In order to meet the capacity needs of LoRa networks, Adaptive Data Rate (ADR) has been proposed and implemented in LoRaWANs. The network server running ADR determines the optimum data-rate and hence spreading factor setting for each LoRa device in a network. This in turn requires the gateway to be capable of receiving all possible spreading factors. Existing gateways achieve this by using multiple RF front ends, increasing their overall cost and complexity. In this work, we propose a Discrete Wavelet Transform based spreading factor detection algorithm that is agnostic to transmitter settings. This computationally light-weight algorithm can be implemented on any off-the-shelf SDR, bringing down the cost and ease of LoRaWAN gateway implementations. Using experimental, real-world datasets, we show that the proposed algorithm can detect the spreading factor of over 99.5% of the received packets at SNRs down to -10dB.

LoRa has seen widespread adoption as a long range IoT technology. As the number of LoRa deployments grow, packet collisions undermine its overall network throughput. In this paper, we propose a novel interference cancellation technique -- Concurrent Interference Cancellation (CIC), that enables concurrent decoding of multiple collided LoRa packets. CIC fundamentally differs from existing approaches as it demodulates symbols by canceling out all other interfering symbols. It achieves this cancellation by carefully selecting a set of sub-symbols -- pieces of the original symbol such that no interfering symbol is common across all sub-symbols in this set. Thus, after demodulating each sub-symbol, an intersection across their spectra cancels out all the interfering symbols. Through LoRa deployments using COTS devices, we demonstrate that CIC can increase the network capacity of standard LoRa by up to 10x and up to 4x over the state-of-the-art research. While beneficial across all scenarios, CIC has even more significant benefits under low SNR conditions that are common to LoRa deployments, in which prior approaches appear to perform quite poorly.