Nonasymptotic Oblivious Relaying and Variable-Length Noisy Lossy Source Coding
Pith reviewed 2026-05-23 05:35 UTC · model grok-4.3
The pith
Finite-blocklength achievability for the oblivious relay channel produces two distinct second-order versions of the information bottleneck, one for fixed-length relay codes and one for variable-length codes.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Finite-blocklength achievability results for the oblivious relay channel with fixed-length and variable-length codes at the relay produce two different second-order versions of the information bottleneck. The bounds are obtained by combining the nonasymptotic noisy lossy source coding results of Kostina and Verdú with the strong functional representation lemma and the Poisson matching lemma, together with a novel nonasymptotic variable-length noisy lossy source coding result.
What carries the argument
The information bottleneck function applied to nonasymptotic settings, with the fixed-length versus variable-length distinction at the relay producing the split in second-order terms.
If this is right
- Short-blocklength rates for oblivious relaying can be bounded separately according to whether the relay employs fixed-length or variable-length codes.
- The novel variable-length noisy lossy source coding result supplies tighter finite-blocklength guarantees for related compression tasks.
- The two different second-order characterizations allow comparison of error-probability decay between the two coding styles at the relay.
- The approach extends the information-bottleneck capacity result to precise nonasymptotic performance limits.
Where Pith is reading between the lines
- In short-packet wireless systems the choice between fixed and variable relay coding may produce measurably different reliability at the same average rate.
- The split in second-order behavior could appear in other rate-limited forwarding problems such as multi-hop networks or distributed compression.
- The variable-length source coding bound may improve finite-blocklength analysis for lossy compression under average-length constraints.
Load-bearing premise
Nonasymptotic noisy lossy source coding bounds can be combined with the strong functional representation lemma and the Poisson matching lemma to derive the stated achievability results for both fixed-length and variable-length relaying.
What would settle it
A direct calculation or simulation for a specific channel showing identical second-order terms for fixed-length and variable-length relay coding would contradict the claimed distinction between the two versions.
Figures
read the original abstract
The information bottleneck channel (or the oblivious relay channel) concerns a channel coding setting where the decoder does not directly observe the channel output. Rather, the channel output is relayed to the decoder by an oblivious relay (which does not know the codebook) via a rate-limited link. The capacity is known to be given by the information bottleneck. We study finite-blocklength achievability results of the channel, where the relay communicates to the decoder via fixed-length or variable-length codes. These two cases give rise to two different second-order versions of the information bottleneck. Our proofs utilize the nonasymptotic noisy lossy source coding results by Kostina and Verd\'{u}, the strong functional representation lemma, and the Poisson matching lemma. Moreover, we also give a novel nonasymptotic variable-length noisy lossy source coding result.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript derives finite-blocklength achievability bounds for the oblivious relay (information bottleneck) channel under both fixed-length and variable-length coding at the relay. These produce two distinct second-order characterizations of the information bottleneck. The proofs combine the Kostina-Verdú nonasymptotic noisy lossy source coding bounds, the strong functional representation lemma, and the Poisson matching lemma, while also introducing a new nonasymptotic variable-length noisy lossy source coding result.
Significance. If the derivations hold without extraneous dispersion or remainder terms in the new variable-length bound, the work supplies the first explicit distinction between fixed-length and variable-length regimes at the second-order level for this channel model. It extends nonasymptotic tools to a relay setting and supplies a new source-coding lemma whose utility may extend beyond the present application.
major comments (2)
- [abstract and proofs of the variable-length case] The central claim of two distinct second-order expressions rests on the new variable-length noisy lossy source coding bound not introducing additional o(1/sqrt(n)) terms or implicit fixed-length approximations when the block length is randomized. The abstract states that the proofs utilize this new result together with Kostina-Verdú bounds, but the composition must be checked to ensure the claimed distinction survives.
- [main achievability theorems] The achievability bounds for the relay channel are obtained by invoking the strong functional representation lemma and Poisson matching lemma on top of the source-coding results. Any mismatch in the second-order remainders between the fixed-length and variable-length source-coding statements would collapse the two information-bottleneck characterizations; explicit tracking of all o(1/sqrt(n)) terms through this composition is required.
minor comments (2)
- [preliminaries] Notation for the variable-length code should be introduced with an explicit definition of the random length random variable and its relation to the fixed-length case.
