A Joint Communication and Computation Design for Probabilistic Semantic Communications
<p>Illustration of a knowledge graph.</p> "> Figure 2
<p>An illustration of the considered probabilistic semantic communication (PSC) network.</p> "> Figure 3
<p>Illustration of the probability graph considered in the PSC system.</p> "> Figure 4
<p>The framework of considered PSC network.</p> "> Figure 5
<p>Illustration of computation load versus semantic compression ratio <math display="inline"><semantics> <mi>ρ</mi> </semantics></math>.</p> "> Figure 6
<p>Sum of equivalent rate vs. number of iterations.</p> "> Figure 7
<p>Sum of equivalent rate vs. number of users.</p> "> Figure 8
<p>Sum of equivalent rate vs. noise power.</p> "> Figure 9
<p>Sum of equivalent rate vs. computation power coefficient.</p> "> Figure 10
<p>Sum of equivalent rate vs. maximum power limit.</p> "> Figure 11
<p>The allocation of the computation power and transmission power with different computation power coefficients.</p> ">
Abstract
:1. Introduction
- We consider a PSC network in which multiple users employ semantic information extraction techniques to compress their original large-sized data and transmit the extracted information to a multi-antenna base station (BS). In our model, users’ large-sized data are extracted as extensive knowledge graphs and are compressed based on the shared probability graph between the users and the BS.
- We formulate an optimization problem that aims to maximize the sum equivalent rate of all users while considering total power and semantic resource limit constraints. This joint optimization problem takes into account the trade-off between the transmission efficiency and computation complexity.
- To solve this non-convex, non-smooth problem, a low-complexity three-stage algorithm is proposed. In stage 1, the received beamforming matrix is optimized using the minimum mean square error (MMSE) strategy. In stage 2, we substitute the transmit power with the semantic compression ratio and develop an alternating optimization (AO) method to perform a rough search for the semantic compression ratio. In stage 3, gradient ascent is used to refine the semantic compression ratio. Numerical results show the effectiveness of the proposed algorithm.
2. System Model and Problem Formulation
2.1. Semantic Communication Model
2.2. Transmission Model
2.3. Computation Model
2.4. Problem Formulation
3. Algorithm Design
3.1. Stage 1: MMSE for the Received Beamforming Matrix
3.2. Stage 2: Rough Search for the Semantic Compression Ratio
Algorithm 1 Alternating Optimization for Determining Integer Matrix |
|
3.3. Stage 3: Refined Search for the Semantic Compression Ratio
Algorithm 2 Gradient Ascent Algorithm for a Refined Search of the Semantic Compression Ratio |
|
3.4. Algorithm Analysis
Algorithm 3 Joint Transmission and Computation Resource Allocation Algorithm for Multi-User PSC Network |
|
4. Simulation Results
- ‘Non-semantic’: This benchmark scheme represents a conventional communication approach where the original data are directly transmitted without employing semantic compression. In this scheme, all users’ power is allocated solely to transmission, without any optimization for joint transmission and computation.
- ‘PSC-S2’: This scheme is a simplified version of the ‘PSC’ scheme, where the optimization process is performed only up to stage 2. The final result is the roughly estimated semantic compression ratio obtained from this stage.
- ‘PSC-ZF’: In this scheme, the ZF strategy is employed at stage 1. This means that the received beamforming matrix is calculated as . The remaining stages are the same with the ‘PSC’ scheme.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A. Proof of Lemma 1
Appendix B. Proof of Theorem 1
References
- Xu, W.; Yang, Z.; Ng, D.W.K.; Levorato, M.; Eldar, Y.C.; Debbah, M. Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing. IEEE J. Sel. Top. Signal Process. 2023, 17, 9–39. [Google Scholar] [CrossRef]
- Saad, W.; Bennis, M.; Chen, M. A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems. IEEE Netw. 2020, 34, 134–142. [Google Scholar] [CrossRef]
- Lu, K.; Zhou, Q.; Li, R.; Zhao, Z.; Chen, X.; Wu, J.; Zhang, H. Rethinking Modern Communication from Semantic Coding to Semantic Communication. IEEE Wirel. Commun. 2023, 30, 158–164. [Google Scholar] [CrossRef]
