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.

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.

This paper investigates dynamic spectrum access (DSA) for cognitive satellite networks (CSNs), where a non-geostationary orbit (NGSO) satellite acting as a secondary user (SU) shares a segment of spectrum licensed to the primary user (PU) geostationary (GSO) satellite system. Considering the influence of spectrum sensing errors on spectrum sharing, a new problem about joint channel selection and power control is formulated as a sequential decision-making process, to maximize a long-term throughput of the NGSO under an interference constraint for the GSO system. Due to the imperfect spectrum information, we employ deep reinforcement learning (DRL) with a double deep Q-learning neural network to solve the problem by learning the set of DSA policies with stabilized convergence. We consider three cases to evaluate the performance of the proposed algorithm and the simulation results show that the throughput and the average transmission power of NGSO can converge quickly in a dynamic spectrum environment. Besides, the spectrum utilization can be maximized with meeting the transmission power constraint proposed by GSO for NGSO.

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.

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.

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.

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).

Unmanned Aerial Vehicle (UAV) technology has been widely applied in both military and civilian applications. With the increasing complexity of application scenarios, the coordination of multiple UAVs has become a hot topic. However, the limited capability of UAVs make it hard to achieve stable and reliable control. Considering this practical problem, we propose a cloud-based UAV system. It extricates the computing and data storage from UAVs and utilizes the cloud to process the sensor data and to maintain the stable operation of multi-UAV systems. Firstly, we analyze the cloud-based system's on-demand service ability and its impact on UAVs' control procedure. Secondly, we propose a UAV cloud control system (CCS) which serves as a network control system. Moreover, the stable condition of the UAV cloud control system is derived. It reveals the relationship between the acquisition rate of sensor data and the stability of the cloud-based UAV system. Finally, simulations are conducted to verify the effectiveness of previous theoretical analysis.

Earth satellite networks are playing an increasingly important role in observation, surveillance and reconnaissance of specific targets or areas with dramatic growing of the demand for such services. The harsh and vulnerable space environment makes satellite nodes susceptible to a variety of attacks and prompts us to protect satellite networks by securing them with efficient defending strategy. In this paper, we propose a satellite scheduling problem in a vulnerable space environment. A set of complex operational constraints is imposed to make the best defending strategy between task requests and satellites under protection. To reduce the complexity, we decompose the original problem into two subproblems of satellite scheduling and satellite protection. A joint optimization algorithm is adopted to find near optimal solution. The results of extensive computational experiments show its effective and superior scheduling performance.

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.

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.

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.

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.