20,576 research outputs found
Joint Secure Beamforming for Cognitive Radio Networks with Untrusted Secondary Users
In this paper, we consider simultaneous wireless information and power
transfer (SWIPT) in orthogonal frequency division multiple access (OFDMA)
systems with the coexistence of information receivers (IRs) and energy
receivers (ERs). The IRs are served with best-effort secrecy data and the ERs
harvest energy with minimum required harvested power. To enhance physical-layer
security and yet satisfy energy harvesting requirements, we introduce a new
frequency-domain artificial noise based approach. We study the optimal resource
allocation for the weighted sum secrecy rate maximization via transmit power
and subcarrier allocation. The considered problem is non-convex, while we
propose an efficient algorithm for solving it based on Lagrange duality method.
Simulation results illustrate the effectiveness of the proposed algorithm as
compared against other heuristic schemes.Comment: To appear in Globecom 201
Secrecy Wireless Information and Power Transfer in OFDMA Systems
In this paper, we consider simultaneous wireless information and power
transfer (SWIPT) in orthogonal frequency division multiple access (OFDMA)
systems with the coexistence of information receivers (IRs) and energy
receivers (ERs). The IRs are served with best-effort secrecy data and the ERs
harvest energy with minimum required harvested power. To enhance physical-layer
security and yet satisfy energy harvesting requirements, we introduce a new
frequency-domain artificial noise based approach. We study the optimal resource
allocation for the weighted sum secrecy rate maximization via transmit power
and subcarrier allocation. The considered problem is non-convex, while we
propose an efficient algorithm for solving it based on Lagrange duality method.
Simulation results illustrate the effectiveness of the proposed algorithm as
compared against other heuristic schemes.Comment: To appear in Globecom 201
Progenitor delay-time distribution of short gamma-ray bursts: Constraints from observations
Context. The progenitors of short gamma-ray bursts (SGRBs) have not yet been
well identified. The most popular model is the merger of compact object
binaries (NS-NS/NS-BH). However, other progenitor models cannot be ruled out.
The delay-time distribution of SGRB progenitors, which is an important property
to constrain progenitor models, is still poorly understood. Aims. We aim to
better constrain the luminosity function of SGRBs and the delay-time
distribution of their progenitors with newly discovered SGRBs. Methods. We
present a low-contamination sample of 16 Swift SGRBs that is better defined by
a duration shorter than 0.8 s. By using this robust sample and by combining a
self-consistent star formation model with various models for the distribution
of time delays, the redshift distribution of SGRBs is calculated and then
compared to the observational data. Results. We find that the power-law delay
distribution model is disfavored and that only the lognormal delay distribution
model with the typical delay tau >= 3 Gyr is consistent with the data.
Comparing Swift SGRBs with T90 > 0.8 s to our robust sample (T90 < 0.8 s), we
find a significant difference in the time delays between these two samples.
Conclusions. Our results show that the progenitors of SGRBs are dominated by
relatively long-lived systems (tau >= 3 Gyr), which contrasts the results found
for Type Ia supernovae. We therefore conclude that primordial NS-NS systems are
not favored as the dominant SGRB progenitors. Alternatively, dynamically formed
NS-NS/BH and primordial NS-BH systems with average delays longer than 5 Gyr may
contribute a significant fraction to the overall SGRB progenitors.Comment: 8 pages, 6 figures, Astron. Astrophys. in pres
Learning how to Active Learn: A Deep Reinforcement Learning Approach
Active learning aims to select a small subset of data for annotation such
that a classifier learned on the data is highly accurate. This is usually done
using heuristic selection methods, however the effectiveness of such methods is
limited and moreover, the performance of heuristics varies between datasets. To
address these shortcomings, we introduce a novel formulation by reframing the
active learning as a reinforcement learning problem and explicitly learning a
data selection policy, where the policy takes the role of the active learning
heuristic. Importantly, our method allows the selection policy learned using
simulation on one language to be transferred to other languages. We demonstrate
our method using cross-lingual named entity recognition, observing uniform
improvements over traditional active learning.Comment: To appear in EMNLP 201
Energy Harvesting for Secure OFDMA Systems
Energy harvesting and physical-layer security in wireless networks are of
great significance. In this paper, we study the simultaneous wireless
information and power transfer (SWIPT) in downlink orthogonal
frequency-division multiple access (OFDMA) systems, where each user applies
power splitting to coordinate the energy harvesting and information decoding
processes while secrecy information requirement is guaranteed. The problem is
formulated to maximize the aggregate harvested power at the users while
satisfying secrecy rate requirements of all users by subcarrier allocation and
the optimal power splitting ratio selection. Due to the NP-hardness of the
problem, we propose an efficient iterative algorithm. The numerical results
show that the proposed method outperforms conventional methods.Comment: Accepted by WCSP 201
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