. On this basis, we design a side information and attention-enhanced OAMP network (SA-OAMPNet), which incorporates a data-driven self-attention mechanism into each iteration of unrolled OAMP network to further exploit the sparsity across the delay-Doppler-angle domain. Simulation results demonstrate that the proposed DUNs outperform conventional algorithms and deep learning methods. Remarkably, the proposed SA-OAMPNet reduces pilot overhead by about 30% without significant degradation in channel estimation accuracy.

With the growing demand for Earth observation, it is important to provide reliable real-time remote sensing inference services to meet the low-latency requirements. The Space Computing Power Network (Space-CPN) offers a promising solution by providing onboard computing and extensive coverage capabilities for real-time inference. This paper presents a remote sensing artificial intelligence applications deployment framework designed for Low Earth Orbit satellite constellations to achieve real-time inference performance. The framework employs the microservice architecture, decomposing monolithic inference tasks into reusable, independent modules to address high latency and resource heterogeneity. This distributed approach enables optimized microservice deployment, minimizing resource utilization while meeting quality of service and functional requirements. We introduce Robust Optimization to the deployment problem to address data uncertainty. Additionally, we model the Robust Optimization problem as a Partially Observable Markov Decision Process and propose a robust reinforcement learning algorithm to handle the semi-infinite Quality of Service constraints. Our approach yields sub-optimal solutions that minimize accuracy loss while maintaining acceptable computational costs. Simulation results demonstrate the effectiveness of our framework.

This paper proposes a novel angle-of-arrival (AoA) estimation approach for planar antenna arrays with hybrid analog and digital architectures. Although such array structure can significantly reduce system cost and hardware complexity compared to the traditional fully digital arrays, in terms of AoA estimation, the available degree-of-freedom (DoF) is also reduced since the received signals are combined in the analog domain before digital processing, introducing fundamental challenges including angle ambiguity and limited multi-source resolution capability. To overcome these limitations, we propose a two-stage coprime matching estimator that systematically eliminates angle ambiguity through strategic analog beamforming and effectively extracts the exact AoA information even in challenging multi-source scenarios. Furthermore, we derive the closed-form expression of the Cramér-Rao lower bound (CRLB) for two-dimensional AoA estimation in planar hybrid arrays, providing a theoretical performance benchmark that accounts for both azimuth and elevation angles. Numerical simulations demonstrate that our proposed approach achieves superior multi-source resolution and estimation accuracy compared to state-of-the-art methods, while asymptotically approaching the CRLB.

In non-geostationary orbit (NGSO) satellite systems, beam hopping (BH) emerges as a promising technique, enabling satellites to efficiently address dynamic and unevenly distributed ground user demands with fewer beams, thereby optimizing resource utilization. However, the orbital dynamics of NGSO satellites cause continual variations in individual satellite service cells and inter-satellite coordination, challenging the real-time performance and generalization of BH algorithms. To tackle this, we propose a cooperative BH architecture leveraging the attention mechanism and multi-agent communication. Firstly, enhanced load balancing is achieved by solving the satellite-user association problem for greater flexibility. Secondly, building upon the centralized training with decentralized execution (CTDE) paradigm, we designed an attention-based network backbone for real-time single-satellite BH to adapt to dynamically changing cell sets. Moreover, a periodic multi-agent communication framework is proposed, allowing satellites to sustain coordination in evolving environments through information exchange over long-term operations. Simulation results show that the proposed algorithm outperforms other benchmark methods. Compared to multi-layer perceptron (MLP)-based multi-agent deep reinforcement learning (MADRL) algorithms, it yields a 17% throughput improvement under equivalent average user delay. Furthermore, we demonstrate the feasibility of inter-satellite multi-agent communication via inter-satellite links (ISLs), revealing that this enhancement improves performance while occupying less than 0.01% of ISL bandwidth per message, underscoring its practicality for real-world deployment.

Providing direct-to-cell (D2C) services via non-geostationary orbit (NGSO) satellites constitutes one of the critical applications in satellite communications for non-terrestrial networks (NTN). To mitigate frequent beam handovers caused by the high mobility of NGSO satellites, an earth-fixed service mode can be employed to maintain beam staring at target areas. Addressing the capacity degradation due to delayed beam updates, we propose a beam tracking method based on dual codebook interpolation. This approach estimates beam update intervals through beam pattern analysis and angular velocity of terminals within satellite’s field of view, while maintaining the codebook. The precoder at any instant is generated through linear interpolation between active and updated codebooks. Simulation results demonstrate that the proposed method adaptively adjusts beam update intervals according to terminal’s position and channel conditions, effectively reducing beam tracking frequency while maintaining system capacity above specified thresholds.

As the adoption of satellite-enabled Internet of Things (IoT) continues to rise, its intricate multi-domain architecture becomes increasingly susceptible to cross-domain cyber threats. Attackers can exploit compromised IoT devices, inject malicious packets into data streams aggregated at the IoT gateway for satellite backhaul, and potentially endanger the satellite network during transmission by exploiting the hardware, software, and protocol vulnerabilities. Compared to single-domain defenses, cooperative defense at the IoT devices, IoT access network, and satellite transmission network provides fine-granularity defense against cross-domain intelligent attacks. However, quantifying cross-domain impacts and tilting incentive misalignment among different participants remain significant challenges, making systematic cooperative defense development a complex task. To address this, we develop a tripartite security game framework to characterize the impacts of attacks and defense methods across both the terrestrial and satellite domains. Leveraging this game model, we devise flow pricing to optimally motivate the IoT Network Operator (IoT-NO) to prevent malicious packet infiltration into the satellite domain. Subsequently, we propose efficient learning algorithms enabling both the IoT-NO to ascertain their ideal flow sampling strategies and the Satellite Service Provider (SAT-SP) to determine optimal flow pricing. The simulation results corroborate the effectiveness of the consolidated game in counteracting cross-domain cyber attacks and facilitating cooperative defense between the IoT-NO and the SAT-SP with non-aligned incentives.

Nongeostationary orbit (NGSO) constellations characterized by seamless coverage and high throughput have been envisioned as a promising solution to 6G, where beam hopping (BH) technique plays an indispensable role to achieve on-demand service. However, owing to the dynamic topology and uneven terrestrial traffic, how to effectively manage multisatellite time-frequency-spatial beam resources to match the heterogeneous demands is challenging for NGSO satellite operators. This article proposes a novel return-link BH scheduling framework for NGSO mega-constellations, where the ground resource management center periodically executes BH scheduling for users in Earth-fixed cells to improve the long-term average service satisfaction (ASS) while reducing the intersatellite handover. Specifically, we decouple the NP-hard scheduling problem into three subproblems, namely, user-satellite association problem, BH pattern design problem and resource block (RB) allocation problem. User-satellite association problem is solved by a low-complexity heuristic algorithm to reduce the handover and avoid intersatellite interference. Furthermore, a graph-based maximum weight method and an iterative scheme based on successive convex approximation are developed to obtain the BH patterns and RB allocations, respectively. Simulation results demonstrate that compared with other benchmarks, such as the best channel association and round-robin BH schemes, the proposed method can reduce the satellite load unevenness by 60% and improve the ASS by 35% while maintaining a low handover overhead.

