Dataset for Delay and Throughput Analysis of Cognitive Go-Back-N HARQ in the Face of Imperfect Sensing
Dataset for Delay and Throughput Analysis of Cognitive Go-Back-N HARQ in the Face of Imperfect Sensing
Dataset supporting:
Rehman, Ateeq Ur, Yang, Lie-Liang and Hanzo, Lajos (2017) Delay and Throughput Analysis of Cognitive Go-Back-N HARQ in the Face of Imperfect Sensing.
In order to mitigate spectrum scarcity, the cognitive radio (CR) paradigm has been invoked for improving the overall exploitation of the licensed spectrum by identifying and filling the free spectrum holes without degrading the transmission of primary users (PUs). Hence, we conceive a CR communication scheme, which enables a cognitive user (CU) to sense the activity of the PUs over a primary radio (PR) channel, which is exploited to transmit data using the modified Go-Back-N hybrid automatic repeat request (GBN-HARQ) protocol, when PR channel is free from the PUs. This arrangement is termed as the cognitive GBN-HARQ (CGBN-HARQ), where the activity of PUs on the PR channel is modelled as a two-state Markov chain having `ON' and `OFF' states. However, the CU may wrongly detect the `ON'/`OFF' activity of the PUs in the channel, hence resulting in false-alarm or mis-detection. Therefore, the two-state Markov chain is extended to four states by explicitly considering all the wrong sensing decisions. In this paper, we analytically modelled the CGBN-HARQ scheme with the aid of a Discrete Time Markov Chain (DTMC). Explicitly, an algorithm is developed for deriving all the legitimate states and for eliminating the illegitimate states, which assists us in reducing both the dimensionality of the state transition matrix and the associated computational complexity. Furthermore, based on DTMC modelling, we derive closed-form expressions for evaluating the throughput, the average packet delay and the end-to-end packet delay of CGBN-HARQ in realistic imperfect sensing environment. The results are also validated by our simulations. Our performance results demonstrate that both the achievable throughput and the delay are significantly affected by the activity of the PUs, as well as by the reliability of the PR channel and by the number of packets transmitted per TS. To attain the maximum throughput and/or the minimum transmission delay, the number of packets transmitted within a TS should be carefully adapted based on the activity level of the PUs and on the quality of the PR channel.
University of Southampton
Rehman, Ateeq, Ur
e5f10d6d-7fc7-45f2-a731-ad97c94ef3ee
Yang, Lieliang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Rehman, Ateeq, Ur
e5f10d6d-7fc7-45f2-a731-ad97c94ef3ee
Yang, Lieliang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Rehman, Ateeq, Ur, Yang, Lieliang and Hanzo, Lajos
(2017)
Dataset for Delay and Throughput Analysis of Cognitive Go-Back-N HARQ in the Face of Imperfect Sensing.
University of Southampton
doi:10.5258/SOTON/404271
[Dataset]
Abstract
Dataset supporting:
Rehman, Ateeq Ur, Yang, Lie-Liang and Hanzo, Lajos (2017) Delay and Throughput Analysis of Cognitive Go-Back-N HARQ in the Face of Imperfect Sensing.
In order to mitigate spectrum scarcity, the cognitive radio (CR) paradigm has been invoked for improving the overall exploitation of the licensed spectrum by identifying and filling the free spectrum holes without degrading the transmission of primary users (PUs). Hence, we conceive a CR communication scheme, which enables a cognitive user (CU) to sense the activity of the PUs over a primary radio (PR) channel, which is exploited to transmit data using the modified Go-Back-N hybrid automatic repeat request (GBN-HARQ) protocol, when PR channel is free from the PUs. This arrangement is termed as the cognitive GBN-HARQ (CGBN-HARQ), where the activity of PUs on the PR channel is modelled as a two-state Markov chain having `ON' and `OFF' states. However, the CU may wrongly detect the `ON'/`OFF' activity of the PUs in the channel, hence resulting in false-alarm or mis-detection. Therefore, the two-state Markov chain is extended to four states by explicitly considering all the wrong sensing decisions. In this paper, we analytically modelled the CGBN-HARQ scheme with the aid of a Discrete Time Markov Chain (DTMC). Explicitly, an algorithm is developed for deriving all the legitimate states and for eliminating the illegitimate states, which assists us in reducing both the dimensionality of the state transition matrix and the associated computational complexity. Furthermore, based on DTMC modelling, we derive closed-form expressions for evaluating the throughput, the average packet delay and the end-to-end packet delay of CGBN-HARQ in realistic imperfect sensing environment. The results are also validated by our simulations. Our performance results demonstrate that both the achievable throughput and the delay are significantly affected by the activity of the PUs, as well as by the reliability of the PR channel and by the number of packets transmitted per TS. To attain the maximum throughput and/or the minimum transmission delay, the number of packets transmitted within a TS should be carefully adapted based on the activity level of the PUs and on the quality of the PR channel.
Archive
CGBN_HARQ_Imperfect_DOI.tar.gz
- Dataset
More information
Published date: 2017
Organisations:
Electronics & Computer Science, Southampton Wireless Group
Projects:
Cooperative Classical and Quantum Communications Systems
Funded by: UNSPECIFIED (EP/L018659/1)
31 October 2014 to 30 October 2017
Cooperative backhaul aided next-generation digital subscriber loops
Funded by: UNSPECIFIED (EP/N004558/1)
13 October 2015 to 12 October 2018
From Radio-Frequency to Giga-Bit Optical- and Quantum-Wireless (BEAM-ME-UP)
Funded by: UNSPECIFIED (321097)
1 March 2013 to 28 February 2018
Identifiers
Local EPrints ID: 404271
URI: http://eprints.soton.ac.uk/id/eprint/404271
PURE UUID: 00f164f5-cf86-4ad2-bd4b-2cc89a2a7d36
Catalogue record
Date deposited: 11 Jan 2017 15:07
Last modified: 05 Nov 2023 02:36
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Contributors
Creator:
Ateeq, Ur Rehman
Creator:
Lieliang Yang
Creator:
Lajos Hanzo
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