Cognitive Multi-Point Free Space Optical Communication: Real-Time Users Discovery Using Unsupervised Machine Learning

Aveta, Federica; Refai, Hazem H.; LoPresti, Peter G.

doi:10.1109/access.2020.3038624

Cited by 20 publications

(13 citation statements)

References 30 publications

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“…In comparison, the DL-based detector with 16 modulation orders is two times faster, three times faster for 64 modulation orders, and 7.5 times faster for 256 modulation orders than the ML-based detector. In addition, in [131], the authors worked on a cognitive FSO communication network that offers some tantalizing advantages. For example, it can overcome the system complexity caused by the heterogeneity of supported services, applications, devices, and transmission technologies, while guaranteeing a high data rate and bandwidth.…”

Section: Free Space Optics (Fso)mentioning

confidence: 99%

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Puspitasari,

An,

Alsharif

et al. 2023

Sensors

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The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

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Section: Free Space Optics (Fso)mentioning

confidence: 99%

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Puspitasari,

An,

Alsharif

et al. 2023

Sensors

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“…Beyond 5G (B5G) must be created with additional features in order to achieve the 6G goal and get rid of 5G limitations. The prior convergence features, such as network densification, high dependability, widespread connection, high throughput, and low energy consumption, are the main components of 6G [4].…”

Section: Introductionmentioning

confidence: 99%

Improving the Efficiency of FSO Communication Using a Hybrid Optimization Based U-NET Model in 6G Applications

Sreeni,

Vasavi

et al. 2023

Preprint

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The sixth generation (6G) wireless communication has significant impact due to massive connectivity and higher data rate. 6G is enhanced with Free Space Optical (FSO) communication in which the optical data is transmitted with a free spectrum license and higher security. But atmospheric circumstances have an impact on how well FSO communication works. The novel detection approach is proposed in this paper to enhance performance by mitigating the effect of atmospheric turbulence. Initially, the input signal is modulated with an On-Off keying (OOK) based modulation technique and transmitted through FSO communication channel. On the receiver side, signal detection can be accomplished with hybrid optimization Particle Swarm Hill Climbing Algorithm based (PSHCA)-U-net. The U-net architecture is modified to enhance the U-net performance by optimizing the hyperparameters with a hybrid PSHCA algorithm. Tests are conducted using various atmospheric weather scenarios on the proposed PSHCA-U-net model.Finally, simulations are performed to determine the performance of a proposed architecture and evaluated using Bit Error rate (BER), accuracy, transmission time, latency, spectral efficiency etc. Using the proposed approach, the spectral efficiency and latency are improved with 100 Gbps/Hz and 1ms. In terms of BER and other performance measures, the proposed solution outperforms the current strategy. It shows the superiority of a proposed approach while considering 6G parameters.

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“…Different deep learning models can be applied to different strengths of FSO turbulent channels to detect OOK modulated signals [21]. A machine-learning-based methodology was presented for improving future optical wireless communication systems from existing fiber-based networks to cognitive networks based on fiber-based learning that provides cognitive capabilities at the physical layer [22].…”

Section: Introductionmentioning

confidence: 99%

Free-space optical channel performance under atmospheric losses using orthogonal frequency division multiplexing

Mani

Kumar

2022

IJEECS

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<span>Free space optics (FSO) is a fast-growing technology that outperforms fiber optics in communication network infrastructure without spectrum licensing. The goal of this work is to evaluate the performance of the FSO communication system using direct detection 4-quadrature amplitude modulation (4-QAM) and 4-phase shift keying (4-PSK) with orthogonal frequency division multiplexing (OFDM) scheme. Simulating a direct detection OFDM-FSO system and comparing constellation diagrams, electrical power, and optical power have been used to conduct the analysis. The model is validated using a constellation diagram of received signals for various FSO channel ranges and weather conditions. According to simulation results, the OFDM-FSO architecture combined with the 4-QAM modulation technique produces the most efficient output with the least amount of power consumption for data rates up to 10 Gb/s and FSO channel ranges of up to <br /> 3 km for clear air, 1.7 km, and 1.3 km for thin and thick fog, and 0.7 km for heavy fog. This investigation can enhance the current fiber optic network's structure connected with the FSO system to provide last-mile connectivity in 5G modules.</span>

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Cognitive Multi-Point Free Space Optical Communication: Real-Time Users Discovery Using Unsupervised Machine Learning

Cited by 20 publications

References 30 publications

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Improving the Efficiency of FSO Communication Using a Hybrid Optimization Based U-NET Model in 6G Applications

Free-space optical channel performance under atmospheric losses using orthogonal frequency division multiplexing

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