5 research outputs found
Power Efficient Visible Light Communication (VLC) with Unmanned Aerial Vehicles (UAVs)
A novel approach that combines visible light communication (VLC) with
unmanned aerial vehicles (UAVs) to simultaneously provide flexible
communication and illumination is proposed. To minimize the power consumption,
the locations of UAVs and the cell associations are optimized under
illumination and communication constraints. An efficient sub-optimal solution
that divides the original problem into two sub-problems is proposed. The first
sub-problem is modeled as a classical smallest enclosing disk problem to obtain
the optimal locations of UAVs, given the cell association. Then, assuming fixed
UAV locations, the second sub-problem is modeled as a min-size clustering
problem to obtain the optimized cell association. In addition, the obtained UAV
locations and cell associations are iteratively optimized multiple times to
reduce the power consumption. Numerical results show that the proposed approach
can reduce the total transmit power consumption by at least 53.8% compared to
two baseline algorithms with fixed UAV locations.Comment: 4 pages, 4 figures. Accepted for publication in IEEE Communications
Letter
Deep Learning for Optimal Deployment of UAVs with Visible Light Communications
In this paper, the problem of dynamical deployment of unmanned aerial
vehicles (UAVs) equipped with visible light communication (VLC) capabilities
for optimizing the energy efficiency of UAV-enabled networks is studied. In the
studied model, the UAVs can simultaneously provide communications and
illumination to service ground users. Since ambient illumination increases the
interference over VLC links while reducing the illumination threshold of the
UAVs, it is necessary to consider the illumination distribution of the target
area for UAV deployment optimization. This problem is formulated as an
optimization problem which jointly optimizes UAV deployment, user association,
and power efficiency while meeting the illumination and communication
requirements of users. To solve this problem, an algorithm that combines the
machine learning framework of gated recurrent units (GRUs) with convolutional
neural networks (CNNs) is proposed. Using GRUs and CNNs, the UAVs can model the
long-term historical illumination distribution and predict the future
illumination distribution. Given the prediction of illumination distribution,
the original nonconvex optimization problem can be divided into two
sub-problems and is then solved using a low-complexity, iterative algorithm.
Then, the proposed algorithm enables UAVs to determine the their deployment and
user association to minimize the total transmit power. Simulation results using
real data from the Earth observations group (EOG) at NOAA/NCEI show that the
proposed approach can achieve up to 68.9% reduction in total transmit power
compared to a conventional optimal UAV deployment that does not consider the
illumination distribution and user association.Comment: This paper has been accepted by IEEE Transactions on Wireless
Communications. arXiv admin note: text overlap with arXiv:1909.0755
Advanced Technique and Future Perspective for Next Generation Optical Fiber Communications
Optical fiber communication industry has gained unprecedented opportunities and achieved rapid progress in recent years. However, with the increase of data transmission volume and the enhancement of transmission demand, the optical communication field still needs to be upgraded to better meet the challenges in the future development. Artificial intelligence technology in optical communication and optical network is still in its infancy, but the existing achievements show great application potential. In the future, with the further development of artificial intelligence technology, AI algorithms combining channel characteristics and physical properties will shine in optical communication. This reprint introduces some recent advances in optical fiber communication and optical network, and provides alternative directions for the development of the next generation optical fiber communication technology