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Heterogeneous Semantic and Bit Communications: A Semi-NOMA Scheme

Xidong Mu, Yuanwei Liu, Li Guo, Naofal Al-Dhahir

TL;DR

This work addresses the coexistence of semantic and traditional bit-based transmissions by introducing a heterogeneous MA framework with one S-user and one B-user. It overcomes the lack of a closed-form semantic similarity by fitting a generalized logistic function to approximate the semantic similarity, enabling tractable analysis of three MA schemes: OMA, NOMA, and semi-NOMA. It defines and characterizes the semantic-versus-bit (SvB) rate region and the power region for each scheme, showing that semi-NOMA strictly contains the SvB regions of the other schemes and achieves the lowest transmit power in many cases, even outperforming NOMA in symmetric-channel scenarios. The results underscore the practical value of semi-NOMA for flexible resource allocation in future networks and highlight design guidelines such as pairing a stronger S-user with a weaker B-user and carefully selecting the semantic encoding parameter $K$ to balance semantic rate and power consumption. The methods and findings have potential implications for efficient resource management in 6G-like systems that integrate semantic and conventional communications, and they open avenues for extending the framework to multi-user, multimodal semantic scenarios and opportunistic transmission policies $within$ heterogeneous networks.$

Abstract

Multiple access (MA) design is investigated for facilitating the coexistence of the emerging semantic transmission and the conventional bit-based transmission in future networks. The semantic rate is considered for measuring the performance of the semantic transmission. However, a key challenge is that there is a lack of a closed-form expression for a key parameter, namely the semantic similarity, which characterizes the sentence similarity between an original sentence and the corresponding recovered sentence. To overcome this challenge, we propose a data regression method, where the semantic similarity is approximated by a generalized logistic function. Using the obtained tractable function, we propose a heterogeneous semantic and bit communication framework, where an access point simultaneously sends the semantic and bit streams to one semantics-interested user (S-user) and one bit-interested user (B-user). To realize this heterogeneous semantic and bit transmission in multi-user networks, three MA schemes are proposed, namely orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), and semi-NOMA. More specifically, the bit stream in semi-NOMA is split into two streams, one is transmitted with the semantic stream over the shared frequency sub-band and the other is transmitted over the separate orthogonal frequency sub-band. To study the fundamental performance limits of the three proposed MA schemes, the semantic-versus-bit (SvB) rate region and the power region are defined. An optimal resource allocation procedure is then derived for characterizing the boundary of the SvB rate region and the power region achieved by each MA scheme. The structures of the derived solutions demonstrate that semi-NOMA is superior to both NOMA and OMA given its highly flexible transmission policy. Our numerical results validate the analysis and show the superiority of semi-NOMA.

Heterogeneous Semantic and Bit Communications: A Semi-NOMA Scheme

TL;DR

This work addresses the coexistence of semantic and traditional bit-based transmissions by introducing a heterogeneous MA framework with one S-user and one B-user. It overcomes the lack of a closed-form semantic similarity by fitting a generalized logistic function to approximate the semantic similarity, enabling tractable analysis of three MA schemes: OMA, NOMA, and semi-NOMA. It defines and characterizes the semantic-versus-bit (SvB) rate region and the power region for each scheme, showing that semi-NOMA strictly contains the SvB regions of the other schemes and achieves the lowest transmit power in many cases, even outperforming NOMA in symmetric-channel scenarios. The results underscore the practical value of semi-NOMA for flexible resource allocation in future networks and highlight design guidelines such as pairing a stronger S-user with a weaker B-user and carefully selecting the semantic encoding parameter to balance semantic rate and power consumption. The methods and findings have potential implications for efficient resource management in 6G-like systems that integrate semantic and conventional communications, and they open avenues for extending the framework to multi-user, multimodal semantic scenarios and opportunistic transmission policies heterogeneous networks.$

Abstract

Multiple access (MA) design is investigated for facilitating the coexistence of the emerging semantic transmission and the conventional bit-based transmission in future networks. The semantic rate is considered for measuring the performance of the semantic transmission. However, a key challenge is that there is a lack of a closed-form expression for a key parameter, namely the semantic similarity, which characterizes the sentence similarity between an original sentence and the corresponding recovered sentence. To overcome this challenge, we propose a data regression method, where the semantic similarity is approximated by a generalized logistic function. Using the obtained tractable function, we propose a heterogeneous semantic and bit communication framework, where an access point simultaneously sends the semantic and bit streams to one semantics-interested user (S-user) and one bit-interested user (B-user). To realize this heterogeneous semantic and bit transmission in multi-user networks, three MA schemes are proposed, namely orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), and semi-NOMA. More specifically, the bit stream in semi-NOMA is split into two streams, one is transmitted with the semantic stream over the shared frequency sub-band and the other is transmitted over the separate orthogonal frequency sub-band. To study the fundamental performance limits of the three proposed MA schemes, the semantic-versus-bit (SvB) rate region and the power region are defined. An optimal resource allocation procedure is then derived for characterizing the boundary of the SvB rate region and the power region achieved by each MA scheme. The structures of the derived solutions demonstrate that semi-NOMA is superior to both NOMA and OMA given its highly flexible transmission policy. Our numerical results validate the analysis and show the superiority of semi-NOMA.
Paper Structure (28 sections, 1 theorem, 49 equations, 12 figures)

This paper contains 28 sections, 1 theorem, 49 equations, 12 figures.

Table of Contents

  1. Introduction
  2. State-of-the-art
  3. Motivations and Contributions
  4. Organization and Notation
  5. Semantic Rate and Approximation
  6. Semantic Rate
  7. Semantic Rate Approximation
  8. System Model
  9. OMA
  10. NOMA
  11. Semi-NOMA
  12. SvB Rate Region Characterization
  13. SvB Rate Region Characterization for OMA
  14. SvB Rate Region Characterization for NOMA
  15. SvB Rate Region Characterization for semi-NOMA
  16. ...and 13 more sections

Key Result

Lemma 1

For the given $0 \le \overline S \le {S_{\max }}$, the optimal bandwidth allocation for the semantic transmission, $W_s^*$, must satisfy $W_s^* \in \left[ {{W^{low}},{W^{up}}} \right]$, where ${W^{low}} \triangleq \frac{{\overline S KL}}{I}$ and ${W^{up}} \triangleq \min \left( {\frac{{\overline SKL

Figures (12)

  • Figure 1: Illustration of the point-to-point DeepSC framework DeepSC_T for text transmission.
  • Figure 2: Generalized logistic regression for approximating the semantic similarity, $\varepsilon \left( {K,\gamma }\right)$.
  • Figure 3: Illustration of the proposed heterogeneous semantic and bit communication framework.
  • Figure 4: OMA based heterogeneous semantic and bit transmission, where the two streams are transmitted via two orthogonal frequency sub-bands.
  • Figure 5: NOMA based heterogeneous semantic and bit transmission, where the two streams are transmitted via the fully shared frequency band.
  • ...and 7 more figures

Theorems & Definitions (4)

  • Lemma 1
  • proof
  • Remark 1
  • Remark 2