The collaboration among multiple unmanned aerial vehicles (UAVs) can overcome the limitation of spatial sensing capabilities of individual UAVs has attracted extensive attention in the field of direction-of-arrival (DoA) estimation. Considering the constrained hardware resources in UAV swarm, maintaining coherence among all elements is extremely challenging. In this paper, we consider a collaborative UAV swarm with partly calibrated subarrays and propose a dual-driven joint-sparse pattern-coupled sparse Bayesian learning unfolding network (DD-JPC-SBLNet) for non-coherent DoA estimation. Specifically, we first propose a novel joint-sparse pattern-coupled sparse Bayesian learning (JPC-SBL) algorithm, which introduces a pattern-coupled model and a distributed noise variance estimation module, to improve the non-coherent DoA estimation accuracy. Then, the JPC-SBL algorithm is unfolded into cascaded customized neural networks, each of which learns the optimal coupling parameter configuration based on the pattern-coupled model and learns the current optimal signal hyperparameter update rule with a data-driven customized neural network. By effectively combining the advantages of model-driven and data-driven, the proposed dual-driven unfolding network exhibits superior convergence performance and speed. Simulation results demonstrate that the proposed method not only outperforms existing methods in terms of estimation accuracy and angular resolution, but also reduces computational complexity by more than 50% compared with other SBL-based algorithms.

In space–air–ground integrated networks (SAGINs), efficient task scheduling is critical to ensuring low latency and energy efficiency for computation-intensive applications. This paper proposes a hierarchical reinforcement learning (HRL)–based task scheduling framework for space–air integrated edge computing systems, consisting of low Earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs). The architecture is structured into two layers: UAVs handle task reception and local execution, while satellites assist in offloaded task processing. To enable intelligent and decentralized decision-making, we employ a multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm for UAVs and a TD3-based controller for satellite-level coordination. A shared reward mechanism is introduced to promote cross-layer optimization. Simulation results demonstrate that the proposed framework significantly reduces task execution delay and energy consumption compared to baseline schemes, achieving faster convergence. These results verify the effectiveness and practicality of the proposed method in dynamic and resource-constrained space–air environments. The proposed method reduces average total cost (ATC) by at least 13% compared to existing methods.

To mitigate co-frequency interference (CFI) with primary users (PUs), secondary systems use spectrum sensing to identify spatial and temporal spectrum holes. In satellite communications, directional antennas create sparsity in the angle domain, offering additional spectrum availability to the secondary system. Therefore, it becomes crucial for secondary systems to accurately understand the Uplink Angle of Departure (UL-AoD) or Downlink Angle of Arrival (DL-AoA) of the PU. However, in nongeostationary orbit (NGSO) systems, this information is time-varying and generally not shared with noncooperative secondary systems. To cope with this, we propose a novel UL-AoD estimation method. First, we leverage spot beams from secondary system satellites to jointly collect the PU’s signal. Then, a two-phase algorithm is designed to select a high-quality signal sample set from the collected signal samples and utilize the set to estimate the UL-AoD. Given the varying processing capabilities of secondary systems with respect to the signal of the primary system, we employ matched filtering (MF) to process the collected signal for scenarios with sufficient prior knowledge of the primary system and energy detection (ED) for scenarios with insufficient knowledge. Finally, combined with the ephemeris data of the primary system, the estimation result is then used to infer the most probable actual UL-AoD. Simulation results show the MF-based method approaches the Cramér-Rao lower bound (CRLB) and achieves error-free AoD estimation with fewer samples using ephemeris data. The ED-based method, with the assistance of the ephemeris data, attains over 85% AoD accuracy in large-scale constellations using more samples.

By decoupling substrate resources, network virtualization (NV) is a promising solution for meeting diverse demands and ensuring differentiated Quality of Service (QoS). In particular, virtual network embedding (VNE) is a critical enabling technology that enhances the flexibility and scalability of network deployment by addressing the coupling of Internet processes and services. However, in the existing deep neural networks (DNNs)-based works, the closed-box nature DNNs limits the analysis, development, and improvement of systems. For example, in the Industrial Internet of Things (IIoT), there is a conflict between decision interpretability and the opacity of DNN-based methods. In recent times, interpretable deep learning (DL) represented by deep neuro fuzzy systems (DNFSs) combined with fuzzy inference has shown promising interpretability to further exploit the hidden value in the data. Motivated by this, we propose a DNFS-based VNE algorithm that aims to provide an interpretable NV scheme. Specifically, data-driven convolutional neural networks (CNNs) are used as fuzzy implication operators to compute the embedding probabilities of candidate substrate nodes through entailment operations. And, the identified fuzzy rule patterns are cached into the weights by forward computation and gradient back-propagation (BP). Moreover, the fuzzy rule base is constructed based on Mamdani-type linguistic rules using linguistic labels. In addition, the DNFS-driven five-block structure-based policy network serves as the agent for deep reinforcement learning (DRL), which optimizes VNE decision making through interaction with the environment. Finally, the effectiveness of evaluation indicators and fuzzy rules is verified by simulation experiments.

Mobile user equipment (UE) which can connect to satellites directly attracts increasing attention. Power consumption is one of the most significant challenges for mobile UEs. In existing satellite communication systems, beam hopping technology has been utilized to improve the satellite efficiency, but the power consumption of UEs is ignored. Discontinuous reception (DRX) mechanism, wherein UEs turn off their receivers intermittently to save power, has been researched and utilized in terrestrial communication systems. However, the satellite efficiency will decrease if the DRX mechanism is applied to beam hopping satellite systems directly. In this paper, we propose a novel DRX mechanism for beam hopping satellite systems (DRX-BHS) and a new beam hopping operation strategy combining traffic-driven strategy and pre-scheduled strategy. Besides, a new pre-scheduled beam hopping strategy based on UE grouping is proposed, which can overcome the limitation of the satellite efficiency in traditional pre-scheduled beam hopping strategy. A semi-Markov model is established to evaluate the performance of the proposed DRX-BHS mechanism. The numerical results reveal the relationship between the performance and the number of UEs in each group, and show that our proposed DRX-BHS mechanism can balance the satellite efficiency and UE power saving.

Space-ground integrated networks hold great promise for providing global connectivity, particularly in remote areas where large amounts of valuable data are generated by Internet of Things (IoT) devices, but lacking terrestrial communication infrastructure. The massive data is conventionally transferred to the cloud server for centralized artificial intelligence (AI) models training, raising huge communication overhead and privacy concerns. To address this, we propose a hierarchical learning and computing framework, which leverages the low-latency characteristic of low-earth-orbit (LEO) satellites and the global coverage of geostationary-earth-orbit (GEO) satellites, to provide global aggregation services for locally trained models on ground IoT devices. Due to the time-varying nature of satellite network topology and the energy constraints of LEO satellites, efficiently aggregating the received local models from ground devices on LEO satellites is highly challenging. By leveraging the predictability of inter-satellite connectivity, modeling the space network as a directed graph, we formulate a network energy minimization problem for model aggregation, which turns out to be aDirected Steiner Tree (DST)problem. We propose a topology-aware energy-efficient routing (TAEER) algorithm to solve theDSTproblem by finding a minimum spanning arborescence on a substitute directed graph. Extensive simulations under real-world space-ground integrated network settings demonstrate that the proposed TAEER algorithm significantly reduces energy consumption and outperforms benchmarks.

