Enhanced CSI Feedback for FD-MIMO with Beamformed CSI-RS in LTE-A Pro Systems

Morozov, G. V.; Davydov, Alexei; Сергеев, В. А.

doi:10.1109/vtcfall.2016.7880998

Cited by 12 publications

(13 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Depending on the CSI-RS type used for CSI acquisition, two types of CSI feedback schemes are supported for FD-MIMO in LTE-A Pro: Class A and Class B CSI feedback [48], [97]. In Class A, the user estimates the CSI values using measurements from non-precoded CSI-RS transmitted by the BS, and in Class B, the user estimates the CSI values using beamformed CSI-RS transmitted by the BS.…”

Section: A Channel Estimation and Csi Feedback In Fd-mimomentioning

confidence: 99%

Elevation Beamforming With Full Dimension MIMO Architectures in 5G Systems: A Tutorial

Nadeem

Kammoun

Alouini

2019

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

Full dimension (FD) multiple-input multiple-output (MIMO) technology has attracted substantial research attention from both wireless industry and academia in the last few years as a promising technique for next-generation wireless communication networks. FD-MIMO scenarios utilize a planar two-dimensional (2D) active antenna system (AAS) that not only allows a large number of antenna elements to be placed within feasible base station (BS) form factors, but also provides the ability of adaptive electronic beam control over both the elevation and the traditional azimuth dimensions. This paper presents a tutorial on elevation beamforming analysis for cellular networks utilizing FD Massive MIMO antenna arrays. In contrast to existing works that focus on the standardization of FD-MIMO in the 3rd Generation Partnership Project (3GPP), this tutorial is distinguished by its depth with respect to the theoretical aspects of antenna array and 3D channel modeling. In an attempt to bridge the gap between industry and academia, this preliminary tutorial introduces the relevant array and transceiver architecture designs proposed in the 3GPP Release 13 that enable elevation beamforming. Then it presents and compares two different 3D channel modeling approaches that can be utilized for the performance analysis of elevation beamforming techniques. The spatial correlation in FD-MIMO arrays is characterized and compared based on both channel modeling approaches and some insights into the impact of different channel and array parameters on the correlation are drawn. All these aspects are put together to provide a mathematical framework for the design of elevation beamforming schemes in single-cell and multi-cell scenarios. Simulation examples associated with comparisons and discussions are also presented. To this end, this paper highlights the state-of-the-art research and points out future research directions.

show abstract

Section: A Channel Estimation and Csi Feedback In Fd-mimomentioning

confidence: 99%

Elevation Beamforming With Full Dimension MIMO Architectures in 5G Systems: A Tutorial

Nadeem

Kammoun

Alouini

2019

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…Beamformed CSI-RS The original 3GPP proposal [8] has shown significant CSIT acquisition gain. The enhanced-CSI-RS (E-CSI-RS) algorithm [9] is an extension of the CSI-RS standard, proposed from Intel Research. E-CSI-RS algorithm adopts dual FD-DFT codebooks.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…However, channel quantization represents a major limitation of the network spatial degrees of freedom (DoFs), regardless of the number of transmit antennas [7]. Hence, the design of the beamformed CSI-reference-signals (CSI-RS) is widely studied in recent standards [8,9], where the DL pilots are distributed across several FD beamforming directions. CSI-RS algorithms have shown scalability and performance improvement with limited feedback overhead; however, they may result in blind coverage spots if scanning precision is insufficient.…”

Section: Introductionmentioning

confidence: 99%

Directional Spatial Channel Estimation for Massive FD-MIMO in Next Generation 5G Networks

Esswie

Dobre

Ikki

2018

2018 IEEE International Conference on Communications (ICC)

View full text Add to dashboard Cite

Full-dimensional (FD) channel state information at transmitter (CSIT) has always been a major limitation of the spectral efficiency of cellular multi-input multi-output (MIMO) networks. This letter proposes an FD-directional spatial channel estimation algorithm for frequency division duplex massive FD-MIMO systems. The proposed algorithm uses the statistical spatial correlation between the uplink (UL) and downlink (DL) channels of each user equipment. It spatially decomposes the UL channel into azimuthal and elevation dimensions to estimate the array principal receive responses. An FD spatial rotation matrix is constructed to estimate the corresponding transmit responses of the DL channel, in terms of the frequency band gap between the UL and DL channels. The proposed algorithm shows significantly promising performance, approaching the ideal perfect-CSIT case without UL feedback overhead.

show abstract

“…When compared to the exhaustive beam-search scanning of all possible beam directions, an effective approach to search for the optimal beam direction is the process of scanning based on codebooks [11]- [13]. The efficiency of search for the optimal beam direction is enhanced by constructing a hierarchical structure in codebooks [14], [15], or providing the channel state information (CSI) as feedback for codebooks [16], [17].…”

Section: A Beam-tracking With Rf Informationmentioning

confidence: 99%

“…, 𝑇/𝜏, the state 𝑠 𝑡 ∈ S is expressed as 𝑠 𝑡 = (x 𝑡 , v 𝑡 , b 𝑡 ). The action 𝑎 𝑡 is the action selected at time step 𝑡, and reward 𝑟 𝑡 is calculated from (17). In the test scenario, when the state 𝑠 is observed, the action 𝑎 that maximizes the predicted discounted reward is selected, which is expressed as 𝑎 = arg max 𝑎 ∈A 𝑄(𝑠, 𝑎 ; θ).…”

Section: Adversarial Rl Algorithmmentioning

confidence: 99%

Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learning

Shinzaki¹,

Koda²,

Yamamoto³

et al. 2021

Preprint

View full text Add to dashboard Cite

This paper discusses the opportunity of bringing the concept of zero-shot adaptation into learning-based millimeterwave (mmWave) communication systems, particularly in environments with unstable urban infrastructures. Here, zero-shot adaptation implies that a learning agent adapts to unseen scenarios during training without any adaptive fine-tuning. By considering learning-based beam-tracking of a mmWave node placed on an overhead messenger wire, we first discuss the importance of zero-shot adaptation. More specifically, we confirm that the gap between the values of wire tension and total wire mass in training and test scenarios deteriorates the beam-tracking performance in terms of the received power. Motivated by this discussion, we propose a robust beam-tracking method to adapt to a broad range of test scenarios in a zero-shot manner, i.e., without requiring any retraining to adapt the scenarios. The key idea is to leverage a recent, robust adversarial reinforcement learning technique, where such training and test gaps are regarded as disturbances from adversaries. In our case, a beam-tracking agent performs training competitively bases on an intelligent adversary who causes beam misalignments. Numerical evaluations confirm the feasibility of zero-shot adaptation by showing that the on-wire node achieves feasible beam-tracking performance without any adaptive fine-tuning in unseen scenarios.

show abstract

Enhanced CSI Feedback for FD-MIMO with Beamformed CSI-RS in LTE-A Pro Systems

Cited by 12 publications

References 2 publications

Elevation Beamforming With Full Dimension MIMO Architectures in 5G Systems: A Tutorial

Elevation Beamforming With Full Dimension MIMO Architectures in 5G Systems: A Tutorial

Directional Spatial Channel Estimation for Massive FD-MIMO in Next Generation 5G Networks

Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learning

Contact Info

Product

Resources

About