Explainable Machine learning models for AI native radio access technologies (XAI-RAT)

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Machine learning (ML) has started to be extensively used to enhance the implementation of various components within the 5G radio access network. In addition, 6G is expected to enable greater levels of autonomy, improve human machines interfacing, and achieve deep connectivity in more diverse environments. As such we are by to embrace a vision where 6G may need to be designed in a way that ML can modify parts of the physical (PHY) and medium access control (MAC) layers. In such an artificial intelligent (AI)-native radio access technologies (RATs), ML can for instance enable the learning of personalized waveforms, modulation schemes, pilot sequences and codes, which not only make a more efficient use of the spectrum but are also optimally adapted to practical limitations of the computational resources, the transceiver hardware and channel. The application of AI within the cellular domain, while promising, is still at its infant stages where significant challenges remain to be overcome. The key challenges for realizing the vision of AI-enabled RATs for beyond 5G and 6G are: (i) The overhead and availability of training data and the uncertainty in generalization that are still serious open issues, (ii) the lack of explainability that stems from AI tools being often treated as black boxes as it is hard to develop analytical models to either test their correctness, or explain their behaviours, in a simple manner, and (iii) deployment concerns that are foreseen from both the lack of interoperability and energy efficient hardware implementation. As such, the long-term goal of the current research program is to develop AI-native radio access technologies with explainable models. The contributions reside (i) in proposing low complexity explainable meta-learning techniques to address real-time on-line training issues and to considerably improve the trustworthiness of AI-enabled RATs, and (ii) in proposing AI-based approximate computing (AC) methodology for an aggressive energy efficient implementation by trading accuracy with energy consumption. The key idea is also on applying AI in making the AC framework input-independent and making better use of error compensation mechanisms to improve the AC performance. Therefore, the main outcome is a novel methodology to investigate the synergy between AI-native RATs design and its AI-based energy efficient implementation where explainability is carried out all along the design and implementation processes. As such, this research program will contribute to improving the trustworthiness of an AI-enabled RAT, speed up industrial adoption and support the standardization bodies toward providing key insights for integrating AI models. The realization of the related projects in institutional/industrial partnership will be privileged. The realization of this program is also based on research results already published in journals and conferences.