Pub Date : 2022-06-26DOI: 10.1109/ISIT50566.2022.9834773
Mohammad Hatami, Markus Leinonen, Zheng Chen, Nikolaos Pappas, M. Codreanu
We consider a resource-constrained IoT network, where users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an energy harvesting sensor. The edge node serves users’ requests by either commanding the corresponding sensor to send a fresh status update or retrieving the most recently received measurement from the cache. We aim to find a control policy at the edge node to minimize the average age of information (AoI) of the received measurements upon requests, i.e., average on-demand AoI, subject to per-slot transmission and energy constraints. We develop a low-complexity algorithm – termed relax-then-truncate – and prove that it is asymptotically optimal as the number of sensors goes to infinity. Numerical results assess the performance of the proposed method.
{"title":"Asymptotically Optimal On-Demand AoI Minimization in Energy Harvesting IoT Networks","authors":"Mohammad Hatami, Markus Leinonen, Zheng Chen, Nikolaos Pappas, M. Codreanu","doi":"10.1109/ISIT50566.2022.9834773","DOIUrl":"https://doi.org/10.1109/ISIT50566.2022.9834773","url":null,"abstract":"We consider a resource-constrained IoT network, where users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an energy harvesting sensor. The edge node serves users’ requests by either commanding the corresponding sensor to send a fresh status update or retrieving the most recently received measurement from the cache. We aim to find a control policy at the edge node to minimize the average age of information (AoI) of the received measurements upon requests, i.e., average on-demand AoI, subject to per-slot transmission and energy constraints. We develop a low-complexity algorithm – termed relax-then-truncate – and prove that it is asymptotically optimal as the number of sensors goes to infinity. Numerical results assess the performance of the proposed method.","PeriodicalId":348168,"journal":{"name":"2022 IEEE International Symposium on Information Theory (ISIT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127721326","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}
Pub Date : 2022-06-26DOI: 10.1109/ISIT50566.2022.9834589
Ashwin Hebbar, Rajesh K. Mishra, S. Ankireddy, Ashok Vardhan Makkuva, Hyeji Kim, P. Viswanath
In this paper, we introduce a neural-augmented decoder for Turbo codes called TINYTURBO . TINYTURBO has complexity comparable to the classical max-log-MAP algorithm but has much better reliability than the max-log-MAP baseline and performs close to the MAP algorithm. We show that TINYTURBO exhibits strong robustness on a variety of practical channels of interest, such as EPA and EVA channels, which are included in the LTE standards. We also show that TINYTURBO strongly generalizes across different rate, blocklengths, and trellises. We verify the reliability and efficiency of TINYTURBO via over-the-air experiments.
{"title":"TinyTurbo: Efficient Turbo Decoders on Edge","authors":"Ashwin Hebbar, Rajesh K. Mishra, S. Ankireddy, Ashok Vardhan Makkuva, Hyeji Kim, P. Viswanath","doi":"10.1109/ISIT50566.2022.9834589","DOIUrl":"https://doi.org/10.1109/ISIT50566.2022.9834589","url":null,"abstract":"In this paper, we introduce a neural-augmented decoder for Turbo codes called TINYTURBO . TINYTURBO has complexity comparable to the classical max-log-MAP algorithm but has much better reliability than the max-log-MAP baseline and performs close to the MAP algorithm. We show that TINYTURBO exhibits strong robustness on a variety of practical channels of interest, such as EPA and EVA channels, which are included in the LTE standards. We also show that TINYTURBO strongly generalizes across different rate, blocklengths, and trellises. We verify the reliability and efficiency of TINYTURBO via over-the-air experiments.","PeriodicalId":348168,"journal":{"name":"2022 IEEE International Symposium on Information Theory (ISIT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127754136","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}
Pub Date : 2022-06-26DOI: 10.1109/ISIT50566.2022.9834901
P. Trifonov
A method for computing the reliability of bit sub-channels arising in multilevel polar codes with shaping is presented. The proposed approach is based on explicit expressions for cumulative density functions of LLRs arising in the SC decoder in multilevel Honda-Yamamoto polar coding scheme.
