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We consider a power constrained downlink communication scenario where energy efficiency, reliability, and latency take precedence over rate, as in some Internet of Things (IoT) applications. To reduce its receiver power consumption and complexity, we assume that the IoT device has a single RF chain and investigate the finite-resolution analog-to-digital converter (ADC) operation with differential Phase Shift Keying (PSK) modulation. A lower ADC resolution leads to an exponential decrease in power consumption, while adopting differential PSK enables the use of a low-cost detector with no channel state information (CSI) or carrier phase recovery circuitry. Our main goal in this article is to compare, both analytically and numerically using representative IoT system design parameters, the receiver energy efficiency of the proposed differential PSK system with a coherent PSK system that uses estimated CSI under reliability and latency constraints in Rayleigh slow-fading and ADC quantization distortion conditions. To mitigate the ADC finite-resolution effects without requiring CSI at the receiver or transmitter, we propose and analyze a PSK differentially-modulated Alamouti space-time block transmission scheme with only two RF chains at the transmitter while still restricting the IoT device to have a single RF chain. Our results demonstrate that in the considered low-rate, and outage and latency-constrained scenario with stringent power consumption requirements, differentially-modulated Alamouti transmissions to a single RF-chain IoT device with a lowresolution ADC is an attractive choice in terms of receiver energy efficiency, reliability, and transmission latency.

In this paper, we investigate enhanced super-resolution range estimators with decimeter-level accuracy for multi-antenna and multipath Bluetooth systems. To enhance the traditional MUSIC range estimator for a two-way frequency-hopping Bluetooth channel model, we apply forward-backward averaging and bandwidth extrapolation using Burg's algorithm to improve ranging accuracy, which is limited by the used Bluetooth bandwidth and the quality of the estimated sample covariance matrix. For the multiantenna case, we compare the Summed Antenna Processing and Individual Antenna Processing methods to process multiple-antenna Bluetooth channel measurements and enhance the range estimation accuracy compared to the single-antenna case. In addition, we investigate a sparsity-aware range estimator which exploits the sparsity of Bluetooth channel impulse response and achieves comparable ranging accuracy to the enhanced MUSIC estimator but at a much lower computational complexity. We apply the greedy Orthogonal Matching Pursuit algorithm to heuristically solve the sparsity-constrained optimization problem for Bluetooth ranging. Furthermore, we evaluate the computational complexity of our investigated Bluetooth range estimators with two complexity-reduction techniques to further reduce the complexity of MUSIC range estimator. Moreover, we analyze the Cramer-Rao Lower Bound (CRLB) on unbiased range estimation using the frequency-hopping Bluetooth channel model and derive a new insightful CRLB expression for a two-path channel model. Finally, we evaluate the Root-Mean-Square Error and Empirical Cumulative Distribution Function performance of our investigated range estimators both on simulated and real-world Bluetooth data that we collected in line-of-sight (LOS) and non-line-of-sight (NLOS) multipath scenarios. Our proposed enhancements on the range estimators improved the ranging accuracy by 58% for our collected Bluetooth data.

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