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

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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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