Message Passing Based Two-Timescale Bayesian Learning for Joint Channel and Memory Hardware Impairments Tracking
arXiv:2607.01660v1 Announce Type: new Abstract: Hardware impairments in massive multiple-input multiple-output (MIMO) receivers introduce inter-symbol memory and inter-element coupling, severely degrading channel estimation. This paper employs a residual recurrent gated unit (RGRU) to model the intra-slot memory of the hardware impairments and proposes a message-passing-based two-timescale Bayesian deep learning (MP-TTBDL) framework for joint channel and impairment tracking. Owing to small-scale fading, the wireless channel varies rapidly across slots, whereas hardware impairments drift slowly
The continuous drive for more efficient and robust communication systems, especially in massive MIMO, necessitates advanced methods to counteract hardware imperfections that increasingly hinder performance.
This research provides a refined approach to channel estimation by simultaneously addressing hardware impairments, which is crucial for the reliability and data throughput of next-generation wireless communication networks.
Current methods for channel estimation often struggle with complex hardware impairments; this new message-passing-based Bayesian learning framework offers improved real-time tracking and compensation.
- · Telecommunications infrastructure providers
- · 5G/6G network operators
- · AI/ML in wireless research
- · Legacy channel estimation techniques
- · Systems highly sensitive to hardware impairments
Improved stability and performance of massive MIMO systems in challenging environments.
Faster adoption and deployment of advanced wireless technologies due to enhanced reliability.
Potential for new classes of applications requiring ultra-reliable low-latency communication over imperfect hardware.
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Read at arXiv cs.LG