- [introduction] The abstract mentions 'two different second-order versions'; the introduction or theorem statements should state the precise dispersion coefficients for each regime side-by-side for immediate comparison.
Simulated Author's Rebuttal
We thank the referee for the careful reading and the insightful comments regarding the second-order characterizations. Below we respond point by point to the major comments, clarifying the term tracking in our proofs while agreeing to strengthen the presentation where helpful.
read point-by-point responses
-
Referee: [abstract and proofs of the variable-length case] The central claim of two distinct second-order expressions rests on the new variable-length noisy lossy source coding bound not introducing additional o(1/sqrt(n)) terms or implicit fixed-length approximations when the block length is randomized. The abstract states that the proofs utilize this new result together with Kostina-Verdú bounds, but the composition must be checked to ensure the claimed distinction survives.
Authors: The variable-length noisy lossy source coding bound is stated and proved directly for randomized block lengths, with all remainder terms controlled at o(1/sqrt(n)) without any fixed-length approximation or additional dispersion contribution. The subsequent composition with the strong functional representation lemma and Poisson matching lemma is performed by first applying the representation to obtain a conditional distribution, then invoking the variable-length source-coding result on the resulting empirical distribution; the second-order term therefore inherits the distinct dispersion coefficient that arises from the variable-length nature. We have verified this composition yields the claimed separation and will add an explicit appendix tabulating every o(1/sqrt(n)) remainder through the entire chain. revision: partial
-
Referee: [main achievability theorems] The achievability bounds for the relay channel are obtained by invoking the strong functional representation lemma and Poisson matching lemma on top of the source-coding results. Any mismatch in the second-order remainders between the fixed-length and variable-length source-coding statements would collapse the two information-bottleneck characterizations; explicit tracking of all o(1/sqrt(n)) terms through this composition is required.
Authors: The proofs apply the strong functional representation lemma to produce a representation of the relay output whose conditional distribution is then fed to the appropriate (fixed- or variable-length) source-coding bound; the Poisson matching lemma is used only for the final channel-coding step and contributes the same o(1/sqrt(n)) remainder in both cases. Because the source-coding statements already carry distinct dispersion coefficients, the overall second-order terms remain separated. We acknowledge that the current write-up leaves some of these remainder calculations implicit and will expand the proof sketches in the revised version to display the full term-by-term accounting. revision: yes
Circularity Check
No significant circularity; derivations rest on external lemmas
full rationale
The paper derives finite-blocklength achievability for oblivious relaying by combining the nonasymptotic noisy lossy source coding bounds of Kostina-Verdú, the strong functional representation lemma, and the Poisson matching lemma, plus one novel variable-length source coding result. No load-bearing step is shown to reduce by the paper's own equations to a fitted parameter, self-defined quantity, or self-citation chain. The two distinct second-order information-bottleneck expressions arise from the fixed-length versus variable-length relay regimes; the abstract and structure give no indication that either collapses to the other by construction. This is the normal case of a self-contained derivation against external benchmarks.
