845 research outputs found
Vehicle Communication using Secrecy Capacity
We address secure vehicle communication using secrecy capacity. In
particular, we research the relationship between secrecy capacity and various
types of parameters that determine secrecy capacity in the vehicular wireless
network. For example, we examine the relationship between vehicle speed and
secrecy capacity, the relationship between the response time and secrecy
capacity of an autonomous vehicle, and the relationship between transmission
power and secrecy capacity. In particular, the autonomous vehicle has set the
system modeling on the assumption that the speed of the vehicle is related to
the safety distance. We propose new vehicle communication to maintain a certain
level of secrecy capacity according to various parameters. As a result, we can
expect safer communication security of autonomous vehicles in 5G
communications.Comment: 17 Pages, 12 Figure
Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks
Spectrum sensing, which aims at detecting spectrum holes, is the precondition
for the implementation of cognitive radio (CR). Collaborative spectrum sensing
among the cognitive radio nodes is expected to improve the ability of checking
complete spectrum usage. Due to hardware limitations, each cognitive radio node
can only sense a relatively narrow band of radio spectrum. Consequently, the
available channel sensing information is far from being sufficient for
precisely recognizing the wide range of unoccupied channels. Aiming at breaking
this bottleneck, we propose to apply matrix completion and joint sparsity
recovery to reduce sensing and transmitting requirements and improve sensing
results. Specifically, equipped with a frequency selective filter, each
cognitive radio node senses linear combinations of multiple channel information
and reports them to the fusion center, where occupied channels are then decoded
from the reports by using novel matrix completion and joint sparsity recovery
algorithms. As a result, the number of reports sent from the CRs to the fusion
center is significantly reduced. We propose two decoding approaches, one based
on matrix completion and the other based on joint sparsity recovery, both of
which allow exact recovery from incomplete reports. The numerical results
validate the effectiveness and robustness of our approaches. In particular, in
small-scale networks, the matrix completion approach achieves exact channel
detection with a number of samples no more than 50% of the number of channels
in the network, while joint sparsity recovery achieves similar performance in
large-scale networks.Comment: 12 pages, 11 figure
Compressive Sensing for Feedback Reduction in MIMO Broadcast Channels
We propose a generalized feedback model and compressive sensing based
opportunistic feedback schemes for feedback resource reduction in MIMO
Broadcast Channels under the assumption that both uplink and downlink channels
undergo block Rayleigh fading. Feedback resources are shared and are
opportunistically accessed by users who are strong, i.e. users whose channel
quality information is above a certain fixed threshold. Strong users send same
feedback information on all shared channels. They are identified by the base
station via compressive sensing. Both analog and digital feedbacks are
considered. The proposed analog & digital opportunistic feedback schemes are
shown to achieve the same sum-rate throughput as that achieved by dedicated
feedback schemes, but with feedback channels growing only logarithmically with
number of users. Moreover, there is also a reduction in the feedback load. In
the analog feedback case, we show that the propose scheme reduces the feedback
noise which eventually results in better throughput, whereas in the digital
feedback case the proposed scheme in a noisy scenario achieves almost the
throughput obtained in a noiseless dedicated feedback scenario. We also show
that for a fixed given budget of feedback bits, there exist a trade-off between
the number of shared channels and thresholds accuracy of the feedback SINR.Comment: Submitted to IEEE Transactions on Wireless Communications, April 200
Performance Limits of Compressive Sensing Channel Estimation in Dense Cloud RAN
Towards reducing the training signaling overhead in large scale and dense
cloud radio access networks (CRAN), various approaches have been proposed based
on the channel sparsification assumption, namely, only a small subset of the
deployed remote radio heads (RRHs) are of significance to any user in the
system. Motivated by the potential of compressive sensing (CS) techniques in
this setting, this paper provides a rigorous description of the performance
limits of many practical CS algorithms by considering the performance of the,
so called, oracle estimator, which knows a priori which RRHs are of
significance but not their corresponding channel values. By using tools from
stochastic geometry, a closed form analytical expression of the oracle
estimator performance is obtained, averaged over distribution of RRH positions
and channel statistics. Apart from a bound on practical CS algorithms, the
analysis provides important design insights, e.g., on how the training sequence
length affects performance, and identifies the operational conditions where the
channel sparsification assumption is valid. It is shown that the latter is true
only in operational conditions with sufficiently large path loss exponents.Comment: 6 pages, two-column format; ICC 201
- …