Leveraging von Mises Message-Passing for Massive MIMO Detection
Information Theory
Summary
The authors address the challenge of detecting signals in large wireless systems called massive MIMO, where traditional best methods are very complex. They develop a detector using belief propagation that treats signal phases on a circle and uses a special distribution to simplify calculations. Their method works well with PSK modulated signals and keeps complexity low regardless of modulation size. They also adapt their approach for cases when the channel information is imperfect and compare its performance to existing Gaussian-based methods.
Authors
Sweta Suresh, Arthur Michon, Charly Poulliat, Maxime Guillaud, Claire Goursaud
Abstract
Detection in massive multiple input multiple output (mMIMO) systems suffers from exponential complexity while using optimal decoders like maximum a posteriori (MAP). We propose a belief propagation-based detector based on directional statistics, applicable to mMIMO systems relying on PSK modulations. Thanks to a continuous relaxation of the PSK modulation to the unit circle, and to the use of von Mises parametric representations of the messages to obtain sparse representations of the (generally infinite dimensional) messages, the proposed method allows for approximate detection with a low complexity which does not depend on the PSK modulation order. Extensions of the algorithm to imperfect channel realizations are also present. We quantify the performance and complexity of the proposed approach and compare it with detection algorithms based on Gaussian approximation.