Chang-Jen Wang

TL;DR: A Bayes-optimal JCD estimator is developed using a recent technique based on approximate message passing that allows the efficient evaluation of the performance of quantized massive MIMO systems and provides insights into effective system design.

TL;DR: This paper develops efficient and universal algorithms for a downlink massive MU-MIMO system with finite-alphabet precodings based on the alternating direction method of multipliers framework and develops two algorithms called iterative discrete estimation (IDE) and IDE2.

TL;DR: In this paper, the authors developed efficient and universal algorithms for a downlink massive MU-MIMO system with finite-alphabet precodings based on the alternating direction method of multipliers (ADMM) framework.

TL;DR: This research considers the channel estimation problem to fill this gap and proposes a two-step channel estimator by utilizing the different features of mixed outputs, which yields significantly lower mean square errors than the conventional maximum likelihood estimator.

TL;DR: A phase retrieval solution that aims to recover signals from noisy phaseless measurements by leveraging the core framework of GEC-SR, and proposes a novel decentralized algorithm called deGEC- SR, which exhibits excellent performance similar to G EC-SR but runs tens to hundreds of times faster.