In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory

Dileep Kalathil; Krishna Narayanan; Matteo Zecchin; Nir Shlezinger; Osvaldo Simeone; Tomer Raviv

REVIEW 3 major objections 6 minor 30 references

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Pretrained sequence models can adapt wireless receivers in a single forward pass, with no online retraining.

desk verdict A polished, useful survey of the authors' own ICL-equalization work; the 'principled' claim is stronger than the cited theory supports, so treat it as a tutorial, not the primary source. read the letter →

arxiv 2506.15176 v2 pith:6MB6E4AN submitted 2025-06-18 cs.IT cs.LGeess.SPmath.IT

Matteo Zecchin , Tomer Raviv , Dileep Kalathil , Krishna Narayanan , Nir Shlezinger , Osvaldo Simeone This is my paper · ORCID

classification cs.ITcs.LGeess.SPmath.IT

keywords in-contextlearningwirelessreceiveradaptationcell-freemassiveMIMOchannelequalizationtransformersstate-spacemodelsgradient-freeBayesianinference

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that a pretrained sequence model can act as a wireless receiver that adapts to a new channel, user population, or interference condition in a single forward pass, with no gradient updates at run time. The claimed mechanism is in-context learning: pilots, contextual information such as fading statistics, and the received signal are all fed into the model as a prompt. The paper reviews Transformer and state-space-model implementations, cites theory that such models can match Bayes-optimal estimation under suitable conditions, and reports experiments in cell-free massive MIMO showing the approach beats joint learning, hypernetworks, meta-learning, and two-step linear equalizers in many settings. If the claim holds, receiver adaptation becomes a matter of prompt design rather than online optimization.

What carries the argument

The load-bearing object is the prompt-to-output mapping of an ICL-capable sequence model, instantiated as a decoder-only Transformer or a structured state-space model. The prompt for user $k$ is $\{C_k, y\} = (r_1, p_{k,1}, r_2, p_{k,2}, \ldots, r_{T_p}, p_{k,T_p}, y)$: interleaved received-pilot columns and known pilot symbols, optionally augmented with contextual tokens such as large-scale fading coefficients, followed by the received data vector. Meta-training minimizes the MSE loss over many simulated tasks; the theoretical anchor is a result that a single-layer self-attention Transformer can provably implement the Bayes-optimal estimator in the large-context limit under suitable linear-channel conditions, so that ICL-based equalization is claimed to converge to the posterior mean $\mathbb{E}[x \mid C, y]$.

What would settle it

In the Section V-A threshold experiment, measure the ICL equalizer's MSE as both the number of pretraining tasks and the pilot length grow: if, for sufficiently large task diversity and context, the MSE does not approach the MMSE benchmark that knows the true channel prior, the claimed Bayesian-optimality of ICL-based equalization is contradicted.

Watch

Extended reading notes

Core claim

On its own terms, the central claim is that in-context learning is a principled and efficient paradigm for gradient-free receiver adaptation: an ICL equalizer, trained offline on a diverse family of network tasks, maps a prompt of quantized pilot observations, known pilot symbols, optional long-term channel statistics, and the current received signal directly to the transmitted symbol estimates. No weights change at run time; adaptation happens inside the forward pass. The paper reports that this design matches the performance of an MMSE receiver with the true channel prior when enough pretraining tasks are seen, outperforms existing neural baselines on in-distribution and out-of-distribution tests (most clearly with short pilots), and, when augmented with large-scale fading tokens, reduces MSE under pilot contamination below even an LMMSE receiver with perfect fronthaul. It also positions SSM-based ICL as a linear-complexity alternative to Transformers with lower parameter and FLOP counts at equal MSE.

Load-bearing premise

The theoretical guarantee that ICL matches optimal Bayesian inference is proven only for idealized linear channels with large context length, but the paper applies it to realistic quantized, multi-user, pilot-contaminated channels without proving those conditions carry over.

