Citizens Broadband Radio Service Band Research Articles

Anonymous Dynamic Spectrum Access and Sharing Mechanisms for the CBRS Band

The Federal Communications Commission (FCC) has released the 3.5 GHz (3550-3700 MHz) band, termed Citizens Broadband Radio Service (CBRS), for shared broadband use between incumbent federal and secondary users through dynamic and opportunistic spectrum access. FCC requires that this band be operated and managed through the use of spectrum access systems ( $\mathit {SAS}$ s), which are to be deployed specifically for this purpose. The challenge is that $\mathit {SAS}$ requires that secondary users provide some of their private operational data, such as their physical location, identity and spectrum usage, in order for them to acquire spectrum availability information. In this paper, we propose a privacy-preserving $\mathit {SAS}$ framework, $\mathit {TrustSAS}$ , that synergizes state-of-the-art cryptographic mechanisms with blockchain technology to enable anonymous access to $\mathit {SAS}$ by protecting users’ privacy while still complying with FCC’s regulatory design requirements and rules. We evaluate the performance of $\mathit {TrustSAS}$ through theoretic analysis, computer simulation and testbed experimentation, and show that it can offer high security guarantees, making it suitable for $\mathit {SAS}$ environments without needing to compromise private information of its secondary users.

Deterministic Move Lists for Federal Incumbent Protection in the CBRS Band.

The 3.5 GHz citizens broadband radio service (CBRS) band in the U.S. is a key portion of mid-band spectrum shared between commercial operators and existing federal and non-federal incumbents. To protect the federal incumbents from harmful interference, a spectrum access system (SAS) is required to use a common, standardized algorithm, called the move list algorithm, to suspend transmissions of some CBRS devices (CBSDs) on channels in which the incumbent becomes active. However, the current reference move list implementation used for SAS testing is non-deterministic in that it uses a Monte Carlo estimate of the 95th percentile of the aggregate interference from CBSDs to the incumbent. This leads to uncertainty in move list results and in the aggregate interference check of the test. This paper uses upper and lower bounds on the aggregate interference distribution to compute deterministic move lists. These include the reference move list used by the testing system and an operational move list used by the SAS itself. We evaluate the performance of the proposed deterministic move lists using reference implementations of the standards and simulated CBSD deployments in the vicinity of federal incumbent dynamic protection areas.

Performance Impact of Coexistence Groups in a GAA-GAA Coexistence Scheme in the CBRS Band

The General Authorized Access (GAA) users in the Citizens Broadband Radio Service (CBRS) band are the lowest priority users. They must make sure that they do not cause harmful interference to the higher tier users while cooperating with each other to minimize potential interference among themselves. Thus, efficient GAA coexistence scheme is essential for the operation of GAA users and to obtain high spectrum utilization. Towards this goal, the Wireless Innovation Forum (WInnForum) has recommended three schemes to facilitate GAA-GAA coexistence. We had reported a performance study of one of these schemes (called Approach 1), but that study did not have any Coexistence Group (CxG). A CxG is responsible for managing interference among its CBRS devices (CBSDs). In this paper, we study the performance of Approach 1 without CxGs as well as with different number of CxGs, in various configurations. We conduct our study around two locations in the USA using actual terrain and land cover data of the continental USA. We evaluate performance of the scheme at different deployment densities, using different propagation models at those two locations with different number of CxGs. We provide some interesting insights into the costs and benefits of having CxGs in the deployment.

Enabling a “Use-or-Share” Framework for PAL–GAA Sharing in CBRS Networks via Reinforcement Learning

By implementing reinforcement learning-aided listen-before-talk (LBT) schemes over a citizens broadband radio service (CBRS) network, we increase the spatial reuse at secondary nodes while minimizing the interference footprint on higher-tier nodes. The federal communications commission encourages “use-or-share” policies in the CBRS band across the priority access license (PAL)–general authorized access (GAA) priority tiers by opportunistically allowing the lower-priority GAA nodes to access unused higher-priority PAL spectrum. However, there is currently no mechanism to enable this cross-tier spectrum sharing. In this paper, we propose and evaluate LBT schemes that allow opportunistic access to PAL spectrum. We find that by allowing LBT in a two carrier, two eNB scenario, we see upward of 50% user perceived throughput (UPT) gains for both eNBs. Furthermore, we examine the use of ${Q}$ -learning to adapt the energy-detection threshold (EDT), combating problematic topologies, such as hidden and exposed nodes. With merely a 4% reduction in primary node UPT, we see up to 350% gains in average secondary node UPT when adapting the EDT of opportunistically transmitting nodes.

