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6G-AUTOR: Autonomic Transceiver via Realtime On-Device Signal Analytics

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Abstract

Next-generation wireless systems aim at fulfilling diverse application requirements but fundamentally rely on point-to-point transmission qualities. Aligning with recent AI-enabled wireless implementations, this paper introduces autonomic radios, 6G-AUTOR, that leverage novel algorithm-hardware separation platforms, softwarization of transmission (TX) and reception (RX) operations, and automatic reconfiguration of RF frontends, to support link performance and resilience. Hence, a software architecture can be provided to enable event-triggered operations for seamless hosting and execution environment. That is, those functions can be executed on an on-demand basis so that no resources will be preoccupied until the point of execution for better device efficiency. As a comprehensive transceiver solution, our design encompasses several ML-driven models, each enhancing a specific aspect of either TX or RX, leading to robust transceiver operation under tight constraints of future wireless systems. As for Tx scenarios, a data-driven spectrum sensing algorithm was implemented to obtain usages of current frequency bands for further use. Also, a data-driven radio management module was developed via deep Q-networks to support fast-reconfiguration of TX resource blocks (RB) and proactive multi-agent access. As for Rx scenarios, a fundamental tool - automatic modulation classification (AMC) which involves a complex correntropy extraction, followed by a convolutional neural network (CNN)-based classification, and a deep learning-based LDPC decoder were added to improve the reception quality and radio performance. Simulations of individual algorithms demonstrate that under appropriate training, each of the corresponding radio functions have either outperformed or have performed on-par with the benchmark solutions.

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Notes

  1. Data availability statement: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by Lockheed Martin Space Systems Company, Lockheed Martin Corporation.

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Authors and Affiliations

  1. Intelligent Wireless Networking Lab, Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, 27695, USA

    Chia-Hung Lin, K. V. S. Rohit & Shih-Chun Lin

  2. Lockheed Martin Space, Lockheed Martin Corporation, Sunnyvale, USA

    Liang C. Chu

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  1. Chia-Hung Lin
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  2. K. V. S. Rohit
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  3. Shih-Chun Lin
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Correspondence to Shih-Chun Lin.

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Lin, CH., Rohit, K.V.S., Lin, SC. et al. 6G-AUTOR: Autonomic Transceiver via Realtime On-Device Signal Analytics. J Sign Process Syst 95, 1277–1295 (2023). https://doi.org/10.1007/s11265-023-01858-8

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