Wireless Networking

Seismic data, obtained from sensors placed on the Earth's surface or subsurface, provides valuable insights into the composition and structure of the Earth's subsurface layers. This data is typically collected in the form of digital recordings, which represent the vibrations produced by seismic waves generated by controlled sources or natural events such as earthquakes. Converting seismic data into speech signals allows researchers and professionals to gain aural insights into the subsurface characteristics.

Radio-Frequency (RF) based User identification enables many attractive applications such as smart homes, and security management. However, laborious data collection is required due to appearance changes, inconsistent walking paths, and environmental variations. Furthermore, multi-user identification persists as an imperative for real-world applications. To this end, we propose an RFID-based user identification system (RF-UI), a few-shot, cross-interference factor, and a continuous user identification system.

Dynamic Spectrum Sharing (DSS) is an enabler for a seamless transition from 4G Long TermEvolution (LTE) to 5G New Radio (NR) by utilizing existing LTE bands without static spectrum re-farming. In this paper, we propose a cross-band DSS scheme that utilizes the Multimedia BroadcastMulticast Service over a Single Frequency Network (MBSFN) feature of an LTE network and theMulticast Broadcast Service (MBS) feature of an NR network.

Dynamic Spectrum Sharing (DSS) is an enabler for a seamless transition from 4G Long TermEvolution (LTE) to 5G New Radio (NR) by utilizing existing LTE bands without static spectrum re-farming. In this paper, we propose a cross-band DSS scheme that utilizes the Multimedia BroadcastMulticast Service over a Single Frequency Network (MBSFN) feature of an LTE network and theMulticast Broadcast Service (MBS) feature of an NR network.

Specific emitter identification (SEI)  is a promising authentication paradigm in physical layer security (PLS). Despite the significant success of existing SEI schemes, most of them assume that the distributions of the training dataset and the test dataset are consistent. However, in most practical scenarios, when the signal parameters change, the distribution of the samples will changes,  resulting in a significant performance degradation.

Extended reality (XR) head-mounted displays (HMDs) are increasingly starting to rely on wireless task
offloading in a bid to allow unobstructed XR user movement, while still rendering high-resolution video on
a remote processing node. An example is the Oculus (Meta) Quest 2. However, congestion and reliability
issues associated with the wireless network can cause high latency and an overall low quality of service (QoS).
Therefore, understanding XR user mobility is of vital importance for supporting XR applications in future
wireless networks.
2 Research question

As communications service providers ponder ways to cater to the diverse traffic requirements of mobile applications that range from the classic telephony to modern augmented reality (AR)-related use cases, the traditional quality of service (QoS)-based radio resource management (RRM) techniques for RAN slicing that are agnostic to the intrinsic workings of applications can result in a poor quality of experience (QoE) for the end-user. We argue that in addition to QoS, RAN slicing strategies should also consider QoE for efficient resource utilization.

The increasing availability of multimodal data holds many promises for developments in millimeter-wave (mmWave) multiple-antenna systems by harnessing the potential for enhanced situational awareness. Specifically, inclusion of non-RF modalities to complement RF-only data in communications-related decisions like beam selection may speed up decision making in situations where an exhaustive search, spanning all candidate options, is required by the standard. However, to accelerate research in this topic, there is a need to collect real-world datasets in a principled manner.

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We created a 5G dataset by measuring 5G traffic directly from a major mobile operator in South Korea. The model name of the mobile terminal used for traffic measurement is the Samsung Galaxy A90 5G, equipped with a Qualcomm Snapdragon X50 5G modem. We installed PCAPdroid, a packet sniffer software, on the terminal via Google Play. Traffic was measured sequentially per application on two stationary terminals (only one terminal is used for noninteractive services) with no background traffic. The dataset contains various types of traffic, and you can find them listed in the table below.