Secondary use of exclusively licensed but inefficiently used spectrum bands has been proposed as a solution to make more efficient use of spectrum resources. An important requirement for such secondary usage is to ensure that the primary services and systems performance are not degraded due to the presence of the secondary system. In this report three licensed primary systems are studied, namely digital video broadcasting, radar and aeronautical systems. In this initial report, the odelled tolerance limits for the primary users are investigated and verified by means of measurements for the case of DVB systems and literature review for the cases of Radar and Aeronautical systems. The impact of interference is evaluated as a functionof Desired to Undesired Power level (D/U) ratio for DVB systems, the pulse detection error for Aeronautical systems and the maximum interference-to-noise ratio (INR) for Radar systems. The main conclusions are that co-channel and adjacent channel interference need to be taken into account when assessing the tolerance limits for secondary systems in the TV bands. Also, non-continuous bursty-type users do not cause any higher interference compared to continuous data traffic users; thus, same tolerance limits can be used for both traffic behaviours.

For aeronautical systems, spatial separation and transmit power of the secondary system determine the feasibility of secondary usage of the L-band. For other radar systems e.g. in S band and C band, the INR value, the spatial separation, the transmit power of the secondary system and the IF separation effects determine the possibilities for secondary users. For DVB systems, multiple interferers cause accumulative interference effects on the co-channels and adjacent channels, and the total amount of interference caused by multiple interferers should be kept below the desired signal level.

The spectacular growth in wireless services imposes scarcity in term of the available radio spectrum. A solution to overcome this scarcity is to adopt what so called cognitive radio based on dynamic spectrum access. With dynamic spectrum access, secondary (unlicensed) users can access spectrum owned by primary (licensed) users when it is temporally and/or geographically unused. This unused spectrum is termed as spectrum opportunity. Finding these spectrum opportunities related aspects are studied in this thesis where two approaches of finding spectrum opportunities, namely spectrum sensing and geo-locations databases are considered.

In spectrum sensing arena, two topics are covered, blind spectrum sensing and sensing time and periodic sensing interval optimization. For blind spectrum sensing, a spectrum scanner based on maximum minimum eigenvalues detector and frequency domain rectangular filtering is developed. The measurements show that the proposed scanner outperforms the energy detector scanner in terms of the probability of detection. Continuing in blind spectrum sensing, a novel blind spectrum sensing technique based on discriminant analysis called spectrum discriminator has been developed in this thesis. Spectrum discriminator has been further developed to peel off multiple primary users with different transmission power from a wideband sensed spectrum. The spectrum discriminator performance is measured and compared with the maximum minimum eigenvalues detector in terms of the probability of false alarm, the probability of detection and the sensing time.

For sensing time and periodic sensing interval optimization, a new approach that aims at maximizing the probability of right detection, the transmission efficiency and the captured opportunities is proposed and simulated. The proposed approach optimizes the sensing time and the periodic sensing interval iteratively. Additionally, the periodic sensing intervals for multiple channels are optimized to achieve as low sensing overhead and unexplored opportunities as possible for a multi channels system.

The thesis considers radar bands and TV broadcasting bands to adopt geo-locations databases for spectrum opportunities. For radar bands, the possibility of spectrum sharing with secondary users in L, S and C bands is investigated. The simulation results show that band sharing is possible with more spectrum opportunities offered by C band than S and L band which comes as the least one. For the TV broadcasting bands, the thesis treats the power assignment for secondary users operate in Gävle area, Sweden. Furthermore, the interference that the TV transmitter would cause to the secondary users is measured in different locations in the same area.

There has been a recent explosive growth in mobile data consumption. This, in turn, imposes many challenges for mobile services providers and regulators in many aspects. One of these primary challenges is maintaining the radio spectrum to handle the current and upcoming expansion in mobile data traffic. In this regard, a radio spectrum regulatory framework based on secondary spectrum access is proposed as one of the solutions for the next generation wireless networks. In secondary spectrum access framework, secondary (unlicensed) systems coexist with primary (licensed) systems and access the spectrum on an opportunistic base.

In this thesis, aspects related to finding the free of use spectrum portions - called spectrum opportunities - are treated. One way to find these opportunities is spectrum sensing which is considered as an enabler of opportunistic spectrum access. In particular, this thesis investigates some topics in blind spectrum sensing where no priori knowledge about the possible co-existing systems is available.

As a standalone contribution in blind spectrum sensing arena, a new blind sensing technique is developed in this thesis. The technique is based on discriminant analysis statistical framework and called spectrum discriminator (SD). A comparative study between the SD and some existing blind sensing techniques was carried out and showed a reliable performance of the SD.

