This paper explores the use of data modulated by a spectrally efficient frequency division multiplexing (SEFDM) waveform for sensing. We first show that, if the presence of a cyclic prefix is assumed in the transmitted signal, the problem of multiple target sensing is tantamount to the detection and estimation of an unknown number of complex two-dimensional complex tones. Then, a novel iterative estimation method, based on a maximum likelihood approach, is developed to solve the last problem. Our simulation results evidence that SEFDM represents a valid technical option over static or slowly varying channels, and that the proposed method achieves a better accuracy-complexity trade-off than other estimation techniques available in the technical literature. In particular, our numerical results show that, in various scenarios, the proposed method achieves a 15% – 50% improvement in estimation accuracy with respect to other techniques with a limited computational complexity.

This paper introduces the spectrally efficient time-frequency space (SETFS) modulation, a two-dimensional (2D) modulation extending the concepts of spectrally efficient frequency division multiplexing (SEFDM) and orthogonal time–frequency space (OTFS) modulation by increasing the spectral overlap at two levels, i.e., in both frequency and Doppler domains. This double compression improves the use of the available spectrum. Moreover, in SETFS, the periodicity in both time and frequency is maintained through the use of a double cyclic prefix (DCP) in order to simplify channel equalization and avoid 2D inter-symbol interference (ISI). However, the presence of a spectral overlap unavoidably induces 2D inter-carrier interference (ICI), that makes symbol detection more complicated. To mitigate channel estimation errors and also support high-resolution sensing, a novel channel estimator, dubbed Newton-based estimation and spectral cancellation algorithm (NESCA), is proposed. Numerical results show that SETFS achieves up to a 24% gain in the achievable communication rate with respect to OTFS at the cost of higher receiver complexity. Combined with NESCA, SETFS provides near optimal delay and Doppler estimation, outperforming other three algorithms available in the technical literature. In terms of communication performance, SETFS employing the NESCA, followed by a minimum mean square error equalizer and iterative detector with soft symbol mapping, reaches quasi optimal capacity, while throughput saturation occurs with an SNR gap of about 10 dB in favor of OTFS-DCP and increasing with less advanced estimators. Nevertheless, SETFS combined with NESCA achieves a better communication-sensing tradeoff, improving spectral efficiency up to 25% with respect to OTFS at the price of a higher detection complexity. Overall, SETFS offers a relevant tradeoff between spectral efficiency, complexity and sensing accuracy, making it a promising candidate for future wireless systems.

In this manuscript, the problem of implementing, on commercial software defined radios, a digital communication system that employs the Orthogonal Time Frequency Space (OTFS) modulation is addressed. Few available manuscripts discuss this issue and they all overlook the use of cyclic prefixes in both the time and frequency domains (i.e., the use of a double cyclic prefix), a crucial aspect to achieve robustness to doubly-selective fading channels. In this paper, we describe the implementation of the OTFS-based communication system developed in [1] on a couple of software defined radios by National Instruments. Various technical information about the signal processing algorithms employed at the transmit and receive sides is provided. Experimental results evidence that the proposed communication system is able to reliably operate in the presence of strong Doppler affecting the communication channel.

In this manuscript, a digital communication system employing the orthogonal time-frequency space modulation with a double cyclic prefix and operating over a doubly selective fading is considered. After providing some guidelines for the allocation of pilot symbols within a single orthogonal timefrequency space modulation symbol, novel algorithms for pilotaided channel estimation and equalization are illustrated. The proposed channel estimator is able to provide a description of the inner structure of the communication channel; therefore, it can be also employed at the transmit side for sensing applications. Our numerical results evidence that our algorithms outperform two technical alternatives outlined in the literature at the price of a limited increase in the required computational effort.

In the last years, substantial attention has been paid to the use of radar systems in health monitoring, due to the availability of both low-cost radar devices and computationally efficient algorithms for processing their measurements. In this article, a tutorial overview of radar-based monitoring of vital signs is provided. More specifically, we first focus on the available radar technologies and the signal processing algorithms developed for the estimation of vital signs. Then, we provide some useful guidelines that should be followed in the selection of radar devices for vital sign monitoring and in their use. Finally, we illustrate various specific applications of radar systems to health monitoring and some relevant research trends in this field.

In this manuscript, an innovative method for the detection and the estimation of multiple targets in a radar system employing orthogonal frequency division multiplexing is illustrated. The core of this method is represented by a novel algorithm for detecting multiple superimposed two-dimensional complex tones in the presence of noise and estimating their parameters. This algorithm is based on a maximum likelihood approach and combines a single tone estimator with a serial cancellation procedure. Our numerical results lead to the conclusion that the developed method can achieve a substantially better accuracy-complexity trade-off than various related techniques in the presence of closely spaced targets.