Low Latency MCS Design for LEO Satellite Communication Based on 5G

This paper presents the performance of outer-loop control for adaptive modulation and coding (AMC) using mutual information (MI) based mapping between the block error rate (BLER) and received signal-to-interference plus noise power ratio (SINR) in the multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) downlink employing maximum likelihood detection (MLD). In AMC, we generate a mapping table between the BLER and MI per bit in advance, which is computed from the received SINR, assuming typical channel conditions including the path model and fading maximum Doppler frequency. Then, in the proposed outer-loop control, the measured MI per bit value for selecting the best modulation and coding scheme (MCS) is adjusted so that the measured BLER after turbo decoding satisfies the prescribed target value. Computer simulation results show that the outer-loop control achieves almost the same throughput as that with a mapping table assuming channel conditions identical to those for the actual measurement, while achieving nearly the identical target BLER. Moreover, we show that the best step size, which provides nearly the maximum throughput, differs according to the change in the maximum Doppler frequency in contrast to the relative feedback delay of the AMC loop. Nevertheless, it is insensitive to the target BLER value and frequency-selectivity of a channel model.

Long Term Evolution (LTE) network offers a high throughput with low latency that makes it the best choice for Evolved Multimedia Broadcast Multicast Service (E-MBMS). E-MBMS transmits data to all users simultaneously using the same channel and same Modulation and Coding Scheme (MCS) level. Each user experiences different channel condition, so an MCS level should be selected properly to satisfy all users. Typically, MCS level is selected on the basis of users' worst channel condition which results in wasting network resources. Since the E-MBMS performance directly depends on the selected MCS level, an efficient Adaptive Modulation and Coding (AMC) scheme is essentially needed. This paper introduces a new mechanism that uses the Standard Deviation (SD) of MCS level of E-MBMS user group operated on single cell mode to efficiently select its optimal MCS level. Simulation results show that the proposed mechanism increase the spectrum efficiency, throughput, and lower Packet Loss Ratio compared to the Worst User Channel Condition mechanism and Fixed Predefined MCS mechanism.

The improvement of BDS Observation Geometry with LEO constellations in Orbit Determination

Recently many companies and consortia have considered launching LEO satellites for such projects as remote sensing, transportation and mining operations. When the multiple low Earth orbit (LEO) satellites are transmitting they can interfere with existing terrestrial microwave and satellite earth‐stations in the fixed and mobile service. The interference problem is related to the number of proposed LEOs and the altitude and consequently the time to orbit the earth. These systems usually consist of many small satellites, and each satellite stays in the beam of a terrestrial station for up to 72 seconds in each 222 minute orbit. Earth coverage could be obtained by 48 (LEO) satellites 1500 km above the earth in polar orbit, and hence at least one LEO would always be interfering with terrestrial networks. A technical evaluation would then be required to determine the resultant BER (bit error rate) effecting existing terrestrial services. A determination can then be made to support such a LEO system or object via official channels such as the ITU.

The Non-Terrestrial Network (NTN) represents one of the major components in future wireless systems. Both academia and industry are interested in Low-Earth Orbit (LEO) satellites orbiting the Earth at low altitudes. Indeed, global connectivity can be achieved through constellations of LEO satellites, which also ensure network access and deliver services anytime and anywhere with low latencies. However, LEO satellite fast speeds around the Earth result in frequent feeder link breaks between the NTN platform and the NTN Gateway (NTN-GW) and, consequently, the network has to perform numerous feeder link switch-over procedures. The Third Generation Partnership Project (3GPP) distinguishes two feeder link switch-over modalities. The soft procedure foresees the establishment of two feeder links with two different NTN-GWs at the same time by releasing the old feeder link once the new one is activated. The hard procedure foresees the establishment of one feeder link per time: first, the old feeder link is released, then the new feeder link is activated, thus causing service interruptions. In this paper, through a simulation campaign conducted by means of the MATLAB tool, we evaluate the system performance in terms of data loss for the two feeder link switch-over modalities. Finally, some future research ideas are considered to tackle the issue of long service disruption above all in the case of hard feeder link switch-over.

Low earth orbit (LEO) satellite system becomes an emerging non-terrestrial network solution for future radio communications. To maximize the system capacity, an LEO satellite could utilize circularly polarized planar antenna array with the frequency reuse technique to form multiple earthmoving beams. With the increasing number of LEO satellites, inline interference from the geosynchronous equatorial orbit (GEO) satellite is an important issue to be resolved when the spectrum resource is shared. With the frequency hopping phenomena in earth-moving LEO networks, the inline interference could be minimized if the GEO users also perform the similar frequency hopping via coordination between LEO and GEO satellite networks. This paper proposes eight spectrum sharing schemes for GEO satellite networks to produce the frequency hopping sequence in earth-moving LEO network. We evaluate the system capacity, fairness between LEO and GEO users, and time costs of all schemes. Simulation results validate the advantage obtained from coordination, and show that the simple greedy scheme performs as good as the optimal scheme due to fixed frequency hopping patterns.

