Nonlinear channel estimation for Internet of Vehicles

Abstract

In order to reduce the power consumption, the Internet of Vehicles (IoV) system mostly adopts the modulation method of a single parameter such as phase. However, in scenarios that require transmitting a large amount of information, the single-information modulation method cannot meet the requirements of high data rates. To address this problem, the paper proposes a scheme of adding a small number of full-information channels containing both amplitude and phase information to form a nonlinear channel. The compressed sensing based algorithm is used to estimate the nonlinear channel. The information of channel is passed iteratively between the single-information channel and the full-information channel to obtain more accurate channel estimation. The paper also studies the influence of the mapping relationship between single-information channel and full-information channel, and the number of iterations on channel estimation accuracy. The results of the simulations show that the proposed scheme can increase the information transmission rate by 6 times at the cost of 2.5 times the power consumption.

Keywords

• iov
• compressed sensing
• sparse reconstruction
• message passing
• channel estimation