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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. 

In this paper, a novel LPWAN technology, ZCNET, is proposed, which achieves over 40 times the network capacity of LoRa using similar or less resource under the most challenging channel conditions. The capacity boost of ZCNET is mainly due to two reasons. First, a ZCNET node transmits signals that occupy a small fraction of the signal space, resulting in a low collision probability. Second, ZCNET supports 8 parallel root channels within a single frequency channel by using 8 Zadoff-Chu (ZC) root sequences. The root channels do not severely interfere with each other, mainly because the interference power is spread evenly over the entire signal space. A simple ALOHA-style protocol is used for medium access, with which a node randomly chooses the root channel and the range it occupies within the root channel, while still achieving high packet receiving ratios such as 0.9 or above. ZCNET has been extensively tested with both real-world experiments on the USRP and simulations. ZCNET will likely better accommodate the explosive growth of IoT network sizes and meet the demand of IoT applications.