Compressed Sensing MRI (CS-MRI) aims at reconstrcuting de-aliased images fromsub-Nyquist samplingk-space datatoaccelerate MRImaging. Inspired by recent deep learning methods, we propose a Cascaded Dilated Dense Network (CDDN)forMRIreconstruction.

Due to its ability to integrate information over large boxes, the new layer facilitates long-range propagation of information and leads to the efficient increase of the receptive fields of network units.

Passive intermodulation (PIM) has emerged as a critical source of self-interference in modern MIMO-OFDM systems, especially under the stringent requirements of 5G and beyond. Conventional cancellation methods often rely on complex nonlinear models with limited scalability and high computational cost. In this work, we propose a lightweight deep learning framework for PIM cancellation that leverages depthwise separable convolutions and dilated convolutions to efficiently capture nonlinear dependencies across antennas and subcarriers. To further enhance convergence, we adopt a cyclic learning rate schedule and gradient clipping. In a controlled MIMO experimental setup, the method effectively suppresses third-order passive intermodulation (PIM) distortion, achieving up to 29dB of average power error (APE) with only 11k trainable parameters. These results highlight the potential of compact neural architectures for scalable interference mitigation in future wireless communication systems.

It consists of various corner cases.

Compressed Sensing MRI (CS-MRI) aims at reconstrcuting de-aliased images from sub-Nyquist sampling k -space data to accelerate MR Imaging.

-- Digital predistortion (DPD) is essential for mitigating nonlinearity in RF power amplifiers, particularly for wideband applications. This paper presents TCN-DPD, a parameter-efficient architecture based on temporal convolutional networks, integrating noncausal dilated convolutions with optimized activation functions. The increasing demand for high-efficiency, wideband communication systems has intensified the need for effective power amplifier (P A) linearization techniques. As modern wireless systems push toward broader bandwidths and higher efficiency requirements, digital predistortion (DPD) has become indispensable for maintaining signal quality while allowing P As to operate in their efficient nonlinear regions [1]. The Generalized Memory Polynomial (GMP) model has long been the industry standard for P A linearization [2].