Massive multiple-input and multiple-output (MIMO) systems with orthogonal frequency division multiplexing (OFDM) are foundational for downlink multiuser (MU) systems in future wireless networks, for their ability to enhance spectral efficiency and support a large number of users. However, high user density intensifies severe inter-user interference (IUI) and pilot overhead. Consequently, existing blind and semi-blind channel estimation (CE) and signal detection (SD) algorithms suffer performance degradation and increased complexity, and are further challenged by frequency-selective channels with high-order modulation demands. To this end, this paper proposes a novel semi-blind joint channel estimation and signal detection (JCESD) method. Specifically, the proposed approach employs a hybrid precoding architecture to suppress IUI. Furthermore, we formulate JCESD as a non-convex optimization exploiting constellation affine invariance. A few pilots are used to achieve coarse estimation for initialization and ambiguity resolution. For high-order modulations, a data augmentation mechanism utilizes the symmetry of quadrature amplitude modulation (QAM) constellations to increase the effective number of samples. To address frequency-selective channels, CE accuracy is then enhanced via an iterative refinement strategy that leverages improved SD results. Simulation results demonstrate an average throughput gain of 11% over pilot-based methods in MU scenarios, highlighting its potential for improving spectral efficiency.

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.

The development of satellite communication is driving the demand for device identification that can respond to security threats from illegal uplink terminals. Specific emitter identification (SEI) is a promising technology of physical layer device identification. Deep learning (DL)-based SEI can effectively learn features for distinguishing emitters through a large number of samples. In the satellite communication scenario, due to the high cost of labeling, wireless signals often face the dilemma of few labeled samples, leading to a decrease in recognition accuracy of existing methods. Therefore, we introduce an innovative FS-SEI method leveraging multi-domain fusion and metric learning (MDF-ML), eliminating the reliance on an auxiliary dataset. Specifically, MDF-ML is proposed to explore additional implicit samples within the sample space, enhancing generalization performance through multi-domain representation and phase rotation. It then uses metric learning to constrain feature distances in the feature space, thereby boosting discriminability. Our simulation results demonstrate that the accuracy of our proposed SEI method exceeds 90%, which outperforms the existing method by 24.21% under 5 sample shots.

The importance of UAV-satellite edge computing networks in disaster relief and scientific exploration has become increasingly prominent, attracting significant attention from both industry and academia. However, under a pre-planned task execution model, fluctuations in data volume often lead to inefficient offloading strategies, significantly increasing the energy consumption risk for UAV-satellite edge computing networks and, in extreme cases, resulting in system failure. Existing offloading approaches either disregard data volume uncertainty, adopt overly conservative robust optimization, or rely on unrealistic distribution assumptions, all of which limit their practicality. To address these limitations, we propose a historical data-driven distributionally robust optimization offloading scheme. Specifically, we first formulate an optimization problem to minimize the total energy consumption and leverage distributionally robust duality theory to derive a tractable formulation. Subsequently, we design an iterative solving algorithm based on the block gradient descent and successive convex approximation methods. Numerical simulations validate that our proposed scheme achieves lower system energy consumption compared to benchmark schemes.

In space-air-ground integrated systems, radar signal analysis is crucial for effective spectrum management. In recent years, time-frequency transforms (TFT) have gained significant attention for radar signal detection and identification. However, challenges such as cross-term interference and low signal-to-noise ratio (SNR) limit the effectiveness in multicomponent signal analysis. Therefore, this paper proposes a novel multitask learning-based TFT framework, named One-Stage TFT (OSTFT), which directly generates high-quality time-frequency representations (TFR) from raw in-phase and quadrature signals. OSTFT incorporates a generative network combined with classification and localization tasks to enhance feature extraction and image clarity. Experimental results demonstrate that OSTFT achieves superior performance in TFR quality and radar signal recognition, with an 81.4% detection rate using the You Only Look Once (YOLO) frame-work, outperforming existing TFT methods under various noise conditions. Compared to the best-performing TFR-denoising method, OSTFT improves the detection rate by 2.7%.

As a complement to terrestrial networks, low-Earth orbit (LEO) satellite networks are promising to provide ubiquitous and continuous services. To accommodate the dynamic topology of LEO satellite networks and increasing traffic demands, this article proposes a distributed routing approach to optimize the end-to-end delay relying on the multiagent deep reinforcement learning (MADRL), where each satellite node is deployed with an autonomous agent and makes its real-time next-hop decisions independently with local observation information. To promote the inner cooperation between decentralized agents, a centralized training scheme with a unified load-balancing reward is utilized by adopting a novel multiagent Transformer-MIX architecture. Moreover, to derive a better decision for each agent, we design an attention-involved agent network to capture more hidden information, and a Transformer-based parameter recurrent mechanism to generate the joint action-value function is used to enhance a more stable training. The simulation results indicate that our proposed scheme achieves faster convergence and demonstrates superior performance across several key performance metrics compared to existing benchmark schemes. Specifically, when the intersatellite link (ISL) failure rate in the network reaches 18%, our scheme achieves a reduction in end-to-end delay by 13.6% and an increase in packet successful delivery rate by 5.4% compared to the benchmark schemes.

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.

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.

Space-aerial-terrestrial integrated networks (SATINs) have gained widespread recognition. Due to the mobility of wireless access points (APs) and user terminals (UTs), handover plays an essential role in ensuring the continuity and quality of communication in SATINs. However, existing handover strategies only consider the instantaneous communication benefits, which makes it difficult to maintain link stability with lower handover frequency. Given that reasonable handover paths help improve the stability of communication, we propose a handover planning (HP) strategy in SATINs, which fully considers the time varying link quality between two handover in planning the handover paths of mobile UTs. Simulation results demonstrate that the proposed HP strategy can maintain high-quality communication links with the lowest handover delay while performing relatively low in handover frequency.

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.

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.

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).