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The concept of signal area (SA), defined as the rectangular time–frequency region in a spectrogram where a signal is detected, plays an important role in spectrum usage measurements. The need for signal area estimation (SAE) is justified by its role in the process of allocating white space spectrum to secondary users in dynamic spectrum access systems as well as in other interesting applications such as compliance verification and enforcement of spectrum regulations, signal interception, and network planning and optimisation. Existing SAE methods are far from perfect and therefore new solutions capable to provide more accurate estimations are thus required. In this study, a novel approach based on image processing techniques is explored. Concretely, the feasibility of using morphological operations (MOs) is explored to examine its usefulness in the context of SAE. By means of extensive simulations, the performance of different MOs (erosion, dilation, opening, and closing) in the context of SAE is investigated under various configurations, including different shapes and sizes of the structuring element (SE), when used both as standalone SAE methods and in combination with other SAE methods from the literature. Based on the obtained results, an MO-based SAE method is formulated based on the optimum MO and SE configuration for each specific SNR regime, which can improve substantially the performance of other proposed SAE methods when used as a pre- or postprocessing technique. Concretely, the accuracy improvement in terms of F1 score is up to 40% in the low-SNR regime while achieving a perfect accuracy of 100% in the high-SNR regime. This is achieved without having a noticeable impact on the associated computational cost (and even reducing it by up to 15% at high SNR). The performance improvement is thus particularly significant in the low-SNR regime, where most methods' performances are limited, and as a result the proposed SAE approach can extend the operational SNR range of the existing SAE methods. [ABSTRACT FROM AUTHOR]

Primary channel statistics have recently gained increasing attention due to its remarkable role in the performance improvement of Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems. These statistics can be calculated from the outcomes of spectrum sensing, which is the well-known method used to identify the available instantaneous opportunities in the spectrum. Computing statistical information from spectrum sensing, however, may sometimes be unreliable due to the fact that spectrum sensing is imperfect in the real world and errors are likely to occur in the sensing decisions. In this context, this work provides a detailed analysis of a broad range of primary channel statistics under Imperfect Spectrum Sensing (ISS) and finds a set of closed-form expressions for the calculated statistics under ISS as a function of the original primary channel statistics, probability of error, and the employed sensing period. In addition, the obtained mathematical expressions are employed to find and propose novel estimators for the primary channel statistics, which outperform the existing estimators in the literature and can provide accurate estimations of the original statistics even under high probability of error of spectrum sensing. The correctness of the obtained analytical expressions and the accuracy of the proposed estimators are corroborated with both simulation and experimental results. [ABSTRACT FROM AUTHOR]

Primary channel activity statistics (such as the minimum idle/busy periods of the channel, the moments or the underlying distributions) can be exploited by cognitive radio (CR) systems to adapt their operation and improve their performance. Such statistics can be directly estimated from periodic observations of the instantaneous idle/busy state of the primary channel (i.e., periodic spectrum sensing). However, the periodicity of such observations (i.e., the sensing period) imposes a fundamental limit on the time resolution in which idle/busy periods can be observed, and consequently on the accuracy of any subsequent estimated statistics. In this context, this paper provides a comprehensive analysis on the estimation of the primary activity statistics based on periodic channel state observations performed with a finite sensing period. In particular, this paper provides a comprehensive set of closed-form expressions for the estimated statistics as a function of the true primary activity statistics and the employed sensing period. These expressions can find a wide range of applications in the analysis, design, and simulation of CR systems. Moreover, several methods to minimize the estimation errors and improve the accuracy are proposed and validated with both simulations and hardware experiments. [ABSTRACT FROM AUTHOR]

For achieving an efficient spectrum sharing in a context of dynamic spectrum access, understanding the spectrum usage by licensed users [primary users (PUs)], is important for secondary users (SUs). Duty cycle (DC) has been used to express the deterministic and stochastic aspects of spectrum usage. Specifically, a deterministic model for the mean of the duty cycle (M-DC) has been proposed in a previous work. The deterministic aspect of M-DC is affected by social behavior, and common habits of users, which can be confirmed in cellular systems. On the other hand, the observed DC (O-DC) during short time duration has randomness and a stochastic model is more suitable, e.g. distribution of O-DC. In this paper, we extend the conventional approach, in which only either the deterministic or stochastic aspect is considered, to a combined deterministic-stochastic (DS) model, which represents both the deterministic and stochastic aspects at once. For the distribution of the O-DC, the beta distribution has been used as stochastic model, but we employ a mixture of beta distributions. The mixture-beta distribution can achieve higher accuracy but requires more capacity for data storage in spectrum usage measurements since it has a higher number of parameters than the beta distribution. For this issue, we employ regression analysis in DS-model since this approach can reduce the number of parameters while retaining the accuracy. We show the validity of DS-model based on exhaustive spectrum measurements in IEEE 802.11-based wireless local area networks and long-term evolution uplink. [ABSTRACT FROM PUBLISHER]