1,374 research outputs found
Energy efficient hybrid satellite terrestrial 5G networks with software defined features
In order to improve the manageability and adaptability
of future 5G wireless networks, the software orchestration mechanism,
named software defined networking (SDN) with Control
and User plane (C/U-plane) decoupling, has become one of the
most promising key techniques. Based on these features, the hybrid
satellite terrestrial network is expected to support flexible
and customized resource scheduling for both massive machinetype-
communication (MTC) and high-quality multimedia requests
while achieving broader global coverage, larger capacity and lower
power consumption. In this paper, an end-to-end hybrid satellite
terrestrial network is proposed and the performance metrics,
e. g., coverage probability, spectral and energy efficiency (SE and
EE), are analysed in both sparse networks and ultra-dense networks.
The fundamental relationship between SE and EE is investigated,
considering the overhead costs, fronthaul of the gateway
(GW), density of small cells (SCs) and multiple quality-ofservice
(QoS) requirements. Numerical results show that compared
with current LTE networks, the hybrid system with C/U split
can achieve approximately 40% and 80% EE improvement in
sparse and ultra-dense networks respectively, and greatly enhance
the coverage. Various resource management schemes, bandwidth
allocation methods, and on-off approaches are compared, and the
applications of the satellite in future 5G networks with software
defined features are proposed
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
Approximations of the aggregated interference statistics for outage analysis in massive MTC
This paper presents several analytic closed-form approximations of the aggregated interference statistics within the framework of uplink massive machine-type-communications (mMTC), taking into account the random activity of the sensors. Given its discrete nature and the large number of devices involved, a continuous approximation based on the Gram–Charlier series expansion of a truncated Gaussian kernel is proposed. We use this approximation to derive an analytic closed-form expression for the outage probability, corresponding to the event of the signal-to-interference-and-noise ratio being below a detection threshold. This metric is useful since it can be used for evaluating the performance of mMTC systems. We analyze, as an illustrative application of the previous approximation, a scenario with several multi-antenna collector nodes, each equipped with a set of predefined spatial beams. We consider two setups, namely single- and multiple-resource, in reference to the number of resources that are allocated to each beam. A graph-based approach that minimizes the average outage probability, and that is based on the statistics approximation, is used as allocation strategy. Finally, we describe an access protocol where the resource identifiers are broadcast (distributed) through the beams. Numerical simulations prove the accuracy of the approximations and the benefits of the allocation strategy.Peer ReviewedPostprint (published version
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