The Purpose of this correspondence is to provide corrections to typos appeared in[1]and to report that[1]contains a missing citation of the reference[2].

We have found a few typos in[1, eqs. (20), (44), and (55)]. In these equations, the term$\lambda _{\min }$must be corrected to$\lambda _{\min }^{-1}$as follows:

LoRa (Long Range), a low power and wide area modulation scheme based on chirp spread spectrum, is the most popular and widely adopted Internet-of-Things (IoT) technique in industry. A notable and interesting property of LoRa modulation is the quasi-orthogonality of signals modulated under different spreading factors (SFs). Unfortunately, in the literature, there has been no analytical effort to establish the theoretical validity of such quasi-orthogonality. This paper, for the first time, theoretically tackles the quasi-orthogonality of the LoRa modulation. First, we derive in both continuous-and discrete-time domains the cross-correlation between two non-synchronized LoRa signals with different SFs, based on which we analyze the quasi-orthogonality of the LoRa modulation and draw some useful engineering insights. Particularly, we analytically show that in the continuous-time domain, the quasi-orthogonality is guaranteed if one of the SFs of the two LoRa signals is large enough; while, in the discrete-time domain, the quasi-orthogonality is ensured if the maximum of the SFs is large enough. Furthermore, for practical values of the SF, the maximum squared magnitudes of the cross-correlation in the continuous-and discrete-time domains are shown to be 1.14% and 1.08%, respectively, compared to their peak values. We demonstrate the validity and accuracy of our analysis through extensive numerical simulations.

Device-to-device (D2D) communications and simultaneous wireless information and power transfer (SWIPT) technologies are key to Internet of Things (IoT) to achieve massive connectivity and to prolong battery lifetime, respectively. In this article, we propose novel schemes for transmit power adaptation to maximize the average data rate and to minimize the outage probability of D2D communications over fast and slow-fading channels, respectively, where the D2D network coexists with a SWIPT-based IoT cellular network operating with the time-switching protocol. Also, different from the existing works, circuit power consumption for information decoding is taken into account in this article. To solve the formulated nonconvex power adaptation problems, we first derive tractable lower bound and upper bound on the average data rate and outage probability of cellular user (CU), respectively. Then, based on the conservative approximation, power adaptation solutions are determined. Intriguingly, we show that in the proposed schemes, the performance tradeoff between information decoding and energy harvesting at the CU is not degraded despite the reception of the D2D signals (i.e., interfering signals). Furthermore, deriving and analyzing closed-form bounds on the D2D performance, we obtain useful insights into the D2D performance improvement by the proposed power adaptation schemes. Numerical results demonstrate that the proposed schemes outperform the baseline scheme, and verify the obtained insights.

In this study, we propose a robust LoRa modulation scheme optimized to mitigate demodulation errors occurring from residual carrier frequency offset (CFO). Furthermore, we analyze its asymptotic performance to obtain useful insights. The performance analysis and advantages of the proposed scheme are confirmed by numerical results.

This article investigates the problem of preamble detection for the long-range (LoRa) modulation and analyzes its performance. For analysis, the inevitable multiuser interference is reasonably modeled as correlated noise. First, the optimal preamble detector is derived and the best preamble is designed to maximize the detection probability while achieving a target value of the false alarm rate. To reduce the required computational complexity and to gain more insights, the preamble detection problem is further investigated for two important special scenarios with: 1) a large spreading factor (SF) and 2) uncorrelated noise, respectively. The analysis is then extended to the case in the presence of phase offset. Our work reveals that the existing preamble detection methods are strictly suboptimal. In addition, various useful and interesting engineering insights into preamble detection with LoRa modulation are provided from the derived results, and the theoretical performance limit of the preamble detection with LoRa modulation is quantified. Extensive numerical results demonstrate the effectiveness and superiority of the proposed scheme. Particularly, the proposed scheme outperforms the existing schemes by more than 10 dB in terms of signal-to-noise ratio (SNR) at the detection probability of 80% and the target false alarm rate of 10%.

Identifying unmanned aerial vehicles(UAVs) has become an important task as the number of UAVs used for various purposes increases. The importance of UAV identification is increasingly emphasized as news of UAV attacks on critical national facilities has been heard. In order to respond effectively to UAV movement, detection, identification, and neutralization phases are expected to be required. Among these, in order to identify UAVs wirelessly, it is necessary to select a wireless communication method suitable for an UAV identification environment. Basically, because UAVs are likely to be high and far from ground-based identifiers, and the UAV’s batteries are limited, it is necessary to consider LPWA-based communication. In this paper, LoRa’s RF system parameters and various propagation models are used to derive the UAV identification success probability using LoRa technology, a representative Low Power Wide Area(LPWA) communication method and we present the probability of UAV identification success with distance by propagation model and spreading factor. In addition, the relationship between the speed of the UAV and the spreading factor and bandwidth of the LoRa system capable of drone identification is also described.