A Low-Complexity Space Time Block Codes Detection for Cell Free Massive MIMO Systems

Saray, Akbar Mazhari; Pourrostam, Jafar; Mousavi, Seyed Hosein; Feghhi, Mahmood Mohassel

doi:10.1109/icee50131.2020.9260979

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…In [133], a joint channel estimation and data detection algorithm for cell-free massive MU MIMO is proposed to minimize the overhead of pilot-based channel estimation for cell-free systems. In [134], a MU cell free massive MIMO system with linear decoders based on approximate matrix inversion methods is proposed. In [135], polynomial expansion detectors and multistage Wiener filters are exploited to propose a low complexity detector.…”

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%