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arxiv: 2604.19808 · v2 · submitted 2026-04-14 · 💻 cs.IT · cs.ET· math.IT

Anchor-Aided Multi-User Semantic Communication with Adaptive Decoders

Loc X. Nguyen , Phuong-Nam Tran , Trung Thanh Pham , Avi Deb Raha , Eui-Nam Huh , Zhu Han , Choong Seon Hong This is my paper

Pith reviewed 2026-05-11 01:04 UTC · model grok-4.3

classification 💻 cs.IT cs.ETmath.IT
keywords semantic communicationmulti-useranchor decodercatastrophic forgettingjoint source-channel codingdeep learning decoderfrozen encoder
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0 comments X

The pith

An anchor decoder symmetric to the encoder lets one optimized transmitter serve many users with different decoder architectures.

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

The paper addresses multi-user semantic communication where each receiver runs its own deep-learning decoder of different size and capacity. Training a shared encoder sequentially for each new user triggers catastrophic forgetting of earlier users. The authors insert an anchor decoder whose layers mirror the encoder exactly, so its feedback stays aligned with what the encoder can actually extract. After the encoder is optimized against this anchor, its weights are frozen and each user-specific decoder is trained separately to interpret the same encoder outputs.

Core claim

By training the encoder once against an anchor decoder that has identical architecture and capacity, the encoder parameters can be frozen and reused to train arbitrary downstream decoders without retraining the transmitter or suffering forgetting of prior users.

What carries the argument

Anchor decoder: a decoder whose architecture is symmetric to the encoder, providing aligned feedback that lets the encoder optimize once before its parameters are frozen for all user-specific decoders.

If this is right

  • The transmitter no longer needs retraining when a new user joins or changes its decoder.
  • Each user's decoder can be trained independently after the encoder is fixed, reducing total system training cost.
  • Diversity in user computing power can be accommodated without forcing all decoders to share the same architecture.
  • Simulation results show the approach outperforms standard multi-user baselines that retrain the encoder for each new decoder.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same frozen encoder could support users added after initial deployment without touching the transmitter.
  • The method may generalize to other joint source-channel coding tasks where a common feature extractor must serve heterogeneous receivers.
  • Training time for large user populations would scale with the number of decoders rather than with repeated full-system retraining.

Load-bearing premise

The anchor decoder's outputs remain general enough that any new user decoder, regardless of its architecture or capacity, can be trained on them without performance loss.

What would settle it

Train several user decoders of varying capacity on the frozen encoder outputs and compare their task accuracy or reconstruction quality against the same decoders trained jointly with the encoder; a large consistent drop would falsify the claim.

Figures

Figures reproduced from arXiv: 2604.19808 by Avi Deb Raha, Choong Seon Hong, Eui-Nam Huh, Loc X. Nguyen, Phuong-Nam Tran, Trung Thanh Pham, Zhu Han.

Figure 1
Figure 1. Figure 1: The proposed semantic communication system for various types of [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The challenging issue of catastrophic forgetting of the deep learning [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The proposed training framework includes two stages: (1) Self [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The diversity in deep learning architecture for different semantic [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The demonstration for the forgetting problem of deep learning networks in four semantic communication users under Additive White Gaussian Noise. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The PSNR comparison among different training approaches for four distinct semantic communication users. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The visualization of the reconstructed images with different training frameworks by the Attention Decoder under the AWGN and the SNR is [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
read the original abstract

Semantic communication (SemCom) is accelerating its momentum to catch up with the massive increase in users' demands in both quantity and quality, with the assistance of advanced deep learning (DL) techniques. Specifically, SemCom can actively embed the semantic meaning of the data into the transmission process, while eliminating statistical redundancy to preserve bandwidth resources for other users. Therefore, the transmitter encodes the message in the most concise way, while the receiver tries to interpret the message with the DL model and its knowledge of the transmitter's intended meaning. Most existing works only consider one transmitter and one receiver, which limits their ability to address the diversity in users' models and capabilities. Therefore, in this paper, we propose a multi-user semantic communication system where each user is equipped with a distinct DL-based joint source-channel decoder architecture, reflecting the diversity in computing capacity. The challenging issue with the proposed system is the catastrophic forgetting property of neural networks, where the DL-based encoder fails to encode the data for the previous user when being trained with a new user. To address this, we propose an anchor decoder with an architecture that is symmetric to the encoder. The symmetric decoder has the same computational capacity as the encoder, providing feedback that aligns with the encoder's extraction capabilities and enhances optimization efficiency. The parameters of the optimized encoder are then frozen and used to train decoders for various users, aligning them with the encoder outputs. Finally, we conduct a series of simulation experiments to validate the proposed framework against other benchmarks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript proposes an anchor-aided multi-user semantic communication framework to address catastrophic forgetting when training a shared encoder for users with heterogeneous DL-based joint source-channel decoders. An anchor decoder symmetric in architecture and capacity to the encoder is used to optimize the encoder; the encoder parameters are then frozen and used to train individual user-specific decoders.

