Search Results for author:
Found 4 papers, 0 papers with code
Deep Dict: Deep Learning-based Lossy Time Series Compressor for IoT Data
We propose Deep Dict, a deep learning-based lossy time series compressor designed to achieve a high compression ratio while maintaining decompression error within a predefined range.
On the Impact of CDL and TDL Augmentation for RF Fingerprinting under Impaired Channels
This work uses a dataset that includes 5G, 4G, and WiFi samples, and it empowers a CDL+TDL-based augmentation technique in order to boost the learning performance of the DL model.
NOMA Computation Over Multi-Access Channels for Multimodal Sensing
This, in return, suggests that our proposed scheme is eminently suitable for multimodal sensor networks.
Low-Complexity Decoder for Overloaded Uniquely Decodable Synchronous CDMA
We consider the problem of designing a low-complexity decoder for antipodal uniquely decodable (UD) /errorless code sets for overloaded synchronous code-division multiple access (CDMA) systems, where the number of signals Kamax is the largest known for the given code length L. In our complexity analysis, we illustrate that compared to maximum-likelihood (ML) decoder, which has an exponential computational complexity for even moderate code lengths, the proposed decoder has a quasi-quadratic computational complexity.