Axiom & Free-Parameter Ledger
axioms (3)
- domain assumption Nonasymptotic noisy lossy source coding results by Kostina and Verdú
- standard math Strong functional representation lemma
- standard math Poisson matching lemma
Reference graph
Works this paper leans on
-
[1]
Communication via decentralized processing,
A. Sanderovich, S. Shamai, Y . Steinberg, and G. Kramer, “Communication via decentralized processing,” IEEE Transactions on Information Theory , vol. 54, no. 7, pp. 3008–3023, 2008
work page 2008
-
[2]
On codebook information for interference relay channels with out-of-band relaying,
O. Simeone, E. Erkip, and S. Shamai, “On codebook information for interference relay channels with out-of-band relaying,” IEEE transactions on information theory , vol. 57, no. 5, pp. 2880–2888, 2011
work page 2011
-
[3]
On the capacity of cloud radio access networks with oblivious relaying,
I. E. Aguerri, A. Zaidi, G. Caire, and S. Shamai, “On the capacity of cloud radio access networks with oblivious relaying,” IEEE Transactions on Information Theory , vol. 65, no. 7, pp. 4575–4596, 2019
work page 2019
-
[4]
On mismatched oblivious relaying,
M. Dikshtein, N. Weinberger, and S. Shamai, “On mismatched oblivious relaying,” in 2023 IEEE International Symposium on Information Theory (ISIT) . IEEE, 2023, pp. 1687–1692
work page 2023
-
[5]
An achievable error exponent for the information bottleneck channel,
H. Wu and H. Joudeh, “An achievable error exponent for the information bottleneck channel,” in 2024 IEEE International Symposium on Information Theory (ISIT) . IEEE, 2024, pp. 1297–1302
work page 2024
-
[6]
The information bottleneck method,
N. Tishby, F. Pereira, and W. Bialek, “The information bottleneck method,” in Proceedings of the 37th Annual Allerton Conference on Communication, Control, and Computing . IEEE, 1999, pp. 368–377
work page 1999
-
[7]
The information bottleneck: Theory and applications,
N. Slonim, “The information bottleneck: Theory and applications,” Ph.D. dissertation, Hebrew University, 2002
work page 2002
-
[8]
A unified framework for one-shot achievability via the Poisson matching lemma,
C. T. Li and V . Anantharam, “A unified framework for one-shot achievability via the Poisson matching lemma,” IEEE Transactions on Information Theory, vol. 67, no. 5, pp. 2624–2651, 2021
work page 2021
-
[9]
Nonasymptotic noisy lossy source coding,
V . Kostina and S. Verdú, “Nonasymptotic noisy lossy source coding,” IEEE Transactions on Information Theory , vol. 62, no. 11, pp. 6111–6123, 2016
work page 2016
-
[10]
Information transmission with additional noise,
R. Dobrushin and B. Tsybakov, “Information transmission with additional noise,” IRE Transactions on Information Theory , vol. 8, no. 5, pp. 293–304, 1962
work page 1962
-
[11]
Strong functional representation lemma and applications to coding theorems,
C. T. Li and A. El Gamal, “Strong functional representation lemma and applications to coding theorems,” IEEE Transactions on Information Theory , vol. 64, no. 11, pp. 6967–6978, Nov 2018
work page 2018
-
[12]
Fronthaul-constrained cloud radio access networks: Insights and challenges,
M. Peng, C. Wang, V . Lau, and H. V . Poor, “Fronthaul-constrained cloud radio access networks: Insights and challenges,”IEEE Wireless Communications, vol. 22, no. 2, pp. 152–160, 2015
work page 2015
-
[13]
On cloud radio access networks with cascade oblivious relaying,
M. Ensan, H. Joudeh, A. Alvarado, U. Gustavsson, and F. M. Willems, “On cloud radio access networks with cascade oblivious relaying,” in 2021 IEEE Information Theory Workshop (ITW) . IEEE, 2021, pp. 1–6
work page 2021
-
[14]
The information bottleneck method
N. Tishby, F. C. Pereira, and W. Bialek, “The information bottleneck method,” arXiv preprint physics/0004057 , 2000
work page internal anchor Pith review Pith/arXiv arXiv 2000
-
[15]
A. Zaidi, I. Estella-Aguerri, and S. Shamai, “On the information bottleneck problems: Models, connections, applications and information theoretic views,” Entropy, vol. 22, no. 2, p. 151, 2020
work page 2020
-
[16]
The information bottleneck problem and its applications in machine learning,
Z. Goldfeld and Y . Polyanskiy, “The information bottleneck problem and its applications in machine learning,” IEEE Journal on Selected Areas in Information Theory , vol. 1, no. 1, pp. 19–38, 2020
