2 research outputs found
Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing
Sparse Mobile CrowdSensing (MCS) is a novel MCS paradigm where data inference
is incorporated into the MCS process for reducing sensing costs while its
quality is guaranteed. Since the sensed data from different cells (sub-areas)
of the target sensing area will probably lead to diverse levels of inference
data quality, cell selection (i.e., choose which cells of the target area to
collect sensed data from participants) is a critical issue that will impact the
total amount of data that requires to be collected (i.e., data collection
costs) for ensuring a certain level of quality. To address this issue, this
paper proposes a Deep Reinforcement learning based Cell selection mechanism for
Sparse MCS, called DR-Cell. First, we properly model the key concepts in
reinforcement learning including state, action, and reward, and then propose to
use a deep recurrent Q-network for learning the Q-function that can help decide
which cell is a better choice under a certain state during cell selection.
Furthermore, we leverage the transfer learning techniques to reduce the amount
of data required for training the Q-function if there are multiple correlated
MCS tasks that need to be conducted in the same target area. Experiments on
various real-life sensing datasets verify the effectiveness of DR-Cell over the
state-of-the-art cell selection mechanisms in Sparse MCS by reducing up to 15%
of sensed cells with the same data inference quality guarantee
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning in communications and networking. Modern networks, e.g.,
Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become
more decentralized and autonomous. In such networks, network entities need to
make decisions locally to maximize the network performance under uncertainty of
network environment. Reinforcement learning has been efficiently used to enable
the network entities to obtain the optimal policy including, e.g., decisions or
actions, given their states when the state and action spaces are small.
However, in complex and large-scale networks, the state and action spaces are
usually large, and the reinforcement learning may not be able to find the
optimal policy in reasonable time. Therefore, deep reinforcement learning, a
combination of reinforcement learning with deep learning, has been developed to
overcome the shortcomings. In this survey, we first give a tutorial of deep
reinforcement learning from fundamental concepts to advanced models. Then, we
review deep reinforcement learning approaches proposed to address emerging
issues in communications and networking. The issues include dynamic network
access, data rate control, wireless caching, data offloading, network security,
and connectivity preservation which are all important to next generation
networks such as 5G and beyond. Furthermore, we present applications of deep
reinforcement learning for traffic routing, resource sharing, and data
collection. Finally, we highlight important challenges, open issues, and future
research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper