EXIT-charts-aided hybrid multiuser detector for multicarrier interleave-division multiple access

A generically applicable hybrid multiuser detector (MUD) concept is proposed by appropriately activating different MUDs in consecutive turbo iterations based on the mutual information (MI) gain. It is demonstrated that the proposed hybrid MUD is capable of approaching the optimal Bayesian MUD's performance despite its reduced complexity, which is at a modestly increased complexity in comparison with that of the suboptimum soft interference cancellation (SoIC) MU

Reconfigurable rateless codes

We propose novel reconfigurable rateless codes, that are capable of not only varying the block length but also adaptively modify their encoding strategy by incrementally adjusting their degree distribution according to the prevalent channel conditions without the availability of the channel state information at the transmitter. In particular, we characterize a reconfigurable ratelesscode designed for the transmission of 9,500 information bits that achieves a performance, which is approximately 1 dB away from the discrete-input continuous-output memoryless channel’s (DCMC) capacity over a diverse range of channel signal-to-noise (SNR) ratios

Reconfigurable rateless codes

We propose novel reconfigurable rateless codes, that are capable of not only varying the block length but also adaptively modify their encoding strategy by incrementally adjusting their degree distribution according to the prevalent channel conditions without the availability of the channel state information at the transmitter. In particular, we characterize a reconfigurable ratelesscode designed for the transmission of 9,500 information bits that achieves a performance, which is approximately 1 dB away from the discrete-input continuous-output memoryless channel’s (DCMC) capacity over a diverse range of channel signal-to-noise (SNR) ratios

Generative Adversarial Method Based on Neural Tangent Kernels

The recent development of Generative adversarial networks (GANs) has driven many computer vision applications. Despite the great synthesis quality, training GANs often confronts several issues, including non-convergence, mode collapse, and gradient vanishing. There exist several workarounds, for example, regularizing Lipschitz continuity and adopting Wasserstein distance. Although these methods can partially solve the problems, we argue that the problems are result from modeling the discriminator with deep neural networks. In this paper, we base on newly derived deep neural network theories called Neural Tangent Kernel (NTK) and propose a new generative algorithm called generative adversarial NTK (GA-NTK). The GA-NTK models the discriminator as a Gaussian Process (GP). With the help of the NTK theories, the training dynamics of GA-NTK can be described with a closed-form formula. To synthesize data with the closed-form formula, the objectives can be simplified into a single-level adversarial optimization problem. We conduct extensive experiments on real-world datasets, and the results show that GA-NTK can generate images comparable to those by GANs but is much easier to train under various conditions. We also study the current limitations of GA-NTK and propose some workarounds to make GA-NTK more practical