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.

翻译：农村医疗面临诸多挑战，包括获取专业医疗服务和诊断设备的机会有限，这延误了患者护理。通过无线网络增强从农村地区向城市医院传输医学影像和数据的能力至关重要。然而，带宽限制、网络不可靠以及对数据安全与隐私的担忧阻碍了高效传输。此外，医疗内容的高数据量以及物联网设备有限的电池寿命带来了进一步挑战。为解决这些挑战，诸如自动编码器（AEs）等数据压缩技术提供了有前景的解决方案，能在不牺牲基本图像质量或细节的情况下显著减少通信开销。同时，农村地区的频谱分配机制通常效率低下，导致资源利用率不佳。拍卖理论提供了一种动态且自适应的优化频谱分配方法。本文提出了一种新型混合框架，将基于AE的数据压缩与基于拍卖的频谱分配相结合，以解决农村无线网络中的通信效率和频谱利用率问题。大量仿真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个实用解决方案。