A DRL-based Reflection Enhancement Method for RIS-assisted Multi-receiver Communications

In reconfigurable intelligent surface (RIS)-assisted wireless communication systems, the pointing accuracy and intensity of reflections depend crucially on the 'profile,' representing the amplitude/phase state information of all elements in a RIS array. The superposition of multiple single-reflection profiles enables multi-reflection for distributed users. However, the optimization challenges from periodic element arrangements in single-reflection and multi-reflection profiles are understudied. The combination of periodical single-reflection profiles leads to amplitude/phase counteractions, affecting the performance of each reflection beam. This paper focuses on a dual-reflection optimization scenario and investigates the far-field performance deterioration caused by the misalignment of overlapped profiles. To address this issue, we introduce a novel deep reinforcement learning (DRL)-based optimization method. Comparative experiments against random and exhaustive searches demonstrate that our proposed DRL method outperforms both alternatives, achieving the shortest optimization time. Remarkably, our approach achieves a 1.2 dB gain in the reflection peak gain and a broader beam without any hardware modifications.Comment: 6 pages, 6 figures. This paper has been accepted for presentation at the VTC2023-Fal

RLOps:Development Life-cycle of Reinforcement Learning Aided Open RAN

Radio access network (RAN) technologies continue to witness massive growth, with Open RAN gaining the most recent momentum. In the O-RAN specifications, the RAN intelligent controller (RIC) serves as an automation host. This article introduces principles for machine learning (ML), in particular, reinforcement learning (RL) relevant for the O-RAN stack. Furthermore, we review state-of-the-art research in wireless networks and cast it onto the RAN framework and the hierarchy of the O-RAN architecture. We provide a taxonomy of the challenges faced by ML/RL models throughout the development life-cycle: from the system specification to production deployment (data acquisition, model design, testing and management, etc.). To address the challenges, we integrate a set of existing MLOps principles with unique characteristics when RL agents are considered. This paper discusses a systematic life-cycle model development, testing and validation pipeline, termed: RLOps. We discuss all fundamental parts of RLOps, which include: model specification, development and distillation, production environment serving, operations monitoring, safety/security and data engineering platform. Based on these principles, we propose the best practices for RLOps to achieve an automated and reproducible model development process.Comment: 17 pages, 6 figrue

Distributed Sensing, Computing, Communication, and Control Fabric: A Unified Service-Level Architecture for 6G

With the advent of the multimodal immersive communication system, people can interact with each other using multiple devices for sensing, communication and/or control either onsite or remotely. As a breakthrough concept, a distributed sensing, computing, communications, and control (DS3C) fabric is introduced in this paper for provisioning 6G services in multi-tenant environments in a unified manner. The DS3C fabric can be further enhanced by natively incorporating intelligent algorithms for network automation and managing networking, computing, and sensing resources efficiently to serve vertical use cases with extreme and/or conflicting requirements. As such, the paper proposes a novel end-to-end 6G system architecture with enhanced intelligence spanning across different network, computing, and business domains, identifies vertical use cases and presents an overview of the relevant standardization and pre-standardization landscape

Identification of pyroptosis-related subtypes and establishment of prognostic model and immune characteristics in asthma

BackgroundAlthough studies have shown that cell pyroptosis is involved in the progression of asthma, a systematic analysis of the clinical significance of pyroptosis-related genes (PRGs) cooperating with immune cells in asthma patients is still lacking.MethodsTranscriptome sequencing datasets from patients with different disease courses were used to screen pyroptosis-related differentially expressed genes and perform biological function analysis. Clustering based on K-means unsupervised clustering method is performed to identify pyroptosis-related subtypes in asthma and explore biological functional characteristics of poorly controlled subtypes. Diagnostic markers between subtypes were screened and validated using an asthma mouse model. The infiltration of immune cells in airway epithelium was evaluated based on CIBERSORT, and the correlation between diagnostic markers and immune cells was analyzed. Finally, a risk prediction model was established and experimentally verified using differentially expressed genes between pyroptosis subtypes in combination with asthma control. The cMAP database and molecular docking were utilized to predict potential therapeutic drugs.ResultsNineteen differentially expressed PRGs and two subtypes were identified between patients with mild-to-moderate and severe asthma conditions. Significant differences were observed in asthma control and FEV1 reversibility between the two subtypes. Poor control subtypes were closely related to glucocorticoid resistance and airway remodeling. BNIP3 was identified as a diagnostic marker and associated with immune cell infiltration such as, M2 macrophages. The risk prediction model containing four genes has accurate classification efficiency and prediction value. Small molecules obtained from the cMAP database that may have therapeutic effects on asthma are mainly DPP4 inhibitors.ConclusionPyroptosis and its mediated immune phenotype are crucial in the occurrence, development, and prognosis of asthma. The predictive models and drugs developed on the basis of PRGs may provide new solutions for the management of asthma