- Gündüz, D.; Qin, Z.; Aguerri, I.E.; Dhillon, H.S.; Yang, Z.; Yener, A.; Wong, K.K.; Chae, C.B. Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications. IEEE J. Sel. Areas Commun. 2023, 41, 5–41. [Google Scholar] [CrossRef]
- Zhao, Z.; Yang, Z.; Hu, Y.; Lin, L.; Zhang, Z. Semantic Information Extraction for Text Data with Probability Graph. In Proceedings of the 2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops), Dalian, China, 10–12 August 2023. [Google Scholar] [CrossRef]
- Chaccour, C.; Saad, W.; Debbah, M.; Han, Z.; Poor, H.V. Less data, more knowledge: Building next generation semantic communication networks. arXiv 2022, arXiv:2211.14343. [Google Scholar]
- Peng, X.; Qin, Z.; Huang, D.; Tao, X.; Lu, J.; Liu, G.; Pan, C. A Robust Deep Learning Enabled Semantic Communication System for Text. In Proceedings of the GLOBECOM 2022-2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 4–8 December 2022; pp. 2704–2709. [Google Scholar] [CrossRef]
- Luo, X.; Chen, H.H.; Guo, Q. Semantic Communications: Overview, Open Issues, and Future Research Directions. IEEE Wirel. Commun. 2022, 29, 210–219. [Google Scholar] [CrossRef]
- Yan, L.; Qin, Z.; Zhang, R.; Li, Y.; Li, G.Y. Resource Allocation for Text Semantic Communications. IEEE Wirel. Commun. Lett. 2022, 11, 1394–1398. [Google Scholar] [CrossRef]
- Mu, X.; Liu, Y.; Guo, L.; Al-Dhahir, N. Heterogeneous Semantic and Bit Communications: A Semi-NOMA Scheme. IEEE J. Sel. Areas Commun. 2023, 41, 155–169. [Google Scholar] [CrossRef]
- Hu, Z.; Liu, T.; You, C.; Yang, Z.; Chen, M. Multiuser Resource Allocation for Semantic-Relay-Aided Text Transmissions. arXiv 2023, arXiv:2311.06854. [Google Scholar]
- Xie, H.; Qin, Z.; Li, G.Y.; Juang, B.H. Deep Learning Enabled Semantic Communication Systems. IEEE Trans. Signal Process. 2021, 69, 2663–2675. [Google Scholar] [CrossRef]
- Zhao, Z.; Yang, Z.; Pham, Q.V.; Yang, Q.; Zhang, Z. Semantic Communication with Probability Graph: A Joint Communication and Computation Design. In Proceedings of the hl2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), Hong Kong, 10–13 October 2023. [Google Scholar] [CrossRef]
- Yang, Z.; Chen, M.; Li, G.; Yang, Y.; Zhang, Z. Secure semantic communications: Fundamentals and challenges. arXiv 2023, arXiv:2301.01421. [Google Scholar]
- Zhao, Z.; Yang, Z.; Gan, X.; Pham, Q.V.; Huang, C.; Xu, W.; Zhang, Z. A Joint Communication and Computation Design for Semantic Wireless Communication with Probability Graph. arXiv 2023, arXiv:2312.13975. [Google Scholar]
- Yang, Z.; Chen, M.; Zhang, Z.; Huang, C. Energy efficient semantic communication over wireless networks with rate splitting. IEEE J. Sel. Areas Commun. 2023, 41, 1484–1495. [Google Scholar] [CrossRef]
- Yan, L.; Qin, Z.; Zhang, R.; Li, Y.; Li, G.Y. QoE-Aware Resource Allocation for Semantic Communication Networks. In Proceedings of the GLOBECOM 2022-2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 4–8 December 2022; pp. 3272–3277. [Google Scholar] [CrossRef]
- Yang, Z.; Chen, M.; Zhang, Z.; Huang, C.; Yang, Q. Performance Optimization of Energy Efficient Semantic Communications over Wireless Networks. In Proceedings of the 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), London, UK, 26–29 September 2022. [Google Scholar] [CrossRef]
- Cang, Y.; Chen, M.; Yang, Z.; Hu, Y.; Wang, Y.; Zhang, Z.; Wong, K.K. Resource Allocation for Semantic-Aware Mobile Edge Computing Systems. arXiv 2023, arXiv:2309.11736. [Google Scholar]
- Qin, Z.; Gao, F.; Lin, B.; Tao, X.; Liu, G.; Pan, C. A Generalized Semantic Communication System: From Sources to Channels. IEEE Wirel. Commun. 2023, 30, 18–26. [Google Scholar] [CrossRef]
- Huang, D.; Gao, F.; Tao, X.; Du, Q.; Lu, J. Toward Semantic Communications: Deep Learning-Based Image Semantic Coding. IEEE J. Sel. Areas Commun. 2023, 41, 55–71. [Google Scholar] [CrossRef]
- Han, T.; Yang, Q.; Shi, Z.; He, S.; Zhang, Z. Semantic-Preserved Communication System for Highly Efficient Speech Transmission. IEEE J. Sel. Areas Commun. 2023, 41, 245–259. [Google Scholar] [CrossRef]
- Weng, Z.; Qin, Z. Semantic Communication Systems for Speech Transmission. IEEE J. Sel. Areas Commun. 2021, 39, 2434–2444. [Google Scholar] [CrossRef]
