Abstract—The mainstay of current spectrum access grants exclusive rights to proprietary occupants who exhibit tidal traffic patterns, leading to low usage of valuable spectrum resources. To remedy this situation, Dynamic Spectrum Access (DSA) is proposed to allow Secondary Users (SUs) to opportunistically exploit idle spectrum slices left by Primary Users (PUs). The key to the success of DSA lies in SUs’ knowledge on radio activities of PUs. To enhance the understanding of PU spectrum tenancy patterns, various mathematical models have been proposed to describe spectrum occupancy dynamics. However, there are still two overlooked aspects in existing studies on spectrum tenancy modeling, i.e., time-varying spectrum tenancy patterns and multi- ple channels within the same Radio Access Technology (RAT). To address the two issues, we apply a change detection algorithm to discover time points where spectrum tenancy patterns vary, and propose to characterize spectrum usage in a multi-channel RAT by the Vector Autoregressive (VAR) model. Through analyzing LTE spectrum tenancy data with the algorithm and the model, we validate that the segment size discovered by the online change detection method coincides with the one obtained by brute force, and VAR outperforms the widely adopted on/off model.
more »
« less
U-CIMAN: Uncover Spectrum and User Information in LTE Mobile Access Networks
Abstract:
With the proliferation of Dynamic Spectrum Access (DSA), Internet of Things (IoT), and Mobile Edge Computing (MEC) technologies, various methods have been proposed to deduce key network and user information in cellular systems, such as available cell bandwidths, as well as user locations and mobility. Not only is such information dominated by cellular networks of vital significance on other systems co-located spectrum-wise and/or geographically, but applications within cellular systems can also benefit remarkably from inferring such information, as exemplified by the endeavours made by video streaming to predict cell bandwidth. Hence, we are motivated to develop a new tool to uncover as much information used to be closed to outsiders or user devices as possible with off-the-shelf products. Given the wide-spread deployment of LTE and its continuous evolution to 5G, we design and implement U-CIMAN, a client-side system to accurately UnCover as much Information in Mobile Access Networks as allowed by LTE encryption. Among the many potential applications of U-CIMAN, we highlight one use case of accurately measuring the spectrum tenancy of a commercial LTE cell. Besides measuring spectrum tenancy in unit of resource blocks, U-CIMAN discovers user mobility and traffic types associated with spectrum usage through decoded control messages and user data bytes. We conduct 4-month detailed accurate spectrum measurement on a commercial LTE cell, and the observations include the predictive power of Modulation and Coding Scheme on spectrum tenancy, and channel off-time bounded under 10 seconds, to name a few.
more »
« less
- Award ID(s):
- 1824518
- NSF-PAR ID:
- 10195761
- Date Published:
- Journal Name:
- IEEE INFOCOM
- Page Range / eLocation ID:
- 1459 to 1468
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The long-term evolution (LTE) has spread around the globe for deploying 4G cellular networks for com-mercial use. These days, it is gaining interest for new applica-tions where mobile broadband services can be of benefit to so-ciety. Whereas the basic concepts of LTE are well understood, its long-term evolution has just started. New areas of R&D look into operation in unlicensed and shared bands, where new ver-sions of LTE need to coexist with other communication systems and radars. Virginia Tech has developed an LTE testbed with unique features to spur LTE research and education. This pa-per introduces Virginia Tech’s LTE testbed, its main features and components, access and configuration mechanisms, and some of the research thrusts that it enables. It is unique in sev-eral aspects, including the extensive use of software-defined radio technology, the combination of industry-grade hardware and software-based systems, and the remote access feature for user-defined configurations of experiments and radio frequency paths.more » « less
-
Increased network wide energy consumption is a paramount challenge that hinders wide scale ultra-dense networks (UDN) deployments. While several Energy Saving (ES) enhancement schemes have been proposed recently, these schemes have one common tenancy. They operate in reactive mode i.e., to increase ES, cells are switched ON/OFF reactively in response to changing cell loads. Though, significant ES gains have been reported for such ON/OFF schemes, the inherent reactiveness of these ES schemes limits their ability to meet the extremely low latency and high QoS expected from future cellular networks vis-a-vis 5G and beyond. To address this challenge, in this paper we propose a novel user mobility prediction based AUtonomous pROactive eneRgy sAving (AURORA) framework for future UDN. Instead of observing changes in cell loads passively and then reacting to them, AURORA uses past hand over (HO) traces to determine future cell loads. This prediction is then used to proactively schedule small cell sleep cycles. AURORA also incorporates the effect of Cell Individual Offsets (CIOs) for balancing load among cells to ensure QoS while maximizing ES. Extensive system level simulations leveraging realistic SLAW model based mobility traces show that AURORA can achieve significant energy reduction gain without noticeable impact on QoS.more » « less
-
Following the current development of the wireless technology landscape, and with respect to the constant growth in user demands, it is inevitable that next-generation mobile wireless networks will use new frequency bands located in the sub-terahertz and terahertz (THz) spectrum to complement the existing microwave and millimeter wave (mmWave) channels. The feasibility of point-to-point stationary THz communication links has already been experimentally demonstrated. To build upon this breakthrough, one of the pressing research targets is making THz communication systems truly mobile. Achieving this target is especially complicated because mobile THz wireless systems (including WLANs and even cellular access) will often operate in the near-field due to the very large (even though physically small) electrical size of the high-gain antenna systems required for making high-rate communication links feasible at such frequencies. This perspective article presents several key prospective research challenges envisioned on the way to designing efficient mobile near-field THz wireless access as a part of 6G and 7G wireless landscapes.more » « less
-
Networking research has witnessed a renaissance from exploring the seemingly unlimited predictive power of machine learning (ML) models. One such promising direction is throughput prediction – accurately predicting the network bandwidth or achievable throughput of a client in real time using ML models can enable a wide variety of network applications to proactively adapt their behavior to the changing network dynamics to potentially achieve significantly improved QoE. Motivated by the key role of newer generations of cellular networks in supporting the new generation of latency-critical applications such as AR/MR, in this work, we focus on accurate throughput prediction in cellular networks at fine time-scales, e.g., in the order of 100 ms. Through a 4-day, 1000+ km driving trip, we collect a dataset of fine-grained throughput measurements under driving across all three major US operators. Using the collected dataset, we conduct the first feasibility study of predicting fine-grained application throughput in real-world cellular networks with mixed LTE/5G technologies. Our analysis shows that popular ML models previously claimed to predict well for various wireless networks scenarios (e.g., WiFi or singletechnology network such as LTE only) do not predict well under app-centric metrics such as ARE95 and PARE10. Further, we uncover the root cause for the poor prediction accuracy of ML models as the inherent conflicting sample sequences in the fine-grained cellular network throughput data.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/INFOCOM41043.2020.9155245
