181 research outputs found
Low-rank matrix completion based malicious user detection in cooperative spectrum sensing
In a cognitive radio (CR) system, cooperative spectrum
sensing (CSS) is the key to improving sensing performance
in deep fading channels. In CSS networks, signals received at the
secondary users (SUs) are sent to a fusion center to make a final
decision of the spectrum occupancy. In this process, the presence
of malicious users sending false sensing samples can severely
degrade the performance of the CSS network. In this paper, with
the compressive sensing (CS) technique being implemented at
each SU, we build a CSS network with double sparsity property. A
new malicious user detection scheme is proposed by utilizing the
adaptive outlier pursuit (AOP) based low-rank matrix completion
in the CSS network. In the proposed scheme, the malicious users
are removed in the process of signal recovery at the fusion center.
The numerical analysis of the proposed scheme is carried out and
compared with an existing malicious user detection algorithm
Compressive Sensing Over TV White Space in Wideband Cognitive Radio
PhDSpectrum scarcity is an important challenge faced by high-speed wireless communications.
Meanwhile, caused by current spectrum assignment policy, a large portion of
spectrum is underutilized. Motivated by this, cognitive radio (CR) has emerged as one
of the most promising candidate solutions to improve spectrum utilization, by allowing
secondary users (SUs) to opportunistically access the temporarily unused spectrum,
without introducing harmful interference to primary users. Moreover, opening of TV
white space (TVWS) gives us the con dence to enable CR for TVWS spectrum. A crucial
requirement in CR networks (CRNs) is wideband spectrum sensing, in which SUs
should detect spectral opportunities across a wide frequency range. However, wideband
spectrum sensing could lead to una ordably high sampling rates at energy-constrained
SUs. Compressive sensing (CS) was developed to overcome this issue, which enables
sub-Nyquist sampling by exploiting sparse property. As the spectrum utilization is low,
spectral signals exhibit a natural sparsity in frequency domain, which motivates the
promising application of CS in wideband CRNs.
This thesis proposes several e ective algorithms for invoking CS in wideband CRNs.
Speci cally, a robust compressive spectrum sensing algorithm is proposed for reducing
computational complexity of signal recovery. Additionally, a low-complexity algorithm is
designed, in which original signals are recovered with fewer measurements, as geolocation
database is invoked to provide prior information. Moreover, security enhancement issue
of CRNs is addressed by proposing a malicious user detection algorithm, in which data
corrupted by malicious users are removed during the process of matrix completion (MC).
One key spotlight feature of this thesis is that both real-world signals and simulated
signals over TVWS are invoked for evaluating network performance. Besides invoking
CS and MC to reduce energy consumption, each SU is supposed to harvest energy from radio frequency. The proposed algorithm is capable of o ering higher throughput by
performing signal recovery at a remote fusion center
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Efficient Spectrum Sensing and Sharing Techniques for Dynamic Wideband Spectrum Access
Besides enabling an enhanced mobile broadband access, fifth-generation (5G) wireless mobile networks are envisioned to support the connectivity of massive, heterogeneous Internet of Things (IoT) devices. Connecting these devices through 5G systems and providing them with their needed data rates require huge amounts of spectrum and power resources, thus calling for the development and design of innovative, dynamic resource identification, access and sharing methods that make effective use of these limited resources. This thesis focuses specifically on wideband spectrum sensing, and presents innovative techniques that enable efficient identification and recovery of unused spectrum opportunities in wideband dynamic spectrum access. Recent research efforts have focused on leveraging compressive sampling (CS) theory to enable wideband spectrum sensing recovery at sub-Nyquist rates. However, these approaches suffer from the following shortcomings. First, they consider homogenous wideband spectrum, where all
bands are assumed to have similar primary users (PU)s traffic characteristics whereas in practice, the wideband spectrum occupancy is heterogeneous. Second, the number of measurements that receiver hardware designs are able to perform is practically way smaller than the number of measurements required by the CS-based sensing approaches. Third, the number of measurements required by the CS-based sensing approaches depends on the number of occupied bands (i.e., sparsity level), which is often unknown
in advance and changes over time. Forth, current wideband spectrum databases suffer from scalability issues in that they incur lots of sensing overhead. This thesis proposes a set of new, complementary techniques that overcome these aforementioned challenges. More specifically, in this thesis,
1. We design efficient spectrum occupancy information recovery techniques for heterogeneous wideband spectrum access. Our proposed techniques exploit the block-like structure of spectrum occupancy behavior observed in wideband spectrum access networks to enable the development of compressed spectrum sensing algorithms. Our proposed spectrum sensing algorithms achieve more stable spectrum information
recovery than that achieved by existing approaches.
