Mohammad Neinavaie

There has been significant interest over the past few years in integrated sensing and communication (ISAC) to enable applications, such as the massive Internet of Things, highly automated transportation systems, and military surveillance. Many ISAC studies in the literature considered designing, from scratch, next-generation systems that are endowed with ISAC capabilities. This article argues that “the truth is out there” and that one can sense and exploit unknown signals, whether non-ISAC “legacy” or ISAC devised. The article presents a framework termed cognitive sensing and opportunistic navigation ( COSON ). COSON can be thought of as an instantiation of ISAC, but, instead of having the “luxury” of designing signals with ISAC capabilities, COSON senses arbitrary, unknown communication signals and exploits them for positioning, navigation, and timing purposes. COSON is composed of four stages: 1) Blind signal acquisition, which comprises spectrum sensing and signal activity detection, blind beacon estimation, initial Doppler and Doppler rate estimation, and blind source enumeration; 2) blind signal tracking and beacon refinement; 3) interference and multipath classification; and 4) sensing and navigation. Extensive experimental results are presented, demonstrating the broad applicability of COSON to terrestrial and nonterrestrial sources, transmitting with various modulation and multiple access schemes: cellular 4G and 5G signals, GPS, and low-Earth orbit satellite (Starlink, Orbcomm, and Iridium) signals. COSON is demonstrated to localize stationary antennas and navigate unmanned aerial vehicles and a ground vehicle, to meter-level accuracy, without global navigation satellite system signals.

A receiver architecture for cognitive sensing and navigation with orthogonal frequency division multiplexing (OFDM)-based systems is proposed. The proposed receiver enables exploiting all the transmitted periodic beacons of 5G new radio (NR) and Starlink low Earth orbit (LEO) signals to draw navigation observables. Reference signals (RSs) of modern OFDM-based systems, such as 5G NR, contain both always-on and on-demand components. These components can be unknown or known but subject to change. To leverage all transmitted signals for navigation purposes, the RS signals should be detected and tracked cognitively. Similar to conventional navigation receivers, the proposed architecture involves acquisition and tracking stages. However, both stages are supplemented by the unorthodox capability of estimating and updating the RS signals. The acquisition stage instructs the tracking stage by reporting performance metrics, which are used to adjust the tracking loop gains to update the RS accordingly. A chirp model is considered to capture the high dynamics of Doppler frequency in intensive Doppler scenarios, where the navigating vehicle is maneuvering or the transmitting source is not static. The effect of Doppler rate estimation error on frame length estimation is analyzed. Experimental results are presented demonstrating the performance of the proposed receiver by: (i) enabling an unmanned aerial vehicle (UAV) to detect and exploit terrestrial 5G NR cellular signals in a blind fashion for navigation purposes, achieving a two-dimensional (2D) root-mean squared error (RMSE) of 4.2 m over a total trajectory of 416 m; (ii) enabling a ground vehicle that traversed a trajectory of 1.79 km to cognitively sense an unknown gNB (blindly detect, track, and exploit transmitted always-on and on-demand signals), localizing it with a 2D error of 5.83 m; and (iii) tracking Starlink LEO OFDM signals, producing Doppler measurements, which were fused to localize a stationary receiver with a 2D error of 6.5 m, starting from an initial estimate 179 km away from the receiver’s true position.

This letter unveils the unknown structure of Starlink low Earth orbit (LEO) satellites' orthogonal frequency division multiplexing (OFDM)-like reference signals (RSs). The spectrum of Starlink's dowlink signals is presented, and the frame length is estimated. A blind receiver is proposed, which acquires via a sequential generalized likelihood ratio test multiple satellites, estimates their RSs and respective Doppler, and tracks their carrier and code phases. Experimental results are presented showing six tracked Starlink LEO satellites, three of which transmitted pure tones, while the other transmitted OFDM-like signals. The achieved horizontal positioning error with the six satellites was 6.5 m.

A cognitive approach is proposed to detect unknown beacons of terrestrial signals of opportunity (SOPs). Two scenarios are considered in the paper: (i) detection of unknown beacons with integer constraints (IC) and (ii) detection of unknown beacons with no integer constraint (NIC). An example of beacons with IC is the pseudo-noise (PN) sequences in cellular code division multiple access (CDMA) signals. On the other hand, the reference signals (RSs) in orthogonal frequency-division multiplexing (OFDM)-based systems can be considered as beacons signals with NIC. Matched subspace detectors are proposed for both scenarios, and it is shown experimentally that the proposed matched subspace detectors are capable of detecting cellular third-generation (3G) cdma2000 signals and fifth-generation (5G) OFDM signals. A low complexity method is derived to simplify the matched subspace detector with IC for M -ary phase shift keying ( M PSK) modulation. The effect of symbol errors in the estimated beacon signal on the carrier to noise ratio (CNR) is characterized analytically. Closed-form expressions for the asymptotic probability of detection and false alarm are derived. Experimental results are presented showing an application of the proposed cognitive approach by enabling an unmanned aerial vehicle (UAV) to detect and exploit terrestrial cellular signals for navigation purposes. In one experiment, the UAV achieved submeter-level accurate navigation over a trajectory of 1.72 km, by exploiting signals from four 3G cdma2000 transmitters. In another experiment, the UAV achieves a position root mean-squared error (RMSE) of 4.63 m over a trajectory of 416 m, by exploiting signals from two 5G transmitters.

