Error probability performance of chirp modulation in uncoded and coded LoRa systems

Baruffa, Giuseppe; Rugini, Luca; Germani, Lorenzo; Frescura, F.

doi:10.1016/j.dsp.2020.102828

“…(18) Note that, for a given 𝛾 b , the bit error probability is a decreasing function of 𝑀 and, for 𝑀 ≥ 64, is the same as the bit error probability of noncoherent LoRa with the spreading factor SF = log 2 𝑀 [16]. This is illustrated in Fig.…”

Section: A Binary ("One Bit Per Pulse") Encodingmentioning

confidence: 96%

M-ary Aggregate Spread Pulse Modulation in LPWANs for IoT applications

Nikitin¹,

Davidchack²

2021

Preprint

0

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In low-power wide-area networks (LPWANs), various trade-offs among the bandwidth, data rates, and energy per bit have different effects on the quality of service under different propagation conditions (e.g. fading and multipath), interference scenarios, multi-user requirements, and design constraints. Such compromises, and the manner in which they are implemented, further affect other technical aspects, such as system's computational complexity and power efficiency. At the same time, this difference in trade-offs also adds to the technical flexibility in addressing a broader range of IoT applications. This paper addresses a physical layer LPWAN approach based on the Aggregate Spread Pulse Modulation (ASPM) and provides a brief assessment of its properties in additive white Gaussian noise (AWGN) channel. In the binary ASPM the control of the quality of service is performed through the change in the spectral efficiency, i.e., the data rate at a given bandwidth. Implementing M-ary encoding in ASPM further enables controlling service quality through changing the energy per bit (in about an order of magnitude range) as an additional trade-off parameter. Such encoding is especially useful for improving the ASPM's energy per bit performance, thus increasing its range and overall energy efficiency, and making it more attractive for use in LPWANs for IoT applications.

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“…and it can be shown that P For the coherent 16-ASPM, the designed pulse trainx[k] is given by (19), where n = 2. For the noncoherent 16-ASPM, the designed pulse train iŝ…”

Section: A Noncoherent M-aspmmentioning

confidence: 99%

M-ary Aggregate Spread Pulse Modulation in LPWANs for IoT applications

Nikitin¹,

Davidchack

²

2021

Preprint

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<div><div><div><p>In low-power wide-area networks (LPWANs), various trade-offs among the bandwidth, data rates, and energy per bit have different effects on the quality of service under different propagation conditions (e.g. various types of fading), interference scenarios, multi-user requirements, and design constraints. Such compromises, and the manner in which they are implemented, fur- ther affect other technical aspects, such as system’s computational complexity and power efficiency. At the same time, this difference in trade-offs also adds to the technical flexibility in addressing a broader range of the Internet of Things (IoT) applications. This paper addresses a physical layer LPWAN approach based on the Aggregate Spread Pulse Modulation (ASPM) and provides a brief assessment of its properties in an additive white Gaussian noise (AWGN) channel. In the binary ASPM, the control of the quality of service is performed through the change in the spectral efficiency, i.e., the data rate at a given bandwidth. Implementing M-ary encoding in ASPM further enables controlling service quality through changing the energy per bit (in about an order of magnitude range) as an additional trade-off parameter. Such encoding is especially useful for improving the ASPM’s energy per bit performance, thus increasing its range and overall energy efficiency, and making it more attractive for use in LPWANs.</p></div></div></div>

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“…3, where the M-ASPM BER performance is compared with the respective performance of the noncoherent LoRa with different spreading factors. For LoRa, the BER approximation proposed in [19] is used, which is expressed as the product of the union bound on the bit error probability and a correction function.…”

Section: A Noncoherent M-aspmmentioning

confidence: 99%

“…and it can be shown that P For the coherent 16-ASPM, the designed pulse train x[k] is given by (19), where n = 2. For the noncoherent 16-ASPM, the designed pulse train is…”

Section: B E B /N 0 Efficiency Of Coherent M-aspmmentioning

confidence: 99%

M-ary Aggregate Spread Pulse Modulation in LPWANs for IoT applications

Nikitin¹,

Davidchack²

2021

Preprint

0

View full text Add to dashboard Cite

<div><div><div><p>In low-power wide-area networks (LPWANs), various trade-offs among the bandwidth, data rates, and energy per bit have different effects on the quality of service under different propagation conditions (e.g. various types of fading), interference scenarios, multi-user requirements, and design constraints. Such compromises, and the manner in which they are implemented, fur- ther affect other technical aspects, such as system’s computational complexity and power efficiency. At the same time, this difference in trade-offs also adds to the technical flexibility in addressing a broader range of the Internet of Things (IoT) applications. This paper addresses a physical layer LPWAN approach based on the Aggregate Spread Pulse Modulation (ASPM) and provides a brief assessment of its properties in an additive white Gaussian noise (AWGN) channel. In the binary ASPM, the control of the quality of service is performed through the change in the spectral efficiency, i.e., the data rate at a given bandwidth. Implementing M-ary encoding in ASPM further enables controlling service quality through changing the energy per bit (in about an order of magnitude range) as an additional trade-off parameter. Such encoding is especially useful for improving the ASPM’s energy per bit performance, thus increasing its range and overall energy efficiency, and making it more attractive for use in LPWANs.</p></div></div></div>

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Error probability performance of chirp modulation in uncoded and coded LoRa systems

Cited by 27 publications

References 18 publications

M-ary Aggregate Spread Pulse Modulation in LPWANs for IoT applications

M-ary Aggregate Spread Pulse Modulation in LPWANs for IoT applications

M-ary Aggregate Spread Pulse Modulation in LPWANs for IoT applications

M-ary Aggregate Spread Pulse Modulation in LPWANs for IoT applications

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