Deep neural networks are used as effective methods for the Low Probability of Intercept (LPI) radar waveform recognition. However, existing models' performance degrades seriously at low Signal-to-Noise Ratios (SNRs) because the effective features extracted by the networks are insufficient under noise jamming. In this paper, we propose a multi-resolution deep feature fusion method for LPI radar waveform recognition. First, we apply the enhanced Fourier-based Synchrosqueezing Transform (FSST), which shows good performance at low SNRs, to convert radar signals into time-frequency images. Then, we construct a multi-resolution deep convolutional network to extract more deep features from each resolution channel. Next, we explore an interactive feature fusion strategy for deep feature fusion. By some down-sampling or up-sampling blocks, different resolution features are fused to generate new features. Finally, we apply a fusion algorithm to the fully connected layer to achieve classification fusion for better performance. Simulation experiments on twelve kinds of LPI radar waveforms show that the overall recognition accuracy of our method can reach 95.2% at the SNR of-8 dB. It is proved that our approach does indeed improve the recognition accuracy effectively at low SNRs. INDEX TERMS radar waveform recognition, multi-resolution, feature fusion, FSST, convolutional neural network.

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Having the ability to provide seamless coverage and alleviate the frequency scarcity, the cognitive satellite terrestrial network becomes a promising candidate for future communication networks. In the cognitive network, spectrum sensing plays an important role in detecting the channel state for opportunistic utilization, where cooperative spectrum sensing is employed to improve the sensing performance. Additionally, it is critical for battery-powered satellite mobile terminals to diminish energy consumption costs. In this regard, this paper proposes a novel sensing-based cognitive satellite terrestrial network (SCSTN), which integrates the cognitive satellite terrestrial network with the distributed cooperative spectrum sensing network. Specifically, we focus on energy-efficient cooperative sensing in the SCSTN, which maximizes the energy efficiency (EE) of the cognitive satellite network by a tradeoff between the average throughput and the average energy consumption. In the SCSTN, the energy detection threshold of the sensing node and the rule threshold of fusion affect the average throughput and the average energy consumption. Hence, the objective of this paper is to identify the energy detection threshold of the sensing node and the rule threshold of fusion to achieve the maximum EE. We first study the EE formulation of the rule threshold of fusion when the energy detection threshold of the sensing node is given, and transform the ratio-type objective function of EE into a parametric formulation. Subsequently, by exploring the relationship between the two formulations and making use of the monotonicity of the parametric formulation, an algorithm to obtain the optimal rule threshold of fusion for the original problem is developed. Furthermore, we study the optimal formulation of the energy sensing threshold of the sensing node and discuss the effect of the sensing duration and the number of distributed cooperative terminals on the EE. Lastly, the performance of the proposed method is evaluated through numerical simulation results.

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Multi-layer satellite networks (MLSNs) is of great potential for the integrated 5G networks to provide diversified services. However, MLSNs confront frequency interference coordination problem between satellite systems in different orbits. This paper investigates a joint user pairing and power allocation scheme in a non-orthogonal multiple access (NOMA)-based geostationary earth orbit (GEO) and low earth orbit (LEO) satellite network. Specifically, a novel NOMA framework with two uplink receivers, i.e. the GEO and LEO satellites is established where the NOMA groups are formed considering the subcarrier assignment of ground users. To maximize the system capacity, an optimization problem is then introduced subject to the decoding threshold and power consumption. Since the formulated problem is non-convex and mathematically intractable, we decompose it into user pairing and power allocation schemes. In the user pairing scheme, virtual GEO users are generated to transform the multi-user pairing problem into a matching problem and a max-min pairing strategy is adopted to ensure the fairness among NOMA groups. In the power allocation scheme, the non-convex problem is transformed into multiple convex subproblems and solved by iterative algorithm. Simulation results validate the effectiveness and superiority of the proposed schemes when compared with several existing schemes.INDEX TERMS Multi-layer satellite networks, non-orthogonal multiple access, user pairing, power allocation.

