4.7 Article

Pilot Assignment for Cell Free Massive MIMO Systems Using a Weighted Graphic Framework

Journal

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 70, Issue 6, Pages 6190-6194

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2021.3076440

Keywords

Contamination; Uplink; Throughput; Massive MIMO; Downlink; Decontamination; Signal to noise ratio; Cell-free massive MIMO; pilot contamination; Max k-Cut; weighted graphic framework

Funding

  1. National Natural Science Foundation of China [51977153, 51577046]
  2. State Key Program of National Natural Science Foundation of China [51637004, 51777050]
  3. National Key Research and Development Plan Important Scientific Instruments and Equipment Development [2016YFF0102200]
  4. Equipment Research Project in Advance [41402040301]

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This paper proposes an efficient pilot assignment scheme using a weighted graphic framework to address pilot contamination in cell-free massive MIMO networks. By introducing a novel metric and constructing a weighted pilot contamination graph, the scheme is capable of significantly improving throughput with low complexity.
Pilot contamination resulting from pilot reuse seriously restricts the communication performance of the cell-free massive multiple-input multiple-output (MIMO) networks. To deal with this problem, an efficient pilot assignment scheme using a weighted graphic framework is proposed in this correspondence paper. Specifically, a novel metric is first introduced for capturing the severity of potential mutual pilot contamination in cell-free topology. On this basis, the weighted pilot contamination graph is constructed to depict the dynamic interference relationship corresponding to the network. Then, the pilot assignment optimization is mapped to the Max k-Cut problem, and a heuristic algorithm is exploited to realize pilot decontamination. Numerical simulation results reveal the superior performance of the proposed scheme, which is capable of achieving a significant improvement in throughput with low complexity.

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