23,908 research outputs found
Spectral Efficient and Energy Aware Clustering in Cellular Networks
The current and envisaged increase of cellular traffic poses new challenges
to Mobile Network Operators (MNO), who must densify their Radio Access Networks
(RAN) while maintaining low Capital Expenditure and Operational Expenditure to
ensure long-term sustainability. In this context, this paper analyses optimal
clustering solutions based on Device-to-Device (D2D) communications to mitigate
partially or completely the need for MNOs to carry out extremely dense RAN
deployments. Specifically, a low complexity algorithm that enables the creation
of spectral efficient clusters among users from different cells, denoted as
enhanced Clustering Optimization for Resources' Efficiency (eCORE) is
presented. Due to the imbalance between uplink and downlink traffic, a
complementary algorithm, known as Clustering algorithm for Load Balancing
(CaLB), is also proposed to create non-spectral efficient clusters when they
result in a capacity increase. Finally, in order to alleviate the energy
overconsumption suffered by cluster heads, the Clustering Energy Efficient
algorithm (CEEa) is also designed to manage the trade-off between the capacity
enhancement and the early battery drain of some users. Results show that the
proposed algorithms increase the network capacity and outperform existing
solutions, while, at the same time, CEEa is able to handle the cluster heads
energy overconsumption
Matching Theory for Future Wireless Networks: Fundamentals and Applications
The emergence of novel wireless networking paradigms such as small cell and
cognitive radio networks has forever transformed the way in which wireless
systems are operated. In particular, the need for self-organizing solutions to
manage the scarce spectral resources has become a prevalent theme in many
emerging wireless systems. In this paper, the first comprehensive tutorial on
the use of matching theory, a Nobelprize winning framework, for resource
management in wireless networks is developed. To cater for the unique features
of emerging wireless networks, a novel, wireless-oriented classification of
matching theory is proposed. Then, the key solution concepts and algorithmic
implementations of this framework are exposed. Then, the developed concepts are
applied in three important wireless networking areas in order to demonstrate
the usefulness of this analytical tool. Results show how matching theory can
effectively improve the performance of resource allocation in all three
applications discussed
Seeing the Unobservable: Channel Learning for Wireless Communication Networks
Wireless communication networks rely heavily on channel state information
(CSI) to make informed decision for signal processing and network operations.
However, the traditional CSI acquisition methods is facing many difficulties:
pilot-aided channel training consumes a great deal of channel resources and
reduces the opportunities for energy saving, while location-aided channel
estimation suffers from inaccurate and insufficient location information. In
this paper, we propose a novel channel learning framework, which can tackle
these difficulties by inferring unobservable CSI from the observable one. We
formulate this framework theoretically and illustrate a special case in which
the learnability of the unobservable CSI can be guaranteed. Possible
applications of channel learning are then described, including cell selection
in multi-tier networks, device discovery for device-to-device (D2D)
communications, as well as end-to-end user association for load balancing. We
also propose a neuron-network-based algorithm for the cell selection problem in
multi-tier networks. The performance of this algorithm is evaluated using
geometry-based stochastic channel model (GSCM). In settings with 5 small cells,
the average cell-selection accuracy is 73% - only a 3.9% loss compared with a
location-aided algorithm which requires genuine location information.Comment: 6 pages, 4 figures, accepted by GlobeCom'1
Spectrum Sharing in mmWave Cellular Networks via Cell Association, Coordination, and Beamforming
This paper investigates the extent to which spectrum sharing in mmWave
networks with multiple cellular operators is a viable alternative to
traditional dedicated spectrum allocation. Specifically, we develop a general
mathematical framework by which to characterize the performance gain that can
be obtained when spectrum sharing is used, as a function of the underlying
beamforming, operator coordination, bandwidth, and infrastructure sharing
scenarios. The framework is based on joint beamforming and cell association
optimization, with the objective of maximizing the long-term throughput of the
users. Our asymptotic and non-asymptotic performance analyses reveal five key
points: (1) spectrum sharing with light on-demand intra- and inter-operator
coordination is feasible, especially at higher mmWave frequencies (for example,
73 GHz), (2) directional communications at the user equipment substantially
alleviate the potential disadvantages of spectrum sharing (such as higher
multiuser interference), (3) large numbers of antenna elements can reduce the
need for coordination and simplify the implementation of spectrum sharing, (4)
while inter-operator coordination can be neglected in the large-antenna regime,
intra-operator coordination can still bring gains by balancing the network
load, and (5) critical control signals among base stations, operators, and user
equipment should be protected from the adverse effects of spectrum sharing, for
example by means of exclusive resource allocation. The results of this paper,
and their extensions obtained by relaxing some ideal assumptions, can provide
important insights for future standardization and spectrum policy.Comment: 15 pages. To appear in IEEE JSAC Special Issue on Spectrum Sharing
and Aggregation for Future Wireless Network
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