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Long Range-Frequency Hopping Spread Spectrum (LR-FHSS) is a new physical layer option that has been recently added to the LoRa family with the promise of achieving much higher network capacity than the previous versions of LoRa. In this article, we present our evaluation of LR-FHSS based on real-world packet traces collected with an LR-FHSS device and a receiver we designed and implemented in software. We overcame challenges due to the lack of documentation of LR-FHSS, and our study is the first of its kind that processes signals transmitted by an actual LR-FHSS device with practical issues such as frequency error. Our results show that LR-FHSS meets its expectations in communication range and network capacity. We also propose customized methods for LR-FHSS that improve its performance significantly, allowing our receiver to achieve higher network capacity than those reported earlier. 

Abstract Low Earth Orbit (LEO) satellite networks provide global data service coverage and has become increasingly popular. Uncoordinated access channels reduce data latency in LEO networks by allowing user terminals to transmit data packets at random times to the satellite without any coordination overhead. In this paper, packet acquisition in uncoordinated access channels of LEO networks is studied and a novel solution, called ChirpPair, is proposed, with which the satellite can detect the packets as well as estimating key parameters of the packets for data demodulation. With ChirpPair, the packet preamble consists of a chirp and its conjugate, where a chirp is a complex vector with constant magnitude and linearly increasing frequency. ChirpPair adopts a multi-stage process that gradually increases the estimation accuracy of the parameters without incurring high computation complexity. ChirpPair has been demonstrated in real-world experiments with over-the-air transmissions. ChirpPair has also been evaluated by simulations with the 3GPP New Radio (NR) Non-Terrestrial Network (NTN) channel model and the results show that ChirpPair achieves high accuracy despite its low computation complexity. 

ABSTRACT LoRa has emerged as one of the main candidates for connecting low-power wireless IoT devices. Packet collisions occur in LoRa networks when multiple nodes transmit wireless signals simultaneously. In this paper, a novel solution, referred to as TnB, is proposed to decode collided LoRa signals. Two major components of TnB are Thrive and Block Error Correction (BEC). Thrive is a simple algorithm to resolve collisions by assigning an observed signal to a node according to a matching cost that reflects the likelihood for the node to have transmitted the signal. BEC is a novel algorithm for decoding the Hamming code used in LoRa, and is capable of correcting more errors than the default decoder by jointly decoding multiple codewords. TnB does not need any modification of the LoRa nodes and can be adopted by simply replacing the gateway. TnB has been tested with real-world experimental traces collected with commodity LoRa devices, and the results show that TnB can increase the median throughput by 1.36× and 2.46× over the state-of-the-art for Spreading Factors (SF) 8 and 10, respectively. Simulations further show that the improvement is even higher under more challenging channel conditions. 

In this paper, MPCast, a novel wireless transmission technology for the downlink of Low Power Wide Area Networks (LPWAN), is proposed. MPCast modulates data on the Zadoff-Chu (ZC) sequence, which generates a peak at the receiving side. Both the location and phase of the peak carry information. Also, multiple peaks are transmitted simultaneously at different power levels to be received by nodes with different channel conditions. A novel preamble design allows the nodes to detect the frame and synchronize with the AP at low computation complexity. MPCast has been validated with real-world experiments on the Powder platform. MPCast has also been evaluated with simulations under a challenging wireless channel model. The results show that MPCast achieves a physical layer data rate of 1.74 kbps in a 125 kHz channel when the Signal to Noise Ratio (SNR) is -7 dB, which is a 9 dB gain over LoRa SF 9.