Analysis of LoRaWAN Uplink with Multiple Demodulating Paths and Capture Effect

Low power wide area networks (LPWANs), such as the ones based on the LoRaWAN protocol, are seen as enablers of large number of IoT applications and services. In this work, we assess the scalability of LoRaWAN by analyzing the frame success probability (FSP) of a LoRa frame while taking into account the capture effect and the number of parallel demodulation paths of the receiving gateway. We have based our model on the commonly used {SX1301 gateway chipset}, which is capable of demodulating {up to} eight frames simultaneously; however, the results of the model can be generalized to architectures with arbitrary number of demodulation paths. We have also introduced and investigated {three} policies for Spreading Factor (SF) allocation. Each policy is evaluated in terms of coverage {probability}, {FSP}, and {throughput}. The overall conclusion is that the presence of multiple demodulation paths introduces a significant change in the analysis and performance of the LoRa random access schemes

Achieving Max-Min Throughput in LoRa Networks

With growing popularity, LoRa networks are pivotally enabling Long Range connectivity to low-cost and power-constrained user equipments (UEs). Due to its wide coverage area, a critical issue is to effectively allocate wireless resources to support potentially massive UEs in the cell while resolving the prominent near-far fairness problem for cell-edge UEs, which is challenging to address due to the lack of tractable analytical model for the LoRa network and its practical requirement for low-complexity and low-overhead design. To achieve massive connectivity with fairness, we investigate the problem of maximizing the minimum throughput of all UEs in the LoRa network, by jointly designing high-level policies of spreading factor (SF) allocation, power control, and duty cycle adjustment based only on average channel statistics and spatial UE distribution. By leveraging on the Poisson rain model along with tailored modifications to our considered LoRa network, we are able to account for channel fading, aggregate interference and accurate packet overlapping, and still obtain a tractable and yet accurate closed-form formula for the packet success probability and hence throughput. We further propose an iterative balancing (IB) method to allocate the SFs in the cell such that the overall max-min throughput can be achieved within the considered time period and cell area. Numerical results show that the proposed scheme with optimized design greatly alleviates the near-far fairness issue, and significantly improves the cell-edge throughput.Comment: 6 pages, 4 figures, published in Proc. International Conference on Computing, Networking and Communications (ICNC), 2020. This paper proposes stochastic-geometry based analytical framework for a single-cell LoRa network, with joint optimization to achieve max-min throughput for the users. Extended journal version for large-scale multi-cell LoRa network: arXiv:2008.0743

Extending the Lora modulation to add further parallel channels and improve the LoRaWAN network performance

In this paper we present a new modulation, called DLoRa, similar in principle to the conventional LoRa modulation and compatible with it in terms of bandwidth and numerology. DLoRa departs from the conventional LoRa modulation as it is using a decreasing instantaneous frequency in the chirps instead of an increasing one as for the conventional LoRa modulation. Furthermore we describe a software environment to accurately evaluate the "isolation" of the different virtual channels created both by LoRa and DLoRa when using different Spreading Factors. Our results are in agreement with the ones present in literature for the conventional LoRa modulation and show that it is possible to double the number of channels by using simultaneously LoRa and DLora. The higher (double) number of subchannels available is the key to improve the network level performance of LoRa based networks.Comment: This work has been submitted on Feb.1 2020 to European Wireless 2020 conference for possible presentation and subsequent publication by the IEE

