Advanced design of space-time training and beamforming for large-scale multi-antenna communication systems
The use of large-scale antenna systems is a promising candidate for next-generation communication systems to provide high data rates and energy efficiency systems with simple signal processing. Realizing the benefits expected form large-scale antenna arrays in practice can be limited by channel estimation accuracy. Much prior work focuses on TDD based networks under the assumption of channel reciprocity between the uplink and downlink channels. On the other hand, most currently deployed commercial wireless systems are based on FDD operation without channel reciprocity. In massive MIMO FDD systems, the problem of channel estimation becomes more challenging due to substantial overhead such as feedback and dedicated time for channel sounding, which scale with the number of antennas. In the first part, we focus on the problem of pilot beam pattern design for channel estimation in massive MIMO systems, and provide a new algorithm for optimal channel estimation. The proposed algorithm generates the pilot beam pattern sequentially by exploiting the properties of Kalman filtering process and prediction error covariance matrices given the channel statistics. In the second part, we tackle the problem of training sequence design that employs a set of training signals and its mapping to the training periods. We consider reduced-dimension training sequence and transmit precoder designs in order to reduce hardware complexity and power consumption. The proposed designs are extended to hybrid analog-digital beamforming schemes by applying the Toeplitz distribution theorem to large-scale linear antenna systems. In the third part, we consider the problem of multi-resolution beam alignment sequence design to make the system search for the dominant channel direction in a mmWave system. The massive MIMO system fits well with communications over mmWave bands in the range of 20-100 GHz. Using mmWave frequencies allows the base station to reduce the size of the antenna array required for achieving the large beamforming gain. The proposed algorithm generates a multilevel beam alignment sequence that strikes a balance between minimizing the training overhead and maximizing beamforming gain, where a subset of multilevel beamforming vectors is chosen adaptively to provide an improved average data rate. We propose an efficient method to design a hierarchical multi-resolution codebook utilizing a Butler matrix, a generalized DFT matrix implemented using analog RF circuitry, which extends to three-dimensional beamforming.
Love, Purdue University.
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