Design and Optimization of a Liquid Cooling Thermal Management System with Flow Distributors and Spiral Channel Cooling Plates for Lithium-Ion Batteries

In this study, a three-dimensional transient simulation model of a liquid cooling thermal management system with flow distributors and spiral channel cooling plates for pouch lithium-ion batteries has been developed. The cooling plates play the role of uniforming temperature distribution and reducing the maximum temperature within each battery, while the flow distributors have the function of reducing the temperature difference between batteries in the battery module. The accuracy of the thermophysical properties and heat generation rate of the battery was verified experimentally. The optimal structure and cooling strategy of the system was determined by single factor analysis as well as orthogonal test and matrix analysis methods. The optimal solution resulted in a maximum battery module temperature of 34.65 °C, a maximum temperature difference of 3.95 °C, and a channel pressure drop of 8.82 Pa. Using the world-harmonized light-duty vehicles test cycle (WLTC) conditions for a battery pack in an electric car, the performance of the optimal battery thermal management system (BTMS) design was tested, and the results indicate that the maximum temperature can be controlled below 25.51 °C and the maximum temperature difference below 0.21 °C, which well meet the requirements of BTMS designs

Bayesian Optimisation-Assisted Neural Network Training Technique for Radio Localisation

Radio signal-based (indoor) localisation technique is important for IoT applications such as smart factory and warehouse. Through machine learning, especially neural networks methods, more accurate mapping from signal features to target positions can be achieved. However, different radio protocols, such as WiFi, Bluetooth, etc., have different features in the transmitted signals that can be exploited for localisation purposes. Also, neural networks methods often rely on carefully configured models and extensive training processes to obtain satisfactory performance in individual localisation scenarios. The above poses a major challenge in the process of determining neural network model structure, or hyperparameters, as well as the selection of training features from the available data. This paper proposes a neural network model hyperparameter tuning and training method based on Bayesian optimisation. Adaptive selection of model hyperparameters and training features can be realised with minimal need for manual model training design. With the proposed technique, the training process is optimised in a more automatic and efficient way, enhancing the applicability of neural networks in localisation.Comment: 5 pages, 4 figures. This paper has been accepted for presentation at the VTC2022-Sprin

Study on Hydrodynamic Characteristics of Wind Turbine Monopile under Nonlinear Wave

As wind power technologies become maturer, the monopile foundation of offshore wind turbine is widely used because of its simple structure, few occupied space and low cost. However, under severe sea conditions, the impact of nonlinear wave load applied against the monopile foundation on the system structure safety cannot be ignored. In this paper, the 5MW offshore wind turbine of the National Renewable Energy Laboratory (NREL) was taken as the research object, and the computational fluid analysis software ‘STARCCM +’ was used to study the hydrodynamic characteristics of the monopile foundation of the wind turbine under different wave parameters. This paper mainly analyzed the upper wave, pressure and wave forces around the monopile foundation of the wind turbine under the same period and different wave heights. And the wave force calculated by CFD was compared with the result based on potential flow theory. The research results showed that with the rise of wave height, the upper wave, pressure and wave force around the monopile foundation increase continuously, and the second-peak phenomenon appeared at some measuring points on the water surface of the monopile foundation. Because the CFD method considers the fluid viscosity and is more in line with the real sea conditions, it is more accurate to obtain wave forces based on this method