研究目的
Investigating the performance of an angular MIMO (A-MIMO) architecture for underwater wireless optical communications (UWOCs) in comparison to conventional MIMO (C-MIMO) systems, focusing on channel modelling and capacity under misalignment and dynamic channel conditions.
研究成果
The angular MIMO (A-MIMO) architecture offers significant advantages over conventional MIMO (C-MIMO) systems for underwater wireless optical communications, including fixed scale imaging and robustness to misalignment errors. Numerical results demonstrate that A-MIMO maintains link capacity better than C-MIMO under misalignment and dynamic channel conditions, making it a promising candidate for underwater wireless sensor networks (UWSNs).
研究不足
The study is limited to numerical simulations using MCRT and does not include practical testing in real-world underwater conditions. The performance of A-MIMO is compared only to C-MIMO under specific conditions, and the impact of more severe environmental factors is not explored.
1:Experimental Design and Method Selection:
The study proposes an angular MIMO (A-MIMO) architecture for UWOCs and contrasts it with conventional MIMO (C-MIMO) systems. It employs Monte Carlo ray tracing (MCRT) for numerical simulations to characterize the channel.
2:Sample Selection and Data Sources:
The study uses clear and coastal seawater conditions for channel modelling.
3:List of Experimental Equipment and Materials:
Parameters for MCRT setup include transmitter and receiver lens diameters, focal lengths, number of LEDs and PDs, spacing between them, optical transmitted power, working wavelength, Gaussian beam waist, divergence angle, receiver field of view, responsitivity, and thermal noise variance.
4:Experimental Procedures and Operational Workflow:
The study involves simulating the propagation of optical rays from the source to the receiver in underwater conditions using MCRT, calculating angle of arrival distributions, and estimating channel capacity.
5:Data Analysis Methods:
The channel capacity is estimated using a Gaussian noise channel model, considering the gain matrix of the channel and noise variance.
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