Editorial extensions

If this is right

ICL equalizers get better as pilot sequences grow, converting additional pilot symbols into lower MSE without any retraining.
With a sufficiently diverse meta-training task set, ICL approaches the performance of an MMSE equalizer that knows the true channel prior, implying the model extrapolates beyond the empirical training prior.
Augmenting the prompt with long-term fading statistics lets the equalizer resolve pilot-contamination ambiguity and beat an LMMSE receiver with perfect fronthaul, not just quantized baselines.
SSM-based ICL achieves comparable MSE to Transformer-based ICL with fewer parameters and fewer FLOPs, making the approach more plausible on resource-constrained hardware.
ICL receivers remain competitive out-of-distribution, degrading more gracefully than joint learning, hypernetworks, and meta-learning when the noise power shifts.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If ICL really converges to the Bayesian posterior, then the same forward pass yields not only point estimates but calibrated uncertainty, which could be fed into channel decoders or used for reliability signaling; the paper does not explore this.
The same prompt mechanism should transfer to other radio tasks the paper names but does not test, such as beam selection and scheduling, since any task describable by context, examples, and a query fits the framework.
Because context is trusted input, an adversary that corrupts pilot or fading tokens could steer the receiver; whether ICL is more or less robust than channel-estimate-based designs is an open question the paper poses but does not answer.
The theory's idealized conditions suggest a sharp testable prediction: in a linear static channel with infinitely long context, ICL and the Bayesian posterior mean should agree exactly, and deviations in nonlinear settings would reveal where the principled guarantee ends and heuristics begin.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

Quick read of arXiv:2506.15176. It's a survey article that repackages the authors' existing ICL-for-MIMO equalization results into a "principles, applications, theory" narrative. The headline claim—ICL is a principled, gradient-free way to adapt receivers—is plausible but, in this manuscript, rests on a cited theorem rather than on anything derived here. The paper does its job well as a tutorial: the taxonomy of joint learning, meta-learning, hypernetworks, and ICL is clear; the description of Transformer and SSM architectures is concise; and the cell-free massive MIMO application with quantization and pilot contamination is a good case study. Credit where due: the optimality theorem is from [24], a peer-reviewed AISTATS paper with proofs, and the paper openly says in Section VI that inverse problems with nonlinear observations remain an open question. That admission matters—it shows the authors know the boundary of the theory.

The soft spot is exactly between that boundary and the application. The theory in [24] guarantees Bayes-optimality for a single-layer softmax-attention Transformer, linear static channel, large context, cross-entropy loss. The experiments use multi-layer transformers, b-bit quantization, finite pilot sequences, non-orthogonal pilots, multi-user interference, and an MSE loss. The paper only says these insights "suggest" the conclusion. That's a real scope gap, not a formal error, and it's not fatal: Section V-A demonstrates Bayesian-matching behavior in a simplified no-quantization case, which is genuine evidence for the mechanism. What's missing is a direct comparison against a Bayesian oracle in the full pilot-contamination/quantization setting, or at least a paragraph stating which of the theory's conditions are believed transferable.

Proportionately, my complaints are moderate. This is a review, so no code or data is expected as a hard requirement, but the reproduced figures have no error bars and the experimental descriptions are high-level. A reader who wants to verify the comparison between ICL and LMMSE under pilot reuse will have to go to the prior papers. The novelty is low if read as a research paper; it's a synthesis.

If the target venue is a magazine or a survey track, this deserves a serious referee. I'd recommend the referee asks for a softened "principled" claim—or a caveat that the transfer of the theorem to the nonlinear/quantized setting is conjectural—and for a note on reproducibility pointing to the original papers. For a primary research venue, I'd desk reject because nothing is new. For a tutorial venue, accept with minor revision.

You could bring it to reading group if you want a compact overview of ICL-based receiver design; just don't cite it for the theoretical guarantee—cite [24].

Referee Report

3 major / 6 minor

Summary. This manuscript proposes in-context learning (ICL) as a gradient-free adaptation mechanism for wireless receivers, with a focus on equalization in cell-free massive MIMO systems. It reviews Transformer and state-space-model architectures for ICL, describes a meta-training procedure, and presents a theoretical justification by citing a recent result that single-layer self-attention transformers can realize Bayes-optimal estimators under certain conditions. Numerical results, drawn from prior references, compare ICL against joint learning, hypernetworks, meta-learning, and linear MMSE equalizers, examining pilot length, task diversity, computational complexity, and robustness to pilot contamination. The central claim is that ICL is a principled and efficient approach to real-time receiver adaptation without online retraining.

