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.

As an alternative positioning, navigation and timing (PNT) method, integrated Doppler positioning is important in emerging direct satellite-to-phone communication and satellite-based remote Internet of Things (IoT) systems to help locate ground terminals. In this work, we offer a fundamental characterization of Doppler positioning by proposing a new scalable and analytical Doppler positioning performance bound that can be used for fast estimation of the Doppler positioning dilution in place of the legacy position dilution of precision (PDOP) expression. Then, to maximize the Doppler estimation accuracy, a Doppler estimation algorithm based on the whole signal packet is proposed, in which the Doppler rate is also considered. Finally, based on mathematical analysis, a Doppler positioning receiver architecture is proposed. Simulations are conducted with a 288-satellite Walker-$\delta ~800$km low-Earth-orbit (LEO) satellite constellation to corroborate the mathematical analysis. The results show that the median relative error of the proposed performance bound for Doppler positioning is less than 2.5% and that the proposed Doppler estimation algorithm outperforms other Doppler estimation algorithms for Doppler positioning and approaches the Cramér-Rao lower bound (CRLB).

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.

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.

Global navigation satellite systems (GNSSs) are used in numerous fields, but their vulnerability is a global problem that has yet to be solved. A promising way to effectively address this problem is by integrating navigation into emerging dense nongeosynchronous orbit (NGSO) megaconstellations. To maximize the downlink efficiency for users, an integrated satellite navigation and communication (ISNAC) framework is proposed in this work, in which a necessary number of packetized bursty downlink satellite communication signals are used directly for navigation purposes. Then, the user terminal estimates the time of arrival (TOA) of these ISNAC signals regardless of their coding and modulation type. To this end, a TOA estimation algorithm is proposed. Specifically, the user terminal first constructs a template signal with demodulated data bits to replicate the transmitted ISNAC signal and then calculates the cross-correlation function (CCF) of the template and the received signal for TOA estimation, based on which the TOA is finally estimated. The performance bound of the proposed TOA estimation algorithm is analyzed. Simulations are conducted to corroborate the mathematical analysis and The results show that the proposed algorithm outperforms other TOA estimators for comparsion by approaching the Cramer-Rao lower bound (CRLB) with a much lower computational complexity.

With the best basis function that compactly describes a discrete-time signal, the Discrete Hirschman Transform (DHT) has been proved to perform better than the Discrete Fourier Transform (DFT) in terms of high resolution and computational complexity. It is reasonable to develop fast algorithms for the DHT computation since the DHT has applied to multiple signal processing applications. In this letter, we propose a split-radix DHT (SRDHT) including mathematical decomposition and comparison of computation complexity. The SRDHT is computationally superior to the DFT and performs more efficiently than our previously developed radix-2/-4 DHTs, with further reduced arithmetic operations. We regard this proposed SRDHT as a more attractive candidate to compute the DHT for those existing and future Hirschman-based applications.

High-speed train (HST) and vehicle-to-vehicle (V2V), as typical scenes of 5G communication, have attracted extensive attention from academia and industry in recent years. Aiming at the channel characteristics of frequency-selective fading, fast time-varying and time-domain non-stationary in high mobility scenarios, a nonlinear Kalman filter-based high-speed channel estimation algorithm for orthogonal frequency-division multiplexing (OFDM) systems is proposed. We adopt basis expansion model (BEM) to eliminate the inter-subcarrier interference (ICI) caused by the fast time-varying characteristics. For the non-stationary characteristics of high mobility channel, a channel interpolation algorithm based on extended Kalman filter (EKF) is introduced to jointly estimate the channel impulse response (CIR) and time correlation coefficients. However, the EKF channel estimation uses structure of decision feedback to construct the observation matrix, which would lead to error propagation. This paper analyzes the generation of error propagation through theoretical derivation. Furthermore, for the error propagation of EKF, we introduce unscented Kalman filter (UKF) algorithm to perform Gaussian approximation of non-Gaussian observing system, and eliminate the influence by Kalman filter (KF). Simulation results demonstrate that the channel estimation accuracy of BEM-UKF is further improved compared with BEM-EKF, and the influence of pilot distance (PD) is smaller, which further improves the robustness of the algorithm.