We consider adapting the transmission rate to maximize the goodput, i.e., the amount of data transmitted without error, over a continuous Markov flat-fading wireless channel. In particular, we consider schemes in which transmitter channel state is inferred from degraded causal error-rate feedback, such as packet-level ACK/NAKs in an automatic repeat request (ARQ) system. In such schemes, the choice of transmission rate affects not only the subsequent goodput but also the subsequent feedback, implying that the optimal rate schedule is given by a partially observable Markov decision process (POMDP). Because solution of the POMDP is computationally impractical, we consider simple suboptimal greedy rate assignment and show that the optimal scheme would itself be greedy if the error-rate feedback was non-degraded. Furthermore, we show that greedy rate assignment using non-degraded feedback yields a total goodput that upper bounds that of optimal rate assignment using degraded feedback. We then detail the implementation of the greedy scheme and propose a reduced-complexity greedy scheme that adapts the transmission rate only once per block of packets. We also investigate the performance of the schemes numerically, and show that the proposed greedy scheme achieves steady-state goodputs that are reasonably close to the upper bound on goodput calculated using non-degraded feedback. A similar improvement is obtained in steady-state goodput, drop rate, and average buffer occupancy in the presence of data buffers. We also investigate an upper bound on the performance of optimal rate assignment for a discrete approximation of the channel and show that such quantization leads to a significant loss in achievable goodput.
Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization
Department of Mathematics, National University of Singapore（数学系，新加坡国立大学）；Department of Mathematics and Department of Electrical and Computer Engineering, National University of Singapore（数学系和电气与计算机工程系，新加坡国立大学）
PRB-WM: Physics-Regularized Beta World Models for Predictive Link Adaptation on Real Time-Varying Underwater Acoustic Channels
School of Electronic and Information Engineering, Beihang University（北京航空航天大学电子信息工程学院）；Peng Cheng Laboratory（鹏城实验室）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；MIIT Key Lab. of Aerospace Mobile Communication（工业和信息化部航空移动通信重点实验室）；State Key Lab. of CNS/ATM（CNS/ATM国家重点实验室）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Hong Kong Generative AI Research and Development Center（香港生成式人工智能研发中心）；College of Underwater Acoustic Engineering, Harbin Engineering University（哈尔滨工程大学水下声学工程学院）；College of Computing & Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Closed-Loop Bayesian Bandit Encoder with GRAND Receiver for a Bursty Interference Channel
Ming Hsieh Department of Electrical and Computer Engineering（明希学校电气与计算机工程系）；Viterbi School of Engineering, University of Southern California（维特比工程学院，南加州大学）
