The next-generation spectrum access system (SAS)
for the Citizens Broadband Radio Service band is equipped with
environmental sensors (ESCs) to detect the presence of noninformed
incumbent users, which allows the SAS to dynamically
reassign spectrum resource for low privilege users to avoid
interference. However, the performance of existing single-node
detection model is limited by the sensor’s geo-locations; whereas
a naive distributed sensing network with improved detection
accuracy introduces a high bandwidth overhead due to the
frequent communication of spectrum data. In addition, many
existing coherent spectrum sensing methods are not feasible for
CBRS band due to the unknown operational characteristics of
incumbent military wireless applications.
To address these issues, we propose a machine learning
based non-coherent spectrum sensing system: (F)eder(a)ted
(I)ncumbent Detection in CB(R)S (FaIR). FaIR leverages a
communication-efficient distributed learning framework, federated
learning, for ESCs to collaborate and train a data-driven
machine learning model for incumbent detection under minimal
communication bandwidth. Our preliminary results show that
the federated learning method can exploit the spatial diversity of
ESCs and obtain an improved detection model comparing to a
naive distributed sensing and centralized model framework. We
evaluate the FaIR model with a variety of spectrum waveforms at
varying SNRs. Our experiments showed that FaIR improves the
average detection accuracy compared to the single-node method,
using a fraction of the bandwidth compared to the naive multinode
method.
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TrustSAS: A Trustworthy Spectrum Access System for the 3.5 GHz CBRS Band
As part of its ongoing efforts to meet the increased spectrum demand, the Federal Communications Commission (FCC) has recently opened up 150 MHz in the 3.5 GHz band for shared wireless broadband use. Access and operations in this band, aka Citizens Broadband Radio Service (CBRS), will be managed by a dynamic spectrum access system (SAS) to enable seamless spectrum sharing between secondary users (SU s) and incumbent users. Despite its benefits, SAS’s design requirements, as set by FCC, present privacy risks to SU s, merely because SU s are required to share sensitive operational information (e.g., location, identity, spectrum usage) with SAS to be able to learn about spectrum availability in their vicinity. In this paper, we propose TrustSAS, a trustworthy framework for SAS that synergizes state-of-the-art cryptographic techniques with blockchain technology in an innovative way to address these privacy issues while complying with FCC’s regulatory design requirements. We analyze the security of our framework and evaluate its performance through analysis, simulation and experimentation. We show that TrustSAS can offer high security guarantees with reasonable overhead, making it an ideal solution for addressing SU s’ privacy issues in an operational SAS environment.
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- Award ID(s):
- 1917627
- NSF-PAR ID:
- 10128464
- Date Published:
- Journal Name:
- IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
- Page Range / eLocation ID:
- 1495 to 1503
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/INFOCOM.2019.8737533
