2,484 research outputs found
Positioning of multiple unmanned aerial vehicle base stations in future wireless network
Abstract. Unmanned aerial vehicle (UAV) base stations (BSs) can be a reliable and efficient alternative to full fill the coverage and capacity requirements when the backbone network fails to provide the requirements during temporary events and after disasters. In this thesis, we consider three-dimensional deployment of multiple UAV-BSs in a millimeter-Wave network. Initially, we defined a set of locations for a UAV-BS to be deployed inside a cell, then possible combinations of predefined locations for multiple UAV-BSs are determined and assumed that users have fixed locations. We developed a novel algorithm to find the feasible positions from the predefined locations of multiple UAVs subject to a signal-to-interference-plus-noise ratio (SINR) constraint of every associated user to guarantees the quality-of-service (QoS), UAV-BS’s limited hovering altitude constraint and restricted operating zone because of regulation policies. Further, we take into consideration the millimeter-wave transmission and multi-antenna techniques to generate directional beams to serve the users in a cell.
We cast the positioning problem as an ℓ₀ minimization problem. This is a combinatorial, NP-hard, and finding the optimum solution is not tractable by exhaustive search. Therefore, we focused on the sub-optimal algorithm to find a feasible solution. We approximate the ℓ₀ minimization problem as non-combinatorial ℓ₁-norm problem. The simulation results reveal that, with millimeter-wave transmission the positioning of the UAV-BS while satisfying the constrains is feasible. Further, the analysis shows that the proposed algorithm achieves a near-optimal location to deploy multiple UVABS simultaneously
Joint Deployment and Resource Management for VLC-enabled RISs-assisted UAV Networks
In this paper, the problem of the deployment and resource management for visible light communication (VLC)-enabled, reconfigurable intelligent surfaces (RISs)-assisted unmanned aerial vehicle (UAV) networks is investigated. In the considered model, UAVs provide terrestrial users with wireless services and illumination simultaneously. Moreover, RISs are utilized to further improve the channel quality between UAVs and users. This joint placement and resource management problem is constructed aiming at acquiring the optimal UAV deployment, RISs phase shift, user and RIS association that satisfies the users’ needs with minimum consumption of the UAVs’ energy. An iterative algorithm that alternately optimizes continuous and binary variables is proposed to solve this mixed-integer programming problem. Specifically, RISs phase shift optimization is solved by phases alignment method and semidefinite program algorithm. Next, the successive convex approximation algorithm is proposed to settle the UAV deployment problem. The user and RIS association variables are relaxed to the continuous ones before adopting the dual method to find the optimal solution. Moreover, a greedy algorithm is proposed as an alternative to RIS association optimization with low complexity. Simulation results show that the proposed two schemes harvest the superior performance of 34.85% and 32.11% energy consumption reduction over the case without RIS, respectively
Joint User Association and UAV Location Optimization for Two-Tired Visible Light Communication Networks
In this paper, an unmanned aerial vehicle (UAVs)-assisted visible light
communication (VLC) has been considered which has two tiers: UAV-to-centroid
and device-to-device (D2D). In the UAV-to-centroid tier, each UAV can
simultaneously provide communications and illumination for the centroids of the
ground users over VLC links. In the D2D tier, the centroids retransmit received
data from UAV over D2D links to the cluster members. For network, the
optimization problem of joint user association and deployment location of UAVs
is formulated to maximize the received data, satisfy illumination constraint,
and the user cluster size. An iterative algorithm is first proposed to
transform the optimization problem into a series of two interdependent sub
problems. Following the smallest enclosing disk theorem, a random incremental
construction method is designed to find the optimal UAV locations. Then,
inspired by unsupervised learning method, a clustering algorithm to find a
suboptimal user association is proposed. Our simulation results show that the
proposed scheme on average guarantees the users brightness 0.77 lux more than
their threshold requirements. Moreover, the received bitrate plus number of D2D
connected users under our proposed method is 50.69% more than the scenario in
which we have RF Link instead of VLC link and do not optimize UAV location.Comment: 7 pages, 5 figures, conferenc
IRS-assisted UAV Communications: A Comprehensive Review
Intelligent reflecting surface (IRS) can smartly adjust the wavefronts in
terms of phase, frequency, amplitude and polarization via passive reflections
and without any need of radio frequency (RF) chains. It is envisaged as an
emerging technology which can change wireless communication to improve both
energy and spectrum efficiencies with low energy consumption and low cost. It
can intelligently configure the wireless channels through a massive number of
cost effective passive reflecting elements to improve the system performance.
