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Research On Hybrid Receiver Architectures For 5G Millimeter Wave Communications

Posted on:2018-05-22Degree:MasterType:Thesis
Country:ChinaCandidate:X JiFull Text:PDF
GTID:2348330536979512Subject:Communication and Information System
Abstract/Summary:PDF Full Text Request
Millimeter wave(mmWave)has been considered as a promising key technique for 5G since it can offer gigabit-per-second data rates thanks to the high frequencies and large bandwidth available at mmWave frequencies.To realize sufficient link margin,mm Wave systems need to employ directional beamforming with large antenna arrays at both the transmitter and receiver.Due to the high cost and power consumption of hardware devices,the MIMO systems at mmWave frequencies are divided into the analog and digital domains.Compared with the MIMO systems at microwave frequencies,the MIMO systems at mmWave frequencies require a special communication algorithm for the mmWave systems.In this context,this paper studies the MIMO systems in mmWave communications from the aspects of system structure,channel model,channel estimation and hybrid combining.Firstly,this paper gives the hybrid architecture for mmWave communications,and studies hybrid architectures based on switching networks to reduce the complexity and the power consumption of the structures based on phase shifters,and then defines a power consumption model to evaluate the energy efficiency of both structures.Secondly,channel estimation of mmWave communications is considered,which is formulated as a sparse reconstruction problem and applies the reconstruction of the compressive sensing to the channel estimation.Besides,least square is applied to channel estimation,and then this paper makes a simulation for the estimation performance of the two algorithms.Finally,using the estimate,hybrid combining is analyzed and the spectral efficiency and bit rate are simulated for each structure.Numerical results show that architectures based on switches obtain better performance.
Keywords/Search Tags:mmWave, hybrid architecture, channel estimation, compressive sensing, hybrid combining
PDF Full Text Request
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