56 research outputs found
Rateless-Coding-Assisted Multi-Packet Spreading over Mobile Networks
A novel Rateless-coding-assisted Multi-Packet Relaying (RMPR) protocol is
proposed for large-size data spreading in mobile wireless networks. With this
lightweight and robust protocol, the packet redundancy is reduced by a factor
of , while the spreading time is reduced at least by a factor of . Closed-form bounds and explicit non-asymptotic results are presented,
which are further validated through simulations. Besides, the packet
duplication phenomenon in the network setting is analyzed for the first time
Faster Information Propagation on Highways: a Virtual MIMO Approach
In vehicular communications, traffic-related information should be spread
over the network as quickly as possible to maintain a safe and reliable
transportation system. This motivates us to develop more efficient information
propagation schemes. In this paper, we propose a novel cluster-based
cooperative information forwarding scheme, in which the vehicles
opportunistically form virtual antenna arrays to boost one-hop transmission
range and therefore accelerate information propagation along the highway. Both
closed-form results of the transmission range gain and the improved Information
Propagation Speed (IPS) are derived and verified by simulations. It is observed
that the proposed scheme demonstrates the most significant IPS gain in moderate
traffic scenarios, whereas too dense or too sparse vehicle density results in
less gain. Moreover, it is also shown that increased mobility offers more
contact opportunities and thus facilitates information propagation.Comment: IEEE 2014 Global Telecommunications Conference (GLOBECOM 2014) -
Communication Theory Symposiu
Mobile Conductance in Sparse Networks and Mobility-Connectivity Tradeoff
In this paper, our recently proposed mobile-conductance based analytical
framework is extended to the sparse settings, thus offering a unified tool for
analyzing information spreading in mobile networks. A penalty factor is
identified for information spreading in sparse networks as compared to the
connected scenario, which is then intuitively interpreted and verified by
simulations. With the analytical results obtained, the mobility-connectivity
tradeoff is quantitatively analyzed to determine how much mobility may be
exploited to make up for network connectivity deficiency.Comment: Accepted to ISIT 201
Soft Consistency Reconstruction: A Robust 1-bit Compressive Sensing Algorithm
A class of recovering algorithms for 1-bit compressive sensing (CS) named
Soft Consistency Reconstructions (SCRs) are proposed. Recognizing that CS
recovery is essentially an optimization problem, we endeavor to improve the
characteristics of the objective function under noisy environments. With a
family of re-designed consistency criteria, SCRs achieve remarkable
counter-noise performance gain over the existing counterparts, thus acquiring
the desired robustness in many real-world applications. The benefits of soft
decisions are exemplified through structural analysis of the objective
function, with intuition described for better understanding. As expected,
through comparisons with existing methods in simulations, SCRs demonstrate
preferable robustness against noise in low signal-to-noise ratio (SNR) regime,
while maintaining comparable performance in high SNR regime
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