419 research outputs found
Instantly Decodable Network Coding for Real-Time Scalable Video Broadcast over Wireless Networks
In this paper, we study a real-time scalable video broadcast over wireless
networks in instantly decodable network coded (IDNC) systems. Such real-time
scalable video has a hard deadline and imposes a decoding order on the video
layers.We first derive the upper bound on the probability that the individual
completion times of all receivers meet the deadline. Using this probability, we
design two prioritized IDNC algorithms, namely the expanding window IDNC
(EW-IDNC) algorithm and the non-overlapping window IDNC (NOW-IDNC) algorithm.
These algorithms provide a high level of protection to the most important video
layer before considering additional video layers in coding decisions. Moreover,
in these algorithms, we select an appropriate packet combination over a given
number of video layers so that these video layers are decoded by the maximum
number of receivers before the deadline. We formulate this packet selection
problem as a two-stage maximal clique selection problem over an IDNC graph.
Simulation results over a real scalable video stream show that our proposed
EW-IDNC and NOW-IDNC algorithms improve the received video quality compared to
the existing IDNC algorithms
Resource Allocation Frameworks for Network-coded Layered Multimedia Multicast Services
The explosive growth of content-on-the-move, such as video streaming to
mobile devices, has propelled research on multimedia broadcast and multicast
schemes. Multi-rate transmission strategies have been proposed as a means of
delivering layered services to users experiencing different downlink channel
conditions. In this paper, we consider Point-to-Multipoint layered service
delivery across a generic cellular system and improve it by applying different
random linear network coding approaches. We derive packet error probability
expressions and use them as performance metrics in the formulation of resource
allocation frameworks. The aim of these frameworks is both the optimization of
the transmission scheme and the minimization of the number of broadcast packets
on each downlink channel, while offering service guarantees to a predetermined
fraction of users. As a case of study, our proposed frameworks are then adapted
to the LTE-A standard and the eMBMS technology. We focus on the delivery of a
video service based on the H.264/SVC standard and demonstrate the advantages of
layered network coding over multi-rate transmission. Furthermore, we establish
that the choice of both the network coding technique and resource allocation
method play a critical role on the network footprint, and the quality of each
received video layer.Comment: IEEE Journal on Selected Areas in Communications - Special Issue on
Fundamental Approaches to Network Coding in Wireless Communication Systems.
To appea
Partitioning Procedure for Polynomial Optimization: Application to Portfolio Decisions with Higher Order Moments
We consider the problem of finding the minimum of a real-valued multivariate polynomial function constrained in a compact set defined by polynomial inequalities and equalities. This problem, called polynomial optimization problem (POP), is generally nonconvex and has been of growing interest to many researchers in recent years. Our goal is to tackle POPs using decomposition. Towards this goal we introduce a partitioning procedure. The problem manipulations are in line with the pattern used in the Benders decomposition [1], namely relaxation preceded by projection. Stengle’s and Putinar’s Positivstellensatz are employed to derive the so-called feasibility and optimality constraints, respectively. We test the performance of the proposed method on a collection of benchmark problems and we present the numerical results. As an application, we consider the problem of selecting an investment portfolio optimizing the mean, variance, skewness and kurtosis of the portfolio.Polynomial optimization, Semidefinite relaxations, Positivstellensatz, Sum of squares, Benders decomposition, Portfolio optimization
Optimized Network-coded Scalable Video Multicasting over eMBMS Networks
Delivery of multicast video services over fourth generation (4G) networks
such as 3GPP Long Term Evolution-Advanced (LTE-A) is gaining momentum. In this
paper, we address the issue of efficiently multicasting layered video services
by defining a novel resource allocation framework that aims to maximize the
service coverage whilst keeping the radio resource footprint low. A key point
in the proposed system mode is that the reliability of multicast video services
is ensured by means of an Unequal Error Protection implementation of the
Network Coding (UEP-NC) scheme. In addition, both the communication parameters
and the UEP-NC scheme are jointly optimized by the proposed resource allocation
framework. Numerical results show that the proposed allocation framework can
significantly increase the service coverage when compared to a conventional
Multi-rate Transmission (MrT) strategy.Comment: Proc. of IEEE ICC 2015 - Mobile and Wireless Networking Symposium, to
appea
Sum Throughput Maximization in Multi-Tag Backscattering to Multiantenna Reader
Backscatter communication (BSC) is being realized as the core technology for
pervasive sustainable Internet-of-Things applications. However, owing to the
resource-limitations of passive tags, the efficient usage of multiple antennas
at the reader is essential for both downlink excitation and uplink detection.
This work targets at maximizing the achievable sum-backscattered-throughput by
jointly optimizing the transceiver (TRX) design at the reader and
backscattering coefficients (BC) at the tags. Since, this joint problem is
nonconvex, we first present individually-optimal designs for the TRX and BC. We
show that with precoder and {combiner} designs at the reader respectively
targeting downlink energy beamforming and uplink Wiener filtering operations,
the BC optimization at tags can be reduced to a binary power control problem.
Next, the asymptotically-optimal joint-TRX-BC designs are proposed for both low
and high signal-to-noise-ratio regimes. Based on these developments, an
iterative low-complexity algorithm is proposed to yield an efficient
jointly-suboptimal design. Thereafter, we discuss the practical utility of the
proposed designs to other application settings like wireless powered
communication networks and BSC with imperfect channel state information.
Lastly, selected numerical results, validating the analysis and shedding novel
insights, demonstrate that the proposed designs can yield significant
enhancement in the sum-backscattered throughput over existing benchmarks.Comment: 17 pages, 5 figures, accepted for publication in IEEE Transactions on
Communication
Growth Optimal Portfolio : Analysis and construction on a discrete multi-period market
This thesis provides an analysis of Growth Optimal Portfolio (GOP) in discrete time. Growth Optimal Portfolio is a portfolio optimization method that aims to maximize expected long-term growth. One of the main properties of GOP is that, as time horizon increases, it outperforms all other trading strategies almost surely. Therefore, when compared with the other common methods of portfolio construction, GOP performs well in the long-term but might provide riskier allocations in the short-term.
The first half of the thesis considers GOP from a theoretical perspective. Connections to the other concepts (numeraire portfolio, arbitrage freedom) are examined and derivations of optimal properties are given. Several examples where GOP has explicit solutions are provided and sufficiency and necessity conditions for growth optimality are derived.
Yet, the main focus of this thesis is on the practical aspects of GOP construction. The iterative algorithm for finding GOP weights in the case of independently log-normally distributed growth rates of underlying assets is proposed. Following that, the algorithm is extended to the case with non-diagonal covariance structure and the case with the presence of a risk-free asset on the market. Finally, it is shown how GOP can be implemented as a trading strategy on the market when underlying assets are modelled by ARMA or VAR models. The simulations with assets from the real market are provided for the time period 2014-2019.
Overall, a practical step-by-step procedure for constructing GOP strategies with data from the real market is developed. Given the simplicity of the procedure and appealing properties of GOP, it can be used in practice as well as other common models such as Markowitz or Black-Litterman model for constructing portfolios
- …