The mobile satellite communication (MSC) system is a vital communication method in which mobile users and network control centers connect via satellites. Since the satellite's communication is wireless, communication security is a critical factor to ensure accountable communication. Key exchange and authentication (KEA) mechanism is widely used to achieve data security for data transmitted in an insecure or open channel. Different authentication systems are suggested in the last many years to establish safe communication, whereas most existing security protocols are based on factorization or discrete logarithm. However, these systems are no longer reliable by Shor's algorithm as any discrete logarithm and factorization can be resolved in polynomial time on quantum computers. Thus, developing a quantum secure KEA protocol for MSC systems is necessary. In this direction, recently a ring learning with an error-based KEA technique is proposed to ensure a quantum-safe environment. This scheme is quantum-safe and satisfies desirable security attributes but has low efficiency in computation and communication. Moreover, establishing a secure session requires six communications among involved entities. As a result, replay attack detection at an early stage is not possible for the central authority (server), which could delay the server response, and the adversary gets the advantage of drawing a denial of service scenario for authorized entities. We propose a lattice-based KEA protocol for the MSC system to improve computation, communication efficiency, and early-stage replay attack detection. The security analysis of the proposed scheme is presented in the random oracle model. Calculation of performance is also presented to observe advantages in computation and communication overhead.
Recently, the Low Earth Orbit (LEO) satellite and Geostationary Earth Orbit (GEO) satellites share the same frequency to enhance spectrum efficiency in response to the scarcity of spectral resources. However, overlapping coverage areas emerge when LEO-GEO and LEO-LEO beams cover the same ground area in the GEO-LEO co-existing satellite system. Thus, the GEO-LEO inter-system interference and the LEO intra-system interference are generated, which leads to more severe and complex interference. To solve the co-channel interference problem without sacrificing the performance of the LEO system, a collaborative interference avoidance technology is proposed for the GEO-LEO co-existing satellite system. Firstly, the LEO satellite and user regrouping strategy (SURS) is employed to effectively avoid severe co-channel interference by changing service satellites. Then, based on regrouping satellite and user pairs, a proximal policy optimization (PPO)-based deep reinforcement learning (DRL) method is adopted to realize further continuous beam pointing optimization (BPO) of the LEO user antenna and reduce the co-channel interference. Finally, simulation results verify that the proposed scheme can achieve the goal of interference mitigation and continuous service.
Recently, the low earth orbit (LEO) satellite has attracted much attention due to its advantages of low latency and construction costs. However, the scarcity of frequency spectrum resources has resulted in these satellites sharing the same spectrum to provide services for ground users. So, challenges such as overlapping beam coverage and inter-beam interference occur in the multi-beam LEO satellite coexistence scenario. Therefore, this paper focuses on the issue of interference coexistence in LEO and LEO (LEO–LEO) co-existing scenario, where the initially established system is the primary system, and the later established system is the secondary system. The interference coexistence problem is modeled as a multi-objective optimization problem. The joint beam power and pointing management (JBPPM) based on particle swarm optimization (PSO) is proposed. The power control is applied to the LEO satellites in the secondary system to reduce the interference to the primary system user. Meanwhile, beam pointing optimization is adopted by the LEO users in the secondary system to avoid interference from the primary system and ensure the minimum communication ability. The simulation results verify that the proposed technique can adapt to various coexistence conditions and outperform the existing method.
