arXiv:2607. 11075v1 Announce Type: new Abstract: The choice of Modulation and Coding (MCS) type for a particular channel condition is made through link adaptation (LA) algorithms that operate at the MAC layer.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
The choice of Modulation and Coding (MCS) type for a particular channel condition is made through link adaptation (LA) algorithms that operate at the MAC layer. These algorithms rely on the ACK/NACK statistics and the channel quality index (CQI) feedback.
arXiv:2604.14908v2 Announce Type: replace-cross Abstract: We study downlink beam and rate adaptation in a multi-user mmWave MISO system where multiple base stations (BSs), each using analog beamformi...
arXiv:2607. 15404v1 Announce Type: cross Abstract: Interleaving mitigates burst errors but introduces decoding delay and removes temporal error structure that a channel-aware decoder could exploit.
arXiv:2409. 18909v2 Announce Type: replace Abstract: Motivated by real-world applications that necessitate responsible experimentation, we introduce the problem of best arm identification (BAI) with minimal regret.
arXiv:2606. 00913v1 Announce Type: cross Abstract: Multi-arm bandit algorithms are increasingly used in online platforms, clinical trials, and social science experiments, but valid statistical inference on their performance remains an open challenge.
This paper studies Restless Multi‑Armed Bandits with individual penalty constraints for dynamic wireless networks, allowing each arm to have distinct performance limits such as energy, activation, or age of information. It introduces the Penalty‑Optimal Whittle (POW) index, which depends only on an arm’s transition kernel and its constraints, making it computable offline and independent of system‑wide parameters. The authors prove the POW index policy is asymptotically optimal, present a deep reinforcement learning method to learn the index online, and show through simulations that it outperforms existing policies.