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Grant-Free Random Access in Uplink LEO Satellite Communications with OFDM

Rui Mao, Yongpeng Wu, Boxiao Shen, Symeon Chatzinotas, Björn Ottersten, Wenjun Zhang

TL;DR

The paper addresses grant-free random access for uplink LEO satellite communications with OFDM, where severe Doppler shifts create a large number of unknown channel parameters. It proposes a transmission scheme based on discrete prolate spheroidal basis expansion (DPS-BEM) to compress the channel representation and uses a vector approximate message passing (VAMP) algorithm with a Bernoulli-Gaussian (BG) prior and a Markov random field (MRF) to exploit angular sparsity, with expectation-maximization (EM) learning of hyperparameters. The proposed EM-MRF-VAMP method performs joint device activity detection and channel estimation and outperforms benchmarks such as MVSP and EM-SBL in NMSE and activity error rate under NTN-TDL-D channel models. The results indicate a scalable, accurate GFRA solution for IoT-style uplink to LEO satellites, enabling global coverage with low latency and high spectral efficiency.

Abstract

This paper investigates joint device activity detection and channel estimation for grant-free random access in Low-earth orbit (LEO) satellite communications. We consider uplink communications from multiple single-antenna terrestrial users to a LEO satellite equipped with a uniform planar array of multiple antennas, where orthogonal frequency division multiplexing (OFDM) modulation is adopted. To combat the severe Doppler shift, a transmission scheme is proposed, where the discrete prolate spheroidal basis expansion model (DPS-BEM) is introduced to reduce the number of unknown channel parameters. Then the vector approximate message passing (VAMP) algorithm is employed to approximate the minimum mean square error estimation of the channel, and the Markov random field is combined to capture the channel sparsity. Meanwhile, the expectation-maximization (EM) approach is integrated to learn the hyperparameters in priors. Finally, active devices are detected by calculating energy of the estimated channel. Simulation results demonstrate that the proposed method outperforms conventional algorithms in terms of activity error rate and channel estimation precision.

Grant-Free Random Access in Uplink LEO Satellite Communications with OFDM

TL;DR

The paper addresses grant-free random access for uplink LEO satellite communications with OFDM, where severe Doppler shifts create a large number of unknown channel parameters. It proposes a transmission scheme based on discrete prolate spheroidal basis expansion (DPS-BEM) to compress the channel representation and uses a vector approximate message passing (VAMP) algorithm with a Bernoulli-Gaussian (BG) prior and a Markov random field (MRF) to exploit angular sparsity, with expectation-maximization (EM) learning of hyperparameters. The proposed EM-MRF-VAMP method performs joint device activity detection and channel estimation and outperforms benchmarks such as MVSP and EM-SBL in NMSE and activity error rate under NTN-TDL-D channel models. The results indicate a scalable, accurate GFRA solution for IoT-style uplink to LEO satellites, enabling global coverage with low latency and high spectral efficiency.

Abstract

This paper investigates joint device activity detection and channel estimation for grant-free random access in Low-earth orbit (LEO) satellite communications. We consider uplink communications from multiple single-antenna terrestrial users to a LEO satellite equipped with a uniform planar array of multiple antennas, where orthogonal frequency division multiplexing (OFDM) modulation is adopted. To combat the severe Doppler shift, a transmission scheme is proposed, where the discrete prolate spheroidal basis expansion model (DPS-BEM) is introduced to reduce the number of unknown channel parameters. Then the vector approximate message passing (VAMP) algorithm is employed to approximate the minimum mean square error estimation of the channel, and the Markov random field is combined to capture the channel sparsity. Meanwhile, the expectation-maximization (EM) approach is integrated to learn the hyperparameters in priors. Finally, active devices are detected by calculating energy of the estimated channel. Simulation results demonstrate that the proposed method outperforms conventional algorithms in terms of activity error rate and channel estimation precision.

Paper Structure

This paper contains 10 sections, 30 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Input-Output Relationship
  4. Problem Formulation
  5. Joint Device Activity Detection And Channel Estimation
  6. Factor Graph
  7. Posterior Distribution Estimation
  8. Learning the Hyperparameters
  9. Numerical Results
  10. Conclusion

Figures (4)

  • Figure 1: Factor graph
  • Figure 2: Performance comparison of channel estimation. $N_{y} = N_{z}=1$, $U = 40$, $p_{\lambda} = 0.2$, $M=2$, $N=64$, $f_{\mathrm{max}}$= 4 kHz.
  • Figure 3: Performance comparison of channel estimation. $N_{y} = N_{z}=4$, $U = 100$, $p_{\lambda} = 0.1$, $M=8$, $N=32$, $f_{\mathrm{max}}$= 30 kHz.
  • Figure 4: Performance comparison of device activity detection. $N_{y} = N_{z}=4$, $U = 100$, $p_{\lambda} = 0.1$, $M=8$, $N=32$, $f_{\mathrm{max}}$= 30 kHz.