Design Trade-offs for Decentralized Baseband Processing in Massive MU-MIMO Systems

Li, Kaipeng; McNaney, James; Tarver, Chance; Castañeda, Oscar; Jeon, Charles; Cavallaro, Joseph R.; Studer, Christoph

doi:10.1109/ieeeconf44664.2019.9048727

Cited by 18 publications

(7 citation statements)

References 16 publications

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“…Another decentralized data detection based on alternating direction method of multipliers (ADMM) [137], partially decentralized (PD) and fully decentralized (FD) data detectors based on the AMP are proposed [139]. The DBP is studied based on free-matrix-inversion methods in different channel conditions [140]. The FD architectures based on the coordinate descent (CD) method and FD data detector based on recursive lease square (RLS), stochastic gradient descent (SGD), and averaged stochastic gradient descent (ASGD) have also been proposed [141], [142].…”

Section: B Detection In Cell-free Massive Mimomentioning

confidence: 99%

Deep Learning for Massive MIMO Uplink Detectors

Albreem

Alhabbash²,

Shahabuddin

et al. 2022

IEEE Commun. Surv. Tutorials

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Detection techniques for massive multiple-input multiple-output (MIMO) have gained a lot of attention in both academia and industry. Detection techniques have a significant impact on the massive MIMO receivers' performance and complexity. Although a plethora of research is conducted using the classical detection theory and techniques, the performance is deteriorated when the ratio between the numbers of antennas and users is relatively small. In addition, most of classical detection techniques are suffering from severe performance loss and/or high computational complexity in real channel scenarios. Therefore, there is a significant room for fundamental research contributions in data detection based on the deep learning (DL) approach. DL architectures can be exploited to provide optimal performance with similar complexity of conventional detection techniques. This paper aims to provide insights on DL based detectors to a generalist of wireless communications. We garner the DL based massive MIMO detectors and classify them so that a reader can find the differences between various architectures with a wider range of potential solutions and variations. In this paper, we discuss the performance-complexity profile, pros and cons, and implementation stiffness of each DL based detector's architecture. Detection in cell-free massive MIMO is also presented. Challenges and our perspectives for future research directions are also discussed. This article is not meant to be a survey of a mature-subject, but rather serve as a catalyst to encourage more DL research in massive MIMO.

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Section: B Detection In Cell-free Massive Mimomentioning

confidence: 99%

Deep Learning for Massive MIMO Uplink Detectors

Albreem

Alhabbash²,

Shahabuddin

et al. 2022

IEEE Commun. Surv. Tutorials

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“…2 To this end, a general framework is considered which can accommodate classical centralized processing architectures, decentralized architectures such as [14], as well as a wide range of intermediate architectures. Unlike [19], where a system-level trade-off between different decentralized architectures, algorithms, and data precision is studied, [28] gives a fundamental trade-off between level of decentralization and decentralized processing complexity. The information-lossy regime of said trade-off is considered in [8], [15], while we restrict our work to the informationlossless regime.…”

Section: Introductionmentioning

confidence: 99%

Trade-Offs in Decentralized Multi-Antenna Architectures: Sparse Combining Modules for WAX Decomposition

Vidal Alegría,

Rusek

2023

IEEE Trans. Signal Process.

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With the increase in the number of antennas at base stations (BSs), centralized multi-antenna architectures have encountered scalability problems from excessive interconnection bandwidth to the central processing unit (CPU), as well as increased processing complexity. Thus, research efforts have been directed towards finding decentralized receiver architectures where a part of the processing is performed at the antenna end (or close to it). A recent paper put forth an informationlossless trade-off between level of decentralization (inputs to CPU) and decentralized processing complexity (multiplications per antenna). This trade-off was obtained by studying a newly defined matrix decomposition-the WAX decomposition-which is directly related to the information-lossless processing that should to be applied in a general framework to exploit the trade-off. The general framework consists of three stages: a set of decentralized filters, a linear combining module, and a processing matrix applied at the CPU; these three stages are linear transformations which can be identified with the three constituent matrices of the WAX decomposition. The previous work was unable to provide explicit constructions for linear combining modules which are valid for WAX decomposition, while it remarked the importance of these modules being sparse with 1s and 0s so they could be efficiently implemented using hardware accelerators. In this work we present a number of constructions, as well as possible variations of them, for effectively defining linear combining modules which can be used in the WAX decomposition. Furthermore, we show how these structures facilitate decentralized calculation of the WAX decomposition for applying information-lossless processing in architectures with an arbitrary level of decentralization.

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“…Many recent publications address the idea of decentralizing the baseband processing in the BS for the M-MIMO systems [4]. A partially decentralized receiver was considered in [5].…”

Section: Introductionmentioning

confidence: 99%

Decentralized Design of Fast Iterative Receivers for Massive and Extreme-Large MIMO Systems

Croisfelt,

Abrão,

Amiri

et al. 2021

Preprint

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Despite the extensive use of a centralized approach to design receivers at the base station for massive multiple-input multiple-output (M-MIMO) systems, their actual implementation is a major challenge due to several bottlenecks imposed by the large number of antennas. One way to deal with this problem is by fully decentralizing the classic zero-forcing receiver across multiple processing nodes based on the gradient descent method. In this paper, we first explicitly relate this decentralized receiver to a distributed version of the Kaczmarz algorithm and to the use of the successive interference cancellation (SIC) philosophy to mitigate the residual across nodes. In addition, we propose two methods to further accelerate the initial convergence of these iterative decentralized receivers by exploring the connection with the Kaczmarz algorithm: 1) a new Bayesian distributed receiver, which can eliminate noise on an iteration basis; 2) a more practical method for choosing the relaxation parameter. The discussion also consider spatial non-stationarities that arise when the antenna arrays are extremely large (XL-MIMO). We were able to improve the numerical results for both spatially stationary and non-stationary channels, but mainly the nonstationary performance can still be improved compared to the centralized ZF receiver. Future research directions are provided with the aim of further improving the applicability of the receiver based on the principle of successive residual cancellation (SRC).

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Design Trade-offs for Decentralized Baseband Processing in Massive MU-MIMO Systems

Cited by 18 publications

References 16 publications

Deep Learning for Massive MIMO Uplink Detectors

Deep Learning for Massive MIMO Uplink Detectors

Trade-Offs in Decentralized Multi-Antenna Architectures: Sparse Combining Modules for WAX Decomposition

Decentralized Design of Fast Iterative Receivers for Massive and Extreme-Large MIMO Systems

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