Beam Squint and Channel Estimation for Wideband mmWave Massive MIMO-OFDM Systems

Abstract

With the increasing scale of antenna arrays in wideband millimeter-wave (mmWave) communications, the physical propagation delays of electromagnetic waves traveling across the whole array will become large and comparable to the time-domain sample period, which is known as the spatial-wideband effect. In this case, different subcarriers in an orthogonal frequency division multiplexing (OFDM) system will "see" distinct angles of arrival (AoAs) for the same path. This effect is known as beam squint, resulting from the spatial-wideband effect, and makes the approaches based on the conventional multipleinput multiple-output (MIMO) model, such as channel estimation and precoding, inapplicable. After discussing the relationship between beam squint and the spatial-wideband effect, we propose a channel estimation scheme for frequency-division duplex (FDD) mmWave massive MIMO-OFDM systems with hybrid analog/digital precoding, which takes the beam squint effect into consideration. A super-resolution compressed sensing approach is developed to extract the frequency-insensitive parameters of each uplink channel path, i.e., the AoA and the time delay, and the frequency-sensitive parameter, i.e., the complex channel gain. With the help of the reciprocity of these frequency-insensitive parameters in FDD systems, the downlink channel estimation can be greatly simplified, where only limited pilots are needed to obtain downlink complex gains and reconstruct downlink channels. Furthermore, the uplink and downlink 2 channel covariance matrices can be constructed from these frequency-insensitive channel parameters rather than through a long-term average, which enables the minimum mean-squared error (MMSE) channel estimation to further enhance performance. Numerical results demonstrate the superiority of the proposed scheme over the conventional methods under general system configurations in mmWave communications.

Compact Multi-Wideband Array for Millimeter-Wave Communications Using Squint Beams

“…CN (0, 1). To simplify the design, the HPBW in step 3 of Algorithm 1 is set to a constant value of Ψ h = Θ x0 (f c ) according to (36), where f c is the center frequency of that tier. Finally, all results are reported for averaged over 10000 random channel realizations.…”

“…12 and 14, the number of users served in tier-3 is less than in tier-2 due to the selection of suitable secondary users within the HPBW in step 3 of Algorithm 1. From (35) and (36), it can be seen that Ψ h decreases as the number of antennas increases, thus the probability of suitable secondary users in tier-3 decreases. We note that the optimal steering beam rate for the primary user in tier-3 is significantly higher than in tier-2.…”

Wideband MIMO Channel Estimation for Hybrid Beamforming Millimeter Wave Systems via Random Spatial Sampling

“…• In Line 2 of the Algorithm 1, the solution of (36) is obtained by employing the SVT operator on the dense non-square matrix Y ∈ C NR×T . Recall that SVT is based on the Singular Value Decomposition (SVD).…”

“…In this section, we briefly discuss the impact of the beam squint effect on our problem formulation. Large size antenna arrays realizing directive beams in wideband communication systems are susceptible to the beam squint phenomenon that imposes selectivity both in the frequency and the spatial domains [35], [36]. This spatio-frequency selectivity needs to carefully accounted for when designing wideband mmWave massive MIMO systems.…”

Fast Antenna and Beam Switching Method for mmWave Handsets with Multiple Subarrays

“…As a solution to the aforementioned optimization problem, numerous algorithms have been developed over the past half-century. Any algorithm with high accuracy [30], [31] can be used in Fast-ABS to estimate the ToA, AoA, and gain of multipath. In this paper, we employ the Newtonized orthogonal matching pursuit (NOMP) algorithm [32] in Fast-ABS, because we find that NOMP provides better performance and lower complexity than many existing algorithms [33].…”