Flexible spot beams have been widely utilized in satellite communication systems to project high power and reuse frequency spectrum. To improve the system efficiency, beams share the same frequency channel and are only steered to the user terminals (UTs) which need to be served. Satellites with numerous beams can serve large numbers of UTs simultaneously, which may lead to severe inter-beam interference because of the non-uniform distributed UTs. Therefore, it is significant to study the effect of beam configuration parameters on the coverage performance to design an efficient satellite communication system. In this paper, we derive analytical expressions for the coverage probability and the average data rate of a flexible beam using stochastic geometry tools. We model the locations of beam centers as a nonhomogeneous spherical binomial point process whose intensity depends on the density of UTs. Meanwhile, the average sum rate of a satellite is derived using two different models of the locations of satellites. From the numerical results, the average sum rate of a satellite first increases but then levels off as the number of beams increases, which may provide insightful design guidelines on satellite communication systems.

In nongeostationary orbit (NGSO) satellite networks, multicast transmission is increasingly important to improve the system performance. Making full use of the beams to cover numerous nonuniform distributed users is a new challenge. User subgrouping techniques are utilized to optimize the coverage of beams and maximize the data rate. However, due to the high-speed movement of NGSO satellites, frequent handovers occur and the user subgroups should be updated frequently, aggravating network control overheads. This article proposes an Earth-fixed multicast user subgrouping scheme for NGSO satellite constellations to reduce network control overheads and improve network throughput (NT). In our proposed scheme, users are partitioned into multiple subgroups according to their geographical locations, and these subgroups remain the same while satellites keep moving along the orbit. First, we formulate a joint user subgrouping and beam optimization problem and decompose it into three subproblems by the approximation of models. An iterative greedy Earth-fixed user subgrouping algorithm is presented to obtain the user subgroups. Each user is chosen into a user subgroup which can maximize the NT, and then the position of each user subgroup is updated according to the positions of users in this subgroup. Next, the subgroup-satellite association relationship is optimized according to the maximum elevation angle criterion. Finally, a simple algorithm is proposed to optimize the direction and beamwidth of the beam serving each subgroup in each satellite. Simulation results demonstrate that our proposed scheme can reduce the update frequency significantly compared with the existing satellite-fixed user subgrouping scheme, and the NT achieved by our proposed scheme is improved by 1~3 times than conventional beam coverage scheme.

The emergence of new applications brings multi-class traffic with diverse quality of service (QoS) requirements to wide area networks (WANs), motivating research in traffic engineering (TE). In recent years, novel centralized and hierarchical TE schemes have used heuristic or machine learning techniques to orchestrate resources in closed systems such as datacenter networks. However, these schemes suffer from long delivery delays and high control overhead when applied to general WANs. To provide low-delay services, this paper proposes an asynchronous multi-class traffic management (AMTM) scheme. We first establish an asynchronous TE paradigm in which distributed nodes locally perform low-complexity and low-delay traffic control based on link prices, and the TE server updates link prices to eliminate decision conflicts between edge nodes. By modeling the asynchronous TE paradigm as a control system with non-negligible control loop delay, we find that the traditional pricing strategy cannot simultaneously achieve a low packet loss rate and a low flow delivery delay. To address this issue, we propose a new pricing strategy based on the observations of virtual queues in intermediate nodes. We also present a system design and related algorithms that utilize a dynamic step size mechanism of link price update. Simulation results show that AMTM can effectively reduce the end-to-end flow delivery delay.

Non-geostationary orbit (NGSO) constellations enabled by beam hopping (BH) technology are characterized by wide coverage and high spectrum efficiency. However, how to efficiently schedule multi-satellite beam resources to satisfy the heterogeneous and uneven terrestrial traffic demands remains a huge challenge for satellite operators. This paper proposes a satellite-terrestrial coordinated multi-satellite BH scheduling framework, where the complex multi-satellite BH problem is formulated into a long-term and a short-term subproblems. The long-term subproblem is cell-satellite association problem, which is solved by a low-complexity iterative algorithm executed in network operation control center (NOCC) to minimize the traffic load gap among satellites while considering the interference avoidance. The short-term subproblem is multi-satellite traffic-driven BH problem and we propose a multi-agent deep reinforcement learning (MADRL) architecture where each satellite can cooperatively make real-time BH decisions using the well-trained model by QMIX algorithm to adapt to time-varying and heterogeneous traffic. Simulation results demonstrate that the traffic load gap and network delay have been reduced by 70% and 50% respectively compared with non-load-balancing scheme. Besides, the proposed algorithm outperforms other benchmarks in terms of the network throughput under various traffic load cases and the average network delay is kept within 4 ms. Furthermore, the proposed QMIX-BH can be applied to real-time scheduling since the execution time is less than 1 ms.

Satellite communications play an indispensable role in serving aviation user. However, since the number of satellite beams is much less than the number of users, aviation users need to share beams by time division multiplexing when the number of users is large, which inevitably leads to the situation that the satellite frequently requires the user to report location information for beam scheduling so as to prevent users from deviating from the coverage of satellite beams. Frequent user location updates result in high interaction overhead between the satellite and aviation users. In such a case, how to free users from frequent location updates and realize low interaction overhead beam scheduling are the key in satellite aviation communications. To solve such a problem, we first propose a dynamic spatio-temporal approximation (DSTA) model to provide large spatial-temporal scale mobility tolerance for users within the time frame permitted by the satellite system, then a novel beam collaboration scheduling algorithm based on this new model is further proposed, aiming to realize low-overhead satellite multi-beam scheduling. Simulation results show that the proposed method with moderate complexity reduces interaction frequency and interaction overhead between the satellite and users by at least 56.9% and 47.1% compared with benchmark approaches respectively, which demonstrates the superiority of our proposed algorithm.

With the explosive growth in demands for mobile video services, the focus of cellular networks is evolving from the core network to the edge network to facilitate resource-intensive services and mitigate backhaul burdens. However, the overlapping in coverage and the user similarity in preferences lead to substantial cache redundancy. In addition, the mobility of users poses a significant challenge to smart devices for device-to-device (D2D) communications, which affects the content richness and hit ratio of the edge cache. In this paper, an adaptive cache partitioning and placement (ACPP) strategy is proposed for mobile edge computing (MEC) networks to minimize the average cost of content access. A practical two-layer collaborative caching model is presented, which comprises 5G base station (gNB) clusters and D2D caching. Besides, a public and private cache partitioning method is designed for gNBs to improve the content richness of local cache, and a static and dynamic cache partitioning method is developed for user devices to address the varying mobility patterns. Simulation results demonstrate the effectiveness of the proposed ACPP strategy in delivering content at a lower average cost, and achieve a better hit ratio with a relatively high energy consumption at user ends.

