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Wireless communication technologies have profoundly impacted the interconnectivity of mobile users and terminals. Nevertheless, the exponential increase in the number of users poses significant challenges, particularly in interference management, which is a major concern in wireless communication. Machine learning (ML) approaches have emerged as powerful tools for solving various problems in this domain. However, existing studies have not fully addressed the problem of interference management for wireless communication using ML techniques. In this paper, we explore the application of recurrent neural network (RNN) approaches to address co-channel interference in wireless communication. Specifically, we investigate the effectiveness of long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and gated recurrent unit (GRU) network architectures in two different network settings. The first network comprises 10 connected devices, while the second network involves 20 devices. Our experimental results demonstrate that Bi-LSTM outperforms LSTM and GRU in terms of mean squared error, normalized mean squared error, and sum rate. While LSTM and GRU produce similar results, LSTM exhibits a marginal advantage over GRU. In addition, a combined RNN approach is also studied, and it can provide better results in dense networks. [ABSTRACT FROM AUTHOR]

With the growing maritime economy, ensuring the quality of communication for maritime users has become imperative. The maritime communication system based on nearshore base stations enhances the communication rate of maritime users through dynamic resource allocation. A virtual queue-based deep reinforcement learning beam allocation scheme is proposed in this paper, aiming to maximize the communication rate. More particularly, to reduce the complexity of resource management, we employ a grid-based method to discretize the maritime environment. For the combinatorial optimization problem of grid and beam allocation under unknown channel state information, we model it as a sequential decision process of resource allocation. The nearshore base station is modeled as a learning agent, continuously interacting with the environment to optimize beam allocation schemes using deep reinforcement learning techniques. Furthermore, we guarantee that grids with poor channel state information can be serviced through the virtual queue method. Finally, the simulation results provided show that our proposed beam allocation scheme is beneficial in terms of increasing the communication rate. [ABSTRACT FROM AUTHOR]

Applying the Delphi method, this study aims at characterizing the perceptions and needs of physicians, individuals with hemophilia, and their caregivers in relation to the management of routine visits during regular follow-ups. A single structured questionnaire, prepared by an advisory board, was administered to 139 participants, comprising hemophilia treaters, patients and caregivers, during the period from May to June 2023. Agreement (defined following the Delphi method as developed by RAND Corporation) was reached on several topics. The Principal Component Analysis methods identified the four most relevant areas where consensus was reached among the interviewees, underscoring the necessity for in-depth discussions during routine visits: (1) medical aspects related to symptoms, life-limitations, pain, etc.; (2) non-medical related aspects (ambitions, lifestyle, network, etc.); (3) logistical-organizational aspects (home–hospital distance, alternative modalities of communication); and (4) visit duration and telemedicine integration. The results of both the Delphi and Principal Component Analysis underline that the care of individuals with hemophilia extends beyond merely prescribing drugs or treatment regimens. Instead, it necessitates consideration of numerous variables from both therapeutic and non-therapeutic domains, all of which are deemed important for the holistic management of the individuals. As a result, these aspects are routinely discussed and addressed during visits. [ABSTRACT FROM AUTHOR]