1,446 research outputs found
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
Comprehensive survey on quality of service provisioning approaches in cognitive radio networks : part one
Much interest in Cognitive Radio Networks (CRNs) has been raised recently by enabling unlicensed (secondary) users to utilize the unused portions of the licensed spectrum. CRN utilization of residual spectrum bands of Primary (licensed) Networks (PNs) must avoid harmful interference to the users of PNs and other overlapping CRNs. The coexisting of CRNs depends on four components: Spectrum Sensing, Spectrum Decision, Spectrum Sharing, and Spectrum Mobility. Various approaches have been proposed to improve Quality of Service (QoS) provisioning in CRNs within fluctuating spectrum availability. However, CRN implementation poses many technical challenges due to a sporadic usage of licensed spectrum bands, which will be increased after deploying CRNs. Unlike traditional surveys of CRNs, this paper addresses QoS provisioning approaches of CRN components and provides an up-to-date comprehensive survey of the recent improvement in these approaches. Major features of the open research challenges of each approach are investigated. Due to the extensive nature of the topic, this paper is the first part of the survey which investigates QoS approaches on spectrum sensing and decision components respectively. The remaining approaches of spectrum sharing and mobility components will be investigated in the next part
Optimal Real-time Spectrum Sharing between Cooperative Relay and Ad-hoc Networks
Optimization based spectrum sharing strategies have been widely studied.
However, these strategies usually require a great amount of real-time
computation and significant signaling delay, and thus are hard to be fulfilled
in practical scenarios. This paper investigates optimal real-time spectrum
sharing between a cooperative relay network (CRN) and a nearby ad-hoc network.
Specifically, we optimize the spectrum access and resource allocation
strategies of the CRN so that the average traffic collision time between the
two networks can be minimized while maintaining a required throughput for the
CRN. The development is first for a frame-level setting, and then is extended
to an ergodic setting. For the latter setting, we propose an appealing optimal
real-time spectrum sharing strategy via Lagrangian dual optimization. The
proposed method only involves a small amount of real-time computation and
negligible control delay, and thus is suitable for practical implementations.
Simulation results are presented to demonstrate the efficiency of the proposed
strategies.Comment: One typo in the caption of Figure 5 is correcte
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