96,597 research outputs found
Multi-access radio resource management using multi-agent system
Coexistence of heterogeneous networks such as cellular, Wireless Local Area Network (WLAN), Ultra-wideband (UWB) etc. brings new challenges. It is perceived that current radio resource management mechanism cannot meet the requirements of multi-radio access technologies (Multi-RATs). This paper proposes a novel intelligent multi-agent radio resource management system, which is self-organized and distributed to ensure the coexistence of Multi-RATs. Radio resource is managed by a macro control and management system using control factors and validation mechanism, instead of micro control for individual users. The goal is to increase radio resource utilization efficiency, maximize system capacity and meet the QoS requirements of different services. ©2006 IEEE.published_or_final_versio
Design and Experimental Validation of a Software-Defined Radio Access Network Testbed with Slicing Support
Network slicing is a fundamental feature of 5G systems to partition a single
network into a number of segregated logical networks, each optimized for a
particular type of service, or dedicated to a particular customer or
application. The realization of network slicing is particularly challenging in
the Radio Access Network (RAN) part, where multiple slices can be multiplexed
over the same radio channel and Radio Resource Management (RRM) functions shall
be used to split the cell radio resources and achieve the expected behaviour
per slice. In this context, this paper describes the key design and
implementation aspects of a Software-Defined RAN (SD-RAN) experimental testbed
with slicing support. The testbed has been designed consistently with the
slicing capabilities and related management framework established by 3GPP in
Release 15. The testbed is used to demonstrate the provisioning of RAN slices
(e.g. preparation, commissioning and activation phases) and the operation of
the implemented RRM functionality for slice-aware admission control and
scheduling
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Deep Reinforcement Learning for Real-Time Optimization in NB-IoT Networks
NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based
technology that offers a range of flexible configurations for massive IoT radio
access from groups of devices with heterogeneous requirements. A configuration
specifies the amount of radio resource allocated to each group of devices for
random access and for data transmission. Assuming no knowledge of the traffic
statistics, there exists an important challenge in "how to determine the
configuration that maximizes the long-term average number of served IoT devices
at each Transmission Time Interval (TTI) in an online fashion". Given the
complexity of searching for optimal configuration, we first develop real-time
configuration selection based on the tabular Q-learning (tabular-Q), the Linear
Approximation based Q-learning (LA-Q), and the Deep Neural Network based
Q-learning (DQN) in the single-parameter single-group scenario. Our results
show that the proposed reinforcement learning based approaches considerably
outperform the conventional heuristic approaches based on load estimation
(LE-URC) in terms of the number of served IoT devices. This result also
indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve
almost the same performance with much less training time. We further advance
LA-Q and DQN via Actions Aggregation (AA-LA-Q and AA-DQN) and via Cooperative
Multi-Agent learning (CMA-DQN) for the multi-parameter multi-group scenario,
thereby solve the problem that Q-learning agents do not converge in
high-dimensional configurations. In this scenario, the superiority of the
proposed Q-learning approaches over the conventional LE-URC approach
significantly improves with the increase of configuration dimensions, and the
CMA-DQN approach outperforms the other approaches in both throughput and
training efficiency
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