Efficient Algorithms for Maximum Link Scheduling in Distributed Computing Models with SINR Constraints

A fundamental problem in wireless networks is the maximum link scheduling problem: given a set $L$ of links, compute the largest possible subset $L’\subseteq L$ of links that can be scheduled simultaneously without interference. This problem is particularly challenging in the physical interference model based on SINR constraints (referred to as the SINR model), which has gained a lot of interest in recent years. Constant factor approximation algorithms have been developed for this problem, but low complexity distributed algorithms that give the same approximation guarantee in the SINR model are not known. Distributed algorithms are especially challenging in this model, because of its non-locality. In this paper, we develop a set of fast distributed algorithms in the SINR model, providing constant approximation for the maximum link scheduling problem under uniform power assignment. We find that different aspects of available technology, such as full/half-duplex communication, and non-adaptive/adaptive power control, have a significant impact on the performance of the algorithm; these issues have not been explored in the context of distributed algorithms in the SINR model before. Our algorithms’ running time is $O(g(L) \log^c m)$, where $c=1,2,3$ for different problem instances, and $g(L)$ is the “link diversity” determined by the logarithmic scale of a communication link length. Since $g(L)$ is small and remains in a constant range in most cases, our algorithms serve as the first set of “sublinear” time distributed solution. The algorithms are randomized and crucially use physical carrier sensing in distributed communication steps.

💡 Research Summary

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The paper tackles the classic Maximum Link Scheduling (MLS) problem in wireless networks under the physical Signal‑to‑Interference‑plus‑Noise Ratio (SINR) model. Given a set L of communication links, the goal is to select the largest subset L′ that can operate simultaneously without violating SINR constraints. While constant‑factor approximation algorithms exist for MLS in the SINR model, they are typically centralized and assume global knowledge, making them unsuitable for large‑scale, dynamic networks where distributed control is essential. The authors bridge this gap by presenting a family of fast, randomized distributed algorithms that achieve a constant‑factor approximation under a uniform power assignment, while explicitly accounting for practical system aspects such as full‑/half‑duplex radios and adaptive versus non‑adaptive power control.

Key technical contributions

1. Link diversity metric g(L).
   The authors define g(L) as the logarithmic spread of link lengths:
   \