We consider the uplink connectivity for massive machine-type communications (mMTC) assisted by intelligent reconfigurable surfaces (IRSs), where device activity detection (DAD) and channel estimation (CE) are challenging due to limited pilot sequences. Moreover, differentiation among device types causes channels to deviate from the assumed characteristics, leading to performance degradation of conventional compressive sensing (CS) algorithms. To this end, two innovative networks driven by the hybrid of model and data are proposed exploiting the iterative frameworks and deep neural networks. We first present a hybrid driven iterative shrinkage thresholding algorithm, dubbed HISTA-Net, where a dual attention network (DAN) is embedded within the iterations to adaptively suppress iterative noise and enhance sparsity properties. Subsequently, we encapsulate data driven network and the intrinsic channel matrix knowledge, and derive a hybrid driven approximate message passing network (HAMP-Net) to further improve the sparse recovery performance. Our experiments demonstrate that the proposed networks outperform existing CS methodologies and deep learning strategies in accuracy, convergence, and generalization ability. Remarkably, the proposed HAMP-Net reduces pilot overhead by 30%, and achieves an NMSE gain of 3 dB for signal-to-noise ratios exceeding 15 dB.

Due to the non-uniform distribution of the ground traffic demand and the high mobility of non-geostationary orbit (NGSO) satellites, how to make full use of the limited beam resources to serve users flexibly and efficiently is a brand-new challenge for NGSO communication systems. In order to achieve efficient spectrum utilization, the combination of full frequency multiplexing and beam hopping is a major trend in future satellite communication systems. However, conventional beam hopping methods are mainly based on geostationary satellites, which do not take into account the interference between satellites. This paper proposes a multi-satellite beam hopping algorithm based on load balancing and interference avoidance, which takes advantage of the multiple coverage features in the NGSO constellation and avoids intra-satellite interference and inter-satellite interference by designing beam-hopping patterns with spatial isolation characteristics. In particular, we decompose the multi-satellite beam hopping problem into three sub-problems, which are the multi-satellite load balancing problem, the single-satellite beam hopping pattern design problem, and the multi-satellite interference avoidance problem. Simulation results demonstrate that the proposed method reduces the load gap among satellites by about 72.5% and the average traffic satisfaction rate can reach 81.4%. Besides, our method has the lowest unmet capacity compared with other benchmarks, achieving better offered-requested data match.

Satellite communications play an important role in future Internet of Things (IoT) networks, and megasatellite constellations can further provide global coverage and high-quality services for IoT communications. In the megaconstellation, large-scale satellites are launched to enhance the capacity. However, the dense distribution of satellites brings intraconstellation interference, limiting the performance. In order to evaluate the restriction of interference caused by system parameters, such as the scale of constellation or the frequency reuse factor, we investigate uplink intraconstellation interference and performance of the megasatellite constellation. First, a multibeam polar constellation with uplink spatial frequency reuse is assumed. Then, the interference model is constructed considering the antenna gain of interfering user terminals and multibeam satellites, where the details of the satellite-fixed frequency reuse scheme and coordinates of co-frequency cells are provided. To evaluate the performance, expressions of outage probability, ergodic capacity, and sum ergodic capacity are driven. The analytical results disclose the impact of system design on the performance, and the accuracy of analysis results is obtained through extensive simulation evaluation. The results show that sum ergodic capacity achieves highest in the case of full frequency reuse for the frequency-limited constellation system, and it gets a linear growth at first but then keeps flat with a trend of fluctuating downward as the scale increases; therefore, the impact of the scale should be considered when constructing megaconstellations.

Non-orthogonal multiple access (NOMA) is a promising technology for next generation multiple access (NGMA). However, traditional NOMA detection methods cannot cope with challenges of NGMA in satellite high-mobility communications. On the one hand, due to the high mobility and heterogeneity of terminals in terms of the velocity and acceleration, time-varying Doppler shifts caused by high-mobility terminals are relatively higher and different for each user, which degrades the demodulation performance seriously and makes multi-user interference cancellation become the bottleneck. On the other hand, owning to wide distribution of high-mobility terminals, the arriving time of users’ signals is different at the receiver, which further incurs more difficulties for NOMA detection. To solve such a problem in satellite high-mobility communications, we propose a novel multi-user detection method based on the new three-dimensional (3D) factor graph for high-mobility environments, named the turbo iterative detection (TID) algorithm. Specially, the proposed algorithm consists of the interference cancellation loop, the Doppler elimination loop and the decoding loop. By means of message passing along edges in the proposed 3D factor graph, these three iterative loops can interact with each other to effectively eliminate time-varying Doppler shifts of heterogeneous high-mobility terminals and interference among multiple users. Simulation results show that the proposed algorithm improves the bit error ratio (BER) performance more than 0.9 dB with less computational complexity compared with traditional algorithms, which demonstrates the superiority of this algorithm in terms of the BER performance and computational complexity.

The integrated satellite-terrestrial network is promising to provide global broadband communication service. However, the long propagation delay of satellite-terrestrial links will lead to high communication delay when users access the Internet via satellites. In this paper, we investigate the cooperative multilayer edge caching in the integrated satellite-terrestrial network to reduce the communication delay, in which the base station cache, the satellite cache, and the gateway cache cooperatively provide content service for ground users. We first propose the three-layer cooperative caching model of the network, based on which we analyze the content retrieving process and derive the cache hit probability for different caching locations. Considering limited cache sizes, we formulate the content placement problem to minimize the average content retrieving delay of users. Then, two caching strategies, the non-cooperative caching strategy and the cooperative caching strategy, are proposed with exhaustive theoretical analysis. By introducing the concept of delay reduction gains, the optimal caching strategies are obtained based on the proposed iterative algorithms. Finally, numerical results are presented to demonstrate the performance of the proposed cooperative caching architecture and the caching strategies.

Recently, the ultra-dense low Earth orbit (LEO) satellite constellation over high-frequency band has served as a potential solution for high-capacity backhaul data services. In this paper, we consider an ultra-dense LEO-based terrestrial-satellite network where terrestrial users can access the network through the LEO-assisted backhaul. We aim to minimize the number of satellites in the constellation while satisfying the backhaul requirement of each user terminal (UT). We first derive the average total backhaul capacity of each UT, based on which a three-dimensional constellation optimization algorithm is proposed to minimize the number of satellites in the constellation. Simulation results verify our theoretical capacity analysis and show that for any given coverage ratio requirement, the corresponding optimized LEO satellite constellation can be obtained by the proposed three-dimensional constellation optimization algorithm. Given the same number of deployed LEO satellites, the average coverage ratio of the proposed LEO satellite constellation is at least 10 percentage points higher than that of Telesat constellation.

The communication satellite equipped with phased array antennas can produce high power density by narrow spotbeams and thus can achieve high data rates. A large number of narrow spotbeams are required when the satellite provides multicast service for a group of distributed ground users, which limits the system capacity. In this paper, we propose a new satellite multicast scheme as taking advantage of beams with flexible direction and beamwidth generated by phased array antennas. Users are partitioned into multiple groups and an appropriate beam is allocated to each group, where all users in the same group are located in the mainlobe of the beam. It is formulated to jointly optimize user grouping and beamwidth for maximizing the average data rate of satellite and guaranteeing the quality-of-service (QoS) for users. Moreover, we develop an iterative algorithm to solve the problem with low complexity. Finally, simulation results show that our proposed method is superior to other schemes in terms of average data rate.

