Complex Neural Network based Joint AoA and AoD Estimation for Bistatic ISAC

Integrated sensing and communication (ISAC) in wireless systems has emerged as a promising paradigm, offering the potential for improved performance, efficient resource utilization, and mutually beneficial interactions between radar sensing and wireless communications, thereby shaping the future of wireless technologies. In this work, we present two novel methods to address the joint angle of arrival and angle of departure estimation problem for bistatic ISAC systems. Our proposed methods consist of a deep learning (DL) solution leveraging complex neural networks, in addition to a parameterized algorithm. By exploiting the estimated channel matrix and incorporating a preprocessing step consisting of a coarse timing estimation, we are able to notably reduce the input size and improve the computational efficiency. In our findings, we emphasize the remarkable potential of our DL-based approach, which demonstrates comparable performance to the parameterized method that explicitly exploits the multiple-input multiple-output (MIMO) model, while exhibiting significantly lower computational complexity.

RIS-Enabled Integrated Sensing and Communication for 6G Systems

The following paper proposes a new target localization system design using an architecture based on reconfigurable intelligent surfaces (RISs) and passive radars (PRs) for integrated sensing and communications systems. The preamble of the communication signal is exploited in order to perform target sensing tasks, which involve detection and localization. The RIS in this case can aid the PR in sensing targets that are otherwise not seen by the PR itself, due to the many obstacles encountered within the propagation channel. Therefore, this work proposes a localization algorithm tailored for the integrated sensing and communications RIS-aided architecture, which is capable of uniquely positioning targets within the scene. The algorithm is capable of detecting the number of targets along with estimating the position of targets via angles and times of arrival. Our simulation results demonstrate the performance of the localization method in terms of different localization and detection metrics and for increasing RIS sizes.

Secure Full Duplex Integrated Sensing and Communications

The following paper models a secure full duplex (FD) integrated sensing and communication (ISAC) scenario, where malicious eavesdroppers aim at intercepting the downlink (DL) as well as the uplink (UL) information exchanged between the dual functional radar and communication (DFRC) base station (BS) and a set of communication users. The DFRC BS, on the other hand, aims at illuminating radar beams at the eavesdroppers in order to sense their physical parameters, while maintaining high UL/DL secrecy rates. Based on the proposed model, we formulate a power efficient secure ISAC optimization framework design, which is intended to guarantee both UL and DL secrecy rates requirements, while illuminating radar beams towards eavesdroppers. The framework exploits artificial noise (AN) generation at the DFRC BS, along with UL/DL beamforming design and UL power allocation. We propose a beamforming design solution to the secure ISAC optimization problem. Finally, we corroborate our findings via simulation results and demonstrate the feasibility, as well as the superiority of the proposed algorithm, under different situations. We also reveal insightful trade-offs achieved by our approach.

Mutual Information Based Pilot Design for ISAC

The following paper presents a novel orthogonal pilot design dedicated for dual-functional radar and communication (DFRC) systems performing multi-user communications and target detection. After careful characterization of both sensing and communication metrics based on mutual information (MI), we propose a multi-objective optimization problem (MOOP) tailored for pilot design, dedicated for simultaneously maximizing both sensing and communication MIs. Moreover, the MOOP is further simplified to a single-objective optimization problem, which characterizes trade-offs between sensing and communication performances. Due to the non-convex nature of the optimization problem, we propose to solve it via the projected gradient descent method on the Stiefel manifold. Closed-form gradient expressions are derived, which enable execution of the projected gradient descent algorithm. Furthermore, we prove convergence to a fixed orthogonal pilot matrix. Finally, we demonstrate the capabilities and superiority of the proposed pilot design, and corroborate relevant trade-offs between sensing MI and communication MI. In particular, significant signal-to-noise ratio (SNR) gains for communication are reported, while re-using the same pilots for target detection with significant gains in terms of probability of detection for fixed false-alarm probability. Other interesting findings are reported through simulations, such as an \textit{information overlap} phenomenon, whereby the fruitful ISAC integration can be fully exploited.

Deep Learning-based Estimation for Multitarget Radar Detection

Target detection and recognition is a very challenging task in a wireless environment where a multitude of objects are located, whether to effectively determine their positions or to identify them and predict their moves. In this work, we propose a new method based on a convolutional neural network (CNN) to estimate the range and velocity of moving targets directly from the range-Doppler map of the detected signals. We compare the obtained results to the two dimensional (2D) periodogram, and to the similar state of the art methods, 2DResFreq and VGG-19 network and show that the estimation process performed with our model provides better estimation accuracy of range and velocity index in different signal to noise ratio (SNR) regimes along with a reduced prediction time. Afterwards, we assess the performance of our proposed algorithm using the peak signal to noise ratio (PSNR) which is a relevant metric to analyse the quality of an output image obtained from compression or noise reduction. Compared to the 2D-periodogram, 2DResFreq and VGG-19, we gain 33 dB, 21 dB and 10 dB, respectively, in terms of PSNR when SNR = 30 dB.

