2,167 research outputs found
Censored Truncated Sequential Spectrum Sensing for Cognitive Radio Networks
Reliable spectrum sensing is a key functionality of a cognitive radio
network. Cooperative spectrum sensing improves the detection reliability of a
cognitive radio system but also increases the system energy consumption which
is a critical factor particularly for low-power wireless technologies. A
censored truncated sequential spectrum sensing technique is considered as an
energy-saving approach. To design the underlying sensing parameters, the
maximum energy consumption per sensor is minimized subject to a lower bounded
global probability of detection and an upper bounded false alarm rate. This way
both the interference to the primary user due to miss detection and the network
throughput as a result of a low false alarm rate is controlled. We compare the
performance of the proposed scheme with a fixed sample size censoring scheme
under different scenarios. It is shown that as the sensing cost of the
cognitive radios increases, the energy efficiency of the censored truncated
sequential approach grows significantly.Comment: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6464630&isnumber=646450
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
Green radios systems
Essential overview of GRS implementation scenarios. The needs of GRS and some concrete cases exampleope
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Spectrum sensing and occupancy prediction for cognitive machine-to-machine wireless networks
A thesis submitted to the University of Bedfordshire, in partial
fulfil ment of the requirements for the degree of Doctor of Philosophy (PhD)The rapid growth of the Internet of Things (IoT) introduces an additional challenge to the existing spectrum under-utilisation problem as large scale deployments of thousands devices are expected to require wireless connectivity. Dynamic Spectrum
Access (DSA) has been proposed as a means of improving the spectrum utilisation of wireless systems. Based on the Cognitive Radio (CR) paradigm, DSA enables unlicensed spectrum users to sense their spectral environment and adapt their operational parameters to opportunistically access any temporally unoccupied bands without causing interference to the primary spectrum users. In the same context, CR inspired Machine-to-Machine (M2M) communications have recently been proposed as a potential solution to the spectrum utilisation problem, which has been driven by the ever increasing number of interconnected devices. M2M communications introduce new challenges for CR in terms of operational environments and design requirements. With spectrum sensing being the key function for CR, this
thesis investigates the performance of spectrum sensing and proposes novel sensing approaches and models to address the sensing problem for cognitive M2M deployments.
In this thesis, the behaviour of Energy Detection (ED) spectrum sensing for cognitive M2M nodes is modelled using the two-wave with dffi use power fading model. This channel model can describe a variety of realistic fading conditions including
worse than Rayleigh scenarios that are expected to occur within the operational environments of cognitive M2M communication systems. The results suggest that ED based spectrum sensing fails to meet the sensing requirements over worse than Rayleigh conditions and consequently requires the signal-to-noise ratio (SNR) to be increased by up to 137%. However, by employing appropriate diversity and node
cooperation techniques, the sensing performance can be improved by up to 11.5dB in terms of the required SNR. These results are particularly useful in analysing the eff ects of severe fading in cognitive M2M systems and thus they can be used
to design effi cient CR transceivers and to quantify the trade-o s between detection performance and energy e fficiency.
A novel predictive spectrum sensing scheme that exploits historical data of past sensing events to predict channel occupancy is proposed and analysed. This approach allows CR terminals to sense only the channels that are predicted to be
unoccupied rather than the whole band of interest. Based on this approach, a spectrum occupancy predictor is developed and experimentally validated. The proposed scheme achieves a prediction accuracy of up to 93% which in turn can lead to up to
84% reduction of the spectrum sensing cost. Furthermore, a novel probabilistic model for describing the channel availability
in both the vertical and horizontal polarisations is developed. The proposed model is validated based on a measurement campaign for operational scenarios where CR terminals may change their polarisation during their operation. A Gaussian approximation is used to model the empirical channel availability data with more than 95% confi dence bounds. The proposed model can be used as a means of improving
spectrum sensing performance by using statistical knowledge on the primary users occupancy pattern
From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks
Strategies to acquire white space information is the single most significant
functionality in cognitive radio networks (CRNs) and as such, it has gone some evolution
to enhance information accuracy. The evolution trends are spectrum sensing, prediction
algorithm and recently, geo-location database technique. Previously, spectrum sensing was
the main technique for detecting the presence/absence of a primary user (PU) signal in a
given radio frequency (RF) spectrum. However, this expectation could not materialized as
a result of numerous technical challenges ranging from hardware imperfections to RF
signal impairments. To convey the evolutionary trends in the development of white space
information, we present a survey of the contemporary advancements in PU detection with
emphasis on the practical deployment of CRNs i.e. Television white space (TVWS) networks.
It is found that geo-location database is the most reliable technique to acquire
TVWS information although, it is financially driven. Finally, using financially driven
database model, this study compared the data-rate and spectral efficiency of FCC and
Ofcom TV channelization. It was discovered that Ofcom TV channelization outperforms
FCC TV channelization as a result of having higher spectrum bandwidth. We proposed the
adoption of an all-inclusive TVWS information acquisition model as the future research
direction for TVWS information acquisition techniques
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