The aggregate interference distribution in cognitive radio networks is studied in a rigorous analytical way using the popular Poisson point process model. While a number of results are available for this model of regular (non-cognitive) networks, cognitive ones present an extra level of difficulty for the analysis, mainly due to the exclusion region around the primary receiver, which are typically addressed via various ad-hoc approximations (e.g. based on the interference cumulants) or via the large-deviation analysis. Unlike the previous studies, here we do not use ad-hoc approximations but rather obtain the asymptotic interference distribution in a systematic and rigorous way. This is in contrast to the large deviation analysis, which provides only the (exponential) order of scaling but not the outage probability itself. Unlike the cumulant-based analysis, our approach provides a guaranteed level of accuracy at the distribution tail. Additionally, our analysis also provides a number of novel insights. In particular, we demonstrate that there is a critical transition point below which the outage probability decays only polynomially but above which it decays exponentially. This provides a solid analytical foundation to the earlier empirical observations in the literature and also reveals how typical outage events occur in different regimes. In addition, the proposed asymptotic expressions are also shown to be accurate in the non-asymptotic regimes.

The aggregate interference distribution in cognitive radio networks is studied in a rigorous analytical way using the popular Poisson point process model. While a number of results are available for this model of regular (non-cognitive) networks, cognitive ones present an extra level of difficulties for the analysis, mainly due to the exclusion region around the primary receiver, which are typically addressed via various ad-hoc approximations (e.g. based on the interference cumulants) or via the large-deviation analysis. Unlike the previous studies, we do not use here ad-hoc approximations but rather obtain the asymptotic interference distribution in a systematic and rigorous way, which also has a guaranteed level of accuracy at the distribution tail. This is in contrast to the large deviation analysis, which provides only the (exponential) order of scaling but not the outage probability itself. Unlike the cumulant-based analysis, our approach provides a guaranteed level of accuracy at the distribution tail. Additionally, our analysis provides a number of novel insights. In particular, we demonstrate that there is a critical transition point below which the outage probability decays only polynomially but above which it decays super-exponentially. This provides a solid analytical foundation to the earlier empirical observations in the literature and also reveals what are the typical ways outage events occur in different regimes. The analysis is further extended to include interference cancelation and fading (from a broad class of distributions). The outage probability is shown to scale down exponentially in the number of canceled nearest interferers in the below-critical region and does not change significantly in the above-critical one. The proposed asymptotic expressions are shown to be accurate in the non-asymptotic regimes.

This paper analyzes the outage probability in cognitive radio networks, based on the Poisson point process model of node spatial distribution and the standard propagation path loss model, including Rayleigh and log-normal fading. To make the analysis tractable, all possible scenarios are classified into three cases based on typical outage events. When the average number of nodes in the forbidden region is much smaller than unity, the aggregate interference can be well approximated by the nearest node for both non-fading and fading scenarios (the nearest node dominates the outage performance). When the average number of nodes in the forbidden region is greater than unity, the aggregate interference can be well approximated by a Gaussian random variable for non-fading scenario (many nodes contribute to outage events, rather than a single dominant one). This approximation also applies to the fading scenario, but its accuracy is a bit worse at the transition region. An alternative approximation is proposed, which is accurate for any outage probability. When the average number of nodes in the forbidden region is slightly smaller than unity, neither the nearest node approximation nor the Gaussian one is accurate for the non-fading scenario (since only a few near-by nodes are dominant), and finding an accurate approximation for the outage probability in this case is an open problem. The alternative approximation above is accurate for the fading scenario. All approximations are validated via Monte-Carlo simulations.

This paper proposes a synchronization method that uses as preamble two identical training parts (Chu sequences) followed by a flipped postfix (FlP) for OFDM and MIMO-OFDM systems. The proposed method provides symbol timing and carrier frequency offset (CFO) estimators. By using this FlP-based preamble, the accuracy of the symbol timing estimator is highly improved without increasing the computation complexity. The time synchronization method includes both coarse and fine time synchronization. In particular, this paper indicates how to set up the threshold for the fine time synchronization. Performance evaluation of the proposed method, applied to MIMO-OFDM systems, with various diversity combining shows that receive diversity gives more gain than transmit diversity. The proposed method for both OFDM and MIMO-OFDM systems is also compared to various synchronization methods such as the Schmidl-Cox (SC) and Minn-Zeng-Bhargava (MZB)'s methods in time invariant and time-variant Rayleigh fading channels in terms of mean and variance of the timing and CFO estimators. Overall, it is seen that the proposed new method offers good advantages compared to existing synchronization methods.

This paper presents a novel synchronization method for OFDM systems that uses preambles containing two identical training parts followed by a flipped postfix (FlP). Specifically, the proposed preamble for AWGN channels consists of a flipped cyclic prefix (FCP) and two identical training parts followed by a FlP The preamble for ISI and fading channels is similar except that the FCP is replaced by a zero padding prefix (ZPP). By using these FlP-based preambles, the accuracy of the timing offset estimator is highly improved without increasing the computation complexity. Performance of the proposed method is evaluated by simulation over AWGN, ISI and fading channels in terms of the mean and variance of the time offset and carrier frequency offset (CFO) estimators. Results show the new time offset estimator has a quite small variance and its mean value is quite close to the exact start point. Comparison to Schmidl-Cox and Minn's classical synchronization methods illustrates the advantages of the proposed method.