In the rapidly evolving field of wireless communication, Multiple Input Multiple Output (MIMO) networks have emerged as a pivotal technology, offering enhanced data rates and spectral efficiency by leveraging multiple antennas at both the transmitter and receiver. The introduction of Open Radio Access Network (O-RAN) architecture has further revolutionized this domain, enabling greater flexibility, scalability, and interoperability through its open interfaces and software-defined approach. This paper presents DRAGON, a novel Deep Reinforcement Learning (DRL)-based framework for joint Layer and Modulation and Coding Scheme (MCS) selection, tailored for downlink single-user MIMO networks under the O-RAN framework. Our approach is designed to be highly scalable, capable of efficiently managing a large number of configuration options in one-shot prediction, including up to 25 MCS and 4 layer choices. The proposed solution has been rigorously evaluated using an O-RAN-based simulation environment, demonstrating up to an 18% performance improvement over the state-of-the-art (SOTA) methods and achieving a best throughput of 87.4% when compared to the collected ground-truth dataset. Furthermore, our method supports real-time prediction, making it viable for practical deployment. In addition to these advancements, we explore the potential integration of our DRL-based solution with real-world platforms and discuss the extension of our approach to handle multi-user (MU) MIMO scenarios, paving the way for broader applications in next-generation wireless networks.

This letter presents a new array pattern synthesis algorithm using the recursive least squares method. This new algorithm designs a minimax array pattern without an ad hoc weighting function, and without solving a matrix inverse at each iteration. Numerical examples are presented to illustrate this new algorithm.

We present a new technique to solve array pattern synthesis problems by using semidefinite programming. We first formulate (or reformulate) the array design problems into semidefinite programming problems, and then use the recently developed efficient numerical algorithms and software to compute the numerical solution of antenna array weights. Using this approach, we can directly solve not only the standard synthesis problems for nonuniform arrays, but also the synthesis problems for arrays having power restrictions and uncertainties. Numerical examples are presented to illustrate our approach.