Tracking the Doppler frequency of a transmitter, where the Doppler is being compensated at the transmitter is considered. A particular example where this occurs is Globalstar low Earth orbit (LEO) satellites. Doppler compensation takes place based on the center of each satellite transmitter beams. When the Doppler is compensated, the measured Doppler by a ground receiver is different from the real Doppler, which renders the measured Doppler unusable for applications such as opportunistic navigation. This paper presents an approach to estimate the true (i.e., uncompensated) Doppler, which is subsequently used for code and carrier phase tracking. Experimental results are presented of the first delay tracking with real Globalstar satellite signals, showing the proposed technique closely matching the tracking predicted by two-line element (TLE) files.

This letter shows the first acquisition, Doppler tracking, and positioning results with Starlink's low Earth orbit satellite signals. A generalized-likelihood-ratio-based test is proposed to acquire Starlink's downlink signals. A Kalman-filter-based algorithm for tracking the Doppler frequency from the unknown Starlink signals is developed. Experimental results show Doppler tracking of six Starlink satellites, achieving a horizontal positioning error of 10 m.

A receiver architecture is proposed to cognitively extract navigation observables from fifth generation (5G) new radio (NR) signals of opportunity. Unlike conventional opportunistic receivers which require knowledge of the signal structure, particularly the reference signals (RSs), the proposed cognitive opportunistic navigation (CON) receiver requires knowledge of only the frame duration and carrier frequency of the signal. In 5G NR, some of these RSs are only transmitted on demand, which limits the existing opportunistic navigation frameworks to signals which are on always-on ; hence, limiting the exploitable RS bandwidth. To exploit the full available bandwidth and improve ranging accuracy, the proposed CON receiver is designed to estimate all the RSs contained in the transmitted signals corresponding to multiple 5G base stations, (i.e., gNBs). Navigation observables (pseudorange and carrier phase) are subsequently derived from the estimated RSs. The proposed receiver operates in two stages: (i) acquisition and (ii) tracking. The acquisition stage of the CON receiver is modeled as a sequential detection problem where the number of gNBs and their corresponding RSs and Doppler frequencies are unknown. The generalized likelihood ratio (GLR) test for sequentially detecting active gNBs is derived and used to estimate the number of gNBs and their RSs. In order for the receiver to refine and maintain the Doppler and RS estimates provided by the acquisition stage, tracking loops are designed. A sufficient condition on the Doppler estimation error to ensure that the proposed GLR asymptotically achieves a constant false alarm rate (CFAR) is derived. The output of the tracking loops, namely carrier phase and code phase, are then used to estimate the receiver’s position. Extensive experimental results are presented demonstrating the capabilities of the proposed CON receiver with real 5G signals on ground and aerial platforms, with an experiment showing the first navigation results with real 5G signals on an unmanned aerial vehicle (UAV) navigating using the CON receiver over a 416 m trajectory with a position root mean-squared error (RMSE) of 4.35 m.

This letter shows the first carrier phase tracking and positioning results with Starlink’s low earth orbit (LEO) satellite signals. An adaptive Kalman filter based algorithm for tracking the beat carrier phase from the unknown Starlink signals is proposed. Experimental results show carrier phase tracking of six Starlink satellites and a horizontal positioning error of 7.7 m with known receiver altitude.

We present a diversity multiplexing tradeoff (DMT) optimal tree pruning sphere decoding algorithm which visits merely a single branch of the search tree of the sphere decoding (SD) algorithm, while maintaining the DMT optimality at high signal to noise ratio (SNR) regime. The search tree of the sphere decoding algorithm is pruned via intersecting one dimensional spheres with the hypersphere of the SD algorithm, and the radii are chosen to guarantee the DMT optimality. In contrast to the conventional DMT optimal SD algorithm, which is known to have a polynomial complexity at high SNR regime, we show that the proposed method achieves the DMT optimality by solely visiting a single branch of the search tree at high SNR regime. The simulation results are corroborated with the claimed characteristics of the algorithm in two different scenarios.

Although zero-forcing (ZF) detection is well-known for its low computational complexity in multiple-input multiple-output (MIMO) communication systems, it suffers from significantly poor performance. The sphere decoder (SD) method, on the other hand, achieves the maximum likelihood (ML) performance yet imposes a high computational complexity. We propose a low-complexity detection scheme, concatenated with the SD method, which verifies the reliability of the ZF equalized observations via some predefined regions and thresholds obtained by the channel realization. We design the threshold analytically, such that the method achieves the ML performance. With the designed threshold, we prove that the method achieves the ML performance and the ZF computational complexity at the same time with probability one, at high signal-to-noise ratio (SNR). The theoretical analysis is corroborated with numerical simulations. The simulation results also show that the proposed method achieves the ML performance very rapidly as the SNR increases.