4.7 Article

A Novel Beam Domain Channel Model for B5G Massive MIMO Wireless Communication Systems

Journal

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 72, Issue 4, Pages 4143-4156

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2022.3222771

Keywords

Channel models; Massive MIMO; Computational modeling; MIMO communication; Antenna arrays; Antennas; Analytical models; B5G; beam domain channel model; GBSM; massive MIMO; statistical properties

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A novel beam domain channel model (BDCM) is proposed for B5G massive MIMO wireless communication systems, considering the realistic spherical wavefront caused by near-field effect. This model is transformed from a geometry-based stochastic model (GBSM) through specific algorithms. The correlations, computational complexity, and channel capacity of both models are compared, showing that the novel BDCM has lower complexity and similar accuracy when the number of beams is sufficiently large.
In this paper, a novel beam domain channel model (BDCM) is proposed for beyond fifth generation (B5G) massive multiple-input multiple-output (MIMO) wireless communication systems. Different from conventional massive MIMO BDCMs which assumed the far-field plane wavefront effect, the proposed BDCM considers more realistic spherical wavefront caused by near-field effect. We transform a massive MIMO geometry-based stochastic model (GBSM) from the antenna domain to the beam domain through specific algorithms to obtain the novel BDCM. The space-time-frequency correlations of both the GBSM and BDCM are studied, and the correlations for both models at the cluster level are similar. We also compare the quasi-stationary distance (QSD), computational complexity, and channel capacity for both models. Results show that in comparison to the GBSM, the novel BDCM has lower complexity and similar accuracy if the number of beams is sufficiently large. Furthermore, we compare the singular value spreads (SVSs) of both channel models with channel measurements under the same conditions. Both the novel BDCM and GBSM are close to the measurement. Through the above analysis, the novel BDCM is proved to be more convenient for information theory and signal processing researches than the conventional GBSMs.

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