Resource Allocation for NOMA-based LPWA Networks Powered by Energy Harvesting

In this paper, we explore perpetual, scalable, Low-powered Wide-area networks (LPWA). Specifically we focus on the uplink transmissions of non-orthogonal multiple access (NOMA)-based LPWA networks consisting of multiple self-powered nodes and a NOMA-based single gateway. The self-powered LPWA nodes use the "harvest-then-transmit" protocol where they harvest energy from ambient sources (solar and radio frequency signals), then transmit their signals. The main features of the studied LPWA network are different transmission times-on-air, multiple uplink transmission attempts, and duty cycle restrictions. The aim of this work is to maximize the time-averaged sum of the uplink transmission rates by optimizing the transmission time-on-air allocation, the energy harvesting time allocation and the power allocation; subject to a maximum transmit power and to the availability of the harvested energy. We propose a low complex solution which decouples the optimization problem into three sub-problems: we assign the LPWA node transmission times (using either the fair or unfair approaches), we optimize the energy harvesting (EH) times using a one-dimensional search method, and optimize the transmit powers using a concave-convex (CCCP) procedure. In the simulation results, we focus on Long Range (LoRa) networks as a practical example LPWA network. We validate our proposed solution and we observe a $15\%$ performance improvement when using NOMA

Spatiotemporal Modelling of Multi-Gateway LoRa Networks with Imperfect SF Orthogonality

Meticulous modelling and performance analysis of Low-Power Wide-Area (LPWA) networks are essential for large scale dense Internet-of-Things (IoT) deployments. As Long Range (LoRa) is currently one of the most prominent LPWA technologies, we propose in this paper a stochastic-geometry-based framework to analyse the uplink transmission performance of a multi-gateway LoRa network modelled by a Matern Cluster Process (MCP). The proposed model is first to consider all together the multi-cell topology, imperfect spreading factor (SF) orthogonality, random start times, and geometric data arrival rates. Accounting for all of these factors, we initially develop the SF-dependent collision overlap time function for any start time distribution. Then, we analyse the Laplace transforms of intra-cluster and inter-cluster interference, and formulate the uplink transmission success probability. Through simulation results, we highlight the vulnerability of each SF to interference, illustrate the impact of parameters such as the network density, and the power allocation scheme on the network performance. Uniquely, our results shed light on when it is better to activate adaptive power mechanisms, as we show that an SF-based power allocation that approximates LoRa ADR, negatively impacts nodes near the cluster head. Moreover, we show that the interfering SFs degrading the performance the most depend on the decoding threshold range and the power allocation scheme.Comment: IEEE Global Communications Conferenc

Average channel capacity bounds of a dynamic vehicle-to-vehicle visible light communication system

As vehicles trajectories are unpredictable and changing dynamically, vehicle-to-vehicle visible light communication (V2V-VLC) experiences a dynamic channel. In this work, we conduct measurements taking into account different realistic inter-vehicle distances and ambient noise levels at different times of the day in order to model and verify the dynamic V2V-VLC channel. We also derive the average channel capacity bounds by considering the impact of traffic at different times of the day, atmospheric turbulence and fog. Considering both peak and average optical power levels constraints, we derive the upper and lower bounds by using sphere packing and constraint relaxation methods, as well as truncated-exponential and truncated Gaussian distributions, respectively. The results show that the constraint relaxation method provides an improved estimation for the upper bound, whereas the truncated exponential distribution tightens the lower bound with a minimum gap of 0.4 bit/s/Hz during rush hour and in a clear weather condition. We also show that the average capacity bounds of V2V-VLC are less affected by atmospheric turbulence and fog, and that the capacity during rush hours is higher by 0.7 bit/s/Hz than during late hours

On the Scalability of Duty-Cycled LoRa Networks with Imperfect SF Orthogonality

This papers uses stochastic geometry and queuing theory to study he scalability of long-range (LoRa) networks, accounting for duty cycling restrictions and imperfect spreading factor (SFs) orthogonality. The scalability is characterised by the joint boundaries of device density and traffic intensity per device. Novel cross-correlation factors are used to quantify imperfect SForthogonality. Our results show that a proper characterisation of LoRa orthogonality extends the scalability of the network. They also highlight that for low/medium densities decreasing the SF extends the spanned spectrum of sensing applications characterised by their traffic requirements (i.e. sensing rate). However, for high density (> 104 nodes/Km2 ), the Pareto frontiers converge to a stability limit governed by the SF allocation scheme and the predefined capture thresholds. The results further evince the importance of capturing threshold distribution among the SFs to mitigate the unfair latency

Minimizing Age of Information in Multi-hop Energy-Harvesting Wireless Sensor Network

Age of information (AoI), a metric measuring the information freshness, has drawn increased attention due to its importance in monitoring applications in which nodes send time-stamped status updates to interested recipients, and timely updates about phenomena are important. In this work, we consider the AoI minimization scheduling problem in multi-hop energy harvesting(EH) wireless sensor networks (WSNs). We design the generation time of updates for nodes and develop transmission schedules under both protocol and physical interference models, aiming at achieving minimum peak AoI and average AoI among all nodes for a given time duration. We prove that it is an NP-Hard problem and propose an energy-adaptive, distributed algorithm called MAoIG. We derive its theoretical upper bounds for the peak and average AoI and a lower bound for peak AoI. The numerical results validate that MAoIG outperforms all of the baseline schemes in all scenarios and that the experimental results tightly track the theoretical upper bound optimal solutions while the lower bound tightness decreases with the number of nodes