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Therefore, at time index d and considering a multipath channel (N p paths), the received sample becomes r d = e j2π dν Nu Np−1 p=0 h p s d−τp + η d, (1) where η d stands for the sample of zero-mean complex Gaussian noise process with variance σ 2 η. Assume a perfect sampling clock recovery and consider the symbol-timing error normalized with respect to the sampling period T b as well as the fractional part of the carrier frequency offset, normalized with respect to the sub-carrier spacing ∆ f and which we denote by ν. To ensure performance communication, synchronization of connected devices is a necessary task to accomplished. to enable a wide range of civilian and commercial applications connecting billions of devices. BACKGROUND Modern wireless communication systems have introduced tremendous paradigms such as the Internet of Things (IoT), , proximity services, , network virtualization, , location awareness services, vehicular-to-everything communication, communication assisted with drones, Wireless Sensors Networks (WSN), ultra-reliable communication etc. Hereafter, we give a state of the art od such techniques. Our work is concerned with preamble-based techniques. Data aided techniques generally provide better detection than the blind techniques. The training symbols must have specific properties which makes them distinguishable from data symbols. On the other hand, data-aided techniques which use known training symbols transmitted at the beginning of each frame to achieve synchronization. On the first hand, blind (non-data aided) techniques which exploit either the redundancy in the Cyclic Prefix or the statistical properties of the received signal.
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These techniques can be roughly classified in two categories. The objective of this work is to provide a survey on existing synchronization techniques proposed in the literature to mitigate channel impairments.
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In particular, we focus on Line Of Site (LOS) propagation, path loss model, interference and fading phenomena in A2G link. Based on the finding of the studied works, a characterization of A2G channel parameters with new aspect is depicted. We present an overview of research works dealing with channel modeling through both simulations and field measurements. The main goal of this paper is to provide a comprehensive survey on A2G channel modeling for cellular networks in order to support UAV-enabled communications. An optimal deployment of UAV-enabled communication systems requires using an accurate air-to-ground (A2G) channel model whose characteristics significantly differ from classical ground communication channels. Besides the promising opportunities of UAVs deployment as aerial mobile devices, they rise many technical challenges in order to effectively use them for each specific networking application. UAVs can also be deployed as aerial User Equipments (UEs) in coexistence with ground users. An attractive feature of using UAVs for communication networks is that they can be quickly deployed to support communication backhaul infrastructure as flying base station.
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Furthermore, UAVs are rapidly growing in networking application and are envisioned as a potential component of 5G wireless technology and beyond. Unmanned Aerial Vehicles (UAVs) have attracted considerable attention in a wide range of applications including military and civilian area, such as surveillance and monitoring, relief operations and package delivery.