Semantic Communication Systems for 6G

• Semantic Communication Systems are innovative frameworks that transmit meaning rather than bits, using AI-based semantic extraction and adaptive coding strategies.
• They integrate joint source-channel coding with multi-modal fusion techniques to enhance spectral efficiency and robustness in 6G networks.
• These systems address challenges like knowledge base alignment, metric standardization, and energy-efficient design, driving new research in cross-layer protocols and network security.

Semantic Communication (SemCom) Systems represent a transformative approach in wireless networks, shifting the focus from bit-level accuracy to the transmission of semantic meaning tailored to the receiver's task or knowledge base. SemCom leverages AI-driven semantic extraction, joint source-channel coding, and adaptive knowledge base (KB) management to optimize spectral efficiency and robustness under resource constraints and harsh channel conditions. The following sections provide a comprehensive technical overview grounded in recent research, with particular emphasis on 6G scenarios (Ahmed et al., 13 Jun 2025).

1. Architectural Principles and End-to-End Pipeline

A SemCom transceiver comprises three core stages:

• Semantic Extraction: The transmitter uses a semantic encoder $f_\mathrm{sem}(x;\mathrm{KB}_\mathrm{tx})$ to project input $x$ (e.g., text, image, audio, video) into a compact representation $s$ within a semantic space $S$. Architectures are modality-dependent: transformers for text, CNN/diffusion models for images, spatiotemporal autoencoders for video/audio.
• Joint Source–Channel Coding (JSCC): Semantic symbols $s$ are mapped to channel inputs $x$ via a deep encoder $f_\mathrm{jscc}(s)$, which simultaneously optimizes semantic fidelity and channel robustness.
• Semantic Reconstruction: Receiver-side decoder $g_\mathrm{jscc}$ estimates the semantic embedding $\hat{s}$, which is mapped back to reconstructed data $$\hat{y} = g_\mathrm{sem}(\hat{s};\mathrm{KB}_\mathrm{rx})$$. Knowledge base alignment between transmitter ($\mathrm{KB}_\mathrm{tx}$) and receiver ($\mathrm{KB}_\mathrm{rx}$) is essential; semantic relay satellites and KB-coordinators synchronize distributed KBs.

This architecture enables dynamic adaptation to underlying physical channels, resource constraints, and semantic requirements, with cross-layer feedback and semantic relaying as illustrated in satellite network scenarios (Ahmed et al., 13 Jun 2025).

2. Information-Theoretic Foundations and Performance Metrics

SemCom systems are characterized using semantic analogs of classical information metrics:

• Semantic Mutual Information: $$I(S;\hat{S}) = \sum_{s,\hat{s}} p(s,\hat{s}) \log\left(\frac{p(s,\hat{s})}{p(s)p(\hat{s})}\right)$$ quantifies the amount of received meaning.
• Semantic Distortion: $D(S,\hat{S}) = E[d(S,\hat{S})]$ measures task-oriented discrepancy, e.g., cosine similarity in embedding space, BERTScore for text.
• Semantic Rate Model:

$S = \frac{W\,I}{K\,L}\,\epsilon(K,\gamma)$

where $W$ is bandwidth, $I$ semantic content (“suts”), $K$ symbols per word, $L$ words, and $\epsilon(K,\gamma)$ is semantic similarity—typically fit by a logistic curve dependent on SNR $\gamma$.

• Bit-to-Semantic Rate Conversion:

$R_{SB} = R_B \frac{I}{\mu L}\epsilon_C$

mapping bit-rate $R_B$ to equivalent semantic rate under knowledge base size $\mu$ and achieved similarity $\epsilon_C$.

Other typical metrics include bandwidth compression ratios, cross-modal semantic fidelity, and task-specific performance (e.g., image quality, scene understanding) (Ahmed et al., 13 Jun 2025).

3. Resource Allocation and Coexistence with Bit-based Communication

SemCom can coexist in heterogeneous networks with BitCom (traditional bit-based communication):

• Resource Allocation Problem:

$\max E[S_\mathrm{sec}(P_\mathrm{sec})]$

$$\text{subject to } E[R_\mathrm{prim}(P_\mathrm{prim},P_\mathrm{sec})] \geq \bar{R}_\mathrm{prim}$$

$0 \leq P_\mathrm{sec} \leq P_\mathrm{max}$

where $P_\mathrm{prim},P_\mathrm{sec}$ are power allocations for bit- and semantic-users.

