Abstract-Open Spectrum systems allow fast deployment of wireless technologies by reusing under-utilized pre-allocated spectrum channels, all with minimal impact on existing primary users. However, existing proposals take a reactive sense-andavoid approach to impulsively reconfigure spectrum usage based solely on the latest observations. This can result in frequent disruptions to operations of both primary and secondary users. In this paper, we propose a proactive spectrum access approach where secondary users utilize past channel histories to make predictions on future spectrum availability, and intelligently schedule channel usage in advance. We propose two channel selection and switching techniques to minimize disruptions to primary users and maintain reliable communication at secondary users. Experiments show that the proactive approach effectively reduces the interferences to primary users by up to 30%, and significantly decreases throughput jitters at secondary users.
Spatial reuse in a mesh network can allow multiple communications to proceed simultaneously, hence proportionally improve the overall network throughput. To maximize spatial reuse, the MAC protocol must enable simultaneous transmitters to maintain the minimal separation distance that is sufficient to avoid interference. This paper demonstrates that physical carrier sensing enhanced with a tunable sensing threshold is effective at avoiding interference in 802.11 mesh networks without requiring the use of virtual carrier sensing. We present an analytical model for deriving the optimal sensing threshold given network topology, reception power, and data rate. A distributed adaptive scheme is also presented to dynamically adjust the physical carrier sensing threshold based on periodic estimation of channel conditions in the network. Simulation results are shown for large-scale 802.11b and 802.11a networks to validate both the analytical model and the adaptation scheme. It is demonstrated that the enhanced physical carrier sensing mechanism effectively improves network throughput by maximizing the potential of spatial reuse. With dynamically tuned physical carrier sensing, the end to end throughput approaches 90% of the predicted theoretical upper-bound assuming a perfect MAC protocol, for a regular chain topology of 90 nodes.
Abstract-Dynamic spectrum management can drastically improve the performance of wireless networks struggling under increasing user demands. However, performing efficient spectrum allocation is a complex and difficult process. Current proposals make the problem tractable by simplifying interference constraints as conflict graphs, but they face potential performance degradation from inaccurate interference estimation. In this paper, we show that conflict graphs, if optimized properly, can produce spectrum allocations that closely match those derived from the physical interference model. Thus we propose PLAN, a systematic framework to produce conflict graphs based on physical interference characteristics. PLAN first applies an analytical framework to derive the criterion for identifying conflicting neighbors, capturing the cumulative effect of interference. PLAN then applies a local conflict adjustment algorithm to address heterogeneous interference conditions and improve spectrum allocation efficiency. Through detailed analysis and experimental evaluations, we show that PLAN builds a conflict graph to effectively represent the complex interference conditions and allow the reuse of efficient graph-based spectrum allocation solutions. PLAN also significantly outperforms the conventional graph model based solutions.
Degree κ−path centrality [5] Ego-betweenness [6] Global property Eigenvector [7] Page rank [8] Closeness [9] Betweenness [10] Edges Edge κ−path centrality (local) [11] Edge betweenness (global) [12] Communities Communities of interest [13] Communities of connectivity Edge betweenness based detection [12] Modularity maximisation based detection [14] Small-world phenomenon Milgram's six degree separation experiment [15] Watts Strogatz (WS) small-world network model [16] Newman Watts (NW) small-world network model [17] Kleinberg's small-world network model [18] Small-world based social search [19][20] 2169-3536 (c)
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.