2,309 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
Cooperative subcarrier sensing using antenna diversity based weighted virtual sub clustering
The idea of cooperation and the clustering amongst cognitive radios (CRs) has recently been focus of attention of research community, owing to its potential to improve performance of spectrum sensing (SS) schemes. This focus has led to the paradigm of cluster based cooperative spectrum sensing (CBCSS). In perspective of high date rate 4th generation wireless systems, which are characterized by orthogonal frequency division multiplexing (OFDM) and spatial diversity, there is a need to devise effective SS strategies. A novel CBCSS scheme is proposed for OFDM subcarrier detection in order to enable the non-contiguous OFDM (NC-OFDM) at the physical layer of CRs for efficient utilization of spectrum holes. Proposed scheme is based on the energy detection in MIMO CR network, using equal gain combiner as diversity combining technique, hard combining (AND, OR and Majority) rule as data fusion technique and antenna diversity based weighted clustering as virtual sub clustering algorithm. Results of proposed CBCSS are compared with conventional CBCSS scheme for AND, OR and Majority data fusion rules. Moreover the effects of antenna diversity, cooperation and cooperating clusters are also discussed
CogCell: Cognitive Interplay between 60GHz Picocells and 2.4/5GHz Hotspots in the 5G Era
Rapid proliferation of wireless communication devices and the emergence of a
variety of new applications have triggered investigations into next-generation
mobile broadband systems, i.e., 5G. Legacy 2G--4G systems covering large areas
were envisioned to serve both indoor and outdoor environments. However, in the
5G-era, 80\% of overall traffic is expected to be generated in indoors. Hence,
the current approach of macro-cell mobile network, where there is no
differentiation between indoors and outdoors, needs to be reconsidered. We
envision 60\,GHz mmWave picocell architecture to support high-speed indoor and
hotspot communications. We envisage the 5G indoor network as a combination of-,
and interplay between, 2.4/5\,GHz having robust coverage and 60\,GHz links
offering high datarate. This requires an intelligent coordination and
cooperation. We propose 60\,GHz picocellular network architecture, called
CogCell, leveraging the ubiquitous WiFi. We propose to use 60\,GHz for the data
plane and 2.4/5GHz for the control plane. The hybrid network architecture
considers an opportunistic fall-back to 2.4/5\,GHz in case of poor connectivity
in the 60\,GHz domain. Further, to avoid the frequent re-beamforming in 60\,GHz
directional links due to mobility, we propose a cognitive module -- a
sensor-assisted intelligent beam switching procedure -- which reduces the
communication overhead. We believe that the CogCell concept will help future
indoor communications and possibly outdoor hotspots, where mobile stations and
access points collaborate with each other to improve the user experience.Comment: 14 PAGES in IEEE Communications Magazine, Special issue on Emerging
Applications, Services and Engineering for Cognitive Cellular Systems
(EASE4CCS), July 201
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
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