Problem
Research questions and friction points this paper is trying to address.
Probabilistic Shaping
MIMO
QAM
From Priors to Projections: Geometry and simplified MIMO demodulation of probabilistic shaping
🤖 AI Summary
该论文研究了在MIMO系统中通过使符号遵循Maxwell-Boltzmann分布简化概率整形解调问题，提出的方法等效于对均匀星座进行简单的预处理步骤。
📝 Abstract
Probabilistic shaping (PS) is a well-known method to achieve improved performance upon a regular quadrature amplitude modulation (QAM) by taking the target constellation and making the distribution of underlying points non-uniform. It has been extensively studied over the years for the additive white Gaussian noise (AWGN) and Rayleigh fading channels. However, the potential of probabilistic shaping in the multiple-input and multiple-output (MIMO) setting needs further investigations.
In this paper, we prove that if shaped symbols follow Maxwell-Boltzmann distribution, the optimal maximum a posteriori (MAP) detection is equivalent to the case of uniform constellation with a simple preprocessing step. Our approach has multiple benefits. It enables to utilize the same processing chain for both shaped and unshaped system, which simplifies the receiver architecture. This technique can be applied for both linear MMSE and nonlinear (near-)MAP demapper types. In addition, the complexity of adaptive methods such as sphere decoding can be reduced.
Innovation
Methods, ideas, or system contributions that make the work stand out.
probabilistic shaping
Maxwell-Boltzmann distribution
MAP detection
receiver architecture simplification
sphere decoding
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