41 research outputs found
Harnessing Maritime Geospatial Information for InsurTech Advancements
peer reviewedThe maritime industry plays a key role in global trade and transportation, but it also poses unique risks and challenges for insurers. In recent years, the availability of maritime geospatial information and advancements in satellite technology and remote sensing capabilities have opened up new opportunities for the insurance sector. This paper explores the potential of harnessing maritime geospatial information for InsurTech advancements. We examine the data sources, including satellite imagery and Automatic Identification System (AIS) that provide valuable insights into vessel movements, traffic patterns, risk factors, and environmental conditions. Furthermore, we explore the challenges and opportunities associated with integrating geospatial information into InsurTech workflows, including issues related to data quality, scalability, and data privacy.
The application of data analytics and learning techniques in harnessing maritime geospatial information is also discussed. By leveraging the power of maritime geospatial information and InsurTech advancements, insurers can gain a comprehensive understanding of the maritime domain, enhance risk management strategies, optimize insurance products and pricing, and provide tailored insurance solutions to the maritime industry.9. Industry, innovation and infrastructur
Improved Gaussian-Bernoulli Restricted Boltzmann Machines for UAV-Ground Communication Systems
Unmanned aerial vehicle (UAV) is steadily growing as a promising technology
for next-generation communication systems due to their appealing features such
as wide coverage with high altitude, on-demand low-cost deployment, and fast
responses. UAV communications are fundamentally different from the conventional
terrestrial and satellite communications owing to the high mobility and the
unique channel characteristics of air-ground links. However, obtaining
effective channel state information (CSI) is challenging because of the dynamic
propagation environment and variable transmission delay. In this paper, a deep
learning (DL)-based CSI prediction framework is proposed to address channel
aging problem by extracting the most discriminative features from the UAV
wireless signals. Specifically, we develop a procedure of multiple Gaussian
Bernoulli restricted Boltzmann machines (GBRBM) for dimension reduction and
pre-training utilization incorporated with an autoencoder-based deep neural
networks (DNNs). To evaluate the proposed approach, real data measurements from
an UAV communicating with base-stations within a commercial cellular network
are obtained and used for training and validation. Numerical results
demonstrate that the proposed method is accurate in channel acquisition for
various UAV flying scenarios and outperforms the conventional DNNs
Carrier Aggregation in Multi-Beam High Throughput Satellite Systems
Carrier Aggregation (CA) is an integral part of current terrestrial networks.
Its ability to enhance the peak data rate, to efficiently utilize the limited
available spectrum resources and to satisfy the demand for data-hungry
applications has drawn large attention from different wireless network
communities. Given the benefits of CA in the terrestrial wireless environment,
it is of great interest to analyze and evaluate the potential impact of CA in
the satellite domain. In this paper, we study CA in multibeam high throughput
satellite systems. We consider both inter-transponder and intra-transponder CA
at the satellite payload level of the communication stack, and we address the
problem of carrier-user assignment assuming that multiple users can be
multiplexed in each carrier. The transmission parameters of different carriers
are generated considering the transmission characteristics of carriers in
different transponders. In particular, we propose a flexible carrier allocation
approach for a CA-enabled multibeam satellite system targeting a proportionally
fair user demand satisfaction. Simulation results and analysis shed some light
on this rather unexplored scenario and demonstrate the feasibility of the CA in
satellite communication systems
Reconfigurable Intelligent Surfaces for Smart Cities: Research Challenges and Opportunities
The concept of Smart Cities has been introduced as a way to benefit from the
digitization of various ecosystems at a city level. To support this concept,
future communication networks need to be carefully designed with respect to the
city infrastructure and utilization of resources. Recently, the idea of 'smart'
environment, which takes advantage of the infrastructure for better performance
of wireless networks, has been proposed. This idea is aligned with the recent
advances in design of reconfigurable intelligent surfaces (RISs), which are
planar structures with the capability to reflect impinging electromagnetic
waves toward preferred directions. Thus, RISs are expected to provide the
necessary flexibility for the design of the 'smart' communication environment,
which can be optimally shaped to enable cost- and energy-efficient signal
transmissions where needed. Upon deployment of RISs, the ecosystem of the Smart
Cities would become even more controllable and adaptable, which would
subsequently ease the implementation of future communication networks in urban
areas and boost the interconnection among private households and public
services. In this paper, we describe our vision of the application of RISs in
future Smart Cities. In particular, the research challenges and opportunities
are addressed. The contribution paves the road to a systematic design of
RIS-assisted communication networks for Smart Cities in the years to come.Comment: Submitted for possible publication in IEEE Open Journal of the
Communications Societ
Channel Estimation for UAV Communication Systems Using Deep Neural Networks
