4,392 research outputs found
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
NOMA based resource allocation and mobility enhancement framework for IoT in next generation cellular networks
With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the internet, forming what is called internet of things (IoT). IoT devices with heterogeneous characteristics and quality of experience (QoE) requirements may engage in dynamic spectrum market due to scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of the paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions, enhance the radio resource utilization efficiency. The results show substantial reduction in the number of sub-carriers required when compared to conventional orthogonal multiple access (OMA) and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has less user or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve required quality of service (QoS) with guaranteed bit rate (GBR) and non-guaranteed bit rate (Non-GBR)
Statistical Multiplexing and Traffic Shaping Games for Network Slicing
Next generation wireless architectures are expected to enable slices of
shared wireless infrastructure which are customized to specific mobile
operators/services. Given infrastructure costs and the stochastic nature of
mobile services' spatial loads, it is highly desirable to achieve efficient
statistical multiplexing amongst such slices. We study a simple dynamic
resource sharing policy which allocates a 'share' of a pool of (distributed)
resources to each slice-Share Constrained Proportionally Fair (SCPF). We give a
characterization of SCPF's performance gains over static slicing and general
processor sharing. We show that higher gains are obtained when a slice's
spatial load is more 'imbalanced' than, and/or 'orthogonal' to, the aggregate
network load, and that the overall gain across slices is positive. We then
address the associated dimensioning problem. Under SCPF, traditional network
dimensioning translates to a coupled share dimensioning problem, which
characterizes the existence of a feasible share allocation given slices'
expected loads and performance requirements. We provide a solution to robust
share dimensioning for SCPF-based network slicing. Slices may wish to
unilaterally manage their users' performance via admission control which
maximizes their carried loads subject to performance requirements. We show this
can be modeled as a 'traffic shaping' game with an achievable Nash equilibrium.
Under high loads, the equilibrium is explicitly characterized, as are the gains
in the carried load under SCPF vs. static slicing. Detailed simulations of a
wireless infrastructure supporting multiple slices with heterogeneous mobile
loads show the fidelity of our models and range of validity of our high load
equilibrium analysis
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