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.

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.

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.

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 communication has become a hot topic in the development of the sixth-generation (6G) mobile communication due to its advantage of wide coverage and broadband transmission. Dual polarization frequency reuse (DPFR) is often used to improve the spectrum utilization in satellite communication system. However, cross-polarization interference (XPI) often occurs owing to the imperfect devices and atmosphere conditions. This paper proposes a joint convolutional neural network (CNN) and long short-term memory (LSTM) network to cancel the XPI for satellite communication. Since the cross-polarization interference cancellation (XPIC) is normally implemented at baseband, it is inevitable that nonlinear effects will be introduced by the high-power amplifier (HPA) on satellite. Conventional XPIC schemes based on transversal filter can not deal with the nonlinear impairments of the received signal, resulting in poor bit error ratio (BER) performance. We combine CNN and LSTM to extract the nonlinear feature and temporal correlation of the received signal, which can effectively eliminate the XPI and nonlinear effects. Simulation results show that, compared with conventional XPIC schemes, the BER performance of proposed scheme is improved by two orders of magnitude when the signal-to-noise ratio (SNR) is 20 dB. Besides, the proposed scheme can achieve fast convergence with small mean square error (MSE).

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.