# Wireless Link Capacity under Shadowing and Fading

## TL;DR

This paper analyzes wireless link capacity considering shadowing and fading, providing bounds and approximation algorithms that work under stochastic shadowing and Rayleigh fading, improving understanding of real-world wireless network performance.

## Contribution

It introduces constant approximation algorithms for link capacity in stochastic shadowing and fading environments, extending deterministic models to more realistic wireless conditions.

## Key findings

- Shadowing effects can be bounded to approximate capacity.
- Temporal fading impacts non-fading solutions only by a constant factor.
- Combined models yield a constant approximation algorithm for real-world scenarios.

## Abstract

We consider the following basic link capacity (a.k.a., one-shot scheduling) problem in wireless networks: Given a set of communication links, find a maximum subset of links that can successfully transmit simultaneously. Good performance guarantees are known only for deterministic models, such as the physical model with geometric (log-distance) pathloss. We treat this problem under stochastic shadowing under general distributions, bound the effects of shadowing on optimal capacity, and derive constant approximation algorithms. We also consider temporal fading under Rayleigh distribution, and show that it affects non-fading solutions only by a constant-factor. These can be combined into a constant approximation link capacity algorithm under both time-invariant shadowing and temporal fading.