Channel Estimation and Hybrid Precoding for
Millimeter Wave Cellular Systems
Ahmed Alkhateeb, Omar El Ayach, Geert Leus, and Robert W. Heath, Jr.
Submitted to the IEEE Journal of Selected Topics in Signal Processing, September 2013.
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. For single-path channels, tools from compressed sensing
are leveraged to derive a lower bound on the estimation success probability using the proposed algorithm,
and find the optimal training power allocation among the adaptive stages of the algorithm to maximize
this success probability. These tools also provide a mathematical basis by which the proposed algorithm is
extended to tackle the multi-path mmWave channel estimation problem. 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
channel estimation and precoding algorithms can approach the coverage probability achieved by perfect
channel knowledge even in the presence of interference.
Available on IEEE
The Matlab code of the paper is available here