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

Wang, Mingjin; Gao, Feifei; Shlezinger, Nir; Flanagan, Mark F.; Eldar, Yonina C.

doi:10.1109/tsp.2019.2956677

Cited by 65 publications

(57 citation statements)

References 45 publications

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“…However, [97], [99] still face the grid mismatch problem. Then, Wang et al [100] proposed a shiftinvariant block-sparsity based channel estimation algorithm which jointly computed the off-grid angles, the off-grid delays, and the complex gains of the wideband mmWave massive MIMO channels.…”

Section: B Training Based Methodsmentioning

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.

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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: B Training Based Methodsmentioning

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.

Self Cite

132

View full text Add to dashboard Cite

show abstract

“…Moreover, the number of antennas, UEs, and time slots is set to N BS = 64, K = 3, and M R = M T = 12, respectively, which may change in the form of variables in subsequent simulations, and we set the variable M P to represent the time slot for better presentation. As in [2,40], the signal-to-noise ratio (SNR) is defined as σ 2 s /σ 2 n , where σ 2 s is the signal power. I denotes the number of Monte Carlo runs, and the normalized mean squared error (NMSE) is defined as:…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) has been generally recognized as a strong potential key technique for enhancing quality in the future wireless communication field [1]. The demand for higher speed data transmission, shorter delays, better user experience, and denser networks is increasing with the rapid development of wireless services such as virtual reality, multimedia, and the Internet of Things [2]. The mmWave band has huge unexploited spectrum resources, which can overcome the spectrum congestion of standard wireless frequency bands and achieve an orders of magnitude increase in spectral efficiency to meet these requirements [3,4].…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Joint Channel Estimation for Multi-User mmWave Massive MIMO Systems

et al. 2020

Electronics

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For multi-user millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the precise acquisition of channel state information (CSI) is a huge challenge. With the increase of the number of antennas at the base station (BS), the traditional channel estimation techniques encounter the problems of pilot training overhead and computational complexity increasing dramatically. In this paper, we develop a step-length optimization-based joint iterative scheme for multi-user mmWave massive MIMO systems to improve channel estimation performance. The proposed estimation algorithm provides the BS with full knowledge of all channel parameters involved in up-and down-links. Compared with existing algorithms, the proposed algorithm has higher channel estimation accuracy with low complexity. Moreover, the proposed scheme performs well even with a small number of training sequences and a large number of users. Simulation results are shown to demonstrate the performance of the proposed channel estimation algorithm.

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“…We can obtain the smallest N c in the m − n plane by the tangent point of the circle denoted by (38) and a cluster of lines denoted by (37). When M = N , from (38) we get m = n = π sin θ max / √ 2β cell , then from (37) we get N c = 2(M − 1)m. Table 2 shows the maximum cell number needed in each tier of the Sierpinski carpet array calculated from (37) and (38) From the perspective of cost-saving, in this section, we use a group of cells to substitute the phase shifters in the array. In addition, the cell number in an array could be decreased by limiting the maximum perpendicular angle of the main beam.…”

Section: How Many Cells Are Needed In a Planar Array?mentioning

confidence: 99%

Compact Multi-Wideband Array for Millimeter-Wave Communications Using Squint Beams

Pan

Hua

et al. 2020

IEEE Access

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Millimeter-wave (mmWave) cellular systems leverage the hybrid analog-digital structure to balance the costs and the capacity performance by using fewer Radio Frequency (RF) chains and the spatial sparsity of the channel. Besides, a more broadband system is the natural outgrowth of the gigantic frequency resource in mmWave band. However, it becomes precarious when the hybrid structure is extended to the wideband systems as the wideband phase shifters are used in the array. In this paper, a multi-wideband planar array structure using the concept of Sierpinski carpet fractal is proposed, which exploits the beam squinting property instead of forming several independent beams through an existing hybrid structure. Moreover, multi-subarray can form a larger antenna array without additional costs of phase shifters for the wider range of values. First, we derive several properties of the steering beams from a wideband linear array. Then, we exploit the beam squinting property of the planar arrays employing wideband ideal phase shifters to form multi-beam. The functionality of the phase shifters in an array is simplified by a set of wideband cells connected in series. The maximum cell number needed in the array is derived, and during this derivation, we find a flaw of the beam squinting method. Finally, we present numerical results on the performance of the proposed arrays with the proposed algorithm, based on a cellular scenario of one base station (BS) and multiuser , where the BS has four subarrays and each user has one subarray.

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

Cited by 65 publications

References 45 publications

An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

Low-Complexity Joint Channel Estimation for Multi-User mmWave Massive MIMO Systems

Compact Multi-Wideband Array for Millimeter-Wave Communications Using Squint Beams

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