104 research outputs found
Performance Analysis of Small Cells' Deployment under Imperfect Traffic Hotspot Localization
Heterogeneous Networks (HetNets), long been considered in operators' roadmaps
for macrocells' network improvements, still continue to attract interest for 5G
network deployments. Understanding the efficiency of small cell deployment in
the presence of traffic hotspots can further draw operators' attention to this
feature. In this context, we evaluate the impact of imperfect small cell
positioning on the network performances. We show that the latter is mainly
impacted by the position of the hotspot within the cell: in case the hotspot is
near the macrocell, even a perfect positioning of the small cell will not yield
improved performance due to the interference coming from the macrocell. In the
case where the hotspot is located far enough from the macrocell, even a large
error in small cell positioning would still be beneficial in offloading traffic
from the congested macrocell.Comment: This article is already published in IEEE Global Communications
Conference (GLOBECOM) 201
Traffic Hotspot localization in 3G and 4G wireless networks using OMC metrics
In recent years, there has been an increasing awareness to traffic
localization techniques driven by the emergence of heterogeneous networks
(HetNet) with small cells deployment and the green networks. The localization
of hotspot data traffic with a very high accuracy is indeed of great interest
to know where the small cells should be deployed and how can be managed for
sleep mode concept. In this paper, we propose a new traffic localization
technique based on the combination of different key performance indicators
(KPI) extracted from the operation and maintenance center (OMC). The proposed
localization algorithm is composed with five main steps; each one corresponds
to the determination of traffic weight per area using only one KPI. These KPIs
are Timing Advance (TA), Angle of Arrival (AoA), Neighbor cell level, the load
of each cell and the Harmonic mean throughput (HMT) versus the Arithmetic mean
throughput (AMT). The five KPIs are finally combined by a function taking as
variables the values computed from the five steps. By mixing such KPIs, we show
that it is possible to lessen significantly the errors of localization in a
high precision attaining small cell dimensions.Comment: 7 pages, 7 figures, published in Proc. IEEE International Symposium
on Personal, Indoor and Mobile Radio Communications 2014 (PIMRC); IEEE
International Symposium on Personal, Indoor and Mobile Radio Communications
2014 (PIMRC
System level analysis of heterogeneous networks under imperfect traffic hotspot localization
We study, in this paper, the impact of imperfect small cell positioning with
respect to traffic hotspots in cellular networks. In order to derive the
throughput distribution in macro and small cells, we firstly perform static
level analysis of the system considering a non-uniform distribution of user
locations. We secondly introduce the dynamics of the system, characterized by
random arrivals and departures of users after a finite service duration, with
the service rates and distribution of radio conditions outfitted from the first
part of the work. When dealing with the dynamics of the system, macro and small
cells are modeled by multi-class processor sharing queues. Macro and small
cells are assumed to be operating in the same bandwidth. Consequently, they are
coupled due to the mutual interferences generated by each cell to the other. We
derive several performance metrics such as the mean flow throughput and the
gain, if any, generated from deploying small cells to manage traffic hotspots.
Our results show that in case the hotspot is near the macro BS (Base Station),
even a perfect positioning of the small cell will not yield improved
performance due to the high interference experienced at macro and small cell
users. However, in case the hotspot is located far enough from the macro BS,
performing errors in small cell positioning is tolerated (since related results
show positive gains) and it is still beneficial in offloading traffic from the
congested macrocell. The best performance metrics depend also on several other
important factors such as the users' arrival intensity, the capacity of the
cell and the size of the traffic hotspot.Comment: This paper is already published in IEEE Transactions on Vehicular
Technology 201
Offloading traffic hotspots using moving small cells
In this paper, the concept of moving small cells in mobile networks is
presented and evaluated taking into account the dynamics of the system. We
consider a small cell moving according to a Manhattan mobility model which is
the case when the small cell is deployed on the top of a bus following a
predefined trajectory in areas which are generally crowded. Taking into account
the distribution of user locations, we study the dynamic level considering a
queuing model composed of multi-class Processor Sharing queues. Macro and small
cells are assumed to be operating in the same bandwidth. Consequently, they are
coupled due to the mutual interferences generated by each cell to the other.
