351 research outputs found
Cellular Underwater Wireless Optical CDMA Network: Potentials and Challenges
Underwater wireless optical communications is an emerging solution to the
expanding demand for broadband links in oceans and seas. In this paper, a
cellular underwater wireless optical code division multiple-access (UW-OCDMA)
network is proposed to provide broadband links for commercial and military
applications. The optical orthogonal codes (OOC) are employed as signature
codes of underwater mobile users. Fundamental key aspects of the network such
as its backhaul architecture, its potential applications and its design
challenges are presented. In particular, the proposed network is used as
infrastructure of centralized, decentralized and relay-assisted underwater
sensor networks for high-speed real-time monitoring. Furthermore, a promising
underwater localization and positioning scheme based on this cellular network
is presented. Finally, probable design challenges such as cell edge coverage,
blockage avoidance, power control and increasing the network capacity are
addressed.Comment: 11 pages, 10 figure
Reconfigurable Intelligent Surfaces in Challenging Environments: Underwater, Underground, Industrial and Disaster
Reconfigurable intelligent surfaces (RISs) have been introduced to improve
the signal propagation characteristics by focusing the signal power in the
preferred direction, thus making the communication environment "smart". The
typical use cases and applications for the "smart" environment include beyond
5G communication networks, smart cities, etc. The main advantage of employing
RISs in such networks is a more efficient exploitation of spatial degrees of
freedom. This advantage manifests in better interference mitigation as well as
increased spectral and energy efficiency due to passive beam steering.
Challenging environments comprise a range of scenarios, which share the fact
that it is extremely difficult to establish a communication link using
conventional technology due to many impairments typically associated with the
propagation medium and increased signal scattering. Although the challenges for
the design of communication networks, and specifically the Internet of Things
(IoT), in such environments are known, there is no common enabler or solution
for all these applications. Interestingly, the use of RISs in such scenarios
can become such an enabler and a game changer technology. Surprisingly, the
benefits of RIS for wireless networking in underwater and underground medium as
well as in industrial and disaster environments have not been addressed yet. In
this paper, we aim at filling this gap by discussing potential use cases,
deployment strategies and design aspects for RIS devices in underwater IoT,
underground IoT as well as Industry 4.0 and emergency networks. In addition,
novel research challenges to be addressed in this context are described.Comment: 16 pages, 13 figures, submitted for publication in IEEE journa
Routing Protocols for Underwater Acoustic Sensor Networks: A Survey from an Application Perspective
Underwater acoustic communications are different from terrestrial radio communications; acoustic channel is asymmetric and has large and variable end‐to‐end propagation delays, distance‐dependent limited bandwidth, high bit error rates, and multi‐path fading. Besides, nodes’ mobility and limited battery power also cause problems for networking protocol design. Among them, routing in underwater acoustic networks is a challenging task, and many protocols have been proposed. In this chapter, we first classify the routing protocols according to application scenarios, which are classified according to the number of sinks that an underwater acoustic sensor network (UASN) may use, namely single‐sink, multi‐sink, and no‐sink. We review some typical routing strategies proposed for these application scenarios, such as cross‐layer and reinforcement learning as well as opportunistic routing. Finally, some remaining key issues are highlighted
Adapting Deep Learning for Underwater Acoustic Communication Channel Modeling
The recent emerging applications of novel underwater systems lead to increasing demand for underwater acoustic (UWA) communication and networking techniques. However, due to the challenging UWA channel characteristics, conventional wireless techniques are rarely applicable to UWA communication and networking. The cognitive and software-defined communication and networking are considered promising architecture of a novel UWA system design. As an essential component of a cognitive communication system, the modeling and prediction of the UWA channel impulse response (CIR) with deep generative models are studied in this work.
Firstly, an underwater acoustic communication and networking testbed is developed for conducting various simulations and field experiments. The proposed test-bed also demonstrated the capabilities of developing and testing SDN protocols for a UWA network in both simulation and field experiments.
Secondly, due to the lack of appropriate UWA CIR data sets for deep learning, a series of field UWA channel experiments have been conducted across a shallow freshwater river. Abundant UWA CIR data under various weather conditions have been collected and studied. The environmental factors that significantly affect the UWA channel state, including the solar radiation rate, the air temperature, the ice cover, the precipitation rate, etc., are analyzed in the case studies. The obtained UWA CIR data set with significant correlations to weather conditions can benefit future deep-learning research on UWA channels.
Thirdly, a Wasserstein conditional generative adversarial network (WCGAN) is proposed to model the observed UWA CIR distribution. A power-weighted Jensen–Shannon divergence (JSD) is proposed to measure the similarity between the generated distribution and the experimental observations. The CIR samples generated by the WCGAN model show a lower power-weighted JSD than conventional estimated stochastic distributions.
Finally, a modified conditional generative adversarial network (CGAN) model is proposed for predicting the UWA CIR distribution in the 15-minute range near future. This prediction model takes a sequence of historical and forecast weather information with a recent CIR observation as the conditional input. The generated CIR sample predictions also show a lower power-weighted JSD than conventional estimated stochastic distributions
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