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

Green cooperative spectrum sensing and scheduling in heterogeneous cognitive radio networks

The motivation behind the cognitive radio networks (CRNs) is rooted in scarcity of the radio spectrum and inefficiency of its management to meet the ever increasing high quality of service demands. Furthermore, information and communication technologies have limited and/or expensive energy resources and contribute significantly to the global carbon footprint. To alleviate these issues, energy efficient and energy harvesting (EEH) CRNs can harvest the required energy from ambient renewable sources while collecting the necessary bandwidth by discovering free spectrum for a minimized energy cost. Therefore, EEH-CRNs have potential to achieve green communications by enabling spectrum and energy self-sustaining networks. In this thesis, green cooperative spectrum sensing (CSS) policies are considered for large scale heterogeneous CRNs which consist of multiple primary channels (PCs) and a large number of secondary users (SUs) with heterogeneous sensing and reporting channel qualities. Firstly, a multi-objective clustering optimization (MOCO) problem is formulated from macro and micro perspectives; Macro perspective partitions SUs into clusters with the objectives: 1) Intra-cluster energy minimization of each cluster, 2) Intra-cluster throughput maximization of each cluster, and 3) Inter-cluster energy and throughput fairness. A multi-objective genetic algorithm, Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is adopted and demonstrated how to solve the MOCO. The micro perspective, on the other hand, works as a sub-procedure on cluster formations given by macro perspective. For the micro perspective, a multihop reporting based CH selection procedure is proposed to find: 1) The best CH which gives the minimum total multi-hop error rate, and 2) the optimal routing paths from SUs to the CHs using Dijkstra\u27s algorithm. Using Poisson-Binomial distribution, a novel and generalized K-out-of-N voting rule is developed for heterogeneous CRNs to allow SUs to have different levels of local detection performance. Then, a convex optimization framework is established to minimize the intra-cluster energy cost subject to collision and spectrum utilization constraints.Likewise, instead of a common fixed sample size test, a weighted sample size test is considered for quantized soft decision fusion to obtain a more EE regime under heterogeneity. Secondly, an energy and spectrum efficient CSS scheduling (CSSS) problem is investigated to minimize the energy cost per achieved data rate subject to collision and spectrum utilization constraints. The total energy cost is calculated as the sum of energy expenditures resulting from sensing, reporting and channel switching operations. Then, a mixed integer non-linear programming problem is formulated to determine: 1) The optimal scheduling subset of a large number of PCs which cannot be sensed at the same time, 2) The SU assignment set for each scheduled PC, and 3) Optimal sensing parameters of SUs on each PC. Thereafter, an equivalent convex framework is developed for specific instances of above combinatorial problem. For the comparison, optimal detection and sensing thresholds are also derived analytically under the homogeneity assumption. Based on these, a prioritized ordering heuristic is developed to order channels under the spectrum, energy and spectrum-energy limited regimes. After that, a scheduling and assignment heuristic is proposed and shown to have a very close performance to the exhaustive optimal solution. Finally, the behavior of the CRN is numerically analyzed under these regimes with respect to different numbers of SUs, PCs and sensing qualities. Lastly, a single channel energy harvesting CSS scheme is considered with SUs experiencing different energy arrival rates, sensing, and reporting qualities. In order to alleviate the half- duplex EH constraint, which precludes from charging and discharging at the same time, and to harvest energy from both renewable sources and ambient radio signals, a full-duplex hybrid energy harvesting (EH) model is developed. After formulating the energy state evolution of half and full duplex systems under stochastic energy arrivals, a convex optimization framework is established to jointly obtain the optimal harvesting ratio, sensing duration and detection threshold of each SU to find an optimal myopic EH policy subject to collision and energy- causality constraints

Cooperative spectrum sensing using adaptive quantization mapping for mobile cognitive radio networks

