564 research outputs found
Spectrum sensing via restricted neyman-pearson approach in the presence of non-Gaussian noise
In this paper, spectrum sensing in cognitive radio systems is studied for non-Gaussian channels in the presence of prior distribution uncertainty. In most practical cases, some amount of prior information about signals of primary users is available to secondary users but that information is never perfect. In order to design optimal spectrum sensing algorithms in such cases, we propose to employ the restricted Neyman-Pearson (NP) approach, which maximizes the average detection probability under constraints on the worst-case detection and false-alarm probabilities. We derive a restricted NP based spectrum sensing algorithm for additive Gaussian mixture noise channels, and compare its performance against the generalized likelihood ratio test (GLRT) and the energy detector. Simulation results show that the proposed spectrum sensing algorithm provides improvements over the other approaches in terms of minimum (worst-case) and/or average detection probabilities. © 2013 IEEE
Restricted Neyman-Pearson approach based spectrum sensing in cognitive radio systems
Ankara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Master's) -- Bilkent University, 2012.Includes bibliographical refences.Over the past decade, the demand for wireless technologies has increased enormously,
which leads to a perceived scarcity of the frequency spectrum. Meanwhile,
static allocation of the frequency spectrum leads to under-utilization of
the spectral resources. Therefore, dynamic spectrum access has become a necessity.
Cognitive radio has emerged as a key technology to solve the conflicts
between spectrum scarcity and spectrum under-utilization. It is an intelligent
wireless communication system that is aware of its operating environment and
can adjust its parameters in order to allow unlicensed (secondary) users to access
and communicate over the frequency bands assigned to licensed (primary)
users when they are inactive. Therefore, cognitive radio requires reliable spectrum
sensing techniques in order to avoid interference to primary users. In this
thesis, the spectrum sensing problem in cognitive radio is studied. Specifically,
the restricted Neyman-Pearson (NP) approach, which maximizes the average detection
probability under the constraints on the minimum detection and false
alarm probabilities, is applied to the spectrum sensing problem in cognitive radio
systems in the presence of uncertainty in the prior probability distribution of
primary users’ signals. First, we study this problem in the presence of Gaussian
noise and assume that primary users’ signals are Gaussian. Then, the problem
is reconsidered for non-Gaussian noise channels. Simulation results are obtained
in order to compare the performance of the restricted NP approach with the
existing methods such as the generalized likelihood ratio test (GLRT) and energy
detection. The restricted NP approach outperforms energy detection in all
cases. It is also shown that the restricted NP approach can provide important
advantages over the GLRT in terms of the worst-case detection probability, and
sometimes in terms of the average detection probability depending on the situation
in the presence of imperfect prior information for Gaussian mixture noise
channels.Turgut, EsmaM.S
Massive MIMO for Wireless Sensing with a Coherent Multiple Access Channel
We consider the detection and estimation of a zero-mean Gaussian signal in a
wireless sensor network with a coherent multiple access channel, when the
fusion center (FC) is configured with a large number of antennas and the
wireless channels between the sensor nodes and FC experience Rayleigh fading.
For the detection problem, we study the Neyman-Pearson (NP) Detector and Energy
Detector (ED), and find optimal values for the sensor transmission gains. For
the NP detector which requires channel state information (CSI), we show that
detection performance remains asymptotically constant with the number of FC
antennas if the sensor transmit power decreases proportionally with the
increase in the number of antennas. Performance bounds show that the benefit of
multiple antennas at the FC disappears as the transmit power grows. The results
of the NP detector are also generalized to the linear minimum mean squared
error estimator. For the ED which does not require CSI, we derive optimal gains
that maximize the deflection coefficient of the detector, and we show that a
constant deflection can be asymptotically achieved if the sensor transmit power
scales as the inverse square root of the number of FC antennas. Unlike the NP
detector, for high sensor power the multi-antenna ED is observed to empirically
have significantly better performance than the single-antenna implementation. A
number of simulation results are included to validate the analysis.Comment: 32 pages, 6 figures, accepted by IEEE Transactions on Signal
Processing, Feb. 201
Noise enhanced detection in restricted Neyman-Pearson framework
Noise enhanced detection is studied for binary composite hypothesis-testing problems in the presence of prior information uncertainty. The restricted Neyman-Pearson (NP) framework is considered, and a formulation is obtained for the optimal additive noise that maximizes the average detection probability under constraints on worst-case detection and false-alarm probabilities. In addition, sufficient conditions are provided to specify when the use of additive noise can or cannot improve performance of a given detector according to the restricted NP criterion. A numerical example is presented to illustrate the improvements obtained via additive noise. © 2012 IEEE
Signal Processing for Compressed Sensing Multiuser Detection
The era of human based communication was longly believed to be the main driver for the development of communication systems. Already nowadays we observe that other types of communication impact the discussions of how future communication system will look like. One emerging technology in this direction is machine to machine (M2M) communication. M2M addresses the communication between autonomous entities without human interaction in mind. A very challenging aspect is the fact that M2M strongly differ from what communication system were designed for. Compared to human based communication, M2M is often characterized by small and sporadic uplink transmissions with limited data-rate constraints. While current communication systems can cope with several 100 transmissions, M2M envisions a massive number of devices that simultaneously communicate to a central base-station. Therefore, future communication systems need to be equipped with novel technologies facilitating the aggregation of massive M2M. The key design challenge lies in the efficient design of medium access technologies that allows for efficient communication with small data packets. Further, novel physical layer aspects have to be considered in order to reliable detect the massive uplink communication. Within this thesis physical layer concepts are introduced for a novel medium access technology tailored to the demands of sporadic M2M. This concept combines advances from the field of sporadic signal processing and communications. The main