TY - GEN
T1 - Information Velocity of Cascaded AWGN Channels with Feedback
AU - Domanovitz, Elad
AU - Khina, Anatoly
AU - Philosof, Tal
AU - Kochman, Yuval
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - We consider a line network of nodes connected by additive white Gaussian noise channels and equipped with local feedback. We study the velocity at which information spreads over this network. For the transmission of a data packet, we derive an explicit positive lower bound on the velocity for any packet size. Furthermore, we consider streaming, that is, transmission of data packets that is generated at a given average arrival rate. We show that a positive velocity exists as long as the arrival rate is below the individual Gaussian channel capacity and provide an explicit lower bound. Our analysis involves applying pulse-amplitude modulation to the data (successively in the streaming case) and using linear mean-squared error estimation at the network nodes. Due to the analog-linear nature of the scheme, the results extend to any additive noise. For general noise, we derive exponential error-probability bounds. Moreover, for (sub-)Gaussian noise, we show doubly-exponential behavior, which reduces to the celebrated Schalkwijk-Kailath scheme when considering a single node. By viewing the constellation as an 'analog source', we also provide bounds on the exponential decay of the mean-squared error of source transmission over the network.
AB - We consider a line network of nodes connected by additive white Gaussian noise channels and equipped with local feedback. We study the velocity at which information spreads over this network. For the transmission of a data packet, we derive an explicit positive lower bound on the velocity for any packet size. Furthermore, we consider streaming, that is, transmission of data packets that is generated at a given average arrival rate. We show that a positive velocity exists as long as the arrival rate is below the individual Gaussian channel capacity and provide an explicit lower bound. Our analysis involves applying pulse-amplitude modulation to the data (successively in the streaming case) and using linear mean-squared error estimation at the network nodes. Due to the analog-linear nature of the scheme, the results extend to any additive noise. For general noise, we derive exponential error-probability bounds. Moreover, for (sub-)Gaussian noise, we show doubly-exponential behavior, which reduces to the celebrated Schalkwijk-Kailath scheme when considering a single node. By viewing the constellation as an 'analog source', we also provide bounds on the exponential decay of the mean-squared error of source transmission over the network.
UR - http://www.scopus.com/inward/record.url?scp=85201651383&partnerID=8YFLogxK
U2 - 10.1109/ISIT57864.2024.10619138
DO - 10.1109/ISIT57864.2024.10619138
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AN - SCOPUS:85201651383
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 499
EP - 504
BT - 2024 IEEE International Symposium on Information Theory, ISIT 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Symposium on Information Theory, ISIT 2024
Y2 - 7 July 2024 through 12 July 2024
ER -