EmergingWireless '22: Proceedings of the 1st International Workshop on Emerging Topics in Wireless

Human sensing with millimeter waves (mmWaves) is rapidly gaining momentum. In particular, mmWave radars are becoming the technology of choice in applications like contactless vital signs monitoring, people tracking, or activity recognition, when preserving the users privacy is a concern. However, single mmWave radar sensors have limited range (up to 6--8 m) and are affected by occlusions. For this reason, covering medium to large indoor spaces requires the deployment of multiple radar devices, i.e., radar networks. Because of the complexity of reflections produced by people moving in real life environments, the development and validation of algorithms for mmWave radar networks can only be fulfilled through extensive experimental campaigns. In this work, we present RadNet, the first experimental testbed for the easy deployment and testing of radar network algorithms. We describe its architecture and functioning and we show experimental results of a multi-radar people tracking algorithm implemented on the RadNet experimental platform.

The advent of Low Power Wide Area Networks (LPWAN) has enabled the feasibility of wireless sensor networks for environmental traffic sensing across urban areas. In this study, we explore the usage of LoRaWAN end nodes as traffic sensing sensors to offer a practical traffic management solution. The monitored Received Signal Strength Indicator (RSSI) factor is reported and used in the gateways to assess the traffic of the environment. Our technique utilizes LoRaWAN as a long-range communication technology to provide a large-scale system. In this work, we present a method of using LoRaWAN devices to estimate traffic flows. LoRaWAN end devices then transmit their packets to different gateways. Their RSSI will be affected by the number of cars present on the roadway. We used SVM and clustering methods to classify the approximate number of cars present. This paper details our experiences with the design and real implementation of this system across an area that stretches for miles in urban scenarios. We continuously measured and reported RSSI at different gateways for weeks. Results have shown that if a LoRaWAN end node is placed in an optimal position, up to 96% of correct environment traffic level detection can be obtained. Additionally, we share the lessons learned from such a deployment for traffic sensing.

Millimeter-wave (mmWave) is one key enabler for high data rates in (beyond-)5G wireless communication networks. The use of directive beams and the shorter transmission range in mmWave communications make it easily obstructed or blocked by humans in dynamic real-world scenarios. Connectivity diversity, a solution to link disruption and blockage, could be achieved by deploying distributed multi-point infrastructure. To this end, a mmWave User Equipment (UE) requires a multi-reception capability. This paper investigates how different multi-point connectivity schemes, established by a set of non-cooperative and cooperative serving Base Stations (BSs), perform in the ideal Line-of-Sight (LoS) and the worst-case blocked scenarios. The results and analysis found that the impact of intra-cluster interference on spectral efficiency is much more significant than the blockage. Without any interference cancellation, regardless of LoS or blocked scenarios, adding extra serving BSs does not necessarily improve the spectral efficiency due to the dominant inter-stream interference within a cluster of services.

The Open Radio Access Network (O-RAN) is an emerging paradigm for developing the next-generation radio access network (RAN) for 5G and beyond. Inspired by the principles from software-defined networks (SDNs), the key innovation of O-RAN is the disaggregation of control logic from the network data plane, by using a centralized RAN intelligent controller (RIC), with customized xApps and service models. The O-RAN's novel design transforms the traditional monolithic network infrastructure into an open, programmable, and interoperable RAN. These distinctive features make O-RAN ideal for deploying extensible security services against a wide range of prevalent threat vectors (e.g., malicious transmitters that spoof, interfere, or flood communications between mobile devices and the 5G RAN), which can compromise the security, privacy, and availability of mobile devices and the network itself, at very low cost. Unfortunately, we find that the existing exemplar xApp models of O-RAN and the underlying telemetry streams that drive these applications are insufficient for developing robust security countermeasures. In this paper, we propose MobiFlow, a fine-grained telemetry stream tailored for security analysis on O-RAN. We envision MobiFlow as an enabling building block upon which novel 5G services can be implemented, offering device and RAN-specific run-time security monitoring, intelligent RAN control, and security-focused AI/ML assisted applications.

In this paper, we investigate how Joint Communication And Sensing (JCAS) can be used to improve the Inertial Measurement Unit (IMU)-based tracking accuracy of eXtended Reality (XR) Head-Mounted Displays (HMDs). Such tracking is used when optical and InfraRed (IR) tracking is lost, and its lack of accuracy can lead to disruption of the user experience. In particular, we analyze the impact of using doppler-based speed estimation to aid the accelerometer-based position estimation, and Angle of Arrival (AoA) estimation to aid the gyroscope-based orientation estimation. Although less accurate than IMUs for short times in fact, the JCAS based methods require one fewer integration step, making the tracking more sustainable over time. Based on the proposed model, we conclude that at least in the case of the position estimate, introducing JCAS can make long lasting optical/IR tracking losses more sustainable.