Our results show that deploying moving small cells could be an efficient
solution to offload traffic hotspots.Comment: This article is already published in IEEE ICC conference 2016, Kuala
Lumpur, Wireless networks symposiu
Optimal Multiphase Investment Strategies for Influencing Opinions in a Social Network
We study the problem of optimally investing in nodes of a social network in a
competitive setting, where two camps aim to maximize adoption of their opinions
by the population. In particular, we consider the possibility of campaigning in
multiple phases, where the final opinion of a node in a phase acts as its
initial biased opinion for the following phase. Using an extension of the
popular DeGroot-Friedkin model, we formulate the utility functions of the
camps, and show that they involve what can be interpreted as multiphase Katz
centrality. Focusing on two phases, we analytically derive Nash equilibrium
investment strategies, and the extent of loss that a camp would incur if it
acted myopically. Our simulation study affirms that nodes attributing higher
weightage to initial biases necessitate higher investment in the first phase,
so as to influence these biases for the terminal phase. We then study the
setting in which a camp's influence on a node depends on its initial bias. For
single camp, we present a polynomial time algorithm for determining an optimal
way to split the budget between the two phases. For competing camps, we show
the existence of Nash equilibria under reasonable assumptions, and that they
can be computed in polynomial time
On the effective bandwidth for resource management in ATM networks
Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 1997.Thesis (Master's) -- Bilkent University, 1997.Includes bibliographical references leaves 81-84.Chahed, TijaniM.S
RIS-assisted Cell-Free MIMO with Dynamic Arrivals and Departures of Users: A Novel Network Stability Approach
Reconfigurable Intelligent Surfaces (RIS) have recently emerged as a hot
research topic, being widely advocated as a candidate technology for next
generation wireless communications. These surfaces passively alter the behavior
of propagation environments enhancing the performance of wireless communication
systems. In this paper, we study the use of RIS in cell-free multiple-input
multiple-output (MIMO) setting where distributed service antennas, called
Access Points (APs), simultaneously serve the users in the network. While most
existing works focus on the physical layer improvements RIS carry, less
attention has been paid to the impact of dynamic arrivals and departures of the
users. In such a case, ensuring the stability of the network is the main goal.
For that, we propose an optimization framework of the phase shifts, for which
we derived a low-complexity solution. We then provide a theoretical analysis of
the network stability and show that our framework stabilizes the network
whenever it is possible. We also prove that a low complexity solution of our
framework stabilizes a guaranteed fraction (higher than 78.5%) of the stability
region. We provide also numerical results that corroborate the theoretical
claims
Initial Access Optimization for RIS-assisted Millimeter Wave Wireless Networks
Reconfigurable Intelligent Surfaces (RIS) are considered a key enabler to
achieve the vision of Smart Radio Environments, where the propagation
environment can be programmed and controlled to enhance the efficiency of
wireless systems. These surfaces correspond to planar sheets comprising a large
number of small and low-cost reflecting elements whose parameters are
adaptively selected with a programmable controller. Hence, by optimizing these
coefficients, the information signals can be directed in a customized fashion.
On the other hand, the initial access procedure used in 5G is beam sweeping,
where the base station sequentially changes the active beam direction in order
to scan all users in the cell. This conventional protocol results in an initial
access latency. The aim of this paper is to minimize this delay by optimizing
the activated beams in each timeslot, while leveraging the presence of the RIS
in the network. The problem is formulated as a hard optimization problem. We
propose an efficient solution based on jointly alternating optimization and
Semi Definite Relaxation (SDR) techniques. Numerical results are provided to
assess the superiority of our scheme as compared to conventional beam sweeping
A Two Phase Investment Game for Competitive Opinion Dynamics in Social Networks
We propose a setting for two-phase opinion dynamics in social networks, where
a node's final opinion in the first phase acts as its initial biased opinion in
the second phase. In this setting, we study the problem of two camps aiming to
maximize adoption of their respective opinions, by strategically investing on
nodes in the two phases. A node's initial opinion in the second phase naturally
plays a key role in determining the final opinion of that node, and hence also
of other nodes in the network due to its influence on them. More importantly,
this bias also determines the effectiveness of a camp's investment on that node
in the second phase. To formalize this two-phase investment setting, we propose
an extension of Friedkin-Johnsen model, and hence formulate the utility
functions of the camps. There is a tradeoff while splitting the budget between
the two phases. A lower investment in the first phase results in worse initial
biases for the second phase, while a higher investment spares a lower available
budget for the second phase. We first analyze the non-competitive case where
only one camp invests, for which we present a polynomial time algorithm for
determining an optimal way to split the camp's budget between the two phases.
We then analyze the case of competing camps, where we show the existence of
Nash equilibrium and that it can be computed in polynomial time under
reasonable assumptions. We conclude our study with simulations on real-world
network datasets, in order to quantify the effects of the initial biases and
the weightage attributed by nodes to their initial biases, as well as that of a
camp deviating from its equilibrium strategy. Our main conclusion is that, if
nodes attribute high weightage to their initial biases, it is advantageous to
have a high investment in the first phase, so as to effectively influence the
biases to be harnessed in the second phase
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