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Cell-Free Massive MIMO with Multi-Antenna Users and Phase Misalignments: A Novel Partially Coherent Transmission Framework

Unnikrishnan Kunnath Ganesan, Tung Thanh Vu, Erik G. Larsson

TL;DR

This work tackles phase misalignment in cell-free massive MIMO by proposing a partially coherent transmission framework that groups APs into phase-aligned clusters while allowing non-coherent transmission across clusters. It introduces a clustering algorithm based on a reference synchronization distance, and develops MMSE-inspired precoding/combining and a greedy data-stream allocation to maximize downlink sum rate within the clustered topology. The results show that, with a carefully chosen reference distance, the PC framework can achieve sum rates close to the ideal fully coherent performance, highlighting a practical path toward scalable CF deployments. The contributions provide a feasible route to harness the gains of multi-antenna CF networks without network-wide phase synchronization, with clear design guidelines for clustering, processing, and data-stream allocation.

Abstract

Cell-free massive multiple-input multiple-output (MIMO) is a promising technology for next-generation communication systems. This work proposes a novel partially coherent (PC) transmission framework to cope with the challenge of phase misalignment among the access points (APs), which is important for unlocking the full potential of cell-free massive MIMO technology. With the PC operation, the APs are only required to be phase-aligned within clusters. Each cluster transmits the same data stream towards each user equipment (UE), while different clusters send different data streams. We first propose a novel algorithm to group APs into clusters such that the distance between two APs is always smaller than a reference distance ensuring the phase alignment of these APs. Then, we propose new algorithms that optimize the combining at UEs and precoding at APs to maximize the downlink sum data rates. We also propose a novel algorithm for data stream allocation to further improve the sum data rate of the PC operation. Numerical results show that the PC operation using the proposed framework with a sufficiently small reference distance can offer a sum rate close to the sum rate of the ideal fully coherent (FC) operation that requires network-wide phase alignment. This demonstrates the potential of PC operation in practical deployments of cell-free massive MIMO networks.

Cell-Free Massive MIMO with Multi-Antenna Users and Phase Misalignments: A Novel Partially Coherent Transmission Framework

TL;DR

This work tackles phase misalignment in cell-free massive MIMO by proposing a partially coherent transmission framework that groups APs into phase-aligned clusters while allowing non-coherent transmission across clusters. It introduces a clustering algorithm based on a reference synchronization distance, and develops MMSE-inspired precoding/combining and a greedy data-stream allocation to maximize downlink sum rate within the clustered topology. The results show that, with a carefully chosen reference distance, the PC framework can achieve sum rates close to the ideal fully coherent performance, highlighting a practical path toward scalable CF deployments. The contributions provide a feasible route to harness the gains of multi-antenna CF networks without network-wide phase synchronization, with clear design guidelines for clustering, processing, and data-stream allocation.

Abstract

Cell-free massive multiple-input multiple-output (MIMO) is a promising technology for next-generation communication systems. This work proposes a novel partially coherent (PC) transmission framework to cope with the challenge of phase misalignment among the access points (APs), which is important for unlocking the full potential of cell-free massive MIMO technology. With the PC operation, the APs are only required to be phase-aligned within clusters. Each cluster transmits the same data stream towards each user equipment (UE), while different clusters send different data streams. We first propose a novel algorithm to group APs into clusters such that the distance between two APs is always smaller than a reference distance ensuring the phase alignment of these APs. Then, we propose new algorithms that optimize the combining at UEs and precoding at APs to maximize the downlink sum data rates. We also propose a novel algorithm for data stream allocation to further improve the sum data rate of the PC operation. Numerical results show that the PC operation using the proposed framework with a sufficiently small reference distance can offer a sum rate close to the sum rate of the ideal fully coherent (FC) operation that requires network-wide phase alignment. This demonstrates the potential of PC operation in practical deployments of cell-free massive MIMO networks.
Paper Structure (20 sections, 2 theorems, 56 equations, 10 figures, 4 algorithms)

This paper contains 20 sections, 2 theorems, 56 equations, 10 figures, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Literature and Discussions
  3. Research Gap and Main Contributions
  4. Motivating Example
  5. Phase Alignment Scenario
  6. Phase Misalignment Scenario
  7. Transmit Only from the Best AP
  8. Transmit Independently Coded Data Streams with Optimal Beamforming and Successive Interference Cancellation
  9. Transmit Independently Coded Data Streams with Sub-optimal Beamforming
  10. System Model and Problem Formulation
  11. AP Clustering Algorithm
  12. Precoding and Combining Optimization Algorithm for Sum Rate Maximization
  13. Data Stream Allocation
  14. Complexity Analysis
  15. AP Clustering Algorithm
  16. ...and 5 more sections

Key Result

Proposition 1

For given $\left\{\overline{\mathbf{W}}_{kc}\right\}$, the instantaneous rate $R_{kc} (\overline{\mathop{\mathrm{\mathbf{V}}}\nolimits}_{kc}) = \log_2\left\vert \mathbf{I}_{d_{kc}} + \rho \overline{\mathbf{H}}_{kc}^\mathrm{H} \overline{\mathbf{Q}}_{kc}^{-1} \overline{\mathbf{H}}_{kc} \right\vert$ i which results in the achievable rate for $k$ in a broadcast channel given by eqn:RateWith_MMSE_exp

Figures (10)

  • Figure 1: Achievable rates with different beamforming designs and transmission schemes for the example considered in \ref{['eqn:analyticalExample1']} and \ref{['eqn:analyticalExample2']}.
  • Figure 2: Clustering
  • Figure 3: Comparison of , , and under different network settings. Parameters for the plot: $K=5$, $N=2$, $D=200$ m, and $d_{kc}=2 ~\forall k,c$.
  • Figure 4: Performance of the proposed clustering algorithm. Parameters for the plot: $L=10$, $K=5$, $M=5$, $N=2$, $D=200$ m, and $d_{kc}=2 ~ \forall k,c$.
  • Figure 5: Performance of scheme compared to scheme in antonioli2023mixed. Parameters for the plot: $L=10$, $M=5$, $K=5$, and $N=1$.
  • ...and 5 more figures

Theorems & Definitions (4)

  • Proposition 1
  • proof
  • Proposition 2
  • proof