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Conference Papers Year : 2019

Low complexity Detector for massive uplink random access with NOMA in IoT LPWA networks

Abstract

We focus on the random uplink transmissions of an unknown subset of nodes, disseminated in a cell. Under the constraints of massive Machine Type Communication (MTC) in cellular Low Power Wide Area Networks (LPWAN) and Ultra Reliable Low Latency Communications (URLLC), we assume a low coordination with the receiver and the usage of Gaussian coded Non Orthogonal Multiple Access (NOMA). We then target direct data transmission and thus propose a low complexity optimal-based detection of the active users: the It-MAP. This algorithm relies on the Maximum A Posteriori (MAP) detector and, similarly to Orthogonal Matching Pursuit (OMP)-like algorithms, proceeds by iteration to decrease its intrinsic complexity. We also show the gain of employing It-MAP rather than an OMP-based detection and the advantage of exploiting the possibility to tune the algorithm, in order to avoid either Missed Detection or False Alarm, depending on the wished trade-off between the reliability, the latency and the resource usage of the full transmission.
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Dates and versions

hal-02146649 , version 1 (04-06-2019)

Identifiers

  • HAL Id : hal-02146649 , version 1

Cite

Diane Duchemin, Jean-Marie Gorce, Claire Goursaud. Low complexity Detector for massive uplink random access with NOMA in IoT LPWA networks. WCNC 2019 - IEEE Wireless Communications and Networking Conference, Apr 2019, Marrakech, Morocco. pp.1-6. ⟨hal-02146649⟩
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