Using Beam-Specific Measurements, Accurate Angular Inference for 802.11ad Devices PROJECT TITLE : Accurate Angular Inference for 802.11ad Devices Using Beam-Specific Measurements ABSTRACT: The 60 GHz channels are characterized by having only a few dominant paths as a result of their sparsity. We are able to develop a variety of applications thanks to the knowledge that we have regarding the angular information of their dominant paths. These applications include the prediction of link performance and the tracking of 802.11ad devices. Even though they have phased arrays, 802.11ad devices still have difficulty with angular inference due to the limited number of RF chains and phase control capabilities they possess. This is despite the fact that the devices are equipped with phased arrays. We propose variation-based angle estimation (VAE), also known as VAE-CIR, by making use of beam-specific channel impulse responses (CIRs) measured under different beams and the directional gains of the corresponding beams to infer the angular information of dominant paths. This is done in consideration of the beam sweeping operation and the high Communication bandwidth of 802.11ad devices. In contrast to the current state of the art, VAE-CIR takes advantage of the differences that exist between beam-specific CIRs in order to make angular inferences, and it guarantees performance even in the presence of a high signal-to-noise ratio. In order to evaluate VAE-CIR, we first generate the beam-specific CIRs by simulating the beam sweeping of 802.11ad devices using the beam patterns measured on off-the-shelf 802.11ad devices. This allows us to compare the simulated results with the actual results. The 60GHz channel is produced by a simulator that is based on ray tracing, and the CIRs are derived from the channel through channel estimation that is based on Golay sequences. Extensive testing has demonstrated that the VAE-CIR method provides a more accurate estimate of the angle being measured than other approaches. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest A Stochastic ON-OFF Queueing Mobility Model for Software-Defined Vehicle Networks is analyzed. A Reinforcement Learning-Based Trust Update Mechanism in Underwater Acoustic Sensor Networks