ATS ToDMA Adaptive Token Selection and Token Domain Multiple Access for Cross Modal Semantic Communications

Jul 16, 2026

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Adaptive token processing is presented as a route to improve semantic communication efficiency, where conventional frameworks under-address token-level multiple access and the effects of semantic interference from simultaneously sent tokens. The paper proposes ATS-ToDMA, a cross-modal semantic communication framework integrating semantic token selection, interference-aware scheduling, and semantic-aware power allocation. A Semantic Signal-to-Interference-plus-Noise Ratio (SSINR) metric models combined channel impairments and interference from token similarity. A transformer scheduler assigns selected tokens to token-domain slots while reducing intra- and cross-modal interference, supported by analytical bounds and closed-form power allocation approximations. Simulations show higher semantic throughput and decoding accuracy with lower total interference and transmit power versus OMA, Semantic NOMA, Random-TS, and Greedy ATS.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

Abstract

Introduction

Proposed ATS-ToDMA Framework

Semantic Token Selection

Interference-Aware Scheduling

Semantic-Aware Power Allocation

SSINR Metric and Analysis

Semantic Interference Bounds

Token Occupancy Characterization

Simulation Results and Comparisons

Conclusion

ATS-ToDMA: Adaptive Token Selection and Token-Domain Multiple Access for Cross-Modal Semantic Communications Sachin Kadam and Dong In Kim , Life Fellow, IEEE arXiv :2607 .03520v 1 [ cs .IT] 3 Jul 2026 Abstract—Adaptive token processing has emerged as a promising approach for improving the efficiency of semantic communication systems. However, existing semantic communication frameworks largely overlook token-level multiple access and the impact of semantic interference among simultaneously transmitted semantic tokens. In this paper, we propose Adaptive Token Selection and Token-Domain Multiple Access (ATS-ToDMA), a novel cross-modal semantic communication framework that jointly performs semantic token selection, interference-aware scheduling, and semantic-aware power allocation. The proposed framework introduces a Semantic Signal-to-Interference-plusNoise Ratio (SSINR) metric that captures the combined effects of channel impairments and semantic interference arising from token similarity. A transformer-based scheduler is developed to allocate selected semantic tokens across token-domain transmission slots while mitigating both intra-modal and cross-modal semantic interference. To characterize the behavior of the proposed system, analytical bounds on semantic interference and feasible token occupancy are derived, together with a closed-form approximation for semantic-aware power allocation. Simulation results demonstrate significant gains in semantic throughput and semantic decoding accuracy while reducing aggregate semantic interference and transmit power compared with OMA, Semantic NOMA, Random-TS, and Greedy ATS benchmarks. Index Terms—Semantic communication, LSTM, adaptive token selection, multiple access, interference modeling. I. INTRODUCTION The rapid proliferation of data-intensive and intelligencedriven applications, such as immersive communications, autonomous systems, and edge intelligence, is fundamentally reshaping the design objectives of wireless networks. Conventional communication systems, grounded in Shannon’s information theory, are primarily designed to ensure reliable bit-level transmission by minimizing distortion between transmitted and reconstructed signals [1] . However, in many emerging applications, the ultimate goal is not perfect data reconstruction but successful task execution. This mismatch leads to inefficient utilization of communication and computation resources, as large amounts of task-irrelevant information are unnecessarily transmitted. To address this limitation, semantic communication (SemCom) has recently emerged as a paradigm shift, aiming to transmit only task-relevant information required for inference or decision-making at the receiver [2]–[5] . By optimizing task-oriented performance metrics rather than symbol-level S. Kadam is with the Department of Electronics and Communication Engineering, Motilal Nehru National Institute of Technology, Prayagraj, UP, 211004, India, (e-mail: sachink@mnnit.ac.in) and D. I. Kim is with the Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea (e-mail: [dongin@skku.edu](dongin@skku.edu)). accuracy, SemCom enables significant reductions in communication overhead while preserving or even enhancing end-task performance. In particular, deep learning-based approaches, including joint source-channel coding (JSCC), have demonstrated promising results for semantic transmission of multimedia data across noisy wireless channels [6]–[8] . Recent advances in foundation models and deep representation learning, including transformers for language and vision, have further enabled compact semantic representations across heterogeneous modalities such as text, images, and speech [9]–[12] . These models map raw inputs into structured semantic token representations, where each token encodes localized semantic information. Although such representations significantly reduce redundancy, tran

FAQs

What problem does ATS-ToDMA address in cross-modal semantic communication?

ATS-ToDMA targets the inefficiency of existing semantic frameworks that ignore token-level multiple access and the degradation caused by semantic interference among simultaneously transmitted semantic tokens.

How does the framework mitigate semantic interference?

It uses an interference-aware scheduling strategy and introduces the SSINR metric to account for both channel impairments and interference driven by semantic similarity among tokens.

What components does ATS-ToDMA jointly optimize?

ATS-ToDMA jointly performs semantic token selection, interference-aware scheduling across token-domain slots, and semantic-aware power allocation to improve throughput and decoding accuracy while reducing aggregate interference and transmit power.

Jul 16, 2026

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