6,280 research outputs found
Single-photon-assisted entanglement concentration of a multi-photon system in a partially entangled W state with weak cross-Kerr nonlinearity
We propose a nonlocal entanglement concentration protocol (ECP) for
-photon systems in a partially entangled W state, resorting to some
ancillary single photons and the parity-check measurement based on cross-Kerr
nonlinearity. One party in quantum communication first performs a parity-check
measurement on her photon in an -photon system and an ancillary photon, and
then she picks up the even-parity instance for obtaining the standard W state.
When she obtains an odd-parity instance, the system is in a less-entanglement
state and it is the resource in the next round of entanglement concentration.
By iterating the entanglement concentration process several times, the present
ECP has the total success probability approaching to the limit in theory. The
present ECP has the advantage of a high success probability. Moreover, the
present ECP requires only the -photon system itself and some ancillary
single photons, not two copies of the systems, which decreases the difficulty
of its implementation largely in experiment. It maybe have good applications in
quantum communication in future.Comment: 7 pages, 3 figure
Dithiolopyrrolone Natural Products : Isolation, Synthesis and Biosynthesis
Peer reviewedPublisher PD
Seeing the Unobservable: Channel Learning for Wireless Communication Networks
Wireless communication networks rely heavily on channel state information
(CSI) to make informed decision for signal processing and network operations.
However, the traditional CSI acquisition methods is facing many difficulties:
pilot-aided channel training consumes a great deal of channel resources and
reduces the opportunities for energy saving, while location-aided channel
estimation suffers from inaccurate and insufficient location information. In
this paper, we propose a novel channel learning framework, which can tackle
these difficulties by inferring unobservable CSI from the observable one. We
formulate this framework theoretically and illustrate a special case in which
the learnability of the unobservable CSI can be guaranteed. Possible
applications of channel learning are then described, including cell selection
in multi-tier networks, device discovery for device-to-device (D2D)
communications, as well as end-to-end user association for load balancing. We
also propose a neuron-network-based algorithm for the cell selection problem in
multi-tier networks. The performance of this algorithm is evaluated using
geometry-based stochastic channel model (GSCM). In settings with 5 small cells,
the average cell-selection accuracy is 73% - only a 3.9% loss compared with a
location-aided algorithm which requires genuine location information.Comment: 6 pages, 4 figures, accepted by GlobeCom'1
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