As the demand for vehicle-to-everything communication (V2X) band in the 5.9 GHz increases, the millimeter-wave spectrum offers alternative options in unlicensed or radar-dedicated bands with wider bandwidth. Joint radar-communication (JRC) systems emerge as a comprehensive solution to effectively utilize these bands by integrating both functions within the same waveform and hardware. In this work, we present a multiple-input and multiple-output (MIMO) JRC testbed, operating in the 24 GHz mmWave band, utilizing orthogonal frequency division multiplexing (OFDM) waveform that simultaneously carries data across all subcarriers. In particular, we develop a real-time operating, full-duplex JRC prototype with a fully-digital front-end and software-defined radios, providing enhanced flexibility and capability. Additionally, for systems with high computational power, we introduce a high-resolution range-angle processing method based on the MUSIC algorithm. Through mobile experiments with multiple targets, we showcase simultaneous data transmission and high-resolution radar processing capabilities enabled by the fully-digital MIMO architecture. By leveraging radar’s tracking capability, we propose a radar-assisted precoding approach, offering a low-complexity beamforming solution with reduced feedback overhead. Our experimental results demonstrate that the proposed precoding method achieves comparable performance compared to the conventional precoding method.

Large-scale deployment of connected vehicles with cooperative sensing technologies increases the demand on the vehicular communication spectrum in 5.9 GHz allocated for the exchange of safety messages. To support the high data rates needed by such applications, the millimeter-wave (mmWave) automotive radar spectrum at 76–81 GHz can be utilized for wideband communication as well. For this purpose, various joint automotive radar-communication (JARC) systems have been proposed in the literature to perform both functions using the same wideband waveform. However, the wideband joint waveforms encounter frequency-selectivity in both radar and communication channels due to multi-path propagation. In this paper, we address the optimal joint waveform design problem to exploit the frequency-selectivity for wideband JARC operations via orthogonal frequency-division multiplexing (OFDM) wherein subcarrier coefficients are designed for optimal power allocation and phase coding. We show that the problem is a non-convex quadratically constrained quadratic programming (QCQP) problem which is known to be NP-hard. Existing approaches to solve QCQP include semidefinite relaxation (SDR) which incurs high time complexity. Instead, we propose approximation methods to solve QCQP more efficiently by leveraging structured matrices and using convex approximations. Finally, we demonstrate the efficacy of the proposed approaches through numerical simulations.

Large-scale deployment of connected vehicles equipped with multiple automotive radar systems increases the demand on both the millimeter-wave (mmWave) automotive radar spectrum in 76–81 GHz and the vehicle-to-everything (V2X) communication spectrum in 5.9 GHz that is mainly allocated for the exchange of safety messages. To supplement V2X communication and support high data rates needed by broadband applications, the automotive radar spectrum with up to 4 GHz of contiguous bandwidth can be leveraged. For this purpose, various joint automotive radar-communication (JARC) systems have been proposed in the literature to perform both functions using the same radio-frequency (RF) signal and transceiver hardware. Combined with the high mobility in traffic, the directionality of RF transmission in mmWave spectrum and interference from other systems prevent JARC systems to achieve optimal communication and radar performance. In this work, we propose a dedicated neighbor discovery and medium access control (MAC) protocol for JARC systems to establish reliable communication links and improve the robustness of radar functionalities without requiring a separate control channel.

Large-scale deployment of connected vehicles with cooperative awareness technologies increases the demand for vehicle-to-everything (V2X) communication spectrum in 5.9 GHz that is mainly allocated for the exchange of safety messages. To supplement V2X communication and support the high data rates needed by broadband applications, the millimeter-wave (mmWave) automotive radar spectrum at 76-81 GHz can be utilized. For this purpose, joint radar-communication systems have been proposed in the literature to perform both functions using the same waveform and hardware. While multiple-input and multiple-output (MIMO) communication with multiple users enables independent data streaming for high throughput, MIMO radar processing provides high-resolution imaging that is crucial for safety-critical systems. However, employing conventional precoding methods designed for communication generates directional beams that impair MIMO radar imaging and target tracking capabilities during data streaming. In this paper, we propose a MIMO joint automotive radar-communication (JARC) framework based on orthogonal frequency division multiplexing (OFDM) waveform. First, we show that the MIMO-OFDM preamble can be exploited for both MIMO radar processing and estimation of the communication channel. Then, we propose an optimal precoder design method that enables high accuracy target tracking while transmitting independent data streams to multiple receivers. The proposed methods provide high-resolution radar imaging and high throughput capabilities for MIMO JARC networks. Finally, we evaluate the efficacy of the proposed methods through numerical simulations.

Database-as-a-service (DBaaS) allows the client to store and manage structured data on the cloud remotely. Despite its merits, DBaaS also brings significant privacy issues. Existing encryption techniques (e.g., SQL-aware encryption) can mitigate privacy concerns, but they still leak information through access patterns, which are vulnerable to statistical inference attacks. Oblivious Random Access Machine (ORAM) can seal such leakages; however, the recent studies showed significant challenges on the integration of ORAM into databases. That is, the direct usage of ORAM on databases is not only costly but also permits very limited query functionalities. In this paper, we propose new oblivious data structures called Oblivious Matrix Structure (OMAT), which allow tree-based ORAM to be integrated into database systems in a more efficient manner with diverse query functionalities supported. OMAT provides special ORAM packaging strategies for table structures, which not only offers a significantly better performance but also enables a broad range of query types that may not be efficient in existing frameworks. On the other hand, OTREE allows oblivious conditional queries to be performed on tree-indexed databases more efficiently than existing techniques. We implemented our proposed techniques and evaluated their performance on a real cloud database with various metrics, compared with state-of-the-art counterparts.

Large scale deployment of connected vehicles with cooperative sensing technologies increases the demand on the vehicular communication spectrum in 5.9 GHz allocated for the exchange of safety messages. To support high data rates needed by such applications, the millimeter-wave (mmWave) automotive radar spectrum at 76-81 GHz can be utilized for communication. For this purpose, joint automotive radar-communication (JARC) system designs have been proposed in the literature to perform both functions using the same waveform. However, employing a large band in the mmWave spectrum deteriorates the performance of both radar and communication functions due to frequency-selectivity. In this paper, we address the optimal joint waveform design problem for wideband JARC systems via Orthogonal Frequency-Division Multiplexing (OFDM). We show that the problem is a non-convex quadratically constrained quadratic fractional programming (QCQFP) problem, which is known to be NP-hard. Existing approaches to solve QCQFP include Semidefinite Relaxation (SDR) and randomization approaches, which have high time complexity. Instead, we propose an approximation method to solve QCQFP more efficiently by leveraging structured matrices in the quadratic fractional objective function. Finally, we evaluate the efficacy of the proposed approach through numerical results.

Oblivious Random Access Machine (ORAM) enables a client to access her data without leaking her access patterns. Existing client-efficient ORAMs either achieve O(log N) client-server communication blowup without heavy computation, or O(1) blowup but with expensive homomorphic encryptions. It has been shown that O(log N) bandwidth blowup might not be practical for certain applications, while schemes with O(1) communication blowup incur even more delay due to costly homomorphic operations.