Scalable Cell-Free Massive MIMO Networks With LEO Satellite Support

被引:10
|
作者
Riera-Palou, Felip [1 ]
Femenias, Guillem [1 ]
Caus, Marius [2 ]
Shaat, Musbah [2 ]
Perez-Neira, Ana, I [2 ]
机构
[1] Univ Balearic Isl, Mobile Commun Grp, Palma De Mallorca 07122, Illes Balears, Spain
[2] Ctr Tecnol Telecomunicac Catalunya, Barcelona 08860, Catalunya, Spain
关键词
Satellites; Low earth orbit satellites; Scalability; Precoding; 5G mobile communication; Proposals; Resource management; Cell-free; massive multiple-input multiple-output (MIMO); low earth orbit (LEO); terrestrial-satellite integrated networks; scalability; NON-TERRESTRIAL NETWORKS; POWER-CONTROL; 5G; OPTIMIZATION; INTEGRATION; CHALLENGES; SYSTEMS; ENERGY;
D O I
10.1109/ACCESS.2022.3164097
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents an integrated network architecture combining a cell-free massive multiple-input multiple-output (CF-M-MIMO) terrestrial layout with a low Earth orbit satellite segment where the scalability of the terrestrial segment is taken into account. The main purpose of such an integrated scheme is to transfer to the satellite segment those users that somehow limit the performance of the terrestrial network. Towards this end, a correspondingly scalable technique is proposed to govern the ground-to-satellite user diversion that can be tuned to different performance metrics. In particular, in this work the proposed technique is configured to result in an heuristic that improves the minimum per-user rate and the sum-rate of the overall network. Simulation results serve to identify under which conditions the satellite segment can become an attractive solution to enhance users' performance. Generally speaking, although the availability of the satellite segment always leads to an improvement of users' rates, it is in those cases where the terrestrial CF-M-MIMO network exhibits low densification traits that the satellite backup becomes crucial.
引用
收藏
页码:37557 / 37571
页数:15
相关论文
共 50 条
  • [21] Distributed Optimization of Uplink Cell-Free Massive MIMO Networks
    Wang, Rui
    Jiang, Yi
    2022 IEEE 96TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-FALL), 2022,
  • [22] Distributed Beam Combining in Cell-Free Massive MIMO Networks
    Singh, Santosh Kumar
    Sah, Abhay Kumar
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2025, 73 (03) : 2077 - 2087
  • [23] Mobility Management in mmWave Cell-Free Massive MIMO Networks
    Zaher, Mahmoud
    Bjornson, Emil
    Petrova, Marina
    ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, 2023, : 6492 - 6497
  • [24] Distributed Learning for Uplink Cell-Free Massive MIMO Networks
    Wang, Rui
    Dai, Weijie
    Jiang, Yi
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2023, 71 (09) : 5595 - 5606
  • [25] SWIPT-Enhanced Cell-Free Massive MIMO Networks
    Femenias, Guillem
    Garcia-Morales, Jan
    Riera-Palou, Felip
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2021, 69 (08) : 5593 - 5607
  • [26] Minimizing Energy Consumption in Cell-Free Massive MIMO Networks
    Jayaweera, Nalin
    Manosha, K. B. Shashika
    Rajatheva, Nandana
    Latva-aho, Matti
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2024, 73 (09) : 13263 - 13277
  • [27] Treating Interference as Noise in Cell-Free Massive MIMO Networks
    Chen, Shuaifei
    Zhang, Jiayi
    Chent, Zheng
    Ai, Bo
    IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022), 2022, : 1385 - 1390
  • [28] Another Twist to the Scalability in Cell-Free Massive MIMO Networks
    Femenias, Guillem
    Riera-Palou, Felip
    Bjornson, Emil
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2023, 71 (11) : 6793 - 6804
  • [29] Topological Pilot Assignment in Cell-Free Massive MIMO Networks
    Yu, Han
    Yi, Xinping
    Caire, Giuseppe
    2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2021,
  • [30] Scalable AP Clustering With Deep Reinforcement Learning for Cell-Free Massive MIMO
    Tsukamoto, Yu
    Ikami, Akio
    Murakami, Takahide
    Amrallah, Amr
    Shinbo, Hiroyuki
    Amano, Yoshiaki
    IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, 2025, 6 : 1552 - 1567