Elias Aboutanios

In this work, we propose the orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation with frequency modulated continuous wave (FMCW) (ODDM-FMCW) waveform to enable integrated sensing and communication (ISAC) with a low peak-to-average power ratio (PAPR). We first propose a square-root-Nyquist-filtered FMCW (SRN-FMCW) waveform to address limitations of conventional linear FMCW waveforms in ISAC systems. To better integrate with ODDM, we generate SRN-FMCW by embedding symbols in the DD domain, referred to as a DD-SRN-FMCW frame. A DD chirp compression receiver is designed to obtain the channel response efficiently. Next, we construct the proposed ODDM-FMCW waveform for ISAC by superimposing a DD-SRN-FMCW frame onto an ODDM data frame. A comprehensive performance analysis of the ODDM-FMCW waveform is presented, covering peak-to-average power ratio, spectrum, ambiguity function, and Cramér-Rao bound for delay and Doppler estimation. Numerical results show that the proposed ODDM-FMCW waveform delivers excellent ISAC performance in terms of root mean square error for sensing and bit error rate for communications.

In this work, we propose the orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation with frequency modulated continuous wave (FMCW) (ODDM-FMCW) waveform to enable integrated sensing and communication (ISAC) with a low peak-to-average power ratio (PAPR). We first propose a square-root-Nyquist-filtered FMCW (SRN-FMCW) waveform to address limitations of conventional linear FMCW waveforms in ISAC systems. To better integrate with ODDM, we generate SRN-FMCW by embedding symbols in the DD domain, referred to as a DD-SRN-FMCW frame. A DD chirp compression receiver is designed to obtain the channel response efficiently. Next, we construct the proposed ODDM-FMCW waveform for ISAC by superimposing a DD-SRN-FMCW frame onto an ODDM data frame. A comprehensive performance analysis of the ODDM-FMCW waveform is presented, covering peak-to-average power ratio, spectrum, ambiguity function, and Cramer-Rao bound for delay and Doppler estimation. Numerical results show that the proposed ODDM-FMCW waveform delivers excellent ISAC performance in terms of root mean square error for sensing and bit error rate for communications.

In airborne radar, target detection performance can be degraded by limited sample support in a non-homogeneous and non-stationary environment. The multistage Wiener filter (MWF) is a reduced rank adaptive detector that compresses the clutter-plus-noise (interference) subspace. However, there is a challenge in selecting the appropriate number of MWF stages to achieve these benefits. Current approaches estimate the rank of the full clutter subspace. However, this is known to be an overestimate of the required number of MWF stages that leads to a degradation in the detection performance. Consequently, we reformulate the problem and propose an algorithm that seeks the minimum number of stages required to achieve high probability of detection. The proposed algorithm utilises the Kullback-Leibler divergence to estimate the number of stages. Our proposed method leads to early truncation of the MWF, reducing sample support requirements and decreasing computational cost. We provide a computationally efficient implementation embedded within the MWF and demonstrate its effectiveness in a variety of space-time adaptive processing simulations.

The terahertz spectrum has the ability to provide high-speed communication and millimeter-level resolution. As a result, terahertz-integrated sensing and communication (ISAC) has been identified as a key enabler for 6G wireless networks. This work discusses a photonics-based D-band communication system for integrated high-resolution localization and high-speed wireless communication. Our empirical results show that a communication rate of 5 Gbps over a distance of 1.5 m and location identification of the target with millimeter-level (${\lt} 4 \;{\rm mm}$) range resolution can be conducted simultaneously using the same signal. We also show that the error due to the thickness of the beam splitter can be eliminated, while the quantization error and the random drift errors are the limiting factors of the resolution achieved. This experimental demonstration using D-band communication indicates that terahertz ISAC can be realized for 6G networks while considering the underlying system restrictions (e.g., bandwidth limit and lens diameter).

A grid-synchronization algorithm for grid-tie inverters must be precise, accurate, robust to noise and harmonic distortion and fast to react to grid voltage disturbances to facilitate stable operation in a modern distribution grid network. Unlike traditional synchronization algorithms such as the phase-locked loop (PLL), the harmonic version of the Aboutanios and Mulgrew (HAM) algorithm performs well in all these characteristics. However, both the three-phase HAM estimator and three single-phase HAM estimators generate some small estimation error during unbalanced grid voltage conditions. This paper proposes a method that combines the adaptive Clarke transform (ACT) with the HAM estimator to reduce this error and cause convergence to the Cramèr Rao Lower Bound in noisy environments under both balanced and unbalanced conditions. Simulated results confirm that the proposed method generates improved performance when compared to several similar competing estimators.

In airborne radar, reduced rank space-time adaptive processing techniques are employed when non-stationarity and non-homogeneity of the clutter causes insufficient sample support. There have been many approaches proposed to address this problem, including principal components, the cross-spectral metric and the multistage Wiener filter. This latter approach is superior to other reduced rank techniques in terms of computational efficiency, sample support and rank requirements. Regarding operation in heterogeneous environments, the single data set approach for clutter suppression has been proposed and operates solely on the cell under test to obtain a clutter covariance estimate. It is therefore highly effective in environments with limited training data that is homogeneous with the test data. In this paper, a single data set detection approach under the framework of the multistage Wiener filter is proposed and analysed to enhance clutter suppression capabilities. The target detection performance of the filter is evaluated using simulated maritime radar data.

Dual-functional radar communications (DFRC) systems, which jointly design radar and communication functionalities on one common platform, have been recognized as an attractive approach for leveraging the scarce spectral efficiently in diverse applications, such as autonomous driving. Index modulation (IM), a promising communication technique with high energy utilization and spectral efficiency, has been successfully integrated into DFRC systems. Most existing IM-based DFRC schemes utilize the degrees of freedom (DoFs) from one or two domains for separate information embedding, without utilizing the full diversity of the integrated waveform. In this paper, we propose a novel DFRC scheme, referred to as hybrid index modulation (HIM), operating on frequency hopping multiple-input multiple-output (FH-MIMO) radar. The HIM embeds communication information into the index of the 3-tuples in the hybrid dictionary, which is the outer product of DoFs from the frequency, phase, and antenna index of the integrated waveform. Since the HIM entwines waveform diversities from different domains, it increases the communication rate by multiples. We also apply the HIM to the non-orthogonal FH-MIMO radar to improve spectral efficiency, in addition to the conventional orthogonal case. Furthermore, we propose a constrained HIM scheme to enhance the radar detection performance, where the FH pattern has been optimized in terms of ambiguity function. System performance is evaluated both theoretically and experimentally in metrics of communication rate, ambiguity function and symbol error rate.