- Hu, L.; Li, Y.; Zhang, H.; Yuan, L.; Zhou, F.; Wu, Q. Robust Semantic Communication Driven by Knowledge Graph. In Proceedings of the 2022 9th International Conference on Internet of Things: Systems, Management and Security (IOTSMS), Milan, Italy, 29 November–1 December 2022. [Google Scholar] [CrossRef]
- Wang, Y.; Chen, M.; Saad, W.; Luo, T.; Cui, S.; Poor, H.V. Performance Optimization for Semantic Communications: An Attention-based Learning Approach. In Proceedings of the 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 7–11 December 2021. [Google Scholar] [CrossRef]
- Gaur, M.; Faldu, K.; Sheth, A. Semantics of the Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable and Explainable? IEEE Internet Comput. 2021, 25, 51–59. [Google Scholar] [CrossRef]
- Farsad, N.; Rao, M.; Goldsmith, A. Deep Learning for Joint Source-Channel Coding of Text. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, 15–20 April 2018; pp. 2326–2330. [Google Scholar] [CrossRef]
- Yao, S.; Niu, K.; Wang, S.; Dai, J. Semantic Coding for Text Transmission: An Iterative Design. IEEE Trans. Cogn. Commun. Netw. 2022, 8, 1594–1603. [Google Scholar] [CrossRef]
- Liu, C.; Guo, C.; Wang, S.; Li, Y.; Hu, D. Task-Oriented Semantic Communication Based on Semantic Triplets. In Proceedings of the 2023 IEEE Wireless Communications and Networking Conference (WCNC), Glasgow, UK, 26–29 March 2023. [Google Scholar] [CrossRef]
- Zhou, F.; Li, Y.; Zhang, X.; Wu, Q.; Lei, X.; Hu, R.Q. Cognitive Semantic Communication Systems Driven by Knowledge Graph. In Proceedings of the ICC 2022-IEEE International Conference on Communications, Seoul, Republic of Korea, 16–20 May 2022; pp. 4860–4865. [Google Scholar] [CrossRef]
- Wang, Y.; Chen, M.; Luo, T.; Saad, W.; Niyato, D.; Poor, H.V.; Cui, S. Performance Optimization for Semantic Communications: An Attention-Based Reinforcement Learning Approach. IEEE J. Sel. Areas Commun. 2022, 40, 2598–2613. [Google Scholar] [CrossRef]
- Erol-Kantarci, M.; Mouftah, H.T. Energy-Efficient Information and Communication Infrastructures in the Smart Grid: A Survey on Interactions and Open Issues. IEEE Commun. Surv. Tuts. 2014, 17, 179–197. [Google Scholar] [CrossRef]
- Li, J.; Sun, A.; Han, J.; Li, C. A Survey on Deep Learning for Named Entity Recognition. IEEE Trans. Knowl. Data Eng. 2022, 34, 50–70. [Google Scholar] [CrossRef]
- Hu, Y.; Shen, H.; Liu, W.; Min, F.; Qiao, X.; Jin, K. A Graph Convolutional Network With Multiple Dependency Representations for Relation Extraction. IEEE Access 2021, 9, 81575–81587. [Google Scholar] [CrossRef]
- Cordeschi, N.; Amendola, D.; Baccarelli, E. Fairness-constrained optimized time-window controllers for secondary-users with primary-user reliability guarantees. Comput. Commun. 2018, 116, 63–76. [Google Scholar] [CrossRef]
- Talebi, S.P. Primary service outage and secondary service performance in cognitive radio networks. Wirel. Commun. Mob. Comput. 2015, 15, 1982–1990. [Google Scholar] [CrossRef]
Parameter | Symbol | Value |
---|---|---|
Number of users | N | 8 |
Number of antennas | M | 16 |
Long-term channel power gain | −90 dB | |
Noise power | −10 dBm | |
Computation power coefficient | 1 | |
Maximum power limit | 30 dBm | |
Parameter in (29) | ||
Initial step size | ||
Scaling factor in (34) | 0.5 | |
Hyper-parameter in (35) | 0.1 | |
Threshold in Algorithm 2 | ||
Maximum iteration limit in Algorithm 2 | 1000 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhao, Z.; Yang, Z.; Chen, M.; Zhang, Z.; Poor, H.V. A Joint Communication and Computation Design for Probabilistic Semantic Communications. Entropy 2024, 26, 394. https://doi.org/10.3390/e26050394
Zhao Z, Yang Z, Chen M, Zhang Z, Poor HV. A Joint Communication and Computation Design for Probabilistic Semantic Communications. Entropy. 2024; 26(5):394. https://doi.org/10.3390/e26050394
Chicago/Turabian StyleZhao, Zhouxiang, Zhaohui Yang, Mingzhe Chen, Zhaoyang Zhang, and H. Vincent Poor. 2024. "A Joint Communication and Computation Design for Probabilistic Semantic Communications" Entropy 26, no. 5: 394. https://doi.org/10.3390/e26050394
APA StyleZhao, Z., Yang, Z., Chen, M., Zhang, Z., & Poor, H. V. (2024). A Joint Communication and Computation Design for Probabilistic Semantic Communications. Entropy, 26(5), 394. https://doi.org/10.3390/e26050394