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Massive MIMO-OFDM Channel Acquisition with Multi-group Adjustable Phase Shift Pilots

Yu Zhao, Li You, Jinke Tang, Mengyu Qian, Bin Jiang, Xiang-Gen Xia, Xiqi Gao

TL;DR

This work tackles the CSI acquisition overhead in massive MIMO-OFDM by introducing multi-group adjustable phase shift pilots (MAPSPs) that exploit angle-delay sparsity. It extends APSP to multiple pilot groups, derives MMSE channel estimation with intra- and inter-group interference analysis, and proposes a Zadoff-Chu sequence-based MAPSP implementation with received-signal pre-processing and a phase scheduling algorithm. The paper provides unified interference expressions and optimality conditions for basic pilot matrices, and demonstrates substantial spectral efficiency gains in mobility scenarios with large numbers of users. The results indicate MAPSP can significantly improve SE without increasing pilot overhead, making it promising for high-mobility, high-density wireless systems, albeit with reliance on prior statistical CSI and increased preprocessing complexity.

Abstract

Massive multiple-input multiple-output - orthogonal frequency division multiplexing (MIMO-OFDM) systems face the challenge of high channel acquisition overhead while providing significant spectral efficiency (SE). Adjustable phase shift pilots (APSPs) are an effective technique to acquire channels with low overhead by exploiting channel sparsity. In this paper, we extend it to multiple groups and propose multi-group adjustable phase shift pilots (MAPSPs) to improve SE further. We first introduce a massive MIMO-OFDM system model and transform the conventional channel model in the space-frequency domain to the angle-delay domain, obtaining a sparse channel matrix. Then, we propose a method of generating MAPSPs through multiple basic sequences and investigate channel estimation processes. By analyzing the components of pilot interference, we elucidate the underlying mechanism by which interference affects MMSE estimation. Building upon this foundation, we demonstrate the benefit of phase scheduling in MAPSP channel estimation and establish the optimal design condition tailored for scheduling. Furthermore, we propose an implementation scheme based on Zadoff-Chu sequences that includes received signal pre-processing and pilot scheduling methods to mitigate pilot interference. Simulation results indicate that the MAPSP method achieves a lower mean square error (MSE) of estimation than APSP and significantly enhances SE in mobility scenarios.

Massive MIMO-OFDM Channel Acquisition with Multi-group Adjustable Phase Shift Pilots

TL;DR

This work tackles the CSI acquisition overhead in massive MIMO-OFDM by introducing multi-group adjustable phase shift pilots (MAPSPs) that exploit angle-delay sparsity. It extends APSP to multiple pilot groups, derives MMSE channel estimation with intra- and inter-group interference analysis, and proposes a Zadoff-Chu sequence-based MAPSP implementation with received-signal pre-processing and a phase scheduling algorithm. The paper provides unified interference expressions and optimality conditions for basic pilot matrices, and demonstrates substantial spectral efficiency gains in mobility scenarios with large numbers of users. The results indicate MAPSP can significantly improve SE without increasing pilot overhead, making it promising for high-mobility, high-density wireless systems, albeit with reliance on prior statistical CSI and increased preprocessing complexity.

Abstract

Massive multiple-input multiple-output - orthogonal frequency division multiplexing (MIMO-OFDM) systems face the challenge of high channel acquisition overhead while providing significant spectral efficiency (SE). Adjustable phase shift pilots (APSPs) are an effective technique to acquire channels with low overhead by exploiting channel sparsity. In this paper, we extend it to multiple groups and propose multi-group adjustable phase shift pilots (MAPSPs) to improve SE further. We first introduce a massive MIMO-OFDM system model and transform the conventional channel model in the space-frequency domain to the angle-delay domain, obtaining a sparse channel matrix. Then, we propose a method of generating MAPSPs through multiple basic sequences and investigate channel estimation processes. By analyzing the components of pilot interference, we elucidate the underlying mechanism by which interference affects MMSE estimation. Building upon this foundation, we demonstrate the benefit of phase scheduling in MAPSP channel estimation and establish the optimal design condition tailored for scheduling. Furthermore, we propose an implementation scheme based on Zadoff-Chu sequences that includes received signal pre-processing and pilot scheduling methods to mitigate pilot interference. Simulation results indicate that the MAPSP method achieves a lower mean square error (MSE) of estimation than APSP and significantly enhances SE in mobility scenarios.

Paper Structure

This paper contains 17 sections, 2 theorems, 65 equations, 5 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Notations
  3. Outline
  4. Massive MIMO-OFDM System Model
  5. System Setup
  6. Channel Model
  7. Channel Acquisition With MAPSPs
  8. Structure of MAPSP
  9. Channel Estimation
  10. Analysis Of Pilot Interference
  11. MAPSP Implementation Scheme
  12. Received Signal Pre-processing Method
  13. Phase Scheduling Algorithm
  14. Numerical Results
  15. Conclusion
  16. ...and 2 more sections

Key Result

Proposition 1

The ADPCM of different basic pilot matrices displays characteristics of cyclic shifts with phase differences, as follows, where is the ADPCM of basic pilot matrices.

Figures (5)

  • Figure 1: Matrix structure diagram of pilot interference term.
  • Figure 2: The absolute values of first column elements of ADPCM generated by dual basic pilot matrices
  • Figure 3: Comparison of channel estimation MMSE by APSP and MAPSP under different UT numbers
  • Figure 4: Comparison of the achievable spectral efficiency between the APSP and MAPSP methods under different UT numbers
  • Figure : MAPSP Scheduling Algorithm

Theorems & Definitions (2)

  • Proposition 1
  • Proposition 2