Uplink Power Control in Cell-Free Massive MIMO via Deep Learning

D’Andrea, Carmen; Zappone, Alessio; Buzzi, Stefano; Debbah, Mérouane

doi:10.1109/camsap45676.2019.9022520

Cited by 59 publications

(52 citation statements)

References 20 publications

(21 reference statements)

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“…Deep Learning [176][177][178][179][180] Channel Model Rician fading [181][182][183] Spatially correlated Rayleigh fading [184][185][186] Miscellaneous Full-duplex [187][188][189] Channel non-reciprocity [190] Asynchronous reception [191] System information broadcast [192] Over-the-air signaling [193,194] Multi-antenna users [195,196] Frequency division duplex [197] Stochastic geometry [198] Dynamic Resource allocation [199] Wireless power transfer [200]…”

Section: Topics Aspects and Referencesmentioning

confidence: 99%

Cell-Free Massive MIMO : Scalability, Signal Processing and Power Control

Interdonato

2020

Linköping Studies in Science and Technology. Dissertations

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Linköping 2020 This is a Swedish Doctor of Philosophy thesis. The Doctor of Philosophy degree comprises 240 ECTS credits of postgraduate studies. Cover: Illustration of the Ericsson Radio Stripes concept, co-invented by the author of this thesis. The Radio Stripes constitute a way to implement a cell-free massive MIMO system. They aim at enabling invisible, low-cost deployment of large number of distributed access points in the vicinity of the users. This is achieved by serial integration of several transmitting and receiving components into a cable (or stripe) which also provides fronthaul communication and power supply.

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Section: Topics Aspects and Referencesmentioning

confidence: 99%

Cell-Free Massive MIMO : Scalability, Signal Processing and Power Control

Interdonato

2020

Linköping Studies in Science and Technology. Dissertations

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confidence: 98%

“…Finally, the work of the present paper is different from the recent work [34]. A cell-free massive MIMO with perfect fronthaul is considered in [34], where the authors consider the max-min rate and sum rate optimization problems without having any throughput requirement constraints. Moreover, in [34], the authors find an unknown mapping between the LSF components and the optimal power elements obtained by having the LSF components at the CPU.…”

mentioning

confidence: 98%

“…A cell-free massive MIMO with perfect fronthaul is considered in [34], where the authors consider the max-min rate and sum rate optimization problems without having any throughput requirement constraints. Moreover, in [34], the authors find an unknown mapping between the LSF components and the optimal power elements obtained by having the LSF components at the CPU. However, in the present work, we find an unknown mapping between the LSF components and the power elements obtained by the knowledge of the SSF components at the CPU.…”

mentioning

confidence: 99%

“…However, in the present work, we find an unknown mapping between the LSF components and the power elements obtained by the knowledge of the SSF components at the CPU. Finally, in [34], the authors use 2,000,000 training samples to train the neural network whereas having only 60,000-70,000 training examples are enough for our proposed network.…”

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confidence: 99%

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Exploiting Deep Learning in Limited-Fronthaul Cell-Free Massive MIMO Uplink

Bashar

Akbari

Cumanan

et al. 2020

IEEE J. Select. Areas Commun.

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A cell-free massive multiple-input multiple-output (MIMO) uplink is considered, where quantize-and-forward (QF) refers to the case where both the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU) whereas in combinequantize-and-forward (CQF), the APs send the quantized version of the combined signal to the CPU. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is exploited to convert the power allocation problem into a standard geometric programme (GP). We exploit the knowledge of the channel statistics to design the power elements. Employing largescale-fading (LSF) with a deep convolutional neural network (DCNN) enables us to determine a mapping from the LSF coefficients and the optimal power through solving the sum rate maximization problem using the quantized channel. Four possible power control schemes are studied, which we refer to as i) small-scale fading (SSF)-based QF; ii) LSF-based CQF; iii) LSF use-and-then-forget (UatF)-based QF; and iv) LSF deep learning (DL)-based QF, according to where channel estimation is performed and exploited and how the optimization problem is solved. Numerical results show that for the same fronthaul rate, the throughput significantly increases thanks to the mapping obtained using DCNN.

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Multi‐input fully CNN for joint pilot decontamination and symbol detection in 5G massive MIMO

Victor,

Mvuma,

Mrutu

2023

IET Communications

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This paper presents a multi‐input deep learning‐based joint pilot decontamination and symbol detection (SD) technique for 5G massive multiple‐input multiple‐output (MAMIMO) systems. It consists of a fully convolutional neural network (FCNN) that finds the 5G channel coefficients using pre‐known sounding reference signals (SRSs) under pilot contamination and a symbol detector derived from projected gradient descent iterations. The study considers pilot contamination caused by inter‐cell interferences between SRSs during channel estimation (CE). The proposed scheme accepts entire 5G orthogonal frequency modulated (OFDM) data and least square estimates and produces the transmitted OFDM signal. Simulation experiments demonstrated that the proposed technique has better CE and SD performance with reduced trainable parameters. Moreover, it is faster due to the lowest elapsed time during end‐to‐end OFDM symbol detection. This paper proposes a joint pilot decontamination and signal detection for 5G MAMIMO systems. It achieves better detection performance with the lowest number of trainable parameters and memory requirements. It is applicable in 5G OFDM symbol detection and channel estimation under pilot contamination.

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Uplink Power Control in Cell-Free Massive MIMO via Deep Learning

Cited by 59 publications

References 20 publications

Cell-Free Massive MIMO : Scalability, Signal Processing and Power Control

Cell-Free Massive MIMO : Scalability, Signal Processing and Power Control

Exploiting Deep Learning in Limited-Fronthaul Cell-Free Massive MIMO Uplink

Multi‐input fully CNN for joint pilot decontamination and symbol detection in 5G massive MIMO

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