On the Spectral Efficiency of Noncooperative Uplink Massive MIMO Systems

Shlezinger, Nir; Eldar, Yonina C.

doi:10.1109/tcomm.2018.2884008

Cited by 21 publications

(19 citation statements)

References 33 publications

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“…According to the physical parameters, e.g., AoA, delay, and Doppler shifts, the channel of massive MIMO system can be classified into different categories, as shown in Tab. I: Channel [30], [33]- [38], though assuming M (W ) to be extremely large, only stick to the spatial narrowband model (9) and (10), while ignoring the spatial wideband phenomenon that actually corresponds to (8) and (6).…”

Section: E Summary Of Wideband-narrowband Channel Modelingmentioning

confidence: 99%

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An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

Wang

Gao

Jin

et al. 2019

IEEE J. Sel. Top. Signal Process.

132

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In the past ten years, there have been tremendous research progresses on massive MIMO systems, most of which stand from the communications viewpoint. A new trend of investigating massive MIMO, especially for the sparse scenario like millimeter wave (mmWave) transmission, is to re-build the transceiver design from array signal processing viewpoint that could deeply exploit the half-wavelength array and provide enhanced performances in many aspects. For example, the high dimensional channel could be decomposed into small amount of physical parameters, e.g., angle of arrival (AoA), angle of departure (AoD), multi-path delay, Doppler shift, etc. As a consequence, transceiver techniques like synchronization, channel estimation, beamforming, precoding, multi-user access, etc., can be re-shaped with these physical parameters, as opposed to those designed directly with channel state information (CSI). Interestingly, parameters like AoA/AoD and multi-path delay are frequency insensitive and thus can be used to guide the downlink transmission from uplink training even for FDD systems. Moreover, some phenomena of massive MIMO that were vaguely revealed previously can be better explained now with array signal processing, e.g., the beam squint effect. In all, the target of this paper is to present an overview of recent progress on merging array signal processing into massive MIMO communications as well as its promising future directions.Index Terms-massive MIMO, array signal processing, mmWave transmission, angle-delay-Doppler reciprocity, beam squint.

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Section: E Summary Of Wideband-narrowband Channel Modelingmentioning

confidence: 99%

“…has been tremendous theoretical research on massive MIMO systems, including capacity analysis [4], channel estimation [5], synchronization [6], beamforming technique [7], user scheduling [8], spectral efficiency [9], etc.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

Wang

Gao

Jin

et al. 2019

IEEE J. Sel. Top. Signal Process.

132

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“…However, when metasurfaces are present, it is no longer reasonable to assume that the moderate frequency variations induced by the DMA are effectively canceled by the transmission scheme. Consequently, in order to account for the frequency selectivity of both the DMA as well as the wireless channel, we explicitly incorporate the effect of multipath into the model in (4).…”

Section: B System Modelmentioning

confidence: 99%

Dynamic Metasurface Antennas for Uplink Massive MIMO Systems

Shlezinger

Dicker²,

Eldar

et al. 2019

IEEE Trans. Commun.

Self Cite

119

117

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Massive multiple-input multiple-output (MIMO) communications are the focus of considerable interest in recent years. While the theoretical gains of massive MIMO have been established, implementing MIMO systems with large-scale antenna arrays in practice is challenging. Among the practical challenges associated with massive MIMO systems are increased cost, power consumption, and physical size. In this work we study the implementation of massive MIMO antenna arrays using dynamic metasurface antennas (DMAs), an emerging technology which inherently handles the aforementioned challenges. Specifically, DMAs realize large-scale planar antenna arrays, and can adaptively incorporate signal processing methods such as compression and analog combining in the physical antenna structure, thus reducing the cost and power consumption. We first propose a mathematical model for massive MIMO systems with DMAs and discuss their constraints compared to ideal antenna arrays. Then, we characterize the fundamental limits of uplink communications with the resulting systems, and propose two algorithms for designing practical DMAs for approaching these limits. Our numerical results indicate that the proposed approaches result in practical massive MIMO systems whose performance is comparable to that achievable with ideal antenna arrays.

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“…To meet all these requirements, it is necessary to exploit higher frequencies, and in particular, the millimeter wave (mmWave) band, to overcome the spectral congestion of standard wireless frequency bands [2]. An additional method to increase the spectral efficiency and to improve spatial resolution is to equip the base station (BS) with a large-scale antenna array [3]- [5]. This technique is commonly referred to as massive MIMO.…”

Section: Introductionmentioning

confidence: 99%

A Block Sparsity Based Estimator for mmWave Massive MIMO Channels With Beam Squint

Wang

Gao

Shlezinger

et al. 2020

IEEE Trans. Signal Process.

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Multiple-input multiple-output (MIMO) millimeter wave (mmWave) communication is a key technology for next generation wireless networks. One of the consequences of utilizing a large number of antennas with an increased bandwidth is that array steering vectors vary among different subcarriers. Due to this effect, known as beam squint, the conventional channel model is no longer applicable for mmWave massive MIMO systems. In this paper, we study channel estimation under the resulting non-standard model. To that aim, we first analyze the beam squint effect from an array signal processing perspective, resulting in a model which sheds light on the angle-delay sparsity of mmWave transmission. We next design a compressive sensing based channel estimation algorithm which utilizes the shiftinvariant block-sparsity of this channel model. The proposed algorithm jointly computes the off-grid angles, the off-grid delays, and the complex gains of the multi-path channel. We show that the newly proposed scheme reflects the mmWave channel more accurately and results in improved performance compared to traditional approaches. We then demonstrate how this approach can be applied to recover both the uplink as well as the downlink channel in frequency division duplex (FDD) systems, by exploiting the angle-delay reciprocity of mmWave channels.

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On the Spectral Efficiency of Noncooperative Uplink Massive MIMO Systems

Cited by 21 publications

References 33 publications

An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

Dynamic Metasurface Antennas for Uplink Massive MIMO Systems

A Block Sparsity Based Estimator for mmWave Massive MIMO Channels With Beam Squint

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