@article{sun_wang_li_2016, title={The Impact of Network Size and Mobility on Information Delivery in Cognitive Radio Networks}, volume={15}, ISSN={["1558-0660"]}, DOI={10.1109/tmc.2015.2398420}, abstractNote={There have been extensive works on the design of opportunistic spectrum access and routing schemes to improve spectrum efficiency in cognitive radio networks (CRNs), which becomes an integral component in the future communication regime. Nonetheless, the potentials of CRNs in boosting network performance yet remain to be explored to reach the full benefits of such a phenomenal technique. In this paper, we study the end-to-end latency in CRNs in order to find the sufficient and necessary conditions for real-time applications in finite networks and large-scale deployments. We first provide a general mobility framework which captures most characteristics of the existing mobility models and takes spatial heterogeneity into account. Under this general mobility framework, secondary users are mobile with an mobility radius a, which indicates how far a mobile node can reach in spatial domain. We find that there exists a cutoff point on a, below which the latency has a heavy tail and above which the tail of the latency is bounded by some Gamma distributions. As the network grows large, the latency is asymptotically scalable (linear) with respect to the dissemination distance (e.g., the number of hops or euclidean distance). An interesting observation is that although the density of primary users adversely impacts the expected latency, it makes no influence on the dichotomy of the latency tail in finite networks and the linearity of latency in large networks. Our results encourage CRN deployment for real-time and large applications, when the mobility radius of secondary users is large enough.}, number={1}, journal={IEEE TRANSACTIONS ON MOBILE COMPUTING}, author={Sun, Lei and Wang, Wenye and Li, Yujin}, year={2016}, month={Jan}, pages={217–231} }
@article{sun_wang_lu_2015, title={On Topology and Resilience of Large-Scale Cognitive Radio Networks Under Generic Failures}, volume={14}, ISSN={["1558-2248"]}, DOI={10.1109/twc.2015.2404919}, abstractNote={It has been demonstrated that in wireless networks, blackholes, which are typically generated by isolated node failures, and augmented by failure correlations, can easily result in devastating impact on network performance. In order to address this issue, we focus on the topology of Cognitive Radio Networks (CRNs) because of their phenomenal benefits in improving spectrum efficiency through opportunistic communications. Particularly, we first define two metrics, namely the failure occurrence probability p and failure connection function g(·), to characterize node failures and their spreading properties, respectively. Then we prove that each blackhole is exponentially bounded based on percolation theory. By mapping failure spreading using a branching process, we further derive an upper bound on the expected size of blackholes. With the observations from our analysis, we are able to find a sufficient condition for a resilient CRN in the presence of blackholes through analysis and simulations.}, number={6}, journal={IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS}, author={Sun, Lei and Wang, Wenye and Lu, Zhuo}, year={2015}, month={Jun}, pages={3390–3401} }
@inproceedings{sun_wang_2013, title={Understanding blackholes in large-scale cognitive radio networks under generic failures}, DOI={10.1109/infcom.2013.6566859}, abstractNote={It has been demonstrated that in wireless networks, Blackholes, which are typically generated by isolated node failures, and augmented by failure correlations, can easily result in devastating impact on network performance. Therefore, many solutions, such as routing protocols and restoration algorithms, are proposed to deal with Blackholes by identifying alternative paths to bypass these holes such that the effect of Blackholes can be mitigated. These advancements are based on an underlying premise that there exists at least one alternative path in the network. However, such a hypothesis remains an open question. In other words, we do not know whether the network is resilient to Blackholes or whether an alternative path exists. The answer to this question can complement our understanding of designing routing protocols, as well as topology evolution in the presence of random failures. In order to address this issue, we focus on the topology of Cognitive Radio Networks (CRNs) because of their phenomenal benefits in improving spectrum efficiency through opportunistic communications. Particularly, we first define two metrics, namely the failure occurrence probability p and failure connection function g(·), to characterize node failures and their spreading properties, respectively. Then we prove that each Blackhole is exponentially bounded based on percolation theory. By mapping failure spreading using a branching process, we further derive an upper bound on the expected size of Blackholes. With the observations from our analysis, we are able to find a sufficient condition for a resilient CRN in the presence of Blackholes through analysis and simulations.}, booktitle={2013 proceedings ieee infocom}, author={Sun, L. and Wang, Wenye}, year={2013}, pages={728–736} }