idea is to exploit the sporadic structure of the M2M traffic to design physical layer algorithms utilizing this side information. This concept considers that the base-station has to jointly detect the activity and the data of the M2M nodes. The whole framework of joint activity and data detection in sporadic M2M is known as Compressed Sensing Multiuser Detection (CS-MUD). This thesis introduces new physical layer concepts for CS-MUD. One important aspect is the question of how the activity detection impacts the data detection. It is shown that activity errors have a fundamentally different impact on the underlying communication system than data errors have. To address this impact, this thesis introduces new algorithms that aim at controlling or even avoiding the activity errors in a system. It is shown that a separate activity and data detection is a possible approach to control activity errors in M2M. This becomes possible by considering the activity detection task in a Bayesian framework based on soft activity information. This concept allows maintaining a constant and predictable activity error rate in a system. Beyond separate activity and data detection, the joint activity and data detection problem is addressed. Here a novel detector based on message passing is introduced. The main driver for this concept is the extrinsic information exchange between different entities being part of a graphical representation of the whole estimation problem. It can be shown that this detector is superior to state-of-the-art concepts for CS-MUD. Besides analyzing the concepts introduced simulatively, this thesis also shows an implementation of CS-MUD on a hardware demonstrator platform using the algorithms developed within this thesis. This implementation validates that the advantages of CS-MUD via over-the-air transmissions and measurements under practical constraints
Noise enhanced hypothesis-testing according to restricted Neyman-Pearson criterion
Cataloged from PDF version of article.Noise enhanced hypothesis-testing is studied according to the restricted Neyman-Pearson (NP) criterion. First, a problem formulation is presented for obtaining the optimal probability distribution of additive noise in the restricted NP framework. Then, sufficient conditions for improvability and nonimprovability are derived in order to specify if additive noise can or cannot improve detection performance over scenarios in which no additive noise is employed. Also, for the special case of a finite number of possible parameter values under each hypothesis, it is shown that the optimal additive noise can be represented by a discrete random variable with a certain number of point masses. In addition, particular improvability conditions are derived for that special case. Finally, theoretical results are provided for a numerical example and improvements via additive noise are illustrated. © 2013 Elsevier Inc
SPECTRUM SENSING AND COOPERATION IN COGNITIVE-OFDM BASED WIRELESS COMMUNICATIONS NETWORKS
The world has witnessed the development of many wireless systems and
applications. In addition to the large number of existing devices, such development of
new and advanced wireless systems increases rapidly the demand for more radio
spectrum. The radio spectrum is a limited natural resource; however, it has been
observed that it is not efficiently utilized. Consequently, different dynamic spectrum
access techniques have been proposed as solutions for such an inefficient use of the
spectrum. Cognitive Radio (CR) is a promising intelligent technology that can identify
the unoccupied portions of spectrum and opportunistically uses those portions with
satisfyingly high capacity and low interference to the primary users (i.e., licensed users).
The CR can be distinguished from the classical radio systems mainly by its awareness
about its surrounding radio frequency environment. The spectrum sensing task is the
main key for such awareness. Due to many advantages, Orthogonal Frequency Division
Multiplexing system (OFDM) has been proposed as a potential candidate for the CR‟s
physical layer. Additionally, the Fast Fourier Transform (FFT) in an OFDM receiver
supports the performance of a wide band spectrum analysis. Multitaper spectrum
estimation method (MTM) is a non-coherent promising spectrum sensing technique. It
tolerates problems related to bad biasing and large variance of power estimates.
This thesis focuses, generally, on the local, multi antenna based, and global
cooperative spectrum sensing techniques at physical layer in OFDM-based CR systems.
It starts with an investigation on the performance of using MTM and MTM with
singular value decomposition in CR networks using simulation. The Optimal MTM
parameters are then found. The optimal MTM based detector theoretical formulae are
derived. Different optimal and suboptimal multi antenna based spectrum sensing
techniques are proposed to improve the local spectrum sensing performance. Finally, a
new concept of cooperative spectrum sensing is introduced, and new strategies are
proposed to optimize the hard cooperative spectrum sensing in CR networks.
The MTM performance is controlled by the half time bandwidth product and
number of tapers. In this thesis, such parameters have been optimized using Monte
Carlo simulation. The binary hypothesis test, here, is developed to ensure that the effect
of choosing optimum MTM parameters is based upon performance evaluation. The
results show how these optimal parameters give the highest performance with minimum
complexity when MTM is used locally at CR.
The optimal MTM based detector has been derived using Neyman-Pearson
criterion. That includes probabilities of detection, false alarm and misses detection
approximate derivations in different wireless environments. The threshold and number
of sensed samples controlling is based on this theoretical work.
In order to improve the local spectrum sensing performance at each CR, in the CR
network, multi antenna spectrum sensing techniques are proposed using MTM and
MTM with singular value decomposition in this thesis. The statistical theoretical
formulae of the proposed techniques are derived including the different probabilities.
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The proposed techniques include optimal, that requires prior information about the
primary user signal, and two suboptimal multi antenna spectrum sensing techniques
having similar performances with different computation complexity; these do not need
prior information about the primary user signalling. The work here includes derivations
for the periodogram multi antenna case.
Finally, in hard cooperative spectrum sensing, the cooperation optimization is
necessary to improve the overall performance, and/or minimize the number of data to be
sent to the main CR-base station. In this thesis, a new optimization method based on
optimizing the number of locally sensed samples at each CR is proposed with two
different strategies. Furthermore, the different factors that affect the hard cooperative
spectrum sensing optimization are investigated and analysed and a new cooperation
scheme in spectrum sensing, the master node, is proposed.Ministry of Interior-Kingdom of Saudi Arabi
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