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

Interdonato, Giovanni

doi:10.3384/diss.diva-167218

Cited by 9 publications

(3 citation statements)

References 164 publications

(272 reference statements)

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“…Moreover, physical influences such as trees and buildings can easily hinder transmission. Massive multiple input and multiple output (massive MIMO) systems have been widely adressed as a solution to the aforementioned issues in recent studies [1,2].…”

Section: Introductionmentioning

confidence: 99%

Hücresiz ÇGÇÇ Sistemlerinde AP Seçimi için Makine Öğrenimi Tabanlı Sınıflandırma Algoritması

DEMİREL

AYCAN

2022

EJOSAT

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With the rapid development of technology, cellular networks in wireless networks are insufficient to meet the demands. In order to provide a correct and good service to each user, communication systems must change. Although cell-free networks have many advantages over cellular networks, since there are too many users and access points (APs) in cell-free networks, AP selection is very important. In this thesis, the AP selection model has been studied and compared five different machine learning classification methods. The campus of Izmir Katip Celebi University has been chosen as the environment where the study will be carried out, and capacity values have been obtained from the users and APs that have been placed on the campus in the simulation environment. Numerical calculation results have been obtained from the Wireless Insite (WI) software. The AP selection to be created with the capacity values has been supported by artificial intelligence algorithm techniques. With two different data sets have been compared, better results have been tried to be obtained with different feature values. As a result of the comparisons made, the best machine learning classification technique used has been proposed.

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Section: Introductionmentioning

confidence: 99%

Hücresiz ÇGÇÇ Sistemlerinde AP Seçimi için Makine Öğrenimi Tabanlı Sınıflandırma Algoritması

DEMİREL

AYCAN

2022

EJOSAT

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“…Contrary to the conventional antenna array, which also refers to as the co‐located antenna arrays, the wireless networks can operate as a distributed antenna array, 12 where a large number of access points (APs) serve a relatively large number of users. In the literature, distributed antenna arrays are also referred to as the cell‐free (CF) network, 13,14 which promises to offer large advantages over the traditional network 15,16 …”

Section: Introductionmentioning

confidence: 99%

On channel estimation in cell‐free massive MIMO for spatially correlated channels with correlated shadowing under Rician fading

Amadid

Belhabib

Zeroual

2021

Int J Communication

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Summary This work addresses channel estimation (CE) in the uplink phase for a cell‐free massive multiple‐input multiple‐output system operating under the time division duplex protocol. Considering that, channels are spatially correlated under the Rician fading model, where the investigated model is composed of two components: deterministic and nondeterministic, with the deterministic component describing the line‐of‐sight paths and the nondeterministic component describing the non‐line‐of‐sight paths. Additionally, we dealt with correlated shadow fading that represents the most realistic situation. On the other hand, this work introduces a dynamic cooperation cluster framework in which the user is not served with the whole network ( i.e., all access points [APs]) but only the APs that present the best channel conditions regarding that user. In other words, this work proposes partial CE for each user because only APs with the best channel conditions are allowed to compute channel estimates. Consequently, we proposed partial channel estimators that perform the CE process with low complexity, namely, a partial minimum mean square error estimator and a partial element‐wise minimum mean square error estimator. In addition, a simple pilot assignment technique is proposed in order to reduce interference signals so that each user experiences low interference from other users. Furthermore, the computational complexity required by each estimator is derived, where it is represented by the number of complex multiplications that each estimator requires in each consistency block. Theoretical and simulated results are provided, where the performance of each estimator is evaluated and analyzed using the normalized mean‐square error metric.

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“…On the other hand, with the distributed configuration of M-MIMO systems, the antennas are distributed over a wide geographical area [7,8,14]. This latter configuration is known in the literature as cell-free (CF) M-MIMO [9,10]. CF M-MIMO offers a significant gain for the fifth generation (5G) wireless communication systems, in which an enormous number of access points (APs) serve a relatively small number of users in the same time-frequency resources.…”

Section: Introductionmentioning

confidence: 99%

On Channel Estimation for Rician Fading with the Phase-Shift in Cell-Free Massive MIMO System

Amadid

Boulouird

Belhabib

et al. 2021

Wireless Pers Commun

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Channel estimation (CE) is a crucial phase in wireless communication systems, especially in cell-free (CF) massive multiple input multiple output (M-MIMO) since it is a dynamic wireless network. Therefore, this work is introduced to study CE for the CF M-MIMO system in the uplink phase, wherein the performance of different estimators are evaluated, discussed, and compared in various situations. We assume the scenario in which each access point has prior knowledge of the channel statistics. The phase-aware-minimum mean square error (PA-MMSE) estimator, the non-phase-aware-MMSE (NPA-MMSE) estimator, and the least-squares estimator are the three estimators which are exploited in this work. Besides, we consider the Rician fading channel in which the line-of-sight path is realized with a phase-shift that models the users' mobility where the considered phase-shift follows a uniform distribution. On the other hand, the mean-squared error metric is employed in order to evaluate the performance of each estimator, where an analytical and simulated result is provided for the PA-MMSE estimator and the NPA-MMSE estimator in order to assert our numerical results.

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

Cited by 9 publications

References 164 publications

Hücresiz ÇGÇÇ Sistemlerinde AP Seçimi için Makine Öğrenimi Tabanlı Sınıflandırma Algoritması

Hücresiz ÇGÇÇ Sistemlerinde AP Seçimi için Makine Öğrenimi Tabanlı Sınıflandırma Algoritması

On channel estimation in cell‐free massive MIMO for spatially correlated channels with correlated shadowing under Rician fading

On Channel Estimation for Rician Fading with the Phase-Shift in Cell-Free Massive MIMO System

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