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Cell-Free Ultra Massive MIMO in 6G and Beyond Networks

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 17157

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Special Issue Editor

Dr. Chunguo Li

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Guest Editor

School of Information Science and Engineering, Southeast University, Nanjing, China
Interests: artificial intelligence-based image/video signal processing; algorithm design; wireless communications; cyberspace security theories and techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, multiple antenna technologies have received considerable attentio from both industry and academia, given that they can provide high coverage probability, spatial multiplexing, and macroscopic diversity. To provide ubiquitous wireless connectivity and achieve orders-of-magnitude improvements in these metrics, the new paradigm shifts away from the transitional massive multiple-input multiple-output (MIMO), cell-free ultra massive MIMO and intelligent reflecting surfaces (IRSs) at the physical layer are expected to achieve this goal. This is because the cell-free ultra massive MIMO technology can effectively avoid excessive inter-cell handover, reduce the effect of detrimental shading, and depress the control signaling interaction. Furthermore, IRSs can dynamically and proactively revize the wireless transmission channel between themselves via highly controllable and intelligent signal reflection. Thus, this combination can provide a new reconfigurable environment to further enhance the wireless communication performance, and this paves the way to realizing a smart and programmable wireless environment. However, several issues must be studied and resolved in the design of practical and efficient cell-free ultra massive MIMO systems, since traditional solutions cannot be used. In this Special Issue, we are interested in high-quality submissions that mainly highlight the emerging cell-free ultra massive MIMOs for future wireless networks.

The potential topics of submissions include, but are not limited to,

channel modeling;
characterization;
signal processing and estimation for B5G/6G Ultra massive MIMOs;
cell-free massive MIMOs;
cloud-RAN cooperative cell-free massive schemes;
wireless communications through reconfigurable intelligent surfaces;
AI;
deep learning;
machine learning for wireless communications;
performance analysis and simulations for integrated networks;
content caching and storage in wireless networks;
energy-efficient and energy-harvesting PHY layer design;
simultaneous wireless information and power transfer;
full duplexing;
PHY layer security and privacy;
ultra-wideband;
mmWave and sub-THz communication for integrated networks;
information-theoretic aspects of wireless communications;

Dr. Chunguo Li
Guest Editor

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Keywords

ultra massive MIMO
cell-free massive MIMO
cloud-RAN

Published Papers (6 papers)

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Research

16 pages, 407 KiB

Open AccessArticle

Deep Learning-Based Channel Estimation for mmWave Massive MIMO Systems in Mixed-ADC Architecture

by Rui Zhang, Weiqiang Tan, Wenliang Nie, Xianda Wu and Ting Liu

Sensors 2022, 22(10), 3938; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103938 - 23 May 2022

Cited by 3 | Viewed by 3029

Abstract

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems can significantly reduce the number of radio frequency (RF) chains by using lens antenna arrays, because it is usually the case that the number of RF chains is often much smaller than the number of antennas, so channel estimation becomes very challenging in practical wireless communication. In this paper, we investigated channel estimation for mmWave massive MIMO system with lens antenna array, in which we use a mixed (low/high) resolution analog-to-digital converter (ADC) architecture to trade-off the power consumption and performance of the system. Specifically, most antennas are equipped with low-resolution ADC and the rest of the antennas use high-resolution ADC. By utilizing the sparsity of the mmWave channel, the beamspace channel estimation can be expressed as a sparse signal recovery problem, and the channel can be recovered by the algorithm based on compressed sensing. We compare the traditional channel estimation scheme with the deep learning channel-estimation scheme, which has a better advantage, such as that the estimation scheme based on deep neural network is significantly better than the traditional channel-estimation algorithm. Full article

(This article belongs to the Special Issue Cell-Free Ultra Massive MIMO in 6G and Beyond Networks)

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22 pages, 5192 KiB

Open AccessArticle

Two Tier Slicing Resource Allocation Algorithm Based on Deep Reinforcement Learning and Joint Bidding in Wireless Access Networks

by Geng Chen, Xu Zhang, Fei Shen and Qingtian Zeng

Sensors 2022, 22(9), 3495; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093495 - 04 May 2022

Cited by 6 | Viewed by 1946

Abstract

Network slicing (NS) is an emerging technology in recent years, which enables network operators to slice network resources (e.g., bandwidth, power, spectrum, etc.) in different types of slices, so that it can adapt to different application scenarios of 5 g network: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable and low-latency communications (URLLC). In order to allocate these sliced network resources more effectively to users with different needs, it is important that manage the allocation of network resources. Actually, in the practical network resource allocation problem, the resources of the base station (BS) are limited and the demand of each user for mobile services is different. To better deal with the resource allocation problem, more effective methods and algorithms have emerged in recent years, such as the bidding method, deep learning (DL) algorithm, ant colony algorithm (AG), and wolf colony algorithm (WPA). This paper proposes a two tier slicing resource allocation algorithm based on Deep Reinforcement Learning (DRL) and joint bidding in wireless access networks. The wireless virtual technology divides mobile operators into infrastructure providers (InPs) and mobile virtual network operators (MVNOs). This paper considers a single base station, multi-user shared aggregated bandwidth radio access network scenario and joins the MVNOs to fully utilize base station resources, and divides the resource allocation process into two tiers. The algorithm proposed in this paper takes into account both the utilization of base station (BS) resources and the service demand of mobile users (MUs). In the upper tier, each MVNO is treated as an agent and uses a combination of bidding and Deep Q network (DQN) allows the MVNO to get more resources from the base station. In the lower tier allocation process, each MVNO distributes the received resources to the users who are connected to it, which also uses the Dueling DQN method for iterative learning to find the optimal solution to the problem. The results show that in the upper tier, the total system utility function and revenue obtained by the proposed algorithm are about 5.4% higher than double DQN and about 2.6% higher than Dueling DQN; In the lower tier, the user service quality obtained by using the proposed algorithm is more stable, the system utility function and Se are about 0.5–2.7% higher than DQN and Double DQN, but the convergence is faster. Full article