Significance. If fully substantiated, the paper would make a useful conceptual contribution by connecting the ICL literature to a concrete wireless application and by highlighting a complexity-accuracy trade-off between Transformer- and SSM-based ICL receivers. The taxonomy of adaptation strategies (joint learning, meta-learning, hypernetworks, ICL) is clearly presented, and the prompt-design principle of augmenting context with long-term fading statistics under pilot reuse is a practical insight. However, the original technical content is limited: the theoretical result is cited rather than derived, and the experiments are high-level summaries of other papers without error bars or code. The load-bearing claim that ICL is 'principled' rests on an extrapolation of theory to a setting that is acknowledged to be an open problem, and the quantitative performance claims are not reproducible from the manuscript alone.

major comments (3)

[Section IV-B, Eq. (8), Section VI-5] The claim that ICL-based equalization is 'principled' is anchored to reference [24]'s Bayesian-optimality theorem, whose stated conditions include a linear static channel, a well-defined prior, large context length, a single-layer self-attention transformer, and cross-entropy loss. The application in Sections III and V involves b-bit entry-wise quantization (a nonlinear observation model), pilot contamination, finite pilot length Tp (e.g., up to 60 in Figure 7), multi-user interference, a multi-layer transformer, and the MSE loss of Eq. (8). The manuscript only states that these theoretical insights 'suggest' the conclusion, without proving that the optimality guarantee carries over. This gap is load-bearing because the paper's headline claim of being 'principled' rather than heuristic depends on it. Moreover, Section VI-5 explicitly lists 'the study of inverse problems involving non-linear observations models' as an open question, which is internally inconsistent with the assertion in Section IV-B that the theory establishes the principled nature of the approach for the actual deployment setting. The authors should either provide a formal extension of the optimality result to the quantized MIMO setting, state clearly which conditions are violated and why the behavior is still expected to be near-Bayesian, or add a direct comparison against a Bayesian oracle that has full knowledge of the channel prior under the exact nonlinearities and pilot contamination considered in the experiments.
[Section V (Figures 6-9)] The empirical evidence is presented as a summary of references [10], [24]-[26] with no error bars, confidence intervals, dataset details, precise training and evaluation protocols, or code release. Since the abstract and Section VII make quantitative claims (e.g., 'outperforms all other baselines' and 'significantly reduces both the number of parameters and the FLOPs'), the experiments as reported are not reproducible from the manuscript. To support the original empirical conclusions, the authors should incorporate a complete experimental description with standard error statistics and a code/data availability statement, or they should reframe the paper as a review and remove the unsubstantiated empirical claims from the abstract and conclusions.
[Section I-D and Section IV-B] The paper describes itself as providing 'theoretical analyses and empirical evidence', but the sole theoretical content is a one-paragraph citation of prior work in Section IV-B. If the intended contribution is an original research article, the novelty is mostly the application framing and the prompt-design/context-augmentation idea, which is not formally analyzed. The authors should clarify whether this is a review paper or a research contribution and align the claims accordingly. For a research article, new analysis is needed, for example a formal statement of sufficient conditions under which ICL achieves Bayes-optimality for the quantized cell-free MIMO equalization problem.

minor comments (6)

[Eq. (8)] The loss in Eq. (8) is called the mean squared error, but the right-hand side uses ∥xk−x̂k∥ without a square; it should be the squared Euclidean norm.
[Section V-B] The sentence 'uniformly distributed between 1 and, which are placed uniformly at random across the deployment area' is missing the upper bound on K; please complete the sentence.
[Section II-A-2] The phrase 'positional encodings is typically not applied' should be 'positional encodings are typically not applied'.
[References] Several references lack complete publication details (e.g., page numbers or DOIs); please provide full bibliographic information, especially for workshop and arXiv items.
[General] The manuscript would benefit from a data and code availability statement, since the experiments are drawn from prior works and no implementation is provided.
[Figure 7 caption] The caption and text refer to 'in-distribution' and 'out-of-distribution' tasks; please clarify that the out-of-distribution condition is an increase in noise power by 10 dB, as the text states.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a self-contained review/application of previously proved external results; no prediction reduces by construction to a fit or to a self-citation.

