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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]

The potential of ad hoc networks, which enable flexible and dynamic network establishment only by mobile terminals equipped with wireless communication devices, has recently attracted attention for the coming IoT era. Although the nature of ad hoc networks shows the advantages of their autonomous and distributed network management, a manifestation of drawbacks owing to the nature of wireless communication and the mobility of terminals are inevitable. Many routing protocols have already been proposed to address the issues by adapting to nature and achieving a certain level of improvement. However, the routing protocols still suffer from difficulties in information collection for routing and adaptive route management during communication. Moreover, there is another issue that end pair-based routing procedures prevent other end pairs from reusing the routing information effectively. To address the drawbacks of conventional routing protocols, this paper proposes VORTEX, a novel routing protocol that employs an opportunistic routing strategy using hierarchization. One of the characteristic features of VORTEX is its network-driven opportunistic forwarding, in which packets travel toward destination terminals using hierarchy as a guide without conventional route discovery procedures. Moreover, another characteristic feature of VORTEX is that the hierarchical structure also contributes to adapting to communication environment changes in an autonomous manner. In other words, VORTEX enables flexible network-wide information-based routing only with the locally collected information. The simulation results show that the proposed VORTEX could provide better service quality and reliability with improved efficiency compared to the conventional routing protocols. Furthermore, the most significant contribution is not only in the communication performance but also VORTEX omits route discovery or route maintenance from routing protocols, and formed networks themselves have a function to deliver packets toward destination terminals. [ABSTRACT FROM AUTHOR]

In factory automation and process control systems, hybrid wired/wireless networks are often deployed to connect devices of difficult reachability such as those mounted on mobile equipment. A widespread implementation of these networks makes use of Access Points (APs) to implement wireless extensions of Real-Time Ethernet (RTE) networks via the IEEE 802.11 Wireless LAN (WLAN). Unfortunately, APs may introduce random delays in frame forwarding, mainly related to their internal behavior (e.g., queue management, processing times), that clearly impact the overall worst case execution time of real-time tasks involved in industrial process control systems. As a consequence, the knowledge of such delays becomes a crucial design parameter, and their estimation is definitely of utter importance. In this scenario, the paper presents an original and effective method to measure the aforementioned delays introduced by APs, exploiting a hybrid loop-back link and a simple, yet accurate set-up with moderate instrumentation requirements. The proposed method, which requires an initial calibration phase by means of a reference AP, has been successfully tested on some commercial APs to prove its effectiveness. The proposed measurement procedure is proven to be general and, as such, can be profitably adopted in even different scenarios. [ABSTRACT FROM AUTHOR]