A Multi-Channel Software Decoder for the LoRa Modulation Scheme

Robyns, Pieter; Quax, Peter; Lamotte, Wim; Thenaers, William

doi:10.5220/0006668400410051

Cited by 89 publications

(77 citation statements)

References 6 publications

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“…LoRa supports multiple spreading factors, coding rates, and packet lengths, to support a very wide range of operating signal-to-noise ratios (SNRs). The LoRa physical layer is proprietary [8], but reverse engineering attempts [9], [10] have led to detailed mathematical descriptions [11]. The effect of carrier-and sampling frequency offset on LoRa digital receivers has been modeled and analyzed in [12].…”

Section: Introductionmentioning

confidence: 99%

On the Error Rate of the LoRa Modulation With Interference

Afisiadis

Cotting

Burg

et al. 2020

IEEE Trans. Wireless Commun.

101

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LoRa is a chirp spread-spectrum modulation developed for the Internet of Things. In this work, we examine the performance of LoRa in the presence of both additive white Gaussian noise and interference from another LoRa user. To this end, we extend an existing interference model, which assumes perfect alignment of the signal of interest and the interference, to the more realistic case where the interfering user is neither chip-nor phase-aligned with the signal of interest and we derive an expression for the error rate. We show that the existing aligned interference model overestimates the effect of interference on the error rate. Moreover, we prove two symmetries in the interfering signal and we derive low-complexity approximate formulas that can significantly reduce the complexity of computing the symbol and frame error rates compared to the complete expression. Finally, we provide numerical simulations to corroborate the theoretical analysis and to verify the accuracy of our proposed approximations. ). layer, which uses an ALOHA-based channel access scheme in which collisions are not explicitly avoided. These collisions lead to same-technology inter-user interference which may ultimately become the capacity-limiting factor in massive IoT scenarios [13]. For this reason, it is of great interest and importance to study the performance of LoRa under sametechnology interference.The authors of [14] present a mathematical network model for LoRa that includes the capture effect, i.e., the fact that a LoRa packet can be correctly decoded even under interference from another LoRa packet. A stochastic geometry framework for modeling the performance of a single gateway LoRa network is used in [15]. An investigation of the latency, collision rate, and throughput for LoRaWAN under duty-cycle restrictions is performed in [16]. Several real-world deployments of LoRa have been tested, but in order to assess the network scalability of LoRaWAN to future network densities that are expected to be orders of magnitude larger, evaluations through network simulators need to be performed. For this reason, the works of [17], [18] added LoRa functionality to the well-known ns-3 network simulator. A simpler Python-based network simulator for the LoRa uplink was first described in [19], and later extended for the LoRa downlink in [20]. The impact of the downlink feedback on LoRa capacity was also studied in [21]. A general overview and performance evaluations of LoRaWAN can be found in [13], [22].The impact of interference coming from different technologies on the performance of the LoRa modulation has received some attention in the literature. Specifically, [23] studies the co-existence of LoRa with IEEE 802.15.4g, while [24] studies the co-existence of LoRa with ultra-narrowband technologies, such as Sigfox. The impact of interference coming from other LoRa nodes has also received some attention. Specifically, the work of [25] extended the simulator of [19] in order to study the impact of imperfect orthogonality between different LoRa spr...

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Section: Introductionmentioning

confidence: 99%

On the Error Rate of the LoRa Modulation With Interference

Afisiadis

Cotting

Burg

et al. 2020

IEEE Trans. Wireless Commun.

101

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“…1. Annotated spectrogram of an example LoRa signal (with settings SF=11, BW=125 kHz) Reproduced with permission from [3].…”

Section: B Nyquist-rate Receiversmentioning

confidence: 99%

“…The reverse engineering efforts mentioned previously [3][4] [5][6] employ wide-band software-defined radios (SDR) and over-sample captured frames emitted by LoRa-compatible RF transmitters. These samples can be stored in memory and post-processed by powerful CPU's running potentially complex synchronization and demodulation algorithms.…”

Section: Related Workmentioning

confidence: 99%

Low Complexity LoRa Frame Synchronization for Ultra-Low Power Software-Defined Radios

Bernier

Dehmas

Deparis

2020

IEEE Trans. Commun.

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Low power wide area (LPWA) wireless networks based on the LoRa physical layer have attracted huge attention in recent years, both from industry and from academic researchers. While this rising popularity is due to this technology's demonstrated effectiveness and low cost, unfortunately, due to their complexity, the timing and frequency synchronization algorithms required to detect LoRa-modulated frames, in the context of minimum sampling rate optimum receivers, have received little attention. The aim of this paper is to fill this gap and describe how robust frame detection can be performed while focusing on minimal complexity implementations of the proposed algorithms. The ultimate goal is to propose frame detection techniques applicable to recently proposed ultra-low power software-defined receivers.

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“…We assume that the LoRa gateway is perfectly synchronized to the user whose message is decoded. Various synchronization techniques for LoRa have been explained in the literature [3]. It has been shown that interferers with different spreading factors can be considered approximately orthogonal [16], [17].…”

Section: Symbol Error Rate Under Awgn and Same-sf Lora Interferencementioning

confidence: 99%

“…LoRa supports multiple spreading factors, coding rates, and packet lengths, to support a wide range of operating signal-to-noise ratios (SNRs). The LoRa physical layer is proprietary, but reverse engineering attempts that have been carried out [2], [3] have revealed the mathematical description [4]. The effect of carrier-and sampling-frequency offset on LoRa digital receivers has been modeled and analyzed in [5].…”

Section: Introductionmentioning

confidence: 99%

LoRa Symbol Error Rate Under Non-Aligned Interference

Afisiadis

Cotting

Burg

et al. 2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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In this work, we examine the performance of the LoRa chirp spread spectrum modulation in the presence of both additive white Gaussian noise and interference from another LoRa user. To this end, we extend an existing interference model to the more realistic case where the interfering user is neither chip-nor phase-aligned with the signal of interest and we derive an expression for the SER. We show that the existing interference model overestimates the effect of interference on the error rate. Moreover, we derive a low-complexity approximate formula that can significantly reduce the complexity of computing the symbol error rate compared to the complete expression.

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A Multi-Channel Software Decoder for the LoRa Modulation Scheme

Cited by 89 publications

References 6 publications

On the Error Rate of the LoRa Modulation With Interference

On the Error Rate of the LoRa Modulation With Interference

Low Complexity LoRa Frame Synchronization for Ultra-Low Power Software-Defined Radios

LoRa Symbol Error Rate Under Non-Aligned Interference

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