29,791 research outputs found
Experimental investigation of high-dimensional quantum key distribution protocols with twisted photons
Quantum key distribution is on the verge of real world applications, where
perfectly secure information can be distributed among multiple parties. Several
quantum cryptographic protocols have been theoretically proposed and
independently realized in different experimental conditions. Here, we develop
an experimental platform based on high-dimensional orbital angular momentum
states of single photons that enables implementation of multiple quantum key
distribution protocols with a single experimental apparatus. Our versatile
approach allows us to experimentally survey different classes of quantum key
distribution techniques, such as the 1984 Bennett \& Brassard (BB84),
tomographic protocols including the six-state and the Singapore protocol, and
to investigate, for the first time, a recently introduced differential phase
shift (Chau15) protocol using twisted photons. This enables us to
experimentally compare the performance of these techniques and discuss their
benefits and deficiencies in terms of noise tolerance in different dimensions.Comment: 13 pages, 4 figures, 1 tabl
Sub-channel Assignment, Power Allocation and User Scheduling for Non-Orthogonal Multiple Access Networks
In this paper, we study the resource allocation and user scheduling problem
for a downlink nonorthogonal multiple access network where the base station
allocates spectrum and power resources to a set of users. We aim to jointly
optimize the sub-channel assignment and power allocation to maximize the
weighted total sum-rate while taking into account user fairness. We formulate
the sub-channel allocation problem as equivalent to a many-to-many two-sided
user-subchannel matching game in which the set of users and sub-channels are
considered as two sets of players pursuing their own interests. We then propose
a matching algorithm which converges to a two-side exchange stable matching
after a limited number of iterations. A joint solution is thus provided to
solve the sub-channel assignment and power allocation problems iteratively.
Simulation results show that the proposed algorithm greatly outperforms the
orthogonal multiple access scheme and a previous non-orthogonal multiple access
scheme.Comment: Accepted as a regular paper by IEEE Transactions on Wireless
Communication
Completion-Time-Driven Scheduling for Uplink NOMA-Enabled Wireless Networks
Efficient scheduling policy is crucial in wireless
networks due to delay-sensitivity of many emerging applications.
In this work, we consider a joint user pairing and scheduling
(UPaS) scheme for multi-carrier non-orthogonal multiple access
(MC-NOMA)-enabled wireless networks to reduce the maximum
completion time of serving uplink users. The NOMA scheduling
problem is shown to be NP-hard and a shortest processing time
(SPT)-based strategy to solve the same problem within affordable
time and complexity is introduced. The simulation results confirm
the efficacy of the proposed scheduling scheme in terms of
the maximum completion time in comparison with orthogonal
multiple access (OMA) and random NOMA pairing
Multivariate discrimination and the Higgs + W/Z search
A systematic method for optimizing multivariate discriminants is developed
and applied to the important example of a light Higgs boson search at the
Tevatron and the LHC. The Significance Improvement Characteristic (SIC),
defined as the signal efficiency of a cut or multivariate discriminant divided
by the square root of the background efficiency, is shown to be an extremely
powerful visualization tool. SIC curves demonstrate numerical instabilities in
the multivariate discriminants, show convergence as the number of variables is
increased, and display the sensitivity to the optimal cut values. For our
application, we concentrate on Higgs boson production in association with a W
or Z boson with H -> bb and compare to the irreducible standard model
background, Z/W + bb. We explore thousands of experimentally motivated,
physically motivated, and unmotivated single variable discriminants. Along with
the standard kinematic variables, a number of new ones, such as twist, are
described which should have applicability to many processes. We find that some
single variables, such as the pull angle, are weak discriminants, but when
combined with others they provide important marginal improvement. We also find
that multiple Higgs boson-candidate mass measures, such as from mild and
aggressively trimmed jets, when combined may provide additional discriminating
power. Comparing the significance improvement from our variables to those used
in recent CDF and DZero searches, we find that a 10-20% improvement in
significance against Z/W + bb is possible. Our analysis also suggests that the
H + W/Z channel with H -> bb is also viable at the LHC, without requiring a
hard cut on the W/Z transverse momentum.Comment: 41 pages, 5 tables, 29 figure
DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC
From MDPI via Jisc Publications RouterHistory: accepted 2021-05-11, pub-electronic 2021-05-14Publication status: PublishedMulti-access edge computing (MEC) and non-orthogonal multiple access (NOMA) are regarded as promising technologies to improve the computation capability and offloading efficiency of mobile devices in the sixth-generation (6G) mobile system. This paper mainly focused on the hybrid NOMA-MEC system, where multiple users were first grouped into pairs, and users in each pair offloaded their tasks simultaneously by NOMA, then a dedicated time duration was scheduled to the more delay-tolerant user for uploading the remaining data by orthogonal multiple access (OMA). For the conventional NOMA uplink transmission, successive interference cancellation (SIC) was applied to decode the superposed signals successively according to the channel state information (CSI) or the quality of service (QoS) requirement. In this work, we integrated the hybrid SIC scheme, which dynamically adapts the SIC decoding order among all NOMA groups. To solve the user grouping problem, a deep reinforcement learning (DRL)-based algorithm was proposed to obtain a close-to-optimal user grouping policy. Moreover, we optimally minimized the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results showed that the proposed algorithm converged fast, and the NOMA-MEC scheme outperformed the existing orthogonal multiple access (OMA) scheme
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