In this thesis, we consider cognitive radio networks (CRN) combined with cooperative transmission, and investigate relay selection and power allocation strategies to maximize network lifetime (NLT). Cognitive radio network enhances spectrum efficiency resource by exploiting capabilities of cognition, learning and coordination against insufficient spectrum resource. In underlay cognitive radio network, however, transmitted energy of secondary user is constrained by interference level observed at primary user (PU). Though cooperation among secondary users (SU), multiple relays from virtual antenna array to improve transmission rate and reliability by exploiting spatial diversity. Most existing works assume that cooperative secondary users are plugged and with infinite energy device. In this thesis, we consider secondary cooperative systems where relays are battery-powered and with finite energy. We will investigate relay-selection schemes to reduce energy consumption of secondary relays and prolong network lifetime under the premises that secondary user¡¦s transmission rate is guaranteed and interference constraint of primary user is met. Our major difference between this work and previous works is the definition of network lifetime, which is defined by the maximum duration that the probability of secondary user¡¦s achievable rate below the guaranteed value, i.e. outage probability, is lower than a predetermined threshold. We proposed four relay-selection methods which take channel state information (CSI) and residual energy information (REI) into considerations to prolong network lifetime. Since the selection metrics of the proposed strategies requires CSI and REI of each individual relay, so the relay-selection can be accomplished in distributed manner through opportunistic sensing. No additional overhead is demanded for information exchange.

Recently, with the fast development of wireless communications, the radio spectrum becomes a precious natural resource. Many researches and reports reveal the problems of inefficient spectrum utilization. Cognitive Radio (CR) technology is now developing for solving this critical problem. This technology will enable various kinds of wireless systems to look for and connect radio frequency spectrum that the locality leave unused by oneself, to offer the best service to user. The CR will pass in and out the idle frequency band according to the demand while receiving and dispatching the signal, avoid the frequency band that has been already used. In CR network, the objective is to maximize the throughput of secondary users while limiting the probability of colliding with primary users below a prescribed level. In this paper, we consider a distributed secondary networks model where users seek spectrum opportunities independently that overlaying the primary networks to analyze the system performance and the effect to the primary users with the existence of both primary users and secondary users under the cognitive radio networks. In the cognitive system, due to the existence of noise and fading effect, error detection cannot be avoided. Therefore, we made a comparison to the difference of the efficiency among environments of different probability of miss detection. We also propose a random hopping method for all secondary users in system will re-sensing after a random period of time. Hereby, efficiently decreases the ratio of time that influences the primary users by the secondary users, and further research the factor that influences its efficiency.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Akyildiz, Ian; Committee Member: Ingram, Mary Ann; Committee Member: Blough, Douglas; Committee Member: Dovrolis, Konstantinos; Committee Member: Li, Ye. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Cognitive Radio is an innovative technology that allows the secondary unlicensed users to share the spectrum with licensed primary users to utilize the spectrum. For maximum utilization of spectrum, in cognitive radio network spectrum sensing is an important issue. Cognitive user under extreme shadowing and channel fading can‟t sense the primary licensed user signal correctly and thus to improve the performance of spectrum sensing, collaboration between secondary unlicensed users is required. In collaborative spectrum sensing the observation of each secondary user is received by a base station acting as a central entity, where a final conclusion about the presence or absence of the primary user signal is made using a particular decision and fusion rule. Due to spatially correlated shadowing the collaborative spectrum sensing performance decreases, and thus optimum secondary users must be selected to, not only improve spectrum sensing performance but also lessen the processing overhead of the central entity. A particular situation is depicted in the project where according to some performance parameters, first those optimum secondary users that have enough spatial separation and high average received SNR are selected using Genetic Algorithm, and then collaboration among these optimum secondary users is done to evaluate the performance. The collaboration of optimal secondary user providing high probability of detection and low probability of false alarm, for sensing the spectrum is compared with the collaboration of all the available secondary users in that radio environment. At the end a conclusion has been made that collaboration of selected optimum secondary users provides better performance, then the collaboration of all the secondary users available. / Cognitive Radio is an innovative technology that allows the secondary unlicensed users to share the spectrum with licensed primary users to utilize the spectrum. For maximum utilization of spectrum, in cognitive radio network spectrum sensing is an important issue. Cognitive user under extreme shadowing and channel fading can‟t sense the primary licensed user signal correctly and thus to improve the performance of spectrum sensing, collaboration between secondary unlicensed users is required. In collaborative spectrum sensing the observation of each secondary user is received by a base station acting as a central entity, where a final conclusion about the presence or absence of the primary user signal is made using a particular decision and fusion rule. Due to spatially correlated shadowing the collaborative spectrum sensing performance decreases, and thus optimum secondary users must be selected to, not only improve spectrum sensing performance but also lessen the processing overhead of the central entity. A particular situation is depicted in the project where according to some performance parameters, first those optimum secondary users that have enough spatial separation and high average received SNR are selected using Genetic Algorithm, and then collaboration among these optimum secondary users is done to evaluate the performance. The collaboration of optimal secondary user providing high probability of detection and low probability of false alarm, for sensing the spectrum is compared with the collaboration of all the available secondary users in that radio environment. At the end a conclusion has been made that collaboration of selected optimum secondary users provides better performance, then the collaboration of all the secondary users available.

