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Asynchronous Cell-Free Massive MIMO-OFDM: Mixed Coherent and Non-Coherent Transmissions

Guoyu Li, Shaochuan Wu, Changsheng You, Wenbin Zhang, Guanyu Shang

TL;DR

Numerical results demonstrate that the mixed coherent and non-coherent transmissions can effectively improve the sum-rate of CF mMIMO-OFDM systems under asynchronous reception.

Abstract

In this letter, we analyze the performance of mixed coherent and non-coherent transmissions approach, which can improve the performance of cell-free multiple-input multiple-output orthogonal frequency division multiplexing (CF mMIMO-OFDM) systems under asynchronous reception. To this end, we first obtain the achievable downlink sum-rate for the mixed coherent and non-coherent transmissions, and then provide a closed-form expression for the case with the maximum ratio precoding. Subsequently, an efficient clustering algorithm is proposed to group access points into different clusters with the same quantized phase shift in each cluster. Numerical results demonstrate that the mixed coherent and non-coherent transmissions can effectively improve the sum-rate of CF mMIMO-OFDM systems under asynchronous reception.

Asynchronous Cell-Free Massive MIMO-OFDM: Mixed Coherent and Non-Coherent Transmissions

TL;DR

Numerical results demonstrate that the mixed coherent and non-coherent transmissions can effectively improve the sum-rate of CF mMIMO-OFDM systems under asynchronous reception.

Abstract

In this letter, we analyze the performance of mixed coherent and non-coherent transmissions approach, which can improve the performance of cell-free multiple-input multiple-output orthogonal frequency division multiplexing (CF mMIMO-OFDM) systems under asynchronous reception. To this end, we first obtain the achievable downlink sum-rate for the mixed coherent and non-coherent transmissions, and then provide a closed-form expression for the case with the maximum ratio precoding. Subsequently, an efficient clustering algorithm is proposed to group access points into different clusters with the same quantized phase shift in each cluster. Numerical results demonstrate that the mixed coherent and non-coherent transmissions can effectively improve the sum-rate of CF mMIMO-OFDM systems under asynchronous reception.
Paper Structure (12 sections, 2 theorems, 19 equations, 2 figures, 1 algorithm)

This paper contains 12 sections, 2 theorems, 19 equations, 2 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Models
  4. Block-fading Channel Model
  5. Asynchronous Reception Channel Model
  6. Uplink Channel Estimation
  7. Mixed Coherent and Non-Coherent Transmissions Under Asynchronous Reception
  8. Mixed Coherent and Non-Coherent Transmissions
  9. AP Clustering Algorithms
  10. Numerical Results
  11. Conclutions
  12. Proff of Proposition 1

Key Result

Proposition 1

Based on the received signal in receive_signal, when user $k$ uses successive interference cancellation (SIC) to detect the signals sent by $L_{k}$ coherent clusters (with the perfect SIC and arbitrary decoding order), the achievable downlink sum-rate under asynchronous reception is given by where the effective SINR is given in SINR, as shown on top of this page.

Figures (2)

  • Figure 1: CDF of sum-rate comparing the synchronous reception, asynchronous reception, and mixed transmissions.
  • Figure 2: Sum-rate of users versus the number of antennas per AP.

Theorems & Definitions (5)

  • Remark 1
  • Proposition 1
  • proof
  • Corollary 1
  • proof