Efficient Radio Resource Allocation (RRA) is of utmost importance for achieving maximum capacity in mobile networks. However, the performance assessment should take into account the main constraints of these networks. This letter presents important enhancements to RRA algorithms proposed in [1]. Prior work [1] ignores some important system constraints such as the impact of inter-cell interference and granularity of frequency allocation blocks. Here we show the performance degradation when these system constraints are assumed on the algorithms in [1] as well as propose some improvements on these algorithms in order to achieve better performance.
With the increasing number of devices performing Machine-Type Communications (MTC), mobile networks are expected to encounter a high load of burst transmissions. One bottleneck in such cases is the Random Access Channel (RACH) procedure, which is responsible for the attachment of devices, among other things. In this paper, we performed a rich-parameter based simulation on RACH to identify the procedure bottlenecks. A finding from the studied scenarios is that the Physical Downlink Control Channel (PDCCH) capacity for the grant allocation is the main limitation for the RACH capacity rather than the number of Physical Random Access Channel (PRACH) preambles. Guided by our simulation results, we proposed improvements to the RACH procedure and to PDCCH.
LoRa has emerged as a prominent technology for the Internet of Things (IoT), with LoRa Wide Area Network (LoRaWAN) emerging as a suitable connection solution for smartthings. The choice of the best location for the installation of gateways, as well as a robust network server configuration, are key to the deployment of a LoRaWAN. In this paper, we present an evaluation of Received Signal Strength Indication (RSSI) values collected from the real-life LoRaWAN deployed in Skellefteå, Sweden, when compared with the values calculatedby a Radio Frequency (RF) planning tool for the Irregular Terrain Model (ITM), Irregular Terrain with Obstructions Model (ITWOM) and Okumura-Hata propagation models. Five sensors are configured and deployed along a wooden bridge, with different Spreading Factors (SFs), such as SF 7, 10 and 12. Our results show that the RSSI values calculated using the RF planning tool for ITWOM are closest to the values obtained from the real-life LoRaWAN. Moreover, we also show evidence that the choice of a propagation model in an RF planning tool has to be made with care, mainly due to the terrain conditions of the area where the network and the sensors are deployed.
LoRaWAN has become popular as an IoT enabler. The low cost, ease of installation and the capacity of fine-tuning the parameters make this network a suitable candidate for the deployment of smart cities. In northern Sweden, in the smart region of Skellefteå, we have deployed a LoRaWAN to enable IoT applications to assist the lives of citizens. As Skellefteå has a subarctic climate, we investigate how the extreme changes in the weather happening during a year affect a real LoRaWAN deployment in terms of SNR, RSSI and the use of SF when ADR is enabled. Additionally, we evaluate two propagation models (Okumura-Hata and ITM) and verify if any of those models fit the measurements obtained from our real-life network. Our results regarding the weather impact show that cold weather improves the SNR while warm weather makes the sensors select lower SFs, to minimize the time-on-air. Regarding the tested propagation models, Okumura-Hata has the best fit to our data, while ITM tends to overestimate the RSSI values.