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1. TnB: resolving collisions in LoRa based on the peak matching cost and block error correction

   https://doi.org/10.1145/3555050.3569132  

   Rathi, Raghav; Zhang, Zhenghao (November 2022, International Conference On Emerging Networking Experiments And Technologies)

   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. 

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2. Cloud-LoRa: Enabling Cloud Radio Access LoRa Networks Using Reinforcement Learning Based Bandwidth-Adaptive Compression

   Shahid, Muhammad Osama; Koch, Daniel; Raghuram, Jayaram; Krishnaswamy, Bhuvana; Chintalapudi, Krishna; Banerjee, Suman (April 2024, 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24))

   The Cloud Radio Access Network (CRAN) architecture has been proposed as a way of addressing the network throughput and scalability challenges of large-scale LoRa networks. CRANs can improve network throughput by coherently combining signals, and scale to multiple channels by implementing the receivers in the cloud. However, in remote LoRa deployments, a CRAN’s demand for high-backhaul bandwidths can be challenging to meet. Therefore, bandwidth-aware compression of LoRa samples is needed to reap the benefits of CRANs. We introduce Cloud-LoRa, the first practical CRAN for LoRa, that can detect sub-noise LoRa signals and perform bandwidth-adaptive compression. To the best of our knowledge, this is the first demonstration of CRAN for LoRa operating in real-time. We deploy Cloud-LoRa in an agricultural field over multiple days with USRP as the gateway. A cellular backhaul hotspot is then used to stream the compressed samples to a Microsoft Azure server. We demonstrate SNR gains of over 6 dB using joint multi-gateway decoding and over 2x throughput improvement using state-of-the-art receivers, enabled by CRAN in real-world deployments. 

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3. Cloud-LoRa: Enabling Cloud Radio Access LoRa Networks Using Reinforcement Learning Based Bandwidth-Adaptive Compression

   Shahid, Mohammad_Osama; Koch, Daniel; Rahghuram, Jayaram; Krishnaswamy, Bhuvana; Chintalapudi, Krishna; Banerjee, Suman (April 2024, 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24))

   The Cloud Radio Access Network (CRAN) architecture has been proposed as a way of addressing the network throughput and scalability challenges of large-scale LoRa networks. CRANs can improve network throughput by coherently combining signals, and scale to multiple channels by implementing the receivers in the cloud. However, in remote LoRa deployments, a CRAN's demand for high-backhaul bandwidths can be challenging to meet. Therefore, bandwidth-aware compression of LoRa samples is needed to reap the benefits of CRANs. We introduce Cloud-LoRa, the first practical CRAN for LoRa, that can detect sub-noise LoRa signals and perform bandwidth-adaptive compression. To the best of our knowledge, this is the first demonstration of CRAN for LoRa operating in real-time. We deploy Cloud-LoRa in an agricultural field over multiple days with USRP as the gateway. A cellular backhaul hotspot is then used to stream the compressed samples to a Microsoft Azure server. We demonstrate SNR gains of over 6 dB using joint multi-gateway decoding and over 2x throughput improvement using state-of-the-art receivers, enabled by CRAN in real-world deployments. 

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4. A Comprehensive Survey of Data-Driven Solutions for LoRaWAN: Challenges and Future Directions

   https://doi.org/10.1145/3711953  

   Maurya, Poonam; Hazra, Abhishek; Kumari, Preti; Sørensen, Troels Bundgaard; Das, Sajal K (February 2025, ACM Transactions on Internet of Things)

   Long-range Wide-area Network (LoRaWAN) is an innovative and prominent communication protocol in the domain of Low-power Wide-area Networks (LPWAN), known for its ability to provide long-range communication with low energy consumption. However, the practical implementation of the LoRaWAN protocol, operating at the Medium Access Control layer and specially built to work upon the LoRa physical layer, presents numerous research challenges, including network congestion, interference, optimal resource allocation, collisions, scalability, and security. To mitigate these challenges effectively, the adoption of cutting-edge data-driven technologies such as Deep Learning (DL) and Machine Learning (ML) emerges as a promising approach. Interestingly, very few existing surveys or tutorials have addressed the importance of ML- or DL-based techniques for LoRaWAN. This article provides a comprehensive survey of current LoRaWAN challenges and recent solutions, particularly using DL and ML algorithms. The primary objective of this survey is to stimulate further research efforts to enhance the performance of LoRa networks and facilitate their practical deployments. We begin by emphasizing the characteristics of LoRaWAN compared to other LPWAN technologies and then examine the technical specifications of LoRaWAN that have been released so far, as well as the current research trends. Furthermore, we discuss an overview of the most utilized DL and ML algorithms for overcoming LoRaWAN challenges. We also present an interoperable reference architecture for LoRaWAN and validate its effectiveness using a wide range of applications. Additionally, we shed light on several evolving challenges of LoRa and LoRaWAN for the future digital network, along with possible solutions. Finally, we conclude our discussion by briefly summarizing our work. 

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5. Concurrent interference cancellation: decoding multi-packet collisions in LoRa

   https://doi.org/10.1145/3452296.3472931  

   Shahid, Muhammad Osama; Philipose, Millan; Chintalapudi, Krishna; Banerjee, Suman; Krishnaswamy, Bhuvana (August 2021, Proceedings of the 2021 ACM SIGCOMM Conference)

   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. 

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