Stochastic Approximation Algorithm for Optimal Throughput Performance of Wireless LANs
Sundaresan Krishnan, Prasanna Chaporkar

TL;DR
This paper introduces a model-independent stochastic approximation algorithm that optimizes throughput in wireless LANs, including networks with hidden nodes, and achieves weighted fairness and superior exponential backoff performance.
Contribution
It presents a novel, model-independent approach for throughput optimization in wireless LANs with hidden nodes, outperforming traditional model-based schemes.
Findings
Achieves maximum throughput in networks with hidden terminals.
Demonstrates exponential backoff can outperform p-persistent schemes.
Provides algorithms for weighted fairness in connected networks.
Abstract
Throughput improvement of the Wireless LANs has been a constant area of research. Most of the work in this area, focuses on designing throughput optimal schemes for fully connected networks (no hidden nodes). But, we demonstrate that the proposed schemes, though perform optimally in fully connected network, achieve significantly lesser throughput even than that of standard IEEE 802.11 in a network with hidden nodes. This motivates the need for designing schemes that provide near optimal performance even when hidden nodes are present. The primary reason for the failure of existing protocols in the presence of hidden nodes is that these protocols are based on the model developed by Bianchi. However this model does not hold when hidden nodes exist. Moreover, analyzing networks with hidden nodes is still an open problem. Thus, designing throughput optimal schemes in networks with hidden…
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Taxonomy
TopicsWireless Networks and Protocols · Cooperative Communication and Network Coding · Mobile Ad Hoc Networks
