Super-resolution modeling of the indoor radio propagation channel

Results from parametric modeling of the indoor channel frequency response are presented. Minimum-norm and autoregressive (AR) are two super-resolution modeling techniques that are used on frequency domain data to form the theoretical sum-of-sinusoids model for the indoor channel-impulse response. Using these techniques, it is possible to check the validity of assuming Turin's model for the indoor channel. It is shown that the modeling techniques estimate the indoor channel-impulse response with a time-domain resolution better than that of current measurement systems. A competing model that matches the measured channel frequency response to a sampled finite-impulse response (FIR) filter (as opposed to AR and minimum-norm, which give arbitrary time delays) is presented. It is shown that FIR filter matching produces a parametric model that can be more compact and more accurate in terms of matching the frequency response of the channel than the conventional super-resolution techniques.