Uplink Precoding Design for Cell-Free Massive MIMO With Iteratively Weighted MMSE

Wang, Zhe; Zhang, Jiayi; Ngo, Hien Quoc; Ai, Bo; Debbah, Mérouane

doi:10.1109/tcomm.2023.3235919

Cited by 16 publications

(13 citation statements)

References 46 publications

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“…Downlink spectral efficiency (SE) of cell-free massive MIMO is derived in [34] considering imperfect channel state information (CSI), nonorthogonal pilots and power control. An iterative weighted minimum-mean-square-error (WMMSE) precoding scheme for uplink cell-free massive MIMO is proposed in [35] which shows a higher SE with a large number of UE antennas. An eigenbasis-based uplink precoding scheme is proposed in [36] to improve the SE.…”

Section: A Related Literature and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Cell-Free Massive MIMO With Multi-Antenna Users and Phase Misalignments: A Novel Partially Coherent Transmission Framework

Kunnath Ganesan,

Vu,

Larsson

2024

IEEE Open J. Commun. Soc.

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C ELL-FREE massive multiple-input multiple-output (MIMO) is a promising technology for nextgeneration communication systems. This work proposes a novel partially coherent (PC) transmission framework to cope with the challenge of phase misalignment among the access points (APs), which is important for unlocking the full potential of cell-free massive MIMO technology. With the PC operation, the APs are only required to be phase-aligned within clusters. Each cluster transmits the same data stream towards each user equipment (UE), while different clusters send different data streams. We first propose a novel algorithm to group APs into clusters such that the distance between two APs is always smaller than a reference distance ensuring the phase alignment of these APs. Then, we propose new algorithms that optimize the combining at UEs and precoding at APs to maximize the downlink sum data rates. We also propose a novel algorithm for data stream allocation to further improve the sum data rate of the PC operation. Numerical results show that the PC operation using the proposed framework with a sufficiently small reference distance can offer a sum rate close to the sum rate of the ideal fully coherent (FC) operation that requires network-wide phase alignment. This demonstrates the potential of PC operation in practical deployments of cell-free massive MIMO networks.INDEX TERMS Cell-free massive MIMO, downlink, coherent transmission, non-coherent transmission, partially coherent transmission, precoding, combining, data stream allocation.

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Section: A Related Literature and Discussionmentioning

confidence: 99%

“…We summarize the steps to solve problem (31) (or problem (24)) in Algorithm 2. Since G kc W kc = l∈Cc G kl W kl , the problem (34) can be equivalently written as problem (35) (see the top of the next page). In light of the per-AP power constraints in (12),…”

Section: Proposition 2 For Givenmentioning

confidence: 99%

Cell-Free Massive MIMO With Multi-Antenna Users and Phase Misalignments: A Novel Partially Coherent Transmission Framework

Kunnath Ganesan,

Vu,

Larsson

2024

IEEE Open J. Commun. Soc.

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“…By connecting all access points (APs) to a central processing unit (CPU) via backhaul links, cell-free systems allow multiple APs to simultaneously collaborate to serve users within the network coverage area, which could overcome many of the interference issues that appear in cellular networks [3], [4]. Nevertheless, popular beamforming design in cell-free systems generally assumes that all APs in the network coverage area serve users simultaneously [5], [6]. This appears to be impractical as long-range APs serving users consume precious power and bandwidth resources while contributing little useful power due to high path losses [7].…”

Section: Introductionmentioning

confidence: 99%

“…It is worth noting that beamforming design or AP clustering in most of these references, e.g., [3], [5], [6], [9], [16], [17], optimistically assumes the availability of perfect CSI, which leads to system performance degradation in practice. To this end, it is highly desired to take the CSI estimation errors into account, and there have been some studies on robust beamforming design, especially for multicellular networks, e.g., in [18], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method: QoS-Aware Joint AP Clustering and Beamforming Design for Cell-Free Networks

Chen,

He,

et al. 2023

IEEE Trans. Commun.

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In this paper, we consider robust joint access point (AP) clustering and beamforming design with imperfect channel state information (CSI) in cell-free systems. Specifically, we jointly optimize AP clustering and beamforming with imperfect CSI to simultaneously maximize the worst-case sum rate and minimize the number of AP clustering under power constraint and the sparsity constraint of AP clustering. By transformations, the semi-infinite constraints caused by the imperfect CSI are converted into more tractable forms for facilitating a computationally efficient unsupervised deep learning algorithm. In addition, to further reduce the computational complexity, a computationally effective unsupervised deep learning algorithm is proposed to implement robust joint AP clustering and beamforming design with imperfect CSI in cell-free systems. Numerical results demonstrate that the proposed unsupervised deep learning algorithm achieves a higher worst-case sum rate under a smaller number of AP clustering with computational efficiency.

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“…To cope with this problem, a distributed mMIMO paradigm called cell-free massive MIMO (CF mMIMO) has been proposed in [35]. Compared with cellular mMIMO, the concept of cell boundary has been removed in CF mMIMO systems, where a large number of distributed access points (APs) are deployed in a wide area to serve the user equipment (UE) by joint transmission and reception [36]- [41]. It is worth noting that, in this setting, all APs are connected to central processing units (CPUs), enabling various processing schemes with different levels of cooperation between the APs and CPUs to improve macro-diversity gain and achieve high SE performance uniformly within the coverage area.…”

Section: A Motivationmentioning

confidence: 99%

Very large scale integration architectures of discrete transforms for digital signal processing applications

Wang¹

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Luo Jianwen, Associate Professor Jong Ching Chuen, and Associate Professor Chang Chip Hong, for their friendship, help, discussions, suggestions and encouragement.I do also appreciate the support and help from my colleagues in the FTD Solutions Pte Ltd and in the Centre for Signal Processing, NTD. Especially, I would like to thank Qian Yue, Lu Dawei, Yang Fan, and Dr. Yu Zhuliang for their valuable comments, suggestions and criticisms.I have enjoyed the company and friendship of all of my friends in Singapore. I would like to thank them for making the past five years an unforgettable and invaluable experience in my life.Last, I wish to express my deepest gratitude to my dear parents, my dear brother, and my lovely four year old nephew. It is indeed their deep love and consistent encouragement that has kept me focused, determined, cheered up, and never to say give up.

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Uplink Precoding Design for Cell-Free Massive MIMO With Iteratively Weighted MMSE

Cited by 16 publications

References 46 publications

Cell-Free Massive MIMO With Multi-Antenna Users and Phase Misalignments: A Novel Partially Coherent Transmission Framework

Cell-Free Massive MIMO With Multi-Antenna Users and Phase Misalignments: A Novel Partially Coherent Transmission Framework

A Deep Learning Method: QoS-Aware Joint AP Clustering and Beamforming Design for Cell-Free Networks

Very large scale integration architectures of discrete transforms for digital signal processing applications

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