Codebook design and beam training for extremely large-scale RIS: Far-field or near-field?

Wei, Xiuhong; Dai, Linglong; Zhao, Yajun; Yu, Guanlong; Duan, Xiangyang

doi:10.23919/jcc.2022.06.015

Cited by 81 publications

(27 citation statements)

References 23 publications

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“…Let M denote the number of relevant phase-shift configurations for a given channel realization, where typically M ≪ M T holds. These relevant configurations can be determined during beam training and do not change during the coherence time of the channel [15]. The focus of this paper is the joint online optimization of the phase-shift configurations of all tiles and the BS precoder.…”

Section: Tile and Codebook Based Irs Configurationmentioning

confidence: 99%

Codebook Based Two-Time Scale Resource Allocation Design for IRS-Assisted eMBB-URLLC Systems

Ghanem¹,

Jamali²,

Schellmann³

et al. 2022

Preprint

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This paper investigates the resource allocation algorithm design for wireless systems assisted by large intelligent reflecting surfaces (IRSs) with coexisting enhanced mobile broadband (eMBB) and ultra reliable low-latency communication (URLLC) users. We consider a two-time scale resource allocation scheme, whereby the base station's precoders are optimized in each mini-slot to adapt to newly arriving URLLC traffic, whereas the IRS phase shifts are reconfigured only in each time slot to avoid excessive base station-IRS signaling. To facilitate efficient resource allocation design for large IRSs, we employ a codebook-based optimization framework, where the IRS is divided into several tiles and the phase-shift elements of each tile are selected from a pre-defined codebook. The resource allocation algorithm design is formulated as an optimization problem for the maximization of the average sum data rate of the eMBB users over a time slot while guaranteeing the quality-of-service (QoS) of each URLLC user in each mini-slot. An iterative algorithm based on alternating optimization (AO) is proposed to find a high-quality suboptimal solution. As a case study, the proposed algorithm is applied in an industrial indoor environment modelled via the Quadriga channel simulator. Our simulation results show that the proposed algorithm design enables the coexistence of eMBB and URLLC users and yields large performance gains compared to three baseline schemes. Furthermore, our simulation results reveal that the proposed two-time scale resource allocation design incurs only a small performance loss compared to the case when the IRSs are optimized in each mini-slot.• We propose a novel resource allocation algorithm design for an eMBB-URLLC system assisted by large IRSs. The proposed resource allocation algorithm is formulated as a two-time scale resource allocation problem, whereby the precoders at the BS are optimized in each mini-slot to adapt to the newly arriving URLLC traffic, whereas the IRS phase shifts are reconfigured only in each time slot to avoid excessive BS-IRSs signaling. Then, the resource allocation algorithm design is formulated as a non-convex

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Section: Tile and Codebook Based Irs Configurationmentioning

confidence: 99%

Codebook Based Two-Time Scale Resource Allocation Design for IRS-Assisted eMBB-URLLC Systems

Ghanem¹,

Jamali²,

Schellmann³

et al. 2022

Preprint

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“…Although near-field BF has been studied in literature e.g., [5], [6], [14], [16]- [18], [25]- [27], to our best knowledge, it has not been considered from a practical implementation perspective that can cooperate with the existing far-field BF scheme such as in 5G-NR. For instance, in [14], the near-field BF vector for a microphone-array in an acoustic system is derived to be equal to the signal propagation vector, which is however, unknown at the transmitter side.…”

Section: Introductionmentioning

confidence: 99%

Design of Near-Field Beamforming for Large Intelligent Surfaces

Hu¹,

Wang²,

ilter³

2023

Preprint

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<p> In this paper, we propose a novel near-field beamforming (BF) design for a Large Intelligent Surface (LIS) implemented as a discretized two-dimensional (2D) antenna array. We firstly investigate the definitions of near-field and far-field regions of the LIS, and determine the Fraunhofer distance which scales up linearly in the surface-area. Then, we derive the Fresnel near-field region where variations of amplitudes and angles are both negligible, as long as the distance from a user-equipment (UE) to the LIS is larger than a threshold that scales up linearly in the diameter of the LIS. Hence, in the majority region of near-field, only phase variations worsen the quality of received signal and yield significant losses of array gains. Following this observation, we present a decomposition theorem proving that in the Fresnel region, the optimal three-dimensional (3D) near-field BF can be decomposed into a conventional 2D far-field BF and a one-dimensional (1D) near-field BF. The 2D far-field BF compensates phase variations caused by the azimuth and elevation angles, while the 1D near-field BF compensates phases variations caused by distance differences among the UE and antenna-elements on the LIS. Such a 2D+1D BF design is fully compatible with existing 2D far-field BF schemes, and the 1D near-field BF can be treated as an additional step. From this perspective, we further derive an optimal codebook design of the 1D near-field BF with Lloyd-Max method. Numerical results show that the proposed 2D+1D BF is effective to recover losses of array gains in the near-field, and also robust against distance mismatches. Moreover, with a few optimized quantization points in the codebook, the 1D near-field BF performs close to optimal. </p>

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“…In particular, to reap the prominent beamforming gain brought by the XL-array, it is indispensable for the XL-array to perform the near-field beam training for establishing high signal-to-noise ratio (SNR) initial links before efficient channel estimation and data transmission [7], [10]. In contrast to the conventional far-field beam training methods that aim to find the best beam direction based on a predefined angular-domain beam codebook, the near-field beam training requires new codebook designs and efficient search for a proper beam to match the specific channel angle and distance according to…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…To address the above issues, several new beam training methods dedicated to the near-field communication have been recently proposed in the literature [7]- [9]. Specifically, the authors in [7] proposed a hierarchical near-field codebook and the corresponding hierarchical near-field beam training scheme, where different levels of sub-codebooks are searched in turn with reduced codebook size. To further reduce the size of the near-field codebook, a novel polar-domain codebook dedicated to the near field was proposed in [8], wherein the angular domain is uniformly sampled while the distance domain is sampled non-uniformly to reduce the codebook size.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fast Near-Field Beam Training for Extremely Large-Scale Array

Zhang

You

2022

IEEE Wireless Commun. Lett.

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In this letter, we study efficient near-field beam training design for the extremely large-scale array (XL-array) communication systems. Compared with the conventional farfield beam training method that searches for the best beam direction only, the near-field beam training is more challenging since it requires a beam search over both the angular and distance domains due to the spherical wavefront propagation model. To reduce the near-field beam-training overhead based on the two-dimensional exhaustive search, we propose in this letter a new two-phase beam training method that decomposes the twodimensional search into two sequential phases. Specifically, in the first phase, the candidate angles of the user is determined by a new method based on the conventional far-field codebook and angle-domain beam sweeping. Then, a customized polardomain codebook is employed in the second phase to find the best effective distance of the user given the shortlisted candidate angles. Numerical results show that our proposed twophase beam training method significantly reduces the training overhead of the exhaustive search and yet achieves comparable beamforming performance for data transmission.

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Codebook design and beam training for extremely large-scale RIS: Far-field or near-field?

Cited by 81 publications

References 23 publications

Codebook Based Two-Time Scale Resource Allocation Design for IRS-Assisted eMBB-URLLC Systems

Codebook Based Two-Time Scale Resource Allocation Design for IRS-Assisted eMBB-URLLC Systems

Design of Near-Field Beamforming for Large Intelligent Surfaces

Fast Near-Field Beam Training for Extremely Large-Scale Array

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