4,329 research outputs found
A novel load-balancing scheme for cellular-WLAN heterogeneous systems with cell-breathing technique
This paper proposes a novel load-balancing scheme for an operator-deployed cellular-wireless local area network (WLAN) heterogeneous network (HetNet), where the user association is controlled by employing a cell-breathing technique for the WLAN network. This scheme eliminates the complex coordination and additional signaling overheads between the users and the network by allowing the users to simply associate with the available WLAN networks similar to the traditional WLAN-first association, without making complex association decisions. Thus, this scheme can be easily implemented in an existing operator-deployed cellular-WLAN HetNet. The performance of the proposed scheme is evaluated in terms of load distribution between cellular and WLAN networks, user fairness, and system throughput, which demonstrates the superiority of the proposed scheme in load distribution and user fairness, while optimizing the system throughput. In addition, a cellular-WLAN interworking architecture and signaling procedures are proposed for implementing the proposed load-balancing schemes in an operator-deployed cellular-WLAN HetNet
Hybrid Spectrum Sharing in mmWave Cellular Networks
While spectrum at millimeter wave (mmWave) frequencies is less scarce than at
traditional frequencies below 6 GHz, still it is not unlimited, in particular
if we consider the requirements from other services using the same band and the
need to license mmWave bands to multiple mobile operators. Therefore, an
efficient spectrum access scheme is critical to harvest the maximum benefit
from emerging mmWave technologies. In this paper, we introduce a new hybrid
spectrum access scheme for mmWave networks, where data is aggregated through
two mmWave carriers with different characteristics. In particular, we consider
the case of a hybrid spectrum scheme between a mmWave band with exclusive
access and a mmWave band where spectrum is pooled between multiple operators.
To the best of our knowledge, this is the first study proposing hybrid spectrum
access for mmWave networks and providing a quantitative assessment of its
benefits. Our results show that this approach provides major advantages with
respect to traditional fully licensed or fully unlicensed spectrum access
schemes, though further work is needed to achieve a more complete understanding
of both technical and non technical implications
Opportunistic Third-Party Backhaul for Cellular Wireless Networks
With high capacity air interfaces and large numbers of small cells, backhaul
-- the wired connectivity to base stations -- is increasingly becoming the cost
driver in cellular wireless networks. One reason for the high cost of backhaul
is that capacity is often purchased on leased lines with guaranteed rates
provisioned to peak loads. In this paper, we present an alternate
\emph{opportunistic backhaul} model where third parties provide base stations
and backhaul connections and lease out excess capacity in their networks to the
cellular provider when available, presumably at significantly lower costs than
guaranteed connections. We describe a scalable architecture for such
deployments using open access femtocells, which are small plug-and-play base
stations that operate in the carrier's spectrum but can connect directly into
the third party provider's wired network. Within the proposed architecture, we
present a general user association optimization algorithm that enables the
cellular provider to dynamically determine which mobiles should be assigned to
the third-party femtocells based on the traffic demands, interference and
channel conditions and third-party access pricing. Although the optimization is
non-convex, the algorithm uses a computationally efficient method for finding
approximate solutions via dual decomposition. Simulations of the deployment
model based on actual base station locations are presented that show that large
capacity gains are achievable if adoption of third-party, open access
femtocells can reach even a small fraction of the current market penetration of
WiFi access points.Comment: 9 pages, 6 figure
A Multi-Game Framework for Harmonized LTE-U and WiFi Coexistence over Unlicensed Bands
The introduction of LTE over unlicensed bands (LTE-U) will enable LTE base
stations (BSs) to boost their capacity and offload their traffic by exploiting
the underused unlicensed bands. However, to reap the benefits of LTE-U, it is
necessary to address various new challenges associated with LTE-U and WiFi
coexistence. In particular, new resource management techniques must be
developed to optimize the usage of the network resources while handling the
interdependence between WiFi and LTE users and ensuring that WiFi users are not
jeopardized. To this end, in this paper, a new game theoretic tool, dubbed as
\emph{multi-game} framework is proposed as a promising approach for modeling
resource allocation problems in LTE-U. In such a framework, multiple,
co-existing and coupled games across heterogeneous channels can be formulated
to capture the specific characteristics of LTE-U. Such games can be of
different properties and types but their outcomes are largely interdependent.
After introducing the basics of the multi-game framework, two classes of
algorithms are outlined to achieve the new solution concepts of multi-games.
Simulation results are then conducted to show how such a multi-game can
effectively capture the specific properties of LTE-U and make of it a
"friendly" neighbor to WiFi.Comment: Accepted for publication at IEEE Wireless Communications Magazine,
Special Issue on LTE in Unlicensed Spectru
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
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