395,443 research outputs found
Drone-assisted emergency communications
Drone-mounted base stations (DBSs) have been proposed to extend coverage and improve communications between mobile users (MUs) and their corresponding macro base stations (MBSs). Different from the base stations on the ground, DBSs can flexibly fly over and close to MUs to establish a better vantage for communications. Thus, the pathloss between a DBS and an MU can be much smaller than that between the MU and MBS. In addition, by hovering in the air, the DBS can likely establish a Line-of-Sight link to the MBS. DBSs can be leveraged to recover communications in a large natural disaster struck area and to fully embody the advantage of drone-assisted communications. In order to retrieve signals from MUs in a large disaster struck area, DBSs need to overcome the large pathloss incurred by the long distance between DBSs and MBSs. This can be addressed by the following two strategies.
First, placing multiple drones in a disaster struck area can be used to mitigate the problem of large backhaul pathloss. In this method, data from MUs in the disaster struck area may be forwarded by more than one drone, i.e., DBSs can enable drone-to-drone communications. Thus, the throughput from the disaster struck area can potentially be enhanced by this multi-drone strategy. A cooperative DBS placement and channel allocation algorithm is proposed to maximize the aggregated data rate from MUs in a disaster struck area. It is demonstrated by simulations that the aggregated data rate can be improved by more than 10%, as compared to the scenario without drone-to-drone communications.
Second, free space optics (FSO) can be used as backhaul links to reduce the backhaul pathloss. FSO can provision a high-speed point-to-point transmission and is thus suitable for backhaul transmission. A heuristic algorithm is proposed to maximize the number of MUs that can be served by the drones by optimizing user association, DBS placement and spectrum allocation iteratively. It is demonstrated by simulations that the proposed algorithm can cover over 15% more MUs at the expense of less than 5% of the aggregated throughput. Equipping DBSs and MBSs with FSO transceivers incurs extra payload for DBSs, hence shortening the hovering time of DBSs. To prolong the hovering time of a DBS, the FSO beam is deployed to facilitate simultaneous communications and charging. The viability of this concept has been studied by varying the distance between a DBS and an MBS, in which an optimal location of the DBS is found to maximize the data throughput, while the charging power directed to the DBS from the MBS diminishes with the increasing distance between them.
Future work is planned to incorporate artificial intelligence to enhance drone-assisted networking for various applications. For example, a drone equipped with a camera can be used to detect victims. By analyzing the captured pictures, the locations of the victims can be estimated by some machine learning based image processing technology
Optimal Constrained Wireless Emergency Network Antennae Placement
With increasing number of mobile devices, newly introduced smart devices, and the Internet of things (IoT) sensors, the current microwave frequency spectrum is getting rapidly congested. The obvious solution to this frequency spectrum congestion is to use millimeter wave spectrum ranging from 6 GHz to 300 GHz. With the use of millimeter waves, we can enjoy very high communication speeds and very low latency. But, this technology also introduces some challenges that we hardly faced before. The most important one among these challenges is the Line of Sight (LOS) requirement. In the emergent concept of smart cities, the wireless emergency network is set to use millimeter waves. We have worked on the problem of efficiently finding a line of sight for such wireless emergency network antennae in minimal time. We devised two algorithms, Sequential Line of Sight (SLOS) and Tiled Line of Sight (TLOS), both perform better than traditional algorithms in terms of execution time. The tiled line of sight algorithm reduces the time required for a single line of sight query from 200 ms for traditional algorithms to mere 1.7 ms on average
Deploy-As-You-Go Wireless Relay Placement: An Optimal Sequential Decision Approach using the Multi-Relay Channel Model
We use information theoretic achievable rate formulas for the multi-relay
channel to study the problem of as-you-go deployment of relay nodes. The
achievable rate formulas are for full-duplex radios at the relays and for
decode-and-forward relaying. Deployment is done along the straight line joining
a source node and a sink node at an unknown distance from the source. The
problem is for a deployment agent to walk from the source to the sink,
deploying relays as he walks, given that the distance to the sink is
exponentially distributed with known mean. As a precursor, we apply the
multi-relay channel achievable rate formula to obtain the optimal power
allocation to relays placed along a line, at fixed locations. This permits us
to obtain the optimal placement of a given number of nodes when the distance
between the source and sink is given. Numerical work suggests that, at low
attenuation, the relays are mostly clustered near the source in order to be
able to cooperate, whereas at high attenuation they are uniformly placed and
work as repeaters. We also prove that the effect of path-loss can be entirely
mitigated if a large enough number of relays are placed uniformly between the
source and the sink. The structure of the optimal power allocation for a given
placement of the nodes, then motivates us to formulate the problem of as-you-go
placement of relays along a line of exponentially distributed length, and with
the exponential path-loss model, so as to minimize a cost function that is
additive over hops. The hop cost trades off a capacity limiting term, motivated
from the optimal power allocation solution, against the cost of adding a relay
node. We formulate the problem as a total cost Markov decision process,
establish results for the value function, and provide insights into the
placement policy and the performance of the deployed network via numerical
exploration.Comment: 21 pages. arXiv admin note: substantial text overlap with
arXiv:1204.432
Impromptu Deployment of Wireless Relay Networks: Experiences Along a Forest Trail
We are motivated by the problem of impromptu or as- you-go deployment of
wireless sensor networks. As an application example, a person, starting from a
sink node, walks along a forest trail, makes link quality measurements (with
the previously placed nodes) at equally spaced locations, and deploys relays at
some of these locations, so as to connect a sensor placed at some a priori
unknown point on the trail with the sink node. In this paper, we report our
experimental experiences with some as-you-go deployment algorithms. Two
algorithms are based on Markov decision process (MDP) formulations; these
require a radio propagation model. We also study purely measurement based
strategies: one heuristic that is motivated by our MDP formulations, one
asymptotically optimal learning algorithm, and one inspired by a popular
heuristic. We extract a statistical model of the propagation along a forest
trail from raw measurement data, implement the algorithms experimentally in the
forest, and compare them. The results provide useful insights regarding the
choice of the deployment algorithm and its parameters, and also demonstrate the
necessity of a proper theoretical formulation.Comment: 7 pages, accepted in IEEE MASS 201
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