Deep Learning Classification of 3.5 GHz Band Spectrograms with Applications to Spectrum Sensing.

In the United States, the Federal Communications Commission has adopted rules permitting commercial wireless networks to share spectrum with federal incumbents in the 3.5 GHz Citizens Broadband Radio Service band. These rules require commercial systems to vacate the band when sensors detect radars operated by the U.S. military; a key example being the SPN-43 air traffic control radar. Such sensors require highly-accurate detection algorithms to meet their operating requirements. In this paper, using a library of over 14,000 3.5 GHz band spectrograms collected by a recent measurement campaign, we investigate the performance of thirteen methods for SPN-43 radar detection. Namely, we compare classical methods from signal detection theory and machine learning to several deep learning architectures. We demonstrate that machine learning algorithms appreciably outperform classical signal detection methods. Specifically, we find that a three-layer convolutional neural network offers a superior tradeoff between accuracy and computational complexity. Last, we apply this three-layer network to generate descriptive statistics for the full 3.5 GHz spectrogram library. Our findings highlight potential weaknesses of classical methods and strengths of modern machine learning algorithms for radar detection in the 3.5 GHz band.

Online Algorithm for Leasing Wireless Channels in a Three-Tier Spectrum Sharing Framework

The three-tier spectrum sharing framework (3-TSF) is a spectrum sharing model adopted by the Federal Communications Commission. According to this model, under-utilized federal spectrum like the Citizens Broadband Radio Service band is released for shared use where the highest preference is given to Tier-1 followed by Tier-2 (T2) and then Tier-3 (T3). In this paper, we study how a wireless operator, who is interested in maximizing its profit, can strategically operate as a T2 and/or a T3 user. T2 is characterized by paid but ”almost” guaranteed and interference-free channel access while T3 access is free but has the lesser guarantee and also faces channel interference. So the operator has to optimally decide between paid but better channel quality and free but uncertain channel quality. Also, the operator has to make these decisions without knowing future market variables like customer demand or channel availability. The main contribution of this paper is a deterministic online algorithm for leasing channels that has finite competitive ratio, low time complexity, and that does not rely on the knowledge of market statistics. Such algorithms are desirable in the early stages of the deployment of 3-TSF because the knowledge of market statistics may be rather inaccurate. We use tools from the ski-rental literature to design the online algorithm. The online optimization problem for leasing channels is a novel generalization of the ski-rental problem. We, therefore, make fundamental contributions to the ski-rental literature, the applications of which extend beyond this paper. We also conduct simulations using synthetic traces to compare our online algorithm with the benchmark and state-of-the-art algorithms.

RF Dataset of Incumbent Radar Signals in the 3.5 GHz CBRS Band.

This Radio Frequency (RF) dataset consists of synthetically generated waveforms of incumbent 3.5GHz radar systems. The intended use of the dataset is for developing and evaluating detectors for the 3.5GHz Citizens Broadband Radio Service (CBRS) \cite{cbrs} or similar bands where the primary users of the band are Federal radar systems. The dataset can be used for developing and testing radar detection algorithms using machine learning/deep learning techniques. The algorithm aims to detect whether the radar signal is present or absent regardless of the signal type. The target signals have a variety of modulation types and parameters chosen from wide ranges. In addition, the start time and the center frequency of the radar signals are randomized in the waveform. The variety of signals and their random parameters makes the detection problem more challenging when using non-naive (e.g., energy detector is a naive signal detector) classical signal processing techniques.