Deep neural network (DNN)-based channel code designs have recently gained interest as an alternative to conventional coding schemes, particularly for channels in which existing codes do not provide satisfactory performance. Coding in the presence of feedback is one such problem, for which promising results have recently been obtained by various DNN-based coding architectures.
Welcome to the ninth (June 2022) issue of the 麻豆传媒AV Journal on Selected Areas in Information Theory (JSAIT), dedicated to 鈥淒istributed Coding and Computation鈥.
Future wireless systems are trending towards higher carrier frequencies that offer larger communication bandwidth but necessitate the use of large antenna arrays. Signal processing techniques for channel estimation currently deployed in wireless devices do not scale well to this 鈥渉igh-dimensional鈥 regime in terms of performance and pilot overhead.
Graph prediction problems prevail in data analysis and machine learning. The inverse prediction problem, namely to infer input data from given output labels, is of emerging interest in various applications. In this work, we develop invertible graph neural network (iGNN), a deep generative model to tackle the inverse prediction problem on graphs by casting it as a conditional generative task.
Countless signal processing applications include the reconstruction of signals from few indirect linear measurements. The design of effective measurement operators is typically constrained by the underlying hardware and physics, posing a challenging and often even discrete optimization task. While the potential of gradient-based learning via the unrolling of iterative recovery algorithms has been demonstrated, it has remained unclear how to leverage this technique when the set of admissible measurement operators is structured and discrete.
We study the problem of reconstructing a high-dimensional signal $\mathrm {x} \in \mathbb {R}^{n}$ from a low-dimensional noisy linear measurement $\mathrm {y}=\mathrm {M}\mathrm {x}+\mathrm {e} \in \mathbb {R}^{\ell }$ , assuming x admits a certain structure.