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Open AccessArticle

Reducing the Cost of Implementing Filters in LoRa Devices

1
Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Dr., Saskatoon, SK S7N 5A9, Canada
2
Cisco Systems Canada, 2123-595 Burrard St., Vancouver, BC V7X 1L7, Canada
3
Cisco Systems, Research Triangle Park, 7100-8 Kit Creek Road, RTP, NC 27560, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(18), 4037; https://doi.org/10.3390/s19184037
Received: 16 August 2019 / Revised: 13 September 2019 / Accepted: 16 September 2019 / Published: 19 September 2019
(This article belongs to the Section Internet of Things)
This paper presents two methods to optimize LoRa (Low-Power Long-Range) devices so that implementing multiplier-less pulse shaping filters is more economical. Basic chirp waveforms can be generated more efficiently using the method of chirp segmentation so that only a quarter of the samples needs to be stored in the ROM. Quantization can also be applied to the basic chirp samples in order to reduce the number of unique input values to the filter, which in turn reduces the size of the lookup table for multiplier-less filter implementation. Various tests were performed on a simulated LoRa system in order to evaluate the impact of the quantization error on the system performance. By examining the occupied bandwidth, fast Fourier transform used for symbol demodulation, and bit-error rates, it is shown that even performing a high level of quantization does not cause significant performance degradation. Therefore, the memory requirements of LoRa devices can be significantly reduced by using the methods of chirp segmentation and quantization so as to improve the feasibility of implementing multiplier-less filters in LoRa devices. View Full-Text
Keywords: LoRa; chirp spread spectrum (CSS); Internet of Things (IoT); pulse shaping filter; multiplier-less filters; sample quantization LoRa; chirp spread spectrum (CSS); Internet of Things (IoT); pulse shaping filter; multiplier-less filters; sample quantization
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Stewart, S.; Nguyen, H.H.; Barton, R.; Henry, J. Reducing the Cost of Implementing Filters in LoRa Devices. Sensors 2019, 19, 4037.

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