Channel modeling of unmanned aerial vehicles (UAVs) from wireless communications has gained great interest for rapid deployment in wireless communication. The UAV channel has its own distinctive characteristics compared to satellite and cellular networks. Many proposed techniques consider and formulate the channel modeling of UAVs as a classification problem, where the key is to extract the discriminative features of the UAV wireless signal. For this issue, we propose a framework of multiple Gaussian–Bernoulli restricted Boltzmann machines (GBRBM) for dimension reduction and pre-training utilization incorporated with an autoencoder-based deep neural network. The developed system used UAV measurements of a town’s already existing commercial cellular network for training and validation. To evaluate the proposed approach, we run ray-tracing simulations in the program Remcom Wireless InSite at a distinct frequency of 28 GHz and used them for training and validation. The results demonstrate that the proposed method is accurate in channel acquisition for various UAV flying scenarios and outperforms the conventional DNNs
Scheduling Design and Performance Analysis of Carrier Aggregation in Satellite Communication Systems
Carrier Aggregation is one of the vital approaches to achieve several orders of magnitude increase in peak data rates. While carrier aggregation benefits have been extensively studied in cellular networks, its application to satellite systems has not been thoroughly explored yet. Carrier aggregation can offer an enhanced and more consistent quality of service for users throughout the satellite coverage via combining multiple carriers, utilizing the unused capacity at other carriers, and enabling effective interference management. Furthermore, carrier aggregation can be a prominent solution to address the issue of the spatially heterogeneous satellite traffic demand. This paper investigates introducing carrier aggregation to satellite systems from a link layer perspective. Deployment of carrier aggregation in satellite systems with the combination of multiple carriers that have different characteristics requires effective scheduling schemes for reliable communications. To this end, a novel load balancing scheduling algorithm has been proposed to distribute data packets across the aggregated carriers based on channel capacities and to utilize spectrum efficiently. Moreover, in order to ensure that the received data packets are delivered without perturbing the original transmission order, a perceptive scheduling algorithm has been developed that takes into consideration channel properties along with the instantaneous available resources at the aggregated carriers. The proposed modifications have been carefully designed to make carrier aggregation transparent above the medium access control (MAC) layer. Additionally, the complexity analysis of the proposed algorithms has been conducted in terms of the computational loads. Simulation results are provided to validate our analysis, demonstrate the design tradeoffs, and to highlight the potentials of carrier aggregation applied to satellite communication systems
Carrier Aggregation in Multi-Beam High Throughput Satellite Systems
Carrier Aggregation (CA) is an integral part of
current terrestrial networks. Its ability to enhance the peak data
rate, to efficiently utilize the limited available spectrum resources
and to satisfy the demand for data-hungry applications has drawn
large attention from different wireless network communities.
Given the benefits of CA in the terrestrial wireless environment,
it is of great interest to analyze and evaluate the potential impact
of CA in the satellite domain. In this paper, we study CA
in multi-beam high throughput satellite systems. We consider
both inter-transponder and intra-transponder CA at the satellite
payload level of the communication stack, and we address the
problem of carrier-user assignment assuming that multiple users
can be multiplexed in each carrier. The transmission parameters
of different carriers are generated considering the transmission
characteristics of carriers in different transponders. In particular,
we propose a flexible carrier allocation approach for a CA enabled
multi-beam satellite system targeting a proportionally
fair user demand satisfaction. Simulation results and analysis
shed some light on this rather unexplored scenario and demonstrate
the feasibility of the CA in satellite communication systems
Performance of Joint Symbol Level Precoding and RIS Phase Shift Design in the Finite Block Length Regime with Constellation Rotation
In this paper, we tackle the problem of joint symbol level precoding (SLP)
and reconfigurable intelligent surface (RIS) phase shift design with
constellation rotation in the finite block length regime. We aim to increase
energy efficiency by minimizing the total transmit power while satisfying the
quality of service constraints. The total power consumption can be
significantly minimized through the exploitation of multiuser interference by
symbol level precoding and by the intelligent manipulation of the propagation
environment using reconfigurable intelligent surfaces. In addition, the
constellation rotation per user contributes to energy efficiency by aligning
the symbol phases of the users, thus improving the utilization of constructive
interference. The formulated power minimization problem is non-convex and
correspondingly difficult to solve directly. Hence, we employ an alternating
optimization algorithm to tackle the joint optimization of SLP and RIS phase
shift design. The optimal phase of each user's constellation rotation is
obtained via an exhaustive search algorithm. Through Monte-Carlo simulation
results, we demonstrate that the proposed solution yields substantial power
minimization as compared to conventional SLP, zero forcing precoding with RIS
as well as the benchmark schemes without RIS.Comment: 6 pages,4 figures. This paper has been accepted by IEEE International
Symposium on Personal, Indoor and Mobile Radio Communication