  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. 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. 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. Also, a ResNet-inspired fast-beamforming solution
was employed to enable robust communication to multiple receivers over the same
RB, which has potential applications in realisation of cell-free
infrastructures. As a receiver the system was equipped with a capability of
ultra-broadband spectrum recognition. Apart from this, 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.
