IOT CONNECTIVITY WITH EDGE COMPUTING

Billions of Internet of Things (IoT) devices will be connected in the next decades. Most devices are for Massive Machine Type Communication (MMTC) applications. This requires the IoT infrastructure to be extremely efficient and scalable (like today’s Internet) to support more and more devices connected to the network over time. The cost per connection needs to be very low (like today’s Web services). The current network design with dedicated HW-based base stations (or IoT gateways) may be too costly. Furthermore, there is a vast amount of IoT radio standards, such as Narrowband-IoT (NB-IoT), LTE-M, BLE, ZigBee, Sigfox, LoRa, to give some examples, which all need to be implemented if they are supposed to be supported. The current approach requires to deploy parallel networks with dedicated base stations for different standards in one place. This further increases network costs. Cloud Radio Access Network (RAN) (c-RAN) has been proposed to centralize and cloudify baseband processing in a cloud infrastructure based on GPPs, which can potentially increase network flexibility and reduce the network Total Cost of Ownership (TCO) significantly. It can also be beneficiary for network performance by increased coordination possibilities. Nowadays, c-RAN still is on a concept level, because it is deemed difficult to implement due to complexity and reliability issues, e.g. for 4G/5G which requires sophisticated processing capabilities. The terminology of C-RAN today refers more to Centralized-RAN based on Digital Signal Processing (DSP) microcontrollers and ASICs, instead of c-RAN. However, the MMTC technologies are usually narrowband and designed with low complexity (considering cost of User Equipment (UE), power consumption, battery life time, etc.). Therefore, they are rather suitable for cloud implementation. Latency may be another issue for c-RAN. However, most of the MMTC applications are based on best-effort strategies and delay-tolerance. Therefore, c-RAN offers a promising solution to deliver the required efficiency and scalability for MMTC services. This master thesis is part of an effort to explore the possibilities, to increase the understanding and to gain hands-on experiences of IoT c-RAN implementation at the edge. It focuses on the NB-IoT downlink (DL) Physical (PHY) implementation as one example. However, IEEE 802.15.4 (PHY layer of e.g. Zigbee) has been integrated into the system within a collaboration between Ericsson and RISE SICS. This also shows, that c-RAN technology is able to unite multiple radio interfaces in one system leveraging Software (SW). In this study, we built a Software Defined Radio (SDR) testbed based on GNU Radio. The USRP B210 is the Hardware (HW) tool to test the implementation. Key components of the NB-IoT DL have been implemented. Orthogonal Frequency-Division Multiplex (OFDM) transmitter and receiver follow the NB-IoT numerology and implement algorithms for signal generation, time and frequency synchronization, as well as equalization and demodulation. The convolutional code of the Voyager missions with a coding rate R = 12 is used for performance evaluation. Different baseband modules have been tested and verified. Investigations have been carried out on the topic of latency. The measurements reveal a latency, which is higher than expected. Most likely, this is due to the large buffers underlying the GNU Radio scheduler in combination with the low speed of NB-IoT. The end-to-end system has been evaluated by field measurements (Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), Packet Error Rate (PER)) conducted in an Ericsson office environment. With no Line-Of-Sight (LOS), the implemented system has a reach of >= 65 m (from the office lab on floor 4 to the other end of the corridor where GFTB ER NAP NIT Fronhaul Technologies is located) with only 0.5 % PER and a SNR of 15.9 dB. In this work, system and SW design of the testbed and implementation are presented, as well as the hands-on experiences. The testbed is ready for human interaction with a fascinating Telegram bot live demo