2. We develop distributed CS-based spectrum sensing techniques for cooperative wideband spectrum access that require lesser measurements while overcoming time-variability of spectrum occupancy and addressing hidden terminal challenges. Also, we propose non-uniform sensing matrices design that exploits the heterogeneity in the wideband spectrum access to further improve the spectrum sensing recovery
accuracy.
3. We develop scalable spectrum occupancy information recovery techniques for database-driven wideband spectrum access networks. The novelty of our developed techniques lies in combining the merit of compressive sampling theory with that of low-rank matrix theory to enable scalable and accurate wideband spectrum occupancy recovery at low sensing overhead.
4. We propose joint data and energy transfer optimization frameworks for powering mobile cellular devices through RF energy harvesting. Our proposed framework accounts for both the consumed power at the base station and the battery power available at the end users to ensure that end users achieve their required data rates with as little battery power consumption as possible. We also analytically derive closed-form expressions of the optimal power allocations required for meeting the data rate requirements of the downlink and uplink communications between the base station and its mobile users
Sparse Representation for Wireless Communications:A Compressive Sensing Approach
Sparse representation can efficiently model signals in different applications to facilitate processing. In this article, we will discuss various applications of sparse representation in wireless communications, with a focus on the most recent compressive sensing (CS)-enabled approaches. With the help of the sparsity property, CS is able to enhance the spectrum efficiency (SE) and energy efficiency (EE) of fifth-generation (5G) and Internet of Things (IoT) networks
Sub-Nyquist Wideband Spectrum Sensing and Sharing
PhDThe rising popularity of wireless services resulting in spectrum shortage has motivated
dynamic spectrum sharing to facilitate e cient usage of the underutilized spectrum.
Wideband spectrum sensing is a critical functionality to enable dynamic spectrum access
by enhancing the opportunities of exploring spectral holes, but entails a major implemen-
tation challenge in compact commodity radios that have limited energy and computation
capabilities. The sampling rates speci ed by the Shannon-Nyquist theorem impose great
challenges both on the acquisition hardware and the subsequent storage and digital sig-
nal processors. Sub-Nyquist sampling was thus motivated to sample wideband signals
at rates far lower than the Nyquist rate, while still retaining the essential information in
the underlying signals.
This thesis proposes several algorithms for invoking sub-Nyquist sampling in wideband
spectrum sensing. Speci cally, a sub-Nyquist wideband spectrum sensing algorithm is
proposed that achieves wideband sensing independent of signal sparsity without sampling
at full bandwidth by using the low-speed analog-to-digital converters based on sparse
Fast Fourier Transform. To lower signal spectrum sparsity while maintaining the channel
state information, the received signal is pre-processed through a proposed permutation
and ltering algorithm. Additionally, a low-complexity sub-Nyquist wideband spectrum
sensing scheme is proposed that locates occupied channels blindly by recovering the sig-
nal support, based on the jointly sparse nature of multiband signals. Exploiting the
common signal support shared among multiple secondary users, an e cient coopera-
tive spectrum sensing scheme is developed, in which the energy consumption on signal
acquisition, processing, and transmission is reduced with the detection performance guar-
antee. To further reduce the computation complexity of wideband spectrum sensing, a
hybrid framework of sub-Nyquist wideband spectrum sensing with geolocation database
is explored. Prior channel information from geolocation database is utilized in the sens-
ing process to reduce the processing requirements on the sensor nodes. The models of
the proposed algorithms are derived and veri ed by numerical analyses and tested on
both real-world and simulated TV white space signals
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