Spectrum sensing for cognitive radios: Algorithms, performance, and limitations

Inefficient use of radio spectrum is becoming a serious problem as more and more wireless systems are being developed to operate in crowded spectrum bands. Cognitive radio offers a novel solution to overcome the underutilization problem by allowing secondary usage of the spectrum resources along with high reliable communication. Spectrum sensing is a key enabler for cognitive radios. It identifies idle spectrum and provides awareness regarding the radio environment which are essential for the efficient secondary use of the spectrum and coexistence of different wireless systems. The focus of this thesis is on the local and cooperative spectrum sensing algorithms. Local sensing algorithms are proposed for detecting orthogonal frequency division multiplexing (OFDM) based primary user (PU) transmissions using their autocorrelation property. The proposed autocorrelation detectors are simple and computationally efficient. Later, the algorithms are extended to the case of cooperative sensing where multiple secondary users (SUs) collaborate to detect a PU transmission. For cooperation, each SU sends a local decision statistic such as log-likelihood ratio (LLR) to the fusion center (FC) which makes a final decision. Cooperative sensing algorithms are also proposed using sequential and censoring methods. Sequential detection minimizes the average detection time while censoring scheme improves the energy efficiency. The performances of the proposed algorithms are studied through rigorous theoretical analyses and extensive simulations. The distributions of the decision statistics at the SU and the test statistic at the FC are established conditioned on either hypothesis. Later, the effects of quantization and reporting channel errors are considered. Main aim in studying the effects of quantization and channel errors on the cooperative sensing is to provide a framework for the designers to choose the operating values of the number of quantization bits and the target bit error probability (BEP) for the reporting channel such that the performance loss caused by these non-idealities is negligible. Later a performance limitation in the form of BEP wall is established for the cooperative sensing schemes in the presence of reporting channel errors. The BEP wall phenomenon is important as it provides the feasible values for the reporting channel BEP used for designing communication schemes between the SUs and the FC

Spectrum sensing, spectrum monitoring, and security in cognitive radios

Spectrum sensing is a key function of cognitive radios and is used to determine whether a primary user is present in the channel or not. In this dissertation, we formulate and solve the generalized likelihood ratio test (GLRT) for spectrum sensing when both primary user transmitter and the secondary user receiver are equipped with multiple antennas. We do not assume any prior information about the channel statistics or the primary user’s signal structure. Two cases are considered when the secondary user is aware of the energy of the noise and when it is not. The final test statistics derived from GLRT are based on the eigenvalues of the sample covariance matrix. In-band spectrum sensing in overlay cognitive radio networks requires that the secondary users (SU) periodically suspend their communication in order to determine whether the primary user (PU) has started to utilize the channel. In contrast, in spectrum monitoring the SU can detect the emergence of the PU from its own receiver statistics such as receiver error count (REC). We investigate the problem of spectrum monitoring in the presence of fading where the SU employs diversity combining to mitigate the channel fading effects. We show that a decision statistic based on the REC alone does not provide a good performance. Next we introduce new decision statistics based on the REC and the combiner coefficients. It is shown that the new decision statistic achieves significant improvement in the case of maximal ratio combining (MRC). Next we consider the problem of cooperative spectrum sensing in cognitive radio networks (CRN) in the presence of misbehaving radios. We propose a novel approach based on the iterative expectation maximization (EM) algorithm to detect the presence of the primary users, to classify the cognitive radios, and to compute their detection and false alarm probabilities. We also consider the problem of centralized binary hypothesis testing in a cognitive radio network (CRN) consisting of multiple classes of cognitive radios, where the cognitive radios are classified according to the probability density function (PDF) of their received data (at the FC) under each hypotheses

Non-uniform sampling and reconstruction of multi-band signals and its application in wideband spectrum sensing of cognitive radio

Sampling theories lie at the heart of signal processing devices and communication systems. To accommodate high operating rates while retaining low computational cost, efficient analog-to digital (ADC) converters must be developed. Many of limitations encountered in current converters are due to a traditional assumption that the sampling state needs to acquire the data at the Nyquist rate, corresponding to twice the signal bandwidth. In this thesis a method of sampling far below the Nyquist rate for sparse spectrum multiband signals is investigated. The method is called periodic non-uniform sampling, and it is useful in a variety of applications such as data converters, sensor array imaging and image compression. Firstly, a model for the sampling system in the frequency domain is prepared. It relates the Fourier transform of observed compressed samples with the unknown spectrum of the signal. Next, the reconstruction process based on the topic of compressed sensing is provided. We show that the sampling parameters play an important role on the average sample ratio and the quality of the reconstructed signal. The concept of condition number and its effect on the reconstructed signal in the presence of noise is introduced, and a feasible approach for choosing a sample pattern with a low condition number is given. We distinguish between the cases of known spectrum and unknown spectrum signals respectively. One of the model parameters is determined by the signal band locations that in case of unknown spectrum signals should be estimated from sampled data. Therefore, we applied both subspace methods and non-linear least square methods for estimation of this parameter. We also used the information theoretic criteria (Akaike and MDL) and the exponential fitting test techniques for model order selection in this case

