A Family of Hybrid Analog–Digital Beamforming Methods for Massive MIMO Systems

Ioushua, Shahar Stein; Eldar, Yonina C.

doi:10.1109/tsp.2019.2911255

Cited by 110 publications

(118 citation statements)

References 25 publications

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“…We therefore consider hybrid acquisition systems as illustrated in Fig. 2, which is a common model in MIMO communication receivers [19], [20]. Hybrid architectures were originally proposed as a method to reduce the number of costly RF chains in MIMO receivers [19], [20], while here we exploit these structures to allow quantization under bit constraints for tasks.…”

Section: Hardware-limited Task-based Quantizationmentioning

confidence: 99%

“…2, which is a common model in MIMO communication receivers [19], [20]. Hybrid architectures were originally proposed as a method to reduce the number of costly RF chains in MIMO receivers [19], [20], while here we exploit these structures to allow quantization under bit constraints for tasks. In such hybrid systems, a set of analog signals can be combined in analog prior to being converted to digital, a property which we exploit in order to facilitate extracting some desired information from them.…”

Section: Hardware-limited Task-based Quantizationmentioning

confidence: 99%

“…This constraint can accounted for by identifying the unconstrained analog combining matrix via, e.g., Theorem 3, and projecting it to the feasible set of fully-connected phase shifter networks, similarly to [20,Alg. 2].…”

Section: A Dedicated Analog Combiner Hardwarementioning

confidence: 99%

“…Finally, we show how to implement MIMO receivers capable of dynamically adjusting their acquisition system in light of the task, thus realizing tunable task-based quantization. Our proposed design builds upon either conventional hybrid receiver architectures [19], [20], dedicated pre-acquisition hardware [21], or on exploiting the inherent configurability of receivers equipped with metasurface antennas [22], [23], and we present hardware prototypes built in our lab, demonstrating the feasibility of task-based quantization in MIMO receivers.…”

Section: Introductionmentioning

confidence: 99%

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Asymptotic Task-Based Quantization With Application to Massive MIMO

Shlezinger

Eldar

Rodrigues

2019

IEEE Trans. Signal Process.

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Multiple-input multiple-output (MIMO) systems are required to communicate reliably at high spectral bands using a large number of antennas, while operating under strict power and cost constraints.In order to meet these constraints, future MIMO receivers are expected to operate with low resolution quantizers, namely, utilize a limited number of bits for representing their observed measurements, inherently distorting the digital representation of the acquired signals. The fact that MIMO receivers use their measurements for some task, such as symbol detection and channel estimation, other than recovering the underlying analog signal, indicates that the distortion induced by bit-constrained quantization can be reduced by designing the acquisition scheme in light of the system task, i.e., by task-based quantization.In this work we survey the theory and design approaches to task-based quantization, presenting modelaware designs as well as data-driven implementations. Then, we show how one can implement a task-based bit-constrained MIMO receiver, presenting approaches ranging from conventional hybrid receiver architectures to structures exploiting the dynamic nature of metasurface antennas. This survey narrows the gap between theoretical task-based quantization and its implementation in practice, providing concrete algorithmic and hardware design principles for realizing task-based MIMO receivers. N. Shlezinger and Y. C. Eldar are with the Faculty of Math and CS, the number of BS antennas grow arbitrarily large [1], [2]. An additional method to increase the network throughput is to explore the millimeter wave (mmWave) frequency range [3], thus overcoming the spectral congestion of traditional wireless bands. Such mmWave communications is particularly suitable for massive MIMO systems: The short wavelengths of mmWave signals allows packing a large number of antenna elements at a small physical size, and the massive number of elements facilitates directed beamforming which is essential at mmWave bands. While the theoretical gains of massive MIMO systems, particularly when combined with mmWave transmission, are clear, implementing such systems in practice under strict cost and power constraints is a challenging task. A major source of this increased cost are the analog-todigital convertor (ADC) components, which allow the analog signals observed by each antenna element to be processed in digital. The power consumption of an ADC is directly related to the signal bandwidth and the number of bits used for digital representation [4], [5]. Consequently, in massive MIMO systems, where the number of antennas and ADCs operating at high frequency bands is large, limiting the number of bits, thus operating under quantization constraints, is crucial to keep cost and power consumption feasible [3].Focusing on uplink communications, i.e., when the BS acts as the receiver, quantization constraints imply that the BS cannot process the channel output directly but rather only an inaccurate distorted digital representation of it. The distor...

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Section: Hardware-limited Task-based Quantizationmentioning

confidence: 99%

Section: Hardware-limited Task-based Quantizationmentioning

confidence: 99%

Section: A Dedicated Analog Combiner Hardwarementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Asymptotic Task-Based Quantization With Application to Massive MIMO

Shlezinger

Eldar

Rodrigues

2019

IEEE Trans. Signal Process.

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“…withỸ i ∈ C L×τ . The condition on the analog combining matrix W i depends on the specific architecture of the analog network [3] [4]. In this work, we consider fully-connected phase shifters network so that the matrix W i is subject to constant modulus constraints stated as [2] |W…”

Section: B Reduced Rf Chain and Analog Combiningmentioning

confidence: 99%

Optimizing Pilots and Analog Processing for Channel Estimation in Cell-Free Massive MIMO with One-Bit ADCs

Park

Simeone

Eldar

et al. 2018

2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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In a cell-free cloud radio access network (C-RAN) architecture, uplink channel estimation is carried out by a centralized baseband processing unit (BBU) connected to distributed remote radio heads (RRHs). When the RRHs have multiple antennas and limited radio front-end resources, the design of uplink channel estimation is faced with the challenges posed by reduced radio frequency (RF) chains and one-bit analogto-digital converters (ADCs) at the RRHs. This work tackles the problem of jointly optimizing the pilot sequences and the pre-RF chains analog combiners with the goal of minimizing the sum of mean squared errors (MSEs) of the estimated channel vectors at the BBU. The problem formulation models the impact of the ADC operation by leveraging Bussgang's theorem. An efficient solution is developed by means of an iterative alternating optimization algorithm. Numerical results validate the advantages of the proposed joint design compared to baseline schemes that randomly choose either pilots or analog combiners.

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Review Paper on Joint Beamforming, Power Control and Interference Coordination for Non-orthogonal Multiple Access in Wireless Communication Networks for Efficient Data Transmission

Bitla

Sakode

2023

Lecture Notes in Electrical Engineering

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A Family of Hybrid Analog–Digital Beamforming Methods for Massive MIMO Systems

Cited by 110 publications

References 25 publications

Asymptotic Task-Based Quantization With Application to Massive MIMO

Asymptotic Task-Based Quantization With Application to Massive MIMO

Optimizing Pilots and Analog Processing for Channel Estimation in Cell-Free Massive MIMO with One-Bit ADCs

Review Paper on Joint Beamforming, Power Control and Interference Coordination for Non-orthogonal Multiple Access in Wireless Communication Networks for Efficient Data Transmission

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