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

Motivated by the fact that the multi-static transmit-receive configuration in a distributed multiple-input multiple-output (MIMO) radar causes non-stationary clutter, we consider the problem of moving target detection (MTD) using a distributed MIMO radar in non-homogeneous clutter environments. A new non-homogeneous clutter model, where the clutter resides in a low-rank subspace with different subspace coefficients for different transmit-receive pairs, is introduced. The subspace clutter model is effective in capturing the non-homogeneity of the clutter and, in particular, the power variations across different aspect angles and resolution cells. A generalized likelihood ratio test (GLRT), which performs local matched subspace detection, noncoherent combining using local decision variables of all transmit-receive pairs and target velocity matching, is proposed. The GLRT is shown to be a constant false alarm rate (CFAR) detector. Computer simulations are provided to verify our statistical analysis of the GLRT, and a comparison with existing detectors is conducted to evaluate the impact of model mismatch on detection performance.

Moving target detection using distributed MIMO radar in non-homogeneous clutter: A subspace approach

Automotive perception involves understanding the external driving environment and the internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving high levels of safety and autonomy in driving. This article provides an overview of different sensor modalities, such as cameras, radars, and light detection and ranging (LiDAR) used commonly for perception, along with the associated data processing techniques. Critical aspects of perception are considered, such as architectures for processing data from single or multiple sensor modalities, sensor data processing algorithms and the role of machine learning techniques, methodologies for validating the performance of perception systems, and safety. The technical challenges for each aspect are analyzed, emphasizing machine learning approaches, given their potential impact on improving perception. Finally, future research opportunities in automotive perception for their wider deployment are outlined.

Sensing and Machine Learning for Automotive Perception: A Review

A frequency modulation continuous wave (FMCW)-based system includes an emitter to transmit at least one linearly modulated wave of radiation to a scene and a receiver to receive a reflection of the transmitted wave from one or multiple objects located at different locations in the scene. The system interferes a copy of the wave outputted by the emitter with the reflection of the transmitted wave received by the receiver to produce a beat signal with spectrum peaks corresponding to reflections from the different locations at the scene. The beat signal is distorted due to the non-linearity of the modulation. The system includes a processor to detect a number of spectrum peaks in the distorted beat signal and, only in response to detecting multiple spectrum peaks, jointly determine coefficients of a basis function approximating the non-linearity of the modulation and distances to the different locations at the scene having the objects causing the reflection resulting in the spectrum peaks in the distorted beat signal.

Reference-free nonlinearity correction for FMCW-based sensing systems

Devices and methods for decoding a symbol transmitted over a millimeter wave (mmWave) channel. Receiving a test symbol transmitted over the mmWave channel. Estimating channel state information (CSI) of the mmWave channel using a block sparse signal recovery on the test symbol, according to a multi-dimensional spreading model with statistics on multi-dimensional paths. The multi-dimensional spreading model with statistics on multi-dimensional paths include an angle of departure (AoD), angle of arrival (AoA), and a path spread for the AoD and a path spread for the AoA, propagating in the mmWave channel. Receiving a symbol over the mmWave channel, and decoding the symbol using the CSI, wherein steps of the method are performed by a processor of a receiver.

Pu Wang

Papers

Moving target detection using distributed MIMO radar in non-homogeneous clutter: A subspace approach

Sensing and Machine Learning for Automotive Perception: A Review

Reference-free nonlinearity correction for FMCW-based sensing systems

Method for decoding symbol and receiver for receiving and decoding symbol

Frequency modulation continuous wave (fmcw)-based system and method for fmcw range estimation