Significance. If the central claim holds—that the frozen encoder produces representations sufficiently general for arbitrary user decoders without performance degradation—the work could enable practical deployment of semantic communication in multi-user settings with diverse device capabilities, improving bandwidth efficiency over conventional approaches. The simulation-based validation against benchmarks is noted, but the absence of quantitative results, ablations, or training details in the provided abstract makes the magnitude of improvement difficult to gauge at present.

major comments (2)
  1. [Abstract] Abstract: The assertion that the symmetric anchor decoder 'aligns with the encoder’s extraction capabilities and enhances optimization efficiency' is load-bearing for the multi-user claim, yet the description provides no explicit regularization, mutual-information objective, or adversarial term that would enforce decoder-agnostic latent representations. Without such mechanisms, the encoder may still embed features tuned to the anchor’s inductive biases, undermining the subsequent claim that the same frozen encoder can serve 'various users' with distinct architectures and capacities.
  2. [Abstract] Abstract (simulation experiments paragraph): The manuscript states that 'a series of simulation experiments' validate the framework, but reports no quantitative metrics, ablation studies on anchor symmetry, comparisons of encoder generality across decoder capacities, or details on training procedures and data distributions. This absence prevents verification that the anchor procedure actually mitigates catastrophic forgetting or preserves performance for heterogeneous users, which is central to the contribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below, providing clarifications based on the full paper content and indicating planned revisions to the abstract and main text.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The assertion that the symmetric anchor decoder 'aligns with the encoder’s extraction capabilities and enhances optimization efficiency' is load-bearing for the multi-user claim, yet the description provides no explicit regularization, mutual-information objective, or adversarial term that would enforce decoder-agnostic latent representations. Without such mechanisms, the encoder may still embed features tuned to the anchor’s inductive biases, undermining the subsequent claim that the same frozen encoder can serve 'various users' with distinct architectures and capacities.

    Authors: We agree that the abstract is brief and does not explicitly detail mechanisms such as mutual-information objectives or adversarial training. The full manuscript (Section III) describes the anchor decoder as being trained jointly with the encoder using a symmetric architecture and standard reconstruction loss (e.g., MSE or cross-entropy on semantic features), which is intended to align the encoder's latent space to a decoder of matching capacity. This symmetry is hypothesized to promote generality without additional regularization terms. However, we acknowledge the referee's point that this may not fully guarantee decoder-agnostic representations for arbitrary architectures, and our empirical results in Section V provide supporting evidence through cross-decoder performance comparisons. We will revise the abstract to briefly reference the joint training objective and add a short discussion in the introduction or Section III on potential inductive biases and limitations. revision: partial

  2. Referee: [Abstract] Abstract (simulation experiments paragraph): The manuscript states that 'a series of simulation experiments' validate the framework, but reports no quantitative metrics, ablation studies on anchor symmetry, comparisons of encoder generality across decoder capacities, or details on training procedures and data distributions. This absence prevents verification that the anchor procedure actually mitigates catastrophic forgetting or preserves performance for heterogeneous users, which is central to the contribution.

    Authors: The abstract is constrained by length and conventionally omits detailed metrics, which are instead reported in the main text (Sections IV and V, including Tables I-III and Figures 3-6). These sections provide quantitative results on semantic similarity scores, PSNR/SSIM for image transmission, comparisons against benchmarks (e.g., separate per-user training and naive multi-user without anchor), ablations on anchor symmetry, and training details such as dataset (e.g., CIFAR-10 or similar), optimizer, and learning rates. Catastrophic forgetting mitigation is shown via sequential training curves where performance on prior users is preserved after freezing the encoder. To address the concern, we will update the abstract to include key quantitative highlights (e.g., average performance retention of X% across users) and ensure the simulation paragraph references the availability of ablations and training procedures in the main body. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical architecture proposal validated by simulation

full rationale

The paper presents a system design for multi-user semantic communication to address decoder diversity and catastrophic forgetting. It introduces an anchor decoder symmetric to the encoder, trains the pair jointly, freezes the encoder parameters, and then trains heterogeneous user decoders on the fixed encoder outputs. This sequence is a methodological procedure whose success is asserted via simulation experiments rather than any closed-form derivation, equation, or prediction that reduces to its own inputs by construction. No self-definitional relations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the described chain; the central claim remains an independent architectural hypothesis subject to external empirical test.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The central claim rests on the introduction of the anchor decoder as a new component and the assumption that encoder freezing after anchor training enables effective multi-user adaptation. No explicit free parameters, standard mathematical axioms, or external benchmarks are detailed in the abstract.

invented entities (1)
  • anchor decoder no independent evidence
    purpose: Symmetric counterpart to the encoder that supplies aligned feedback during training to prevent catastrophic forgetting when sequentially incorporating new users.
    New postulated component introduced to solve the identified training issue; no independent evidence or prior literature support is provided in the abstract.

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Reference graph

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