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Is Downlink Training Necessary for User-Centric Cell-Free RSMA Systems With Mobile Users?

Ravi Kiran Palla, Dheeraj Naidu Amudala, Ronit Budhiraja

TL;DR

The paper addresses spectral efficiency in RSMA-enabled, multi-cluster user-centric CF mMIMO with mobile UEs, showing that downlink DL CSI pilots are essential to unlock RSMA gains under channel aging and pilot contamination. It proposes a DL-pilot-aided RSMA framework with common and private DL training phases and derives LMMSE-based channel estimates under aging for effective SIC and interference mitigation. Through simulations, it demonstrates that RSMA achieves superior SE over SDMA when DL pilots are used, with performance improvements amplified by more clusters, especially at higher UE velocities, albeit with sensitivity to resource block length. The findings highlight practical design guidelines for deploying RSMA in dynamic CF mMIMO environments, where DL CSI accuracy and cluster management critically influence performance gains and system scalability.

Abstract

We study the spectral efficiency (SE) of a ratesplitting multiple access (RSMA) enabled multi-clustered cell-free (CF) massive multiple-input multiple-output (mMIMO) system. The access points (APs) in each cluster serve mobile user equipments (UEs) by employing RSMA. The UEs employ successive interference cancellation to decode their data. This work emphasizes the role of downlink (DL) pilots in realizing RSMA benefits in practical CF systems with spatially-correlated Rician channels which observe random phase shifts, pilot contamination, and channel aging due to UE mobility. We numerically show that DL pilots are required for RSMA in user-centric CF mMIMO systems with channel aging to outperform spatial division multiple access. We show that the degraded channel quality due to higher UE velocity and longer resource block lengths significantly reduces the RSMA SE. Increasing the number of clusters can compensate for the SE loss.

Is Downlink Training Necessary for User-Centric Cell-Free RSMA Systems With Mobile Users?

TL;DR

The paper addresses spectral efficiency in RSMA-enabled, multi-cluster user-centric CF mMIMO with mobile UEs, showing that downlink DL CSI pilots are essential to unlock RSMA gains under channel aging and pilot contamination. It proposes a DL-pilot-aided RSMA framework with common and private DL training phases and derives LMMSE-based channel estimates under aging for effective SIC and interference mitigation. Through simulations, it demonstrates that RSMA achieves superior SE over SDMA when DL pilots are used, with performance improvements amplified by more clusters, especially at higher UE velocities, albeit with sensitivity to resource block length. The findings highlight practical design guidelines for deploying RSMA in dynamic CF mMIMO environments, where DL CSI accuracy and cluster management critically influence performance gains and system scalability.

Abstract

We study the spectral efficiency (SE) of a ratesplitting multiple access (RSMA) enabled multi-clustered cell-free (CF) massive multiple-input multiple-output (mMIMO) system. The access points (APs) in each cluster serve mobile user equipments (UEs) by employing RSMA. The UEs employ successive interference cancellation to decode their data. This work emphasizes the role of downlink (DL) pilots in realizing RSMA benefits in practical CF systems with spatially-correlated Rician channels which observe random phase shifts, pilot contamination, and channel aging due to UE mobility. We numerically show that DL pilots are required for RSMA in user-centric CF mMIMO systems with channel aging to outperform spatial division multiple access. We show that the degraded channel quality due to higher UE velocity and longer resource block lengths significantly reduces the RSMA SE. Increasing the number of clusters can compensate for the SE loss.
Paper Structure (23 sections, 2 theorems, 26 equations, 2 figures, 1 table)

This paper contains 23 sections, 2 theorems, 26 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Transmission Protocol
  4. UL Training Interval
  5. DL Training Interval
  6. Data Transmission Interval
  7. Channel Model With Aging
  8. UL training phase
  9. DL Training
  10. DL common training
  11. DL private training
  12. RSMA Data Transmission
  13. RSMA Decoding at the UE
  14. Common Signal Decoding
  15. Private Data
  16. ...and 8 more sections

Key Result

Proposition 1

The LMMSE estimate of the DL common channel $a_{lk}^{c}[\lambda]$ in a clustered user-centric CF system with spatially-correlated Rician fading channels is given as follows The simplified closed-form expressions for the scalars $\bar{a}_{lk}^{c}[\lambda]$, $\theta_{lk}^{c}$, $\psi_{lk}^{c}$ and $\bar{y}_{lk}^{c}[\tilde{t}_l]$ are given in Appendix DL common channel estimation. The covariances of

Figures (2)

  • Figure 1: Resource block schematics for DL training based CF RSMA system.
  • Figure 2: RSMA SE, with and without DL pilots, by varying the number of UEs $K$; RSMA and SDMA comparison by varying b) number of clusters $L$ and UE velocity $v_k$; and c) resource block length $\tau_c$ for different UE velocity $v_k$.

Theorems & Definitions (2)

  • Proposition 1
  • Proposition 2