The thesis also contributes by exploring sensing parameters optimization for two existing techniques, namely, energy detector (ED) and maximum-minimum eigenvalue detector (MME). For ED, the sensing time and periodic sensing interval are optimized to achieve as high detection accuracy as possible. Moreover, a study of sensing parameters optimization in a real-life coexisting scenario, that is, LTE cognitive femto-cells, is carried out with an objective of maximizing cognitive femto-cells throughput. In association with this work, an empirical statistical model for LTE channel occupancy is accomplished. The empirical model fits the channels' active and idle periods distributions to a linear combination of multiple exponential distributions. For the MME, a novel solution for the filtering problem is introduced. This solution is based on frequency domain rectangular filtering. Furthermore, an optimization of the observation bandwidth for MME with respect to the signal bandwidth is analytically performed and verified by simulations.

After optimizing the parameters for both ED and MME, a two-stage fully-blind self-adapted sensing algorithm composed of ED and MME is introduced. The combined detector is found to outperform both detectors individually in terms of detection accuracy with an average complexity lies in between the complexities of the two detectors. The combined detector is tested with measured TV and wireless microphone signals.

The performance evaluation in the different parts of the thesis is done through measurements and/or simulations. Active measurements were performed for sensing performance evaluation. Passive measurements on the other hand were used for LTE downlink channels occupancy modeling and to capture TV and wireless microphone signals.

In this paper we present a new spectrum sensing technique for cognitive radios based on discriminant analysis called spectrum discriminator and compare it with the maximum minimum eigenvalue detector. The common feature between those two techniques is that neither prior knowledge about the system noise level nor the primary user signal, that might occupy the band under sensing, is required. Instead the system noise level will be derived from the received signal. The main difference between both techniques is that the spectrum discriminator is a non-parametric technique while the maximum minimum eigenvalue detector is a parametric technique. The comparative study between both has been done based on two performance metrics: the probability of false alarm and the probability of detection. For the spectrum discriminator an accuracy factor called noise uncertainty is defined as the level over which the noise energy may vary. Simulations are performed for different values of noise uncertainty for the spectrum discriminator and different values for the number of received samples and smoothing factor for the maximum minimum eigenvalue detector.

In this paper a new approach of optimizing the sensing time and periodic sensing interval for energy detectors has been explored. This new approach is built upon maximizing the probability of right detection, captured opportunities and transmission efficiency. The probability of right detection is defined as the probability of having no false alarm and correct detection. Optimization of the sensing time relies on maximizing the summation of the probability of right detection and the transmission efficiency while optimization of periodic sensing interval subjects to maximizing the summation of transmission efficiency and the captured opportunities. The optimum sensing time and periodic sensing interval are dependent on each other, hence, iterative approach to optimize them is applied and convergence criterion is defined. The simulations show that both converged sensing time and periodic sensing interval increase with the increase of the channel utilization factor, moreover, the probability of false alarm, the probability of detection, the probability of right detection, the transmission efficiency and the captured opportunities have been taken as the detector performance metrics and evaluated for different values of channel utilization factor and signal-to-noise ratio.

Using frequency hopping for fair resources allocation in TV white spaces is proposed and evaluated in this paper. The degree of fairness is judged by the achieved throughput by different secondary users. The throughput of the secondary users is determined by their permissible transmission power and the interference from the TV and other secondary users. The permissible transmission power for secondary users in TV white spaces in different channels is investigated. The main concern of calculating the permissible secondary user transmission power is protecting the primary TV receivers from harmful interference. With the aid of SPLAT (RF Signal Propagation, Loss, And Terrain analysis tool), the received TV signal power in a study case of the surroundings of the city of Gävle is fetched. The interference from the TV transmission into the free channels is measured in six different locations. The simulated system is a deployed Wi-Fi access points in a building representing an office environment in an urban area. Moreover, the size of the hopping set and the number of APs influences are investigated.

In this paper a model to investigate the spectrum opportunities for cognitive radio networks in three radar frequency bands L, S and C at a specific location is introduced. We consider underlay unaware spectrum sharing model. The Secondary System we assume is an OFDM based system. The followed strategy is built upon defining a specific co or adjacent channel as a spectrum opportunity if -and only if- the interference generated by the secondary system occupying that channel  into the radar system is less than the permissible interference defined by the value of Interference to Noise ratio (INR) and the radar receiver inherited noise level. The simulation results show that for the same transmission parameters C band offer more spectrum opportunities than S band which is itself offers more spectrum opportunities than L band.