Robust Integrated Sensing and Communication Beamforming for Dual-functional Radar and Communications: Method and Insights

This work presents a novel robust beamforming design dedicated for dual-functional radar and communication (DFRC) base stations (BSs) in the context of integrated sensing and communications (ISAC). The architecture is intended for circumstances with imperfect channel state information (CSI). Our suggested approach demonstrates several tradeoffs for joint radar-communication deployment. Due to the DFRC nature of the design, the beamformer can simultaneously point towards an intended target, while optimizing communication quality of service. We unveil several insights regarding closed form expressions, as well as optimality of the proposed beamformer. Lastly, simulation results demonstrate the effectiveness of the proposed ISAC beamformer.

On Hybrid Radar Fusion for Integrated Sensing and Communication

The following paper introduces a novel integrated sensing and communication (ISAC) scenario termed hybrid radar fusion. In this setting, the dual-functional radar and communications (DFRC) base station (BS) acts as a mono-static radar in the downlink (DL), for sensing purposes, while performing its DL communication tasks. Meanwhile, the communication users act as distributed bi-static radar nodes in the uplink (UL) following a frequency-division duplex protocol. The DFRC BS fuses the information available at different DL and UL resource bands to estimate the angles-of-arrival (AoAs) of the multiple targets existing in the scene. In this work, we derive the maximum likelihood (ML) criterion for the hybrid radar fusion problem at hand. Additionally, we design efficient estimators; the first algorithm is based on an alternating optimization approach to solve the ML criterion, while the second one designs an optimization framework that leads to an alternating subspace approach to estimate AoAs for both the target and users. Finally, we demonstrate the superior performance of both algorithms in different scenarios, and the gains offered by these proposed methods through numerical simulations.

Dual-Mode Time Domain Multiplexed Chirp Spread Spectrum

We propose a dual-mode (DM) time domain multiplexed (TDM) chirp spread spectrum (CSS) modulation for spectral and energy-efficient low-power wide-area networks (LPWANs). DM-CSS modulation that uses both the even and odd cyclic time shifts has been proposed for LPWANs to achieve noteworthy performance improvement over classical counterparts. However, its spectral efficiency (SE) is half of the in-phase and quadrature (IQ)-TDM-CSS scheme that employs IQ components with both up and down chirps, resulting in a SE that is four times relative to Long Range (LoRa) modulation. Nevertheless, the IQ-TDM-CSS scheme only allows coherent detection. Furthermore, it is also sensitive to carrier frequency and phase offsets, making it less practical for low-cost battery-powered LPWANs for Internet-of-Things (IoT) applications. DM-CSS uses either an up-chirp or a down-chirp. DM-TDM-CSS consists of two chirped symbols that are multiplexed in the time domain. One of these symbols consisting of even and odd frequency shifts (FSs) is chirped using an up-chirp. The second chirped symbol also consists of even and odd FSs, but they are chirped using a down-chirp. It shall be demonstrated that DM-TDM-CSS attains a maximum achievable SE close to IQ-TDM-CSS while also allowing both coherent and non-coherent detection. Additionally, unlike IQ-TDM-CSS, DM-TDM-CSS is robust against carrier frequency and phase offsets.

On Integrated Sensing and Communication Waveforms with Tunable PAPR

We present a novel approach to the problem of dual-functional radar and communication (DFRC) waveform design with adjustable peak-to-average power ratio (PAPR), while minimizing the multi-user communication interference and maintaining a similarity constraint towards a radar chirp signal. The approach is applicable to generic radar chirp signals and for different constellation sizes. We formulate the waveform design problem as a non convex optimization problem. As a solution, we adopt the alternating direction method of multipliers (ADMM), hence iterating towards a stable waveform for both radar and communication purposes. Additionally, we prove convergence of the proposed iterative waveform design and demonstrate its superior performance by computer simulations, in comparison to state-of-the-art radar-communication waveform designs.

A Survey on Chirp Spread Spectrum-based Waveform Design for IoT

Long Range (LoRa) is one of the most promising and widespread chirp spread spectrum (CSS)-based physical (PHY) layer technique for low-power wide-area networks (LPWANs). Using different spreading factors, LoRa can attain different spectral/energy efficiencies, and can target multitude of Internet-of-Thing (IoT) applications. However, one of the limiting factors for LoRa is the low bit rate. Little to no effort has been made in order to improve the achievable rate of LoRa, until recently, when a number of CSS-based PHY layer LoRa alternative are proposed for LPWANs. In this survey, for the first time, we present a comprehensive waveform design of these CSS-based schemes that have been proposed between 2019 to 2022. In total, fifteen alternatives to LoRa are compared. Other survey articles related to LoRa mostly tackle different issues, such as LoRa networking, LoRa deployment in massive IoT networks, and LoRa architectures, etc. This survey, on the other hand, comprehensively elucidates the waveform design of LoRa alternatives. The CSS schemes studied in this survey are divided into single chirp, multiple chirp, and index modulation based on the multiplexing pattern of the chirps. Complete transceiver architecture of these CSS schemes is studied, and performance is evaluated in terms of energy efficiency (EE), spectral efficiency (SE), bit-error rate (BER) performance in additive white Gaussian noise, BER in the presence of phase and frequency offsets. It has been observed that the EE, SE and robustness against the offsets is primarily linked to transmit symbol frame structure. The public versions of the MATLAB codes for the CSS schemes studied in this survey shall be provided to promote reproducible research.