Cellular networks are projected to deal with an immense rise in data traffic, as well as an enormous and diverse device, plus advanced use cases, in the nearest future; hence, future 5G networks are being developed to consist of not only 5G but also different radio access technologies (RATs) integrated. In addition to 5G, the user's device (UD) will be able to connect to the network via LTE, WiMAX, WiFi, Satellite and other technologies. On the other hand, Satellite has been suggested as a preferred network to support 5G use cases. However, achieving load balancing is essential to guarantee an equal amount of traffic distributed between different RATs in a heterogeneous wireless network; this would enable optimal utilisation of the radio resources and lower the likelihood of call blocking/dropping. This study presented an artificial intelligent-based application in heterogeneous wireless networks and proposed an enhanced particle optimisation (EPSO) algorithm to solve the load balancing problem in 5G-Satellite networks. The algorithm uses a call admission control strategy to admit users into the network to ensure that users are evenly distributed on the network. The proposed algorithm was compared with the Artificial Bee Colony and Simulated Annealing algorithm using three performance metrics: throughput, call blocking and fairness. Finally, based on the experimental findings, results outcomes were analysed and discussed.
Modern satellite communication systems are designed to serve dispersed users with changing operational requirements. Allocating resources to meet these requirements becomes highly complex when the size, location, and allocated frequency of satellite beams are flexible. We developed solution techniques that generate feasible beam patterns and frequency allocations to maximize the total demand met across all users. In our approach, an integer program selects an optimal set of beams from a heuristically generated pool of feasible candidate beams. The challenge is in how to efficiently build a pool of good quality candidate beams from exponentially many possible solutions. An innovative column generation-style heuristic to generate mathematically justifiable beams is presented to address this challenge. We also derived two other heuristic candidate beam generation algorithms to compare and contrast performance and robustness characteristics of different algorithmic choices. We tested the performance of our three new approaches on 12 operational instances that vary in user distribution, user numbers, and demand distribution. While the methods performed differently under varying operational scenarios, the column generation-based methods provided the best trade-off between computation time and solution quality in most cases. We further tested our two best performing algorithms for scalability. Our column generation-based methods were able to provide good quality solutions with up to 400 beams and 5,000 users. Our work provides valuable insights for real-life implementation: an end-user of our system can select the solution approach based on their business need, computational (time) budget, and the desired solution quality.
The study of multi-interferences plays a pivotal role in advancing the development of next-generation, high-performance communication devices. In this paper, we propose a design approach for auxiliary antennas aimed at mitigating multi-interference challenges within satellite communication earth stations. We introduce an eight-element auxiliary antenna with an elliptical beam comprising dual radiation patches and a T-shaped power divider, to attenuate interferences originating from three distinct directions. To enhance the azimuthal beamwidth, we adjust the dimensions of the ground plane and extend the substrate. At a frequency of 12.5 GHz, the antenna exhibits a 3-dB beam spacing of 38° and an azimuthal width of 162°, with a maximum gain of 7.9 dBi. We employ a sidelobe canceller to optimize the antenna's performance, and both simulations and measurements affirm that the auxiliary antenna achieves an extinction interference cancelation ratio exceeding 27 dB under all circumstances.
Multiple-input multiple-output (MIMO) technology can boost the capacity of conventional satellite communications, and full frequency reuse (FFR) can further improve the capacity by making full use of frequency resources. However, FFR will cause severe co-channel interference and lead to a degradation of channel capacity. Blind source separation (BSS) algorithm can be used to eliminate co-channel interference and does not require prior information, which avoids the extra occupation of channel frequency resources. While the multi-satellite MIMO communication system have delay mixing characteristic, the separation performance will be degraded if the delay is ignored. In this paper, DMBSS algorithm for delay mixing is proposed. Firstly, the DMBSS algorithm utilizes Taylor expansion to achieve dimension expansion of observed signals and so as to transform the delay mixing into instantaneous mixing with extended dimension. Secondly, complex non-orthogonal joint diagonalization algorithm is used to solve the separation matrix equation of extended observation signals and extended source signals and thus to eliminate the co-channel interference. Simulation results show that for multi-satellite MIMO communication scenarios with FFR, the proposed algorithm can effectively improve the performance of co-channel interference cancellation and reduce the value of BER. Furthermore, the influence of order of Taylor expansion on the separation performance is also analyzed and simulated in this paper to show the cost performance of the proposed algorithm.
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