Gravity field recovery of inter-satellite links between Beidou navigation satellite system (BDS) and LEO based on geodesy and time reference in space (GETRIS)

Cognitive Radio (CR) systems allow optimizing the use of the shared radio spectrum and enhancing the coexistence among different technologies by efficiently changing certain operating parameters of the radios such as transmit-power, carrier frequency, and modulation and coding scheme in real-time. Dynamic Spectrum Access (DSA), which allows radios to dynamically access and use the unused spectrum, is one of the tasks that are fundamental for a better use of the spectrum. In this paper, we extend the previous work on Automatic Modulation Classification (AMC) by using Deep Neural Network (DNNs) and evaluate the performance of these architectures on signals that are not only modulated but are also encoded. We call this the Automatic Modulation and Coding Scheme Classification problem, or \(AMC^2\). In this problem, radio signals are classified according to the Modulation and Coding Scheme (MCS) used during their transmission. Evaluations on a data set of 802.11 radio signals, transmitted with different MCS and Signal to Noise Ratio (SNR), provide important results on the impact of some DNN hyperparameters, e.g. number of layers, batch size, etc., in the classification accuracy.

The efficient utilization of limited spectral resources in space presents a significant challenge given the rapidly increasing number of satellites (SATs) in various orbits. To address this challenge, it is paramount to enhance spectral efficiency by facilitating inter-operation between SATs in different orbits. In this letter, we investigate the impact of dynamic spectrum access (DSA) on the spectral efficiency of a SAT network that integrates geostationary (GEO) and low Earth orbit (LEO) SATs. Specifically, in the context of DSA, the secondary LEO SATs can opportunistically reuse the frequency bands originally assigned to primary GEO SATs. To characterize the resultant spectral efficiency in a realistic time-varying SAT network topology, we analyze the signal-to-interference-plus-noise ratio (SINR) at the nadir user of a randomly selected LEO SAT and an upper bound on average throughput of the LEO SAT. Simulation results demonstrate the significant advantages of DSA in enhancing the spectral efficiency of the integrated GEO-LEO SAT network.

Machine learning (ML) frameworks are investigated for use in simultaneous tracking and navigation (STAN) with low Earth orbit (LEO) satellites. STAN is a navigation paradigm that utilizes specialized LEO receivers to extract navigation observables (e.g., pseudorange and Doppler) from LEO satellite signals. Two neural network architectures are compared: Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN). Additionally, two ML-based orbit determination frameworks are compared: ephemeris propagation and residual error propagation. The objective of the comparison is to select an approach with the lowest open-loop propagation error as well as computational cost. Based on simulation results, a nonlinear autoregressive neural network with exogenous inputs (NARX) embedded within the residual error modeling framework is selected as the best ML approach among the compared candidates. Experimental results are presented demonstrating a ground vehicle navigating for a total of 258 seconds, while receiving signals from two Orbcomm LEO satellites. Global navigation satellite system (GNSS) signals were artificially cut off for the last 30 seconds, during which the vehicle traversed a trajectory of 871 m. Two navigation frameworks are compared to estimate the vehicle’s trajectory: (i) LEO signal-aided inertial navigation system (INS) STAN framework using Simplified General Perturbation (SGP4) as its propagator and (ii) the proposed LEO signal-aided INS STAN framework using ML as its propagator. The STAN with SGP4 achieved a three-dimensional (3-D) position root-mean squared error (RMSE) of 30 m. In contrast, the proposed STAN with SGP4+NARX framework achieved a 3-D position RMSE of 3.6 m.

An opportunistic navigation framework using low Earth orbit (LEO) satellites is analyzed. This framework, termed simultaneous tracking and navigation (STAN), estimates a navigating vehicle’s state along with the states of orbiting LEO satellites. STAN employs an extended Kalman filter (EKF) to fuse measurements from satellite receivers and an inertial navigation system (INS). The navigation performance is analyzed due to: (i) the vehicle being equipped with (1) different inertial measurement unit (IMU) grades: consumer, industrial, and tactical and (2) different receiver clock quality: temperature-compensated crystal oscillators (TCXO) and oven-controlled crystal oscillators (OCXO) and (ii) the LEO satellites being equipped with different transmitter clock quality: OCXO and chip-scale atomic clock (CSAC). Additionally, the effect of utilizing a large number of LEO satellites for navigation is investigated. This analysis provides insight into the achievable performance of STAN, which can serve as an alternative navigation system in global navigation satellite system (GNSS)-denied environments. The performance predictions from simulations are compared with experimental results with real signals from the Orbcomm LEO constellation. A close match between the simulation and experimental results is demonstrated for an unmanned aerial vehicle (UAV) navigating via the STAN framework with signals from two Orbcomm LEO satellites for 160 seconds, the last 35 seconds of which are without GNSS signals. The UAV’s position root-mean squared error (RMSE) from simulations was 8.6 m, while the experimental position RMSE was 10 m.

