Opportunistic Spatial Orthogonalization and Its Application in Fading Cognitive Radio Networks

TL;DR

This paper introduces Opportunistic Spatial Orthogonalization (OSO), a scheme that enhances cognitive radio network throughput by exploiting spatial dimensions to orthogonalize users without harming primary users, especially in MIMO channels.

Contribution

The paper develops OSO, a novel spatial orthogonalization scheme that leverages channel randomness and independence to improve secondary user throughput in cognitive radio networks.

Findings

OSO increases system throughput without affecting primary users.

In MIMO channels, channel peaks can be exploited to boost throughput.

Ill-conditioned MIMO channels can be beneficial for sum throughput.

Abstract

Opportunistic Spatial Orthogonalization (OSO) is a cognitive radio scheme that allows the existence of secondary users and hence increases the system throughput, even if the primary user occupies all the frequency bands all the time. Notably, this throughput advantage is obtained without sacrificing the performance of the primary user, if the interference margin is carefully chosen. The key idea is to exploit the spatial dimensions to orthogonalize users and hence minimize interference. However, unlike the time and frequency dimensions, there is no universal basis for the set of all multi-dimensional spatial channels, which motivated the development of OSO. On one hand, OSO can be viewed as a multi-user diversity scheme that exploits the channel randomness and independence. On the other hand, OSO can be interpreted as an opportunistic interference alignment scheme, where the interference from multiple secondary users is opportunistically aligned at the direction that is orthogonal to the primary user's signal space. In the case of multiple-input multiple-output (MIMO) channels, the OSO scheme can be interpreted as "riding the peaks" over the eigen-channels, and ill-conditioned MIMO channel, which is traditionally viewed as detrimental, is shown to be beneficial with respect to the sum throughput. Throughput advantages are thoroughly studied, both analytically and numerically.