42 research outputs found
Relay Assisted Cooperative OSTBC Communication with SNR Imbalance and Channel Estimation Errors
In this paper, a two-hop relay assisted cooperative Orthogonal Space-Time
Block Codes (OSTBC) transmission scheme is considered for the downlink
communication of a cellular system, where the base station (BS) and the relay
station (RS) cooperate and transmit data to the user equipment (UE) in a
distributed fashion. We analyze the impact of the SNR imbalance between the
BS-UE and RS-UE links, as well as the imperfect channel estimation at the UE
receiver. The performance is analyzed in the presence of Rayleigh flat fading
and our results show that the SNR imbalance does not impact the spatial
diversity order. On the other hand, channel estimation errors have a larger
impact on the system performance. Simulation results are then provided to
confirm the analysis.Comment: 5 pages, 3 figures, IEEE 69th Vehicular Technology Conferenc
Summary Statistic Privacy in Data Sharing
We study a setting where a data holder wishes to share data with a receiver,
without revealing certain summary statistics of the data distribution (e.g.,
mean, standard deviation). It achieves this by passing the data through a
randomization mechanism. We propose summary statistic privacy, a metric for
quantifying the privacy risk of such a mechanism based on the worst-case
probability of an adversary guessing the distributional secret within some
threshold. Defining distortion as a worst-case Wasserstein-1 distance between
the real and released data, we prove lower bounds on the tradeoff between
privacy and distortion. We then propose a class of quantization mechanisms that
can be adapted to different data distributions. We show that the quantization
mechanism's privacy-distortion tradeoff matches our lower bounds under certain
regimes, up to small constant factors. Finally, we demonstrate on real-world
datasets that the proposed quantization mechanisms achieve better
privacy-distortion tradeoffs than alternative privacy mechanisms
Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding
This work aims at decreasing the end-to-end generation latency of large
language models (LLMs). One of the major causes of the high generation latency
is the sequential decoding approach adopted by almost all state-of-the-art
LLMs. In this work, motivated by the thinking and writing process of humans, we
propose "Skeleton-of-Thought" (SoT), which guides LLMs to first generate the
skeleton of the answer, and then conducts parallel API calls or batched
decoding to complete the contents of each skeleton point in parallel. Not only
does SoT provide considerable speed-up (up to 2.39x across 11 different LLMs),
but it can also potentially improve the answer quality on several question
categories in terms of diversity and relevance. SoT is an initial attempt at
data-centric optimization for efficiency, and reveal the potential of pushing
LLMs to think more like a human for answer quality.Comment: Technical report, work in progres
Efficiently Computing Similarities to Private Datasets
Many methods in differentially private model training rely on computing the
similarity between a query point (such as public or synthetic data) and private
data. We abstract out this common subroutine and study the following
fundamental algorithmic problem: Given a similarity function and a large
high-dimensional private dataset , output a
differentially private (DP) data structure which approximates for any query . We consider the cases where is a kernel
function, such as (also known as DP kernel
density estimation), or a distance function such as , among
others.
Our theoretical results improve upon prior work and give better
privacy-utility trade-offs as well as faster query times for a wide range of
kernels and distance functions. The unifying approach behind our results is
leveraging `low-dimensional structures' present in the specific functions
that we study, using tools such as provable dimensionality reduction,
approximation theory, and one-dimensional decomposition of the functions. Our
algorithms empirically exhibit improved query times and accuracy over prior
state of the art. We also present an application to DP classification. Our
experiments demonstrate that the simple methodology of classifying based on
average similarity is orders of magnitude faster than prior DP-SGD based
approaches for comparable accuracy.Comment: To appear at ICLR 202
An Unsupervised Machine Learning Scheme for Index-Based CSI Feedback in Wi-Fi
With the ever-increasing demand for high-speed wireless data transmission,
beamforming techniques have been proven to be crucial in improving the data
rate and the signal-to-noise ratio (SNR) at the receiver. However, they require
feedback mechanisms that need an overhead of information and increase the
system complexity, potentially challenging the efficiency and capacity of
modern wireless networks. This paper investigates novel index-based feedback
mechanisms that aim at reducing the beamforming feedback overhead in Wi-Fi
links. The proposed methods mitigate the overhead by generating a set of
candidate beamforming vectors using an unsupervised learning-based framework.
The amount of feedback information required is thus reduced by using the index
of the candidate as feedback instead of transmitting the entire beamforming
matrix. We explore several methods that consider different representations of
the data in the candidate set. In particular, we propose five different ways to
generate and represent the candidate sets that consider the covariance matrices
of the channel, serialize the feedback matrix, and account for the effective
distance, among others. Additionally, we also discuss the implications of using
partial information in the compressed beamforming feedback on the link
performance and compare it with the newly proposed index-based methods.
Extensive IEEE 802.11 standard-compliant simulation results show that the
proposed methods effectively minimize the feedback overhead, enhancing the
throughput while maintaining an adequate link performance
Enhanced Index-Based Feedback Overhead Reduction for WLANs
Compressed beamforming algorithm is used in the current Wi-Fi standard to
reduce the beamforming feedback overhead (BFO). However, with each new
amendment of the standard the number of supported antennas in Wi-Fi devices
increases, leading to increased BFO and hampering the throughput despite using
compressed beamforming. In this paper, a novel index-based method is presented
to reduce the BFO in Wi-Fi links. In particular, a k-means clustering-based
approach is presented to generate candidate beamforming feedback matrices,
thereby reducing the BFO to only the index of the said candidate matrices. With
extensive simulation results, we compare the newly proposed method with the
IEEE 802.11be baseline and our previously published index-based method. We show
approximately 54% gain in throughput at high signal-to-noise (SNR) against the
IEEE 802.11be baseline. Our comparison also shows approximately 4 dB gain
compared to our previously published method at the packet-error-rate (PER) of
0.01 using MCS index 11. Additionally, we also discuss the impact of the
distance metric chosen for clustering as well as candidate selection on the
link performance