Channel Estimation for Hybrid Architecture-Based Wideband Millimeter Wave Systems

Abstract

Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the hybrid precoders and combiners, though, is usually based on knowledge of the channel. Prior work on mmWave channel estimation with hybrid architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for hybrid architectures based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing based solutions for the wideband mmWave channel estimation problem for hybrid architectures. First, we leverage the sparse structure of the frequency selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains.Our solutions are suitable for both SC-FDE and OFDM systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead.Leveraging the hybrid architecture at the transceivers gives further improvement in estimation error performance and achievable rates. Index TermsMillimeter wave communications, channel estimation, frequency selective channel, hybrid precodercombiner, compressed sensing, sparse recovery, multi-stream MIMO, IEEE 802.11ad.

Distributed compressed sensing‐based multiuser channel estimation for frequency‐selective hybrid mmWave MIMO system

“…This also indicates that a large number of subcarriers that satisfies (21), the more precise estimation of noise variance can be achieved. This also indicates that a large number of subcarriers that satisfies (21), the more precise estimation of noise variance can be achieved.…”

“…A distributed compressed sensing (DCS)-based 18 approach was proposed to estimate channel coefficients of the line-of-sight (LoS) path with Rician fading. In Venugopal et al, 21 the frequency-selective mmWave channel estimation was formulated as a sparse recovery problem in both time and frequency domains by leveraging the intrinsic sparse structure. In Zhou et al, 20 a tensor-based method was proposed for the downlink channel estimation of orthogonal frequency division multiplexing (OFDM)-based mmWave MIMO system, which organized the received signal at the BS into a third-order tensor and utilized a CP decomposition to obtain the frequency-selective channel.…”

Millimeter Wave Communications for Future Mobile Networks

“…Some improved schemes following the similar idea can be found in [146], [149]. The second approach is to regard (7) as a classical sparse signal recovery problem [150]- [152]. Then, it designs f m and w m to make the sensing matrix ΨA D enjoy sufficiently low mutual coherence, which is crucial to achieve high signal recovery accuracy in CS theory [153].…”

DoA and DoD Estimation and Hybrid Beamforming for Radar-Aided mmWave MIMO Vehicular Communication Systems

“…In this section, we address the problem of designing the hybrid beamformers [30][31][32][33][34]. In the design of hybrid beamforming, the power is usually maximized, so the direction of beam can be designed to the users.…”