Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Convex Optimization
Exploiting the capture effect for collision detection and recovery
EmNets '05 Proceedings of the 2nd IEEE workshop on Embedded Networked Sensors
MDG: measurement-driven guidelines for 802.11 WLAN design
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Designing high performance enterprise Wi-Fi networks
NSDI'08 Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation
Measurement-driven guidelines for 802.11 WLAN design
IEEE/ACM Transactions on Networking (TON)
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The provisioning of high throughput performance infrastructure wireless networks necessitates the deployment of a high density of Access Points. While the latter improves wireless link quality to the clients, it can also introduce additional interference unless the network is carefully planned and tuned. It has been shown that the reaction of CSMA/CA protocol to interference is unnecessarily conservative in high density environments. In this work, we study the problem of infrastructure wireless network design, and the interaction between high density and MAC parameter tuning. Through analysis and numerical results, we provide recommendations on (i) optimum dimensioning of high density networks, and (ii) optimum tuning of their MAC parameters. We demonstrate that 802.11a networks are inherently noise-dominated, while 802.11g networks are interference-dominated, thus requiring different network design approaches. In sharp contrast to previous work, we establish that MAC parameter tuning has limited benefit in properly planned 802.11a networks. On the other hand, analytical results on the optimal tuning of MAC parameters in interference-dominated 802.11g deployments show substantial throughput improvements. Using the insight gained through our analysis, we propose an algorithm for the optimal tuning of MAC parameters in unstructured high density environments. Opnet simulations show that the proposed algorithm results in up to 260% improvement in network throughput.