Performance Analysis And Resource Management Of Ambient Backscatter Communication

In recent years,Internet-of-Things(IoT)has gained rapid development,and related “Reading China”,smart city,etc.,have also gained spotlights.In IoT scenarios,a huge amount of small and micro devices will be deployed.A portion of these devices,e.g.,some in workshops,demand to continuously work for a long time with low power consumption and low cost,which puts forward higher requirements for convenient powering and low-power green communication.As to powering,cables for a large amount of devices cost a lot and result in high complexity.Therefore,convenient and flexible wireless powering is more preferred.Moreover,wireless powering is in rapid development in recent years.Short-distance powering techniques based on inductive electromagnetic coupling have been applied to commercial products.Radio frequency powering(RF powering)is another kind of wireless powering that utilizes radio frequency to transfer energy,with a relatively higher powering distance.And it is a better choice for lots of devices.In addition,radio frequencies are already existing in various producing and living environments,making it possible that devices get energy from these signals.As for communication of devices,wireless communication is also preferred.However,traditional techniques,e.g.,cellular network,Wi-Fi and Bluetooth,have a high cost of energy which is not practical for small and micro devices.In contrast,ambient backscatter communication(AmBC)that has gained focus in recent years,is a type of ultra-low-power green communication.This technique performs communication by utilizing existing signals in the environment,costs low even no energy,needs no dedicated spectrum resource and leads to low deployment and operating cost.Therefore,AmBC is highly suitable for small and micro devices having low communication demands.However,there are not many researches on AmBC,including modeling and analysis of large-scale application of it.In this thesis,under cellular network,we analyze large-scale AmBC with integrated RF powering and cognitive radio,which is a technique that allows devices to sense and utilize existing signals to increase utilization of spectrum resource.This technique can be well integrated with AmBC,resulting in more reliable and more lowcost green communication.In this thesis,firstly we use space point processes to model the distributions of devices in the network,including base stations,cellular users and small devices as secondary users.Besides,we analyze network details like time splits and interference.After that,stochastic geometry is used to analyze the performance of both native cellular users and secondary users.Results show that secondary users have good performance while quite limited effect on cellular users.Besides,an energy storage and reusing model is proposed to increase energy utilization of secondary users.Moreover,we use evolutionary game to model the resource allocation in multi-channel scenario.And an iterative selection algorithm based on replicator dynamics is proposed to analyze the evolutionary game.The analysis and results of this thesis show promising effect on green wireless communication from ambient backscattering,and provide reference for practical applications.