LoRa is widely viewed as a promising wireless technology to support connections of IoT devices to the gateway. The downlink of LoRa carries traffic such as acknowledgments and faces challenges as the network size grows. In this paper, we propose 2-Pipe, a novel method that significantly enhances the downlink capacity of LoRa without modifying any nodes. With 2-Pipe, the gateway can transmit two downlink packets simultaneously with the same Spreading Factor (SF) to two distinct nodes. Simultaneous transmissions are achieved by modulating packets with intentional misalignment both in the time and frequency domains, so that a node can demodulate its own packet correctly without being affected by the other because the intentional misalignment reduces the impact of interference. Experiments with commodity LoRa devices show that the throughput of 2-Pipe is 1.62× that of the existing LoRa.

Low Earth Orbit (LEO) satellite networks have been growing very rapidly in recent years. In this paper, we propose a novel method, called StarAngle, which estimates user orientation with the beacon signals of Starlink satellites. StarAngle measures the beacon phase difference between two receiving antennas because the phase difference is a function of the user orientation. The phase measurements are compared with mathematical calculations based on known orbital parameters of Starlink satellites and the value that leads to the best agreement is used as the estimation. We overcome challenges due to asynchronous clocks in our commodity antennas by subtracting the phase measurements of one satellite by another which cancels the biases caused by clock mismatch. We experimentally test StarAngle in 10 locations under challenging weather conditions and our results show that the median estimation error is 7.5 degrees. Our results also confirm that the phase information of Starlink satellites can be measured reliably and may be used to support other applications in addition to user orientation estimation.

In cellular networks, the User Equipment (UE) often needs to send control information to the base station. For example, to initiate a connection, a UE transmits a Zadoff-Chu (ZC) sequence on a channel called PRACH, where the sequence is randomly selected from up to 64 sequences. Also, a connected UE may send the Scheduling Request (SR) bit on a channel called PUCCH to start an uplink data transmission. In this paper, novel access methods, namely, ABF PRACH and ABF SR, are proposed based on the Analog Bloom Filter (ABF). With ABF PRACH, a UE transmits a pattern, which consists of multiple ZC sequences. The performance is improved, because the number of patterns, currently 2304, is much larger than the number of sequences, resulting in a much lower collision probability. Similarly, with ABF SR, each connected UE is assigned a unique pattern. The system capacity is improved, because the resource is shared by many UEs, instead of dedicated to individual UEs. Both ABF PRACH and ABF SR have been demonstrated in real-world experiments. Simulations show that ABF PRACH significantly outperforms the existing PRACH. Also, ABR SR achieves a 2.52-fold increase of capacity, while maintaining very low error ratios.

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, a novel LPWAN technology, ZCNET, is proposed, which achieves significantly higher network capacity than existing solutions, such as LoRa, Sigfox, and RPMA. 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. ZCNET has been extensively tested with both real-world experiments on the USRP and simulations, and the results confirm that ZCNET achieves significant gains over LoRa, Sigfox, and RPMA. ZCNET will likely better accommodate the explosive growth of IoT network sizes and meet the demand of IoT applications.