full rationale

The paper's central claim is that ICL is a principled and efficient gradient-free receiver adaptation paradigm. The theoretical grounding (Section IV-B) is explicitly a citation to reference [24], a peer-reviewed AISTATS paper by partially overlapping authors (Narayanan and Kalathil are co-authors of both). Under the review rules, this citation is real evidence rather than circular self-citation because [24] is a peer-reviewed paper containing a parameter-free theorem with stated assumptions; it is not an unverified assertion imported to force a conclusion. The present paper does not derive the theorem, does not redefine ICL in terms of its own conclusion, and does not rename a fitted quantity as a prediction. The empirical sections draw on the authors' own prior work ([10], [25], [26]), but these are externally falsifiable experimental demonstrations, not logically load-bearing reductions. The only substantive weakness is a scope mismatch: the theorem in [24] assumes a linear static channel and large context, while the application uses quantized observations, pilot contamination, and nonlinearities. However, the paper itself flags this gap in Section VI ('Open questions include the study of inverse problems involving non-linear observations models'), and a scope mismatch between a cited theorem and an application is a correctness/risk concern, not circularity. No equation in the paper is equivalent to its input by construction, and no fitted parameter is relabeled as a prediction. The correct circularity verdict is therefore 0.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new free parameters or invented entities. It relies on standard channel and quantization models, on the assumption that meta-training distribution matches test conditions, and on external theoretical results about transformers as in-context estimators.

assumptions (5)

domain assumption Channel model (5): H_m,k are complex Gaussian random matrices, noise n_m ~ CN(0, sigma^2 I), and data symbols come from known constellations.
The entire equalization problem and the claim that ICL learns the channel prior are defined with respect to this model; real channels may deviate significantly.
domain assumption Fronthaul quantization Q_b(.) is applied entry-wise and separately to in-phase and quadrature components, with b-bit resolution.
Quantization is a central nonlinear impairment; the specific model affects the distortion that ICL is claimed to handle.
domain assumption The meta-training task distribution T_train matches the test-time task distribution for in-distribution results.
ICL performance depends on the pre-training distribution covering the test scenarios; out-of-distribution robustness is only tested with a +10 dB noise shift.
domain assumption A single-layer Transformer can provably realize optimal Bayesian estimators in the limit of large context length (result from reference [24]).
Section IV-B relies on this external theorem to argue ICL is principled, but the conditions are more restrictive than the considered nonlinear quantized MIMO setting.
domain assumption Transformers and SSMs, when sufficiently large and well trained, exhibit in-context learning capabilities (background from [13], [14], [15]).
The paper assumes ICL emerges from meta-training based on prior literature; no new proof or mechanism is provided in this manuscript.

how reviews work

0 comments

Cite this review

Pith. "Pith review of In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory." pith.science (2026). https://pith.science/paper/6MB6E4AN

@misc{pith2026250615176,
  author       = {Pith},
  title        = {Pith review of: In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6MB6E4AN}},
  note         = {Machine review of arXiv:2506.15176}
}

read the original abstract

In recent years, deep learning has facilitated the creation of wireless receivers capable of functioning effectively in conditions that challenge traditional model-based designs. Leveraging programmable hardware architectures, deep learning-based receivers offer the potential to dynamically adapt to varying channel environments. However, current adaptation strategies, including joint training, hypernetwork-based methods, and meta-learning, either demonstrate limited flexibility or necessitate explicit optimization through gradient descent. This paper presents gradient-free adaptation techniques rooted in the emerging paradigm of in-context learning (ICL). We review architectural frameworks for ICL based on Transformer models and structured state-space models (SSMs), alongside theoretical insights into how sequence models effectively learn adaptation from contextual information. Further, we explore the application of ICL to cell-free massive MIMO networks, providing both theoretical analyses and empirical evidence. Our findings indicate that ICL represents a principled and efficient approach to real-time receiver adaptation using pilot signals and auxiliary contextual information-without requiring online retraining.

Figures

Figures reproduced from arXiv: 2506.15176 by the authors.

