Published in 2017 International Conference on Circuits, Controls, and Communications (CCUBE), 2017
The dynamic environment of a vehicular communication system poses a difficult task of estimating the channel at minimal complexity. A time-varying multipath channel is estimated by computationally intensive algorithms that are generally not suitable for implementation on resource limited consumer hardware. Compressed Sensing (CS) schemes have been established to provide an accurate estimate by exploiting the inherent sparsity of a wireless communication channel. Correspondingly, the Rake-Matching Pursuit (RMP) and its low complexity variant, the Gradient Rake-Matching Pursuit (GRMP) algorithm, first identify different delay taps in the environment. The Doppler is then implicitly estimated by a tracking stage of respective tap coefficients. Although their performance is encouraging even under high Doppler shifts, its adoption for a static multipath environment is excessive due to the required computational resources. A low complexity scheme, like Least Squares (LS), is sufficient to estimate and compensate such channels. The cognitive framework envisages the switch between a high mobility scheme, like RMP, and a low mobility scheme, like LS, based on the channel conditions. In this paper, an enhanced cognitive framework is proposed to interchange between the channel estimation schemes to provide an adequate Bit Error Rate (BER) performance at optimum complexity. Even though the experimentation is performed for the IEEE 802.11p standard, the proposed metrics are relevant for any Orthogonal Frequency- Division Multiplexing (OFDM) based wireless communication system.