A 5G Radio-Light SDN Architecture for Wireless and Mobile Network Access in Buildings

Cosmas, John; Meunier, Ben; Ali, Kareem; Jawad, Nawar; Salih, Mukhald; Zhang, Y.; Hadad, Zion; Globen, Baruch; Gokmen, Haluk; Malkos, Sibel; Cakan, M. Emre; Koumaras, Harilaos; Kourtis, Anastasios; Sakkas, Christos; Negru, Daniel; Lacaud, Mathias; Ran, Moshe; Ran, Einat; Garcia, Jorge; Li, W.; Huang, L-K; Zetík, Rudolf; Cabaj, Krzysztof; Mazurczyk, Wojciech; Zhang, Xun; Kapovits, Adam

doi:10.1109/5gwf.2018.8516970

Cited by 12 publications

(9 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specific examples of set of performance KPIs selected in various use cases for 5G combined with LiFi is described in [10], [11], [15] and [16] including: In this work, we consider having LiFi technologies as part of new spectrum for 5G/B5G and focus on the following subset of key criteria:…”

Section: Methodology To Address Use Cases and Kpis For 5g Combinmentioning

confidence: 99%

See 1 more Smart Citation

Which LiFi's apps may fit mostly to 5G and beyond-5G Technology?

Albert¹,

Dekel²,

Kurland³

et al. 2019

2019 Global LIFI Congress (GLC)

Self Cite

View full text Add to dashboard Cite

LiFi technologies are expected to be an integrated part of future broadband wireless technologies. However, it is still not fully clear which apps will fit and benefit future 5G and beyond-5G. The purpose of this paper is to retrieve through Analytic Hierarchy Process model (AHP) and qualitative analysis the most promising apps. Apps for homes, supermarket, museum, communication between vehicles, in-flight entertainment and hospitals were considered. Relevant criteria include latency, data security, data-rate per square-meter, number of customers in square meter and acceptance. The results indicate that acceptance criteria found to be most dominant and hospital driven 5G apps most promising.

show abstract

Section: Methodology To Address Use Cases and Kpis For 5g Combinmentioning

confidence: 99%

“…Seven key apps are analysed: Communications between Vehicles (CBV) [9], Home and in-building (HO) [10], [11]. Hospitals (HS) [12], In-Flight Entertainment (IFE) [13], Museum (MU) [14], Supermarket (SU) [15] and Underground Train (UT) [16].…”

Section: A Apps Mapping: Current State Of Lifi's Apps Sugested Formentioning

confidence: 99%

Which LiFi's apps may fit mostly to 5G and beyond-5G Technology?

Albert¹,

Dekel²,

Kurland³

et al. 2019

2019 Global LIFI Congress (GLC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…For this purpose, attention to focus on addressing the performance and efficiency challenges of existing ML techniques is required. However, traditional ML techniques need a certain amount of private information to be analyzed at the central device where ML model training of the data is performed [177]. Such a training procedure at the centralized server may cause potential security threats and privacy leakages for 5G/B5G network UEs.…”

Section: E Secure Frl (Secfrl) Framework For 5g/b5g Systemsmentioning

confidence: 99%

URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

et al. 2021

View full text Add to dashboard Cite

The tactile internet (TI) is believed to be the prospective advancement of the internet of things (IoT), comprising human-to-machine and machine-to-machine communication. TI focuses on enabling realtime interactive techniques with a portfolio of engineering, social, and commercial use cases. For this purpose, the prospective 5 th generation (5G) technology focuses on achieving ultra-reliable low latency communication (URLLC) services. TI applications require an extraordinary degree of reliability and latency. The 3 rd generation partnership project (3GPP) defines that URLLC is expected to provide 99.99% reliability of a single transmission of 32 bytes packet with a latency of less than one millisecond. 3GPP proposes to include an adjustable orthogonal frequency division multiplexing (OFDM) technique, called 5G new radio (5G NR), as a new radio access technology (RAT). Whereas, with the emergence of a novel physical layer RAT, the need for the design for prospective next-generation technologies arises, especially with the focus of network intelligence. In such situations, machine learning (ML) techniques are expected to be essential to assist in designing intelligent network resource allocation protocols for 5G NR URLLC requirements. Therefore, in this survey, we present a possibility to use the federated reinforcement learning (FRL) technique, which is one of the ML techniques, for 5G NR URLLC requirements and summarizes the corresponding achievements for URLLC. We provide a comprehensive discussion of MAC layer channel access mechanisms that enable URLLC in 5G NR for TI. Besides, we identify seven very critical future use cases of FRL as potential enablers for URLLC in 5G NR.

show abstract

“…Further in this paragraph we present how LED fingerprinting could be used in real-life systems. As an exemplary test system, we choose IoRL project [ 32 ]. The main subsystems of the IoRL system are optical RAN, UEs (User Equipment) and Intelligent Home IP Gateway (IHIPG).…”

Section: Led Fingerprint Verification Mechanismmentioning

confidence: 99%

On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method

Shi

Zhang

Shi

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

In this paper, a novel device identification method is proposed to improve the security of Visible Light Communication (VLC) in 5G networks. This method extracts the fingerprints of Light-Emitting Diodes (LEDs) to identify the devices accessing the 5G network. The extraction and identification mechanisms have been investigated from the theoretical perspective as well as verified experimentally. Moreover, a demonstration in a practical indoor VLC-based 5G network has been carried out to evaluate the feasibility and accuracy of this approach. The fingerprints of four identical white LEDs were extracted successfully from the received 5G NR (New Radio) signals. To perform identification, four types of machine-learning-based classifiers were employed and the resulting accuracy was up to 97.1%.

show abstract

A 5G Radio-Light SDN Architecture for Wireless and Mobile Network Access in Buildings

Cited by 12 publications

References 2 publications

Which LiFi's apps may fit mostly to 5G and beyond-5G Technology?

Which LiFi's apps may fit mostly to 5G and beyond-5G Technology?

URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method

Contact Info

Product

Resources

About