Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Raviv, Tomer"'
Autor:
Raviv, Tomer, Shlezinger, Nir
Deep neural networks (DNNs) were shown to facilitate the operation of uplink multiple-input multiple-output (MIMO) receivers, with emerging architectures augmenting modules of classic receiver processing. Current designs consider static DNNs, whose a
Externí odkaz:
http://arxiv.org/abs/2408.11920
We study iterative blind symbol detection for block-fading linear inter-symbol interference channels. Based on the factor graph framework, we design a joint channel estimation and detection scheme that combines the expectation maximization (EM) algor
Externí odkaz:
http://arxiv.org/abs/2408.02312
Deep learning is envisioned to facilitate the operation of wireless receivers, with emerging architectures integrating deep neural networks (DNNs) with traditional modular receiver processing. While deep receivers were shown to operate reliably in co
Externí odkaz:
http://arxiv.org/abs/2407.09134
We investigate the application of the factor graph framework for blind joint channel estimation and symbol detection on time-variant linear inter-symbol interference channels. In particular, we consider the expectation maximization (EM) algorithm for
Externí odkaz:
http://arxiv.org/abs/2401.12627
Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with deep neural networks (DNNs) enabling digital receivers to learn to operate in challenging communication scenarios. However, wireless receiver design p
Externí odkaz:
http://arxiv.org/abs/2305.07309
Deep learning is envisioned to play a key role in the design of future wireless receivers. A popular approach to design learning-aided receivers combines deep neural networks (DNNs) with traditional model-based receiver algorithms, realizing hybrid m
Externí odkaz:
http://arxiv.org/abs/2302.02436
Polar codes have promising error-correction capabilities. Yet, decoding polar codes is often challenging, particularly with large blocks, with recently proposed decoders based on list-decoding or neural-decoding. The former applies multiple decoders
Externí odkaz:
http://arxiv.org/abs/2301.06060
Autor:
Raviv, Tomer, Shlezinger, Nir
Deep neural networks (DNNs) allow digital receivers to learn to operate in complex environments. To do so, DNNs should preferably be trained using large labeled data sets with a similar statistical relationship as the one under which they are to infe
Externí odkaz:
http://arxiv.org/abs/2209.01362
Recent years have witnessed growing interest in the application of deep neural networks (DNNs) for receiver design, which can potentially be applied in complex environments without relying on knowledge of the channel model. However, the dynamic natur
Externí odkaz:
http://arxiv.org/abs/2203.14359
Autor:
Raviv, Tomer, Park, Sangwoo, Shlezinger, Nir, Simeone, Osvaldo, Eldar, Yonina C., Kang, Joonhyuk
Deep neural networks (DNNs) based digital receivers can potentially operate in complex environments. However, the dynamic nature of communication channels implies that in some scenarios, DNN-based receivers should be periodically retrained in order t
Externí odkaz:
http://arxiv.org/abs/2103.13483