[Paper Review] Distributed Link Scheduling with Constant Overhead

Sujay Sanghavi, Loc Bui+1 more|ArXiv.org|Nov 14, 2006

Advanced Wireless Network Optimization16 references4 citations

TL;DR

This paper proposes a novel class of distributed wireless scheduling algorithms that achieve k/(k+2) of the network's capacity region with constant, small overhead—specifically, less than 4k+2 round-trip times and at most three control transmissions per node. The algorithms use localized, randomized link augmentation to dynamically adjust schedules while explicitly accounting for control overhead, enabling tunable trade-offs between performance and signaling cost in large-scale networks.

ABSTRACT

This paper proposes a new class of simple, distributed algorithms for scheduling in wireless networks. The algorithms generate new schedules in a distributed manner via simple local changes to existing schedules. The class is parameterized by integers $k\geq 1$. We show that algorithm $k$ of our class achieves $k/(k+2)$ of the capacity region, for every $k\geq 1$. The algorithms have small and constant worst-case overheads: in particular, algorithm $k$ generates a new schedule using {\em (a)} time less than $4k+2$ round-trip times between neighboring nodes in the network, and {\em (b)} at most three control transmissions by any given node, for any $k$. The control signals are explicitly specified, and face the same interference effects as normal data transmissions. Our class of distributed wireless scheduling algorithms are the first ones guaranteed to achieve any fixed fraction of the capacity region while using small and constant overheads that do not scale with network size. The parameter $k$ explicitly captures the tradeoff between control overhead and scheduler throughput performance and provides a tuning knob protocol designers can use to harness this trade-off in practice.

Motivation & Objective

• Address the critical gap in distributed wireless scheduling where control overheads grow with network size, undermining practical efficiency.
• Develop a distributed algorithm class that guarantees a fixed fraction of the capacity region while maintaining constant, small overhead independent of network size.
• Provide a tunable trade-off between scheduling performance and control signaling cost through the parameter k.
• Ensure that control signals face the same interference constraints as data transmissions, making the overhead model realistic and resource-aware.
• Design a protocol that is both simple and provably efficient, avoiding heuristic or unaccounted-for control signaling common in prior work.

Proposed method

• Propose a distributed scheduling framework where each node independently generates a new schedule using local information and randomized control signaling.
• Use a cycle of 4k+2 phases—each lasting one round-trip time—during which nodes exchange control signals to identify potential link augmentations.
• Each node selects a random seed and broadcasts a target length (1 to k) to initiate local link augmentation processes.
• Construct disjoint augmentations via a randomized process: nodes attempt to extend active links in a way that avoids interference, using local queue weight information.
• Apply a greedy selection of augmentations with positive gain (i.e., net improvement in total queue weight) to form the next active schedule.
• Ensure all control signals are subject to the same interference model as data, making overhead realistic and non-idealized.

Experimental results

Research questions

• RQ1 Can a distributed wireless scheduling algorithm achieve a fixed fraction of the capacity region while keeping control overhead constant, regardless of network size?
• RQ2 How can control signaling be designed to be both minimal and effective in distributed scheduling, without growing with network scale?
• RQ3 What is the trade-off between scheduling performance (throughput) and control overhead in distributed wireless networks?
• RQ4 Can a randomized, localized algorithm achieve provable performance guarantees in terms of capacity region approximation?
• RQ5 How can interference constraints be uniformly applied to both data and control signals in a distributed scheduling protocol?

Key findings

• The proposed algorithm class achieves k/(k+2) of the capacity region for any k ≥ 1, with performance improving as k increases.
• The worst-case time overhead is bounded by 4k + 2 round-trip times between neighboring nodes, independent of network size.
• Each node generates at most three control transmissions per scheduling cycle, regardless of network scale.
• The probability of generating the optimal augmentation set A* is uniformly bounded below by a positive constant δ > 0, independent of queue state or current schedule.
• The control signaling is subject to the same interference model as data, ensuring realistic resource accounting and avoiding optimistic assumptions.
• The algorithm provides a tunable trade-off: increasing k improves throughput performance at the cost of slightly higher control overhead, enabling protocol designers to select an optimal operating point.