Satellite communication systems usually work in low signal-to-noise ratio (SNR) circumstances owning to the limited satellites' link budgets. Large doppler frequency offset in low-SNR satellite communication systems severely influences the performance of frequency synchronization, while the frequency offset correction still remains an open problem under low SNR condition especially for short burst transmission. To solve such a problem in satellite communications, we present a novel method named GP-MASO-MLE, which comprises a coarse correction based on the objective function with Gaussian Process (GP) search and a fine correction based on Maximum Likelihood Estimation (MLE) jointly with turbo iterations. Specifically, the proposed method is appropriate for non-data-aided frequency offset correction in satellite communication systems. Simulation results show that the proposed algorithm can approach to the bit error rate (BER) performance bound of ideal frequency offset correction within 0.1 dB, moreover, the proposed algorithm has lower computational complexity compared with traditional multi-step search algorithms.

This paper proposes a distributed power control scheme based on the constrained model predictive control (MPC) for the underlay cognitive satellite terrestrial networks (CSTNs), where the primary satellite communication network coexists with the secondary terrestrial mobile network. We model this power control problem as a closed-loop dynamic control system with the inner loop and outer loop. On the basis of combining target power control (TPC) algorithm in the inner loop and tracking of flexible target signal to interference plus noise ratio (SINR) in the outer loop, we develop a corresponding state space expression of the problem where the fluctuation of each channel power gain is formulated as the exogenous disturbance input so that we do not need the accurate instantaneous channel state information (CSI). Then we design a SINR regulator in the outer loop, which is a constrained model predicted controller with rolling optimal operation subject to the interference temperature constraint obtained by calculating a linear matrix inequality. Finally, we obtain our constrained model predictive power control algorithm. In contrast to the previous static power control schemes based on the optimization theory that highly depend on the known instantaneous CSI and large signalling exchanges, the proposed scheme only needs locally measured information and outdate feedbacks. The performance of the proposed algorithm is shown to be effective through computer simulations.

The development of satellite networks is drawing much more attention in recent years due to the wide coverage ability. Composed of geosynchronous orbit (GEO), medium earth orbit (MEO), and low earth orbit (LEO) satellites, the satellite network is a three-layer heterogenous network of high complexity, for which comprehensive theoretical analysis is still missing. In this paper, we investigate the capacity performance of the three-layer heterogenous satellite network. We first construct the network model and the capacity model of the network with detailed design of the network parameters. Then, taking time structure into account, we propose a time structure based augmenting path searching method, which can significantly reduce the computing complexity. Finally, based on the model and method proposed, we analyze the capacity performance of the three-layer heterogenous satellite network with numerical results.

Providing reliable wireless communications for high-mobility terminals remains one of the main challenges faced by satellite high-mobility communication systems. Because the high Doppler frequency offset and Doppler rate caused by the high-mobility nature of the mobile terminal, and low signal-tonoise ratio (SNR) circumstances caused by limited satellites' link budgets degrade the system performance seriously. To solve such a problem in high-mobility satellite communications, we propose a novel modulation method named second order time-frequency (SOTF) modulation, which consists of index modulation (IM) and liner frequency modulation (LFM). Simulation results show that the bit error ratio (BER) performance of the proposed modulation method with different parameters is better than traditional modulation methods. Specially, the BER performance loss is about 0. 05dB in high-mobility communication scenarios, which demonstrates that the proposed modulation method is insensitive to Doppler frequency offset and Doppler rate. Overall, the proposed method can be well applied in high-mobility satellite communication systems for its good performance with moderate complexity.

Satellite communication systems are able to provide diverse services for ground terminals in ubiquitous global coverage, which play a vital role in high-mobility communication environments. Existing technologies developed primarily for satellite communications cannot be readily applied to satellite high-mobility communication scenarios, since high Doppler frequency offset caused by the fast movement of wireless terminals, and low signal-to-noise ratio (SNR) circumstances caused by limited link budgets in satellites incur more difficulty of the synchronization, especially for short burst transmission. To solve such a problem in satellite high-mobility communications, we propose a novel method named GP-MASO-MLE, which consists of a coarse estimation algorithm based on the Gaussian process (GP) model and Newton-Raphson method, and a fine correction algorithm based on the improved maximum likelihood estimation (MLE) jointly with turbo decoding iterations. Simulation results show that the proposed algorithm can approach to the bit error rate (BER) performance bound of ideal Doppler frequency offset correction within 0.1 dB, which can be well applied in code-aided (CA) satellite high-mobility communication systems for its good performance. In addition, the computational complexity of the proposed algorithm is lower than other traditional turbo synchronization algorithms.

Random access is a key technology in satellite communication, as a large number of machine- type communication (MTC) terminals accessing the satellite makes it difficult to guarantee the access quality. Irregular repetition slotted ALOHA (IRSA) is one random access protocol relying on transmitting irregular number of replicas in multiple time slots, achieving a peak throughput at 0.8 in practical implementations. However, the probability of sending a certain number of replicas stays the same when given degree distribution, without considering the effects of different loads, which means there are extra useless packets sent and brings power waste in IRSA. Therefore, enhanced irregular repetition slotted ALOHA (EIRSA) based on tracking degree distribution control (TDDC) algorithm is proposed in this paper with adaptive degree distribution adjustment scheme to reduce the number of replicas while maintaining the same access performance with adaptation. Simulation results show that proposed protocol can achieve higher performance at the same power level and it is adaptive to load change.

The multibeam satellite system (MBSS) has great potential for mobile communications in 5G era due to its superiority in terms of extensive coverage, large capacity and real-time service. In order to integrate the resource allocation in multiple dimensions and maximize the system capacity of the MBSS, the scenario of a sparse network with dense users is selected to investigate the resource allocation method in time dimension, frequency dimension, space dimension and power dimension. We first propose a multilevel clustering algorithm and a cross-cluster grouping algorithm to realize the beam scheduling, by which the interference is reduced in time dimension and space dimension. Based on the beam scheduling scheme, we further explore the relationship between the system capacity and the resource allocation in frequency dimension and power dimension, where a joint power allocation and subchannel selection algorithm is proposed to optimize the spectral efficiency. Our simulation results show that the proposed multiple-dimension resource allocation method is superior to the existing methods in system capacity and convergence, which is not only applicable for the resource allocation in the MBSS but also provides an efficient approach to solve the coupling resource allocation problem.

Recently, the random access (RA) protocols are widely used in modern wireless communication systems. However, message collisions in RA usually cause low channel efficiency and high time delay. In this paper, a novel RA scheme named multi-power contention resolution diversity slotted aloha (MP-CRDSA) is proposed. In MP-CRDSA, the RA packets are transmitted in random power levels and multiple slots. In the receiver, the collisions are solved by iterative successive interference cancellation (SIC). Theoretical analysis and numerical results show that the proposed RA scheme performs high throughput, low packet loss ratio and low time delay, outperforming the ordinary RA scheme.