{"title":"Design of Multilevel Polar Codes with Shaping","authors":"P. Trifonov","doi":"10.1109/ISIT50566.2022.9834901","DOIUrl":"https://doi.org/10.1109/ISIT50566.2022.9834901","url":null,"abstract":"A method for computing the reliability of bit sub-channels arising in multilevel polar codes with shaping is presented. The proposed approach is based on explicit expressions for cumulative density functions of LLRs arising in the SC decoder in multilevel Honda-Yamamoto polar coding scheme.","PeriodicalId":348168,"journal":{"name":"2022 IEEE International Symposium on Information Theory (ISIT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127932081","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}
Pub Date : 2022-06-26DOI: 10.1109/ISIT50566.2022.9834397
H. Boche, U. Mönich, Yannik N. Böck
The bandwidth of a signal is an important physical property that is of relevance in many signal processing applications. In this paper we study questions related to the computability of the bandwidth of bandlimited signals. To this end we employ the concept of Turing computability, which exactly describes what is theoretically feasible and can be computed on a digital machine. Recently, it has been shown that there exist bandlimited signals, the actual bandwidth of which cannot be algorithmically determined, i.e., computed on a digital machine. In this work, we consider the most general class of bandlimited signals and analyze whether it is at least possible to compute nontrivial upper or lower bounds for the actual bandwidth of its members. We show that this is not possible in general.
{"title":"Computing Upper and Lower Bounds for the Bandwidth of Bandlimited Signals","authors":"H. Boche, U. Mönich, Yannik N. Böck","doi":"10.1109/ISIT50566.2022.9834397","DOIUrl":"https://doi.org/10.1109/ISIT50566.2022.9834397","url":null,"abstract":"The bandwidth of a signal is an important physical property that is of relevance in many signal processing applications. In this paper we study questions related to the computability of the bandwidth of bandlimited signals. To this end we employ the concept of Turing computability, which exactly describes what is theoretically feasible and can be computed on a digital machine. Recently, it has been shown that there exist bandlimited signals, the actual bandwidth of which cannot be algorithmically determined, i.e., computed on a digital machine. In this work, we consider the most general class of bandlimited signals and analyze whether it is at least possible to compute nontrivial upper or lower bounds for the actual bandwidth of its members. We show that this is not possible in general.","PeriodicalId":348168,"journal":{"name":"2022 IEEE International Symposium on Information Theory (ISIT)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133462372","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}
Pub Date : 2022-06-26DOI: 10.48550/arXiv.2207.10580
Oron Sabag, V. Kostina, B. Hassibi
We consider the feedback capacity of a MIMO channel whose channel output is given by a linear state-space model driven by the channel inputs and a Gaussian process. The generality of our state-space model subsumes all previous studied models such as additive channels with colored Gaussian noise, and channels with an arbitrary dependence on previous channel inputs or outputs. The main result is a computable feedback capacity expression that is given as a convex optimization problem subject to a detectability condition. We demonstrate the capacity result on the auto-regressive Gaussian noise channel, where we show that even a single time-instance delay in the feedback reduces the feedback capacity significantly in the stationary regime. On the other hand, for large regression parameters, the feedback capacity can be achieved with delayed feedback. Finally, we show that the detectability condition is satisfied for scalar models and conjecture that it is true for MIMO models.
