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

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 wi...

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Veröffentlicht in:2019 IEEE Wireless Communications and Networking Conference Workshop (WCNCW) S. 1 - 6
Hauptverfasser: Duchemin, Diane, Gorce, Jean-Marie, Goursaud, Claire
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2019
Schlagworte:
Complexity theory
Detectors
Matching pursuit algorithms
Mathematical model
Multiple User Detection
NOMA
Non Orthogonal Multiple Access
Random Access
Ultra reliable low latency communication
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Zusammenfassung: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.
DOI:10.1109/WCNCW.2019.8902806