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The objective of our research is to create efficient methods and tools for the quick and thorough assessment of emerging digital circuit devices, facilitating the adoption of promising ones. In this work, we develop methods and tools for hybrid technology that combines memristors with MOS transistors and demonstrates their effectiveness. Although several types of memristor-transistor logic have been proposed, 15 years of research has created a small set of logic cells. We propose a systematic method for generating new and efficient memristor-transistor single-phase combinational logic cells. At the core of our approach is a cell enumerator, which enables us to explore a wide range of cell designs, including nonintuitive ones, and a data-driven inductive learning method, which identifies new properties of such cells and scales up our explorations. In conjunction with other completely new tools, these create a comprehensive and definitive library of logic cells. Our new cells provide significant improvements or significantly distinct Pareto-optimal alternatives for the few logic functions for which prior research has created cells. Importantly, our methods enable us to discover a previously unknown synergistic operation between memristors and transistors that occurs for specific cell topologies. We harness this synergy to develop a method for adding memristors to low-area pass-transistor circuits such that they produce strong output voltages and low power, including for patterns that cause ratioed operation. We have also developed a new memristor-transistor logic family, namely controlled-AND (cAND)/controlled-OR (cOR), which includes many of the best cells. We have also developed a constructive method for designing such cells. 

This paper explores the use of reconfigurable intelligent surfaces (RIS) in mitigating cross-system interference in spectrum sharing and secure wireless applications. Unlike conventional RIS that can only adjust the phase of the incoming signal and essentially reflect all impinging energy, or active RIS, which also amplify the reflected signal at the cost of significantly higher complexity, noise, and power consumption, an absorptive RIS (ARIS) is considered. An ARIS can in principle modify both the phase and modulus of the impinging signal by absorbing a portion of the signal energy, providing a compromise between its conventional and active counterparts in terms of complexity, power consumption, and degrees of freedom (DoFs). We first use a toy example to illustrate the benefit of ARIS, and then we consider three applications: 1) spectral coexistence of radar and communication systems, where a convex optimization problem is formulated to minimize the Frobenius norm of the channel matrix from the communication base station to the radar receiver; 2) spectrum sharing in device-to-device (D2D) communications, where a max-min scheme that maximizes the worst-case signal-to-interference-plus-noise ratio (SINR) among the D2D links is developed and then solved via fractional programming; 3) physical layer security of a downlink communication system, where the secrecy rate is maximized and the resulting nonconvex problem is solved by a fractional programming algorithm together with a sequential convex relaxation procedure. Numerical results are then presented to show the significant benefit of ARIS in these applications. 

This paper explores the use of reconfigurable intelligent surfaces (RISs) for moving target detection in multi-input multi-output (MIMO) radar. Unlike previous related works that ignore the propa-gation delay difference between the direct path and the RIS-reflected path, we examine the detection problem in RIS-assisted MIMO radar by taking into account the effect of asynchronous propagation. Specifically, we first develop a general signal model for RIS-aided MIMO radar with multiple asynchronous RISs and arbitrary wave-forms. Next, we formulate the RIS design problem by maximizing the overall received signal energy. The resulting optimization problem is non-convex, which is solved with semidefinite relaxation (SDR) techniques. A coherent detector is introduced for target detection. Finally, numerical results are presented to demonstrate the performance of the RIS-aided MIMO radar in comparison with the conventional MIMO radar. 

This paper explores reconfigurable intelligent surfaces (RIS) for mitigating cross-system interference in spectrum sharing applications. Unlike conventional reflect-only RIS that can only adjust the phase of the incoming signal, a hybrid RIS is considered that can configure the phase and modulus of the impinging signal by absorbing part of the signal energy. We investigate two spectrum sharing scenarios: (1) Spectral coexistence of radar and communication systems, where a convex optimization problem is formulated to minimize the Frobenius norm of the channel matrix from the communication base station to the radar receiver, and (2) Spectrum sharing in device-to-device (D2D) communications, where a max-min scheme that optimizes the worst-case signal-to-interference-plus-noise ratio (SINR) among the D2D links is formulated, and then solved through fractional programming. Numerical results show that with a sufficient number of elements, the hybrid RIS can in many cases completely eliminate the interference, unlike a conventional non-absorptive RIS. 

Reconfigurable intelligent surface (RIS) technology is a promising approach being considered for future wireless communications due to its ability to control signal propagation with low-cost elements. This paper explores the use of an RIS for clutter mitigation and target detection in radar systems. Unlike conventional reflect-only RIS, which can only adjust the phase of the reflected signal, or active RIS, which can also amplify the reflected signal at the cost of significantly higher complexity, noise, and power consumption, we exploit hybrid RIS that can configure both the phase and modulus of the impinging signal by absorbing part of the signal energy. Such RIS can be considered as a compromise solution between conventional reflect-only and active RIS in terms of complexity, power consumption, and degrees of freedoms (DoFs). We consider two clutter suppression scenarios: with and without knowledge of the target range cell. The RIS design is formulated by minimizing the received clutter echo energy when there is no information regarding the potential target range cell. This turns out to be a convex problem and can be efficiently solved. On the other hand, when target range cell information is available, we maximize the received signal-to-noise-plus-interference ratio (SINR). The resulting non-convex optimization problem is solved through fractional programming algorithms. Numerical results are presented to demonstrate the performance of the proposed hybrid RIS in comparison with conventional RIS in clutter suppression for target detection.