4,216 research outputs found
An Energy-Efficient Controller for Wirelessly-Powered Communication Networks
In a wirelessly-powered communication network (WPCN), an energy access point
(E-AP) supplies the energy needs of the network nodes through radio frequency
wave transmission, and the nodes store their received energy in their batteries
for possible data transmission. In this paper, we propose an online control
policy for energy transfer from the E-AP to the wireless nodes and for data
transfer among the nodes. With our proposed control policy, all data queues of
the nodes are stable, while the average energy consumption of the network is
shown to be within a bounded gap of the minimum energy required for stabilizing
the network. Our proposed policy is designed using a quadratic Lyapunov
function to capture the limitations on the energy consumption of the nodes
imposed by their battery levels. We show that under the proposed control
policy, the backlog level in the data queues and the stored energy level in the
batteries fluctuate in small intervals around some constant levels.
Consequently, by imposing negligible average data drop rate, the data buffer
size and the battery capacity of the nodes can be significantly reduced
Optimal Online Transmission Policy for Energy-Constrained Wireless-Powered Communication Networks
This work considers the design of online transmission policy in a
wireless-powered communication system with a given energy budget. The system
design objective is to maximize the long-term throughput of the system
exploiting the energy storage capability at the wireless-powered node. We
formulate the design problem as a constrained Markov decision process (CMDP)
problem and obtain the optimal policy of transmit power and time allocation in
each fading block via the Lagrangian approach. To investigate the system
performance in different scenarios, numerical simulations are conducted with
various system parameters. Our simulation results show that the optimal policy
significantly outperforms a myopic policy which only maximizes the throughput
in the current fading block. Moreover, the optimal allocation of transmit power
and time is shown to be insensitive to the change of modulation and coding
schemes, which facilitates its practical implementation.Comment: 7 pages, accepted by ICC 2019. An extended version of this paper is
accepted by IEEE TW
Fast-Convergent Learning-aided Control in Energy Harvesting Networks
In this paper, we present a novel learning-aided energy management scheme
() for multihop energy harvesting networks. Different from prior
works on this problem, our algorithm explicitly incorporates information
learning into system control via a step called \emph{perturbed dual learning}.
does not require any statistical information of the system
dynamics for implementation, and efficiently resolves the challenging energy
outage problem. We show that achieves the near-optimal
utility-delay tradeoff with an
energy buffers (). More interestingly,
possesses a \emph{convergence time} of , which is much faster than the time of
pure queue-based techniques or the time of approaches
that rely purely on learning the system statistics. This fast convergence
property makes more adaptive and efficient in resource
allocation in dynamic environments. The design and analysis of
demonstrate how system control algorithms can be augmented by learning and what
the benefits are. The methodology and algorithm can also be applied to similar
problems, e.g., processing networks, where nodes require nonzero amount of
contents to support their actions
Throughput Maximization for UAV-Aided Backscatter Communication Networks
This paper investigates unmanned aerial vehicle (UAV)-aided backscatter communication (BackCom) networks, where the UAV is leveraged to help the backscatter device (BD) forward signals to the receiver. Based on the presence or absence of a direct link between BD and receiver, two protocols, namely transmit-backscatter (TB) protocol and transmit-backscatter-relay (TBR) protocol, are proposed to utilize the UAV to assist the BD. In particular, we formulate the system throughput maximization problems for the two protocols by jointly optimizing the time allocation, reflection coefficient and UAV trajectory. Different static/dynamic circuit power consumption models for the two protocols are analyzed. The resulting optimization problems are shown to be non-convex, which are challenging to solve. We first consider the dynamic circuit power consumption model, and decompose the original problems into three sub-problems, namely time allocation optimization with fixed UAV trajectory and reflection coefficient, reflection coefficient optimization with fixed UAV trajectory and time allocation, and UAV trajectory optimization with fixed reflection coefficient and time allocation. Then, an efficient iterative algorithm is proposed for both protocols by leveraging the block coordinate descent method and successive convex approximation (SCA) techniques. In addition, for the static circuit power consumption model, we obtain the optimal time allocation with a given reflection coefficient and UAV trajectory and the optimal reflection coefficient with low computational complexity by using the Lagrangian dual method. Simulation results show that the proposed protocols are able to achieve significant throughput gains over the compared benchmarks
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