Millimeter wave (mmWave) cellular systems will enable gigabit-per-second data
rates thanks to the large bandwidth available at mmWave frequencies. To realize
sufficient link margin, mmWave systems will employ directional beamforming with
large antenna arrays at both the transmitter and receiver. Due to the high cost
and power consumption of gigasample mixed-signal devices, mmWave precoding will
likely be divided among the analog and digital domains. The large number of
antennas and the presence of analog beamforming requires the development of
mmWave-specific channel estimation and precoding algorithms. This paper
develops an adaptive algorithm to estimate the mmWave channel parameters that
exploits the poor scattering nature of the channel. To enable the efficient
operation of this algorithm, a novel hierarchical multi-resolution codebook is
designed to construct training beamforming vectors with different beamwidths.
For single-path channels, an upper bound on the estimation error probability
using the proposed algorithm is derived, and some insights into the efficient
allocation of the training power among the adaptive stages of the algorithm are
obtained. The adaptive channel estimation algorithm is then extended to the
multi-path case relying on the sparse nature of the channel. Using the
estimated channel, this paper proposes a new hybrid analog/digital precoding
algorithm that overcomes the hardware constraints on the analog-only
beamforming, and approaches the performance of digital solutions. Simulation
results show that the proposed low-complexity channel estimation algorithm
achieves comparable precoding gains compared to exhaustive channel training
algorithms. The results also illustrate that the proposed algorithms can
approach the coverage probability achieved by perfect channel knowledge even in
the presence of interference.Comment: 36 pages, 10 figures, submitted to IEEE Journal of Selected Topics in
Signal Processin