Ieee 2013 matlab abstracts part b

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IEEE 2013 MATLAB PROJECTS ACADEMIC YEAR 2013-2014 FOR M.Tech/ B.E/B.Tech
20. Phase Noise in MIMO Systems: Bayesian Cram´er-Rao Bounds and Soft-Input Estimation
Abstract: This paper addresses the problem of estimating time varying phase noise caused by
imperfect oscillators in multiple-input multiple-output (MIMO) systems. The estimation
problem is parameterized in detail and based on an equivalent signal model its dimensionality
is r
-Rao lower bounds (BCRLBs) and soft-input
maximum a posteriori (MAP) estimators for online, i.e., filtering, and offline, i.e., smoothing,
estimation of phase noise over the length of a frame are derived. Simulations demonstrate that
the proposed MAP estimators' mean-square error (MSE) performances are very close to the
derived BCRLBs at moderate-to-high signal-to-noise ratios. To reduce the overhead and
complexity associated with tracking the phase noise processes over the length of a frame, a
novel soft-input extended Kalman filter (EKF) and extended Kalman smoother (EKS) that use
soft statistics of the transmitted symbols given the current observations are proposed.
Numerical results indicate that by employing the proposed phase tracking approach, the biterror rate performance of a MIMO system affected by phase noise can be significantly
improved. In addition, simulation results indicate that the proposed phase noise estimation
scheme allows for application of higher order modulations and larger numbers of antennas in
MIMO systems that employ imperfect oscillators.
Published in: Signal Processing, IEEE Transactions on (Volume:61 , Issue: 10 )
Issue Date : May15, 2013
Index Terms—Multi-input multi-output (MIMO), Wiener phase noise, Bayesian Cram´er Rao
lower bound (BCRLB), maximum-a-posteriori (MAP), soft-decision extended Kalman filter (EKF),
and extended Kalman smoother (EKS).
21. Multi scale Gossip for Efficient Decentralized Averaging in Wireless Packet Networks
Abstract: This paper describes and analyzes a hierarchical algorithm called Multi
scale Gossip for solving the distributed average consensus problem in wireless sensor networks.
The algorithm proceeds by recursively partitioning a given network. Initially, nodes at the finest
scale gossip to compute local averages. Then, using multi-hop communication and geographic
routing to communicate between nodes that are not directly connected, these
local averages are progressively fused up the hierarchy until the global average is computed.
We show that the proposed hierarchical scheme with k=Θ(loglogn) levels of hierarchy is
competitive with state-of-the-art randomized gossip algorithms in terms of message
complexity, achieving ε-accuracy with high probability after O(n loglogn log*1/(ε)+ ) single-hop
messages. Key to our analysis is the way in which the network is recursively partitioned. We


find that the above scaling law is achieved when sub networks at scale j contain O(n(2/3)j) nodes;
then the message complexity at any individual scale is O(n log*1/ε+). Another important
consequence of the hierarchical construction is that the longest distance over which messages
are exchanged is O(n1/3) hops (at the highest scale), and most messages (at lower scales) travel
shorter distances. In networks that use link-level acknowledgements, this results in less
congestion and resource usage by reducing message retransmissions. Simulations illustrate that
the proposed scheme is more efficient than state-of-the-art randomized gossip algorithms
based on averaging along paths.
Date of Publication: May1, 2013
22. Joint Estimation of Channel and Oscillator Phase Noise in MIMO Systems
Abstract: Oscillator phase noise limits the performance of high speed communication systems since
it results in time varying channels and rotation of the signal constellation from symbol to
symbol. In this paper, jointestimation of channel gains and Wiener phase noise in multi-input multioutput (MIMO) systems is analyzed. The signal model for the estimation problem is
outlined in detail and
-Rao lower bounds (CRLBs) for the multiparameter estimation problem are derived. A data-aided least-squares (LS) estimator for jointly
obtaining the channel gains and phase noise parameters is derived. Next, a decision-directed
weighted least-squares (WLS) estimator is proposed, where pilots and estimated data symbols are
employed to track the time-varying phase noise parameters over a frame. In order to reduce the
overhead and delay associated with the estimation process, a new decision-directed extended Kalman
filter (EKF) is proposed for tracking the MIMO phase noise throughout a frame. Numerical results show
that the proposed LS, WLS, and EKF estimators' performances are close to the CRLB. Finally, simulation
results
demonstrate
that
by
employing
the
proposed channel and timevarying phase noise estimators the bit-error rate performance of a MIMO system can be significantly
improved.

