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

Design of a Delta-Sigma (Δ-Σ) Based Digital-to-Analog Converter for TETRA-2 Transmitter

This paper describes the design of a delta-sigma (Δ-Σ) based Digital-to-Analog Converter (DAC) for a TETRA-2 transmitter along with the on-chip analog filtering to meet adjacent channel specifications. The proposed design meets the adjacent channel requirements with a margin of over 10 dB which is adequate to cater for hardware implementation. As the design specifications are in process of being finalised for TETRA-2, this paper will provide a valuable reference for designers involved with development of TETRA-2 radio equipment

Accurate stability prediction of 1-bit higher-order Δ-Σ modulators for multiple-sinusoidal inputs

The present approaches on predicting stability of Delta-Sigma (Δ-Σ) modulators are mostly confined to DC inputs. This poses limitations as practical applications of Δ-Σ modulators involve a wide range of signals other than DC such as multiple sinusoidal inputs for speech modeling. In this paper, a quasi-linear model for Δ-Σ modulators with nonlinear feedback control analysis is presented that accurately predicts stability of single-loop 1-bit higher-order Δ-Σ modulators for multiple sinusoids. Theoretical values are shown to match closely with simulation results. The results of this paper would enable optimization of the design of higher-order single-loop Δ-Σ modulators with increased dynamic ranges for various applications that require multiple-sinusoidal inputs or any general input composed of a finite number of sinusoidal components

Low Power Analog Processing for Ultra-High-Speed Receivers with RF Correlation

Ultra-high-speed data communication receivers (Rxs) conventionally require analog digital converters (ADC)s with high sampling rates which have design challenges in terms of adequate resolution and power. This leads to ultra-high-speed Rxs utilising expensive and bulky high-speed oscilloscopes which are extremely inefficient for demodulation, in terms of power and size. Designing energy-efficient mixed-signal and baseband units for ultra-high-speed Rxs requires a paradigm approach detailed in this paper that circumvents the use of power-hungry ADCs by employing low-power analog processing. The low-power analog Rx employs direct-demodulation with RF correlation using low-power comparators. The Rx is able to support multiple modulations with highest modulation of 16-QAM reported so far for direct-demodulation with RF correlation. Simulations using Matlab, Simulink R2020a® indicate sufficient symbol-error rate (SER) performance at a symbol rate of 8 GS/s for the 71 GHz Urban Micro Cell and 140 GHz indoor channels. Power analysis undertaken with current analog, hybrid and digital beamforming approaches requiring ADCs indicates considerable power savings. This novel approach can be adopted for ultra-high-speed Rxs envisaged for beyond fifth generation (B5G)/sixth generation (6G)/ terahertz (THz) communication without the power-hungry ADCs, leading to low-power integrated design solutions

Stability analysis of higher-order delta-sigma modulators for sinusoidal inputs

The aim of this paper is to determine the stability of higher-order Δ-Σ modulators for sinusoidal inputs. The nonlinear gains for the single bit quantizer for a dual sinusoidal input have been derived and the maximum stable input limits for a fifth-order Chebyshev Type II based Δ-Σ modulators are established. These results are useful for optimising the design of higher-order Δ-Σ modulators

SmartDR: A Device-to-Device Communication for Post-Disaster Recovery

Natural disasters, such as earthquakes, can cause severe destruction and create havoc in the society.Buildings and other structures may collapse during disaster incidents causing injuries and deaths to victims trapped under debris and rubble. Immediately after a natural disaster incident, it becomes extremely difficult for first responders and rescuers to find and save trapped victims. Often searches are carried out blindly in random locations, which delay the rescue of the victims. This paper introduces a Smartphone Assisted Disaster Recovery (SmartDR) method for post-disaster communication using Smartphones. SmartDR utilizes the device-to-device (D2D) communication technology in Fifth Generation (5G) networks, which enables direct communication between proximate devices without the need of relaying through a network infrastructure, such as mobile access points or mobile base stations. We examine a scenario of multi-hop D2D communication where smartphones carried by trapped victims and other people in disaster affected areas can self-detect the occurrence of a disaster incident by monitoring the radio environment and then can self-switch to a disaster mode to transmit emergency help messages with their location coordinates to other nearby smartphones. To locate other nearby smartphones also operating in the disaster mode and in the same channel, each smartphone runs a rendezvous process. The emergency messages are thus relayed to the functional base station or rescue centre. To facilitate routing of the emergency messages, we propose a path selection algorithm, which considers both delay and the leftover energy of a device (a smartphone in this case). Thus, the SmartDR method includes: (i) a multi-channel channel hopping rendezvous protocol to improve the victim localization or neighbor discovery, and (ii) an energy-aware multi-path routing (Energy-aware ad-hoc on-demand distance vector or E-AODV) protocol to overcome the higher energy depletionrate at devices associated with single shortest path routing. The SmartDR method can guide search and rescue operations and increase the possibility of saving lives immediately aftermath a disasterincident. A simulation-based performance study is conducted to evaluate the protocol performance in post-disaster scenario. Simulation results show that a significant performance gain is achievable when a device utilises the channel information for the rendezvous process and the leftover energy

