This paper is concerned with the channel estimation problem in Millimeter
wave (mmWave) wireless systems with large antenna arrays. By exploiting the
inherent sparse nature of the mmWave channel, we first propose a fast channel
estimation (FCE) algorithm based on a novel overlapped beam pattern design,
which can increase the amount of information carried by each channel
measurement and thus reduce the required channel estimation time compared to
the existing non-overlapped designs. We develop a maximum likelihood (ML)
estimator to optimally extract the path information from the channel
measurements. Then, we propose a novel rate-adaptive channel estimation (RACE)
algorithm, which can dynamically adjust the number of channel measurements
based on the expected probability of estimation error (PEE). The performance of
both proposed algorithms is analyzed. For the FCE algorithm, an approximate
closed-form expression for the PEE is derived. For the RACE algorithm, a lower
bound for the minimum signal energy-to-noise ratio required for a given number
of channel measurements is developed based on the Shannon-Hartley theorem.
Simulation results show that the FCE algorithm significantly reduces the number
of channel estimation measurements compared to the existing algorithms using
non-overlapped beam patterns. By adopting the RACE algorithm, we can achieve up
to a 6dB gain in signal energy-to-noise ratio for the same PEE compared to the
existing algorithms.

本文提出了一种基于重叠波束模式设计的快速信道估计算法，利用毫米波通信信道固有的稀疏性，优化了模型与提高了信道测量信息的利用效率，能够更快速地实现信道估计，并提出了一种能够根据所需误差概率动态调整信道测量数量的新型自适应算法，在优化算法的性能方面取得了重要进展。