Location Aware Training Scheme for D2D Networks

We consider the problem of acquiring Channel State Information (CSI) in base-station (BS) controlled Device To Device (D2D) networks. Obtaining high-quality CSI requires a trade-off between interference, outdatedness of CSI, and noise. Thus, the goal is to find an efficient pilot scheduling scheme that minimizes errors in the estimates. In this paper we present the Location Aware Training Scheme (LATS) as simple yet efficient training technique. Assuming that users know their location, LATS distributes the users onto segments and assigns a frequency reuse pattern to them. To identify the parameters of this scheme (size of segments and guard spaces), we numerically minimize the Mean Square Error (MSE) as a metric that combines the outdatedness, noise, and interference. Simulations shows that LATS outperforms other existing schemes.