Multi-Modal Semantic Communication Framework

• Multi-modal semantic communication is an advanced paradigm that fuses audio, visual, and textual data at the semantic level for task-optimized wireless transmission.
• It integrates specialized encoders, cross-modal fusion, and adaptive channel modeling to achieve efficient, context-aware information transfer in dynamic environments.
• Applications include audio-visual event localization, scenario-aware visual coding, and multi-task fusion systems, demonstrating significant performance gains over traditional methods.

A multi-modal semantic communication framework is an advanced communication paradigm that fuses multiple data modalities—such as audio, visual, and textual streams—at the semantic level, enabling robust, low-overhead information transfer tailored to end-task requirements and the physical characteristics of wireless channels. Unlike traditional bit-oriented approaches, multi-modal semantic communication directly encodes, fuses, and transmits information most relevant to the receiver’s inference or application, often leveraging deep learning to adapt to context, channel dynamics, and task objectives.

1. System Architectures and Foundational Modules

Modern frameworks for multi-modal semantic communication are unified transmitter–channel–receiver systems partitioned into tightly coupled functional blocks:

• Multi-Modal Semantic Encoders: Modality-specific extractors (e.g., CNNs for vision, LSTMs for audio, Transformers for text) project input data to semantic embeddings. Many frameworks introduce further cross-modal alignment modules, such as time-frequency transforms for audio and attention-based fusion for video–audio pairs (Yu et al., 9 Dec 2024).
• Fusion and Alignment Modules: Advanced frameworks, such as those based on BERT (Zhu et al., 1 Jul 2024) or Kolmogorov–Arnold Networks (KAN) (Jiang et al., 23 Feb 2025), employ cross-attention or neural decomposition to align heterogeneous semantic vectors in a shared space, supporting both single-task and multi-task inference.
• Semantic Enhancement and Contextual Modulation: Techniques including attention-guided fusion (e.g., AGVA/Positive Sample Propagation), scenario-aware importance weightings by MLLMs (Zhang et al., 9 Sep 2025), and task- or query-driven patch prioritization (Mortaheb et al., 17 Dec 2025), target efficient, relevance-driven resource allocation across modalities.
• Channel Modeling and Adaptive Encoding: Systems integrate analog (JSCC) and digital (LDPC/Turbo) coding, supported by sophisticated channel estimation modules (e.g., pilot symbol multiplexing (Yu et al., 9 Dec 2024), GAN-based channel estimators (Jiang et al., 2023)), as well as real-time adaptation to time-varying SNR, Rayleigh/Rician fading, and other impairments. Some frameworks use a unified semantic–channel representation space (Euler encoding (Yu et al., 9 Dec 2024), VIB-based compression (Fu et al., 10 Nov 2025)) for both robustness and equalization.
• Task-Aware Decoders: Decoding modules map recovered semantic vectors to task outputs via multi-headed architectures (classification, regression, sequence generation, etc.), possibly with scenario-specific or user-personalized decoders (Jiang et al., 2023, Jiang et al., 23 Feb 2025).

These system designs enable adaptive, context-sensitive operation and are extensible to multi-user and multi-task settings (Jiang et al., 23 Feb 2025, Yu et al., 9 Dec 2024).

2. Mathematical Modeling and Semantic Information Measures

The mathematical backbone of multi-modal semantic communication is an explicit representation of semantic content, distortion metrics, and information-theoretic trade-offs:

• Signal Models: The observed multi-modal stream is encoded as semantic vectors $\mathbf{s}^m$ for modality $m$. For audio-visual tasks, representations may be in the Euler domain $\tilde{\mathbf{a}} = \lambda e^{i\theta}$ (Yu et al., 9 Dec 2024).
• Fusion Operations: Fused representations are constructed via cross-attention (Zhu et al., 1 Jul 2024), concatenation, or neural operators (e.g., KAN: $f(x_1,\dots,x_n) = \sum_i g_i(\sum_j h_{ij}(x_j))$) (Jiang et al., 23 Feb 2025).
• Semantic Distortion: End-to-end loss functions typically penalize information loss at the semantic level, either through task-oriented metrics ($\mathrm{CrossEntropy}$, F1, BLEU, etc.), semantic MSE, or fusion-based PSNR (Zhu et al., 1 Jul 2024). Weighted distortion, as in SA-OOSC, uses scenario-based importance masks: $$\mathrm{SAD}(S,\widehat{S}) = \sum_{i=1}^{L} w_i \;\mathrm{PSNR}_i$$ (Zhang et al., 9 Sep 2025).
• Channel Model: The physical channel is represented as $\mathbf{Y} = \mathbf{H}\mathbf{X} + \mathbf{N}$, where $\mathbf{H}$ may be time-varying and estimated from embedded pilots (Yu et al., 9 Dec 2024), or predicted from semantic environment features (Qin et al., 2023).
• Optimization: Many systems formalize a trade-off between semantic distortion and transmission rate: $\min \mathbb{E}[D(S, \hat{S})] + \lambda R$ (Qin et al., 2023), or employ variational IB approaches: $\min I(X;Z) - \beta I(Z;Y)$, both within and across modalities (Fu et al., 10 Nov 2025, Zhou et al., 5 Oct 2025).
• Rate/Resource Control: Adaptive mechanisms assign variable protection or code lengths per semantic unit based on modality/task importance, transmission entropy, and feedback from the channel (Zhang et al., 9 Sep 2025, He et al., 2023).