In this paper we introduce a technique for spectrum scanning with the maximum minimum eigenvalue detection based spectrum sensing. The fundamental problem we address in this paper is the inability of using maximum minimum eigenvalue detection with filtering in time domain where the white noise becomes coloured. The solution we propose here is based on frequency domain rectangular filtering. By frequency domain rectangular filtering we take the spectral lines inside each sub-band and throw out the rest. After doing the frequency domain rectangular filtering, we generate the corresponding time domain signal and inject it to the maximum minimum eigenvalue detector. An experimental verification has been performed and the obtained results show that the technique is implementable with a performance better than the energy detector as a reference technique in terms of the probability of detection when both technique have the same probability of false alarm.

The impact of the signal bandwidth and observation bandwidth on the detection performance of the maximumminimum eigenvalue detector is studied in this letter. The considered signals are the Gaussian signals. The optimum ratio between the signal and the observation bandwidth is analytically proven to be 0.5 when reasonable values of the system dimensionality are used. The analytical proof is verified by simulations.

In this chapter the authors propose a new approach for optimizing the sensing time and periodic sensing interval for energy detectors in cognitive radio networks. The optimization of the sensing time depends on maximizing the summation of the probability of right detection and transmission efficiency, while the optimization of periodic sensing interval is subject to maximizing the summation of transmission efficiency and captured opportunities. Since the optimum sensing time and periodic sensing interval are dependent on each other, an iterative approach to optimize them simultaneously is proposed and a convergence criterion is devised. In addition, the probability of detection, probability of false alarm, probability of right detection, transmission efficiency, and captured opportunities are taken as performance metrics for the detector and evaluated for various values of channel utilization factors and signal-to-noise ratios.

In this paper, a comparison between energy and maximum-minimum eigenvalue detectors is performed. The comparison has been made concerning the sensing complexity and the sensing accuracy in terms of the receiver operating characteristics curves. The impact of the signal bandwidth compared to the observation bandwidth is studied for each detector. For the energy detector, the probability of detection increases monotonically with the increase of the signal bandwidth. For the maximum-minimum eigenvalue detector, an optimal value of the ratio between the signal bandwidth and the observation bandwidth is found to be 0.5 when reasonable values of the system dimensionality are used. Based on the comparison findings, a combined two-stage detector is proposed. The combined detector performance is evaluated based on simulations and measurements. The combined detector achieves better sensing accuracy than the two individual detectors with a complexity lies in between the two individual complexities. The combined detector is fully-blind and self-adapted as the maximum-minimum eigenvalue detector estimates the noise and feeds it back to the energy detector. The performance of the noise estimation process is evaluated in terms of the normalized mean square error.

This paper proposes an optimized sensing based channel access framework for the LTE cognitive femto cells with an objective of maximizing femto-cells downlink throughput. Energy detection is used by the LTE cognitive femto cells to locate and thereafter utilize the free channels. Moreover, periodic sensing is adopted to detect any changes of the sensing outcomes. The developed channel access framework is based on an objective of maximization the femto-cell downlink throughput which varies with the macro-cell channel occupancy. Therefore, the LTE macro-cell occupancy statistics are empirically modelled using exponential distributions mixture. The LTE cognitive femto-cell downlink throughput is maximized by compromising the transmission efficiency, the explored spectrum opportunities and the interference from the macro-cell. An analytical solution for the optimal periodic sensing interval that maximizes the throughput is found and verified by simulations. The obtained results show that there is indeed a single periodic sensing interval value that maximizes the LTE cognitive femto-cell downlink throughput which changes with the change of the macro-cell channel occupancy. Yet, at a specific channel occupancy statistical parameters, our framework provides the optimal throughput. At the peak of the macro-cell traffic, our framework increased the femto-cell throughput by $\simeq 15\%$ compared to the senseless case. The impact of the available channels for opportunistic access is studied. The simulation results show that increasing the number of the available channels becomes less significant for more than three channels.

This paper develops an empirical statistical channel occupancy model for downlink long-term evolution (LTE) cellular systems. The model is based on statistical distributions mixtures for the holding times of the channels. Moreover, statistical distribution of the time when the channels are free is also considered. The data is obtained through an extensive measurement campaign performed in Stockholm, Sweden. Two types of mixtures are considered, namely, exponential and log-normal distributions to fit the measurement findings. The log-likelihood of both mixtures is used as a quantitative measure of the goodness of fit. Moreover, finding the optimal number of linearly combined distributions using the Akaike information criterion (AIC) is investigated. The results show that good fitting can be obtained by using either exponential or log-normal distributions mixture. Even though, the fitting is done for a representative case with a tempo-spatial consideration, the model is yet applicable in general for LTE and other cellular systems in a wider sense.

