This paper presents the implementation and investigation of the quarter phase shift keying low power detection. The receiver is designed and modeled using Matlab. It was tested for different values of SNR with a constant input signal power of 100 mW. The FFT based receiver performance is measured theoretically by transmitting 1000 information symbols from the transmitter end with 7 dB improvement at Bit Error Rate BER of 10e−4, Multi Modulation Techniques Recommended and not Recommended are simulated in this research. In this research the modulation method was selected on the basis of reducing rates of the interference in the UWB communication techniques and the essential modulation should have several types of spectrum random distribution methods to reduce the level of the interference on account of the impulse train that transmitted.
Wireless fidelity (Wi-Fi) is common technology for indoor environments that use to estimate required distances, to be used for indoor localization. Due to multiple source of noise and interference with other signal, the receive signal strength (RSS) measurements unstable. The impression about targets environments should be available to estimate accurate targets location. The Wi-Fi fingerprint technique is widely implemented to build database matching with real data, but the challenges are the way of collect accurate data to be the reference and the impact of different environments on signals measurements. In this paper, optimum system proposed based on modify nearest point (MNP). To implement the proposal, 78 points measured to be the reference points recorded in each environment around the targets. Also, the case study building is separated to 7 areas, where the segmentation of environments leads to ability of dynamic parameters assignments. Moreover, database based on optimum data collected at each time using 63 samples in each point and the average will be final measurements. Then, the nearest point into specific environment has been determined by compared with at least four points. The results show that the errors of indoor localization were less than (0.102 m).
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