These formulations are further extended in distributed settings, e.g., the probabilistic modality-selection framework of PoM²-DIB (Zhou et al., 5 Oct 2025), and in dynamic resource allocation games integrating semantic freshness and user utility (Liu et al., 26 Sep 2024).

3. Adaptive Coding, Fusion, and Resource Allocation

A core objective is to optimize rate–distortion trade-offs in fluctuating environments and under task-specific constraints:

• Pilot-guided Dynamic Adaptation: Pilot-guided semantic communication (Yu et al., 9 Dec 2024) inserts known pilot waveforms for robust real-time channel estimation and phase/frequency adaptation in the semantic encoder (Euler-domain modulation and phase pre-compensation).
• Scenario-Aware Distillation and Patchwise Allocation: Integration of MLLMs for semantic importance estimation enables per-patch, scenario-driven code allocation (e.g., in SA-OOSC, importance scores from LLMs guide variable-length rate assignment (Zhang et al., 9 Sep 2025)), assigning higher bandwidth to object patches most relevant to the driving or VQA context.
• Feature Selection and Redundancy Suppression: Lightweight feature-selection modules (FSM) (Zhang et al., 2022), mutual information minimization (adversarial training) (Fu et al., 10 Nov 2025), and probabilistic mask selection (Zhou et al., 5 Oct 2025) all seek to suppress intra- and inter-modal redundancy while preserving informative content.
• Semantic Sharing for Multi-User: In multi-user systems, the semantic vector is explicitly partitioned into public (shared across users) and private (user-specific) components, reducing aggregate bandwidth through redundancy-aware coding (Jiang et al., 23 Feb 2025).
• Fusion Architectures: Cross-modal fusion leverages bidirectional attention (e.g., BERT-based (Zhu et al., 1 Jul 2024)) and joint latent spaces (e.g., contrastive alignment in mm-GESCO (Fu et al., 10 Aug 2024)) to dynamically couple features prior to channel encoding.

Resource adaptation also extends to cross-layer controls, such as hybrid analog–digital path selection under channel and application constraints (Lu et al., 24 Sep 2024).

4. Robustness to Physical Channel Dynamics

Channel-varying phenomena present critical challenges for semantic fidelity:

• Pilot-Based Channel Tracking: Embedding pilot symbols allows estimation of instantaneous channel gains/matrices, enabling zero-forcing detection and error correction tailored to the current state, with channel update rules such as periodic pilot re-estimation (Yu et al., 9 Dec 2024).
• Complex Fading and GAN-Enhanced Channel Models: Systems accommodate AWGN, Rayleigh, and Rician fading, and frameworks like LAM-MSC (Jiang et al., 2023) and G-MSC (Lu et al., 24 Sep 2024) propose GAN/discriminator-guided channel modeling for data-driven nonparametric channel simulation in end-to-end learning.
• Semantic Layer Equalization: Euler-domain semantic encoding preserves inner products and supports complex-domain equalization, providing robustness under deep fading and low SNR (Yu et al., 9 Dec 2024).
• Diffusion-Based/Generative Decoders: Some frameworks use DDIM or LDM decoders to reconstruct high-fidelity modality outputs from noisy semantic representations, compensating for missing or corrupted channel information (Liu et al., 26 Sep 2024, Fu et al., 10 Aug 2024).

Performance under varying SNRs is a standard benchmark—pilot-guided frameworks demonstrate 10–25% higher accuracy under fading/channel uncertainty compared to classical methods (Yu et al., 9 Dec 2024). Disabling the channel estimation module yields a >50% drop in accuracy in such regimes.

