Abstract
The article describes a numerical approach for Massive MIMO channel modeling that accounts for the effects of electromagnetic coupling between a user and the receiving device. The modeling is performed by a combination of the Finite-Difference Time-Domain and the Ray-Tracing methods, supplemented with a stochastic geometry model of the propagation environment. The influence of user-coupling on the channel properties was studied statistically using the singular value spread and matrix power ratio metrics of the channel correlation matrix. The time-averaged Poynting vector distribution in the near-field of the receiver was evaluated using a realistic human phantom model and the Maximum Ratio Transmission precoding scheme in the downlink. The average enhancement of the time-average Poynting vector magnitude at the receiver location, compared to the surrounding area, was found to be around 10 dB when using 36 antenna elements at the base station. The electromagnetic field exposure of the phantom was assessed in terms of the 10g-average peak-spatial Specific Absorption Rate and compared with the existing public guidelines. Comparison of the EMF and exposure results provides a new perspective on the future regulatory procedures.
Highlights
R ECENT years brought many advances to the development of generation wireless networking
A global maximum of |A| was observed around this azimuth angle in the horizontal plane (θ = π/2)
A novel numerical approach to massive Multiple-Input Multiple-Output (MIMO) channel modeling based on the RT and the Finite-Difference Time-Domain (FDTD) methods was presented
Summary
R ECENT years brought many advances to the development of generation wireless networking. Increased throughput, capacity and connection density are some of the requirements that 5G wireless technology must meet. Massive Multiple-Input Multiple-Output (MIMO) is one of the promising candidates to fulfill these requirements. Its operation is based on simultaneous utilization of a very large number of base-station (BS) antennas, (linear) transmission precoding and reception decoding schemes. An accurate channel model is needed to facilitate its design and deployment. Both deterministic [1] and probabilistic [2], [3] approaches to Massive MIMO channel modeling are currently being developed
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