1,428 research outputs found
Deep Reinforcement Learning for Resource Management in Network Slicing
Network slicing is born as an emerging business to operators, by allowing
them to sell the customized slices to various tenants at different prices. In
order to provide better-performing and cost-efficient services, network slicing
involves challenging technical issues and urgently looks forward to intelligent
innovations to make the resource management consistent with users' activities
per slice. In that regard, deep reinforcement learning (DRL), which focuses on
how to interact with the environment by trying alternative actions and
reinforcing the tendency actions producing more rewarding consequences, is
assumed to be a promising solution. In this paper, after briefly reviewing the
fundamental concepts of DRL, we investigate the application of DRL in solving
some typical resource management for network slicing scenarios, which include
radio resource slicing and priority-based core network slicing, and demonstrate
the advantage of DRL over several competing schemes through extensive
simulations. Finally, we also discuss the possible challenges to apply DRL in
network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201
Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing
Radio access network (RAN) slicing is an important pillar in cross-domain
network slicing which covers RAN, edge, transport and core slicing. The
evolving network architecture requires the orchestration of multiple network
resources such as radio and cache resources. In recent years, machine learning
(ML) techniques have been widely applied for network management. However, most
existing works do not take advantage of the knowledge transfer capability in
ML. In this paper, we propose a deep transfer reinforcement learning (DTRL)
scheme for joint radio and cache resource allocation to serve 5G RAN slicing.
We first define a hierarchical architecture for the joint resource allocation.
Then we propose two DTRL algorithms: Q-value-based deep transfer reinforcement
learning (QDTRL) and action selection-based deep transfer reinforcement
learning (ADTRL). In the proposed schemes, learner agents utilize expert
agents' knowledge to improve their performance on target tasks. The proposed
algorithms are compared with both the model-free exploration bonus deep
Q-learning (EB-DQN) and the model-based priority proportional fairness and
time-to-live (PPF-TTL) algorithms. Compared with EB-DQN, our proposed DTRL
based method presents 21.4% lower delay for Ultra Reliable Low Latency
Communications (URLLC) slice and 22.4% higher throughput for enhanced Mobile
Broad Band (eMBB) slice, while achieving significantly faster convergence than
EB-DQN. Moreover, 40.8% lower URLLC delay and 59.8% higher eMBB throughput are
observed with respect to PPF-TTL.Comment: Under review of IEEE Transactions on Cognitive Communications and
Networkin
GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing
Network slicing is a key technology in 5G communications system. Its purpose
is to dynamically and efficiently allocate resources for diversified services
with distinct requirements over a common underlying physical infrastructure.
Therein, demand-aware resource allocation is of significant importance to
network slicing. In this paper, we consider a scenario that contains several
slices in a radio access network with base stations that share the same
physical resources (e.g., bandwidth or slots). We leverage deep reinforcement
learning (DRL) to solve this problem by considering the varying service demands
as the environment state and the allocated resources as the environment action.
In order to reduce the effects of the annoying randomness and noise embedded in
the received service level agreement (SLA) satisfaction ratio (SSR) and
spectrum efficiency (SE), we primarily propose generative adversarial
network-powered deep distributional Q network (GAN-DDQN) to learn the
action-value distribution driven by minimizing the discrepancy between the
estimated action-value distribution and the target action-value distribution.
We put forward a reward-clipping mechanism to stabilize GAN-DDQN training
against the effects of widely-spanning utility values. Moreover, we further
develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to
learn the action-value distribution by estimating the state-value distribution
and the action advantage function. Finally, we verify the performance of the
proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive
simulations
Explanation-Guided Deep Reinforcement Learning for Trustworthy 6G RAN Slicing
The complexity of emerging sixth-generation (6G) wireless networks has
sparked an upsurge in adopting artificial intelligence (AI) to underpin the
challenges in network management and resource allocation under strict service
level agreements (SLAs). It inaugurates the era of massive network slicing as a
distributive technology where tenancy would be extended to the final consumer
through pervading the digitalization of vertical immersive use-cases. Despite
the promising performance of deep reinforcement learning (DRL) in network
slicing, lack of transparency, interpretability, and opaque model concerns
impedes users from trusting the DRL agent decisions or predictions. This
problem becomes even more pronounced when there is a need to provision highly
reliable and secure services. Leveraging eXplainable AI (XAI) in conjunction
with an explanation-guided approach, we propose an eXplainable reinforcement
learning (XRL) scheme to surmount the opaqueness of black-box DRL. The core
concept behind the proposed method is the intrinsic interpretability of the
reward hypothesis aiming to encourage DRL agents to learn the best actions for
specific network slice states while coping with conflict-prone and complex
relations of state-action pairs. To validate the proposed framework, we target
a resource allocation optimization problem where multi-agent XRL strives to
allocate optimal available radio resources to meet the SLA requirements of
slices. Finally, we present numerical results to showcase the superiority of
the adopted XRL approach over the DRL baseline. As far as we know, this is the
first work that studies the feasibility of an explanation-guided DRL approach
in the context of 6G networks.Comment: 6 Pages, 6 figure
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