work page 2020
-
[17]
Distributed variational representation learning,
I. E. Aguerri and A. Zaidi, “Distributed variational representation learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, no. 1, pp. 120–138, 2019
work page 2019
-
[18]
Indirect rate distortion problems,
H. Witsenhausen, “Indirect rate distortion problems,” IEEE Transactions on Information Theory , vol. 26, no. 5, pp. 518–521, 1980
work page 1980
-
[19]
Multiterminal source coding under logarithmic loss,
T. A. Courtade and T. Weissman, “Multiterminal source coding under logarithmic loss,” IEEE Transactions on Information Theory , vol. 60, no. 1, pp. 740–761, 2013
work page 2013
-
[20]
The dispersion of lossy source coding,
A. Ingber and Y . Kochman, “The dispersion of lossy source coding,” in 2011 Data Compression Conference , March 2011, pp. 53–62
work page 2011
-
[21]
Fixed-length lossy compression in the finite blocklength regime,
V . Kostina and S. Verdú, “Fixed-length lossy compression in the finite blocklength regime,” IEEE Transactions on Information Theory , vol. 58, no. 6, pp. 3309–3338, June 2012
work page 2012
-
[22]
Pointwise redundancy in lossy data compression and universal lossy data compression,
I. Kontoyiannis, “Pointwise redundancy in lossy data compression and universal lossy data compression,” IEEE Transactions on Information Theory , vol. 46, no. 1, pp. 136–152, 2000
work page 2000
-
[23]
Universal almost sure data compression,
D. S. Ornstein and P. C. Shields, “Universal almost sure data compression,” The Annals of Probability , pp. 441–452, 1990
work page 1990
-
[24]
A rate of convergence result for a universal d-semifaithful code,
B. Yu and T. P. Speed, “A rate of convergence result for a universal d-semifaithful code,” IEEE Transactions on Information Theory , vol. 39, no. 3, pp. 813–820, 1993
work page 1993
-
[25]
The redundancy of source coding with a fidelity criterion. 1. known statistics,
Z. Zhang, E.-H. Yang, and V . K. Wei, “The redundancy of source coding with a fidelity criterion. 1. known statistics,” IEEE Transactions on Information Theory, vol. 43, no. 1, pp. 71–91, 1997
work page 1997
-
[26]
Arbitrary source models and bayesian codebooks in rate-distortion theory,
I. Kontoyiannis and J. Zhang, “Arbitrary source models and bayesian codebooks in rate-distortion theory,” IEEE Transactions on information theory , vol. 48, no. 8, pp. 2276–2290, 2002
work page 2002
-
[27]
Asymptotic properties on codeword lengths of an optimal FV code for general sources,
H. Koga and H. Yamamoto, “Asymptotic properties on codeword lengths of an optimal FV code for general sources,” IEEE Transactions on Information Theory, vol. 51, no. 4, pp. 1546–1555, 2005
work page 2005
-
[28]
Variable-length compression allowing errors,
V . Kostina, Y . Polyanskiy, and S. Verdú, “Variable-length compression allowing errors,” IEEE Transactions on Information Theory , vol. 61, no. 8, pp. 4316–4330, 2015
work page 2015
-
[29]
Indirect rate distortion functions with f-separable distortion criterion,
P. A. Stavrou, Y . Shkel, and M. Kountouris, “Indirect rate distortion functions with f-separable distortion criterion,” in2023 IEEE International Symposium on Information Theory (ISIT) . IEEE, 2023, pp. 1050–1055
work page 2023
-
[30]
Privacy-constrained remote source coding,
K. Kittichokechai and G. Caire, “Privacy-constrained remote source coding,” in 2016 IEEE International Symposium on Information Theory (ISIT) . IEEE, 2016, pp. 1078–1082
work page 2016
-
[31]
Fixed-length lossy compression in the finite blocklength regime,
V . Kostina and S. Verdú, “Fixed-length lossy compression in the finite blocklength regime,” IEEE Transactions on Information Theory , vol. 58, no. 6, pp. 3309–3338, 2012
work page 2012
-
[32]
S. Saito, H. Yagi, and T. Matsushima, “New results on variable-length lossy compression allowing positive overflow and excess distortion probabilities,” in 2018 International Symposium on Information Theory and Its Applications (ISITA) . IEEE, 2018, pp. 359–363
work page 2018
-
[33]
H. Yang, Y . Shi, S. Shao, and X. Yuan, “Indirect lossy source coding with observed source reconstruction: Nonasymptotic bounds and second-order asymptotics,” IEEE Transactions on Communications , pp. 1–1, 2024
work page 2024
-
[34]
