Cognitive Framework for the Estimation of Doubly Selective Channels

Vehicular Communication Systems also known as Vehicle-to-Vehicle communication systems (V2V) specify the use of Orthogonal Frequency-Division Multiplexing (OFDM) in the physical layer. High mobility in vehicular communication systems results in a time-varying multipath or a doubly selective channel. Such channels exhibit selectivity in both the time as well as the frequency domain. The estimation of the channel parameters plays a vital role in overcoming the effects of high mobility at the receiver. Moreover, it is known that the estimation of such a channel is a non-trivial task. The Matching Pursuit (MP) algorithm is a Compressed Sensing (CS) scheme that works well in scenarios of high mobility. However, it is shown that under less severe channel conditions, simpler channel estimation and equalization schemes can be more power-efficient and hence increase battery-life without reducing the quality of the equalization. In this paper, we propose a cognitive framework optimizing the estimation scheme based on the channel conditions and certain measurements from the receiver chain. The result is a channel estimation scheme that is robust, precise and reliable in all channel conditions while adapting the complexity to be optimal for the channel being estimated. In addition to this, a simulation tool with the proposed scheme is provided for the IEEE 802.11p standard. The proposed cognitive framework is implemented for the IEEE 802.11p standard and is applicable in any OFDM based wireless system that is expected to work in highly mobile environments.