Large models based high-fidelity voice services over 6G narrowband non-terrestrial networks

Non-Terrestrial Networks (NTN) can be used to provide emergency voice services in Sixth-Generation (6G) communication systems. However, Internet of Things (IoT) terminals, which comprise restricted bandwidth resources and weak computing power, which make ensuring high-quality voice services over NTN challenging. Recent advancements in Artificial Intelligence (AI) techniques have been increasingly applied to enhance the audio quality and reduce the bit rate. However, applying models with high computational complexity to IoT terminals is difficult. In this study, we propose a voice-services-over NTN solution including a novel 6G non-terrestrial and ground network integrated framework and a lightweight Large Models (LMs)-driven codec operating at 450 bits per second. We also designed a new voice packet header and deployed an agent on-ground gateway to reduce the bandwidth overhead. The non-standard Session Initiation Protocol header was converted to the standard format while re-encapsulating Internet Protocol and User Datagram Protocol headers, replacing the conventional implementations. Additionally, an operational NTN satellite was used to evaluate the proposed ReCodec. The experimental results demonstrate that the ReCodec decreases the computational complexity by 96.61% while increasing the voice quality by 17.55% when compared with the state-of-the-art mechanisms. Furthermore, the design of the packet header reduced the voice frame header to 50 bytes.

Authors

Publication Details

Journal
Digital Communications and Networks
Published
2025-08-11
DOI
https://doi.org/10.1016/j.dcan.2025.07.009
Citations
2
Primary Topic
Telecommunications and Broadcasting Technologies
Type
article
Field-Weighted Citation Impact
2.27
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article

Large models based high-fidelity voice services over 6G narrowband non-terrestrial networks

Shijing Yuan, Chengxiao Yu, Jingyuan Han, Gang Liu et al.
2 citations
Digital Communications and Networks
Telecommunications and Broadcasting Technologies
2.27
article

Large models based high-fidelity voice services over 6G narrowband non-terrestrial networks

Shijing Yuan, Chengxiao Yu, Jingyuan Han, Gang Liu, Zhongkai Tong
article en
2 citations

Abstract

Non-Terrestrial Networks (NTN) can be used to provide emergency voice services in Sixth-Generation (6G) communication systems. However, Internet of Things (IoT) terminals, which comprise restricted bandwidth resources and weak computing power, which make ensuring high-quality voice services over NTN challenging. Recent advancements in Artificial Intelligence (AI) techniques have been increasingly applied to enhance the audio quality and reduce the bit rate. However, applying models with high computational complexity to IoT terminals is difficult. In this study, we propose a voice-services-over NTN solution including a novel 6G non-terrestrial and ground network integrated framework and a lightweight Large Models (LMs)-driven codec operating at 450 bits per second. We also designed a new voice packet header and deployed an agent on-ground gateway to reduce the bandwidth overhead. The non-standard Session Initiation Protocol header was converted to the standard format while re-encapsulating Internet Protocol and User Datagram Protocol headers, replacing the conventional implementations. Additionally, an operational NTN satellite was used to evaluate the proposed ReCodec. The experimental results demonstrate that the ReCodec decreases the computational complexity by 96.61% while increasing the voice quality by 17.55% when compared with the state-of-the-art mechanisms. Furthermore, the design of the packet header reduced the voice frame header to 50 bytes.

Digital Communications and NetworksVol. 11(6)
Openalex Percentile: Top 10%
Telecommunications and Broadcasting Technologies
2.27
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Large models based high-fidelity voice services over 6G narrowband non-terrestrial networks — Shijing Yuan, Chengxiao Yu, et al. · Digital Communications and Networks (2025) | TGRS Research Map | TGRS