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Effect of Realistic Oscillator Phase Noise on the Performance of Cell-Free Massive MIMO Systems

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arxiv 2405.04099 v2 pith:XGWBFSFO submitted 2024-05-07 cs.NI eess.SP

classification cs.NIeess.SP
keywords cell-freesystemsphasemassivemimomodelnoiseoscillator
verification ladder T0 review T1 audit T2 compute T3 formal
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As the demand for 6G technologies continues to grow, the radio access infrastructure is expected to become increasingly dense. Cell-free (CF) Massive MIMO systems provide remarkable flexibility by enabling coherent service to users through multiple Access Points (APs). This innovative paradigm necessitates precise and stable phase synchronization. This paper examines the standardized 5G New Radio (NR) framework, focusing on subcarrier spacing, OFDM symbol duration, and allocation, while investigating the impact of Phase Noise (PN) on the performance of scalable massive MIMO cell-free systems. Unlike existing studies that typically employ a simplified model of a free-running oscillator characterized by a Wiener process, we present a realistic phase noise model inspired by actual hardware, designed to accurately capture the Local Oscillator (LO) phase drift. Furthermore, our PN model extends its applicability beyond cell-free systems, making it relevant for any RF system operating within the sub-6 GHz band. This model provides a robust foundation for the practical design of cell-free systems, encompassing numerology and pilot allocation strategies. Our findings reveal that even cost-effective low-cost Local Oscillators can achieve sufficient stability, resulting in negligible degradation of uplink Spectral Efficiency (SE) within the standardized 5G Transmission Time Interval of 1 ms. These results affirm the viability of cell-free massive MIMO systems based on 5G standards and their potential integration into future 6G networks.

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Cited by 1 Pith paper

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    A hybrid CNN-ShuffleNet-Transformer model claims the best accuracy-efficiency trade-off on a new 3,060-scenario CSI prediction benchmark, but FDD gains and robustness claims rest on point estimates without error bars.

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