**Figure 1.** The challenge of ensuring the adaptivity of neural receivers to varying traffic, channel, and environmental conditions has been addressed using [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** An ICL-based receiver is optimized during an offline meta-training phase that leverages data from multiple tasks, i.e., multiple traffic, channel, [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 4.** A State Space Model operates sequentially on the tokenized [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

Figures from the paper (5 more)

**Figure 5.** Figure 5: In a cell-free MIMO system, multiple user equipments (UEs) are [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: Test MSE as a function of the number of pre-training tasks [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: Test MSE as a function of pilot symbol sequence length of neural [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

**Figure 8.** Figure 8: Test MSE obtained by ICL equalizers implemented using [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]

**Figure 9.** Figure 9: Test MSE with varying fronthaul capacity under orthogonal pilot [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

30 extracted references · 24 canonical work pages

[24]

Transformers are provably optimal in-context estimators for wireless communications,

V . T . Kunde, V . Rajagopalan, C. S. K. V almeekam, K. Narayanan, J.-F . Chamberland, D. Kalathil, and S. Shakkottai, “Transformers are provably optimal in-context estimators for wireless communications, ” inProceedings of The 28th International Conference on Artificial Intelligence and Statistics, vol. 258 ofProceedings of Machine Learning Research, pp....

work page 2025
[10]

Cell-free multi-user MIMO equalization via in-context learning,

M. Zecchin, K. Zu, and O. Simeone, “Cell-free multi-user MIMO equalization via in-context learning,”IEEE International W orkshop on Signal Processing Advances in Wireless Communications (SP A WC), 2024

work page 2024
[26]

In-Context Learned Equalization in Cell-Free Massive MIMO via State-Space Models

Z. Song, M. Zecchin, B. Rajendran, and O. Simeone, “In-context learned equalization in cell-free massive MIMO via state-space models, ”arXiv preprint arXiv:2410.23882, 2024

work page Pith review arXiv 2024
[1]

Modeling interference for the coexistence of 6G networks and passive sensing systems,

P . Testolina, M. Polese, J. M. Jornet, T . Melodia, and M. Zorzi, “Modeling interference for the coexistence of 6G networks and passive sensing systems, ”IEEE Trans. Wireless Commun., 2024

work page 2024
[2]

Making cell-free massive MIMO competitive with mmse processing and centralized implementation,

E. Bj¨ornson and L. Sanguinetti, “Making cell-free massive MIMO competitive with mmse processing and centralized implementation,” IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 77–90, 2019

work page 2019
[3]

Adaptive coding and channel shaping through reconfigurable intelligent surfaces: An information-theoretic analysis,

R. Karasik, O. Simeone, M. Di Renzo, and S. S. Shitz, “ Adaptive coding and channel shaping through reconfigurable intelligent surfaces: An information-theoretic analysis, ”IEEE Trans. Commun., vol. 69, no. 11, pp. 7320–7334, 2021

work page 2021
[4]

Joint Communication and Sensing for 6G -- A Cross-Layer Perspective

H. W ymeersch, S. Saleh, A. Nimr, R. Halili, R. Berkvens, M. H. Moghaddam, J. M. Mateos-Ramos, A. Stavridis, S. W ¨anstedt, S. Barmpounakis,et al., “Joint communication and sensing for 6G–a cross-layer perspective, ”arXiv preprint arXiv:2402.09120, 2024. xii

work page Pith review arXiv 2024
[5]

Adaptive and flexible model-based AI for deep receivers in dynamic channels,

T . Raviv, S. Park, O. Simeone, Y . C. Eldar, and N. Shlezinger, “ Adaptive and flexible model-based AI for deep receivers in dynamic channels, ”IEEE Wireless Commun., vol. 31, no. 4, pp. 163–169, 2024

work page 2024

Show all 30 references

[6]

Design of a standard-compliant real-time neural receiver for 5G NR,

R. Wiesmayr, S. Cammerer, F . A. Aoudia, J. Hoydis, J. Zakrzewski, and A. Keller, “Design of a standard-compliant real-time neural receiver for 5G NR, ”arXiv preprint arXiv:2409.02912, 2024

2024 arXiv
[7]

Simeone,Machine Learning for Engineers

O. Simeone,Machine Learning for Engineers. Cambridge University Press, 2022

2022
[8]

Learning with limited samples: Meta-learning and applications to communication systems,

