6 research outputs found
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
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%
Codebook Configuration for 1-bit RIS-aided Systems Based on Implicit Neural Representations
Reconfigurable intelligent surfaces (RISs) have become one of the key
technologies in 6G wireless communications. By configuring the reflection
beamforming codebooks, RIS focuses signals on target receivers. In this paper,
we investigate the codebook configuration for 1-bit RIS-aided systems. We
propose a novel learning-based method built upon the advanced methodology of
implicit neural representations. The proposed model learns a continuous and
differentiable coordinate-to-codebook representation from samplings. Our method
only requires the information of the user's coordinate and avoids the
assumption of channel models. Moreover, we propose an encoding-decoding
strategy to reduce the dimension of codebooks, and thus improve the learning
efficiency of the proposed method. Experimental results on simulation and
measured data demonstrated the remarkable advantages of the proposed method
Wireless Communications in Cavity: A Reconfigurable Boundary Modulation based Approach
This paper explores the potential wireless communication applications of
Reconfigurable Intelligent Surfaces (RIS) in reverberant wave propagation
environments. Unlike in free space, we utilize the sensitivity to boundaries of
the enclosed electromagnetic (EM) field and the equivalent perturbation of
RISs. For the first time, we introduce the framework of reconfigurable boundary
modulation in the cavities . We have proposed a robust boundary modulation
scheme that exploits the continuity of object motion and the mutation of the
codebook switch, which achieves pulse position modulation (PPM) by
RIS-generated equivalent pulses for wireless communication in cavities. This
approach achieves around 2 Mbps bit rate in the prototype and demonstrates
strong resistance to channel's frequency selectivity resulting in an extremely
low bit error rate (BER)
Design of Reconfigurable Intelligent Surfaces for Wireless Communication: A Review
Existing literature reviews predominantly focus on the theoretical aspects of
reconfigurable intelligent surfaces (RISs), such as algorithms and models,
while neglecting a thorough examination of the associated hardware components.
To bridge this gap, this research paper presents a comprehensive overview of
the hardware structure of RISs. The paper provides a classification of RIS cell
designs and prototype systems, offering insights into the diverse
configurations and functionalities. Moreover, the study explores potential
future directions for RIS development. Notably, a novel RIS prototype design is
introduced, which integrates seamlessly with a communication system for
performance evaluation through signal gain and image formation experiments. The
results demonstrate the significant potential of RISs in enhancing
communication quality within signal blind zones and facilitating effective
radio wave imaging