18 research outputs found
Compressed Sensing with General Frames via Optimal-dual-based -analysis
Compressed sensing with sparse frame representations is seen to have much
greater range of practical applications than that with orthonormal bases. In
such settings, one approach to recover the signal is known as
-analysis. We expand in this article the performance analysis of this
approach by providing a weaker recovery condition than existing results in the
literature. Our analysis is also broadly based on general frames and
alternative dual frames (as analysis operators). As one application to such a
general-dual-based approach and performance analysis, an optimal-dual-based
technique is proposed to demonstrate the effectiveness of using alternative
dual frames as analysis operators. An iterative algorithm is outlined for
solving the optimal-dual-based -analysis problem. The effectiveness of
the proposed method and algorithm is demonstrated through several experiments.Comment: 34 pages, 8 figures. To appear in IEEE Transactions on Information
Theor
Joint Beamforming Design and 3D DoA Estimation for RIS-aided Communication System
In this paper, we consider a reconfigurable intelligent surface
(RIS)-assisted 3D direction-of-arrival (DoA) estimation system, in which a
uniform planar array (UPA) RIS is deployed to provide virtual line-of-sight
(LOS) links and reflect the uplink pilot signal to sensors. To overcome the
mutually coupled problem between the beamforming design at the RIS and DoA
estimation, we explore the separable sparse representation structure and
propose an alternating optimization algorithm. The grid-based DoA estimation is
modeled as a joint-sparse recovery problem considering the grid bias, and the
Joint-2D-OMP method is used to estimate both on-grid and off-grid parts. The
corresponding Cram\'er-Rao lower bound (CRLB) is derived to evaluate the
estimation. Then, the beampattern at the RIS is optimized to maximize the
signal-to-noise (SNR) at sensors according to the estimated angles. Numerical
results show that the proposed alternating optimization algorithm can achieve
lower estimation error compared to benchmarks of random beamforming design.Comment: 6 pages, 6 figure
Optimal Discrete Beamforming of RIS-Aided Wireless Communications: an Inner Product Maximization Approach
This paper addresses non-convex optimization problems in communication
services using reconfigurable intelligent surfaces (RISs). Specifically, we
focus on optimal beamforming in RIS-aided communications, and formulate it as a
discrete inner product maximization problem. To solve this problem, we propose
a highly efficient divide-and-sort (DaS) search framework that guarantees
global optima with linear search complexity, both in the number of discrete
levels and reflecting cells. This approach is particularly effective for
large-scale problems. Our numerical studies and prototype experiments
demonstrate the speed and effectiveness of the proposed DaS. We also show that
for moderate resolution quantization (4-bits and above), there is no noticeable
difference between continuous and discrete phase configurations
The Capacity Region of Information Theoretic Secure Aggregation with Uncoded Groupwise Keys
This paper considers the secure aggregation problem for federated learning
under an information theoretic cryptographic formulation, where distributed
training nodes (referred to as users) train models based on their own local
data and a curious-but-honest server aggregates the trained models without
retrieving other information about users' local data. Secure aggregation
generally contains two phases, namely key sharing phase and model aggregation
phase. Due to the common effect of user dropouts in federated learning, the
model aggregation phase should contain two rounds, where in the first round the
users transmit masked models and, in the second round, according to the
identity of surviving users after the first round, these surviving users
transmit some further messages to help the server decrypt the sum of users'
trained models. The objective of the considered information theoretic
formulation is to characterize the capacity region of the communication rates
in the two rounds from the users to the server in the model aggregation phase,
assuming that key sharing has already been performed offline in prior. In this
context, Zhao and Sun completely characterized the capacity region under the
assumption that the keys can be arbitrary random variables. More recently, an
additional constraint, known as "uncoded groupwise keys," has been introduced.
This constraint entails the presence of multiple independent keys within the
system, with each key being shared by precisely S users. The capacity region
for the information-theoretic secure aggregation problem with uncoded groupwise
keys was established in our recent work subject to the condition S > K - U,
where K is the number of total users and U is the designed minimum number of
surviving users. In this paper we fully characterize of the the capacity region
for this problem by proposing a new converse bound and an achievable scheme.Comment: 37 pages, 3 figure
RIS-aided Real-time Beam Tracking for a Mobile User via Bayesian Optimization
The conventional beam management procedure mandates that the user equipment
(UE) periodically measure the received signal reference power (RSRP) and
transmit these measurements to the base station (BS). The challenge lies in
balancing the number of beams used: it should be large enough to identify
high-RSRP beams but small enough to minimize reporting overhead. This paper
investigates this essential performance-versus-overhead trade-off using
Bayesian optimization. The proposed approach represents the first application
of real-time beam tracking via Bayesian optimization in RIS-assisted
communication systems. Simulation results validate the effectiveness of this
scheme
A Wi-Fi Signal-Based Human Activity Recognition Using High-Dimensional Factor Models
Passive sensing techniques based on Wi-Fi signals have emerged as a promising
technology in advanced wireless communication systems due to their widespread
application and cost-effectiveness. However, the proliferation of low-cost
Internet of Things (IoT) devices has led to dense network deployments,
resulting in increased levels of noise and interference in Wi-Fi environments.
This, in turn, leads to noisy and redundant Channel State Information (CSI)
data. As a consequence, the accuracy of human activity recognition based on
Wi-Fi signals is compromised. To address this issue, we propose a novel CSI
data signal extraction method. We established a human activity recognition
system based on the Intel 5300 network interface cards (NICs) and collected a
dataset containing six categories of human activities. Using our approach,
signals extracted from the CSI data serve as inputs to machine learning (ML)
classification algorithms to evaluate classification performance. In comparison
to ML methods based on Principal Component Analysis (PCA), our proposed
High-Dimensional Factor Model (HDFM) method improves recognition accuracy by
6.8%