3,946 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
Cognitive Orthogonal Precoder for Two-tiered Networks Deployment
In this work, the problem of cross-tier interference in a two-tiered
(macro-cell and cognitive small-cells) network, under the complete spectrum
sharing paradigm, is studied. A new orthogonal precoder transmit scheme for the
small base stations, called multi-user Vandermonde-subspace frequency division
multiplexing (MU-VFDM), is proposed. MU-VFDM allows several cognitive small
base stations to coexist with legacy macro-cell receivers, by nulling the
small- to macro-cell cross-tier interference, without any cooperation between
the two tiers. This cleverly designed cascaded precoder structure, not only
cancels the cross-tier interference, but avoids the co-tier interference for
the small-cell network. The achievable sum-rate of the small-cell network,
satisfying the interference cancelation requirements, is evaluated for perfect
and imperfect channel state information at the transmitter. Simulation results
for the cascaded MU-VFDM precoder show a comparable performance to that of
state-of-the-art dirty paper coding technique, for the case of a dense cellular
layout. Finally, a comparison between MU-VFDM and a standard complete spectrum
separation strategy is proposed. Promising gains in terms of achievable
sum-rate are shown for the two-tiered network w.r.t. the traditional bandwidth
management approach.Comment: 11 pages, 9 figures, accepted and to appear in IEEE Journal on
Selected Areas in Communications: Cognitive Radio Series, 2013. Copyright
transferred to IEE
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Location-free Spectrum Cartography
Spectrum cartography constructs maps of metrics such as channel gain or
received signal power across a geographic area of interest using spatially
distributed sensor measurements. Applications of these maps include network
planning, interference coordination, power control, localization, and cognitive
radios to name a few. Since existing spectrum cartography techniques require
accurate estimates of the sensor locations, their performance is drastically
impaired by multipath affecting the positioning pilot signals, as occurs in
indoor or dense urban scenarios. To overcome such a limitation, this paper
introduces a novel paradigm for spectrum cartography, where estimation of
spectral maps relies on features of these positioning signals rather than on
location estimates. Specific learning algorithms are built upon this approach
and offer a markedly improved estimation performance than existing approaches
relying on localization, as demonstrated by simulation studies in indoor
scenarios.Comment: 14 pages, 12 figures, 1 table. Submitted to IEEE Transactions on
Signal Processin
A Distributed Approach to Interference Alignment in OFDM-based Two-tiered Networks
In this contribution, we consider a two-tiered network and focus on the
coexistence between the two tiers at physical layer. We target our efforts on a
long term evolution advanced (LTE-A) orthogonal frequency division multiple
access (OFDMA) macro-cell sharing the spectrum with a randomly deployed second
tier of small-cells. In such networks, high levels of co-channel interference
between the macro and small base stations (MBS/SBS) may largely limit the
potential spectral efficiency gains provided by the frequency reuse 1. To
address this issue, we propose a novel cognitive interference alignment based
scheme to protect the macro-cell from the cross-tier interference, while
mitigating the co-tier interference in the second tier. Remarkably, only local
channel state information (CSI) and autonomous operations are required in the
second tier, resulting in a completely self-organizing approach for the SBSs.
The optimal precoder that maximizes the spectral efficiency of the link between
each SBS and its served user equipment is found by means of a distributed
one-shot strategy. Numerical findings reveal non-negligible spectral efficiency
enhancements with respect to traditional time division multiple access
approaches at any signal to noise (SNR) regime. Additionally, the proposed
technique exhibits significant robustness to channel estimation errors,
achieving remarkable results for the imperfect CSI case and yielding consistent
performance enhancements to the network.Comment: 15 pages, 10 figures, accepted and to appear in IEEE Transactions on
Vehicular Technology Special Section: Self-Organizing Radio Networks, 2013.
Authors' final version. Copyright transferred to IEE
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