Flexible Multi-standard Digital Front-End for LPWA Technologies

Savelli, Patrick; Savaux, Vincent; Desnos, Pauline; Zeineddine, Ali; Kanj, Matthieu; Delacourt, Christophe

doi:10.36227/techrxiv.11961264

Cited by 4 publications

(3 citation statements)

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“…Hence, by setting q = K − 1 − b, we obtain (17) where q ∈ [[0, .., K − 1]], which concludes the proof.…”

Section: B Recovering Concavity Through G [K]supporting

confidence: 54%

“…This paper deals with both theoretical and implementation aspects of the synchronization in LoRa. To go further, it is noteworthy that a real-time LoRa receiver including the suggested synchronization algorithm has been implemented, such as described in [17], [18]. Different alternative solutions to the proposed one could be investigated to take advantage of the concavity of g and further improve the complexity of the synchronization method, such as a gradient descent.…”

Section: B Discussionmentioning

confidence: 99%

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On Time-Frequency Synchronization in LoRa System: From Analysis to Near-Optimal Algorithm

Savaux¹,

Delacourt²,

Savelli³

2022

IEEE Internet Things J.

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This paper deals with time and frequency synchronization in LoRa system based on the preamble symbols. A thorough analysis of the maximum likelihood (ML) estimator of the delay (time offset) and the frequency offset shows that the resulting cost function is not concave. As a consequence the a priori solution to the maximization problem consists in exhaustively searching over all the possible values of both the delay and the frequency offset. Furthermore, it is shown that these parameters are intertwined and therefore they must be jointly estimated, leading to an extremely complex solution. Alternatively, we show that it is possible to recover the concavity of the cost function, from which we suggest a low-complexity synchronization algorithm, whose steps are described in detail. Simulations results show that the suggested method reaches the same performance as the ML exhaustive search, while the complexity is drastically reduced, allowing for a real-time implementation of a LoRa receiver.

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“…Hence, by setting q = K − 1 − b, we obtain (17) where q ∈ [[0, .., K − 1]], which concludes the proof.…”

Section: B Recovering Concavity Through G [K]supporting

confidence: 54%

Section: B Discussionmentioning

confidence: 99%

On Time-Frequency Synchronization in LoRa System: From Analysis to Near-Optimal Algorithm

Savaux¹,

Delacourt²,

Savelli³

2022

IEEE Internet Things J.

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“…Basically, this hardware contains the antennas, the RF analog parts, the high-speed analog-to-digital and digital-to-analog converters, and field-programmable gate array (FPGA) reconfigurable hardware to perform functions such as rate adaptation, filtering, channel multiplexing (on the transmit side) and demultiplexing (on the receive side), and generation of the modulation signal. An overview of the possible RRH solutions is provided in [3].…”

Section: Introductionmentioning

confidence: 99%

A Cloud RAN Architecture for LoRa

Delacourt¹,

Savelli²,

Savaux

2021

RSL

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This article deals with a cloud radio access network (CRAN) architecture for the LoRa system. In the suggested design, the gateway embeds a limited remote radio head (RRH), including the analog RF part, the digital-to-analog and analog-to-digital conversion, and the digital front end (DFE). The other LoRa network functions, including the physical (PHY) layer, the LoRaWAN medium access control layer, and the application and customer servers, are implemented as cloud resources. This approach leads to a flexible RAN that is robust to the variations of capacity needs. Furthermore, it allows us to test very specific LoRa features, such as detection or demodulation, while bypassing others, including the hardware RRH. The methodology and tools we used to deploy a LoRa CRAN are detailed, and results of the performance indicator (CPU load, memory consumption) are provided.

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