Grant-free non-orthogonal multiple access (NOMA) is highly expected to support massive connectivity and reduce the transmission latency for future wireless communications. In this paper, we present a joint active user and data detection with no priori knowledge of the active users relying on expectation propagation (EP) and Gaussian approximation (GA) algorithm. To detect the user activity, a structured spike and slab prior is introduced to present the sparsity of transmission signal. Further, the parameters unknown are learned via expectation maximization (EM), which improves the performance of active user detection. Specifically, the active user detection problem in NOMA is firstly formulated under EM framework by parameter learning, and then the transmission data can be detected accurately by message-passing algorithms (MPA). Simulation experiments demonstrate the superiority of our proposed EP-GA-EM algorithm both in the performance of reconstruction and the bit error rate (BER).

In the scenario of frequency coexistence between the GEO (geostationary) and NGEO (non-geostationary) satellite networks, the NGEO system should not incur harmful interference to the GEO system according to the policy of the Radio Regulations. Therefore, spectrum sensing as a promising solution is applied widely in this scenario. With the increasing number of NGEO satellites in the space, one NGEO system could be affected by other NGEO systems while sensing the signal from the GEO system. Given these preconditions, the cognitive radio (CR) scenario considered in this paper is that: the GEO system is regarded as the primary user, one NGEO system is regarded as the secondary user, while another NGEO system is regarded as the interfering user. Meanwhile, all the satellite systems are supposed to operate with more than one discrete transmit power levels which is practical and fits the concept of adaptive power control. In our context, we propose a spectrum strategy using hypothesis testing as well as maximum a posterior (MAP) to differentiate the GEO signal from the interfering NGEO and noise, and then identify the specific power level utilized by the GEO system. Moreover, we derive the closed-form expressions for threshold of verifying the status of the GEO, and for decision regions to determine its power level. Finally, extensive simulations are provided to verify the proposed studies.

In the tracking and data relay satellite system, users compete for the limited inter-satellite link antenna beam (ILAB) resource by excessive resource requests. This can lead to serious user request conflicts and considerably degrade the QoS (quality of service) of the system. Since those selfish users have no incentive to cooperate in a one-shot resource request, in this paper we construct a cooperation mechanism to maximize users' payoffs and meanwhile to reduce the conflict relying on a repeated game framework. Moreover, an efficient punishment and forgiveness strategy is proposed to prevent any user from breaking the cooperation. Simulation results confirm that the proposed scheme can significantly improve the system's operation performance. Compared with the payoffs under the Nash equilibrium in the one-shot game which is the traditional scheme, our repeated ILAB resource allocation game can acquire 1.36 to 3.46 times gain in respect to 2 to 10 users, respectively.

In this paper, we propose an architecture of cloud based integrated terrestrial-satellite networks, in which satellite and terrestrial networks that belong to the same operator cooperatively provide seamless coverage for mobile users. Meanwhile, a resource pool at the cloud acts as the integrated resource management and control center of the entire network. Then, based on the delay constraint of users, we formulate the resource allocation problem for the operator to minimize the energy consumption. By decomposing the optimization problem into two subproblems and utilizing the theory of multidimensional knapsack problem, we eventually obtain the optimal resource allocation strategies for the operator. Furthermore, numerical results are provided to evaluate the performance of the proposed strategies.

In remote mobile social networks, caching is a very promising technique to alleviate the burden of space cloudlet (e.g., cache-enabled satellite user terminal) and to improve subscribers' user experience in terms of content retrieval latency. In this paper, we proposed a social relationship aware caching placement approach for remote mobile social networks. Social relationships between users are used to designate a set of helpers with caching capability, which can cache popular files proactively when the cloudlet is idle. Furthermore, the caching placement problem is formulated as an optimization problem to minimize the average content retrieval latency. Then, we reformulate the problem into a monotone submodular optimization problem with a partition matroid constraint; moreover, an efficient greedy algorithm with 1-[1/e] approximation ratio is proposed to solve it. Simulation results show that the proposed social aware greedy caching placement approach significantly outperforms the traditional approaches in terms of content retrieval latency and hit ratio.

In this paper, we investigate the downlink cooperative multigroup multicast transmission in the integrated terrestrial-satellite network, in which base stations (BSs) and the satellite provide the multicast service for ground users in a cooperative manner while reusing the entire bandwidth. For both terrestrial BSs and the satellite, multiantennas are equipped and beamforming techniques are utilized for improving the system performance. Based on the architecture, we formulate a weighted max-min fair (MMF) beamforming design problem to jointly optimize the beamforming vectors of BSs and the satellite, which is solved based on the relation between the quality of service problem and the MMF problem. When it comes to the large scale case, where large numbers of BSs are distributed within the coverage of the satellite, we propose a time division cooperative multigroup multicast scheme consisting of two phases for the BSs and the satellite. Then, an iterative algorithm is proposed to solve the weighted MMF problem in the time division case. Finally, numerical results are provided to evaluate the cooperative multicast schemes as well as the proposed algorithms.

In millimeter wave (mm-wave) massive multiple-input multiple-output (MIMO) systems, acquiring accurate channel state information is essential for efficient beamforming (BF) and multiuser interference cancellation, which is a challenging task since a low signal-to-noise ratio is encountered before BF in large antenna arrays. The mm-wave channel exhibits a 3-D clustered structure in the virtual angle of arrival (AOA), angle of departure (AOD), and delay domain that is imposed by the effect of power leakage, angular spread, and cluster duration. We extend the approximate message passing (AMP) with a nearest neighbor pattern learning algorithm for improving the attainable channel estimation performance, which adaptively learns and exploits the clustered structure in the 3-D virtual AOA-AOD-delay domain. The proposed method is capable of approaching the performance bound described by the state evolution based on vector AMP framework, and our simulation results verify its superiority in mm-wave systems associated with a broad bandwidth.

With the rapid increase of mission demands for the tracking and data relay satellite system (TDRSS), the technical issue of high-efficient scheduling has attracted more attention in recent years. Most of previous scheduling algorithms are designed based on the assumption of missions' uniform time- space distribution, which have showed unsatisfactory performance in real scenarios with non-uniform distribution of mission demands. In this paper, we first transform the TDRSS scheduling problem into the heterogeneous inter- satellite link antenna (ILA) pointing route problem. Then, a two-stage heuristic algorithm with hierarchical scheduling strategies is proposed with the consideration of non-uniform time-space distribution of missions. Finally, we employ the TDRSS dataset to verify our proposed algorithm by comparing with the improved Rojanasoonthon's greedy randomized adaptive search procedure (GRASP) algorithm. Experimental results show that our proposed two-stage heuristic algorithm can schedule 2.41%, 4.43% and 6.02% more missions and consume 11.84%, 10.38% and 9.54% less setup times of SA antennas than the improved GRASP algorithm for the mission scale of 200, 400 and 600, respectively. In addition, setup times of SA antennas in those instances with non-uniform distribution in space can be more efficiently compressed by our proposed two-stage heuristic algorithm.

