Iterative Matrix Inversion Methods for Precoding in Cell-Free Massive MIMO Systems

Ataeeshojai, Mahtab; Elliott, Robert C.; Krzymien, W.A.; Tellambura, Chintha; Maljević, Ivo

doi:10.1109/tvt.2022.3194870

Cited by 8 publications

(5 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This subsection presents a literature review about conventional CF systems [50][51][52][53][54][55] and UC CF mMIMO [36][37][38][39][40][41][42] under sub-6GHz frequencies. Table 2 summarizes the main similarities and differences among the cited studies [36][37][38][39][40][41][42][50][51][52][53][54][55], in terms of the data transmission, the fading channel model, the use of digital beamforming, and finally the coordinated beamforming implementation.…”

Section: Sub-6 Ghzmentioning

confidence: 99%

“…User-Centric CF mMIMO Both [50] 2017 [51] 2019 [52] 2019 [53] 2022 [54] 2021 [55] 2022 [36] 2018 [37] 2020 [38] 2020 [39] 2022 [40] 2022 [41] 2022 [42] 2021 The work of both [50,51] considered the conjugate beamforming and ZF schemes to evaluate the proposed systems. In [50], the authors designed low-complexity optimal and sub-optimal power optimization algorithms using max-min criteria with those simple linear precoders, while in [51], a tight approximation rate expression was computed to evaluate the effects of the multiple antenna AP, channel estimation error, pilot contamination, and power control.…”

Section: Conventional Cf Mmimomentioning

confidence: 99%

“…Besides, they developed an optimal max-min fairness power control algorithm that is only dependent on LSF quantities. Different iterative matrix inversion algorithms were designed in [55], aiming to avoid the direct matrix inversion for the linear ZF precoder scheme in order to obtain a near-optimal performance with less computational complexity.…”

Section: Conventional Cf Mmimomentioning

confidence: 99%

See 2 more Smart Citations

A Review on Cell-Free Massive MIMO Systems

et al. 2023

View full text Add to dashboard Cite

Cell-free massive multiple-input multiple-output (CF mMIMO) can be considered as a potential physical layer technology for future wireless networks since it can benefit from all the advantages of distributed antenna systems (DASs) and network MIMOs, such as macro-diversity gain, high channel capacity, and link reliability. CF mMIMO systems offer remarkable spatial degrees of freedom and array gains to mitigate the inherent inter-cell interference (ICI) of cellular networks. In such networks, several distributed access points (APs) together with precoding/detection processing can serve many users while sharing the same time-frequency resources. Each AP can be equipped with single or multiple antennas, and hence, can provide a consistently adequate service to all users regardless of their locations in the network. This paper presents a detailed overview of the current state-of-the-art on CF systems. First, it performs a literature review of the conventional CF and scalable user-centric (UC) CF mMIMO systems in terms of the limited capacity of the fronthaul links and the connection between APs and user equipments (UEs). As beyond networks will rely on higher frequency bands, it is of paramount importance to discuss the impact of beamforming techniques that are being investigated. Finally, some of the CF promising enabling technologies are presented to emphasize the main applications in these networks.

show abstract

Section: Sub-6 Ghzmentioning

confidence: 99%

Section: Conventional Cf Mmimomentioning

confidence: 99%

Section: Conventional Cf Mmimomentioning

confidence: 99%

See 1 more Smart Citation

A Review on Cell-Free Massive MIMO Systems

et al. 2023

View full text Add to dashboard Cite

show abstract

“…As an alternative, NAFD CF-mMIMO networks can be deployed, where certain APs are designated for the target sensing, while the remaining APs are allocated for communication purposes. Inspired by the WPT efficiency of CF-mMIMO, which is due to the significantly lower average distance between the energy sources and receiver, the coexistence of cell-free and symbiotic backscatter communication was investigated in [271], [272]. The underlaid backscatter devices utilize ambient "legacy" radio signals from the cell-free network as both a harvested energy source and a carrier on which to modulate data.…”

Section: Miscellaneous Topicsmentioning

confidence: 99%

Next Generation Multiple Access with Cell-Free Massive MIMO

Mohammadi,

Mobini,

Ngo

et al. 2023

Preprint

View full text Add to dashboard Cite

<p>To meet the unprecedented mobile traffic demands of future wireless networks, a paradigm shift from conventional cellular networks to distributed communication systems is imperative. Cell-free massive multiple-input multiple-output (CF-mMIMO) represents a practical and scalable embodiment of distributed/network MIMO systems. It inherits not only the key benefits of co-located massive MIMO systems but also the macro-diversity gains from distributed systems. This innovative architecture has demonstrated significant potential in enhancing network performance from various perspectives, outperforming co-located mMIMO and conventional small-cell systems. Moreover, CF-mMIMO offers flexibility in integration with emerging wireless technologies such as full-duplex (FD), non-orthogonal transmission schemes, millimeter-wave (mmWave) communications, ultra-reliable low-latency communication (URLLC), unmanned aerial vehicle (UAV)-aided communication, and reconfigurable intelligent surfaces (RISs). In this paper, we provide an overview of current research efforts on CF-mMIMO systems and their promising future application scenarios. We then elaborate on new requirements for CF-mMIMO networks in the context of these technological breakthroughs. We also present several current open challenges and outline future research directions aimed at fully realizing the potential of CF mMIMO systems in meeting the evolving demands of future wireless networks.</p>

show abstract

“…A common example of this is matrix inversion. M. Ataeeshojai et al [43] and M. Albreem [44] show the complexity challenges in matrix inversion despite modern computing. The cited work addresses the complexity issue on desktops which would be more staggering on smartphones.…”

Section: Scientific Calculations 421 Memory Cost Of Algorithmsmentioning

confidence: 99%

Challenges and Solutions for Engineering Applications on Smartphones

Khoury,

Kaddaha,

Saade

et al. 2023

Software

View full text Add to dashboard Cite

This paper starts by presenting the concept of a mobile application. A literature review is conducted, which shows that there is still a certain lack with regard to smartphone applications in the domain of engineering as independent simulation applications and not only as extensions of smartphone tools. The challenges behind this lack are then discussed. Subsequently, three case studies of engineering applications for both smartphones and the internet are presented, alongside their solutions to the challenges presented. The first case study concerns an engineering application for systems control. The second case study focuses on an engineering application for composite materials. The third case study focuses on the finite element method and structure generation. The solutions to the presented challenges are then described through their implementation in the applications. The three case studies show a new system of thought concerning the development of engineering smartphone applications.

show abstract

Iterative Matrix Inversion Methods for Precoding in Cell-Free Massive MIMO Systems

Cited by 8 publications

References 47 publications

A Review on Cell-Free Massive MIMO Systems

A Review on Cell-Free Massive MIMO Systems

Next Generation Multiple Access with Cell-Free Massive MIMO

Challenges and Solutions for Engineering Applications on Smartphones

Contact Info

Product

Resources

About