Date of Publication: Sept. 2012
Index Terms—Channel estimation, Cramér-Rao lower bound (CRLB), extended Kalman filter
(EKF), multi-input multi-output (MIMO), weighted least squares (WLS), Wiener phase noise.
23. Accurate Computation of the MGF of the Lognormal Distribution and its Application to
Sum of Lognormals
Abstract:

Sums of lognormal random
communications and other

variables

(RVs)

are of wide interest in wireless
areas of science and engineering.



Since the distribution of lognormal sums is not log-normal and does not have a closed-form
analytical expression, many approximations and bounds have been developed. This paper develops two
computational methods for the moment generating function (MGF) or the characteristic function
(CHF) of a single lognormal RV. The first method uses classical complex integration techniques based
on steepest-descent integration. The saddle point of the integrand is explicitly expressed
by the Lambert function. The steepest-descent (optimal) contour and two closely-related closed-form
contours are derived. A simple integration rule (e.g., the midpoint rule) along any of these contours
computes the MGF/CHF with high accuracy. The second approach uses a variation on the trapezoidal
rule due to Ooura and Mori. Importantly, the cumulative distribution function of lognormalsums is
derived as an alternating series and convergence acceleration via the Epsilon algorithm is used
to reduce, in some cases, the computational load by a factor of 106! Overall, accuracy levels of 13 to 15
significant digits are readily achievable.

Published in: Communications, IEEE Transactions on (Volume:58 , Issue: 5 )
Date of Publication: May 2010
Index Terms—Sum of lognormals, moment-generating function, characteristic function.
24. Compressed Sensing of EEG for Wireless Tele monitoring with Low Energy Consumption
and Inexpensive Hardware
Abstract: Tele monitoring of electroencephalogram (EEG) through wireless body-area networks
is an evolving direction in personalized medicine. Among various constraints in designing such a
system, three important constraints are energy consumption, data compression, and device
cost. Conventional data compression methodologies, although effective in data compression,
consumes significant energy and cannot reduce device cost. Compressed sensing (CS), as an
emerging data compression methodology, is promising in catering to these constraints.
However, EEG is non sparse in the time domain and also non sparse in transformed domains
(such as the wavelet domain). Therefore, it is extremely difficult for current CS algorithms to
recover EEG with the
quality
that
satisfies
the
requirements of clinical
diagnosis and engineering applications. Recently, block sparse Bayesian learning (BSBL) was
proposed as a new method to the CS problem. This study introduces the technique to the tele
monitoring of EEG. Experimental results show that its recovery quality is better than state-ofthe-art CS algorithms, and sufficient for practical use. These results suggest that BSBL is very
promising for tele monitoring of EEG and other non sparse physiological signals.
Published in: Biomedical Engineering, IEEE Transactions on (Volume:60 , Issue: 1 )
Date of Publication: Jan. 2013



Index Terms—Telemonitoring, Healthcare, Wireless Body- Area Network (WBAN), Compressed
Sensing (CS), Block Sparse Bayesian Learning (BSBL), electroencephalogram (EEG)
25. Compressed Sensing for Energy-Efficient Wireless Tele monitoring of Non-Invasive Fetal
ECG via Block Sparse Bayesian Learning
Abstract: Fetal ECG (FECG) tele monitoring is an important branch in telemedicine. The
design of a
tele
monitoring system via a wireless body
area
network
with
low energy consumption for ambulatory use is highly desirable. As an emerging
technique, compressed sensing (CS) shows great promise in compressing/reconstructing data
with low energy consumption. However, due to some specific characteristics of raw FECG
recordings such as non sparsity and strong noise contamination, current CS algorithms
generally fail in this application. This paper proposes to use the block sparse Bayesian
learning framework to compress/reconstruct non sparse raw FECG recordings. Experimental
results show that the framework can reconstruct the raw recordings with high quality.
Especially, the reconstruction does not destroy the interdependence relation among the
multichannel recordings. This ensures that the independent component analysis
decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework
allows the use of a sparse binary sensing matrix with much fewer nonzero entries
to compress recordings. Particularly, each column of the matrix can contain only two nonzero
entries. This shows that the framework, compared to other algorithms such as current CS
algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data
compression stage.
Published in: Biomedical Engineering, IEEE Transactions on (Volume:60 , Issue: 2 )
Date of Publication: Feb. 2013
Index Terms—Fetal ECG (FECG), Tele monitoring, Telemedicine, Healthcare, Block Sparse
Bayesian Learning (BSBL), Compressed Sensing (CS), Independent Component Analysis (ICA)


Ieee 2013 matlab abstracts part b

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Ieee 2013 matlab abstracts part b