11,240 research outputs found
Capacity Analysis of LTE-Advanced HetNets with Reduced Power Subframes and Range Expansion
The time domain inter-cell interference coordination techniques specified in
LTE Rel. 10 standard improves the throughput of picocell-edge users by
protecting them from macrocell interference. On the other hand, it also
degrades the aggregate capacity in macrocell because the macro base station
(MBS) does not transmit data during certain subframes known as almost blank
subframes. The MBS data transmission using reduced power subframes was
standardized in LTE Rel. 11, which can improve the capacity in macrocell while
not causing high interference to the nearby picocells. In order to get maximum
benefit from the reduced power subframes, setting the key system parameters,
such as the amount of power reduction, carries critical importance. Using
stochastic geometry, this paper lays down a theoretical foundation for the
performance evaluation of heterogeneous networks with reduced power subframes
and range expansion bias. The analytic expressions for average capacity and 5th
percentile throughput are derived as a function of transmit powers, node
densities, and interference coordination parameters in a heterogeneous network
scenario, and are validated through Monte Carlo simulations. Joint optimization
of range expansion bias, power reduction factor, scheduling thresholds, and
duty cycle of reduced power subframes are performed to study the trade-offs
between aggregate capacity of a cell and fairness among the users. To validate
our analysis, we also compare the stochastic geometry based theoretical results
with the real MBS deployment (in the city of London) and the hexagonal-grid
model. Our analysis shows that with optimum parameter settings, the LTE Rel. 11
with reduced power subframes can provide substantially better performance than
the LTE Rel. 10 with almost blank subframes, in terms of both aggregate
capacity and fairness.Comment: Submitted to EURASIP Journal on Wireless Communications and
Networking (JWCN
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
An Efficient Requirement-Aware Attachment Policy for Future Millimeter Wave Vehicular Networks
The automotive industry is rapidly evolving towards connected and autonomous
vehicles, whose ever more stringent data traffic requirements might exceed the
capacity of traditional technologies for vehicular networks. In this scenario,
densely deploying millimeter wave (mmWave) base stations is a promising
approach to provide very high transmission speeds to the vehicles. However,
mmWave signals suffer from high path and penetration losses which might render
the communication unreliable and discontinuous. Coexistence between mmWave and
Long Term Evolution (LTE) communication systems has therefore been considered
to guarantee increased capacity and robustness through heterogeneous
networking. Following this rationale, we face the challenge of designing fair
and efficient attachment policies in heterogeneous vehicular networks.
Traditional methods based on received signal quality criteria lack
consideration of the vehicle's individual requirements and traffic demands, and
lead to suboptimal resource allocation across the network. In this paper we
propose a Quality-of-Service (QoS) aware attachment scheme which biases the
cell selection as a function of the vehicular service requirements, preventing
the overload of transmission links. Our simulations demonstrate that the
proposed strategy significantly improves the percentage of vehicles satisfying
application requirements and delivers efficient and fair association compared
to state-of-the-art schemes.Comment: 8 pages, 8 figures, 2 tables, accepted to the 30th IEEE Intelligent
Vehicles Symposiu
Multiobjective auction-based switching-off scheme in heterogeneous networks: to bid or not to bid?
©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The emerging data traffic demand has caused a massive deployment of network infrastructure, including Base Stations (BSs) and Small Cells (SCs), leading to increased energy consumption and expenditures. However, the network underutilization during low traffic periods enables the Mobile Network Operators (MNOs) to save energy by having their traffic served by third party SCs, thus being able to switch off their BSs. In this paper, we propose a novel market approach to foster the opportunistic utilization of the unexploited SCs capacity, where the MNOs, instead of requesting the maximum capacity to meet their highest traffic expectations, offer a set of bids requesting different resources from the third party SCs at lower costs. Motivated by the conflicting financial interests of the MNOs and the third party, the restricted capacity of the SCs that is not adequate to carry the whole traffic in multi-operator scenarios, and the necessity for energy efficient solutions, we introduce a combinatorial auction framework, which includes i) a bidding strategy, ii) a resource allocation scheme, and iii) a pricing rule. We propose a multiobjective framework as an energy and cost efficient solution for the resource allocation problem, and we provide extensive analytical and experimental results to estimate the potential energy and cost savings that can be achieved. In addition, we investigate the conditions under which the MNOs and the third party companies should take part in the proposed auction.Peer ReviewedPostprint (author's final draft
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