{"title":"Feedback Capacity of Gaussian Channels with Memory","authors":"Oron Sabag, V. Kostina, B. Hassibi","doi":"10.48550/arXiv.2207.10580","DOIUrl":"https://doi.org/10.48550/arXiv.2207.10580","url":null,"abstract":"We consider the feedback capacity of a MIMO channel whose channel output is given by a linear state-space model driven by the channel inputs and a Gaussian process. The generality of our state-space model subsumes all previous studied models such as additive channels with colored Gaussian noise, and channels with an arbitrary dependence on previous channel inputs or outputs. The main result is a computable feedback capacity expression that is given as a convex optimization problem subject to a detectability condition. We demonstrate the capacity result on the auto-regressive Gaussian noise channel, where we show that even a single time-instance delay in the feedback reduces the feedback capacity significantly in the stationary regime. On the other hand, for large regression parameters, the feedback capacity can be achieved with delayed feedback. Finally, we show that the detectability condition is satisfied for scalar models and conjecture that it is true for MIMO models.","PeriodicalId":348168,"journal":{"name":"2022 IEEE International Symposium on Information Theory (ISIT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133713133","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}
Pub Date : 2022-06-26DOI: 10.1109/ISIT50566.2022.9834480
Yuval Ben-Hur, Asaf Goren, Da El Klang, Yongjune Kim, Yuval Cassuto
In data-intensive applications, it is advantageous to perform some partial processing close to the data, and communicate to a central processor the partial results instead of the data itself. When the communication medium is noisy, one must mitigate the resulting degradation in computation quality. We study this problem for the setup of binary classification performed by an ensemble of functions communicating real-valued confidence levels. We propose a noise-mitigation solution that works by optimizing the aggregation coefficients at the central processor. Toward that, we formulate a post-training gradient algorithm that minimizes the error probability given the dataset and the noise parameters. We further derive lower and upper bounds on the optimized error probability, and show empirical results that demonstrate the enhanced performance achieved by our scheme on real data.
{"title":"Mitigating Noise in Ensemble Classification with Real-Valued Base Functions","authors":"Yuval Ben-Hur, Asaf Goren, Da El Klang, Yongjune Kim, Yuval Cassuto","doi":"10.1109/ISIT50566.2022.9834480","DOIUrl":"https://doi.org/10.1109/ISIT50566.2022.9834480","url":null,"abstract":"In data-intensive applications, it is advantageous to perform some partial processing close to the data, and communicate to a central processor the partial results instead of the data itself. When the communication medium is noisy, one must mitigate the resulting degradation in computation quality. We study this problem for the setup of binary classification performed by an ensemble of functions communicating real-valued confidence levels. We propose a noise-mitigation solution that works by optimizing the aggregation coefficients at the central processor. Toward that, we formulate a post-training gradient algorithm that minimizes the error probability given the dataset and the noise parameters. We further derive lower and upper bounds on the optimized error probability, and show empirical results that demonstrate the enhanced performance achieved by our scheme on real data.","PeriodicalId":348168,"journal":{"name":"2022 IEEE International Symposium on Information Theory (ISIT)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131768816","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}
Pub Date : 2022-06-26DOI: 10.1109/ISIT50566.2022.9834521
Jesús Gutiérrez-Gutiérrez, Adam Podhorski, Xabier Insausti, M. Zárraga-Rodríguez
In this paper the Pisarenko spectral estimation method for wide sense stationary (WSS) 1-dimensional (scalar) processes is extended to autoregressive (AR) multidimensional (vector) processes.
{"title":"The Pisarenko spectral estimation method: Extension to AR vector processes","authors":"Jesús Gutiérrez-Gutiérrez, Adam Podhorski, Xabier Insausti, M. Zárraga-Rodríguez","doi":"10.1109/ISIT50566.2022.9834521","DOIUrl":"https://doi.org/10.1109/ISIT50566.2022.9834521","url":null,"abstract":"In this paper the Pisarenko spectral estimation method for wide sense stationary (WSS) 1-dimensional (scalar) processes is extended to autoregressive (AR) multidimensional (vector) processes.","PeriodicalId":348168,"journal":{"name":"2022 IEEE International Symposium on Information Theory (ISIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131818961","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}
Pub Date : 2022-06-26DOI: 10.1109/ISIT50566.2022.9834781
Joakim Algrøy, A. Barbero, Øyvind Ytrehus
A simple trellis based algorithm to compute the equivocation of a transmitted codeword, conditioned on the channel output, is presented.
