Optimus: SINR-Driven Spectrum Distribution via Constraint Transformation

Abstract

How to distribute radio spectrum across network nodes is a critical problem in spectrum auctions and management. In this paper, we consider the problem of distributing spectrum using SINR-driven physical interference models. We propose Optimus, a new line of approximation algorithms that perform within a constant distance of min {2 α + 1, 10} from the optimum in terms of spectrum usage efficiency, where α ≥ 2 is the pathloss exponent. Different from conventional greedy solutions, Optimus applies a global optimization mechanism that transforms the spatial interference constraints into a set of linear constraints, reducing the original optimization into a linear/convex/separable programming problem. While linearization techniques have been applied in prior works, Optimus makes a new and important contribution by deriving a highly efficient constraint transformation applicable to general network configurations. Experiments using real network measurements and sophisticated propagation models show that Optimus outperforms existing solutions by 20-50% in spectrum utilization and is within 20% gap from the optimum. Optimus supports a wide variety of objective functions, and is applicable to many spectrum-driven applications such as spectrum auctions and spectrum admission control.

Practical conflict graphs for dynamic spectrum distribution

“…This simple interference structure greatly simplifies spectrum allocation design, leading to a series of highly efficient allocation algorithms with bounded performance and polynomial complexity [10,13,22,32,38,43,50]. In contrast, alternative physical interference models are highly complex, and have been shown in existing studies to produce unbounded performance loss when used to build allocation algorithms [9,47].…”

Practical Conflict Graphs in the Wild

“…Aside from conflict graphs, researchers have looked into other interference models such as the SINR model [10]- [12]. Most works target cellular networks and jointly optimize the placement (and transmission power) of base stations [26], [27], [34]- [36].…”

“…Here, represents the percentage of coverage holes that a spectrum user is willing to tolerate to maximize capacity [42]. When , (1) reduces to the minimal SINR-based criterion [10], [12], [21].…”

“…1 This model simplifies spectrum allocation design, leading to a series of highly efficient allocation algorithms with bounded performance and polynomial-time complexity [3]- [9]. In contrast, alternative physical interference models are complex and entail unbounded performance loss when used to develop allocation algorithms [10]- [12].…”

NASCOR: Network Assisted Spectrum Coordination Service for Coexistence between Heterogeneous Radio Systems

“…The goal of this work is to advance the state-of-the-art in dynamic spectrum access (DSA) technology [2]- [4] by taking advantage of ubiquitous Internet connectivity at wireless devices. The basic idea is to create an overlay network service for dissemination of spectrum usage information between otherwise independent radio devices and systems, enabling them to implement decentralized spectrum coexistence policies that reduce interference and improve spectrum packing efficiency.…”