Investigation of Performance-Complexity Tradeoff in Filtering LoRa Signals

Stewart, Shania; Nguyen, Ha H.; Barton, Robert; Henry, Jérôme

doi:10.1109/ccece.2019.8861909

Cited by 2 publications

(4 citation statements)

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“…Figure 1 illustrates a block diagram of a LoRa system that implements a pulse shaping filter at the transmitter and a matched filter at the receiver. While the performance benefits brought by implementing these filters are demonstrated in detail in [6,8], this paper focuses on reducing the complexity of implementing the pulse shaping filter in a LoRa end device’s transmitter.…”

Section: Sample Reduction Methodsmentioning

confidence: 99%

“…This example considers a standard LoRa device that does not use chirp segmentation or a multiplier-less filter (Nfilt=0), and two devices using chirp segmentation with length-17 and 81 multiplier-less SRRC filters, respectively. The filter lengths were selected based on their ability to reduce the occupied bandwidth of LoRa signals for different BW settings [8]. When supporting individual spreading factors, the length-17 filter requires almost double the number of stored samples compared to the standard system.…”

Section: Sample Reduction Methodsmentioning

confidence: 99%

“…One promising solution is to implement a set of pulse shaping and matched square-root raised cosine (SRRC) filters in LoRa transmitters and receivers, respectively. The use of these filters can significantly reduce the bandwidth containing 99% of the total mean signal power while also reducing the out-of-band emissions created by LoRa devices [6,7,8]. The increase in spectral efficiency allows us to accommodate a larger number of IoT devices.…”

Section: Introductionmentioning

confidence: 99%

“…The challenge is that since LoRa devices are characterized by their low complexity, it is more difficult to justify the added resources required for filtering, especially when longer filters are required (which is often the case for LoRa devices with lower bandwidth settings [8]). Since the cost of implementing multipliers in hardware can prove significant, a preferred solution should eliminate the need for multiplications altogether.…”

Section: Introductionmentioning

confidence: 99%

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Reducing the Cost of Implementing Filters in LoRa Devices

Stewart

Nguyen

Barton

et al. 2019

Sensors

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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.

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Section: Sample Reduction Methodsmentioning

confidence: 99%