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Advanced Techniques for Massive MIMO Systems in Next-Generation Wireless Communication...
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Advanced Techniques for Massive MIMO Systems in Next-Generation Wireless Communication and Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (15 March 2025) | Viewed by 2687

Special Issue Editors

Dr. Wei Cheng

E-Mail Website
Guest Editor
The School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China
Interests: wireless ad hoc networks; Internet of Things; next-generation mobile wireless communication; UAV communication; radar signal processing
Dr. Fangxin Xu

E-Mail Website
Guest Editor
Shenzhen Longsailing Semiconductor Co. Ltd, Shenzhen 518057, China
Interests: next-generation wireless LAN technology; full-duplex technology; backscatter technology; software-defined wireless networking; machine learning in networks
Dr. Limeng Dong

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China
Interests: wireless communications; physical layer security; intelligent reflecting surface-aided communication

Special Issue Information

Dear Colleagues,

Massive MIMO systems represent a significant advancement in wireless communication, with ongoing research and development aiming to overcome the technical challenges and fully realize their potential in 6G and future networks. Massive MIMO systems employ a large number of antennas at the base station to serve multiple users simultaneously, thereby increasing the capacity of the network. By transmitting multiple data streams to different users at the same time–frequency resource, Massive MIMO leverages spatial multiplexing to enhance the spectral efficiency. Advanced beamforming techniques are used to direct signals towards specific users, enhancing signal quality and reducing interference. Massive MIMO is a foundational technology for 5G networks, facilitating the deployment of large antenna arrays and advanced signal processing techniques.

Despite its potential, Massive MIMO faces challenges such as pilot contamination, hardware impairments, and the need for sophisticated signal processing algorithms. Research is progressing in various aspects of Massive MIMO, including transmission schemes, hardware complexity, and computational efficiency. This technology exhibits significant applicative potential, including in enhanced wireless video surveillance, streaming services, AI robotics, unmanned aerial systems, and more.

This Special Issue focuses on Massive MIMO systems for next-generation wireless communication and networks. Therefore, the scope of this Special Issue includes, but is not limited to, the following topics:

  • Millimeter wave massive MIMO systems
  • Terahertz massive MIMO systems
  • Massive MIMO ISAC systems
  • Massive MIMO NOMA systems
  • RIS-assisted massive MIMO systems
  • Full-duplex massive MIMO systems
  • Massive MIMO Visible Light Communication
  • Ultra-massive MIMO systems
  • XL-MIMO systems
  • Cell-free massive MIMO system
  • Massive MIMO systems in UAS
  • Massive MIMO systems in smart city infrastructures

Dr. Wei Cheng
Dr. Fangxin Xu
Dr. Limeng Dong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • massive MIMO
  • wireless communication
  • cell-free
  • NOMA
  • ISAC
  • visible light communication

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Published Papers (2 papers)

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Research

28 pages, 1238 KiB  
Article
Resource Allocation in UAV-D2D Networks: A Scalable Heterogeneous Multi-Agent Deep Reinforcement Learning Approach
by Huayuan Wang, Hui Li, Xin Wang, Shilin Xia, Tao Liu and Ruonan Wang
Electronics 2024, 13(22), 4401; https://doi.org/10.3390/electronics13224401 - 10 Nov 2024
Cited by 1 | Viewed by 1577
Abstract
In unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) caching networks, the uncertainty from unpredictable content demands and variable user positions poses a significant challenge for traditional optimization methods, often making them impractical. Multi-agent deep reinforcement learning (MADRL) offers significant advantages in optimizing multi-agent system [...] Read more.
In unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) caching networks, the uncertainty from unpredictable content demands and variable user positions poses a significant challenge for traditional optimization methods, often making them impractical. Multi-agent deep reinforcement learning (MADRL) offers significant advantages in optimizing multi-agent system decisions and serves as an effective and practical alternative. However, its application in large-scale dynamic environments is severely limited by the curse of dimensionality and communication overhead. To resolve this problem, we develop a scalable heterogeneous multi-agent mean-field actor-critic (SH-MAMFAC) framework. The framework treats ground users (GUs) and UAVs as distinct agents and designs cooperative rewards to convert the resource allocation problem into a fully cooperative game, enhancing global network performance. We also implement a mixed-action mapping strategy to handle discrete and continuous action spaces. A mean-field MADRL framework is introduced to minimize individual agent training loads while enhancing total cache hit probability (CHP). The simulation results show that our algorithm improves CHP and reduces transmission delay. A comparative analysis with existing mainstream deep reinforcement learning (DRL) algorithms shows that SH-MAMFAC significantly reduces training time and maintains high CHP as GU count grows. Additionally, by comparing with SH-MAMFAC variants that do not include trajectory optimization or power control, the proposed joint design scheme significantly reduces transmission delay. Full article
(This article belongs to the Special Issue Advanced Techniques for Massive MIMO Systems in Next-Generation Wireless Communication and Networks)
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21 pages, 1724 KiB  
Article
Lower Energy Consumption in Multi-CPU Cell-Free Massive MIMO Systems
by Heng Zhang, Hui Li and Xin Wang
Electronics 2024, 13(22), 4392; https://doi.org/10.3390/electronics13224392 - 8 Nov 2024
Viewed by 788
Abstract
Under the ideal assumption of deploying only one central processing unit (CPU) in the entire system, cell-free (CF) systems can achieve significant macro-diversity gain, thereby providing uniformly reliable service to each user equipment (UE). However, due to limitations in system scalability and the [...] Read more.
Under the ideal assumption of deploying only one central processing unit (CPU) in the entire system, cell-free (CF) systems can achieve significant macro-diversity gain, thereby providing uniformly reliable service to each user equipment (UE). However, due to limitations in system scalability and the feasibility of strict phase synchronization, CF systems require a multi-CPU setup and perform coherent transmission at a smaller scale. Moreover, conventional CF systems typically operate in time-division duplex (TDD) mode and utilize statistical channel state information (CSI) for downlink (DL) decoding, but the channel hardening effect is not significant. These factors reduce downlink spectral efficiency (SE) and increase DL transmission time, leading to higher energy consumption in CF systems. To address these issues, we introduce downlink channel estimation (DLCE) in multi-CPU CF systems and derive the approximate achievable DL SE. To reduce DL pilot overhead, we propose an uplink–pilot-reuse-constrained DL pilot allocation principle. Based on this principle, we develop a farthest distance pilot allocation (FDPA) algorithm to mitigate pilot contamination. In addition, leveraging the characteristics of the heuristic distributed power allocation algorithm, we propose two access point (AP) clustering algorithms: one based on CSI (BCSI) and the other based on coherent group size (BCGS). Simulation results indicate that the introduction of DLCE significantly improves DL SE in multi-CPU CF massive MIMO systems, while the proposed FDPA algorithm further enhances DL SE. The BCSI and BCGS algorithms also effectively improve DL SE and help reduce energy consumption. By combining DLCE, the FDPA algorithm, and the proposed AP clustering algorithms, the energy consumption of multi-CPU CF systems can be significantly reduced. Full article
(This article belongs to the Special Issue Advanced Techniques for Massive MIMO Systems in Next-Generation Wireless Communication and Networks)
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