In this paper, a newly developed SNR estimation algorithm is presented. The new algorithm is based on the eigenvalues of the samples covariance matrix of the recieved signal. The presented algorithm is blind in the sense that both the noise and the signal power are unknown and estimated from the received samples. The Minimum Descriptive Length (MDL) criterion is used to split the signal and noise corresponding eigenvalues. The experimental results are judged using the Normalized Mean Square Error (NMSE) between the estimated and the actual SNRs. The results show that depending on the value of received vectors size, N, and the number of received vectors, L, the NMSE is changed and down to −55 dB NMSE can be achieved for the highest used values of N and L.

In this paper, we present a new spectrum sensing technique for cognitive radios based on discriminant analysis called spectrum discriminator. The presented technique uses the knowledge of the noise uncertainty and a probabilistic validation to overcome the limitations of the discriminant analysis. A comparative study between the proposed technique and the maximum-minimum eigenvalue detection has been performed based on two performance metrics: the probability of false alarm and the probability of detection. The spectrum discriminator has been further developed to a peel-off technique where all primary users can be detected. The performance of the spectrum discriminator and the peel-off technique has been tested on simulations and experimentally verified. The comparative study is based on simulations as well as measurements.

In this paper a technique to detect multiple impulsive interference in an industrial environment is proposed and evaluated. The technique is based on discriminant analysis which iteratively peels-off the impulsive interferences. The probability of detection of the technique is tested with and without the iterative peeling-off part. The simulations show that the SIR can be improved by applying the detection technique and then blank or clip the impulsive interference components. The improvement in the SIR depends on the impulsive interference parameters and it can reach up to 17 dB.

Efficient use of the available licensed radio spectrum is becoming increasingly difficult as the demand and usage of the radio spectrum increases. This usage of the spectrum is not uniform within the licensed band but concentrated in certain frequencies of the spectrum while other parts of the spectrum are inefficiently utilized. In cognitive radio environments, the primary users are allocated licensed frequency bands while secondary cognitive users can dynamically allocate the empty frequencies within the licensed frequency band, according to their requested quality of service specifications. In this chapter, the authors investigate and assess the performance of MAC layer sensing schemes in cognitive radio networks. Two performance metrics are used to assess the performance of the sensing schemes: the available spectrum utilization and the idle channel search delay for reactive and proactive sensing schemes. In proactive sensing, the adapted and non-adapted sensing period schemes are also assessed. Simulation results show that proactive sensing with adapted periods provides superior performance at the expense of higher computational cost performed by network nodes.

One of the most critical issues regarding wireless networksregulation agencies is how to manage the available electromagneticradio spectrum in a way that satisfies the needs of the huge growingin wireless systems both economically and technically, especiallywith the recent crowding in the available spectrum. Hence, buildingcognitive radio systems supporting dynamic access to the availablespectrum has appeared recently as a novel solution for the wirelesssystem huge expansion. In this paper we investigate the MAC layersensing schemes in cognitive radio networks, where both reactiveand proactive sensing are considered. In proactive sensing theadapted and non-adapted sensing periods schemes are also assessed.The assessment of these sensing schemes has been held via twoperformance metrics: available spectrum utilization and idlechannel search delay. Simulation results show that with proactivesensing adapted periods we achieve the best performance but withan observable overhead computational tasks to be done by thenetwork nodes

By any measure, wireless communications is one of the most evolving fields in engineering. This, in return, has imposed many challenges, especially in handling the hunger for higher data rates in the next generation wireless networks. Among these challenges is how to provide the needed resources in terms of the electromagnetic radio spectrum for these networks. In this regard, cognitive radio (CR) based on dynamic spectrum access (DSA) has been attracting huge attention as a promising solution for more efficient utilization of the available radio spectrum. DSA is based on finding and opportunistically accessing the free-of-use portions of spectrum. To facilitate DSA, spectrum sensing can be used. However, spectrum sensing faces many challenges in different aspects. Such aspects include blind sensing and sensing optimization, which are both to a great extent measurement challenges. We discuss different contributions in addressing these two challenges in this article.

A brain stroke is defined as a disturbance in the blood supply of the brain. This can be due to either an obstruction in the blood vessels of the brain or a rupture in the blood vessels which causes a leakage of blood in the brain. In many cases, a stroke results in the death of the patient within 24 hours. Hence, it is crucial that the neurologist has immediately contact with the patient in the first 30 minutes after the stroke. This means that a direct broadband communication link between the ambulance and the hospital is needed in order to transmit all necessary physiological parameters, such as blood pressure and glucose level as well as video images. In this paper, we present a new architecture of a wireless communication link between the ambulance and the hospital based on the concept of cognitive radios. The sender/receiver module in the ambulance will allow measuring the wideband spectrum and search for a suitable empty frequency band to send the data.