Current medical imaging technologies allow visualization of tissue anatomy in the human body at resolutions ranging from 100 micrometers to 1 millimeter. These technologies are generally not sensitive enough to detect early-stage tissue abnormalities associated with diseases such as cancer and atherosclerosis, which require micrometer-scale resolution. Here, optical coherence tomography was adapted to allow high-speed visualization of tissue in a living animal with a catheter-endoscope 1 millimeter in diameter. This method, referred to as "optical biopsy," was used to obtain cross-sectional images of the rabbit gastrointestinal and respiratory tracts at 10-micrometer resolution.

In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography

The ever-increasing demand for higher data rates in wireless communications in the face of limited or underutilized spectral resources has motivated the introduction of cognitive radio. Traditionally, licensed spectrum is allocated over relatively long time periods and is intended to be used only by licensees. Various measurements of spectrum utilization have shown substantial unused resources in frequency, time, and space [1], [2]. The concept behind cognitive radio is to exploit these underutilized spectral resources by reusing unused spectrum in an opportunistic manner [3], [4]. The phrase cognitive radio is usually attributed to Mitola [4], but the idea of using learning and sensing machines to probe the radio spectrum was envisioned several decades earlier (cf., [5]).

Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances

Time-Frequency Analysis.

The future of mobile communications looks exciting with the potential new use cases and challenging requirements of future 6th generation (6G) and beyond wireless networks. Since the beginning of the modern era of wireless communications, the propagation medium has been perceived as a randomly behaving entity between the transmitter and the receiver, which degrades the quality of the received signal due to the uncontrollable interactions of the transmitted radio waves with the surrounding objects. The recent advent of reconfigurable intelligent surfaces in wireless communications enables, on the other hand, network operators to control the scattering, reflection, and refraction characteristics of the radio waves, by overcoming the negative effects of natural wireless propagation. Recent results have revealed that reconfigurable intelligent surfaces can effectively control the wavefront, e.g., the phase, amplitude, frequency, and even polarization, of the impinging signals without the need of complex decoding, encoding, and radio frequency processing operations. Motivated by the potential of this emerging technology, the present article is aimed to provide the readers with a detailed overview and historical perspective on state-of-the-art solutions, and to elaborate on the fundamental differences with other technologies, the most important open research issues to tackle, and the reasons why the use of reconfigurable intelligent surfaces necessitates to rethink the communication-theoretic models currently employed in wireless networks. This article also explores theoretical performance limits of reconfigurable intelligent surface-assisted communication systems using mathematical techniques and elaborates on the potential use cases of intelligent surfaces in 6G and beyond wireless networks.

Wireless Communications Through Reconfigurable Intelligent Surfaces

Transmission through reconfigurable intelligent surfaces (RISs), which control the reflection/scattering characteristics of incident waves in a deliberate manner to enhance the signal quality at the receiver, appears as a promising candidate for future wireless communication systems. In this paper, we bring the concept of RIS-assisted communications to the realm of index modulation (IM) by proposing RIS-space shift keying (RIS-SSK) and RIS-spatial modulation (RIS-SM) schemes. These two schemes are realized through not only intelligent reflection of the incoming signals to improve the reception but also utilization of the IM principle for the indices of multiple receive antennas in a clever way to improve the spectral efficiency. Maximum energy-based suboptimal (greedy) and exhaustive search-based optimal (maximum likelihood) detectors of the proposed RIS-SSK/SM schemes are formulated and a unified framework is presented for the derivation of their theoretical average bit error probability. Extensive computer simulation results are provided to assess the potential of RIS-assisted IM schemes as well as to verify our theoretical derivations. Our findings also reveal that RIS-based IM, which enables high data rates with remarkably low error rates, can become a potential candidate for future wireless communication systems in the context of beyond multiple-input multiple-output (MIMO) solutions.

Reconfigurable Intelligent Surface-Based Index Modulation: A New Beyond MIMO Paradigm for 6G

Commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) to jointly exchange data and monitor indoor environment. In this paper, we investigate a proof-of-concept approach using automated quantum machine learning (AutoQML) framework called AutoAnsatz to recognize human gesture. We address how to efficiently design quantum circuits to configure quantum neural networks (QNN). The effectiveness of AutoQML is validated by an in-house experiment for human pose recognition, achieving state-of-the-art performance greater than 80% accuracy for a limited data size with a significantly small number of trainable parameters.

AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications

In this paper, we propose a variational Bayesian inference approach for a low-complexity symbol detection for massive MIMO systems with symbol- dependent transmit-side impairments. This study is motivated by observations that realworld communication transceivers are often affected by the hardware impairments, such as non-linearities of power amplifiers, I/Q imbalance, phase drifts due to non-ideal oscillators, and carrier frequency offsets. Particularly, symbol-dependent perturbations are fully accounted into the designed hierarchical signal model as unknown model parameters. The developed variational Bayesian symbol detector is able to learn the unknown perturbations in an iterative fashion. Numerical evaluation confirms the effectiveness of the proposed approach.

Variational Bayesian Symbol Detection for Massive MIMO Systems with Symbol-Dependent Transmit Impairments

This paper introduces the hierarchical truncated Gaussian model in representing automotive radar measurements for extended object tracking. The model aims at a flexible spatial distribution with adaptive truncation bounds to account for partial-view measurements caused by self-occlusion. Built on a random matrix approach, we propose a new state update step together with an adaptively update of the truncation bounds. This is achieved by introducing spatial-domain pseudo measurements and by aggregating partial-view measurements over consecutive time-domain scans. The effectiveness of the proposed algorithm is verified on a synthetic dataset and an independent dataset generated using the MathWorks Automated Driving toolbox.

Extended Object Tracking Using Hierarchical Truncation Model with Partial-View Measurements

Recent advances in solving imaging inverse problems have witnessed the combination of deep learning models with classical image models for better signal representation. One such approach, DeepRED, combines the deep image prior (DIP) with the regularization by denoising (RED) framework to boost the performance of image deblurring and super resolution tasks. In this paper, we formulate DeepRED as a consensus equilibrium problem and set up a fixed-point algorithm for solving the equilibrium equations. We also derive sufficient conditions that the DIP generative prior should satisfy to ensure that the corresponding fixed-point operator is non-expansive. We then demonstrate that the fixed-point algorithm that solves the CE equations results in improved image reconstruction quality in a deblurring setting compared to state-of-the-art methods.

Pu Wang

Papers

AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications

Variational Bayesian Symbol Detection for Massive MIMO Systems with Symbol-Dependent Transmit Impairments

Extended Object Tracking Using Hierarchical Truncation Model with Partial-View Measurements

A Consensus Equilibrium Solution For Deep Image Prior Powered By Red

Logging-While-Drilling Dipole Shear Logging in Fast Formations Using Higher-Order Formation Flexural Mode