Subgroup-Centric Multicast Cell-Free Massive MIMO

被引:0
|
作者
de la Fuente, Alejandro [1 ]
Femenias, Guillem [2 ]
Riera-Palou, Felip [2 ]
Interdonato, Giovanni [3 ]
机构
[1] Univ Rey Juan Carlos, Dept Signal Theory & Commun, Fuenlabrada 28942, Spain
[2] Univ Balear Isl, Mobile Commun Grp, Palma De Mallorca 07122, Spain
[3] Univ Cassino & Southern Lazio, Dept Elect & Informat Engn, I-03043 Cassino, Italy
来源
IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY | 2024年 / 5卷
关键词
Precoding; Unicast; Multicast communication; Channel estimation; 6G mobile communication; Spectral efficiency; Resource management; Reliability; Massive MIMO; Internet of Things; Cell-free massive MIMO; multicasting; user subgrouping; scalability; COVARIANCE-MATRIX ESTIMATION; SYSTEMS; CHALLENGES; ALLOCATION; NETWORKS; VISION; 5G;
D O I
10.1109/OJCOMS.2024.3487912
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Cell-free massive multiple-input multiple-output (CF-mMIMO) is an emerging technology for beyond fifth-generation (5G) systems aimed at enhancing the energy and spectral efficiencies of future mobile networks while providing nearly uniform quality of service to all users. Moreover, multicasting has garnered increasing attention in recent years, as physical-layer multicasting proves to be an efficient approach for serving multiple users simultaneously, all with identical service demands while sharing radio resources. A multicast service is typically delivered using either unicast or a single multicast transmission. In contrast, this work introduces a subgroup-centric multicast CF-mMIMO framework that splits the users into several multicast subgroups. The subgroup creation is based on the similarities in the spatial channel characteristics of the multicast users. This framework benefits from efficiently sharing the pilot sequence used for channel estimation and the precoding filters used for data transmission. The proposed framework relies on two scalable precoding strategies, namely, the centralized improved partial MMSE (IP-MMSE) and the distributed conjugate beamforming (CB). Numerical results demonstrate that the centralized IP-MMSE precoding strategy outperforms the CB precoding scheme in terms of sum SE when multicast users are uniformly distributed across the service area. In contrast, in cases where users are spatially clustered, multicast subgrouping significantly enhances the sum spectral efficiency (SE) of the multicast service compared to both unicast and single multicast transmission. Interestingly, in the latter scenario, distributed CB precoding outperforms IP-MMSE, particularly in terms of per-user SE, making it the best solution for delivering multicast content. Heterogeneous scenarios that combine uniform and clustered distributions of users validate multicast subgrouping as the most effective solution for improving both the sum and per-user SE of a multicast CF-mMIMO service.
引用
收藏
页码:6872 / 6889
页数:18
相关论文
共 50 条
  • [41] Impact of Channel Aging on Cell-Free Massive MIMO Over Spatially Correlated Channels
    Zheng, Jiakang
    Zhang, Jiayi
    Bjornson, Emil
    Ai, Bo
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2021, 20 (10) : 6451 - 6466
  • [42] Secrecy Performance Analysis of Mixed-ADC/DAC Cell-Free Massive MIMO in the Presence of Multiple Eavesdroppers
    Wang, Xiaoyu
    Gao, Yuanyuan
    Sha, Nan
    Guo, Mingxi
    Li, Na
    IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING, 2023, 7 (02): : 759 - 771
  • [43] Cell-Free Massive MIMO for Wireless Federated Learning
    Vu, Tung Thanh
    Ngo, Duy Trong
    Tran, Nguyen H.
    Ngo, Hien Quoc
    Dao, Minh Ngoc
    Middleton, Richard H.
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2020, 19 (10) : 6377 - 6392
  • [44] Power Allocation in Cell-Free Massive MIMO: A Deep Learning Method
    Zhao, Yu
    Niemegeers, Ignas G.
    De Groot, Sonia Heemstra
    IEEE ACCESS, 2020, 8 : 87185 - 87200
  • [45] Impacts of Asynchronous Reception on Cell-Free Distributed Massive MIMO Systems
    Li, Jiamin
    Liu, Mimi
    Zhu, Pengcheng
    Wang, Dongming
    You, Xiaohu
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (10) : 11106 - 11110
  • [46] Soft Handover Procedures in mmWave Cell-Free Massive MIMO Networks
    Zaher, Mahmoud
    Bjornson, Emil
    Petrova, Marina
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2024, 23 (06) : 6124 - 6138
  • [47] Mobile Edge Computing Aided Cell-Free Massive MIMO Networks
    Femenias, Guillem
    Riera-Palou, Felip
    IEEE TRANSACTIONS ON MOBILE COMPUTING, 2024, 23 (02) : 1246 - 1261
  • [48] Scalable Cell-Free Massive MIMO with Multiple CPUs
    Li, Feiyang
    Sun, Qiang
    Ji, Xiaodi
    Chen, Xiaomin
    MATHEMATICS, 2022, 10 (11)
  • [49] Pilot Assignment With Approximation Ratio in Cell-Free Massive MIMO Systems
    Wang, Zhaoyang
    Liu, Guanghua
    Bi, Ting
    Feng, Fangzheng
    Jiang, Tao
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2025, 74 (01) : 803 - 815
  • [50] Fractional Programming-Based Uplink Transmission Power Allocation for User-Centric Cell-Free Massive MIMO Systems
    Sarker, Manobendu
    Fapojuwo, Abraham O.
    IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING, 2024, 8 (01): : 50 - 63