L. Chen, S. T . Jose, I. Nikoloska, S. Park, T . Chen, O. Simeone,et al., “Learning with limited samples: Meta-learning and applications to communication systems,”F oundations and Trends® in Signal Processing, vol. 17, no. 2, pp. 79–208, 2023

2023
[9]

Online meta-learning for hybrid model-based deep receivers,

T . Raviv, S. Park, O. Simeone, Y . C. Eldar, and N. Shlezinger, “Online meta-learning for hybrid model-based deep receivers,”IEEE Trans. Wireless Commun., vol. 22, no. 10, pp. 6415–6431, 2023

2023
[11]

Hypernetworks,

D. Ha, A. Dai, and Q. V . Le, “Hypernetworks,”arXiv preprint arXiv:1609.09106, 2016

2016 arXiv
[12]

Modular hypernetworks for scalable and adaptive deep MIMO receivers,

T . Raviv and N. Shlezinger, “Modular hypernetworks for scalable and adaptive deep MIMO receivers,”IEEE Open Journal of Signal Processing, vol. 6, pp. 256–265, 2025

2025
[13]

An explanation of in-context learning as implicit Bayesian inference,

S. M. Xie, A. Raghunathan, P . Liang, and T . Ma, “ An explanation of in-context learning as implicit Bayesian inference, ”arXiv preprint arXiv:2111.02080, 2021

2021 arXiv
[14]

Can Mamba learn how to learn? A comparative study on in-context learning tasks,

J. Park, J. Park, Z. Xiong, N. Lee, J. Cho, S. Oymak, K. Lee, and D. Papailiopoulos, “Can Mamba learn how to learn? A comparative study on in-context learning tasks,” inInternational Conference on Machine Learning, pp. 39793–39812, PMLR, 2024

2024
[15]

What can transformers learn in-context? A case study of simple function classes,

S. Garg, D. Tsipras, P . S. Liang, and G. V aliant, “What can transformers learn in-context? A case study of simple function classes, ”Advances in Neural Information Processing Systems, vol. 35, pp. 30583–30598, 2022

2022
[16]

An introduction to transformers,

R. E. Turner, “ An introduction to transformers,”arXiv preprint arXiv:2304.10557, 2023

2023
[17]

Rethinking invariance in in-context learning,

L. Fang, Y . W ang, K. Gatmiry, L. Fang, and Y . W ang, “Rethinking invariance in in-context learning,” inThe Thirteenth International Conference on Learning Representations, 2025

2025
[18]

Model-agnostic meta-learning for fast adaptation of deep networks,

C. Finn, P . Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” inInternational conference on machine learning, pp. 1126–1135, PMLR, 2017

2017
[19]

Uplink spectral and energy efficiency of cell-free massive MIMO with optimal uniform quantization,

M. Bashar, H. Q. Ngo, K. Cumanan, A. G. Burr, P . Xiao, E. Bj¨ornson, and E. G. Larsson, “Uplink spectral and energy efficiency of cell-free massive MIMO with optimal uniform quantization,”IEEE Trans. Commun., vol. 69, no. 1, pp. 223–245, 2020

2020
[20]

Massive MIMO networks: Spectral, energy, and hardware efficiency,

E. Bj ¨ornson, J. Hoydis, L. Sanguinetti,et al., “Massive MIMO networks: Spectral, energy, and hardware efficiency, ”F oundations and Trends® in Signal Processing, vol. 11, no. 3-4, pp. 154–655, 2017

2017
[21]

Deep MIMO detection,

N. Samuel, T . Diskin, and A. Wiesel, “Deep MIMO detection,” in IEEE International W orkshop on Signal Processing Advances in Wireless Communications (SP A WC), 2017

2017
[22]

Linear transformers are secretly fast weight programmers,

I. Schlag, K. Irie, and J. Schmidhuber, “Linear transformers are secretly fast weight programmers,” inInternational conference on machine learning, pp. 9355–9366, PMLR, 2021

2021
[23]

Explaining emergent in-context learning as kernel regression,

C. Han, Z. W ang, H. Zhao, and H. Ji, “Explaining emergent in-context learning as kernel regression, ”arXiv preprint arXiv:2305.12766, 2023

2023 arXiv
[25]

In-context learning for MIMO equalization using transformer-based sequence models,

