1,688 research outputs found
Optimal Power Allocation over Multiple Identical Gilbert-Elliott Channels
We study the fundamental problem of power allocation over multiple
Gilbert-Elliott communication channels. In a communication system with time
varying channel qualities, it is important to allocate the limited transmission
power to channels that will be in good state. However, it is very challenging
to do so because channel states are usually unknown when the power allocation
decision is made. In this paper, we derive an optimal power allocation policy
that can maximize the expected discounted number of bits transmitted over an
infinite time span by allocating the transmission power only to those channels
that are believed to be good in the coming time slot. We use the concept belief
to represent the probability that a channel will be good and derive an optimal
power allocation policy that establishes a mapping from the channel belief to
an allocation decision.
Specifically, we first model this problem as a partially observable Markov
decision processes (POMDP), and analytically investigate the structure of the
optimal policy. Then a simple threshold-based policy is derived for a
three-channel communication system. By formulating and solving a linear
programming formulation of this power allocation problem, we further verified
the derived structure of the optimal policy.Comment: 10 pages, 7 figure
Post-transient relaxation in graphene after an intense laser pulse
High intensity laser pulses were recently shown to induce a population
inverted transient state in graphene [T. Li et al. Phys. Rev. Lett. 108, 167401
(2012)]. Using a combination of hydrodynamic arguments and a kinetic theory we
determine the post-transient state relaxation of hot, dense, population
inverted electrons towards equilibrium. The cooling rate and charge-imbalance
relaxation rate are determined from the Boltzmann-equation including
electron-phonon scattering. We show that the relaxation of the population
inversion, driven by inter-band scattering processes, is much slower than the
relaxation of the electron temperature, which is determined by intra-band
scattering processes. This insight may be of relevance for the application of
graphene as an optical gain medium.Comment: 10 pages, 4 figures, submitted as contribution of the IMPACT Special
Topics series of the EP
A Hybrid Deep Learning Framework for Stock Price Prediction Considering the Investor Sentiment of Online Forum Enhanced by Popularity
Stock price prediction has always been a difficult task for forecasters.
Using cutting-edge deep learning techniques, stock price prediction based on
investor sentiment extracted from online forums has become feasible. We propose
a novel hybrid deep learning framework for predicting stock prices. The
framework leverages the XLNET model to analyze the sentiment conveyed in user
posts on online forums, combines these sentiments with the post popularity
factor to compute daily group sentiments, and integrates this information with
stock technical indicators into an improved BiLSTM-highway model for stock
price prediction. Through a series of comparative experiments involving four
stocks on the Chinese stock market, it is demonstrated that the hybrid
framework effectively predicts stock prices. This study reveals the necessity
of analyzing investors' textual views for stock price prediction
A Network Resource Allocation Recommendation Method with An Improved Similarity Measure
Recommender systems have been acknowledged as efficacious tools for managing
information overload. Nevertheless, conventional algorithms adopted in such
systems primarily emphasize precise recommendations and, consequently, overlook
other vital aspects like the coverage, diversity, and novelty of items. This
approach results in less exposure for long-tail items. In this paper, to
personalize the recommendations and allocate recommendation resources more
purposively, a method named PIM+RA is proposed. This method utilizes a
bipartite network that incorporates self-connecting edges and weights.
Furthermore, an improved Pearson correlation coefficient is employed for better
redistribution. The evaluation of PIM+RA demonstrates a significant enhancement
not only in accuracy but also in coverage, diversity, and novelty of the
recommendation. It leads to a better balance in recommendation frequency by
providing effective exposure to long-tail items, while allowing customized
parameters to adjust the recommendation list bias
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