Similarly, unmanned aerial vehicle (UAV) communication has gained a viable
attention due to flexible deployment, high mobility and ease of integration
with several technologies. However, UAV communication is prone to security
issues and obstructions in real-time applications. Recently, it is foreseen
that UAV and IRS both can integrate together to attain unparalleled
capabilities in difficult scenarios. Both technologies can ensure improved
performance through proactively altering the wireless propagation using smart
signal reflections and maneuver control in three dimensional (3D) space. IRS
can be integrated in both aerial and terrene environments to reap the benefits
of smart reflections. This study briefly discusses UAV communication, IRS and
focuses on IRS-assisted UAC communications. It surveys the existing literature
on this emerging research topic and highlights several promising technologies
which can be implemented in IRS-assisted UAV communication. This study also
presents several application scenarios and open research challenges. This study
goes one step further to elaborate research opportunities to design and
optimize wireless systems with low energy footprint and at low cost. Finally,
we shed some light on future research aspects for IRS-assisted UAV
communication
Q-Learning for Sum-Throughput Optimization in Wireless Visible-Light UAV Networks
Unmanned Aerial Vehicles (UAVs) Have Been Adopted as Aerial Base Stations (ABSs) to Provide Wireless Connectivity to Ground Users in Events of Increased Network Demand, and Points-Of-Failure Infrastructure (Such as in Disasters). However, with the Existing Crowded Radio Frequency (RF) Spectrum, UAV ABSs Cannot Provide High-Data-Rate Communication Required in 5G and beyond. to Address This Challenge, Visible Light Communication (VLC) is Proposed to Be Equipped on UAVs to Take Advantage of the Flexible and On-Demand Deployment Feature of the UAV, and the High-Data-Rate Communication of the VLC. However, VLC Has Strong Alignment Requirements between Transceivers, Therefore, How to Determine the Position and Orientation of the UAV is Critically Important for Sum-Throughput Improvement. in This Paper, We Propose Two Q-Learning based Methods to Maximize the Sum throughput of the Wireless Visible-Light UAV Network by Jointly Controlling the Position and Orientation of the UAV. the Results Show that the Proposed Approaches Can Achieve a Network-Wide Data Rate Very Close to the Optimal Solution Obtained by Exhaustive Search and Outperform Up to 18% Compared with the Intuitive Centroid-Based Method. Computation Complexity is Also Evaluated, Where Results Showing that the Proposed Two Q-Learning based Methods Can Both Consume Less Computational Time, I.e., Approximately 9 Times and 210 Times Less on Average Than that of the Exhaustive Search Approach
Sum-Rate Optimization for Visible-Light-Band UAV Networks based on Particle Swarm Optimization
The mobility nature of unmanned aerial vehicles (UAVs) takes them into high consideration in military, public, and civilian applications in recent years. However, scaling out millions of UAVs in the air will inevitably lead to a more crowded radio frequency (RF) spectrum. Therefore, researchers have been focused on new technologies such as millimeter-wave, Terahertz, and visible light communications (VLCs) to alleviate the spectrum crunch problem. VLC has shown its great potential for UAV networking because of its high data rate, interference-free to legacy RF spectrum, and low-complex frontends. While the physical layer design of the VLC system has been extensively investigated, visible-light-band networking is still in its infancy because of the intermittent link availability caused by blockage and miss-alignment among transceivers. Fortunately, drones can be deployed dynamically at network runtime to establish line-of-sight (LOS) links to users in blockage-rich environments. In this article, we first formulate a sum-rate optimization problem for visible-light-band UAV networks by jointly control the real-time position and orientations of drones. We then propose a solution algorithm based on particle swarm optimization (PSO). The simulation results show that the proposed algorithm can converge in 10 to 20 iteration time and can result in up to 24% performance gain compared to that in heuristic-central-point drone deployment
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