5. Representative Applications and Empirical Results

Multi-modal semantic communication underpins a range of high-impact applications:

• Audio-Visual Event Localization: The pilot-guided, Euler-domain encoding system achieves multimodal event localization accuracy improvements of 20–30% over single-modality or separately coded baselines, particularly under channel fading and at low SNRs (Yu et al., 9 Dec 2024).
• Scenario-Aware Visual Coding: MLLM-augmented variable-length JSCC coding (SA-OOSC) achieves up to 35.55 dB average PSNR at a CBR of 0.0201 (vs. OOSC 35.01 dB/0.0230 and NTSCC 31.68 dB/0.0241) with high per-patch fidelity for objects marked important by scenario context (Zhang et al., 9 Sep 2025).
• Multi-Task Fusion Systems: BERT-based fusion with joint training over text, images, speech, and video enables robust multi-task inference with minimal overhead—for VQA and genre recognition, fusion-based frameworks reduce overhead by 98% vs. non-fused or conventional cascades (Zhu et al., 1 Jul 2024).
• Context-Aware Feature Gating: LLM-driven gating and MoE architectures dynamically prune modalities based on task/channel metadata, cutting bandwidth by 40–60% and enabling rapid convergence and high semantic accuracy in multi-user scenarios (Liu et al., 29 May 2025).
• Distributed and Multi-User Scenarios: Probabilistic, reinforcement-driven modality selection (PoM²-DIB (Zhou et al., 5 Oct 2025)) and redundancy-aware sharing (M4SC (Jiang et al., 23 Feb 2025)) demonstrate that inference quality can be maintained—often improved—while drastically lowering sum-rate, especially in redundant or over-provisioned networks.

6. Design Principles, Open Issues, and Future Directions

Key insights guiding framework design include:

• Cross-Modal and Task-Aware Adaptivity: Modality-specialized encoders, BERT/Transformer attention for fusion, and LLM-driven scenario guidance enable task-specific, context-sensitive prioritization of semantic content.
• Hierarchical Redundancy Reduction: Statistical independence across modality embeddings maximizes complementarity, and dynamic pruning eliminates bandwidth waste.
• Integrated Channel–Semantic Processing: Embedding pilot signals, supporting dynamic channel model estimation, and jointly optimizing source–channel coding (down to the Euler or variational domain) are crucial for real-world operation over unpredictable links.
• Scalability: Fusion and codebook sharing, semantic sharing in multi-user settings, and efficient environment semantic exploitation enable deployment at scale with reduced computational/storage duplication.

Open challenges include formalizing the semantic capacity of wireless channels (including tight achievability bounds), integrating environment semantics and side information for further adaptation, and scaling to include additional modalities (e.g., LIDAR, radar, haptics). There are also underexplored opportunities for hybrid digital–analog semantic coding, real-time feedback and adaptation, and privacy-preserving multi-agent semantic inference networks (Qin et al., 2023, Liu et al., 26 Sep 2024).

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References:

• "Pilot-guided Multimodal Semantic Communication for Audio-Visual Event Localization" (Yu et al., 9 Dec 2024)
• "SA-OOSC: A Multimodal LLM-Distilled Semantic Communication Framework for Enhanced Coding Efficiency with Scenario Understanding" (Zhang et al., 9 Sep 2025)
• "Multi-Modal Fusion-Based Multi-Task Semantic Communication System" (Zhu et al., 1 Jul 2024)
• "Context-Aware Semantic Communication for the Wireless Networks" (Liu et al., 29 May 2025)
• "M4SC: An MLLM-based Multi-modal, Multi-task and Multi-user Semantic Communication System" (Jiang et al., 23 Feb 2025)
• "Multi-Modal Multi-Task Semantic Communication: A Distributed Information Bottleneck Perspective" (Zhou et al., 5 Oct 2025)
• "Robust Multi-modal Task-oriented Communications with Redundancy-aware Representations" (Fu et al., 10 Nov 2025)
• "Generative AI-Enhanced Multi-Modal Semantic Communication in Internet of Vehicles: System Design and Methodologies" (Lu et al., 24 Sep 2024)
• "Multimodal generative semantic communication based on latent diffusion model" (Fu et al., 10 Aug 2024)
• "Large AI Model Empowered Multimodal Semantic Communications" (Jiang et al., 2023)
• "A Generalized Semantic Communication System: from Sources to Channels" (Qin et al., 2023)
• "Rate-Adaptive Coding Mechanism for Semantic Communications With Multi-Modal Data" (He et al., 2023)
• "A Unified Multi-Task Semantic Communication System for Multimodal Data" (Zhang et al., 2022)
• "Multi-Modal Semantic Communication" (Mortaheb et al., 17 Dec 2025)