提出了一种基于栅格的计算传输码字歧义的简单算法，该算法以信道输出为条件。
{"title":"Determining the equivocation in coded transmission over a noisy channel","authors":"Joakim Algrøy, A. Barbero, Øyvind Ytrehus","doi":"10.1109/ISIT50566.2022.9834781","DOIUrl":"https://doi.org/10.1109/ISIT50566.2022.9834781","url":null,"abstract":"A simple trellis based algorithm to compute the equivocation of a transmitted codeword, conditioned on the channel output, is presented.","PeriodicalId":348168,"journal":{"name":"2022 IEEE International Symposium on Information Theory (ISIT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133540301","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}
Pub Date : 2022-06-26DOI: 10.1109/ISIT50566.2022.9834685
Geewon Suh, Changho Suh
We consider a matrix completion problem that exploits social graph as side information. We develop a computationally efficient algorithm that achieves the optimal sample complexity for the entire regime of graph information under the multiple cluster setting (to be detailed). The key idea is to incorporate a switching mechanism which selects the information employed in the first clustering step, between the following two types: graph & matrix ratings. Our experimental results on both synthetic and real data corroborate our theoretical result as well as demonstrate that our algorithm outperforms prior algorithms that leverage graph side information.
{"title":"Graph-assisted Matrix Completion in a Multi-clustered Graph Model","authors":"Geewon Suh, Changho Suh","doi":"10.1109/ISIT50566.2022.9834685","DOIUrl":"https://doi.org/10.1109/ISIT50566.2022.9834685","url":null,"abstract":"We consider a matrix completion problem that exploits social graph as side information. We develop a computationally efficient algorithm that achieves the optimal sample complexity for the entire regime of graph information under the multiple cluster setting (to be detailed). The key idea is to incorporate a switching mechanism which selects the information employed in the first clustering step, between the following two types: graph & matrix ratings. Our experimental results on both synthetic and real data corroborate our theoretical result as well as demonstrate that our algorithm outperforms prior algorithms that leverage graph side information.","PeriodicalId":348168,"journal":{"name":"2022 IEEE International Symposium on Information Theory (ISIT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133820657","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}
Pub Date : 2022-06-26DOI: 10.1109/ISIT50566.2022.9834775
Ziv Aharoni, Dor Tsur, H. Permuter
Density estimation plays an important role in modeling random variables (RVs) with continuous alphabets. This work provides an algorithm that estimates the probability density function (PDF) of stationary and ergodic random processes using recurrent neural networks (RNNs). The main idea is to decompose the target PDF into a known auxiliary PDF and a likelihood ratio between the target and auxiliary PDFs. The algorithm focuses on estimating the likelihood ratio using the Donsker Vardhan (DV) variational formula of Kullback Leibler (KL) divergence. Together, the maximizer of the DV formula and the auxiliary PDF are used to construct the estimator of the target PDF in the form of a Gibbs density. The obtained estimator converges to the target PDF in total variation (TV) and in distribution. Also, we show that proposed estimator minimizes the cross entropy (CE) between the target and auxiliary distribution, and that with a proper choice of the auxiliary distribution, it defines a tight upper bound on the entropy rate. We demonstrate this approach by estimating the density of a Gaussian hidden Markov model.
{"title":"Density Estimation of Processes with Memory via Donsker Vardhan","authors":"Ziv Aharoni, Dor Tsur, H. Permuter","doi":"10.1109/ISIT50566.2022.9834775","DOIUrl":"https://doi.org/10.1109/ISIT50566.2022.9834775","url":null,"abstract":"Density estimation plays an important role in modeling random variables (RVs) with continuous alphabets. This work provides an algorithm that estimates the probability density function (PDF) of stationary and ergodic random processes using recurrent neural networks (RNNs). The main idea is to decompose the target PDF into a known auxiliary PDF and a likelihood ratio between the target and auxiliary PDFs. The algorithm focuses on estimating the likelihood ratio using the Donsker Vardhan (DV) variational formula of Kullback Leibler (KL) divergence. Together, the maximizer of the DV formula and the auxiliary PDF are used to construct the estimator of the target PDF in the form of a Gibbs density. The obtained estimator converges to the target PDF in total variation (TV) and in distribution. Also, we show that proposed estimator minimizes the cross entropy (CE) between the target and auxiliary distribution, and that with a proper choice of the auxiliary distribution, it defines a tight upper bound on the entropy rate. We demonstrate this approach by estimating the density of a Gaussian hidden Markov model.","PeriodicalId":348168,"journal":{"name":"2022 IEEE International Symposium on Information Theory (ISIT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130347675","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}
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