Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAnalysis of Latency and MAC-layer Performance for Class A LoRaWAN
234
0
0.0
(
0
)
Added by
Ren\\'e S{\\o}rensen
Publication date
2017
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
We propose analytical models that allow us to investigate the performance of long range wide area network (LoRaWAN) uplink in terms of latency, collision rate, and throughput under the constraints of the regulatory duty cycling, when assuming exponential inter-arrival times. Our models take into account sub-band selection and the case of sub-band combining. Our numerical evaluations consider specifically the European ISM band, but the analysis is applicable to any coherent band. Protocol simulations are used to validate the proposed models. We find that sub-band selection and combining have a large effect on the quality of service (QoS) experienced in an LoRaWAN cell for a given load. The proposed models allow for the optimization of resource allocation within a cell given a set of QoS requirements and a traffic model.
rate research
Read More
Predictable network performance is key in many low-power wireless sensor network applications. In this paper, we use machine learning as an effective technique for real-time characterization of the communication performance as observed by the MAC layer. Our approach is data-driven and consists of three steps: extensive experiments for data collection, offline modeling and trace-driven performance evaluation. From our experiments and analysis, we find that a neural networks prediction model shows best performance.
In September 2020, the Broadband Forum published a new industry standard for measuring network quality. The standard centers on the notion of quality attenuation. Quality attenuation is a measure of the distribution of latency and packet loss between two points connected by a network path. A vital feature of the quality attenuation idea is that we can express detailed application requirements and network performance measurements in the same mathematical framework. Performance requirements and measurements are both modeled as latency distributions. To the best of our knowledge, existing models of the 802.11 WiFi protocol do not permit the calculation of complete latency distributions without assuming steady-state operation. We present a novel model of the WiFi protocol. Instead of computing throughput numbers from a steady-state analysis of a Markov chain, we explicitly model latency and packet loss. Explicitly modeling latency and loss allows for both transient and steady-state analysis of latency distributions, and we can derive throughput numbers from the latency results. Our model is, therefore, more general than the standard Markov chain methods. We reproduce several known results with this method. Using transient analysis, we derive bounds on WiFi throughput under the requirement that latency and packet loss must be bounded.
We propose that clusters interconnected with network topologies having minimal mean path length will increase their overall performance for a variety of applications. We approach our heuristic by constructing clusters of up to 36 nodes having Dragonfly, torus, ring, Chvatal, Wagner, Bidiakis and several other topologies with minimal mean path lengths and by simulating the performance of 256-node clusters with the same network topologies. The optimal (or sub-optimal) low-latency network topologies are found by minimizing the mean path length of regular graphs. The selected topologies are benchmarked using ping-pong messaging, the MPI collective communications, and the standard parallel applications including effective bandwidth, FFTE, Graph 500 and NAS parallel benchmarks. We established strong correlations between the clusters performances and the network topologies, especially the mean path lengths, for a wide range of applications. In communication-intensive benchmarks, clusters with optimal network topologies out-perform those with mainstream topologies by several folds. It is striking that a mere adjustment of the network topology suffices to reclaim performance from the same computing hardware.
Reconfigurable intelligent surface (RIS) is a promising reflective radio technology for improving the coverage and rate of future wireless systems by reconfiguring the wireless propagation environment. The current work mainly focuses on the physical layer design of RIS. However, enabling multiple devices to communicate with the assistance of RIS is a crucial challenging problem. Motivated by this, we explore RIS-assisted communications at the medium access control (MAC) layer and propose an RIS-assisted MAC framework. In particular, RISassisted transmissions are implemented by pre-negotiation and a multi-dimension reservation (MDR) scheme. Based on this, we investigate RIS-assisted single-channel multi-user (SCMU) communications. Wherein the RIS regarded as a whole unity can be reserved by one user to support the multiple data transmissions, thus achieving high efficient RIS-assisted connections at the user. Moreover, under frequency-selective channels, implementing the MDR scheme on the RIS group division, RISassisted multi-channel multi-user (MCMU) communications are further explored to improve the service efficiency of the RIS and decrease the computation complexity. Besides, a Markov chain is built based on the proposed RIS-assisted MAC framework to analyze the system performance of SCMU/MCMU. Then the optimization problem is formulated to maximize the overall system capacity of SCMU/MCMU with energy-efficient constraint. The performance evaluations demonstrate the feasibility and effectiveness of each
In this document, we prove the convergence of the model proposed in [1], which aims at estimating the LoRaWAN network performance in a single-gateway scenario. First, we provide an analytical proof of the existence of a fixed point solution for such a system. Then, we report experimental results, showing that the system of the two inter-dependent equations provided by the model can be solved through fixed-point iterations, and that a limited number of iterations is enough to reach convergence.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
