9,269 research outputs found
Interference Alignment for Cognitive Radio Communications and Networks: A Survey
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe
Energy-Efficient NOMA Enabled Heterogeneous Cloud Radio Access Networks
Heterogeneous cloud radio access networks (H-CRANs) are envisioned to be
promising in the fifth generation (5G) wireless networks. H-CRANs enable users
to enjoy diverse services with high energy efficiency, high spectral
efficiency, and low-cost operation, which are achieved by using cloud computing
and virtualization techniques. However, H-CRANs face many technical challenges
due to massive user connectivity, increasingly severe spectrum scarcity and
energy-constrained devices. These challenges may significantly decrease the
quality of service of users if not properly tackled. Non-orthogonal multiple
access (NOMA) schemes exploit non-orthogonal resources to provide services for
multiple users and are receiving increasing attention for their potential of
improving spectral and energy efficiency in 5G networks. In this article a
framework for energy-efficient NOMA H-CRANs is presented. The enabling
technologies for NOMA H-CRANs are surveyed. Challenges to implement these
technologies and open issues are discussed. This article also presents the
performance evaluation on energy efficiency of H-CRANs with NOMA.Comment: This work has been accepted by IEEE Network. Pages 18, Figure
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
Generalized Area Spectral Efficiency: An Effective Performance Metric for Green Wireless Communications
Area spectral efficiency (ASE) was introduced as a metric to quantify the
spectral utilization efficiency of cellular systems. Unlike other performance
metrics, ASE takes into account the spatial property of cellular systems. In
this paper, we generalize the concept of ASE to study arbitrary wireless
transmissions. Specifically, we introduce the notion of affected area to
characterize the spatial property of arbitrary wireless transmissions. Based on
the definition of affected area, we define the performance metric, generalized
area spectral efficiency (GASE), to quantify the spatial spectral utilization
efficiency as well as the greenness of wireless transmissions. After
illustrating its evaluation for point-to-point transmission, we analyze the
GASE performance of several different transmission scenarios, including
dual-hop relay transmission, three-node cooperative relay transmission and
underlay cognitive radio transmission. We derive closed-form expressions for
the GASE metric of each transmission scenario under Rayleigh fading environment
whenever possible. Through mathematical analysis and numerical examples, we
show that the GASE metric provides a new perspective on the design and
optimization of wireless transmissions, especially on the transmitting power
selection. We also show that introducing relay nodes can greatly improve the
spatial utilization efficiency of wireless systems. We illustrate that the GASE
metric can help optimize the deployment of underlay cognitive radio systems.Comment: 11 pages, 8 figures, accepted by TCo
Coordinated Dynamic Spectrum Management of LTE-U and Wi-Fi Networks
This paper investigates the co-existence of Wi-Fi and LTE in emerging
unlicensed frequency bands which are intended to accommodate multiple radio
access technologies. Wi-Fi and LTE are the two most prominent access
technologies being deployed today, motivating further study of the inter-system
interference arising in such shared spectrum scenarios as well as possible
techniques for enabling improved co-existence. An analytical model for
evaluating the baseline performance of co-existing Wi-Fi and LTE is developed
and used to obtain baseline performance measures. The results show that both
Wi-Fi and LTE networks cause significant interference to each other and that
the degradation is dependent on a number of factors such as power levels and
physical topology. The model-based results are partially validated via
experimental evaluations using USRP based SDR platforms on the ORBIT testbed.
Further, inter-network coordination with logically centralized radio resource
management across Wi-Fi and LTE systems is proposed as a possible solution for
improved co-existence. Numerical results are presented showing significant
gains in both Wi-Fi and LTE performance with the proposed inter-network
coordination approach.Comment: Accepted paper at IEEE DySPAN 201
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