Motivated by the cell-free and massive MIMO (multiple input multiple outputs) communication scenarios, the number of power amplifiers (PA), digital to analog converters (DAC), etc., is increased. Thus, using coarse quantized transmission reduces the channel's hardware cost and nonlinear effects. More details can be found here.

In this project, we investigate algorithms for mapping modulated data to coarsely quantized signals and compare their information rates. 

The student needs an understanding of information theory and communication systems.

The estimation of a linearly transformed random vector from noisy measurements can be done efficiently by Approximate Message Passing (AMP). AMP is a computationally efficient algorithm that approximates max-sum and sum-product loopy belief propagation (BP). The generalization [1] extends AMP to allow arbitrary separable prior distributions and (non-linear) measurement channels.

The student should be able to understand and interpret the blocks of the generalized AMP. In addition, it should be understood that generalized AMP is a quadratic approximation of loopy BP for MAP estimation.

The literature research should include at least [1].

[1] S. Rangan, "Generalized approximate message passing for estimation with random linear mixing," 2011 IEEE International Symposium on Information Theory Proceedings, St. Petersburg, Russia, 2011, pp. 2168-2172, doi: 10.1109/ISIT.2011.6033942.

Prerequisites