Terrestrial-satellite networks (TSNs) play a significant role in achieving 100\% geographic coverage in the next generation of wireless networks. In TSNs, multimedia transmission is an important application scenario, where efficient content delivery solutions are required for effectively alleviating network congestion. In this paper, we study multicast beamforming problems in TSNs by reusing the entire bandwidth for providing efficient solutions for content delivery. In order to mitigate the interference imposed by multicast transmission of the satellite, we formulate the cooperative multicast beamforming problems for the TSN under the quality of service (QoS) constraints. Then, according to different considerations, the semidefinite relaxation (SDR) method is applied for the beamforming design of the satellite, whilst the recently developed feasible point pursuit successive convex approximation (FPP-SCA) approach is adopted for tackling the beamforming design problem of the base station. The system performance is studied by simulation results. Our investigations show that our TSN is capable of attaining a sufficient high rate for supporting multimedia transmission.

In data relay satellite (DRS) systems, the performance of tasks scheduling is influenced by the variation of task and resources, which degrades the processing capacity of relay satellites. Considering this problem, we investigate the dynamic scheduling in the application of DRS. To achieve the efficient resource utilization and reliable data transfer, the strategies of task preemptive switching and decomposition are designed. Based on the initial scheme, we construct a dynamic scheduling model with multiple objectives, including maximizing the total weight of scheduled tasks, minimizing the change of scheduling scheme and minimizing the number of decomposed subtasks. Meanwhile, a preemptive dynamic scheduling algorithm (PDSA) is designed to solve the proposed model. Explicitly, our simulation results show that PDSA is superior to the whole rescheduling algorithm (WRA) in quantities of completed tasks, rescheduling rate of scheme and processing time, which can efficiently improve the performance of dynamic scheduling in DRS systems.

This paper investigates a multimedia multicast beamforming scheme in the integrated terrestrial-satellite networks, where base stations (BSs) and the satellite work cooperatively provide ubiquitous services for ground users. Due to the contents diversity of multimedia services, users that request the same contents can be served as a group using multicasting. By utilizing multiple transmission antennas, multicast beamforming is performed among groups while reusing the entire bandwidth, which, however, can inevitably cause the co-channel interference among users. Taking both system performance and user fairness into account, we optimize the total system capacity performance under the satellite capacity constraint and derive the optimal power allocation schemes. Numerical results are presented in the end to evaluate the effectiveness of the proposed scheme compared with the greedy and suboptimal searching strategies.

The increasing traffic demand in both ground and satellite communication systems will lead to increasing spectrum demand. Spectrum sharing would become a challenging issue in future between terrestrial and satellite systems with frequency reusing, as well as the interference management. Upon this, we propose the concept of the Cloud Based Integrated Terrestrial-Satellite Network (CTSN), where both base stations of the cellular networks and the satellite are connected to a cloud central unit and the signal processing procedures are executed centrally at the cloud. By utilizing the channel state information (CSI), the interference from the mixed signal can be mitigated. When it comes to the case of imperfect CSI, we propose a resource allocation scheme in respect to subchannel and power to maximize the total capacity of the terrestrial system while limiting the total interference to the satellite. The optimization problem is solved by means of the dual decomposition method. Simulation results are provided to evaluate the effectiveness of the algorithm.

In the fifth-generation (5G) networks, millimeter- wave (mmWave) bands have drawn great attention for the large amount of possible bandwidth. Meanwhile, satellite communications have also shown great interest in the mmWave bands, especially the Ka band. Under such a circumstance, the spectrum sharing between the satellite and terrestrial communication systems becomes prominent. In this paper, we analyze the interference caused by terrestrial cellular systems to the geostationary (GEO) system in two transmission modes. In order to protect the GEO system, we construct a protection radius where the terrestrial transmitters must locate outside. Simulations are conducted to verify the effectiveness of the proposed scheme.

In a cooperative network, the user equipment (UE) shares information for cooperatively achieving a common goal. However, owing to the concerns of privacy or cost, UEs may be reluctant to share genuine information, which raises the information credibility problem addressed. Diverse techniques have been proposed for enhancing the information credibility in various scenarios. However, there is a paucity of information on modeling the UEs' decision making behavior, namely as to whether they are willing/able to share genuine information, even though this directly affects the information credibility across the network. Hence, we propose a game theoretic framework for the associated information credibility modeling by taking into account the users' information sharing strategies and utilities. This framework is investigated under both a homogeneous model and a heterogeneous model. The spontaneous information credibility equilibria of both models are derived and analyzed, including the closed-form analysis of the homogeneous model based on a sophisticated evolutionary game model and on the reinforcement learning-based analysis of the heterogeneous model. Moreover, a credit mechanism is designed for encouraging the UEs to share genuine information. Experimental results relying on real-world data traces support our utility function formulation, while our simulation results verify the theoretical analysis and show that all the UEs are encouraged by the proposed algorithm to share genuine information with a probability of one, when a credit mechanism is invoked. The proposed modeling techniques may be applied in diverse cooperative networks, including classic wireless networks, vehicular networks, as well as social networks.

In this paper, we investigate the downlink transmission of a non-orthogonal multiple access (NOMA)-based integrated terrestrial-satellite network, in which the NOMA-based terrestrial networks and the satellite cooperatively provide coverage for ground users while reusing the entire bandwidth. For both terrestrial networks and the satellite network, multi-antennas are equipped and beamforming techniques are utilized to serve multiple users simultaneously. A channel quality-based scheme is proposed to select users for the satellite, and we then formulate the terrestrial user pairing as a max-min problem to maximize the minimum channel correlation between users in one NOMA group. Since the terrestrial networks and the satellite network will cause interference to each other, we first investigate the capacity performance of the terrestrial networks and the satellite networks separately, which can be decomposed into the designing of beamforming vectors and the power allocation schemes. Then, a joint iteration algorithm is proposed to maximize the total system capacity, where we introduce the interference temperature limit for the satellite since the satellite can cause interference to all base station users. Finally, numerical results are provided to evaluate the user paring scheme as well as the total system performance, in comparison with some other proposed algorithms and existing algorithms.

In this paper, we address the design of message-passing receiver for massive multiple-input multiple-output orthogonal frequency division multiplex (MIMO-OFDM) systems. With the aid of the central limit argument and Taylor-series approximation, a computationally efficient receiver that performs joint channel estimation and decoding is devised by the framework of expectation propagation. In particular, the local belief defined at the channel transition function is expanded up to the second order with Wirtinger calculus, to transform the messages sent by the channel transition function to a tractable form. As a result, the channel impulse response between each pair of antennas is estimated by Gaussian message passing. In addition, a variational expectation-maximization-based method is derived to learn the channel power-delay profiles. The proposed scheme is assessed in 3D massive MIMO-OFDM systems with spatially correlated channels, and the empirical results corroborate its superiority in terms of performance and complexity.

Multibeam satellites allow for significant boost in capacity by reusing the available spectrum and regularized zero-forcing (RZF) precoding promises to be one efficient technique to manage the inter-beam interference in the forward link. In this paper, the RZF precoding problem is first cast within an equivalent Bayesian inference framework. Then, we propose two kinds of message passing based precoding approaches, namely variational message passing based RZF (VMP-RZF) and approximate message passing based RZF (AMP-RZF). Compared with evaluating RZF directly, our proposed methods circumvent the matrix inversion operation and thus enjoy a much lower complexity. Simulation results demonstrate that the normalized mean square errors of both VMP-RZF and AMP-RZF with respect to the true RZF are negligible while AMP-RZF converges faster than VMP-RZF.

