基于自动编码器压缩的拍卖驱动频谱分配：一种用于可靠远程医疗的新框架
Auction-Driven Spectrum Allocation With AutoEncoder-Based Compression in Rural Wireless Networks: A Novel Framework for Reliable Telemedicine
AI总结：
本文提出一种结合自动编码器压缩与拍卖机制的新型框架，旨在提升农村无线网络的频谱利用率和通信效率，以改善远程医疗服务。
摘要：
农村医疗面临诸多挑战，包括难以获得专业医疗服务和诊断设备，这会延迟患者护理。增强通过无线网络将农村地区的医疗影像和数据传输到城市医院的能力至关重要。然而，带宽限制、不可靠的网络以及数据安全和隐私的担忧阻碍了高效传输。此外，医疗内容的高数据量和物联网设备的有限电池寿命也提出了进一步的挑战。为了解决这些挑战，数据压缩技术如自动编码器（AEs）提供了有希望的解决方案，通过显著减少通信开销而不牺牲关键图像质量和细节。此外，农村地区的频谱分配机制往往效率低下，导致资源利用不佳。拍卖理论提供了一种动态和适应性的方法来优化频谱分配。本文提出了一种新的混合框架，将基于自动编码器的数据压缩与基于拍卖的频谱分配相结合，解决农村无线网络中的通信效率和频谱利用问题。广泛的模拟验证了该框架在提高频谱利用率、传输效率和整体连接性方面的能力，为增强农村远程医疗基础设施提供了一种实用的解决方案。
英文摘要：
Rural healthcare faces numerous challenges, including limited access to specialized medical services and diagnostic equipment, which delays patient care. Enhancing the ability to transmit medical images and data from rural areas to urban hospitals via wireless networks is critical. However, bandwidth limitations, unreliable networks, and concerns over data security and privacy hinder efficient transmission. Additionally, the high data volume of medical content and the limited battery life of IoT devices pose further challenges. To address these challenges, data compression techniques such as Autoencoders (AEs) offer promising solutions by significantly reducing the communication overhead without sacrificing essential image quality or details. Additionally, spectrum allocation mechanisms in rural areas are often inefficient, leading to poor resource utilization. Auction theory presents a dynamic and adaptive approach to optimize spectrum allocation. This paper proposes a novel hybrid framework that integrates AE-based data compression with auction-based spectrum allocation, addressing both communication efficiency and spectrum utilization in rural wireless networks. Extensive simulations validate the framework's ability to improve spectrum utilization, transmission efficiency, and overall connectivity, offering a practical solution for enhancing rural telemedicine infrastructure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
