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 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.

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.

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.

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.

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 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 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.

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.