Learning to Transmit Over Unknown Erasure Channels with Empirical Erasure Rate Feedback
This work addresses the learning-exploitation tradeoff in finite-horizon communication for unknown channels, providing theoretical regret guarantees.
The paper tackles reliable data transmission over a binary erasure channel with unknown erasure probability, using infrequent feedback of empirical erasure rate. It proposes two strategies achieving regret of O(T^{2/3}) with one query and O(√T) with O(log T) queries.
We address the problem of reliable data transmission within a finite time horizon $T$ over a binary erasure channel with unknown erasure probability. We consider a feedback model wherein the transmitter can query the receiver infrequently and obtain the empirical erasure rate experienced by the latter. We aim to minimize a regret quantity, i.e. how much worse a strategy performs compared to an oracle who knows the probability of erasure, while operating at the same block error rate. A learning vs. exploitation dilemma manifests in this scenario -- specifically, we need to balance between (i) learning the erasure probability with reasonable accuracy and (ii) utilizing the channel to transmit as many information bits as possible. We propose two strategies: (i) a two-phase approach using rate estimation followed by transmission that achieves an $O({T}^{\frac 23})$ regret using only one query, and (ii) a windowing strategy using geometrically-increasing window sizes that achieves an $O({\sqrt{T}})$ regret using $O(\log(T))$ queries.