Feature-based spectrum sensing techniques have emerged as good balance between energy-based techniques and coherent-based techniques, where the former require minimal prior information of the observed signal, and the latter have robust detection performance when the observed signal is very weak. In this thesis, we focus on pilot tone-aided detection as a feature-based detection class. We propose an improved pilot tone-aided spectrum sensor that utilizes the presence of the pilot tone and the overall energy of the received signal. We show that the optimal Neyman-Pearson detector is a weighted summation of a feature-based component and an energy-based component. The former provides coherent gains at the low signal-to-noise ratio (SNR) regime, whereas the latter provides non-coherent gains at moderate SNR levels. The proposed detector intelligently adapts its weights based on the SNR of the observed signal and the power allocation factor of the pilot tone. This helps it attain significant performance gains compared with the conventional pilot tone-aided detectors. In addition, we present suboptimal detectors that reduce the computational complexity. For instance, we demonstrate that moment estimators are effective techniques for spectrum sensing. Motivated by insights gained from the derivations of these moment estimators, we present a selective mean-variance estimator that performs well in the absence of the prior knowledge about the pilot tone. Moreover, we analyze the impact of two model uncertainties on the detection performance of the proposed detector: Noise uncertainty and imperfect pilot-matching. We show that unlike the energy detector, the proposed detector does not suffer from the SNR wall under the noise uncertainty model due to the coherent gains embedded in the feature-based component. Also, unlike existing pilot tone-aided detectors, the proposed detector is resilient against imperfect synchronization due to the non-coherent gains embedded in computing the overall energy of the signal. Also, we show that the proposed detector achieves the lowest sample complexity, leading to tangible improvements to the aggregate throughput of the secondary user. Extensive simulation and analytical results are provided to verify these conclusions. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2014-04-15 15:31:27.253

To meet the ever increasing spectrum demand, developing a mechanism for dynamic spectrum access seems inevitable. Spectrum sensing enables cognitive radios (CRs) to identify and use frequency bands (channels) that are not being used by primary users (PUs) at a particular place and time. However, sensing errors and limited sensing resources, such as sensing hardware and sensing time, introduce significant technical challenges to the development of such an ideal capability. Adaptive sensing strategies allow the sensing resources to be spent on more promising primary channels. This is achieved by exploiting past sensing outcomes of one secondary user (SU), or, as proposed in this research, multiple spatially distributed SUs. We propose adaptive sensing strategies for three different scenarios. First, we assume that a SU sequentially senses a number of primary channels to find the first available channel. We propose a two-stage spectrum detection strategy that allows the spectrum detector to quickly detect and skip though most of busy channels and spend most of its time on channels that are more likely to be idle. Second, we consider the case where multiple SUs jointly try to locate idle channels within a given sensing time, which itself is divided into a number of sensing slots. We propose a cooperative spectrum search strategy that specifies the channel to be sensed by each SU in each slot in such a way to maximize the expected number of identified idle channels. Third, we consider a primary network that operates in a synchronous time-framed fashion. We assume that the occupancy state of each primary channel over different time frames follows a discrete-time Markov process. We propose a cooperative sensing strategy that decides which channel should be sensed by which SU in each frame. The goal is to maximize a utility function that accounts for both the number of detected idle channel-frames and the number of miss-detected busy channel-frames. We present analytical and numerical results to demonstrate the effectiveness of the proposed sensing strategies in increasing identified time-frequency spectrum opportunities and/or reducing interference with licensed systems.

To meet the ever increasing spectrum demand, developing a mechanism for dynamic spectrum access seems inevitable. Spectrum sensing enables cognitive radios (CRs) to identify and use frequency bands (channels) that are not being used by primary users (PUs) at a particular place and time. However, sensing errors and limited sensing resources, such as sensing hardware and sensing time, introduce significant technical challenges to the development of such an ideal capability. Adaptive sensing strategies allow the sensing resources to be spent on more promising primary channels. This is achieved by exploiting past sensing outcomes of one secondary user (SU), or, as proposed in this research, multiple spatially distributed SUs. We propose adaptive sensing strategies for three different scenarios. First, we assume that a SU sequentially senses a number of primary channels to find the first available channel. We propose a two-stage spectrum detection strategy that allows the spectrum detector to quickly detect and skip though most of busy channels and spend most of its time on channels that are more likely to be idle. Second, we consider the case where multiple SUs jointly try to locate idle channels within a given sensing time, which itself is divided into a number of sensing slots. We propose a cooperative spectrum search strategy that specifies the channel to be sensed by each SU in each slot in such a way to maximize the expected number of identified idle channels. Third, we consider a primary network that operates in a synchronous time-framed fashion. We assume that the occupancy state of each primary channel over different time frames follows a discrete-time Markov process. We propose a cooperative sensing strategy that decides which channel should be sensed by which SU in each frame. The goal is to maximize a utility function that accounts for both the number of detected idle channel-frames and the number of miss-detected busy channel-frames. We present analytical and numerical results to demonstrate the effectiveness of the proposed sensing strategies in increasing identified time-frequency spectrum opportunities and/or reducing interference with licensed systems.

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Electrical and Computer Engineering." Includes bibliographical references (p. 120-126).

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Electrical and Computer Engineering." Includes bibliographical references (p. 101-105).