282 research outputs found
Wireless Powered Cognitive Radio Networks With Compressive Sensing and Matrix Completion
In this paper, we consider cognitive radio networks in which energy constrained secondary users (SUs) can harvest energy from the randomly deployed power beacons. A new frame structure is proposed for the considered networks. In the considered network, a wireless power transfer model is proposed, and the closed-form expressions for the power outage probability are derived. In addition, in order to reduce the energy consumption at SUs, sub-Nyquist sampling are performed at SUs. Subsequently, compressive sensing and matrix completion techniques are invoked to recover the original signals at the fusion center by utilizing the sparsity property of spectral signals. Throughput optimizations of the secondary networks are formulated into two linear constrained problems, which aim to maximize the throughput of a single SU and the whole cooperative network, respectively. Three methods are provided to obtain the maximal throughput of secondary networks by optimizing the time slots allocation and the transmit power. Simulation results show that the maximum throughput can be improved by implementing compressive spectrum sensing in the proposed frame structure design
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
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
20 Years of Evolution from Cognitive to Intelligent Communications
It has been 20 years since the concept of cognitive radio (CR) was proposed,
which is an efficient approach to provide more access opportunities to connect
massive wireless devices. To improve the spectrum efficiency, CR enables
unlicensed usage of licensed spectrum resources. It has been regarded as the
key enabler for intelligent communications. In this article, we will provide an
overview on the intelligent communication in the past two decades to illustrate
the revolution of its capability from cognition to artificial intelligence
(AI). Particularly, this article starts from a comprehensive review of typical
spectrum sensing and sharing, followed by the recent achievements on the
AI-enabled intelligent radio. Moreover, research challenges in the future
intelligent communications will be discussed to show a path to the real
deployment of intelligent radio. After witnessing the glorious developments of
CR in the past 20 years, we try to provide readers a clear picture on how
intelligent radio could be further developed to smartly utilize the limited
spectrum resources as well as to optimally configure wireless devices in the
future communication systems.Comment: The paper has been accepted by IEEE Transactions on Cognitive
Communications and Networkin
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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
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