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Abstract—As wireless communication systems strive to improve spectral efficiency, there has been a growing interest in employing machine learning (ML)-based approaches for adaptive modulation and coding scheme (MCS) selection. In this paper, we introduce a new adaptive MCS selection framework for massive MIMO systems that operates without any feedback from users by solely relying on instantaneous uplink channel estimates. Our proposed method can effectively operate in multi-user scenarios where user feedback imposes excessive delay and bandwidth overhead. To learn the mapping between the user channel matrices and the optimal MCS level of each user, we develop a Convolutional Neural Network (CNN)-Long Short-Term Memory Network (LSTM)-based model and compare the performance with the state-of-the-art methods. Finally, we validate the effectiveness of our algorithm by evaluating it experimentally using real-world datasets collected from the RENEW massive MIMO platform. Index Terms—Adaptive MCS Selection, Machine Learning, Convolutional Neural Network, Long Short-Term Memory Network, Channel State Information, Feedback Delay. 

The large number of antennas in massive MIMO systems allows the base station to communicate with multiple users at the same time and frequency resource with multi-user beamforming. However, highly correlated user channels could drastically impede the spectral efficiency that multi-user beamforming can achieve. As such, it is critical for the base station to schedule a suitable group of users in each time and frequency resource block to achieve maximum spectral efficiency while adhering to fairness constraints among the users. In this paper, we consider the resource scheduling problem for massive MIMO systems with its optimal solution known to be NP-hard. Inspired by recent achievements in deep reinforcement learning (DRL) to solve problems with large action sets, we propose \name{}, a dynamic scheduler for massive MIMO based on the state-of-the-art Soft Actor-Critic (SAC) DRL model and the K-Nearest Neighbors (KNN) algorithm. Through comprehensive simulations using realistic massive MIMO channel models as well as real-world datasets from channel measurement experiments, we demonstrate the effectiveness of our proposed model in various channel conditions. Our results show that our proposed model performs very close to the optimal proportionally fair (Opt-PF) scheduler in terms of spectral efficiency and fairness with more than one order of magnitude lower computational complexity in medium network sizes where Opt-PF is computationally feasible. Our results also show the feasibility and high performance of our proposed scheduler in networks with a large number of users and resource blocks. 

Northern Hemisphere forests changed drastically in the early Eocene with the diversification of the oak family (Fagaceae). Cooling climates over the next 20 million years fostered the spread of temperate biomes that became increasingly dominated by oaks and their chestnut relatives. Here we use phylogenomic analyses of nuclear and plastid genomes to investigate the timing and pattern of major macroevolutionary events and ancient genome-wide signatures of hybridization across Fagaceae. Innovation related to seed dispersal is implicated in triggering waves of continental radiations beginning with the rapid diversification of major lineages and resulting in unparalleled transformation of forest dynamics within 15 million years following the K-Pg extinction. We detect introgression at multiple time scales, including ancient events predating the origination of genus-level diversity. As oak lineages moved into newly available temperate habitats in the early Miocene, secondary contact between previously isolated species occurred. This resulted in adaptive introgression, which may have further amplified the diversification of white oaks across Eurasia.