Optimal Cooperative Spectrum Sensing for Cognitive Radio

The rapid increasing interest in wireless communication has led to the continuous development of wireless devices and technologies. The modern convergence and interoperability of wireless technologies has further increased the amount of services that can be provided, leading to the substantial demand for access to the radio frequency spectrum in an efficient manner. Cognitive radio (CR) an innovative concept of reusing licensed spectrum in an opportunistic manner promises to overcome the evident spectrum underutilization caused by the inflexible spectrum allocation. Spectrum sensing in an unswerving and proficient manner is essential to CR. Cooperation amongst spectrum sensing devices are vital when CR systems are experiencing deep shadowing and in a fading environment. In this thesis, cooperative spectrum sensing (CSS) schemes have been designed to optimize detection performance in an efficient and implementable manner taking into consideration: diversity performance, detection accuracy, low complexity, and reporting channel bandwidth reduction. The thesis first investigates state of the art spectrums sensing algorithms in CR. Comparative analysis and simulation results highlights the different pros, cons and performance criteria of a practical CSS scheme leading to the problem formulation of the thesis. Motivated by the problem of diversity performance in a CR network, the thesis then focuses on designing a novel relay based CSS architecture for CR. A major cooperative transmission protocol with low complexity and overhead - Amplify and Forward (AF) cooperative protocol and an improved double energy detection scheme in a single relay and multiple cognitive relay networks are designed. Simulation results demonstrated that the developed algorithm is capable of reducing the error of missed detection and improving detection probability of a primary user (PU). To improve spectrum sensing reliability while increasing agility, a CSS scheme based on evidence theory is next considered in this thesis. This focuses on a data fusion combination rule. The combination of conflicting evidences from secondary users (SUs) with the classical Dempster Shafter (DS) theory rule may produce counter-intuitive results when combining SUs sensing data leading to poor CSS performance. In order to overcome and minimise the effect of the counter-intuitive results, and to enhance performance of the CSS system, a novel state of the art evidence based decision fusion scheme is developed. The proposed approach is based on the credibility of evidence and a dissociability degree measure of the SUs sensing data evidence. Simulation results illustrate the proposed scheme improves detection performance and reduces error probability when compared to other related evidence based schemes under robust practcial scenarios. Finally, motivated by the need for a low complexity and minmum bandwidth reporting channels which can be significant in high data rate applications, novel CSS quantization schemes are proposed. Quantization methods are considered for a maximum likelihood estimation (MLE) and an evidence based CSS scheme. For the MLE based CSS, a novel uniform and optimal output entropy quantization scheme is proposed to provide fewer overhead complexities and improved throughput. While for the Evidence based CSS scheme, a scheme that quantizes the basic probability Assignment (BPA) data at each SU before being sent to the FC is designed. The proposed scheme takes into consideration the characteristics of the hypothesis distribution under diverse signal-to-noise ratio (SNR) of the PU signal based on the optimal output entropy. Simulation results demonstrate that the proposed quantization CSS scheme improves sensing performance with minimum number of quantized bits when compared to other related approaches

Cross-layer design for multimedia applications in cognitive radio networks.

