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The exponential growth in demand for mobile data has led to significant research efforts aimed at more efficient methods of utilizing the scarce RF spectrum resource. One potential solution to this scarcity problem is Cognitive Radio (CR) which involves dynamic spectrum access in which a set of unlicensed users occupy spectrum holes without causing significant degradation of performance to the incumbent users. A key enabling technology for CR networks is accurate spectrum sensing which aims to learn the radio environment in order to adapt the CR transmission. Traditional spectrum sensing techniques have mainly focused on determining only the presence or absence of a licensed user. Recent work in the past few years have shown however that more detailed knowledge pertaining to radio-scene analysis can be used to improve the performance of CR networks. The more the secondary user knows about the active licensed users, the better it can adapt its transmission strategies. In this work, we put forward the concept of advance spectrum sensing which takes a multi-dimensional approach to radio-scene analysis that estimates various parameters of the active transmitters through sensing, localization and tracking, modulation classification, PHY parameter estimation, and MAC-layer classification. In this work we investigate the elements of such an advance spectrum sensing system. Firstly, we will look at the problem of conventional spectrum sensing, or detecting the presence or absence of transmitting Primary Users. In particular, we study how detection performance could be improved through the use of cyclostationary feature detection and how it could be made robust to fading, noise uncertainty, and co-channel interferers through the optimal use of multiple sensors. Second, we attack the problem of modulation classification which we argue is a critical piece of information for future cognitive radio systems. We present a new type of pattern classification algorithm based on the concept of sampled distribution distance which offers a low computational complexity alternative to maximum-likelihood based classification. Through our extensive analysis, we have derived the optimal form of this type of classifier and applied it to the modulation classification problem. Finally, we propose a system of MAC-layer classification based on 4th-order cumulants which distinguishes between TDMA, OFDMA, CDMA and contention-based schemes. In addition, it is also able to jointly perform modulation classification with channel access method. The analysis of the statistics of the 4th-order cumulant used in our work also offers large potential for applications in different areas including modulation classification, channel estimation, and estimation of number of users. |
