Abstract
Our aim with this paper is to present a solution suitable for vehicle-to-everything (V2X) communications, particularly, when employing single-carrier modulations combined with frequency-domain equalization (SC-FDE). In fact, we consider the V2X channel to be doubly-selective, where the variation of the channel in time is due to the presence of a Doppler term. Accordingly, the equalization procedure is dealt by a low-complexity iterative frequency-domain equalizer based on the iterative block decision-feedback equalization (IB-DFE) while the tracking procedure is conducted employing an extended Kalman filter (EKF). The proposed system is very efficient since it allows a very low density of training symbols, even for fast-varying channels. Furthermore only two training symbols are required to initialize the tracking procedure. Thus, ensuring low latency together with reduced channel estimation overheads.
Highlights
Vehicles in intelligent transportation systems (ITS) are expected to be equipped with high-end video cameras as well as advanced environmental sensors [1], [2]
We consider that the channel tracking is done using an extended Kalman filter (EKF) without DD channel estimation (EKF w/o DD)
We consider that the channel tracking is done using an EKF with DD channel estimation (EKF w/ DD)
Summary
Vehicles in intelligent transportation systems (ITS) are expected to be equipped with high-end video cameras as well as advanced environmental sensors [1], [2]. We consider the state-space vector to be formed by the phases of the complex gains and their associated Doppler terms. These assumptions results in a very simple state-transition model that has a clear and evident relation with the physics of the problem. The complex channel gains and the Doppler terms This a very important element in our investigation since through the knowledge of the BCRB it is possible to assess the performance of the proposed algorithm regarding its theoretical limits. We have investigated the convergence rate of the proposed algorithms This was made through simulations which considered different distributions of the training symbols and lengths of the initial training stage. In Algorithm 1, and Algorithm 2 operators a == b, and a == b test integers a and b for equality, and inequality, respectively; mod(·, ·) is the modulo operation
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