Distributed Cell-Free Massive MIMO versus Cellular Massive MIMO under UE Hardware Impairments

LI Ning; FAN Pingzhi

doi:10.23919/cje.2023.00.045

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Ning LI and Pingzhi FAN, “Distributed Cell-Free Massive MIMO versus Cellular Massive MIMO under UE Hardware Impairments,” Chinese Journal of Electronics, vol. 33, no. 4, pp. 1–12, 2024 doi: 10.23919/cje.2023.00.045

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Ning LI and Pingzhi FAN, “Distributed Cell-Free Massive MIMO versus Cellular Massive MIMO under UE Hardware Impairments,” Chinese Journal of Electronics, vol. 33, no. 4, pp. 1–12, 2024 doi: 10.23919/cje.2023.00.045

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Distributed Cell-Free Massive MIMO versus Cellular Massive MIMO under UE Hardware Impairments

doi: 10.23919/cje.2023.00.045

LI Ning^,,
FAN Pingzhi

1.
Southwest Jiaotong University, Chengdu and 611756, China

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Author Bio:
Ning LI is currently working toward the Ph.D. degree with the School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China. His research interests include signal processing and optimization in wireless communications, massive MIMO, resource allocation, estimation theory, energy efficiency, deep learning, and green communications. (Email: lining@my.swjtu.edu.cn)

Pingzhi FAN received the M.S. degree in Computer Science from Southwest Jiaotong University, China, in 1987, and the Ph.D. degree in Electronic Engineering from Hull University, U.K., in 1994. He is currently a chair professor in the School of Information Science & Technology, Southwest Jiaotong University, China. His research interests include high mobility wireless communications, signal design & coding, etc. (Email: pzfan@swjtu.edu.cn)
Corresponding author: Email: lining@my.swjtu.edu.cn
Received Date: 2023-02-14
Accepted Date: 2023-09-07

Available Online: 2024-03-07

Abstract

Abstract

This paper first investigates and compares the uplink spectral efficiency (SE) of distributed cell-free (CF) massive multiple-input multiple-output (mMIMO) and cellular mMIMO networks, both with user equipment (UE) hardware impairments. We derive a lower bound on the uplink ergodic channel capacity of the cellular mMIMO with UE hardware impairments, based on which we determine the optimal receive combining that maximizes the instantaneous effective signal-to-interference-and-noise ratio. Then, a lower bound on the uplink capacity of a distributed CF mMIMO with UE hardware impairments is derived using the use-and-then-forget technique. On this basis, the optimum large-scale fading decoding vector is found using generalized Rayleigh entropy. By using three combining schemes of minimum mean-square error (MMSE), regularized zero-forcing (RZF), and maximum ratio, the uplink SEs of distributed CF mMIMO and cellular mMIMO networks are analyzed and compared. The results show that the two-layer decoding distributed CF mMIMO network with MMSE combining outperforms the cellular mMIMO network, and the advantage is more evident as the hardware impairment factor increases. Finally, the uplink energy efficiency (EE) of the distributed CF mMIMO networks is analyzed and evaluated through the established realistic power consumption model with hardware impairments. Simulation results show that two-layer decoding provides higher SE and EE than single-layer decoding. In addition, RZF achieves almost the same SE and EE as MMSE in a two-layer decoding architecture.
- Cell-free mMIMO,
- cellular mMIMO,
- hardware impairments,
- uplink spectral efficiency,
- optimal receive combining,
- uplink energy efficiency

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References(45)

References

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