2013Unpublished venueRequires access

Who interrupted me? Analyzing the effect of PU activity on cognitive user performance

Fidan Mehmeti, Thrasyvoulos Spyropoulos

Open publisher page 6 citations

Abstract

Cognitive Networks have been proposed to opportunistically discover and exploit (temporarily) unused licensed spectrum bands. With the exception of TV white spaces, secondary users (SUs) can access the medium only intermittently, due to deferring to primary user (PU) transmissions and scanning for new channels. This raises the following questions: (i) what sort of delays can an SU expect on a channel given the PU utilization of this channel? (ii) how do specific characteristics of the PU activity patterns (e.g. burstiness) further affect performance? These questions are of key importance for the design of efficient algorithms for scheduling, spectrum handoff, etc. In this paper, we propose a queueing analytical model to answer them. We model the PU activity pattern as an ON-OFF alternating renewal process with generic ON and OFF durations, and derive a closed form expression for packet delays by solving a variant of the M/G/1 queue. Contrary to the common belief that low utilization channels are good channels, we show that the expected SU delay on a channel, and thus the best channel to use, is a subtle interplay between the ON and OFF duration distributions of the primary users, and the SU traffic load. We validate our analysis against simulations for different PU activity profiles.

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What this paper is about

Cognitive Networks have been proposed to opportunistically discover and exploit (temporarily) unused licensed spectrum bands. With the exception of TV white spaces, secondary users (SUs) can access the medium only intermittently, due to deferring to primary user (PU) transmissions and scanning for new channels. This raises the following questions: (i) what sort of delays can an SU expect on a channel given the PU utilization of this channel? (ii) how do specific characteristics of the PU activity patterns (e.g. burstiness) further affect performance? These questions are of key importance for the design of efficient algorithms for scheduling, spectrum handoff, etc. In this paper, we propose a queueing analytical model to answer them. We model the PU activity pattern as an ON-OFF alternating renewal process with generic ON and OFF durations, and derive a closed form expression for packet delays by solving a variant of the M/G/1 queue. Contrary to the common belief that low utilization channels are good channels, we show that the expected SU delay on a channel, and thus the best channel to use, is a subtle interplay between the ON and OFF duration distributions of the primary users, and the SU traffic load. We validate our analysis against simulations for different PU activity profiles.

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Available abstract

Cognitive Networks have been proposed to opportunistically discover and exploit (temporarily) unused licensed spectrum bands. With the exception of TV white spaces, secondary users (SUs) can access the medium only intermittently, due to deferring to primary user (PU) transmissions and scanning for new channels. This raises the following questions: (i) what sort of delays can an SU expect on a channel given the PU utilization of this channel? (ii) how do specific characteristics of the PU activity patterns (e.g. burstiness) further affect performance? These questions are of key importance for the design of efficient algorithms for scheduling, spectrum handoff, etc. In this paper, we propose a queueing analytical model to answer them. We model the PU activity pattern as an ON-OFF alternating renewal process with generic ON and OFF durations, and derive a closed form expression for packet delays by solving a variant of the M/G/1 queue. Contrary to the common belief that low utilization channels are good channels, we show that the expected SU delay on a channel, and thus the best channel to use, is a subtle interplay between the ON and OFF duration distributions of the primary users, and the SU traffic load. We validate our analysis against simulations for different PU activity profiles.

Key concepts: Burstiness, Queueing theory, Computer science, Scheduling (production processes), Computer network, Channel (broadcasting), Queue, Network packet

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