Millimeter Wave Cell-Free Massive MIMO Systems: Joint Beamforming and AP-User Association

Wang, Jionghui; Wang, Bin; Fang, Jun; Li, Hongbin

doi:10.1109/lwc.2021.3126389

Cited by 16 publications

(14 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on cell-free massive MIMO networks, a novel federated learning framework in wireless environments was proposed in [252]. So far, the cellfree architecture has been applied to many new communication scenarios, including mmWave communications [253], VLC [259], satellite and UAV communications [260], and communications in indoor factory environments [254]. To make the cellfree/less architecture into reality, there are still many issues to be explored.…”

Section: B Development Trends Of the 6g Networkmentioning

confidence: 99%

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

Wang

You

Gao

et al. 2023

IEEE Commun. Surv. Tutorials

589

View full text Add to dashboard Cite

show abstract

Section: B Development Trends Of the 6g Networkmentioning

confidence: 99%

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

Wang

You

Gao

et al. 2023

IEEE Commun. Surv. Tutorials

589

View full text Add to dashboard Cite

show abstract

“…In their settings, service providers priced themselves and let the users bid for services. User association can also be jointly optimized with beamforming [14], [15]. For example, Dong et al [14] considered jointly optimizing cooperative beamforming and user association to maximize the downlink capacity based on statistical channel state information (CSI).…”

Section: A Related Workmentioning

confidence: 99%

“…For example, Dong et al [14] considered jointly optimizing cooperative beamforming and user association to maximize the downlink capacity based on statistical channel state information (CSI). Wang et al [15] proposed to jointly consider user association and a beamforming design to cope with the significant path loss in a millimeter wave (mmWave) CF-mMIMO network.…”

Section: A Related Workmentioning

confidence: 99%

Deep Reinforcement Learning for Energy Efficiency Maximization in Cache-Enabled Cell-Free Massive MIMO Networks: Single- and Multi-Agent Approaches

Chuang

Chiu

Chang

et al. 2023

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Cell-free massive multiple-input multiple-output (CF-mMIMO) is an emerging beyond fifth-generation (5G) technology that improves energy efficiency (EE) and removes cell structure limitation by using multiple access points (APs). This study investigates the EE maximization problem. Forming proper cooperation clusters is crucial when optimizing EE, and it is often done by selecting AP-user pairs with good channel quality or aligning AP cache contents with user requests. However, the result can be suboptimal if we determine the clusters based solely on either aspect. This motivates our joint design of user association and content caching. Without knowing the user content preferences in advance, two deep reinforcement learning (DRL) approaches, i.e., single-agent reinforcement learning (SARL) and multi-agent reinforcement learning (MARL), are proposed for different scenarios. The SARL approach operates in a centralized manner which has lower computational requirements on edge devices. The MARL approach requires more computation resources at the edge devices but enables parallel computing to reduce the computation time and therefore scales better than the SARL approach. The numerical analysis shows that the proposed approaches outperformed benchmark algorithms in terms of network EE in a small network. In a large network, the MARL yielded the best EE performance and its computation time was reduced significantly by parallel computing.

show abstract

“…Downlink precoding is crucial to unleash the full potential of cell-free networks. Currently, most existing precoding schemes for cell-free systems can be generally classified into noncooperative [7], [22], [23] and cooperative [13], [24]- [26]. Non-cooperative precoding assumes that each BS can only utilize local channel state information (CSI) acquired through uplink channel estimation, without performing any CSI exchange among BSs.…”

Section: A Prior Workmentioning

confidence: 99%

Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks

Xu¹,

An²,

Li³

et al. 2023

Preprint

View full text Add to dashboard Cite

The user-centric cell-free network has emerged as an appealing technology to improve the nextgeneration wireless network's capacity thanks to its ability to eliminate inter-cell interference effectively.However, the cell-free network inevitably brings in higher hardware cost and backhaul overhead as a larger number of base stations (BSs) are deployed. Additionally, severe channel fading in high-frequency bands constitutes another crucial issue that limits the practical application of the cell-free network.In order to address the above challenges, we amalgamate the cell-free system with another emerging technology, namely reconfigurable intelligent surface (RIS), which can provide high spectrum and energy efficiency with low hardware cost by reshaping the wireless propagation environment intelligently. To this end, we formulate a weighted sum-rate (WSR) maximization problem for RIS-assisted cell-free systems by jointly optimizing the BS precoding matrix and the RIS reflection coefficient vector. Subsequently, we transform the complicated WSR problem to a tractable optimization problem and propose a distributed cooperative alternating direction method of multipliers (ADMM) to fully utilize parallel computing resources. Inspired by the model-based algorithm unrolling concept, we unroll our solver to a learningbased deep distributed ADMM (D 2 -ADMM) network framework. To improve the efficiency of the D 2 -ADMM in distributed BSs, we develop a monodirectional information exchange strategy with a small signaling overhead. In addition to benefiting from domain knowledge, D 2 -ADMM adaptively learns hyper-parameters and non-convex solvers of the intractable RIS design problem through datadriven end-to-end training. Finally, numerical results demonstrate that the proposed D 2 -ADMM achieve W. Xu and L. Gan are with the

show abstract

Millimeter Wave Cell-Free Massive MIMO Systems: Joint Beamforming and AP-User Association

Cited by 16 publications

References 18 publications

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

Deep Reinforcement Learning for Energy Efficiency Maximization in Cache-Enabled Cell-Free Massive MIMO Networks: Single- and Multi-Agent Approaches

Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks

Contact Info

Product

Resources

About