A vision towards integrated 6G communication networks: Promising technologies, architecture, and use-cases

Jain, Preksha; Gupta, Akhil; Kumar, Neeraj

doi:10.1016/j.phycom.2022.101917

Cited by 20 publications

(8 citation statements)

References 291 publications

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“…With low-complexity signal processing, the proposed CFmMM systems guarantee that UEs may achieve comparable traditional performance independent of location [224]. Due to improved service quality, practically uniform attainable rates throughout the coverage area, and smooth handover for all mobile users regardless of their network position [225]. The CSI estimation's precision, acquired by training sequence transmission among the access points and UE, determines how well cell-free massive MIMO performs.…”

Section: F Cell-free Massive Mimo (Cfmmm)mentioning

confidence: 99%

Integration of Hybrid Networks, AI, Ultra Massive-MIMO, THz Frequency, and FBMC Modulation Toward 6G Requirements: A Review

Alhaj,

Jamlos,

Manap

et al. 2024

IEEE Access

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The fifth-generation (5G) wireless communications have been deployed in many countries with the following features: wireless networks at 20 Gbps as peak data rate, a latency of 1 ms, reliability of 99.999%, maximum mobility of 500 km/h, a bandwidth of 1 GHz, and a capacity of 10 6 up to Mbps/m 2 . Nonetheless, the rapid growth of applications, such as extended/virtual reality (XR/VR), online gaming, telemedicine, cloud computing, smart cities, the Internet of Everything (IoE), and others, demand lower latency, higher data rates, ubiquitous coverage, and better reliability. These higher requirements are the main problems that have challenged 5G while concurrently encouraging researchers and practitioners to introduce viable solutions. In this review paper, the sixth-generation (6G) technology could solve the 5G limitations, achieve higher requirements, and support future applications. The integration of multiple access techniques, terahertz (THz), visible light communications (VLC), ultra-massive multiple-input multiple-output (um-MIMO), hybrid networks, cell-free massive MIMO, and artificial intelligence (AI)/machine learning (ML) have been proposed for 6G. The main contributions of this paper are a comprehensive review of the 6G vision, KPIs (key performance indicators), and advanced potential technologies proposed with operation principles. Besides, this paper reviewed multiple access and modulation techniques, concentrating on Filter-Bank Multicarrier (FBMC) as a potential technology for 6G. This paper ends by discussing potential applications with challenges and lessons identified from prior studies to pave the path for future research.

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Section: F Cell-free Massive Mimo (Cfmmm)mentioning

confidence: 99%

Integration of Hybrid Networks, AI, Ultra Massive-MIMO, THz Frequency, and FBMC Modulation Toward 6G Requirements: A Review

Alhaj,

Jamlos,

Manap

et al. 2024

IEEE Access

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“…The set of

cell-edge users is represented as ƣ = {

}. The cell-edge users are grouped in clusters using the angle selection scheme (ASS) [ 22 , 23 , 24 , 25 , 26 , 27 ] and a total of

clusters are assumed. All the users are assumed to be moving with a velocity

m/s.…”

Section: System Modelmentioning

confidence: 99%

Massive MIMO NOMA: Double-Mode Model towards Green 5G Networks

Jain

Gupta

Tanwar

et al. 2023

Sensors

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With the development of the Internet of Things (IoT), the number of devices will also increase tremendously. However, we need more wireless communication resources. It has been shown in the literature that non-orthogonal multiple access (NOMA) offers high multiplexing gains due to the simultaneous transfer of signals, and massive multiple-input–multiple-outputs (mMIMOs) offer high spectrum efficiency due to the high antenna gain and high multiplexing gains. Therefore, a downlink mMIMO NOMA cooperative system is considered in this paper. The users at the cell edge in 5G cellular system generally suffer from poor signal quality as they are far away from the BS and expend high battery power to decode the signals superimposed through NOMA. Thus, this paper uses a cooperative relay system and proposes the mMIMO NOMA double-mode model to reduce battery expenditure and increase the cell edge user’s energy efficiency and sum rate. In the mMIMO NOMA double-mode model, two modes of operation are defined. Depending on the relay’s battery level, these modes are chosen to utilize the system’s energy efficiency. Comprehensive numerical results show the improvement in the proposed system’s average sum rate and average energy efficiency compared with a conventional system. In a cooperative NOMA system, the base station (BS) transmits a signal to a relay, and the relay forwards the signal to a cluster of users. This cluster formation depends on the user positions and geographical restrictions concerning the relay equipment. Therefore, it is vital to form user clusters for efficient and simultaneous transmission. This paper also presents a novel method for efficient cluster formation.

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“…Wireless systems predominantly rely on radio frequency (RF) communication networks [ 1 ]. Presently, the research focus has shifted toward sixth generation (6G) cellular networks, which exhibit the potential to deliver data rates ranging from 1 to 10 Tbps [ 2 ]. Despite the growing interest in 6G, it is imperative to acknowledge the numerous disadvantages associated with RF-based communications.…”

Section: Introductionmentioning

confidence: 99%

Proximity-Based Optical Camera Communication with Multiple Transmitters Using Deep Learning

Nasution,

Herfandi,

Sitanggang

et al. 2024

Sensors

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In recent years, optical camera communication (OCC) has garnered attention as a research focus. OCC uses optical light to transmit data by scattering the light in various directions. Although this can be advantageous with multiple transmitter scenarios, there are situations in which only a single transmitter is permitted to communicate. Therefore, this method is proposed to fulfill the latter requirement using 2D object size to calculate the proximity of the objects through an AI object detection model. This approach enables prioritization among transmitters based on the transmitter proximity to the receiver for communication, facilitating alternating communication with multiple transmitters. The image processing employed when receiving the signals from transmitters enables communication to be performed without the need to modify the camera parameters. During the implementation, the distance between the transmitter and receiver varied between 1.0 and 5.0 m, and the system demonstrated a maximum data rate of 3.945 kbps with a minimum BER of 4.2×10−3. Additionally, the system achieved high accuracy from the refined YOLOv8 detection algorithm, reaching 0.98 mAP at a 0.50 IoU.

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A vision towards integrated 6G communication networks: Promising technologies, architecture, and use-cases

Cited by 20 publications

References 291 publications

Integration of Hybrid Networks, AI, Ultra Massive-MIMO, THz Frequency, and FBMC Modulation Toward 6G Requirements: A Review

Integration of Hybrid Networks, AI, Ultra Massive-MIMO, THz Frequency, and FBMC Modulation Toward 6G Requirements: A Review

Massive MIMO NOMA: Double-Mode Model towards Green 5G Networks

Proximity-Based Optical Camera Communication with Multiple Transmitters Using Deep Learning

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