Probabilistic shaping simplifies mimo demodulation for improved wireless reception
From Priors to Projections: Geometry and simplified MIMO demodulation of probabilistic shaping
Information Theory
Summary
Wireless devices send data using signals with many possible states, called constellations. Probabilistic shaping changes how often each state is used to improve signal quality. The authors show that if these signals follow a certain pattern, detecting them in complex multi-antenna setups can be done using the same steps as simpler signals, just with a quick adjustment first. This makes receivers simpler and faster without losing performance.
What this means in practice
- •For wireless communication engineers: Optimize receiver design for multi-antenna systems by reusing existing demodulation methods with minor adjustments to handle shaped signals.
- •For signal processing teams: Reduce computational complexity of adaptive decoding algorithms in MIMO receivers when using probabilistic shaping.
Authors
Kirill Ivanov, Wei Yang, Mahmoud Taherzadeh Boroujeni, Jing Jiang
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.