126 research outputs found
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
Energy-Efficient Multicast Transmission for Underlay Device-to-Device Communications: A Social-Aware Perspective
In this paper, by utilizing the social relationships among mobile users, we present a framework of energy-efficient cluster formation and resource allocation for multicast D2D transmission. In particular, we first deal with D2D multicast cluster/group formation strategy from both physical distance and social trust level. Then we aim to maximize the overall energy-efficiency of D2D multicast groups through resource allocation and power control scheme, which considers the quality-of-service (QoS) requirements of both cellular user equipment and D2D groups. A heuristic algorithm is proposed to solve above energy-efficiency problem with less complexity. After that, considering the limited battery capacity of mobile users, we propose an energy and social aware cluster head update algorithm, which incorporates both the energy constraint and social centrality measurement. Numerical results indicate that the proposed social-tie based D2D multicast group formation and update algorithm form a multicast group in an energy efficient way. Moreover, the proposed resource and power allocation scheme achieves better energy efficiency in terms of throughput per energy consumption. These results show that, by exploiting social domain information, underlay D2D multicast transmission has high practical potential in saving the source on wireless links and in the backhaul
Robust neurooptimal control for a robot via adaptive dynamic programming
We aim at the optimization of the tracking control of a robot to improve the robustness, under the effect of unknown nonlinear perturbations. First, an auxiliary system is introduced, and optimal control of the auxiliary system can be seen as an approximate optimal control of the robot. Then, neural networks (NNs) are employed to approximate the solution of the Hamilton-Jacobi-Isaacs equation under the frame of adaptive dynamic programming. Next, based on the standard gradient attenuation algorithm and adaptive critic design, NNs are trained depending on the designed updating law with relaxing the requirement of initial stabilizing control. In light of the Lyapunov stability theory, all the error signals can be proved to be uniformly ultimately bounded. A series of simulation studies are carried out to show the effectiveness of the proposed control
DGMem: Learning Visual Navigation Policy without Any Labels by Dynamic Graph Memory
In recent years, learning-based approaches have demonstrated significant
promise in addressing intricate navigation tasks. Traditional methods for
training deep neural network navigation policies rely on meticulously designed
reward functions or extensive teleoperation datasets as navigation
demonstrations. However, the former is often confined to simulated
environments, and the latter demands substantial human labor, making it a
time-consuming process. Our vision is for robots to autonomously learn
navigation skills and adapt their behaviors to environmental changes without
any human intervention. In this work, we discuss the self-supervised navigation
problem and present Dynamic Graph Memory (DGMem), which facilitates training
only with on-board observations. With the help of DGMem, agents can actively
explore their surroundings, autonomously acquiring a comprehensive navigation
policy in a data-efficient manner without external feedback. Our method is
evaluated in photorealistic 3D indoor scenes, and empirical studies demonstrate
the effectiveness of DGMem.Comment: 8 pages, 6 figure
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