Finite blocklength lossy source coding for discrete memoryless sources,
L. Zhou, M. Motani et al., “Finite blocklength lossy source coding for discrete memoryless sources,” F oundations and Trends® in Communications and Information Theory , vol. 20, no. 3, pp. 157–389, 2023
work page 2023
-
[35]
Entanglement-assisted capacity of a quantum channel and the reverse Shannon theorem,
C. H. Bennett, P. W. Shor, J. Smolin, and A. V . Thapliyal, “Entanglement-assisted capacity of a quantum channel and the reverse Shannon theorem,” IEEE Transactions on Information Theory , vol. 48, no. 10, pp. 2637–2655, 2002
work page 2002
-
[36]
The quantum reverse Shannon theorem and resource tradeoffs for simulating quantum channels,
C. H. Bennett, I. Devetak, A. W. Harrow, P. W. Shor, and A. Winter, “The quantum reverse Shannon theorem and resource tradeoffs for simulating quantum channels,” IEEE Transactions on Information Theory , vol. 60, no. 5, pp. 2926–2959, May 2014
work page 2014
-
[37]
Distributed channel synthesis,
P. Cuff, “Distributed channel synthesis,” IEEE Transactions on Information Theory , vol. 59, no. 11, pp. 7071–7096, Nov 2013
work page 2013
-
[38]
The communication complexity of correlation,
P. Harsha, R. Jain, D. McAllester, and J. Radhakrishnan, “The communication complexity of correlation,” IEEE Transactions on Information Theory , vol. 56, no. 1, pp. 438–449, Jan 2010
work page 2010
-
[39]
Greedy Poisson rejection sampling,
G. Flamich, “Greedy Poisson rejection sampling,” Advances in Neural Information Processing Systems , vol. 36, 2023
work page 2023
-
[40]
Algorithms for the communication of samples,
L. Theis and N. Yosri, “Algorithms for the communication of samples,” in International Conference on Machine Learning . PMLR, 2022, pp. 21 308– 21 328
work page 2022
-
[41]
One-shot coding over general noisy networks,
Y . Liu and C. T. Li, “One-shot coding over general noisy networks,” in 2024 IEEE International Symposium on Information Theory (ISIT) , 2024, pp. 3124–3129. 9
work page 2024
-
[42]
Universal exact compression of differentially private mechanisms,
Y . Liu, W.-N. Chen, A. Özgür, and C. T. Li, “Universal exact compression of differentially private mechanisms,” Advances in Neural Information Processing Systems, 2024
work page 2024
-
[43]
Minimax learning for distributed inference,
C. T. Li, X. Wu, A. Özgür, and A. El Gamal, “Minimax learning for distributed inference,” IEEE Transactions on Information Theory , vol. 66, no. 12, pp. 7929–7938, 2020
work page 2020
-
[44]
Neural estimation of the rate-distortion function with applications to operational source coding,
E. Lei, H. Hassani, and S. S. Bidokhti, “Neural estimation of the rate-distortion function with applications to operational source coding,” IEEE Journal on Selected Areas in Information Theory , vol. 3, no. 4, pp. 674–686, 2022
work page 2022
-
[45]
Channel simulation: Theory and applications to lossy compression and differential privacy,
C. T. Li, “Channel simulation: Theory and applications to lossy compression and differential privacy,” F oundations and Trends® in Communications and Information Theory , vol. 21, no. 6, pp. 847–1106, 2024. [Online]. Available: http://dx.doi.org/10.1561/0100000141
-
[46]
Pointwise redundancy in one-shot lossy compression via Poisson functional representation,
——, “Pointwise redundancy in one-shot lossy compression via Poisson functional representation,” in arXiv preprint; short version presented at 2024 International Zurich Seminar on Information and Communication , 2024, pp. 28–29. [Online]. Available: https://arxiv.org/pdf/2401.14805.pdf
-
[47]
A method for the construction of minimum-redundancy codes,
D. A. Huffman, “A method for the construction of minimum-redundancy codes,” Proceedings of the IRE , vol. 40, no. 9, pp. 1098–1101, 1952
work page 1952
-
[48]
Channel coding rate in the finite blocklength regime,
Y . Polyanskiy, H. V . Poor, and S. Verdú, “Channel coding rate in the finite blocklength regime,” IEEE Transactions on Information Theory , vol. 56, no. 5, pp. 2307–2359, 2010. APPENDIX A. Remainder of the Proof of Theorem 2 We use the strategy in [11, Theorem 2] to remove the common randomness G := ( ¯Zi, Ti)i. Consider two values g0, g1 of the common r...
work page 2010
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.