IRS-Aided Wireless Channel Prediction with FASLSTM for Implantable Medical Devices

Implantable medical devices have important applications in the medical and healthcare field, but their channel environment is complex and significantly affected by factors such as human tissue, radio frequency losses and multipath effects, which makes it difficult to obtain accurate channel state information (CSI).At the same time, the quality of the wireless communication channel in indoor environments is usually affected by obstacles such as walls, furniture and the human body, and problems such as signal attenuation and multipath effects will occur. This poses a great challenge to traditional wireless communication systems. Intelligent Reflective Surface (IRS), as an emerging technology, can improve communication quality by installing reflective units in indoor environments to adjust the propagation path of signals. Channel estimation is essential in IRS-based wireless communication, and additional channel overhead is usually required when the number of IRS reflective elements is large. To solve this problem, we design a channel prediction framework with frequency self-attentive LSTM, based on which we can effectively extract the nonlinear relationship between IRS configuration and channel dynamics by capturing the temporal correlation of the channel, obtain direct and reflected channels with low channel training overhead, and achieve highly accurate prediction of the future channel state, thereby improving the communication stability and efficiency of implantable medical devices, while reducing the power consumption and system overhead of the devices. Experimental results show that this framework can effectively predict the channel information, with some improvement over other algorithms, and also has good performance in non-implantable device scenarios.