Iterative decoding of orthogonal convolutional code is widely used for its excellent performance. However, the iterations lead to high complexity and long decoding delay, which is unsuitable for low- rate voice service in mobile satellite communications with on-board processing. In this paper, a novel scheme of orthogonal convolutional coding as well as an associated non-iterative joint decoding algorithm based on factor graph are proposed. Due to its non-iterative nature, this novel scheme has low decoding complexity and short latency, which indicates its potential on-board use in the low-rate satellite voice service, or other kinds of services with a high bit error rate (BER) tolerance and a tight delay requirement. Simulation results demonstrate the efficacy of the proposed novel coding scheme and non-iterative decoding algorithm in both AWGN and Rician channels.

In frequency-hopping systems, the multiple-access interference (MAI) occurs when more than one user appear on the same frequency at the same time. In this paper, we address the multi-user detection (MUD) problem for slow frequencyhopping (SFH) system with binary phase shift keying (BPSK). We first built a MAI model taking the hopping patterns and the phase offset into account, whereby the MAI is considered as independent yet unnecessarily identically distributed random variable. Under the proposed model, we further simplify the MAI by exploiting the Lyapunov central limit theorem and propose an iterative per-chip multiuser detection (PC-MUD) algorithm. By simulations, it's verified that the performance of proposed algorithm is near to the performance of single user bound in additive white Gaussian noise environment.

An alternative derivation for the well-known approximate message passing (AMP) algorithm proposed by Donoho is presented in this letter. Compared with the original derivation, which exploits central limit theorem and Taylor expansion to simplify belief propagation (BP), our derivation resorts to expectation propagation (EP) and the neglect of high-order terms in large system limit. This alternative derivation leads to a different yet provably equivalent form of message passing, which explicitly establishes the intrinsic connection between AMP and EP, thereby offering some new insights in the understanding and improvement of AMP.

We propose a message-passing algorithm of joint channel estimation and decoding for OFDM systems, where expectation propagation is exploited to deal with channel estimation. Specially, the message updating is formulated into a recursive form. As a result, for system with K subcarriers and L channel taps, only O(K + L) messages need to be tracked, and meanwhile they can be efficiently calculated using FFT with complexity O(K|A| + K log2K), where |A| denotes the constellation size. Numerical experiments show that our algorithm achieves BER performance within 0.5 dB of the known-channel bound.

Because of the high system capacity and excellent capability against narrowband interference (NBI), Direct Sequence-Code Division Multiple Access (DS-CDMA) is widely used in Geosynchronous Earth Orbit satellite systems. However, due to the existence of the uncertain non-cooperative external interference, traditional colored noise suppression methods cannot achieve high performance in DS-CDMA systems. In this paper, based on spectrum shaping, combining with the feature analysis of the external interference, an eigen-based spreading sequences design framework for CDMA satellite systems is proposed. In this proposal, by the uniform orthogonal transformation (UOT), the eigen-based spreading sequences can combat not only the multiple access interference (MAI) but also the external interference and support multiple users' performance fairness. Furthermore, the design physical significance is analyzed. By simulations, it's verified that both MAI and the external interference can be eliminated by the proposed eigen-based spreading sequences and the fairness of different users can be efficiently guaranteed.

In this paper, we propose an expectation propagation based iterative multi-user detection algorithm for multiple input multiple output interleave-division multiple access (MIMO-IDMA) systems with high-order modulation. The proposed detector can be well integrated into the traditional structure of turbo receivers for MIMO-IDMA systems. By formulating a scalar factor graph representation of the multi-user detector and choosing Gaussian distribution as the projection set for the symbol belief, the overall detection complexity can be reduced to scaling linearly with the number of users, the number of receive antennas and transmit antennas. Numerical results for coded MIMO-IDMA systems with 16-QAM modulation show that our proposed algorithm outperforms the factor graph based detection with Gaussian approximation in terms of the bit error rate (BER) performance with lower complexity.

For the spatially correlated multiuser MIMO-OFDM channels, the conventional iterative MMSE-SIC detection suffers from a considerable performance loss. In this paper, we use the factor graph framework to design robust detection algorithms by clustering a group of symbols to combat the spatial correlation and using the principle of expectation propagation to improve message passing. Furthermore, as the complexity of detection becomes one of the issues in the design of large-scale multiuser MIMO-OFDM systems, we propose a low-complexity approximate message-passing algorithm by opening the channel transition node, which eliminates the expensive matrix inversions involved in the MMSE-SIC based algorithms. Finally, numerical results are presented to verify the proposed algorithms.

This paper presents a new carrier frequency offset (CFO) estimation algorithm with low-complexity for uplink OFDMA systems. Extended Kalman Filter (EKF) is employed in time domain, while the multiple access interference (MAI) cancellation strategy is combined in every recursion. In addition, an adaptive noise variance estimator is contrived for EKF, and a robust EKF method is derived for CFO estimation with data symbols. The proposed algorithm is suitable for online estimation, and can be applied to both preamble and data symbols. Simulation results show that it has a large estimation range, while approaching the Cramer-Rao Bound (CRB) closely with quite reduced complexity.

Multi-path fading and multiple access interference largely degrade the performance of carrier-frequency offset and timing offset estimations in OFDMA uplink systems. This paper proposes a robust algorithm with low complexity for joint estimation of carrier-frequency offset and timing offset. The phase differences in the received sub-carrier signals between two consecutive training symbols are employed and a robust sequential least squares algorithm is used to improve the performance of the estimation. Simulation results show that the proposed low complexity algorithm outperforms the linear least squares (LLS) estimator and approaches the performance of the iteratively reweighted least squares (IRLS) estimator

Acquisition and tracking are two crucial stages necessary to the carrier frequency synchronization in orthogonal frequency division multiplexing (OFDM) systems. In this letter, by employing the rotation property of OFDM data subcarriers, a simple time-frequency decision-feedback loop without the use of pilot subcarriers is proposed for the fine carrier frequency offset (CFO) tracking. Specifically, with proper loop parameters, a residual CFO less than 10% of the subcarrier spacing may be well tracked for quarternary phase-shift keying (QPSK) modulation in the presence of noise, while for systems using QPSK, 16-QAM, and 64-QAM modulation schemes, the bit-error rate (BER) performance very close to that of an offset-free system may be achieved in both additive white Gaussian noise (AWGN) and frequency selective fading channels. Moreover, a hardware implementation in a practical OFDM system is fulfilled which verifies the effectiveness of the proposed scheme.

A carrier frequency offset (CFO) tracking scheme without the use of any pilot subcarriers for the uplink orthogonal frequency division multiple access (OFDMA) system is proposed in this paper. We first analyze the interference due to CFO in uplink OFDMA systems, then, an offset tracking scheme is presented. The proposed scheme employs multiple time-frequency decision-feedback carrier recovery loops for individual users to restore the original symbols. Moreover, a modularized and flexible structure design is achieved for a low-complexity implementation of the loop. Simulation results show that the proposed scheme is resistant to multipath fading, while the BER (bit-error-rate) performance is very close to that of an ideal reception system without CFO.