Hybrid Precoding for WideBand Millimeter Wave MIMO Systems in the Face of Beam Squint

Chen, Yun; Xiong, Yifeng; Chen, Da; Jiang, Tao; Ng, Soon Xin; Hanzo, Lajos

doi:10.1109/twc.2020.3036945

Cited by 74 publications

(57 citation statements)

References 34 publications

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“…corresponding to the transmitted signal of the lth BS is given by (32) on the bottom of this page. In (32), the second entry is the interference from other BSs, n 32) is regarded as the additional noise…”

Section: Path Delay and Channel Gain Estimationmentioning

confidence: 99%

“…To be specific, the impact of beam squint on compressive subspace estimation and the optimality of frequency-flat beamforming was studied in [31]. By projecting all frequencies to the central frequency and constructing the common analog Transmit Precoding (TPC) matrix for all subcarriers, several hybrid TPC schemes were proposed in [32] to design the analog and digital TPC matrices and mitigate the beam squint effect. The channel estimation schemes were proposed to exploit the characteristics of mmWave channels affected by beam squint for estimating the wideband mmWave massive MIMO channels [33]- [35], where the beam squint effect is not mitigated.…”

Section: Introductionmentioning

confidence: 99%

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Terahertz Ultra-Massive MIMO-Based Aeronautical Communications in Space-Air-Ground Integrated Networks

Liao

Gao

Wang

et al. 2021

IEEE J. Select. Areas Commun.

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The emerging space-air-ground integrated network has attracted intensive research and necessitates reliable and efficient aeronautical communications. This paper investigates terahertz Ultra-Massive (UM)-MIMO-based aeronautical communications and proposes an effective channel estimation and tracking scheme, which can solve the performance degradation problem caused by the unique triple delay-beam-Doppler squint effects of aeronautical terahertz UM-MIMO channels. Specifically, based on the rough angle estimates acquired from navigation information, an initial aeronautical link is established, where the delay-beam squint at transceiver can be significantly mitigated by employing a Grouping True-Time Delay Unit (GTTDU) module (e.g., the designed Rotman lens-based GTTDU module). According to the proposed prior-aided iterative angle estimation algorithm, azimuth/elevation angles can be estimated, and these angles are adopted to achieve precise beam-alignment and refine GTTDU module for further eliminating delay-beam squint. Doppler shifts can be subsequently estimated using the proposed prior-aided iterative Doppler shift estimation algorithm. On this basis, path delays and channel gains can be estimated accurately, where the Doppler squint can be effectively attenuated via compensation process. For data transmission, a data-aided decision-directed based channel tracking algorithm is developed to track the beam-aligned effective channels. When the dataaided channel tracking is invalid, angles will be re-estimated at the pilot-aided channel tracking stage with an equivalent sparse digital array, where angle ambiguity can be resolved based on the previously estimated angles. The simulation results and the derived Cramér-Rao lower bounds verify the effectiveness of our solution.Index Terms-Terahertz communications, aeronautical communications, ultra-massive MIMO, channel estimation and tracking, space-air-ground integrated network.The codes and some other associated materials of this work may be available at https://gaozhen16.github.io.

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Section: Path Delay and Channel Gain Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Terahertz Ultra-Massive MIMO-Based Aeronautical Communications in Space-Air-Ground Integrated Networks

Liao

Gao

Wang

et al. 2021

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

show abstract

“…Due to the sparsity of mmwave channels, an extended Saleh-Valenzuela geometric model is considered in this paper. The wideband mmwave transmission is suffered from beam squint effect [22], [23]. For simplicity, the narrow-band channel model is considered in this paper.…”

Section: System Modelmentioning

confidence: 99%

Learning-Based Predictive Transmitter-Receiver Beam Alignment in Millimeter Wave Fixed Wireless Access Links

Zhang

Masouros

2021

IEEE Trans. Signal Process.