M. Zecchin, K. Y u, and O. Simeone, “In-context learning for MIMO equalization using transformer-based sequence models,” inIEEE International Conference on Communications W orkshops (ICC W orkshops), pp. 1573–1578, 2024

2024
[27]

Further advancements for E-UTRA physical layer aspects (release 9),

3GPP , “Further advancements for E-UTRA physical layer aspects (release 9), ”3GPP TS 36.814, March 2017

2017
[28]

Post- training quantization for vision transformer,

Z. Liu, Y . W ang, K. Han, W . Zhang, S. Ma, and W . Gao, “Post- training quantization for vision transformer,”Advances in Neural Information Processing Systems, vol. 34, pp. 28092–28103, 2021

2021
[29]

Neuromorphic in-context learning for energy-efficient MIMO symbol detection,

Z. Song, O. Simeone, and B. Rajendran, “Neuromorphic in-context learning for energy-efficient MIMO symbol detection,” inIEEE International W orkshop on Signal Processing Advances in Wireless Communications (SP A WC), 2024

2024
[30]

Turbo-ICL: In-context learning-based turbo equalization,

Z. Song, M. Zecchin, B. Rajendran, and O. Simeone, “Turbo-ICL: In-context learning-based turbo equalization,”arXiv preprint arXiv:2505.06175, 2025

2025 arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [24]

Transformers are provably optimal in-context estimators for wireless communications,

V . T . Kunde, V . Rajagopalan, C. S. K. V almeekam, K. Narayanan, J.-F . Chamberland, D. Kalathil, and S. Shakkottai, “Transformers are provably optimal in-context estimators for wireless communications, ” inProceedings of The 28th International Conference on Artificial Intelligence and Statistics, vol. 258 ofProceedings of Machine Learning Research, pp....

work page 2025

[2] [10]

Cell-free multi-user MIMO equalization via in-context learning,

M. Zecchin, K. Zu, and O. Simeone, “Cell-free multi-user MIMO equalization via in-context learning,”IEEE International W orkshop on Signal Processing Advances in Wireless Communications (SP A WC), 2024

work page 2024

[3] [26]

In-Context Learned Equalization in Cell-Free Massive MIMO via State-Space Models

Z. Song, M. Zecchin, B. Rajendran, and O. Simeone, “In-context learned equalization in cell-free massive MIMO via state-space models, ”arXiv preprint arXiv:2410.23882, 2024

work page Pith review arXiv 2024

[4] [1]

Modeling interference for the coexistence of 6G networks and passive sensing systems,

P . Testolina, M. Polese, J. M. Jornet, T . Melodia, and M. Zorzi, “Modeling interference for the coexistence of 6G networks and passive sensing systems, ”IEEE Trans. Wireless Commun., 2024

work page 2024

[5] [2]

Making cell-free massive MIMO competitive with mmse processing and centralized implementation,

E. Bj¨ornson and L. Sanguinetti, “Making cell-free massive MIMO competitive with mmse processing and centralized implementation,” IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 77–90, 2019

work page 2019

[6] [3]

Adaptive coding and channel shaping through reconfigurable intelligent surfaces: An information-theoretic analysis,

R. Karasik, O. Simeone, M. Di Renzo, and S. S. Shitz, “ Adaptive coding and channel shaping through reconfigurable intelligent surfaces: An information-theoretic analysis, ”IEEE Trans. Commun., vol. 69, no. 11, pp. 7320–7334, 2021

work page 2021

[7] [4]

Joint Communication and Sensing for 6G -- A Cross-Layer Perspective

H. W ymeersch, S. Saleh, A. Nimr, R. Halili, R. Berkvens, M. H. Moghaddam, J. M. Mateos-Ramos, A. Stavridis, S. W ¨anstedt, S. Barmpounakis,et al., “Joint communication and sensing for 6G–a cross-layer perspective, ”arXiv preprint arXiv:2402.09120, 2024. xii

work page Pith review arXiv 2024

[8] [5]

Adaptive and flexible model-based AI for deep receivers in dynamic channels,

T . Raviv, S. Park, O. Simeone, Y . C. Eldar, and N. Shlezinger, “ Adaptive and flexible model-based AI for deep receivers in dynamic channels, ”IEEE Wireless Commun., vol. 31, no. 4, pp. 163–169, 2024

work page 2024