Signal and Quantizer Designs for MillimeterWave Large-Scale MIMO Communication Systems Using Advanced Channel Training
Abstract
Large-scale antenna systems at millimeter wave (mmWave) frequencies are being considered as a prime candidate to allow fifth generation (5G) communication systems to provide the needed throughput enhancements. To exploit the full benefit of large-scale antenna systems in frequency division duplexing (FDD), the downlink channel should be estimated, quantized, and fed back to the transmitter. However, it is difficult to accurately estimate and quantize the channel due to its large dimensionality. In this dissertation, we develop advanced downlink channel training and channel quantization algorithms for FDD massive multiple-input multiple-output (MIMO) systems. First, we propose a practical beam alignment algorithm that exploits orthogonal polarizations at mmWave frequencies. The beam alignment algorithm enables the mmWave system to align a large number of narrow beams to the channel subspace. Then, we propose a method to adapt the channel sounding time to the channel environment. Second, we introduce beam design algorithms for mmWave systems exploiting a common codebook for channel sounding and data transmission. We focus on designing a set of beamformers generating beams adapted to the directional characteristics of mmWave links. Lastly, we develop narrowband and wideband channel quantizers for full dimension (FD)-MIMO taking the properties of realistic channels into consideration. We carry out performance analysis of Kronecker product (KP) codebooks to provide design guidelines on how to develop practical quantizers. We also develop a hierarchical beam search approach, which scans both spatial domains jointly with moderate complexity.
Degree
Ph.D.
Advisors
Love, Purdue University.
Subject Area
Engineering
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