Coordinated Beam Selection for Training Overhead Reduction in FDD Massive MIMO

Maschietti, Flavio; Fodor, Gábor; Gesbert, David; Kerret, Paul de

doi:10.1109/iswcs.2019.8877306

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…The number of such components depends on the propagation environment, which is in general beyond the designer's control. An interesting twist to the story arises when multiple antennas are considered at the UE side as well, as we did in [1]. In fact, in that case, an extra degree of freedom is obtained by letting the UEs steer energy into suitable spatial regions.…”

Section: A Related Workmentioning

confidence: 96%

“…While this approach makes much sense in a single-user scenario, it fails to exploit all degrees of freedom in a multi-user setting. In fact, [1] set forth the idea that beam selection at the UE side could be designed differently, with the aim to reduce the number of relevant components to estimate (so as to reduce the training overhead). In general, the decision on which beams to activate at both the BS and UE sides is not a trivial one, as several factors participate in the sum-rate optimization problem, including i) the beamforming gain, ii) the multi-user interference and iii) the training overhead.…”

Section: A Related Workmentioning

confidence: 99%

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User Coordination for Fast Beam Training in FDD Multi-User Massive MIMO

Maschietti¹,

Fodor²,

Gesbert³

et al. 2020

Preprint

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Massive multiple-input multiple-output (mMIMO) communications are one of the enabling technologies of 5G and beyond networks. While prior work indicates that mMIMO networks employing time division duplexing have a significant capacity growth potential, deploying mMIMO in frequency division duplexing (FDD) networks remains problematic. The two main difficulties in FDD networks are the scalability of the downlink reference signals and the overhead associated with the required uplink feedback for channel state information (CSI) acquisition. To address these difficulties, most existing methods utilize assumptions on the radio environment such as channel sparsity or angular reciprocity. In this work, we propose a novel cooperative method for a scalable and low-overhead approach to FDD mMIMO under the so-called grid-of-beams architecture. The key idea behind our scheme lies in the exploitation of the near-common signal propagation paths that are often found across several mobile users located in nearby regions, through a coordination mechanism. In doing so, we leverage the recently specified device-to-device communications capability in 5G networks. Specifically, we design beam selection algorithms capable of striking a balance between CSI acquisition overhead and multi-user interference mitigation. The selection exploits statistical information, through so-called covariance shaping. Simulation results demonstrate the effectiveness of the proposed algorithms, which prove particularly well-suited to rapidly-varying channels with short coherence time.

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Section: A Related Workmentioning

confidence: 96%

Section: A Related Workmentioning

confidence: 99%

User Coordination for Fast Beam Training in FDD Multi-User Massive MIMO

Maschietti¹,

Fodor²,

Gesbert³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In particular, one orthogonal RS is allocated to each beam in the GoB codebook. Thus, estimating such effective channels reduces the overhead, as it becomes proportional to the codebook size (the number of possible beams) rather than to the number of antenna elements [35]. Unfortunately, the reduction in training overhead due to coarse granularity of the codebook, typically incurs some performance degradation [36], as the digital precoder for data transmission is based on a reduced channel representation, rather than a perantenna complex channel coefficient.…”

Section: Recent Advances In Csit Acquisition Techniquesmentioning

confidence: 99%

Recent Advances in Acquiring Channel State Information in Cellular MIMO Systems

Fodor¹,

Pap²,

Telek³

2019

Infocommunications journal

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In cellular multiuser multiple input multiple output (MU-MIMO) systems the quality of the available channel state information (CSI) has a large impact on the system performance. Specifically, reliable CSI at the transmitter is required to determine the appropriate modulation and coding scheme, transmit power and the precoder vector, while CSI at the receiver is needed to decode the received data symbols. Therefore, cellular MU-MIMO systems employ predefined pilot sequences and configure associated time, frequency, code and power resources to facilitate the acquisition of high quality CSI for data transmission and reception. Although the trade-off between the resources used for pilot and user data transmission has been known for long, the near-optimal configuration of the available system resources for pilot and data transmission is a topic of current research efforts. Indeed, since the fifth generation of cellular systems utilizes heterogeneous networks in which base stations are equipped with a large number of transmit and receive antennas, the appropriate configuration of pilot-data resources becomes a critical design aspect. In this article, we review recent advances in system design approaches that are designed for the acquisition of CSI and discuss some of the recent results that help to dimension the pilot and data resources specifically in cellular MU-MIMO systems. Index Terms-Multi-antenna systems, channel state information, estimation techniques, receiver algorithms.

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