190 research outputs found
Difference Antenna Selection and Power Allocation for Wireless Cognitive Systems
In this paper, we propose an antenna selection method in a wireless cognitive
radio (CR) system, namely difference selection, whereby a single transmit
antenna is selected at the secondary transmitter out of possible antennas
such that the weighted difference between the channel gains of the data link
and the interference link is maximized. We analyze mutual information and
outage probability of the secondary transmission in a CR system with difference
antenna selection, and propose a method of optimizing these performance metrics
of the secondary data link subject to practical constraints on the peak
secondary transmit power and the average interference power as seen by the
primary receiver. The optimization is performed over two parameters: the peak
secondary transmit power and the difference selection weight . We show that, difference selection using the optimized parameters
determined by the proposed method can be, in many cases of interest, superior
to a so called ratio selection method disclosed in the literature, although
ratio selection has been shown to be optimal, when impractically, the secondary
transmission power constraint is not applied. We address the effects that the
constraints have on mutual information and outage probability, and discuss the
practical implications of the results.Comment: 29 pages, 9 figures, to be submitted to IEEE Transactions on
Communication
An Approximation of the First Order Marcum -Function with Application to Network Connectivity Analysis
An exponential-type approximation of the first order Marcum -function is
presented, which is robust to changes in its first argument and can easily be
integrated with respect to the second argument. Such characteristics are
particularly useful in network connectivity analysis. The proposed
approximation is exact in the limit of small first argument of the Marcum
-function, in which case the optimal parameters can be obtained
analytically. For larger values of the first argument, an optimization problem
is solved, and the parameters can be accurately represented using regression
analysis. Numerical results indicate that the proposed methods result in
approximations very close to the actual Marcum -function for small and
moderate values of the first argument. We demonstrate the accuracy of the
approximation by using it to analyze the connectivity properties of random ad
hoc networks operating in a Rician fading environment.Comment: 6 pages, 4 figures, 1 tabl
Quantum Enhanced Classical Sensor Networks
The quantum enhanced classical sensor network consists of clusters of
entangled quantum states that have been trialled times, each feeding
into a classical estimation process. Previous literature has shown that each
cluster can {ideally} achieve an estimation variance of for
sufficient . We begin by deriving the optimal values for the minimum mean
squared error of this quantum enhanced classical system. We then show that if
noise is \emph{absent} in the classical estimation process, the mean estimation
error will decay like . However, when noise is
\emph{present} we find that the mean estimation error will decay like
, so that \emph{all} the sensing gains obtained from the
individual quantum clusters will be lost
On the Distribution of Random Geometric Graphs
Random geometric graphs (RGGs) are commonly used to model networked systems
that depend on the underlying spatial embedding. We concern ourselves with the
probability distribution of an RGG, which is crucial for studying its random
topology, properties (e.g., connectedness), or Shannon entropy as a measure of
the graph's topological uncertainty (or information content). Moreover, the
distribution is also relevant for determining average network performance or
designing protocols. However, a major impediment in deducing the graph
distribution is that it requires the joint probability distribution of the
distances between nodes randomly distributed in a bounded
domain. As no such result exists in the literature, we make progress by
obtaining the joint distribution of the distances between three nodes confined
in a disk in . This enables the calculation of the probability
distribution and entropy of a three-node graph. For arbitrary , we derive a
series of upper bounds on the graph entropy; in particular, the bound involving
the entropy of a three-node graph is tighter than the existing bound which
assumes distances are independent. Finally, we provide numerical results on
graph connectedness and the tightness of the derived entropy bounds.Comment: submitted to the IEEE International Symposium on Information Theory
201
Adaptive OFDM Index Modulation for Two-Hop Relay-Assisted Networks
In this paper, we propose an adaptive orthogonal frequency-division
multiplexing (OFDM) index modulation (IM) scheme for two-hop relay networks. In
contrast to the traditional OFDM IM scheme with a deterministic and fixed
mapping scheme, in this proposed adaptive OFDM IM scheme, the mapping schemes
between a bit stream and indices of active subcarriers for the first and second
hops are adaptively selected by a certain criterion. As a result, the active
subcarriers for the same bit stream in the first and second hops can be varied
in order to combat slow frequency-selective fading. In this way, the system
reliability can be enhanced. Additionally, considering the fact that a relay
device is normally a simple node, which may not always be able to perform
mapping scheme selection due to limited processing capability, we also propose
an alternative adaptive methodology in which the mapping scheme selection is
only performed at the source and the relay will simply utilize the selected
mapping scheme without changing it. The analyses of average outage probability,
network capacity and symbol error rate (SER) are given in closed form for
decode-and-forward (DF) relaying networks and are substantiated by numerical
results generated by Monte Carlo simulations.Comment: 30 page
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