5,711 research outputs found
Unsupervised Massive MIMO Channel Estimation with Dual-Path Knowledge-Aware Auto-Encoders
In this paper, an unsupervised deep learning framework based on dual-path
model-driven variational auto-encoders (VAE) is proposed for angle-of-arrivals
(AoAs) and channel estimation in massive MIMO systems. Specifically designed
for channel estimation, the proposed VAE differs from the original VAE in two
aspects. First, the encoder is a dual-path neural network, where one path uses
the received signal to estimate the path gains and path angles, and another
uses the correlation matrix of the received signal to estimate AoAs. Second,
the decoder has fixed weights that implement the signal propagation model,
instead of learnable parameters. This knowledge-aware decoder forces the
encoder to output meaningful physical parameters of interests (i.e., path
gains, path angles, and AoAs), which cannot be achieved by original VAE.
Rigorous analysis is carried out to characterize the multiple global optima and
local optima of the estimation problem, which motivates the design of the
dual-path encoder. By alternating between the estimation of path gains, path
angles and the estimation of AoAs, the encoder is proved to converge. To
further improve the convergence performance, a low-complexity procedure is
proposed to find good initial points. Numerical results validate theoretical
analysis and demonstrate the performance improvements of our proposed
framework
Privacy-Preserving Community Detection for Locally Distributed Multiple Networks
Modern multi-layer networks are commonly stored and analyzed in a local and
distributed fashion because of the privacy, ownership, and communication costs.
The literature on the model-based statistical methods for community detection
based on these data is still limited. This paper proposes a new method for
consensus community detection and estimation in a multi-layer stochastic block
model using locally stored and computed network data with privacy protection. A
novel algorithm named privacy-preserving Distributed Spectral Clustering
(ppDSC) is developed. To preserve the edges' privacy, we adopt the randomized
response (RR) mechanism to perturb the network edges, which satisfies the
strong notion of differential privacy. The ppDSC algorithm is performed on the
squared RR-perturbed adjacency matrices to prevent possible cancellation of
communities among different layers. To remove the bias incurred by RR and the
squared network matrices, we develop a two-step bias-adjustment procedure. Then
we perform eigen-decomposition on the debiased matrices, aggregation of the
local eigenvectors using an orthogonal Procrustes transformation, and k-means
clustering. We provide theoretical analysis on the statistical errors of ppDSC
in terms of eigen-vector estimation. In addition, the blessings and curses of
network heterogeneity are well-explained by our bounds
Privacy-Preserving Distributed SVD via Federated Power
Singular value decomposition (SVD) is one of the most fundamental tools in
machine learning and statistics.The modern machine learning community usually
assumes that data come from and belong to small-scale device users. The low
communication and computation power of such devices, and the possible privacy
breaches of users' sensitive data make the computation of SVD challenging.
Federated learning (FL) is a paradigm enabling a large number of devices to
jointly learn a model in a communication-efficient way without data sharing. In
the FL framework, we develop a class of algorithms called FedPower for the
computation of partial SVD in the modern setting. Based on the well-known power
method, the local devices alternate between multiple local power iterations and
one global aggregation to improve communication efficiency. In the aggregation,
we propose to weight each local eigenvector matrix with Orthogonal Procrustes
Transformation (OPT). Considering the practical stragglers' effect, the
aggregation can be fully participated or partially participated, where for the
latter we propose two sampling and aggregation schemes. Further, to ensure
strong privacy protection, we add Gaussian noise whenever the communication
happens by adopting the notion of differential privacy (DP). We theoretically
show the convergence bound for FedPower. The resulting bound is interpretable
with each part corresponding to the effect of Gaussian noise, parallelization,
and random sampling of devices, respectively. We also conduct experiments to
demonstrate the merits of FedPower. In particular, the local iterations not
only improve communication efficiency but also reduce the chance of privacy
breaches
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