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

TL;DR

This paper presents 6G-AUTOR, an AI-enabled, autonomic transceiver for future wireless systems that integrates ML-driven models for adaptive, resilient, and spectrum-efficient communication, supporting rapid reconfiguration and multi-receiver operation.

Contribution

It introduces a comprehensive AI-based transceiver architecture with novel algorithms for reconfiguration, beamforming, spectrum recognition, and modulation classification, advancing beyond existing solutions.

Findings

ML models outperform or match benchmarks in simulations.

Supports fast reconfiguration of transmission resources.

Enables robust multi-receiver communication in cell-free networks.

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. 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.