This paper proves an impossibility result for stochastic network utility maximization for multi-user wireless systems, including multi-access and broadcast systems. Every time slot an access point observes the current channel states and opportunistically selects a vector of transmission rates. Channel state vectors are assumed to be independent and identically distributed with an unknown probability distribution. The goal is to learn to make decisions over time that maximize a concave utility function of the running time average transmission rate of each user. Recently it was shown that a stochastic Frank-Wolfe algorithm converges to utility-optimality with an error of O(log(T)/T ), where T is the time the algorithm has been running. An existing O(1/T ) converse is known. The current paper improves the converse to O(log(T)/T), which matches the known achievability result. The proof uses a reduction from the opportunistic scheduling problem to a Bernoulli estimation problem. Along the way, it refines a result on Bernoulli estimation.
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A Wringing-Based Proof of a Second-Order Converse for the Multiple-Access Channel under Maximal Error Probability
The second-order converse bound of multiple access channels is an intriguing problem in information theory. In this work, in the setting of the two-user discrete memoryless multiple access channel (DM-MAC) under the maximal error probability criterion, we investigate the gap between the best achievable rates and the asymptotic capacity region. With “wringing techniques” and meta-converse arguments, we show that gap at blocklength n is upper bounded by O(1/√n) .
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- Award ID(s):
- 1908725
- PAR ID:
- 10296643
- Date Published:
- Journal Name:
- 2021 IEEE International Symposium on Information Theory (ISIT)
- Page Range / eLocation ID:
- 2220 to 2225
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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