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Joint and Streamwise Distributed MIMO Satellite Communications with Multi-Antenna Ground Users

Parisa Ramezani, Emil Björnson

Abstract

We consider a low Earth orbit downlink communication, where multiple satellites jointly serve multi-antenna ground users, transmitting multiple spatial streams per user. Using a line-of-sight-dominant satellite channel model with statistical channel state information, including angular information and large-scale fading, we study two distributed transmission modes with different fronthaul requirements. First, for joint transmission, where all satellites transmit all user streams, we formulate a sum spectral efficiency (SE) maximization problem under general convex power constraints and address the intractability of the exact ergodic SE expression by adopting a tractable approximation. Exploiting the equivalence between sum SE maximization and weighted sum mean square error minimization, we derive a novel iterative transceiver design. Second, to reduce fronthaul load, we propose streamwise transmission, where each stream is sent by a single satellite, and develop an eigenmode-based stream-satellite association using participation factors and a maximum-weight bipartite matching problem solved by the Hungarian algorithm. Numerical simulations evaluate the validity of the SE approximation, demonstrate conditions under which streamwise transmission performs nearly optimally or trades SE for lower overhead, highlight the impact of stream/user loading, and show substantial performance gains over conventional benchmarks.

Joint and Streamwise Distributed MIMO Satellite Communications with Multi-Antenna Ground Users

Abstract

We consider a low Earth orbit downlink communication, where multiple satellites jointly serve multi-antenna ground users, transmitting multiple spatial streams per user. Using a line-of-sight-dominant satellite channel model with statistical channel state information, including angular information and large-scale fading, we study two distributed transmission modes with different fronthaul requirements. First, for joint transmission, where all satellites transmit all user streams, we formulate a sum spectral efficiency (SE) maximization problem under general convex power constraints and address the intractability of the exact ergodic SE expression by adopting a tractable approximation. Exploiting the equivalence between sum SE maximization and weighted sum mean square error minimization, we derive a novel iterative transceiver design. Second, to reduce fronthaul load, we propose streamwise transmission, where each stream is sent by a single satellite, and develop an eigenmode-based stream-satellite association using participation factors and a maximum-weight bipartite matching problem solved by the Hungarian algorithm. Numerical simulations evaluate the validity of the SE approximation, demonstrate conditions under which streamwise transmission performs nearly optimally or trades SE for lower overhead, highlight the impact of stream/user loading, and show substantial performance gains over conventional benchmarks.
Paper Structure (15 sections, 40 equations, 9 figures, 3 algorithms)

This paper contains 15 sections, 40 equations, 9 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Contributions
  4. Organization
  5. Notations
  6. System Model
  7. Channel Model
  8. Sum SE Maximization Problem Formulation
  9. Problem Reformulation
  10. Precoding Design for Non-Coherent Joint Transmission
  11. Fronthaul-Aware Streamwise Transmission with Limited Message Sharing
  12. Precoding Design
  13. Streamwise Satellite Association
  14. Numerical Results
  15. Conclusions

Figures (9)

  • Figure 1: Distributed multi-satellite MIMO downlink with multi-stream transmission to multi-antenna users.
  • Figure 2: Satellite participation factors of the aggregated channel eigenmodes when UE-side array responses are orthogonal. In this case, each SAT contributes essentially to one eigenmode and close to zero to the others.
  • Figure 3: Satellite participation factors of the aggregated channel eigenmodes when UE-side array responses are not orthogonal. In this case, the eigenmodes are shared across multiple SATs, so each SAT contributes to more than one eigenmode.
  • Figure 4: Comparison between the exact and approximate SE expressions in \ref{['eq:achievable_rate']} and \ref{['eq:achievable_rate_approx']}, respectively.
  • Figure 5: Comparison of joint and streamwise transmissions under UE-side orthogonality.
  • ...and 4 more figures

Theorems & Definitions (2)

  • Remark 1
  • Remark 2