System-level timing fluctuations caused by unstable low-cost clocks and end-to-end communication delays are the main sources of uncertainties in existing synchronization mechanisms that rely on timestamp exchanges. This paper introduces a timestamp-free clock syntonization approach, carrier frequency offset (CFO)-assisted syntonization (CFOSynt), to estimate the clock skew between a pair of nodes by utilizing carrier frequency offset. To enable CFOSynt, we leverage the fact that RF oscillators in the radio can be used as the reference to calibrate the system clock oscillators, and the pairwise RF clock information is carried in the transmission carrier frequency. By incorporating CFO and capturing the clock frequency relationship in system clock skew estimation, CFOSynt can eliminate the need for timestamping and the impact of delay uncertainties. To validate the design, CFOSynt is implemented on two common off-the-shelf (COTS) IoT platforms with access to the CFO estimation from the radio chip. Extensive experiments are conducted to evaluate CFOSynt, and CFOSynt can estimate the clock skew of 32 kHz low-cost electronic oscillators with a mean error of$-2.46$Hz. In comparison with timestamp-based approaches, CFOSynt achieves up to 70-90 percent improvement in skew estimation error and shows significant reliability when low-cost oscillators are used.

The (inverse) discrete Fourier transform (DFT/ IDFT) is often perceived as essential to orthogonal frequency-division multiplexing (OFDM) systems. In this paper, a deep complex-valued convolutional network (DCCN) is developed to recover bits from time-domain OFDM signals without relying on any explicit DFT/IDFT. The DCCN can exploit the cyclic prefix (CP) of OFDM waveform for increased SNR by replacing DFT with a learned linear transform, and has the advantage of combining CP-exploitation, channel estimation, and intersymbol interference (ISI) mitigation, with a complexity ofO(N2). Numerical tests show that the DCCN receiver can outperform the legacy channel estimators based on ideal and approximate linear minimum mean square error (LMMSE) estimation and a conventional CP-enhanced technique in Rayleigh fading channels with various delay spreads and mobility. The proposed approach benefits from the expressive nature of complex-valued neural networks, which, however, currently lack support from popular deep learning platforms. In response, guidelines of exact and approximate implementations of a complex-valued convolutional layer are provided for the design and analysis of convolutional networks for wireless PHY. Furthermore, a suite of novel training techniques are developed to improve the convergence and generalizability of the trained model in fading channels. This work demonstrates the capability of deep neural networks in processing OFDM waveforms and the results suggest that the FFT processor in OFDM receivers can be replaced by a hardware AI accelerator.

Construction workers who usually work in high altitudes and hazardous construction zones are prone to accidents that may lead to injuries and fatalities. This calls for advanced monitoring technologies that can locate workers and hazardous areas within a dynamically evolving outdoor/indoor area. More accurate localization techniques indicate more safety for protecting them from an accident. In this paper, a multiple transmit power assisted sequence-based localization (MPSBL) solution is developed to achieve high localization accuracy. The theoretical analysis and system model design illustrate the feasibility of MPSBL. Simulations and empirical experiments in construction zones have been conducted, which show that MPSBL outperforms state-of-the-art approaches.

System-level timing inconsistency and wireless communication delays are the main uncertainties of existing synchronization mechanisms for wireless sensor networks. Existing solutions mainly rely on timestamp exchanges to estimate clock offset and skew, which results in frequent synchronization, high overhead, and high energy consumption to maintain a well-synchronized network. This paper introduces a novel clock syntonization approach to estimate the differences between clock frequencies of network nodes without the need for timestamp exchanges. The carrier frequency offset (CFO) assisted syntonization (CFOSynt) utilizes the carrier information obtained from wireless packet transmission for clock skew compensation. The key idea of CFOSynt is that, in any wireless communication system, where carrier modulation is employed, carrier frequency delivers information about the transmitter RF clock. Consequently, clock frequency offset between a pair of sensor nodes will result in a carrier frequency offset detected by the receiver node. By leveraging the CFO information, CFOSynt can estimate the system clock skew based on digital counter theory. Extensive experiments and numerical analysis have been demonstrated to evaluate clock skew estimation.