@inproceedings{sun_wang_2012, title={On latency distribution and scaling: From finite to large cognitive radio networks under general mobility}, DOI={10.1109/infcom.2012.6195491}, abstractNote={Cognitive Radio Networks (CRNs), as a phenomenal technique to improve spectrum efficiency for opportunistic communications, become an integral component in the future communication regime. In this paper, we study the end-to-end latency in CRNs because many CRN applications, such as military networks and emergency networks, are either time-sensitive or dependent on delay performance. In particular, we consider a general mobility framework that captures most characteristics of the existing models and accounts for spatial heterogeneity resulting from the scenario that some locations are more likely to be visited by mobile nodes (these can be home in the case of people, or garage in the case of vehicles). By assuming that secondary users are mobile under this general framework, we find that there exists a cutoff point on the mobility radius #, which indicates how far a mobile node can reach in the spatial domain, below which the latency has a heavy-tailed distribution and above which the tail distribution is bounded by some Gamma (light-tailed) distribution. A heavy tail of the latency implies a significant probability that it takes long time to disseminate a message from the source to the destination and thus a light-tailed latency is crucial for time-critical applications. Moreover, as the network grows large, we notice that the latency is asymptotically scalable (linear) with the dissemination distance (e.g., the number of hops or Euclidean distance). Another interesting observation is that although the density of primary users adversely impacts the expected latency, it makes no influence on the dichotomy of the tail distribution of the latency in finite networks and the linearity of latency in large networks. Our results encourage the CRN deployment for real-time and large applications, when the mobility radius of secondary users is large enough.}, booktitle={2012 Proceedings IEEE infocom}, author={Sun, L. and Wang, Wenye}, year={2012}, pages={1287–1295} }
@inproceedings{sun_wang_2012, title={On the connectivity of large multi-channel cognitive radio networks}, DOI={10.1109/icc.2012.6363893}, abstractNote={Cognitive Radio Networks (CRNs) have become promising network components to improve spectrum utilization efficiency, where secondary (unlicensed) users exploit spectrum opportunistically without interfering with the coexisting primary users. A challenging yet open question is how to ensure that information can be disseminated to the entire CRN, which is a prerequisite to applications of wireless networks. In this paper, we address the connectivity of large multi-channel CRNs. Particularly, we study full connectivity and percolation of secondary networks. The former is the existence of a communication path between any two nodes and the latter is the existence of a large component of secondary users. We find that the sufficient and necessary condition to achieve full connectivity is λ = Θ(log n/πr ^{2} P _{s} ), where λ is the density, n is the number and r is the transmission range of secondary users respectively, and P _{s} is the probability that any two secondary users can communicate with each other without interfering with primary users. We further show that the required density for percolation is a constant, and identify an upper bound on λ, above which the network is percolated and a lower bound on λ below which the network is not percolated. Our results provide a theoretical understanding of connectivity in large multi-channel cognitive radio networks.}, booktitle={2012 ieee international conference on communications (icc)}, author={Sun, L. and Wang, Wenye}, year={2012}, pages={1854–1858} }
@inproceedings{sun_wang_2012, title={Understanding the tempo-spatial limits of information dissemination in multi-channel cognitive radio networks}, DOI={10.1109/infcom.2012.6195489}, abstractNote={Cognitive Radio Networks (CRNs) have emerged to become promising network components for exploiting spectrum opportunistically in order that information can be delivered in circumstances otherwise impossible. Challenging yet open questions are how fast and how far a packet can be delivered in such networks, in temporal and spatial domains, respectively. The answers to these questions offer a straightforward interpretation of the potentials of CRNs for time-sensitive applications. To tackle these questions, we define two metrics, dissemination radius ∥ℒ(t)∥ and propagation speed S(d). The former is the maximum Euclidean distance that a packet can reach in time t, and the latter is the speed that a packet transmits between a source and destination at Euclidean distance d apart, which can be used to measure the transmission delay. Further, we determine the sufficient and necessary conditions under which there exist spatial and temporal limits of information dissemination in CRNs. We find that when information cannot be disseminated to the entire network, the limiting dissemination radius is statistically dominated by an exponential distribution, while the limiting information propagation speed approaches to zero. Otherwise, the dissemination radius approaches to infinity and the propagation speed S(d) is no lower than some constant k for large d. The results are validated through simulations.}, booktitle={2012 Proceedings IEEE infocom}, author={Sun, L. and Wang, Wenye}, year={2012}, pages={1278–1286} }
@inproceedings{sun_wang_2011, title={on distribution and limits of information dissemination latency and speed in mobile cognitive radio networks}, DOI={10.1109/infcom.2011.5935069}, abstractNote={Dissemination latency and speed are central to the applications of cognitive radio networks, which have become an important component of current communication infrastructure. In this paper, we investigate the distributions and limits of information dissemination latency and speed in a cognitive radio network where licensed users (primary users) are static and cognitive radio users (secondary users) are mobile. We show that the dissemination latency depends on the stationary spatial distribution and mobility capability α (characterizing the region that a mobile secondary user can reach) of secondary users. Given any stationary spatial distribution, we find that there exists a critical value on α, below which the latency and speed are heavy-tailed and above which the right tails of their distribution are bounded by Gamma random variables. We further show that as the network grows to infinity, the latency asymptotically scales linearly with the “distance” (characterized by transmission hops or Euclidean distance) between the source and the destination. Our results are validated through simulations.}, booktitle={2011 proceedings ieee infocom}, author={Sun, L. and Wang, Wenye}, year={2011}, pages={246–250} }