{"title":"Channel Codes for Relayless Networks with General Message Access Structure","authors":"J. Muramatsu","doi":"10.1109/ITW55543.2023.10161628","DOIUrl":"https://doi.org/10.1109/ITW55543.2023.10161628","url":null,"abstract":"Channel codes for relayless networks with the general message access structure is introduced. It is shown that the multi-letter characterized capacity region of this network is achievable with this code. The capacity region is characterized in terms of entropy functions and provides an alternative to the regions introduced by [Somekh-Baruch and Verdú, ISIT2006][Muramatsu and Miyake, ISITA2018].","PeriodicalId":439800,"journal":{"name":"2023 IEEE Information Theory Workshop (ITW)","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120871443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Error Exponents of the Dirty-Paper and Gel’fand-Pinsker Channels","authors":"Ran Tamir, N. Merhav","doi":"10.1109/ITW55543.2023.10161668","DOIUrl":"https://doi.org/10.1109/ITW55543.2023.10161668","url":null,"abstract":"We derive various error exponents for communication channels with random states, which are available non-causally at the encoder only. For both the finite-alphabet Gel’fand-Pinsker channel and its Gaussian counterpart, the dirty-paper channel, we derive random coding exponents, error exponents of the typical random codes (TRCs), and error exponents of expurgated codes. For the two channel models, we analyze some sub-optimal bin-index decoders, which turn out to be asymptotically optimal, at least for the random coding error exponent. For the dirty-paper channel, we show explicitly via a numerical example, that at rates below capacity, the optimal values of the dirty-paper design parameter α in the random coding sense and in the TRC exponent sense are different from one another, and they are both different from the optimal α that is required for attaining the channel capacity. For the Gel’fand-Pinsker channel, we allow for a variable-rate random binning code construction, and prove that the previously proposed maximum penalized mutual information decoder is asymptotically optimal within a given class of decoders, at least for the random coding error exponent.","PeriodicalId":439800,"journal":{"name":"2023 IEEE Information Theory Workshop (ITW)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116860380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On the Distribution of Partially-Symmetric Codes for Automorphism Ensemble Decoding","authors":"Charles Pillet, Valerio Bioglio","doi":"10.1109/ITW55543.2023.10161664","DOIUrl":"https://doi.org/10.1109/ITW55543.2023.10161664","url":null,"abstract":"Automorphism Ensemble (AE) decoding has recently drawn attention as a possible alternative to list decoding of polar codes. In this letter, we investigate the distribution of Partially-Symmetric Reed-Muller (PS-RM) codes, a family of polar codes yielding good performances under AE decoding. We prove the existence of these codes for almost all code dimensions for code lengths N ≤ 256. Moreover, we analyze the absorption group of this family of codes under SC decoding, proving that valuable permutations in AE decoding always exist. Finally, we experimentally show that PS-RM codes can outperform state-of-the-art polar-code-construction algorithms in terms of error-correction performance for short code lengths, while reducing decoding latency.","PeriodicalId":439800,"journal":{"name":"2023 IEEE Information Theory Workshop (ITW)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126596319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Transfer Learning for Quantum Classifiers: An Information-Theoretic Generalization Analysis","authors":"Sharu Theresa Jose, O. Simeone","doi":"10.1109/ITW55543.2023.10160236","DOIUrl":"https://doi.org/10.1109/ITW55543.2023.10160236","url":null,"abstract":"A key component of a quantum machine learning model operating on classical inputs is the design of an embedding circuit mapping inputs to a quantum state. This paper studies a transfer learning setting in which classical-to-quantum embedding is carried out by an arbitrary parametric quantum circuit that is pre-trained based on data from a source task. At run time, a binary quantum classifier of the embedding is optimized based on data from the target task of interest. The average excess risk, i.e., the optimality gap, of the resulting classifier depends on how (dis)similar the source and target tasks are. We introduce a new measure of (dis)similarity between the binary quantum classification tasks via the trace distances. An upper bound on the optimality gap is derived in terms of the proposed task (dis)similarity measure, two Rényi mutual information terms between classical input and quantum embedding under source and target tasks, as well as a measure of complexity of the combined space of quantum embeddings and classifiers under the source task. The theoretical results are validated on a simple binary classification example.","PeriodicalId":439800,"journal":{"name":"2023 IEEE Information Theory Workshop (ITW)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114421495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}