Estimation of FBMC/OQAM Fading Channels Using Dual Kalman Filters

Abstract

We address the problem of estimating time-varying fading channels in filter bank multicarrier (FBMC/OQAM) wireless systems based on pilot symbols. The standard solution to this problem is the least square (LS) estimator or the minimum mean square error (MMSE) estimator with possible adaptive implementation using recursive least square (RLS) algorithm or least mean square (LMS) algorithm. However, these adaptive filters cannot well-exploit fading channel statistics. To take advantage of fading channel statistics, the time evolution of the fading channel is modeled by an autoregressive process and tracked by Kalman filter. Nevertheless, this requires the autoregressive parameters which are usually unknown. Thus, we propose to jointly estimate the FBMC/OQAM fading channels and their autoregressive parameters based on dual optimal Kalman filters. Once the fading channel coefficients at pilot symbol positions are estimated by the proposed method, the fading channel coefficients at data symbol positions are then estimated by using some interpolation methods such as linear, spline, or low-pass interpolation. The comparative simulation study we carried out with existing techniques confirms the effectiveness of the proposed method.

Review of Channel Estimation for Candidate Waveforms of Next Generation Networks

“…ICI comes next in estimation performance while estimation in the presence of both ICI and ISI offers the worst CE performance. Estimation of time-varying channels is adopted in [123] where dual optimal Kalman filters are introduced for estimating fading channel statistics as well as their unknown pth order autoregressive parameters in an OFDM-OQAM based wireless system. This proposed method estimates the fading coefficients at pilot symbol positions while linear, spline and low-pass interpolation are adopted for estimating data position fading coefficients.…”

“…Synthesis filter block The conversion of the complex input symbols to real symbols is mapped mathematically as [123,131]:…”

Performance evaluation of filterbank multicarrier systems in an underwater acoustic channel

“…represents the real-valued symbols for subcarrier k. The prototype filter p[m] is shifted in frequency to produce the subchannels which cover the whole bandwidth [12]. The kth synthesis filter can be expressed as…”

“…The length L p depends on the size of the filter bank (M subcarriers) and the number of OQAM symbol waveforms K that overlap in the time domain as L p = KM [12]. Assuming perfect reconstruction (PR) (achieved in an ideal transmission channel), the kth analysis filter is a timereversed and complex-conjugated version of the corresponding synthesis filter which is defined as:…”

“…The prototype filter can be designed using the frequency sampling method or window-based technique [12]. In the former method, the impulse response of the filter coefficients are obtained by taking the IFFT of the KM samples from the desired frequency response of the filter.…”