Ph. D. University of KwaZulu-Natal, Durban 2015.The exponential growth in wireless services and the current trend of development in wireless communication technologies have resulted into an overcrowded radio spectrum band in such a way that it can no longer meet the ever increasing requirements of wireless applications. In contrary however, literature surveys indicate that a large amount of the licensed radio spectrum bands are underutilized. This has necessitated the need for efficient ways to be implemented for spectrum sharing among different systems, applications and services in dynamic wireless environment. Cognitive radio (CR) technology emerges as a way to improve the overall efficiency of radio spectrum utilization by allowing unlicensed users (also known as secondary user) to utilize a licensed band when it is vacant. Multimedia applications are being targeted for CR networks. However, the performance and success of CR technology will be determined by the quality of service (QoS) perceived by secondary users. In order to transmit multimedia contents which have stringent QoS requirements over the CR networks, many technical challenges have to be addressed that are constrained by the layered protocol architecture. Cross-layer design has shown a promise as an approach to optimize network performance among different layers. This work is aimed at addressing the question on how to provide QoS guarantee for multimedia transmission over CR networks in terms of throughput maximization while ensuring that the interference to primary users is avoided or minimized. Spectrum sensing is a fundamental problem in cognitive radio networks for the protection of primary users and therefore the first part of this work provides a review of some low complex spectrum sensing schemes. A cooperative spectrum sensing scheme where multi-users are independently performing spectrum sensing is also developed. In order to address a hidden node problem, a cooperate relay based on amplify-and-forward technique (AF) is formulated. Usually the performance of a spectrum sensor is evaluated using receiver operating characteristic (ROC) curve which provides a trade-off between the probability of miss detection and the probability of false alarm. Due to hardware limitations, the spectrum sensor can not sense the whole range of radio spec- trum which results into partial information of the channel state. In order to model a media access control(MAC) protocol which is able to make channel access decision under partial information about the state of the system we apply a partially observable Markov decision process (POMDP) technique as a suitable tool in making decision under uncertainty. A throughput optimization MAC scheme in presence of spectrum sensing errors is then devel- oped using the concept of cross-layer design which integrates the design of spectrum sensing at physical layer (PHY) and sensing and access strategies at MAC layer in order to maximize the overall network throughput. A problem is formulated as a POMDP and the throughput performance of the scheme is evaluated using computer simulations under greedy sensing algorithm. Simulation results demonstrate an improved overall throughput performance. Further more, multiple channels with multiple secondary users having random message ar- rivals are considered during simulation and the throughput performance is evaluated under greedy sensing scheme which forms a benchmark for cross-layer MAC scheme in presence of spectrum sensing errors. By realizing that speech communication is still the most dom- inant and common service in wireless application, we develop a cross-layer MAC scheme for speech transmission in CR networks. The design is aimed at maximizing throughput of secondary users by integrating the design of spectrum sensing at PHY, quantization param- eter of speech traffic at application layer (APP), together with strategy for spectrum access at MAC layer with the main goal to improve the QoS perceived by secondary users in CR networks. Simulation results demonstrate throughput performance improvement and hence QoS is improved. One of the main features of the modern communication systems is the parameterized operation at different layers of the protocol stack. The feature aims at providing them with the capability of adapting to the rapidly changing traffic, channel and system conditions. Another interesting research problem in this thesis is the combination of individual adap- tation mechanisms into a cross-layer that can maximize their effectiveness. We propose a joint cross-layer design MAC scheme that integrates the design of spectrum sensing at PHY layer, access at MAC layer and APP information in order to improve the QoS for video transmission in CR networks. The end-to-end video distortion which is considered as an APP parameter resides in the video encoder. This is integrated in the state space and the problem is formulated as a constrained POMDP. H.264 coding algorithm which is one of the high efficient video coding standards is considered. The objective is to minimize this end-to- end video distortion while maximizes the overall network throughput for video transmission in CR networks. The end-to-end video distortion has signifficant effects to the QoS the per- ceived by the user and is viewed as the cost in the overall system design. Given the target system throughput, the packet loss ration when the system is in the state i and a composite action is taken in time slot t, the system immediate cost is evaluated. The expected total cost for overall end-to-end video distortion over the total time slots is then computed. A joint optimal policy which minimizes the expected total end-to-end distortion in total time slots is computed iteratively. The minimum expected cost (which also known as the value function) is also evaluated iteratively for the total time slots. The throughput performance of the proposed scheme is evaluated through computer simulation. In order to study the throughput performance of the proposed scheme, we considered four simulation scenarios namely simulation scenario A, simulation scenario B, simulation scenario C, and simulation scenario D. These simulation scenarios enabled us to study the throughput performance of the proposed scheme by by computer simulations. In the simulation scenario A, the av- erage throughput performance as a function of time horizon is studied. The throughput performance under channel access decision based on belief vector and that of channel access decision based on the end-to-end distortion are compared. Simulation results show that the channel access decision based on end-to-end distortion outperforms that of channel access decision based on a belief vector. In the simulation scenario B we aimed at studying the spectral efficiency as a function of prescribed collision probability. The simulation results show that, at large values of collision probability the overall spectral efficiency performs poorly. However, there is an optimal value of collision probability of which the spectral efficiency approaches that of the perfect channel access decision. In the simulation scenario C, we aimed at studying the average throughput performance and the spectral efficiency both as a function of prescribed collision probability. The simulation results show that both average throughput and the spectral efficiency are highly affected by the increase in collision probability. However, there is an optimal prescribed collision probability which achieves the maximum average throughput and maximum spectral efficiency

Green Cellular Networks: A Survey, Some Research Issues and Challenges

Energy efficiency in cellular networks is a growing concern for cellular operators to not only maintain profitability, but also to reduce the overall environment effects. This emerging trend of achieving energy efficiency in cellular networks is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. In this article, we present a brief survey of methods to improve the power efficiency of cellular networks, explore some research issues and challenges and suggest some techniques to enable an energy efficient or "green" cellular network. Since base stations consume a maximum portion of the total energy used in a cellular system, we will first provide a comprehensive survey on techniques to obtain energy savings in base stations. Next, we discuss how heterogeneous network deployment based on micro, pico and femto-cells can be used to achieve this goal. Since cognitive radio and cooperative relaying are undisputed future technologies in this regard, we propose a research vision to make these technologies more energy efficient. Lastly, we explore some broader perspectives in realizing a "green" cellular network technologyComment: 16 pages, 5 figures, 2 table