Joint Allocation Strategies of Power and Spreading Factors With Imperfect Orthogonality in LoRa Networks

• Published in: IEEE transactions on communications Vol. 68; no. 6; pp. 3750 - 3765
• Main Authors: Amichi, Licia, Kaneko, Megumi, Fukuda, Ellen Hidemi, El Rachkidy, Nancy, Guitton, Alexandre
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Computer Science; Energy consumption; Interference; Internet of Things; Logic gates; LoRa; matching theory; Networking and Internet Architecture; Optimization; Orthogonality; Performance evaluation; Power consumption; Power management; resource allocation optimization; Resource management; spreading factor; Throughput; Uplink; Wide area networks; Matching Theory; Index Terms LoRa; Spreading Factor; Resource Allocation Optimization
• ISSN: 0090-6778; 1558-0857; 1558-0857
• DOI: 10.1109/TCOMM.2020.2974722

The LoRa physical layer is one of the most promising Low Power Wide-Area Network (LPWAN) technologies for future Internet of Things (IoT) applications. It provides a flexible adaptation of coverage and data rate by allocating different Spreading Factors (SFs) and transmit powers to end-devices. We focus on improving throughput fairness while reducing energy consumption. Whereas most existing methods assume perfect SF orthogonality and ignore the harmful effects of inter-SF interferences, we formulate a joint SF and power allocation problem to maximize the minimum uplink throughput of end-devices, subject to co-SF and inter-SF interferences and power constraints. This results into a mixed-integer non-linear optimization, which, for tractability, is split into two sub-problems: firstly, the SF assignment for fixed transmit powers, and secondly, the power allocation given the previously obtained assignment solution. For the first sub-problem, we propose a low-complexity many-to-one matching algorithm between SFs and end-devices. For the second one, given its intractability, we transform it using two types of constraints' approximation: a linearized and a quadratic version. Our performance evaluation demonstrates that the proposed SF allocation and power optimization methods enable to drastically enhance various performance objectives such as throughput, fairness and power consumption, and that they outperform baseline schemes.