Sparsity in spectrum is the result of spectrum underutilization. Cognitive radio (CR) technology has been proposed to address inefficiency of spectrum utilisation through dynamic spectrum access technique. CR in general allows secondary node (SN) users to access the licensed or primary users’ (PU) band without disrupting their activities. In CR cooperative spectrum sensing (CSS), a group of SNs share their spectrum sensing information to provide a better picture of the spectrum usage over the area where the SNs are located. In centralised CCS approach, all the SNs report their sensing information to a master node (MN) through a control reporting channel before the MN decides the spectrum bands that can be used by the SNs. To reduce unnecessary reporting information by the cooperating nodes, orthogonal frequency division multiplexing (OFDM) Subcarrier Mapping (SCM) spectrum exchange information was proposed. In this technique, the detection power level from each secondary SN user is quantized and mapped into a single OFDM subcarrier number before delivering it to the MN. Most researches in cooperative spectrum sensing often stated that the SNs are absolutely in stationary condition. So far, the mobility effect on OFDM based SCM spectrum exchange information has not been addressed before. In this thesis, the benchmarking of SCM in mobility environment is carried out. The results showed that during mobility, the performance of OFDM-based SCM spectrum exchange information degraded significantly. To alleviate the degradation, OFDM-based spectrum exchange information using adaptive quantization is proposed, which is known as Dynamic Subcarrier Mapping (DSM). The method is proposed to adapt to changes in detected power level during mobility. This new nonuniform subcarrier mapping considers the range of received power, threshold level and dynamic subcarrier width. The range of received power is first compressed or expanded depending on the intensity of the received power against a pre-determined threshold level before the OFDM subcarrier number is computed. The results showed that OFDM-based DSM spectrum exchange information is able to enhance the probability of detection for cooperative sensing by up to 43% and reduce false alarm by up to 28%. The DSM spectrum exchange information method has the potential to improve cooperative spectrum sensing for future CR mobile wireless networks

Joint Optimization of both m and K for the m-out-of-K Rule for Cooperative Spectrum Sensing

In this paper, we present closed form expressions that jointly optimizes the fusion rule (m) and the number of secondary users (K) for the m-out-of-K rule by minimizing the Bayes risk at the fusion center (FC) in the presence of erroneous reporting channels and then show that various existing and new results are special cases of the proposed solution. The results are applicable to any detector used in cooperative spectrum sensing (CSS). Numerical results are presented using energy detector (ED) which shows that CSS obtained using joint optimized values of m and K results in significant performance improvement

SMARAD - Centre of Excellence in Smart Radios and Wireless Research - Activity Report 2011 - 2013

Centre of Excellence in Smart Radios and Wireless Research (SMARAD), originally established with the name Smart and Novel Radios Research Unit, is aiming at world-class research and education in Future radio and antenna systems, Cognitive radio, Millimetre wave and THz techniques, Sensors, and Materials and energy, using its expertise in RF, microwave and millimeter wave engineering, in integrated circuit design for multi-standard radios as well as in wireless communications. SMARAD has the Centre of Excellence in Research status from the Academy of Finland since 2002 (2002-2007 and 2008-2013). Currently SMARAD consists of five research groups from three departments, namely the Department of Radio Science and Engineering, Department of Micro and Nanosciences, and Department of Signal Processing and Acoustics, all within the Aalto University School of Electrical Engineering. The total number of employees within the research unit is about 100 including 8 professors, about 30 senior scientists and about 40 graduate students and several undergraduate students working on their Master thesis. The relevance of SMARAD to the Finnish society is very high considering the high national income from exports of telecommunications and electronics products. The unit conducts basic research but at the same time maintains close co-operation with industry. Novel ideas are applied in design of new communication circuits and platforms, transmission techniques and antenna structures. SMARAD has a well-established network of co-operating partners in industry, research institutes and academia worldwide. It coordinates a few EU projects. The funding sources of SMARAD are diverse including the Academy of Finland, EU, ESA, Tekes, and Finnish and foreign telecommunications and semiconductor industry. As a by-product of this research SMARAD provides highest-level education and supervision to graduate students in the areas of radio engineering, circuit design and communications through Aalto University and Finnish graduate schools. During years 2011 – 2013, 18 doctor degrees were awarded to the students of SMARAD. In the same period, the SMARAD researchers published 197 refereed journal articles and 360 conference papers

