Adaptive Training for Correlated Fading Channels with Feedback
Manish Agarwal, Michael Honig, and Baris Ata

TL;DR
This paper develops an optimal adaptive training scheme with feedback for correlated Rayleigh fading channels, significantly improving data rates by dynamically adjusting pilot and data powers based on channel estimates.
Contribution
It explicitly derives the optimal power control policies in a continuous-time model with feedback, introducing a bang-bang control strategy for pilot power adaptation.
Findings
Adaptive training increases achievable data rates.
Power control adapts to channel fading, reducing training power during fades.
Significant gains at low SNRs and fast fading conditions.
Abstract
We consider data transmission through a time-selective, correlated (first-order Markov) Rayleigh fading channel subject to an average power constraint. The channel is estimated at the receiver with a pilot signal, and the estimate is fed back to the transmitter. The estimate is used for coherent demodulation, and to adapt the data and pilot powers. We explicitly determine the optimal pilot and data power control policies in a continuous-time limit where the channel state evolves as an Ornstein-Uhlenbeck diffusion process, and is estimated by a Kalman filter at the receiver. The optimal pilot policy switches between zero and the maximum (peak-constrained) value (``bang-bang'' control), and approximates the optimal discrete-time policy at low Signal-to-Noise Ratios (equivalently, large bandwidths). The switching boundary is defined in terms of the system state (estimated channel mean and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
