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

The identifiability problem of the cubic phase function (CPF) for multicomponent quadratic FM (QFM) signals is verified both in theory and in simulations. A computationally efficient technique based on the one dimensional (1-D) Radon-CPF transform (RCT) is proposed for this problem. The RCT first reassigns the time-frequency rate distribution (TFRD) with the information of the second-order coefficient, and then implements the 1-D Radon transform with only angle variety. Although this technique is supposed for multicomponent QFM signals, it is also efficient for monocomponent. The simulation results verify the proposed method both in illustrative examples and performance evaluations.

Parameter estimation of multicomponent quadratic FM signals using computationally efficient Radon-CPF transform

In millimeter wave (mmWave) communication, analog and hybrid beamforming systems are proposed to reduce the number of RF chains when a large antenna array is utilized to achieve a high beamforming gain. In this paper, a new transmission scheme is first proposed by leveraging the hardware architecture of analog and hybrid beamforming to improve the spectral efficiency. The new scheme is called spatial scattering modulation (SSM), since it exploits the spatial scattering dimension to modulate information bits. And then, an adaptive transmission strategy (ATS) which chooses the best transmission scheme under instantaneous channel state information, is proposed to improve the performance. Link-level simulation results demonstrate the superiority of the proposed ATS in all the simulated signal-to-noise (SNR) values.

Millimeter wave adaptive transmission using spatial scattering modulation

An apparatus is provided for extracting slowness dispersion characteristics of sonic wave forms in broadband acoustic waves received by multiple sensors including means to digitize the sonic wave forms to form discrete time wave forms and converting the discrete time wave forms into frequency domain wave forms and means to divide a processing band of the wave forms into frequency sub-bands. For each sub-band approximating a family of candidate dispersion curves for multiple modes, parameterizing each of the curves by phase and group slowness, and forming a frequency dependent over-complete dictionary of basis elements, each corresponding to a pair of phase and group slownesses. In addition, forming multiple measurement vectors from the frequency domain data and implementing a sparse Bayesian learning (SBL) algorithm on the vectors with a block sparse signal model and outputting the results. Also, a means for generating a final dispersion curve.

Apparatus for mode extraction using multiple frequencies

In this paper, we provide theoretical analysis on range estimation in frequency modulated continuous wave (FMCW) systems when the source nonlinearity is present. Existing literature on the effect of source nonlinearity on the range estimation is either based on a heuristic approach or limited to specific algorithms. To provide a unified analysis, we introduce the framework of misspecified Cramer-Rao bound (MCRB) and derive analytical lower bounds on the range estimation. Our analysis reveals that the range estimation accuracy is a function of the nonlinearity function, system parameters (e.g., bandwidth, sampling frequency) and noise variance. It is also noted that our result converges to the conventional accuracy analysis for range estimation when the nonlinearity becomes negligible. Finally, numerical results are provided to verify the analytical bounds.

Range Accuracy Analysis for FMCW Systems with Source Nonlinearity

This letter introduces a new coupled mixture of polynomial phase signal (PPS) and sinusoidal frequency modulated (FM) signal, motivated by real-world applications, for example, contactless linear encoders. Specifically, the coupling is introduced to express the sinusoidal FM frequency as a function of the PPS-related parameters. Given the coupling mixture, it generalizes two existing models: the pure PPS model and the independent mixture model. Performance bounds of parameter estimation for the coupled mixture model are established in terms of the Cramer–Rao bound (CRB). Unlike the pure PPS case, the derived CRB shows its dependence on the PPS-related and sinusoidal FM-related parameters due to the coupling mixture. On the other hand, the derived CRBs for the PPS-related parameters are lower than their counterparts of the independent mixture model, as the sinusoidal FM frequency provides additional information on the PPS parameters.

Pu Wang

Papers

Parameter estimation of multicomponent quadratic FM signals using computationally efficient Radon-CPF transform

Millimeter wave adaptive transmission using spatial scattering modulation

Apparatus for mode extraction using multiple frequencies

Range Accuracy Analysis for FMCW Systems with Source Nonlinearity

Cramér–Rao Bounds for a Coupled Mixture of Polynomial Phase and Sinusoidal FM Signals