Performance analysis of energy detector over generalised wireless channels in cognitive radio

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.This thesis extensively analyses the performance of an energy detector which is widely employed to perform spectrum sensing in cognitive radio over different generalised channel models. In this analysis, both the average probability of detection and the average area under the receiver operating characteristic curve (AUC) are derived using the probability density function of the received instantaneous signal to noise ratio (SNR). The performance of energy detector over an ŋ --- µ fading, which is used to model the Non-line-of-sight (NLoS) communication scenarios is provided. Then, the behaviour of the energy detector over к --- µ shadowed fading channel, which is a composite of generalized multipath/shadowing fading channel to model the lineof- sight (LoS) communication medium is investigated. The analysis of the energy detector over both ŋ --- µ and к --- µ shadowed fading channels are then extended to include maximal ratio combining (MRC), square law combining (SLC) and square law selection (SLS) with independent and non-identically (i:n:d) diversity branches. To overcome the problem of mathematical intractability in analysing the energy detector over i:n:d composite fading channels with MRC and selection combining (SC), two different unified statistical properties models for the sum and the maximum of mixture gamma (MG) variates are derived. The first model is limited by the value of the shadowing severity index, which should be an integer number and has been employed to study the performance of energy detector over composite α --- µ /gamma fading channel. This channel is proposed to represent the non-linear prorogation environment. On the other side, the second model is general and has been utilised to analyse the behaviour of energy detector over composite ŋ --- µ /gamma fading channel. Finally, a special filter-bank transform which is called slantlet packet transform (SPT) is developed and used to estimate the uncertain noise power. Moreover, signal denoising based on hybrid slantlet transform (HST) is employed to reduce the noise impact on the performance of energy detector. The combined SPT-HST approach improves the detection capability of energy detector with 97% and reduces the total computational complexity by nearly 19% in comparison with previously implemented work using filter-bank transforms. The aforementioned percentages are measured at specific SNR, number of selected samples and levels of signal decompositionMartyrs Foundatio

Performance analysis of spectrum sensing techniques for future wireless networks

In this thesis, spectrum sensing techniques are investigated for cognitive radio (CR) networks in order to improve the sensing and transmission performance of secondary networks. Specifically, the detailed exploration comprises of three areas, including single-node spectrum sensing based on eigenvalue-based detection, cooperative spectrum sensing under random secondary networks and full-duplex (FD) spectrum sensing and sharing techniques. In the first technical chapter of this thesis, eigenvalue-based spectrum sensing techniques, including maximum eigenvalue detection (MED), maximum minimum eigenvalue (MME) detection, energy with minimum eigenvalue (EME) detection and the generalized likelihood ratio test (GLRT) eigenvalue detector, are investigated in terms of total error rates and achievable throughput. Firstly, in order to consider the benefits of primary users (PUs) and secondary users (SUs) simultaneously, the optimal decision thresholds are investigated to minimize the total error rate, i.e. the summation of missed detection and false alarm rate. Secondly, the sensing-throughput trade-off is studied based on the GLRT detector and the optimal sensing time is obtained for maximizing the achievable throughput of secondary communications when the target probability of detection is achieved. In the second technical chapter, the centralized GLRT-based cooperative sensing technique is evaluated by utilizing a homogeneous Poisson point process (PPP). Firstly, since collaborating all the available SUs does not always achieve the best sensing performance under a random secondary network, the optimal number of cooperating SUs is investigated to minimize the total error rate of the final decision. Secondly, the achievable ergodic capacity and throughput of SUs are studied and the technique of determining an appropriate number of cooperating SUs is proposed to optimize the secondary transmission performance based on a target total error rate requirement. In the last technical chapter, FD spectrum sensing (FDSS) and sensing-based spectrum sharing (FD-SBSS) are investigated. There exists a threshold pair, not a single threshold, due to the self-interference caused by the simultaneous sensing and transmission. Firstly, by utilizing the derived expressions of false alarm and detection rates, the optimal decision threshold pair is obtained to minimize total error rate for the FDSS scheme. Secondly, in order to further improve the secondary transmission performance, the FD-SBSS scheme is proposed and the collision and spectrum waste probabilities are studied. Furthermore, different antenna partitioning methods are proposed to maximize the achievable throughput of SUs under both FDSS and FD-SBSS schemes