LoRa 네트워크에서 에너지 효율성을 위한 노드 기반 ADR 메커니즘

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2020. 8. 김종권.Recently, as Internet of Things (IoT) systems have increased and Wireless Sensor Network (WSN) has been expanding, studies related to them are increasing. Among them, the interest in long range communication technologies has increased. In this regard, Low Power Wide Area (LPWA) network technologies such as Long Range (LoRa), Weightless, and Sigfox have emerged. Also, various studies related to LoRa and LoRaWAN, which are available in Industrial Scientific and Medical (ISM) bands, are being conducted. In LoRa networks, the nodes are connected to the gateway by one hop to form a start topology. LoRa nodes use the transmission parameters such as Spreading Factor (SF), Transmission Power (TP), Bandwidth (BW), Coding Rate (CR), and Carrier Frequency (CF) to transmit frames. In this process, the frame losses and the collisions between frames may occur because of the channel condition and transmission timing. To alleviate this problem, LoRaWAN utilizes the ADR mechanism to select appropriate transmission parameters considering the channel condition on the node side. In addition, there is the ADR mechanism for allocating the transmission parameters on the server side. The ADR mechanisms maintain the connection between the server and the nodes, and set appropriate transmission parameters. However, these existing ADR mechanisms have some limitations. First, the server side ADR mechanism increases the overhead of the server in proportion to the transmitted frames. Second, it is difficult to quickly and efficiently respond to dynamic channel. Third, the transmission parameters selected by these ADR mechanisms may not be the optimal transmission parameters for energy efficiency. These problems cause large energy consumption of the battery-powered nodes and decrease performance when the channel condition changes dynamically. In this paper, we propose a Node-based ADR Mechanism (NbADR), which is the ADR mechanism for Class A nodes in confirmed mode to minimize the server load and maximize energy efficiency. The proposed mechanism responds quickly to the channel condition based on the downlink pattern and selects the transmission parameters for efficient energy consumption by utilizing Efficiency of Energy (EoE) metric. We analyze the efficiency of the transmission parameters selected through EoE, and conduct extensive experiments. In conclusion, NbADR is more effective in terms of energy efficiency than the existing ADR mechanisms. Additionally, NbADR guarantees throughput of LoRa networks even in dynamically changing channel environments and improves fairness between the nodes.최근 IoT 시스템이 증가하고 무선 센서 네트워크가 넓어지면서 이와 관련된 연구가 증가하고 있다. 그 중에서도 장거리 통신 기술에 대한 관심이 증가하고 있다. 이와 관련하여 LoRa, Weightless, Sigfox와 같은 LPWA네트워크 기술들이 등장하고 있다. 또한, ISM 밴드에서 사용 가능한 LoRa와 LoRaWAN 관련 다양한 연구가 진행되고 있다. LoRa 네트워크에서 노드들은 스타 토폴로지를 구성하기 위하여 게이트웨이와 1홉으로 연결되어 있다. LoRa 노드들은 프레임을 전송하기 위하여 SF, TP, BW, CR, CF와 같은 전송 파라미터를 사용한다. 이 과정에서 채널 상태와 전송 타이밍으로 인한 프레임 손실과 프레임 간 충돌이 발생할 수 있다. 이러한 문제를 완화하기 위하여 LoRaWAN에서는 노드 측에서 네트워크 상황을 고려하여 적절한 전송 파라미터를 선택하기 위한 ADR 메커니즘을 사용한다. 게다가 서버 측에서 전송 파라미터를 할당하는 ADR 메커니즘이 존재한다. ADR 메커니즘들은 서버와 노드의 연결을 유지하고 적절한 전송 파라미터를 설정한다. 하지만 기존의 ADR 메커니즘들은 일부 한계점을 가지고 있다. 첫 번째, 서버 측 ADR 메커니즘은 전송하는 프레임에 비례하여 서버의 부하를 증가시킨다. 두 번째, 동적인 채널에서 빠르고 효율적으로 대처하기 어렵다. 세 번째, 이러한 ADR 메커니즘들에서 선택된 전송 파라미터들이 에너지 효율성을 위한 최적의 전송 파라미터가 아닐 수 있다. 이러한 문제점들은 배터리로 동작하는 노드들의 큰 에너지 소모를 야기하고 LoRa 네트워크의 채널이 동적으로 변경되는 환경에서 성능을 감소시킨다. 본 논문에서 우리는 서버의 부하를 최소화하며 에너지 효율성을 최대화하는 노드 기반의 ADR 메커니즘인 NbADR을 제안한다. 제안하는 메커니즘은 노드 측에서 전송 받은 다운링크 패턴을 기반으로 채널 상황에 빠르게 대응하고, Efficiency of Energy (EoE) 메트릭을 활용하여 효율적인 에너지 소모를 위한 전송 파라미터를 선택한다. 우리는 EoE 기반으로 선택한 전송 파라미터의 효율성을 분석하고, 광범위한 실험을 진행한다. 결론적으로, NbADR은 기존의 ADR 메커니즘들과 비교하여 에너지 효율성 측면에서 효과적이다. 추가적으로, NbADR은 급격하게 변화하는 채널 환경에서LoRa 네트워크의 처리량을 보장하고 노드 간 공평성을 향상시킨다.Chapter 1 Introduction 1 Chapter 2 Related Work 4 Chapter 3 Preliminaries 7 3.1 LoRa/LoRaWAN 7 3.2 Transmission Parameters 8 3.3 ADR Mechanism 9 Chapter 4 Channel Modeling 10 4.1 Loss 10 4.2 Collision 12 Chapter 5 Node-based ADR Mechanism 14 5.1 Approach for Energy Efficiency 15 5.2 Node-based ADR Mechanism (NbADR) 17 Chapter 6 Evaluation 21 6.1 Simulation Settings 22 6.2 Simulation Results 23 Chapter 7 Conclusion 33 Bibliography 35Maste