Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems

Abstract

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 channel estimation and precoding algorithms can approach the coverage probability achieved by perfect channel knowledge even in the presence of interference.

5G Position and Orientation Estimation through Millimeter Wave MIMO

“…Since ∂a Tx,F /∂θ Tx = F H D Tx a Tx , we find (13). The term Φ(θ Tx , θ Rx ) is obtained as follows:…”

Channel Models and Beamforming at Millimeter-Wave Frequency Bands

“…For example, [171] uses a gain focusing lens and a part of feeding antennas attached to the lens for pencil analog-beamforming to yield the best receive SNR, and after that, digital beamspace precoding and combining is performed. [172] proposes hierarchical multi-resolution beamforming codebooks to realize different antenna beamwidths with joint analog-digital processing, and [16] demonstrates the hybrid beamforming by hardware prototyping in microcellular mobile access. Recalling the fact that the eigen-and pencil-beamforming defined in Sect.…”

Channel estimation and training design for hybrid multi-carrier MmWave massive MIMO systems: The beamspace ESPRIT approach

“…To exploit the MIMO gain under a reasonable cost, one promising solution is to deploy hybrid analog-digital precoding schemes, realized using phase shifters or switches in the RF domain [3], and digital precoding schemes, implemented in the digital baseband domain as in conventional MIMO. If analog precoding is achieved using phase shifters only, the analog precoding matrix should have only constant modulus entries [4], [5], [6]. Furthermore, when a wideband multicarrier system is considered, we have to use the same phase shifts for all subcarriers [7].…”

“…Furthermore, when a wideband multicarrier system is considered, we have to use the same phase shifts for all subcarriers [7]. These two constraints are stringent so that they lead to significant challenges not only for the precoding of the transmitted data but also for the required channel estimation tasks [4], [5], [8]. When a frequencyflat channel is considered, in [5] an adaptive compressed sensing (CS) based channel estimation algorithm is proposed to estimate the channel of a hybrid analog-digital massive MIMO system.…”

“…These two constraints are stringent so that they lead to significant challenges not only for the precoding of the transmitted data but also for the required channel estimation tasks [4], [5], [8]. When a frequencyflat channel is considered, in [5] an adaptive compressed sensing (CS) based channel estimation algorithm is proposed to estimate the channel of a hybrid analog-digital massive MIMO system. This CS based channel estimation algorithm has been further extended in [9] by involving multiple measurement vectors (MMV) to improve the channel estimation accuracy.…”