Multiuser Detection for Downlink Communication in LoRa- Like Networks

Abstract

Internet of Things (IoT) technology has become ubiquitous in a multitude of applications and its use is growing. However, the expansion of IoT faces a major difficulty: scalability, that is, very dense deployment of communicating devices is currently limited. In long-range networks, such as LoRa, the downlink is critical because it limits the number of acknowledgements that can be sent, and consequently reliability. It also limits the possibility to update the devices, which could be critical when they are deployed for decades. To overcome those problems, we propose a solution, inspired by Non Orthogonal Multiple Access (NOMA) techniques, to increase by at least one order of magnitude the number of devices that can be addressed. While the approach differentiates the devices by the power allocated to them, it differs from the vast majority of previous works on power domain NOMA because it does not require interference cancellation. Instead, it benefits from the spectrum spreading of the modulation scheme (chirp spread spectrum), where, at the end of the decoding phase, the information carried by a symbol is found in the position of a peak in the Fourier domain. In the vast majority of cases, the information from different users results in different peak positions, not creating any interference. In that sense, we get closer to avoidance schemes such as time or frequency hopping, but without using a code. In this paper, we propose a new solution for NOMA in the power domain that does not suffer from the limitations induced by interference cancellation residues. The proposed system, including preamble detection and channel estimation, is presented and evaluated by simulations. We demonstrate that our proposed scheme increases the number of devices by one order of magnitude compared to the current system which allows addressing only one user at a time and maintains full compatibility with the LoRa physical layer standard.

Deep Learning-based Signal Detection for Uplink in LoRa-like Networks

“…The computational complexity of the classical LoRa receiver is of O(M log(M )) [9]. For the DFNN-based receiver, the complexity results in O(M 2 ).…”

Fast generalized threshold‐based signal detection for multiuser downlink NOMA systems with arbitrary power allocation

“…Note that in () is derived from (), which is given by where is defined as the instantaneous SNR. The probability density function of γ u is given by 9 where .…”

“…In addition to the SIC and the threshold‐based detection schemes, a machine learning‐based blind detection is proposed in Zhang et al 8 and a multiuser detection scheme is studied in Tesfay et al 9 in the DL‐NOMA systems. In the above detection schemes, the threshold‐based signal detection has low detection complexity.…”

Susceptibility of LoRa Communications to Intentional Electromagnetic Interference with Different Sweep Periods

“…In most papers about LoRa communications, the terms interference and jammers usually refer to other LoRa devices. When such devices are licit LoRa users, the goal is to design algorithms that allow a gateway to precisely identify each LoRa user in a scenario where multiple LoRa signals are simultaneously received [19]. On the other hand, some authors explore the use of illicit LoRa transmitters, which try to cause collisions between licit and illicit LoRa signals at the gateway input terminal.…”