SOLA: Second-Order Link Adaptation from ACK/NACK Feedback

Link adaptation is a central control loop in cellular networks: a transmitter must choose a spectral-efficiency target from limited radio-state information and receives only binary ACK/NACK feedback after transmission. We study a contextual threshold-feedback model in which the latent decodable capacity is a linear function of the observed context plus calibrated log-concave fading noise. Under this stationary, model-specified setting, we propose Second-Order Link Adaptation (SOLA), an Online-Newton-style method that updates the channel parameter through the ACK/NACK negative log-likelihood and then selects the expected-throughput-maximizing rate. For a fixed unknown channel parameter, known fading CDF, bounded continuous rate set, and adversarially chosen contexts, we prove $\mathcal{O}(d\log T)$ expected effective-throughput regret, including boundary rate optima. Experiments compare SOLA with OLLA, GLM-UCB-style methods, and SALAD, test CDF misspecification, and evaluate discounted D-SOLA under abrupt changes and across 77 recorded 5G context trajectories with synthetic stochastic feedback. These controlled studies support the empirical value of second-order ACK/NACK learning; the theorem does not cover discounting, finite MCS, misspecified CDFs, or safety margins.