Abstract
This article takes an analytical approach to investigate the temporal dynamics of interference in wireless networks. We propose a framework to calculate the autocorrelation of interference in Poisson networks and derive closed-form expressions for the case of Nakagami fading. The framework takes three correlation sources into account: the location of interferers, the wireless channel, and the data traffic. We introduce the interference coherence time-in analogy to the well-established channel coherence time-and show how its basic qualitative behavior depends on the source of correlation. The insights gained can be useful in the design of medium access control and retransmission protocols.
Highlights
IN mobile communication and computer networks, the way how the received signal power varies over time has significant impact on the system design and performance. This dynamics can be quantified by the autocorrelation of the power signal, which describes how similar two values separated by a time lag t are expected to be, indicating how fast the reception power typically changes over time
Expressions for the autocorrelation function and coherence time are known for different types of fading channels but always without consideration of interference—they basically describe the signal received from a single source
We consider a Poisson network: a wireless network consisting of nodes randomly located in a plane according to a Poisson point process (PPP) F on R2
Summary
IN mobile communication and computer networks, the way how the received signal power varies over time has significant impact on the system design and performance. There is some recent work on interference dynamics (see [2], [3], [4], [5], [6]), but general expressions for the autocorrelation and coherence time are largely missing Such expressions would serve a similar purpose as the ones for channel correlation. SCHILCHER ET AL.: AUTOCORRELATION AND COHERENCE TIME OF INTERFERENCE IN POISSON NETWORKS with Rayleigh fading according to Clarke’s model considering three key sources of correlation. These expressions shed light on the interference dynamics beyond existing results and may lead to new techniques to exploit the temporal features of interference.
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