Variable Forgetting Factor LS Algorithm for Polynomial Channel Model

Abstract

Variable forgetting factor (VFF) least squares (LS) algorithm for polynomial channel paradigm is presented for improved tracking performance under nonstationary environment. The main focus is on updating VFF when each time-varying fading channel is considered to be a first-order Markov process. In addition to efficient tracking under frequency-selective fading channels, the incorporation of proposed numeric variable forgetting factor (NVFF) in LS algorithm reduces the computational complexity.

Space-time block-coded systems using numeric variable forgetting factor least squares channel estimator

“…For simulations, the BPSK, 4-QAM and 8-QAM signal constellations mentioned in Su and Xia (2004) 99, 0.75 and 0.005 respectively for all cases. The tracking performance results presented in Kohli et al (2011) depict that LSn2 algorithm (with second-order channel model) combats lag noise more efficiently than LSn algorithm, but at the cost of increased computational complexity. Subsequently, it is apparent from the simulation results shown in Kohli et al (2011) that LSn-VFF and LSn-NVFF algorithms supersede LSn2-VFF and LSn2-NVFF algorithms under time-varying environment.…”

“…The tracking performance results presented in Kohli et al (2011) depict that LSn2 algorithm (with second-order channel model) combats lag noise more efficiently than LSn algorithm, but at the cost of increased computational complexity. Subsequently, it is apparent from the simulation results shown in Kohli et al (2011) that LSn-VFF and LSn-NVFF algorithms supersede LSn2-VFF and LSn2-NVFF algorithms under time-varying environment. Both variable forgetting factors overwhelm the loss in tracking capability caused due to the first-order channel model.…”

“…It results in ( ) ( , which is a modification of LSn algorithm presented in Song et al (2002). Further, the tracking capability of LSn2 algorithm can be improved by the incorporation of variable forgetting factor (VFF) using LMS adaptation algorithm (Song et al, 2002) and numeric variable forgetting factor (NVFF) (Kohli et al, 2011) without the explicit knowledge of the process noise variance. The LSn2-NVFF algorithm uses forgetting factor ( ) n λ to update the channel state after ( ) s PT symbol duration.…”

Full-diversity high-rate space-time block-coded systems using estimated channel state information for symbol detection

“…The main focus/emphasis is on the usage/incorporation of channel matrix obtained by using the LSn-NVFF (first-order polynomial based approach) and LSn2-NVFF (secondorder polynomial based approach) pilot channel estimation algorithms (Grover and Kohli, 2011), which is the estimated CSI. The value of NVFF increases under stationary conditions for the accurate channel estimation and its value decreases under nonstationary conditions to reduce the lag noise (Kohli et al, 2011). Finally, conclusions and future scope are given for the proposed high data-rate STBC, that is,…”

“…The LSn-VFF algorithm is reported to perform well at high SNRs at the cost of increased computational complexity (Song et al, 2000). However, Kohli et al (2011) presented a computationally efficient channel estimation method using the linear-least-squares algorithm in combination with the numeric-variable-forgetting-factor (LSn-NVFF), which is based on the extended estimation error criterion. In this correspondence, we propose the utilization of LSn-NVFF algorithm based estimated complex channel coefficient matrix for the information symbol detection in high-rate STBC wireless systems akin to that of Grover and Kohli (2011), which is expected to improve the bit-error-rate (BER) performance of the high-rate quasi-orthogonal STBCs (QSTBCs) even at the low SNRs.…”