Q-enhancement with on-chip inductor optimization for reconfigurable Δ-Σ radio-frequency ADC

The paper details on-chip inductor optimization for a reconfigurable continuous-time delta-sigma (Δ-Σ) modulator based radio-frequency analog-to-digital converter. Inductor optimisation enables the Δ-Σ modulator with Q enhanced LC tank circuits employing a single high Q-factor on-chip inductor and lesser quantizer levels thereby reducing the circuit complexity for excess loop delay, power dissipation and dynamic element matching. System level simulations indicate at a Q-factor of 75 Δ- Σ modulator with a 3-level quantizer achieves dynamic ranges of 106, 82 dB and 84 dB for RFID, TETRA, and Galileo over bandwidths of 200 kHz, 10 MHz and 40 MHz respectively

5G Uniform linear arrays with beamforming and spatial multiplexing at 28 GHz, 37 GHz, 64 GHz and 71 GHz for outdoor urban communication: A two-level approach

Multiple-input multiple-output (MIMO) spatial multiplexing and beamforming are regarded as key technology enablers for the fifth-generation (5G) millimeter wave (mmWave) mobile radio services. Spatial multiplexing requires sufficiently separated and incoherent antenna array elements, while in the case of beamforming, the antenna array elements need to be coherent and closely spaced. Extensive 28-, 60-, and 73-GHz ultra-wideband propagation measurements in cities of New York City and Austin have indicated formation of two or more spatial lobes for the angles-of-departure and angles-of-arrival even for line-of-sight (LOS) transmission, which is an advantageous feature of mmWave channels, indicating that the transmitting and receiving array antenna elements can be co-located, thus enabling a single architecture for both spatial multiplexing and beamforming. In this paper, a two-level beamforming architecture for uniform linear arrays is proposed that leverages the formation of these spatial lobes. The antenna array is composed of sub-arrays, and the impact of sub-array spacing on the spectral efficiency is investigated through simulations using a channel simulator named NYUSIM developed based on extensive measured data at mmWave frequencies. Simulation results indicate spectral efficiencies of 18.5–28.1 bits/s/Hz with a sub-array spacing of 16 wavelengths for an outdoor mmWave urban LOS channel. The spectral efficiencies obtained are for single-user (SU) MIMO transmission at the recently allocated 5G carrier frequencies in July 2016. The method and results in this paper are useful for designing antenna array architectures for 5G wireless systems

Dictionary selection for Compressed Sensing of EEG signals using sparse binary matrix and spatiotemporal sparse Bayesian learning

Online monitoring of electroencephalogram (EEG) signals is challenging due to the high volume of data and power requirements. Compressed sensing (CS) may be employed to address these issues. Compressed sensing using sparse binary matrix, owing to its low power features, and reconstruction/decompression using spatiotemporal sparse Bayesian learning have been shown to constitute a robust framework for fast, energy efficient and accurate multichannel bio-signal monitoring. EEG signal, however, does not show a strong temporal correlation. Therefore, the use of sparsifying dictionaries has been proposed to exploit the sparsity in a transformed domain instead. Assuming sparsification adds values, a challenge, therefore, in employing this CS framework for the EEG signal is to identify the suitable dictionary. Using real multichannel EEG data from 15 subjects, in this paper, we systematically evaluated the performance of the framework when using various wavelet bases while considering their key attributes of number of vanishing moments and coherence with sensing matrix. We identified Beylkin as the wavelet dictionary leading to the best performance. Using the same dataset, we then compared the performance of Beylkin with discrete cosine basis, often used in the literature, and the case of using no sparsifying dictionary. We further demonstrate that using dictionaries (Beylkin and DCT) may improve performance tangibly only for a high compression ratio (CR) of 80% and with smaller block sizes; as compared to when using no dictionaries

Power control in cognitive radios, Internet-of Things (IoT) for factories and industrial automation

Cognitive radio (CR) is fast emerging as a promising technology that can meet the machine-to machine (M2M) communication requirements for spectrum utilization and power control for large number of machines/devices expected to be connected to the Internet-of Things (IoT). Power control in CR as a secondary user can been modelled as a non-cooperative game cost function to quantify and reduce its effects of interference while occupying the same spectrum as primary user without adversely affecting the required quality of service (QoS) in the network. In this paper a power loss exponent that factors in diverse operating environments for IoT is employed in the non-cooperative game cost function to quantify the required power of transmission in the network. The approach would enable various CRs to transmit with lesser power thereby saving battery consumption or increasing the number of secondary users thereby optimizing the network resources efficiently