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

Abstract

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 joint SF allocation and power optimization enables to drastically enhance various performance objectives such as throughput, fairness and power consumption, and that it outperforms baseline schemes. 1 DRAFT 2 I. INTRODUCTIONA wide range of applications will be enabled by the advent of Internet of Things (IoT) technology, among which smart cities, intelligent transportation systems and environmental monitoring.Given the expected proliferation of such IoT devices in the near future, providing tailored wireless communication protocols with high spectral efficiency and low power consumption is becoming more and more urgent. Indeed, many of these services will depend on the future IoT Wireless Sensor Networks (WSNs), supported by the newly developed Low-Power Wide-Area Network (LPWAN) technologies such as LoRa, SigFox or Ingenu [2-5]. The LoRa physical layer uses the Chirp Spread Spectrum (CSS) modulation technique, where each chirp encodes 2 m values, for Spreading Factor (SF) m = 7 to 12 [6], and which allows multiple end-devices to use the same channel simultaneously. Based on the LoRa physical layer, LoRaWAN defines the MAC layer protocol standardized by LoRa Alliance [7]. It is an increasingly used LPWAN technology, as it operates in the ISM unlicensed bands and enables a flexible adaptation of transmission rates and coverages under low energy consumption [6]. The LoRaWAN architecture is a star topology,where end-devices communicate with the network server through gateways over several channels based on ALOHA mechanism, with duty cycle limitations [4]. In LoRaWAN, smaller SFs provide higher data rates but reduced ranges, while larger SFs allow longer ranges but lower rates [5].The main issue of LoRa-based networks such as LoRaWAN is the throughput limitation: the physical bitrate varies...

A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges

“…In this paper, we performed a comprehensive analysis of solutions that have been developed to optimize ADR algorithms. The proposed optimization techniques address specific challenges such as scalability [ 28 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], throughput [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ] and energy efficiency [ 20 , 46 , 47 , 48 , 49 , 50 , 51 ]. Our analysis distinguished the approaches used and highlighted the challenges and performance in the studies considered.…”

“…The computation time is reduced without a significant effect on QoS performance. In [ 45 ] the running time of the developed SF-Allocation algorithm is upper bounded by . The complexity of the matching algorithm is not a constraint in an actual LoRaWAN because the algorithm operates on the NS whose computational capacity is expansive.…”

“…ADR schemes that maximize throughput maximizing solutions for the ADR scheme were discussed in [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. The ordered waterfalling technique used in [ 37 ] for even distribution of channel load between the end nodes in the system by equally distributing SFs and then channel utilization.…”

“…Network slicing brings an interesting dimension in ADR optimization which sets the LoRa SF and TP parameters efficiently to improve the performance of every slice in terms of QoS. In our opinion the algorithm in [ 45 ] provides the best approach for maximizing throughput, outperforming standard algorithms, jointly with regards to minimum end node data rates, user fairness, and average end node throughput.…”

“…The joint SF assignment and transmit power allocation algorithm has been investigated in [ 45 ]. They consider both co-SF and inter-SF interferences for the improvement of throughput fairness.…”