Low Computational Sensing with Goertzel Filtering for Mobile Industrial IoT Devices

Internet-of Things (IoT) is getting connected to an increasing number of mobile devices such as autonomous vehicles, drones and robots. Termed as Mobile Industrial Internet-of Things (MI²oT) devices in this paper, a key requirement of these devices is to accurately estimate range and Doppler in various applications, in addition to data communication. Research efforts therefore include incorporating MI²-oT devices with high-data rate communications together with Frequency Modulated Continuous Wave Radar (FMCW) sensing capabilities. Range and Doppler sensing, in FMCW radars is undertaken by a twostage Fast Fourier Transform (FFT) which is computationally demanding. It is challenging to design baseband processing with FFTs that can be implemented as low computational hardware or application specific integrated circuits (ASIC) in MI²-oT devices. This paper, presents a novel range and Doppler sensing technique based on Goertzel filtering, leading to considerable reduction in computations compared to the FFT. FMCW radar with Goertzel filtering and FFT are examined in three cases viz., sensing the range and velocity of a car, vibration and respiration monitoring. Simulation results show a computation reduction of the order of 6.3×, 7.7× and 8.1× \u1d422\u1d427Giga-operations per second (GOPS) for the three cases respectively. The reduced computations increase the feasibility of implementing range and Doppler sensing in MI²oT devices which have restricted computational resources

THz Communications – A Candidate for a 6G Radio?

W the first 5G networks are about to be launched, the discussion within the scientific community about the next generation Beyond 5G or 6G has already kicked-off. One of the candidate technologies for a 6G radio access technology is THz communications, which uses spectrum mainly beyond 275 GHz enabling the use of channel bandwidths of several 10s of GHz. This contribution provides an overview on current state-of-the art of THz communications in research, standardization and regulation and discusses the challenges to make THz communications a promising candidate for a 6G radio

Can Automotive Radars Form Vehicular Networks?

Radar communications (RadCom) is a spectrally efficient way for removing automotive radar interference and thereby enhancing reliable radar sensing, via a single hardware for both radar and communications. When interference coordination does not use all the RadCom resources, opportunities for communicating additional data arise. We propose a new communication protocol, termed RadNet (for radar network), which forms a vehicular ad-hoc multi-hop network by automotive radars in a distributed manner. Simulation results obtained for high-way use cases show that RadNet can enable several Mbps data links without degrading the radar performance

Synchronization-free radchat for automotive radar interference mitigation

Automotive radar interference mitigation is expected to be inherent in all future ADAS and AD vehicles. Joint radar communications is a candidate technology for removing this interference by coordinating radar sensing through communication. Coordination of radars requires strict time synchronization among vehicles, and our formerly proposed protocol (RadChat) achieves this by a precise absolute time, provided by GPS clocks of vehicles. However, interference might appear if synchronization among vehicles is lost in case GPS is spoofed, satellites are blocked over short intervals, or GPS is restarted/updated. Here we present a synchronization-free version of RadChat (Sync-free RadChat), which relies on using the relative time for radar coordination, eliminating the dependency on the absolute time provided by GPS. Simulation results obtained for various use cases show that Sync-free RadChat is able to mitigate interference without degrading the radar performance

Sensing Integrated DFT-Spread OFDM Waveform and Deep Learning-powered Receiver Design for Terahertz Integrated Sensing and Communication Systems

Terahertz (THz) communications are envisioned as a key technology of next-generation wireless systems due to its ultra-broad bandwidth. One step forward, THz integrated sensing and communication (ISAC) system can realize both unprecedented data rates and millimeter-level accurate sensing. However, THz ISAC meets stringent challenges on waveform and receiver design to fully exploit the peculiarities of THz channel and transceivers. In this work, a sensing integrated discrete Fourier transform spread orthogonal frequency division multiplexing (SI-DFT-s-OFDM) system is proposed for THz ISAC, which can provide lower peak-to-average power ratio than OFDM and is adaptive to flexible delay spread of the THz channel. Without compromising communication capabilities, the proposed SI-DFT-s-OFDM realizes millimeter-level range estimation and decimeter-per-second-level velocity estimation accuracy. In addition, the bit error rate (BER) performance is improved by 5 dB gain at the $10^{-3}$ BER level compared with OFDM. At the receiver, a deep learning based ISAC receiver with two neural networks is developed to recover transmitted data and estimate target range and velocity, while mitigating the imperfections and non-linearities of THz systems. Extensive simulation results demonstrate that the proposed deep learning methods can realize mutually enhanced performance for communication and sensing, and is robust against Doppler effects, phase noise, and multi-target estimation

RadChat: Spectrum Sharing for Automotive Radar Interference Mitigation

In the automotive sector, both radars and wireless communication are susceptible to interference. However, combining the radar and communication systems, i.e., radio frequency (RF) communications and sensing convergence, has the potential to mitigate interference in both systems. This article analyses the mutual interference of spectrally coexistent frequency modulated continuous wave (FMCW) radar and communication systems in terms of occurrence probability and impact, and introduces RadChat, a distributed networking protocol for mitigation of interference among FMCW based automotive radars, including self-interference, using radar and communication cooperation. The results show that RadChat can significantly reduce radar mutual interference in single-hop vehicular networks in less than 80 ms