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Millimeter wave (mmwave) fixed wireless access is a key enabler of 5G and beyond small cell network deployment, exploiting the abundant mmwave spectrum to provide Gbps backhaul and access links. Large antenna arrays and extremely directional beamforming are necessary to combat the mmwave path loss. However, narrow beams increase sensitivity to physical perturbations caused by environmental factors. To address this issue, in this paper we propose a predictive transmit-receive beam alignment process. We construct an explicit mapping between transmit (or receive) beams and physical coordinates via a Gaussian process, which can incorporate environmental uncertainties. To make full use of underlying correlations between transmitter and receiver and accumulated experiences, we further construct a hierarchical Bayesian learning model and design an efficient beam predictive algorithm. To reduce dependency on physical position measurements, a reverse mapping that predicts physical coordinates from beam experiences is further constructed. The designed algorithms enjoy two folds of advantages. Firstly, thanks to Bayesian learning, a good performance can be achieved even for a small sample setting as low as 10 samples in our scenarios, which drastically reduces training time and is therefore very appealing for wireless communications. Secondly, in contrast to most existing algorithms that only predict one beam in each timeslot, the designed algorithms generate the most promising beam subset, which improves robustness to environment uncertainties. Simulation results demonstrate the effectiveness and superiority of the designed algorithms against the state of the art. Index Terms-Beam alignment, Bayesian learning, beam prediction, beam training, fixed wireless access, Gaussian processes, millimeter wave communications. I. INTRODUCTIONM ILLIMETER wave (mmwave) fixed wireless access (FWA), which is also referred to as mmwave distribution network and supported by the IEEE 802.11ay, enables different deployment scenarios, including broadband residential access, WiFi access point (AP), small cell backhaul, and home media sharing [1]. By operating in the mmwave bands, mmwave FWA provides much increased capacity compared to other WiFi systems that operate in the microwave bands. Moreover, since mmwave links are highly directional, it also presents significant opportunities for spatial reuse,

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“…Where P is the total power of BS transmission. Refer to the MS precoding design, the BS can also design the RF combiner in multiple iterations, but this does not work in 5G communications [18]. The huge communication capacity and low latency of 5G require BS to respond quickly, but the traditional iterative calculation is large.…”

Section: Hybrid Beamforming Design At Bsmentioning

confidence: 99%

Hybrid Beamforming With Deep Learning for Large-Scale Antenna Arrays

Jiang

2021

IEEE Access

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The emergence of highly directional beamforming technology makes millimeter wave frequency band communication possible in future wireless communication networks. Based on the multipath characteristics of millimeter wave frequency communication, a high-precision multipath channel estimation algorithm based on signal subspace is proposed. In the mobile terminal, an iterative heuristic radio frequency combination algorithm based on spatial points is proposed. The analog precoding at the base station uses deep learning to accelerate the calculation, and then the multi-user communication is modeled to design the digital precoding. The simulation results show that the multi-channel estimation algorithm can estimate 4 paths with an error of no more than 0.3 rad. The proposed DL algorithm takes only 20% of the time when it is close to the 87% spectral efficiency of the traditional algorithm.INDEX TERMS Millimeter wave, hybrid beamforming, large-scale antenna arrays, multiple-input multipleoutput (MIMO), channel estimation, space point iteration, deep learning (DL).

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Hybrid Precoding for WideBand Millimeter Wave MIMO Systems in the Face of Beam Squint

Cited by 74 publications

References 34 publications

Terahertz Ultra-Massive MIMO-Based Aeronautical Communications in Space-Air-Ground Integrated Networks

Terahertz Ultra-Massive MIMO-Based Aeronautical Communications in Space-Air-Ground Integrated Networks

Learning-Based Predictive Transmitter-Receiver Beam Alignment in Millimeter Wave Fixed Wireless Access Links

Hybrid Beamforming With Deep Learning for Large-Scale Antenna Arrays

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