Distributed Broadcasting in Wireless Networks under the SINR Model

In the advent of large-scale multi-hop wireless technologies, such as MANET, VANET, iThings, it is of utmost importance to devise efficient distributed protocols to maintain network architecture and provide basic communication tools. One of such fundamental communication tasks is broadcast, also known as a 1-to-all communication. We propose several new efficient distributed algorithms and evaluate their time performance both theoretically and by simulations. First randomized algorithm accomplishes broadcast in O(D+log(1/d)) rounds with probability at least 1-d on any uniform-power network of n nodes and diameter D, when equipped with local estimate of network density. Additionally, we evaluate average performance of this protocols by simulations on two classes of generated networks - uniform and social - and compare the results with performance of exponential backoff heuristic. Ours is the first provably efficient and well-scalable distributed solution for the (global) broadcast task. The second randomized protocol developed in this paper does not rely on the estimate of local density, and achieves only slightly higher time performance O((D+log(1/d))log n). Finally, we provide a deterministic algorithm achieving similar time O(D log^2 n), supported by theoretical analysis.

💡 Research Summary

The paper tackles one of the most fundamental communication tasks in large‑scale multi‑hop wireless systems—global broadcast (1‑to‑all)—under the realistic physical interference model known as SINR (Signal‑to‑Interference‑plus‑Noise Ratio). While many prior works on broadcast have relied on abstract graph models that ignore cumulative interference, this study embraces the SINR model and assumes a uniform transmission power for all nodes, thereby aligning the theoretical analysis with the constraints of real MANET, VANET, and IoT deployments.

Three distributed algorithms are presented, each targeting a different set of assumptions about the information available to the nodes.

1. Density‑aware randomized broadcast – Each node is assumed to have a local estimate of the number of neighbours within its transmission range (i.e., the local network density). Using this estimate, nodes adapt their transmission probability so that the expected number of simultaneous transmitters stays below the SINR feasibility threshold. The algorithm guarantees that, with probability at least 1 − δ, the broadcast finishes in
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