4,292 research outputs found
Optimized Training Design for Wireless Energy Transfer
Radio-frequency (RF) enabled wireless energy transfer (WET), as a promising
solution to provide cost-effective and reliable power supplies for
energy-constrained wireless networks, has drawn growing interests recently. To
overcome the significant propagation loss over distance, employing
multi-antennas at the energy transmitter (ET) to more efficiently direct
wireless energy to desired energy receivers (ERs), termed \emph{energy
beamforming}, is an essential technique for enabling WET. However, the
achievable gain of energy beamforming crucially depends on the available
channel state information (CSI) at the ET, which needs to be acquired
practically. In this paper, we study the design of an efficient channel
acquisition method for a point-to-point multiple-input multiple-output (MIMO)
WET system by exploiting the channel reciprocity, i.e., the ET estimates the
CSI via dedicated reverse-link training from the ER. Considering the limited
energy availability at the ER, the training strategy should be carefully
designed so that the channel can be estimated with sufficient accuracy, and yet
without consuming excessive energy at the ER. To this end, we propose to
maximize the \emph{net} harvested energy at the ER, which is the average
harvested energy offset by that used for channel training. An optimization
problem is formulated for the training design over MIMO Rician fading channels,
including the subset of ER antennas to be trained, as well as the training time
and power allocated. Closed-form solutions are obtained for some special
scenarios, based on which useful insights are drawn on when training should be
employed to improve the net transferred energy in MIMO WET systems.Comment: 30 pages, 9 figures, to appear in IEEE Trans. on Communication
Cellular-Enabled UAV Communication: Trajectory Optimization Under Connectivity Constraint
In this paper, we study a cellular-enabled unmanned aerial vehicle (UAV)
communication system consisting of one UAV and multiple ground base stations
(GBSs). The UAV has a mission of flying from an initial location to a final
location, during which it needs to maintain reliable wireless connection with
the cellular network by associating with one of the GBSs at each time instant.
We aim to minimize the UAV mission completion time by optimizing its
trajectory, subject to a quality of connectivity constraint of the GBS-UAV link
specified by a minimum received signal-to-noise ratio (SNR) target, which needs
to be satisfied throughout the mission. This problem is non-convex and
difficult to be optimally solved. We first propose an effective approach to
check its feasibility based on graph connectivity verification. Then, by
examining the GBS-UAV association sequence during the UAV mission, we obtain
useful insights on the optimal UAV trajectory, based on which an efficient
algorithm is proposed to find an approximate solution to the trajectory
optimization problem by leveraging techniques in convex optimization and graph
theory. Numerical results show that our proposed trajectory design achieves
near-optimal performance.Comment: submitted for possible conference publicatio
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