Introduction to indoor networking concepts and challenges in LiFi

LiFi is networked, bidirectional wireless communication with light. It is used to connect fixed and mobile devices at very high data rates by harnessing the visible light and infrared spectrum. Combined, these spectral resources are 2600 times larger than the entire radio frequency (RF) spectrum. This paper provides the motivation behind why LiFi is a very timely technology, especially for 6th generation (6G) cellular communications. It discusses and reviews essential networking technologies, such as interference mitigation and hybrid LiFi/Wi-Fi networking topologies. We also consider the seamless integration of LiFi into existing wireless networks to form heterogeneous networks across the optical and RF domains and discuss implications and solutions in terms of load balancing. Finally, we provide the results of a real-world hybrid LiFi/Wi-Fi network deployment in a software defined networking testbed. In addition, results from a LiFi deployment in a school classroom are provided, which show that Wi-Fi network performance can be improved significantly by offloading traffic to the LiFi

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Downlink Performance of Optical OFDM in Outdoor Visible Light Communication

Visible light communication (VLC) is a promising ubiquitous design alternative for supporting high data rates. Its application has been primarily oriented to indoor scenarios, but the proliferation of light-emitting diodes in the streets warrants its investigation in outdoor scenarios as well. This paper studies the feasibility of VLC in a conventional outdoor scenario, when optical orthogonal frequency division multiplexing techniques are employed. The presence of sunlight reduces the system's performance, hence sophisticated adaptive techniques must be applied. Closed-form expressions of the signal-to-noise ratio and of the mean cell data rate are derived and our simulations demonstrate their accuracy. Besides, the outage probability when adaptive modulation and coding schemes are employed is analytically expressed. It is shown that, when modulation bandwidth adaptation is carried out depending on the time of day and the illuminance from ambient light, the mean cell data rate is increased and the outage probability is reduced.This work was supported in part by the Spanish National ELISA Project under Grant TEC2014-59255-C3-3-R, the TERESA-ADA Project under MINECO/AEI/FEDER, UE Grant TEC2017-90093-C3-2-R and the 5RANVIR Project under MINECO/AEI/FEDER, UE Grant TEC2016-80090-C2-1-R. The work of B. Genovés Guzmán was supported by the Spanish MECD FPU Fellowship Program. The work of M. C. Aguayo-Torres was supported by the Universidad de Málaga. The work of H. Haas was supported in part by EPSRC through the Established Career Fellowship Extension under Grant EP/R007101/1 and in part by the Wolfson Foundation and the Royal Society. The work of L. Hanzo was supported in part by EPSRC under Project EP/Noo4558/1 and Project EP/PO34284/1, in part by the Royal Society's GRFC Grant, and in part by the European Research Council's Advanced Fellow Grant QuantCom