1,050 research outputs found
Auction-Based Coopetition between LTE Unlicensed and Wi-Fi
Motivated by the recent efforts in extending LTE to the unlicensed spectrum,
we propose a novel spectrum sharing framework for the coopetition (i.e.,
cooperation and competition) between LTE and Wi-Fi in the unlicensed band.
Basically, the LTE network can choose to work in one of the two modes: in the
competition mode, it randomly accesses an unlicensed channel, and interferes
with the Wi-Fi access point using the same channel; in the cooperation mode, it
delivers traffic for the Wi-Fi users in exchange for the exclusive access of
the corresponding channel. Because the LTE network works in an
interference-free manner in the cooperation mode, it can achieve a much larger
data rate than that in the competition mode, which allows it to effectively
serve both its own users and the Wi-Fi users. We design a second-price reverse
auction mechanism, which enables the LTE provider and the Wi-Fi access point
owners (APOs) to effectively negotiate the operation mode. Specifically, the
LTE provider is the auctioneer (buyer), and the APOs are the bidders (sellers)
who compete to sell their channel access opportunities to the LTE provider. In
Stage I of the auction, the LTE provider announces a reserve rate. In Stage II
of the auction, the APOs submit their bids. We show that the auction involves
allocative externalities, i.e., the cooperation between the LTE provider and
one APO benefits other APOs who are not directly involved in this cooperation.
As a result, a particular APO's willingness to cooperate is affected by its
belief about other APOs' willingness to cooperate. This makes our analysis much
more challenging than that of the conventional second-price auction, where
bidding truthfully is a weakly dominant strategy. We show that the APOs have a
unique form of the equilibrium bidding strategies in Stage II, based on which
we analyze the LTE provider's optimal reserve rate in Stage I.Comment: 32 page
Music Source Separation with Band-split RNN
The performance of music source separation (MSS) models has been greatly
improved in recent years thanks to the development of novel neural network
architectures and training pipelines. However, recent model designs for MSS
were mainly motivated by other audio processing tasks or other research fields,
while the intrinsic characteristics and patterns of the music signals were not
fully discovered. In this paper, we propose band-split RNN (BSRNN), a
frequency-domain model that explictly splits the spectrogram of the mixture
into subbands and perform interleaved band-level and sequence-level modeling.
The choices of the bandwidths of the subbands can be determined by a priori
knowledge or expert knowledge on the characteristics of the target source in
order to optimize the performance on a certain type of target musical
instrument. To better make use of unlabeled data, we also describe a
semi-supervised model finetuning pipeline that can further improve the
performance of the model. Experiment results show that BSRNN trained only on
MUSDB18-HQ dataset significantly outperforms several top-ranking models in
Music Demixing (MDX) Challenge 2021, and the semi-supervised finetuning stage
further improves the performance on all four instrument tracks
Complexity Scaling for Speech Denoising
Computational complexity is critical when deploying deep learning-based
speech denoising models for on-device applications. Most prior research focused
on optimizing model architectures to meet specific computational cost
constraints, often creating distinct neural network architectures for different
complexity limitations. This study conducts complexity scaling for speech
denoising tasks, aiming to consolidate models with various complexities into a
unified architecture. We present a Multi-Path Transform-based (MPT)
architecture to handle both low- and high-complexity scenarios. A series of MPT
networks present high performance covering a wide range of computational
complexities on the DNS challenge dataset. Moreover, inspired by the scaling
experiments in natural language processing, we explore the empirical
relationship between model performance and computational cost on the denoising
task. As the complexity number of multiply-accumulate operations (MACs) is
scaled from 50M/s to 15G/s on MPT networks, we observe a linear increase in the
values of PESQ-WB and SI-SNR, proportional to the logarithm of MACs, which
might contribute to the understanding and application of complexity scaling in
speech denoising tasks.Comment: Submitted to ICASSP202
Lanthanum exerts acute toxicity and histopathological changes in gill and liver tissue of rare minnow (Gobiocypris rarus)
We evaluated the acute toxicity effects of lanthanum (La(III)) on gill and liver of rare minnow (Gobiocypris rarus). The median lethal concentration of La (III) at 96 h was 1.92 mg L-1. Rare minnow were reared in freshwater and exposed to 0.04, 0.08, 0.16, 0.32 and 0.80 mg L-1 La (III) for 21 d. Gill and liver samples were analyzed by light microscopy. The main histopathological changes induced by La (III) in gills were epithelial lifting, filamentary epithelial proliferation,edema, lamellar fusion, desquamation, and necrosis. Histopathological changes induced by La (III) in the liver included dilation of sinusoids, focal congestion, pyknotic nuclei, karyohexis and karyolysis, vacuolar degeneration, and numerous necrosis areas. Hypsometric analysis indicated significant changes in the measures of gill dimensions (average length, width, area), suggesting metabolic disturbance (gas exchange) upon La (III) exposure. The result showed that La (III) severely affects fish gill and liver.</p
- …