Researches On Communication Technology Of Dynamic Wireless Body Area Networks Based On Incomplete Channel State Information

As the aging of the population accelerates and the demand for higher-quality medical services surges,the development of smart medical care has attracted significant attention from the society.Wireless Body Area Network(WBAN),as a key technology of smart medical care,can collect physiological data of the human body in real time,thereby realizing remote medical monitoring and diagnosis.However,since most of the sensor nodes are implanted into the human bodies,it is not convenient to charge.Therefore,improving energy efficiency is a critical issue for improving the performance of WBANs.The application of key technologies such as transmission power control and relay cooperative transmission to improve energy efficiency needs to obtain real-time channel state information(CSI).However,due to the dynamic nature of WBANs,the link quality of the wireless communication varies with the swing of limbs,which make it difficult to obtain the real-time CSI.In addition,the freshness of medical data is required to be as high as possible in the medical monitoring scenario.The above features of WBANs have brought new technical challenges to the communication resource allocation.Based on incomplete CSI,this paper aims to improve the performance of WBANs,conducts in-depth research on the dynamic characteristics of link quality,designs energy-efficient resource scheduling strategies while ensuring the reliability and low delay of data transmission.This paper focuses on the key technology of the WBANs system and carries out the following research.First of all,this paper studies the characteristics of the WBAN channel condition in a periodic motion scenario.Through the WBAN channel measurement experiment,it is found that the path loss of the wireless link under periodic motion exhibits the same periodic fluctuations,and further discovers that the correlation between the acceleration signal and the received RSSI.Based on the above two experimental phenomena,this paper proposes a gait cycle-aware scheduling strategy.According to the acceleration signal of the sensor nodes,the periodicity can be judged and the gait cycle can be identified,so as to arrange the sensor node to transmit data in the time slots when the link quality is good.Experimental results show that this strategy can significantly improve the energy efficiency and transmission reliability of WBAN in periodic motion scenarios,and can save 13.56% of energy consumption in actual motion scenarios compared with the traditional random scheduling strategy.Then,this paper studies the characteristics of WBAN channel in practical scenarios.In daily activities,the human bodies have a large number of random and non-periodic swings of limbs,which makes the channel condition complex and dynamic.It's difficult for the traditional channel prediction methods to extract the channel characteristics of WBANs,this paper proposes a dynamic WBAN communication framework based on temporal convolution network adopting temporal convolutional network to make channel prediction,and jointly optimize the power control,time-slot allocation and relay selection to maximize network energy efficiency while ensuring high reliability and low latency.The simulation results show that the channel prediction accuracy based on temporal convolution network is higher than that of traditional methods.The jointly optimization algorithm proposed in this paper dynamically adjusts the relay selection according to the predicted value of the channel.Furthermore,the proposed algorithm takes into account the additional delay caused by relay transmission and constrain the scheduling sequence to reduce the extra delay as much as possible.At the same time,the algorithm arranges the nodes to transmit data at the time when the link quality is good,thereby significantly improving the system energy efficiency.Finally,this paper considers the transient value of medical data,takes the average age of information as an important optimization goal of the system,proposes a utility function that comprehensively considers the information age and energy consumption,and proposes the scheduling strategy that minimizes the utility function.The simulation results show that the proposed scheduling strategy is superior.Compared with the traditional random scheduling strategy,it reduces the average age of information by about 10% and the energy consumption by 12%.