# Massive MIMO Transmission for LEO Satellite Communications

arXiv: https://arxiv.org/abs/2002.08148 · HTML: https://mlsanity.com/paper/2002.08148/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2020.

## Built on by (most cited first, 7 of 7)

- [SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensing](https://mlsanity.com/paper/2405.15542/) (2024)
- [Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling](https://mlsanity.com/paper/2110.06787/) (2021)
- [Integrated Space Domain Awareness and Communication System](https://mlsanity.com/paper/2211.10260/) (2022)
- [Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning](https://mlsanity.com/paper/2409.18718/) (2024)
- [Graph Attention Networks for Channel Estimation in RIS-assisted Satellite IoT Communications](https://mlsanity.com/paper/2104.00735/) (2021)
- [Intermittent Jamming against Telemetry and Telecommand of Satellite Systems and A Learning-driven Detection Strategy](https://mlsanity.com/paper/2107.06181/) (2021)
- [SmartUT: Receive Beamforming for Spectral Coexistence of NGSO Satellite Systems](https://mlsanity.com/paper/2505.07714/) (2025)

## Abstract (arXiv)

Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks, in particular 5G and beyond networks, to provide global wireless access with enhanced data rates. Massive MIMO techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI (iCSI) at the transmitter. We first establish the massive MIMO channel model for LEO satellite communications and simplify the transmission designs via performing Doppler and delay compensations at user terminals (UTs). Then, we develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL) receiver in closed-form, aiming to maximize the average signal-to-leakage-plus-noise ratio (ASLNR) and the average signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some channel condition. Motivated by this, we propose a space angle based user grouping (SAUG) algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. The proposed algorithm is asymptotically optimal in the sense that the lower and upper bounds of the achievable rate coincide when the number of satellite antennas or UT groups is sufficiently large. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems. Notably, the proposed sCSI based precoder and receiver achieve the similar performance with the iCSI based ones that are often infeasible in practice.