The signal detection is presented based on deep learning (DL) for Multiple-in-Multiple-out Orthogonal Frequency Division Multiplexing (MIMO-OFDM) Systems. In MIMO-OFDM systems, a receiver is designed to eliminate successive interference cancellation based on DL for multiple users. The signal detection and channel estimation are carried out using deep neural network (DNN) which is trained offline depend on simulation data. And the symbols online are recovered directly. The simulation results show that Deep learning (DL) method is better than those traditional methods for channel estimation. The error propagation effects are reduced by DNN in the signal detector. The inter-symbol interference (ISI) of systems is serious, which shows that the DL approach can achieve the better performance by the DL approach than the maximum likelihood approach.
For modern medical monitoring systems data acquisition is very important. To achieve the strict requirements and
communication we design the Zigbee-based wireless infusion monitoring system, which has the following excellent
features: It adopts a high performance price ratio chip CC2530, which was integrated of AD converter, strengthened
MCU, and wireless RF unit. It can also communicate with peripheral device via serial ports and then instruct infusion
equipments to realize system real time control. The communication module based on Zigbee has a simple structure, low
power consumption. The system can obtain high control precision but also adjust infusion parameters accurately.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.