• Multiple Access Techniques:
  • Power-domain NOMA: Bit-user and semantic-user share the spectrum, e.g., in uplink:

  $$R_1 \leq \log_2\left[1 + \frac{P_1|h_1|^2}{P_2|h_2|^2 + \sigma^2}\right]$$

  $$S_2 \leq \frac{W\,I}{K\,L}\,\epsilon(K,\gamma_2), \quad \gamma_2 = \frac{P_2|h_2|^2}{\sigma^2}$$ - Hybrid NOMA/OMA: Time-slot or power coefficients $\alpha_i$ per user adapt to optimize joint semantic/bit constraints.

The interplay between semantic and bit resource allocation is central to adaptive and energy-efficient 6G system designs (Ahmed et al., 13 Jun 2025).

4. Multi-Modal Semantic Fusion and Frameworks

Modern SemCom must support heterogeneous sources:

• Modality-specific Encoders: Transformers for text, CNNs/diffusion models for images, spectrogram-based methods for audio, 3D autoencoders for video.

• Central Semantic Fusion: All modality-specific embeddings are projected into a shared latent space, fused via concatenation and attention mechanisms or unified JSCC coding.

• Examples:

  • MU-DeepSC: Visual Question Answering combining image and text queries.
  • U-DeepSC: Unified multi-task model for feature selection based on task/channel.
  • MFMSC: Multi-modal embeddings aligned using BERT.

Such frameworks enable cross-modal, task-oriented semantic communication essential for future intelligent networks (Ahmed et al., 13 Jun 2025).

5. Semantic Communication over Satellite Networks

SemCom is highly advantageous for satellite channels subject to severe path-loss, limited bandwidth, and Doppler effects:

• Channel Model:
  • Path-loss: $\rho(d) = \rho_0(\frac{d_0}{d})^\beta$
  • Fading: Rayleigh block-fading
  • AWGN noise: variance $\sigma^2$
• Semantic Rate with Satellite SNR:

$$S = \frac{W\,I}{K\,L}\,\epsilon(K,\gamma_\mathrm{sat})$$

incorporating satellite-specific SNR.

• QoS Optimization:

$$\min L \qquad \text{subject to } D(S,\hat{S}) \leq \bar{D}, \quad \sum_k P_k \leq P_\mathrm{tot}$$

addressed via hybrid meta-heuristics (e.g., discrete whale optimization) and JSCC.

Advanced proposals integrate OTFS modulation with semantic feature extraction to improve resilience against Doppler and multipath (Ahmed et al., 13 Jun 2025).

6. Open Challenges and Research Directions

Despite substantial spectral and energy advantages, SemCom is impeded by several critical challenges:

• KB Alignment: Maintaining knowledge consistency across distributed, dynamic networks (satellites, UAVs) is unresolved.
• Semantic Metric Standardization: Universal, task-agnostic definitions for $D(S,\hat{S})$ and $\epsilon(\cdot)$ remain an open research frontier.
• Cross-layer Protocols: Integrating semantic awareness from physical to network layers (e.g., semantic routing, AI-native air interfaces) requires novel protocol stacks.
• Complexity and Energy Efficiency: Deep semantic encoders and decoders are computationally intensive; lightweight and energy-aware models are needed for edge and satellite devices.
• Interpretability and Security: Explaining semantic errors and defending against adversarial semantic attacks are prerequisites for trustworthy adoption.

Advancements in these areas are essential for the realization of intelligent, meaning-driven 6G networks (Ahmed et al., 13 Jun 2025).

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Semantic Communication Systems in 6G represent a complex interplay between deep semantic understanding, advanced resource allocation, cross-modal fusion, and robust network/satellite transmission. The current research frontier encompasses information-theoretic modeling, integrated multi-access strategies, multi-modal architecture, and adaptive knowledge base management, with significant open questions regarding metric standardization, KB synchronization, network protocol design, and security. These aspects converge to define the future landscape of meaning-centric, intelligent wireless communications.