Low Earth Orbit (LEO) satellite communication networks have become an important means to provide internet access services for areas with limited infrastructure. Compared with the Geostationary Earth Orbit (GEO) satellites, the LEO satellites have limited on-board communication caching and calculating resources. Furthermore, the distribution of traffic requests is dynamically changing and uneven due to the relative movement between the LEO satellites and the ground. Therefore, how to schedule the multi-dimensional resources is an important issue for the LEO satellite communication networks. Beam-hopping is an efficient approach to improve the resource utilization by dynamically allocating time, power, and frequency according to the traffic requests. This paper proposes an efficient multi-dimensional resource allocation mechanism for beam-hopping in LEO satellite networks, which simultaneously satisfies the GEO interference avoidance. First, we construct the beam-hopping model of LEO satellites, and formulate the resource optimization problem. Second, we provide the weighted greedy strategy to determine the illumination pattern. In order to reduce the search space, the cells are clustered to non-interference clusters. Then, an improved genetic algorithm is provided to jointly allocate the communication resources. Finally, we construct various simulations to evaluate our proposed mechanism. Compared with the random-BH, polling-BH and traditional genetic algorithm, our algorithm achieves better performance in terms of both system throughput, access success rate, average delay and fairness between cells. The performance improvement is more significant in scenarios where traffic demand is unevenly distributed.

6G non-terrestrial networks (NTNs) will use satellites for global internet access, where low earth orbit (LEO) satellites will be used frequently due to the lower latency and higher link budget that can be provided compared to higher altitude satellites. However, LEO satellites require many satellites due to their fast movement and limited coverage, leading to frequent handovers (HOs). This paper presents an intelligent LEO satellite conditional handover (ILCHO) scheme that improves the NTN performance by addressing the frequent HO issues while reducing the signal strength variation between cells and outdated measurement values. The ILCHO scheme derives the LEO satellite's state information (position and orbital path), which is used to obtain the conditions for a LEO satellite to be accessible to enable optimized target LEO satellite selection that will maximize the overall network service performance while minimizing the number of HOs. The ILCHO scheme uses multi-agent reinforcement learning (MARL) to maintain an optimized performance through early preparation by incorporating a distance-based event in the execution phase. The simulation results show that the proposed ILCHO scheme can increase the throughput and signal to noise ratio (SNR) performance compared to other HO algorithms, thereby providng improved and more stable communication conditions.

Low earth orbit (LEO) satellite has the advantages of global coverage, low propagation delay and low path losses, etc.Combined with orthogonal frequency division multiplexing (OFDM) transmission, LEO satellite system can support very high capacity suitable for wideband multimedia traffic.This paper analyzes the channel model and throughput for LEO OFDM satellite communication system.First, several channel models for LEO satellite system are expounded with simulations because channel model is the basis of throughput analysis.Second, the beam model and the signaling model for LEO satellite are introduced.Considering high speed movement of LEO satellite, channel fading and the channel quality information (CQI), link throughput is simulated.Finally, compared with MF-TDMA system, simulation results demonstrate that OFDM system outperforms the former because its feasibility and superiority in throughput.

<p><strong>Abstract</strong> Global Navigation Satellite Systems (GNSS), which are fundamental to Positioning, Navigation, and Timing (PNT), play also a critical role for the determination of ITRF and the monitoring of global geodetic parameters, such as geocenter and Earth Rotation Parameters (ERP), thanks to the globally distributed network. The Low Earth Orbit (LEO) satellites with onboard GNSS receivers as moving stations can significantly improve the observation geometry of a ground tracking network, and thus enhance the solutions. Previous studies have demonstrated that integrated processing of ground GNSS stations and LEO satellites can improve the GNSS satellite orbits and the determination of the geocenter, especially for spare networks. In this study, we further investigate the contribution of having five LEO satellites (two GRACE-FO satellites and three SWARM satellites) to a rather robust ground GNSS network, i.e., 120 stations. We focus not only on the GNSS satellite orbits, and the geocenter and ERP, but also on some other parameters like ambiguities. We further investigate the impact of the LEO orbit modelling and the weighting strategies of the LEO observations in order to properly consider the fact that onboard GNSS observations can be modelled more precisely than that from ground stations due to the differences in station environment and atmosphere.</p>