Spectrum Sensing in Cognitive Radio: Bootstrap and Sequential Detection Approaches

In this thesis, advanced techniques for spectrum sensing in cognitive radio are addressed. The problem of small sample size in spectrum sensing is considered, and resampling-based methods are developed for local and collaborative spectrum sensing. A method to deal with unknown parameters in sequential testing for spectrum sensing is proposed. Moreover, techniques are developed for multiband sensing, spectrum sensing in low signal to noise ratio, and two-bits hard decision combining for collaborative spectrum sensing. The assumption of using large sample size in spectrum sensing often raises a problem when the devised test statistic is implemented with a small sample size. This is because, for small sample sizes, the asymptotic approximation for the distribution of the test statistic under the null hypothesis fails to model the true distribution. Therefore, the probability of false alarm or miss detection of the test statistic is poor. In this respect, we propose to use bootstrap methods, where the distribution of the test statistic is estimated by resampling the observed data. For local spectrum sensing, we propose the null-resampling bootstrap test which exhibits better performances than the pivot bootstrap test and the asymptotic test, as common approaches. For collaborative spectrum sensing, a resampling-based Chair-Varshney fusion rule is developed. At the cognitive radio user, a combination of independent resampling and moving-block resampling is proposed to estimate the local probability of detection. At the fusion center, the parametric bootstrap is applied when the number of cognitive radio users is large. The sequential probability ratio test (SPRT) is designed to test a simple hypothesis against a simple alternative hypothesis. However, the more realistic scenario in spectrum sensing is to deal with composite hypotheses, where the parameters are not uniquely defined. In this thesis, we generalize the sequential probability ratio test to cope with composite hypotheses, wherein the thresholds are updated in an adaptive manner, using the parametric bootstrap. The resulting test avoids the asymptotic assumption made in earlier works. The proposed bootstrap based sequential probability ratio test minimizes decision errors due to errors induced by employing maximum likelihood estimators in the generalized sequential probability ratio test. Hence, the proposed method achieves the sensing objective. The average sample number (ASN) of the proposed method is better than that of the conventional method which uses the asymptotic assumption. Furthermore, we propose a mechanism to reduce the computational cost incurred by the bootstrap, using a convex combination of the latest K bootstrap distributions. The reduction in the computational cost does not impose a significant increase on the ASN, while the protection against decision errors is even better. This work is motivated by the fact that the sequential probability ratio test produces a smaller sensing time than its counterpart of fixed sample size test. A smaller sensing time is preferable to improve the throughput of the cognitive radio network. Moreover, multiband spectrum sensing is addressed, more precisely by using multiple testing procedures. In a context of a fixed sample size, an adaptive Benjamini-Hochberg procedure is suggested to be used, since it produces a better balance between the familywise error rate and the familywise miss detection, than the conventional Benjamini-Hochberg. For the sequential probability ratio test, we devise a method based on ordered stopping times. The results show that our method has smaller ASNs than the Bonferroni procedure. Another issue in spectrum sensing is to detect a signal when the signal to noise ratio is very low. In this case, we derive a locally optimum detector that is based on the assumption that the underlying noise is Student's t-distributed. The resulting scheme outperforms the energy detector in all scenarios. Last but not least, we extend the hard decision combining in collaborative spectrum sensing to include a quality information bit. In this case, the multiple thresholds are determined by a distance measure criterion. The hard decision combining with quality information performs better than the conventional hard decision combining