Efficient Channel State Information Acquisition for Device-to-Device 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 tradeoff 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 the devices are aware of their location, LATS groups the devices into geographical segments and assigns a frequency reuse pattern to them. To identify the parameters of the scheme (segmentation and guard parameters, $S_{rm{ n}}$ and $G_{rm{ n}}$, respectively), we use the average normalized mean square error (NMSE) as a metric, which combines the effects of outdatedness, noise, and interference. We derive an approximation of the average NMSE based on statistics of the devices and LATS structure. We present simulation results that illustrate LATS behavior and show that it outperforms TDMA- and CSMA-based schemes. Furthermore, we consider some practical challenges in using location information and evaluate their effects on the scheme.