Wireless Body Area Network (WBAN) is considered to apply to both medical healthcare and entertainment applications. A requirement for each application is different, i.e. high reliability for medical healthcare whereas...Wireless Body Area Network (WBAN) is considered to apply to both medical healthcare and entertainment applications. A requirement for each application is different, i.e. high reliability for medical healthcare whereas high throughput for entertainment application. However, for both applications, low energy consumption is requested. Multiple hops technics have been researching in many fields of wireless system, e.g., ad hod, mobile, ITS etc. and its energy-efficiency is reported to be high. We propose the multiple hops technic for WBAN, however, WBAN is different to another systems, almost sensors forward the vital data packet of another sensors while sensing and generating the data packet of itself. Therefore, according to a packet generation rate of all sensors, probabilities of successful transmission and packet loss because of collision, timeout and overflow, are changed. It means that the vital data is lost and the transmit power is wasted due to packet loss. In order to obtain the highest throughput and save the power, the successful transmission probability is analyzed and the packet generation rate is optimized for multiple hops WBAN that using CSMA/CA based on IEEE802.15.6. The numerical calculation result indicates that the optimized packet generation rate depends on the system model. Moreover, the relation between the system model, the optimized packet generation rate and the throughput is discussed in the paper.展开更多
针对IEEE 802.11p标准中导频数量有限,难以准确追踪车联万物(Vehicle-to-Everything,V2X)通信中时变信道的问题,学者们研究了数据导频辅助(Data Pilot Aided,DPA)信道估计方案。然而,这些经典DPA方案不能在完整的信噪比(Signal to Noise...针对IEEE 802.11p标准中导频数量有限,难以准确追踪车联万物(Vehicle-to-Everything,V2X)通信中时变信道的问题,学者们研究了数据导频辅助(Data Pilot Aided,DPA)信道估计方案。然而,这些经典DPA方案不能在完整的信噪比(Signal to Noise Ratio,SNR)范围内给出令人满意的效果,并且其估计结果的可靠性易受误差传播的影响。研究了一种新的信道估计方案,基于使用虚拟子载波的最小均方误差(Minimum Mean Square Error Using Virtual Pilots,MMSE-VP)方案,提出一种带有时间平均操作的改进MMSE(Improved MMSE,IMMSE)方案。IMMSE方案通过利用相邻正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)符号间信道的相关性来提高MMSE-VP方案在低SNR区域的性能,达到在整个SNR区域有良好表现的目的。联合深度学习技术,采用全连接神经网络(Fully Connected Neural Network,FCNN)作为IMMSE方案的非线性后处理模块,减少误差并获得更好的估计性能。在不同实验条件下的仿真结果表明,提出的信道估计方案可以适应调制方式和车辆速度的变化,能有效应对V2X通信中的信道估计问题。展开更多
文摘Wireless Body Area Network (WBAN) is considered to apply to both medical healthcare and entertainment applications. A requirement for each application is different, i.e. high reliability for medical healthcare whereas high throughput for entertainment application. However, for both applications, low energy consumption is requested. Multiple hops technics have been researching in many fields of wireless system, e.g., ad hod, mobile, ITS etc. and its energy-efficiency is reported to be high. We propose the multiple hops technic for WBAN, however, WBAN is different to another systems, almost sensors forward the vital data packet of another sensors while sensing and generating the data packet of itself. Therefore, according to a packet generation rate of all sensors, probabilities of successful transmission and packet loss because of collision, timeout and overflow, are changed. It means that the vital data is lost and the transmit power is wasted due to packet loss. In order to obtain the highest throughput and save the power, the successful transmission probability is analyzed and the packet generation rate is optimized for multiple hops WBAN that using CSMA/CA based on IEEE802.15.6. The numerical calculation result indicates that the optimized packet generation rate depends on the system model. Moreover, the relation between the system model, the optimized packet generation rate and the throughput is discussed in the paper.
文摘针对IEEE 802.11p标准中导频数量有限,难以准确追踪车联万物(Vehicle-to-Everything,V2X)通信中时变信道的问题,学者们研究了数据导频辅助(Data Pilot Aided,DPA)信道估计方案。然而,这些经典DPA方案不能在完整的信噪比(Signal to Noise Ratio,SNR)范围内给出令人满意的效果,并且其估计结果的可靠性易受误差传播的影响。研究了一种新的信道估计方案,基于使用虚拟子载波的最小均方误差(Minimum Mean Square Error Using Virtual Pilots,MMSE-VP)方案,提出一种带有时间平均操作的改进MMSE(Improved MMSE,IMMSE)方案。IMMSE方案通过利用相邻正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)符号间信道的相关性来提高MMSE-VP方案在低SNR区域的性能,达到在整个SNR区域有良好表现的目的。联合深度学习技术,采用全连接神经网络(Fully Connected Neural Network,FCNN)作为IMMSE方案的非线性后处理模块,减少误差并获得更好的估计性能。在不同实验条件下的仿真结果表明,提出的信道估计方案可以适应调制方式和车辆速度的变化,能有效应对V2X通信中的信道估计问题。
