Seamless 5G Automotive Connectivity with Integrated Satellite Terrestrial Networks in C-Band

This paper examines integrated satellite-terrestrial networks (ISTNs) in urban environments, where terrestrial networks (TNs) and non-terrestrial networks (NTNs) share the same frequency band in the C-band which is considered the promising band for both systems. The dynamic issues in ISTNs, arising from the movement of low Earth orbit satellites (LEOSats) and the mobility of users (UEs), are addressed. The goal is to maximize the sum rate by optimizing link selection for UEs over time. To tackle this challenge, an efficient iterative algorithm is developed. Simulations using a realistic 3D map provide valuable insights into the impact of urban environments on ISTNs and also demonstrates the effectiveness of the proposed algorithm.

An Experimental Study of C-Band Channel Model in Integrated LEO Satellite and Terrestrial Systems

This paper studies the channel model for the integrated satellite-terrestrial networks operating at C-band under deployment in dense urban and rural areas. Particularly, the interference channel from the low-earth-orbit (LEO) satellite to the dense urban area is analyzed carefully under the impact of the environment's characteristics, i.e., the building density, building height, and the elevation angle. Subsequently, the experimental results show the strong relationships between these characteristics and the channel gain loss. Especially, the functions of channel gain loss are obtained by utilizing the model-fitting approach that can be used as the basis for studying future works of integration of satellite and terrestrial networks (ISTNs).

ML-based PBCH symbol detection and equalization for 5G Non-Terrestrial Networks

This paper delves into the application of Machine Learning (ML) techniques in the realm of 5G Non-Terrestrial Networks (5G-NTN), particularly focusing on symbol detection and equalization for the Physical Broadcast Channel (PBCH). As 5G-NTN gains prominence within the 3GPP ecosystem, ML offers significant potential to enhance wireless communication performance. To investigate these possibilities, we present ML-based models trained with both synthetic and real data from a real 5G over-the-satellite testbed. Our analysis includes examining the performance of these models under various Signal-to-Noise Ratio (SNR) scenarios and evaluating their effectiveness in symbol enhancement and channel equalization tasks. The results highlight the ML performance in controlled settings and their adaptability to real-world challenges, shedding light on the potential benefits of the application of ML in 5G-NTN.

Two-tier User Association and Resource Allocation Design for Integrated Satellite-Terrestrial Networks

This paper presents a study of an integrated satellite-terrestrial network, where Low-Earth-Orbit (LEO) satellites are used to provide the backhaul link between base stations (BSs) and the core network. The mobility of LEO satellites raises the challenge of determining the optimal association between LEO satellites, BSs, and users (UEs). The goal is to satisfy the UE demand while ensuring load balance and optimizing the capacity of the serving link between the BS and the LEO satellite. To tackle this complex optimization problem, which involves mixed-integer non-convex programming, we propose an iterative algorithm that leverages approximation and relaxation methods. The proposed solution aims to find the optimal two-tier satellite-BS-UE association, sub-channel assignment, power and bandwidth allocation in the shortest possible time, fulfilling the requirements of the integrated satellite-terrestrial network.

Large-Scale Beam Placement and Resource Allocation Design for MEO-Constellation SATCOM

This paper presents a centralized framework for optimizing the joint design of beam placement, power, and bandwidth allocation in an MEO satellite constellation to fulfill the heterogeneous traffic demands of a large number of global users. The problem is formulated as a mixed integer programming problem, which is computationally complex in large-scale systems. To overcome this challenge, a three-stage solution approach is proposed, including user clustering, cluster-based bandwidth and power estimation, and MEO-cluster matching. A greedy algorithm is also included as a benchmark for comparison. The results demonstrate the superiority of the proposed algorithm over the benchmark in terms of satisfying user demands and reducing power consumption.

LEO-to-User Assignment and Resource Allocation for Uplink Transmit Power Minimization

This paper aims to develop satellite-user association and resource allocation mechanisms to minimize the total transmit power for integrated terrestrial and non-terrestrial networks wherein a constellation of LEO satellites provides the radio access services to both terrestrial base stations (BSs) and the satellite-enabled users (SUEs). In this work, beside maintaining the traditional SatCom connection for SUEs, the LEO satellites provide backhaul links to the BSs to upload the data received from their ground customers. Taking the individual SUE traffic demands and the aggregated BS demands, we formulate a mixed integer programming which consists of the binary variables due to satellite association selection, power control and bandwidth allocation related variables. To cope with this challenging problem, an iterative optimization-based algorithm is proposed by relaxing the binary components and alternating updating all variables. A greedy mechanism is also presented for comparison purpose. Then, numerical results are presented to confirm the effectiveness of our proposed algorithms.