85,871 research outputs found
Sum-Rate Analysis for High Altitude Platform (HAP) Drones with Tethered Balloon Relay
High altitude platform (HAP) drones can provide broadband wireless
connectivity to ground users in rural areas by establishing line-of-sight (LoS)
links and exploiting effective beamforming techniques. However, at high
altitudes, acquiring the channel state information (CSI) for HAPs, which is a
key component to perform beamforming, is challenging. In this paper, by
exploiting an interference alignment (IA) technique, a novel method for
achieving the maximum sum-rate in HAP-based communications without CSI is
proposed. In particular, to realize IA, a multiple-antenna tethered balloon is
used as a relay between multiple HAP drones and ground stations (GSs). Here, a
multiple-input multiple-output X network system is considered. The capacity of
the considered M*N X network with a tethered balloon relay is derived in
closed-form. Simulation results corroborate the theoretical findings and show
that the proposed approach yields the maximum sum-rate in multiple HAPs-GSs
communications in absence of CSI. The results also show the existence of an
optimal balloon's altitude for which the sum-rate is maximized.Comment: Accepted in IEEE Communications Letter
Competitive Assessments for HAP Delivery of Mobile Services in Emerging Countries
In recent years, network deployment based on High Altitude Platforms (HAPs)
has gained momentum through several initiatives where air vehicles and
telecommunications payloads have been adapted and refined, resulting in more
efficient and less expensive platforms. In this paper, we study HAP as an
alternative or complementary fast-evolving technology to provide mobile
services in rural areas of emerging countries, where business models need to be
carefully tailored to the reality of their related markets. In these large
areas with low user density, mobile services uptake is likely to be slowed by a
service profitability which is in turn limited by a relatively low average
revenue per user. Through three architectures enabling different business roles
and using different terrestrial, HAP and satellite backhaul solutions, we
devise how to use in an efficient and profitable fashion these multi-purpose
aerial platforms, in complement to existing access and backhauling satellite or
terrestrial technologies
The Coverage, Capacity and Coexistence of Mixed High Altitude Platform and Terrestrial Segments
This thesis explores the coverage, capacity and coexistence of High Altitude Platform (HAP) and terrestrial segments in the same service area. Given the limited spectrum available, mechanisms to manage the co-channel interference to enable effective coexistence between the two infrastructures are examined. Interference arising from the HAP, caused by the relatively high transmit power and the antenna beam profile, has the potential to significantly affect the existing terrestrial system on the ground if the HAP beams are deployed without a proper strategy. Beam-pointing strategies exploiting phased array antennas on the HAPs are shown to be an effective way to place the beams, with each of them forming service cells onto the ground in the service area, especially dense user areas. Using a newly developed RF clustering technique to better point the cells over an area of a dense group of users, it is shown that near maximum coverage of 96% of the population over the service area can be provided while maintaining the coexistence with the existing terrestrial system.
To improve the user experience at the cell edge, while at the same time improving the overall capacity of the system, Joint Transmission – Coordinated Multipoint (JT-CoMP) is adapted for a HAP architecture. It is shown how the HAP can potentially enable the tight scheduling needed to perform JT-CoMP due to the centralisation of all virtual E-UTRAN Node Bs (eNodeBs) on the HAP. A trade-off between CINR gain and loss of capacity when adapting JT-CoMP into the HAP system is identified, and strategies to minimise the trade-off are considered. It is shown that 57% of the users benefit from the JT-CoMP.
In order to enable coordination between the HAP and terrestrial segments, a joint architecture based on a Cloud – Radio Access Network (C-RAN) system is introduced. Apart from adapting a C-RAN based system to centrally connect the two segments together, the network functional split which varies the degree of the centralised processing is also considered to deal with the limitations of HAP fronthaul link requirements. Based on the fronthaul link requirements acquired from the different splitting options, the ground relay station diversity to connect the HAP to centralised and distributed units (CUs and DUs) is also considered
Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation
The combination of mobile edge computing (MEC) and radio frequency-based
wireless power transfer (WPT) presents a promising technique for providing
sustainable energy supply and computing services at the network edge. This
study considers a wireless-powered mobile edge computing system that includes a
hybrid access point (HAP) equipped with a computing unit and multiple Internet
of Things (IoT) devices. In particular, we propose a novel muti-user
cooperation scheme to improve computation performance, where collaborative
clusters are dynamically formed. Each collaborative cluster comprises a source
device (SD) and an auxiliary device (AD), where the SD can partition the
computation task into various segments for local processing, offloading to the
HAP, and remote execution by the AD with the assistance of the HAP.
Specifically, we aims to maximize the weighted sum computation rate (WSCR) of
all the IoT devices in the network. This involves jointly optimizing
collaboration, time and data allocation among multiple IoT devices and the HAP,
while considering the energy causality property and the minimum data processing
requirement of each device. Initially, an optimization algorithm based on the
interior-point method is designed for time and data allocation. Subsequently, a
priority-based iterative algorithm is developed to search for a near-optimal
solution to the multi-user collaboration scheme. Finally, a deep learning-based
approach is devised to further accelerate the algorithm's operation, building
upon the initial two algorithms. Simulation results show that the performance
of the proposed algorithms is comparable to that of the exhaustive search
method, and the deep learning-based algorithm significantly reduces the
execution time of the algorithm.Comment: Accepted to IEEE Open Journal of the Communications Societ
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