(This article belongs to the Special Issue Cell-Free Ultra Massive MIMO in 6G and Beyond Networks)

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22 pages, 2303 KiB

Open AccessArticle

Introducing a Novel Model-Free Multivariable Adaptive Neural Network Controller for Square MIMO Systems

by Arash Mehrafrooz, Fangpo He and Ali Lalbakhsh

Sensors 2022, 22(6), 2089; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062089 - 08 Mar 2022

Cited by 2 | Viewed by 1598

Abstract

In this study, a novel Multivariable Adaptive Neural Network Controller (MANNC) is developed for coupled model-free n-input n-output systems. The learning algorithm of the proposed controller does not rely on the model of a system and uses only the history of the system inputs and outputs. The system is considered as a ‘black box’ with no pre-knowledge of its internal structure. By online monitoring and possessing the system inputs and outputs, the parameters of the controller are adjusted. Using the accumulated gradient of the system error along with the Lyapunov stability analysis, the weights’ adjustment convergence of the controller can be observed, and an optimal training number of the controller can be selected. The Lyapunov stability of the system is checked during the entire weight training process to enable the controller to handle any possible nonlinearities of the system. The effectiveness of the MANNC in controlling nonlinear square multiple-input multiple-output (MIMO) systems is demonstrated via three simulation studies covering the cases of a time-invariant nonlinear MIMO system, a time-variant nonlinear MIMO system, and a hybrid MIMO system, respectively. In each case, the performance of the MANNC is compared with that of a properly selected existing counterpart. Simulation results demonstrate that the proposed MANNC is capable of controlling various types of square MIMO systems with much improved performance over its existing counterpart. The unique properties of the MANNC will make it a suitable candidate for many industrial applications. Full article

(This article belongs to the Special Issue Cell-Free Ultra Massive MIMO in 6G and Beyond Networks)

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13 pages, 2838 KiB

Open AccessArticle

Multi-User Scheduling for 6G V2X Ultra-Massive MIMO System

by Shibiao He, Jieru Du and Yong Liao

Sensors 2021, 21(20), 6742; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206742 - 11 Oct 2021

Cited by 8 | Viewed by 2151

Abstract

6G vehicle-to-everything (V2X) communication will be combined with vehicle automatic driving technology and play an important role in automatic driving. However, in 6G V2X systems, vehicle users have the characteristics of high-speed movement. Therefore, how to provide stable and reliable wireless link quality and improve channel gain has become a problem that must be solved. To solve this problem, a new multi-user scheduling algorithm based on block diagonalization (BD) precoding for 6G ultra-massive multiple-input multiple-output (MIMO) systems is proposed in this paper. The algorithm takes advantage of the sensitive nature of BD precoding to channel correlation, uses the Pearson coefficient after matrix vectorization to measure the channel correlation between users, defines the scheduling factor to measure the channel quality according to the user noise enhancement factor, and jointly considers the influence of the correlation between user channels and channel quality, ensuring the selection of high-quality channels while minimizing channel correlation. Simulation results show that compared with the multi-user scheduling algorithm based on subspace correlation, condition number, and geometric angle, the proposed algorithm can obtain higher user channel gain, effectively reduce the system bit error rate, and can be applied to 6G V2X communication. Full article

(This article belongs to the Special Issue Cell-Free Ultra Massive MIMO in 6G and Beyond Networks)

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14 pages, 359 KiB

Open AccessArticle

Satellite-Assisted Cell-Free Massive MIMO Systems with Multi-Group Multicast

by Jiamin Li, Lingling Chen, Pengcheng Zhu, Dongming Wang and Xiaohu You

Sensors 2021, 21(18), 6222; https://0-doi-org.brum.beds.ac.uk/10.3390/s21186222 - 16 Sep 2021

Cited by 1 | Viewed by 1879

Abstract

In this paper, we use satellite-assisted and multi-group multicast mechanisms to relieve ground traffic pressure and improve data transmission efficiency of cell-free massive MIMO systems. We propose to estimate channel state information (CSI) by common pilot scheme. Given the estimated CSI, we derive the closed-form expressions of achievable rate with maximum ratio transmission (MRT) and zero-forcing (ZF) precoding. The correctness of the closed-form expressions is verified through simulations. The results show that with the help of satellite and multicast, the average system spectrum efficiency (SE) can be significantly improved. Full article

(This article belongs to the Special Issue Cell-Free Ultra Massive MIMO in 6G and Beyond Networks)

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24 pages, 15702 KiB

Open AccessArticle

Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

by Mengxing Huang, Qianhao Zhai, Yinjie Chen, Siling Feng and Feng Shu

Sensors 2021, 21(8), 2628; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082628 - 08 Apr 2021

Cited by 39 | Viewed by 3940

Abstract

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions. Full article

(This article belongs to the Special Issue Cell-Free Ultra Massive MIMO in 6G and Beyond Networks)

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