User-Centric Access Point Selection in Cell-Free Massive MIMO Systems: A Game-Theoretic Approach

Wei, Chen; Xu, Kui; Su, Qiao; Shen, Maiying; Xie, Wei; Li, Chunguo

doi:10.1109/lcomm.2022.3186350

Cited by 17 publications

(12 citation statements)

References 15 publications

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“…Furthermore, this approach has the potential to enhance SINRs by limiting the interference term to only a fraction of UEs. Numerous studies have investigated the integration of APS schemes into signal transmission and detection techniques for CF mMIMO networks [69,78,87,[93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][108][109][110][111].…”

Section: Access Point Selection For Optimized Uplink User Allocationmentioning

confidence: 99%

“…On the other hand, ref. [102] tackles the UL APS problem using game theory, modeling the formation of an UE-centric AP service cluster as a local altruistic game. The authors of [103] prioritize the DL SE of UEs with poor channel conditions through the implementation of an APS using a linear assignment algorithm.…”

Section: Access Point Selection For Optimized Uplink User Allocationmentioning

confidence: 99%

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Survey on Resource Allocation for Future 6G Network Architectures: Cell-Free and Radio Stripe Technologies

Conceição,

Gomes,

Silva

et al. 2024

Electronics

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Future beyond fifth-generation (B5G) and sixth-generation (6G) communication systems require a higher quality of service (QoS) along with meeting multiple objectives and traffic demands. Consequently, new multi-antenna technologies and massive multiple-input-multiple-output (mMIMO) architectures have been proposed in recent years. This paper delves into the foundational concepts that form the basis for the design of two potential future mMIMO network topologies: cell-free (CF) network and its successor, the radio stripe (RS) system. Key aspects of the mMIMO and CF network concepts are addressed, along with a practical sequential implementation based on RSs. This exploration encompasses intricate details of the channel estimation (CE) phase, as well as the uplink (UL) and downlink (DL) transmission and reception phases. We then focus on analyzing optimization concepts that underpin resource allocation (RA) algorithms, specifically those applied in UL power allocation and access point selection (APS) schemes in both CF and RS networks. This comprehensive understanding serves as a robust foundation for addressing the challenges inherent in achieving the conflicting B5G and 6G major key performance indicators (KPIs), such as enhancements on spectral efficiency (SE), power efficiency (PE), and computational complexity or load balance (LB).

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Section: Access Point Selection For Optimized Uplink User Allocationmentioning

confidence: 99%

Section: Access Point Selection For Optimized Uplink User Allocationmentioning

confidence: 99%

Survey on Resource Allocation for Future 6G Network Architectures: Cell-Free and Radio Stripe Technologies

Conceição,

Gomes,

Silva

et al. 2024

Electronics

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“…These optimization approaches can form the AP clusters with the most efficient interference suppression on a small scale. Other AP cluster selection methods based on game theory [26] and machine learning-based AP cluster selection methods have also been proposed [27], [28]. This paper focuses on distributed CPU deployments for a large-scale Cell Free implementation.…”

Section: Problems Of Inter-site Cpu Cooperationmentioning

confidence: 99%

Cooperation Method Between CPUs in Large-Scale Cell-Free Massive MIMO for User-Centric RAN

Ikami,

Aihara,

Tsukamoto

et al. 2023

IEEE Access

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We have been studying a user-centric radio access network (RAN) for the realization of uniform radio quality "anywhere anytime" with Cell-free massive MIMO (CF-mMIMO) technology. In usercentric RAN, the central processing unit (CPU) that processes CF-mMIMO signals is assumed to be deployed in multiple sites to address the scalability problem for large-scale CF-mMIMO. However, the distributed deployment of CPUs results in radio quality degradation due to interference between UEs connected to CPUs at different sites. To address this problem, multiple CPU cooperation methods between CPUs at different sites are being studied. However, for cooperation, conventional methods require the exchange of radio signals and channel state information between CPUs, which significantly increases the transmission load of the backhaul connecting the sites. To resolve this issue, we propose an inter-site CPU cooperation method that maintains high radio quality while reducing the amount of data transmitted between sites to suppress intersite interference. The proposed method is realized by deploying a channel estimation processing function for inter-site interference at each site and suppressing inter-site interference independently. Furthermore, we introduce optimization management that adjusts the degree of cooperation among CPUs based on the proposed method according to the required radio quality and computation and transmission resources in the area. We evaluate the proposed method by computational simulation. We show that the proposed method reduce the transmission load by 53% with the same area throughput compared to the existing CPU cooperation schemes.INDEX TERMS Cell-free massive MIMO, user-centric RAN, RAN management, 6G.

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“…From the game theory perspective, a user-centric access point scheduling for cell-free massive MIMO systems has been investigated in [ 20 ]. Authors have developed a user-centric access-point cluster model as a local altruistic game.…”

Section: Related Workmentioning

confidence: 99%

Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning

Naeem

Coronato

Ullah

et al. 2022

Sensors

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Multiple Input Multiple Output (MIMO) systems have been gaining significant attention from the research community due to their potential to improve data rates. However, a suitable scheduling mechanism is required to efficiently distribute available spectrum resources and enhance system capacity. This paper investigates the user selection problem in Multi-User MIMO (MU-MIMO) environment using the multi-agent Reinforcement learning (RL) methodology. Adopting multiple antennas’ spatial degrees of freedom, devices can serve to transmit simultaneously in every time slot. We aim to develop an optimal scheduling policy by optimally selecting a group of users to be scheduled for transmission, given the channel condition and resource blocks at the beginning of each time slot. We first formulate the MU-MIMO scheduling problem as a single-state Markov Decision Process (MDP). We achieve the optimal policy by solving the formulated MDP problem using RL. We use aggregated sum-rate of the group of users selected for transmission, and a 20% higher sum-rate performance over the conventional methods is reported.

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User-Centric Access Point Selection in Cell-Free Massive MIMO Systems: A Game-Theoretic Approach

Cited by 17 publications

References 15 publications

Survey on Resource Allocation for Future 6G Network Architectures: Cell-Free and Radio Stripe Technologies

Survey on Resource Allocation for Future 6G Network Architectures: Cell-Free and Radio Stripe Technologies

Cooperation Method Between CPUs in Large-Scale Cell-Free Massive MIMO for User-Centric RAN

Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning

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