In recent years, low earth orbit (LEO) satellite constellation systems have been developed rapidly. However, the scarcity of satellite spectrum resources has become one of the major obstacles to this trend. LEO satellite constellation communication systems sharing the spectrum of incumbent geostationary earth orbit (GEO) satellite system is a feasible way to alleviate spectrum scarcity. Therefore, it has practical significance to study the optimization of satellite resources allocation (RA) in a spectrum sharing scenario. This paper focuses on the RA problem that LEO satellites share a GEO high throughput satellite's spectrum in a beam-hopping (BH) manner. The GEO satellite system is served as the primary system and the LEO satellite constellation system is served as the secondary system whose frequency bands and transmitting power are strictly limited. Compared with conventional multibeam satellites, BH satellites have the advantage of flexibility in the time dimension. Therefore, we make full use of the flexibility of LEO BH satellites to realize the matching of traffic demand and traffic supply. The RA problem is decomposed into three sub-problems, namely, frequency band selection (FBS) problem, illuminated cell selection (ICS) problem, and transmitting power allocation (TPA) problem. We solve each sub-problem in order and finally form a complete RA scheme. The performance evaluation of the proposed RA scheme is carried out in real-time and simulation results show that the LEO BH satellite paired with the RA scheme we proposed has good adaptability to the uneven distribution of traffic demand in the spectrum sharing scenario.

The beam-hopping (BH) technology applied to low earth orbit (LEO) satellite communication networks is a superior choice, but the long transmission delay partly caused by data packets waiting in the queue of satellite transponders will seriously affect the user experience. To shorten the packet queueing delay, in this paper, we propose an optimization method of dynamic beam position division for LEO BH satellite communication systems. Firstly, we analyze the packet queueing delay problem in BH satellites to find out the factors related to the queueing delay, and we find that the number of beam positions is negatively correlated with the queueing delay. Then, we turn the beam position division problem into a p-center problem to try to cover all users with the least number of beam positions. The beam positions among the footprint of LEO satellites are determined dynamically by the user distribution and the traffic distribution. Finally, the performance evaluation of the proposed optimization method is carried out in realtime and the simulation shows that the beam position division optimized system we proposed can shorten the queueing delay up to 40% compare to the benchmark system without sacrificing throughput.INDEX TERMS LEO satellite communication system, beam-hopping, beam position optimization, packet queueing delay, p-center problem

This paper presents a cognitive satellite communication based wireless sensor network, which combines the wireless sensor network and the cognitive satellite terrestrial network. To address the conflict between the continuously increasing demand and the spectrum scarcity in the space network, the cognitive satellite terrestrial network becomes a promising candidate for future hybrid wireless networks. With the higher transmit capacity demand in satellite networks, explicit concerns on efficient resource allocation in the cognitive network have gained more attention. In this background, we propose a sensing-based dynamic spectrum sharing scheme for the cognitive satellite user, which is able to maximize the ergodic capacity of the satellite user with the interference of the primary terrestrial user below an acceptable average level. Firstly, the cognitive satellite user monitors the channel allocated to the terrestrial user through the wireless sensor network; then, it adjusts the transmit power based on the sensing results. If a terrestrial user is busy, the satellite user can access the channel with constrained power to avoid deteriorating the communication quality of the terrestrial user. Otherwise, if the terrestrial user is idle, the satellite user allocates the transmit power based on its benefit to enhance the capacity. Since the sensing-based dynamic spectrum sharing optimization problem can be modified into a nonlinear fraction programming problem in perfect/imperfect sensing conditions, respectively, we solve them by the Lagrange duality method. Computer simulations have shown that, compared with the opportunistic spectrum access, the proposed method can increase the channel capacity more than 20% for Pav=10dB in a perfect sensing scenario. In an imperfect sensing scenario, Pav=15 dB and Qav=5 dB, the optimal sensing time achieving the highest ergodic capacity is about 2.34 ms when the frame duration is 10 ms.