In this letter, a novel channel impulse response (CIR)-based fingerprinting positioning method using kernel principal component analysis (KPCA) has been proposed. During the offline phase of the proposed method, a survey is performed to collect all CIRs from access points, and a fingerprint database is constructed, which has vectors including CIR and physical location. During the online phase, KPCA is first employed to solve the nonlinearity and complexity in the CIR-position dependencies and extract the principal nonlinear features in CIRs, and support vector regression is then used to adaptively learn the regress function between the KPCA components and physical locations. In addition, the iterative narrowing-scope step is further used to refine the estimation. The performance comparison shows that the proposed method outperforms the traditional received signal strength based positioning methods.

The information of channel impulse response (CIR) length and noise variance play an important role in blind identification and equalization of wireless multipath channels. In orthogonal frequency division multiplexing (OFDM) systems, multipath fading channels introduce interference between adjacent symbols which can be prevented by inserting a cyclic prefix (CP) before each symbol. In this letter, we find that the interference power in the cyclic prefix (CP) interval and its variation can be used to estimate the CIR length and noise variance jointly and blindly.

In this letter we purpose adaptive neuro-fuzzy inference system (ANFIS) for channel estimation in orthogonal frequency division multiplexing (OFDM) systems. To evaluate the performance of this estimator, we compare the ANFIS with least square (LS) algorithm, minimum mean square error (MMSE) algorithm by using bit error rate (BER) and mean square error (MSE) criterias. According to computer simulations the performance of ANFIS has better performance than LS algorithm and close to MMSE algorithm. Besides there is unnecessity to send pilot when used the ANFIS.

This letter presents a method to simulate a phase-included UWB channel impulse responses for a given indoor channel. In this method we decompose a UWB pulse into a finite number of spectral components. This enables the received signal to be determined by the sum of the convolutions between each spectral component and a corresponding frequency-dependent UWB channel impulse response. The ray-tracing algorithm is applied to calculate the amplitude and the phase of each frequency-dependent channel impulse response. Based on the calculated results, we finally show the simulation of the UWB channel impulse response.