140 research outputs found
Beam Selection and Discrete Power Allocation in Opportunistic Cognitive Radio Systems with Limited Feedback Using ESPAR Antennas
We consider an opportunistic cognitive radio (CR) system consisting of a
primary user (PU), secondary transmitter (SUtx), and secondary receiver (SUrx),
where SUtx is equipped with an electrically steerable parasitic array radiator
(ESPAR) antenna with the capability of choosing one beam among M beams for
sensing and communication, and there is a limited feedback channel from SUrx to
SUtx. Taking a holistic approach, we develop a framework for integrated
sector-based spectrum sensing and sector-based data communication. Upon sensing
the channel busy, SUtx determines the beam corresponding to PU's orientation.
Upon sensing the channel idle, SUtx transmits data to SUrx, using the selected
beam corresponding to the strongest channel between SUtx and SUrx. We formulate
a constrained optimization problem, where SUtx-SUrx link ergodic capacity is
maximized, subject to average transmit and interference power constraints, and
the optimization variables are sensing duration, thresholds of channel
quantizer at SUrx, and transmit power levels at SUtx. Since this problem is
non-convex we develop a suboptimal computationally efficient iterative
algorithm to find the solution. Our results demonstrate that our CR system
yields a significantly higher capacity, and lower outage and symbol error
probabilities, compared with a CR system that its SUtx has an omni-directional
antenna.Comment: This paper has been submitted to IEEE Transactions on Cognitive
Communications and Networkin
Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks
In this paper, a sequential probing method for interference constraint
learning is proposed to allow a centralized Cognitive Radio Network (CRN)
accessing the frequency band of a Primary User (PU) in an underlay cognitive
scenario with a designed PU protection specification. The main idea is that the
CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire
the binary ACK/NACK packet. This feedback indicates whether the probing-induced
interference is harmful or not and can be used to learn the PU interference
constraint. The cognitive part of this sequential probing process is the
selection of the power levels of the Secondary Users (SUs) which aims to learn
the PU interference constraint with a minimum number of probing attempts while
setting a limit on the number of harmful probing-induced interference events or
equivalently of NACK packet observations over a time window. This constrained
design problem is studied within the Active Learning (AL) framework and an
optimal solution is derived and implemented with a sophisticated, accurate and
fast Bayesian Learning method, the Expectation Propagation (EP). The
performance of this solution is also demonstrated through numerical simulations
and compared with modified versions of AL techniques we developed in earlier
work.Comment: 14 pages, 6 figures, submitted to IEEE JSTSP Special Issue on Machine
Learning for Cognition in Radio Communications and Rada
Dual antenna selection in secure cognitive radio networks
This paper investigates data transmission and physical layer secrecy in cognitive radio network. We propose to apply full duplex transmission and dual antenna selection at secondary destination node. With the full duplex transmission, the secondary destination node can simultaneously apply the receiving and jamming antenna selection to improve the secondary data transmission and primary secrecy performance respectively. This describes an attractive scheme in practice: unlike that in most existing approaches, the secrecy performance improvement in the CR network is no longer at the price of the data transmission loss. The outage probabilities for both the data transmission and physical layer secrecy are analyzed. Numerical simulations are also included to verify the performance of the proposed scheme
Transmitter Optimization Techniques for Physical Layer Security
Information security is one of the most critical issues in wireless networks as the signals transmitted through wireless medium are more vulnerable for interception. Although the existing conventional security techniques are proven to be safe, the broadcast nature of wireless communications introduces different challenges in terms of key exchange and distributions. As a result, information theoretic physical layer security has been proposed to complement the conventional security techniques for enhancing security in wireless transmissions. On the other hand, the rapid growth of data rates introduces different challenges on power limited mobile devices in terms of energy requirements. Recently, research work on wireless power transfer claimed that it has been considered as a potential technique to extend the battery lifetime of wireless networks. However, the algorithms developed based on the conventional optimization approaches often require iterative techniques, which poses challenges for real-time processing. To meet the demanding requirements of future ultra-low latency and reliable networks, neural network (NN) based approach can be employed to determine the resource allocations in wireless communications.
This thesis developed different transmission strategies for secure transmission in wireless communications. Firstly, transmitter designs are focused in a multiple-input single-output simultaneous wireless information and power transfer system with unknown eavesdroppers. To improve the performance of physical layer security and the harvested energy, artificial noise is incorporated into the network to mask the secret information between the legitimate terminals. Then, different secrecy energy efficiency designs are considered for a MISO underlay cognitive radio network, in the presence of an energy harvesting receiver. In particular, these designs are developed with different channel state information assumptions at the transmitter. Finally, two different power allocation designs are investigated for a cognitive radio network to maximize the secrecy rate of the secondary receiver: conventional convex optimization framework and NN based algorithm
On the Performance of Cognitive Underlay SIMO Networks over Equally Correlated Rayleigh Fading Channels
The performance of single-input multiple-output (SIMO) cognitive spectrum sharing networks with the presence of equally correlated Rayleigh fading channels is investigated. In particular, based on the truncated infinitive series of cumulative distribution function (CDF) and probability density function (PDF) of the end-to-end signal-to-noise ratios (SNRs), close-form expressions are provided for the system outage performance, bit error rate and ergodic capacity. It is shown that the system performance merely depends on the correlation coefficient between antennas. Monte-Carlo simulations are also contributed to confirm the accuracy of our analysis
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