* This is the dataset of the accepted paper (Nov, 2016):  Ateeq Ur Rehman, Chen Dong, Varghese Antony Thomas, Lie-liang Yang and L. Hanzo, "Throughput and Delay Analysis of Cognitive Go-Back-N Hybrid Automatic Repeat reQuest Using Discrete-Time Markov Modelling"




* Paper Abstract:
Cognitive radio (CR) techniques have been proposed for improving the  spectral efficiency by exploiting the temporarily unoccupied segments of the licensed spectrum, provided that the transmission of primary users (PUs) is not hampered.     
 In this paper, we propose a cognitive Go-Back-N Hybrid Automatic Repeat reQuest (CGBN-HARQ) scheme that enables the cognitive user (CU) to opportunistically transmit data over a primary radio (PR) channel. Based on the sensing decisions by the  cognitive user (CU), it decides about the availability of  the PU's channel for its own transmission using the proposed CGBN-HARQ scheme.
 Additionally, it enables the CR transmitter to receive feedback concerning the success/failure of its prior transmissions during the sensing and transmission phases of the time-slot (TS).    A Discrete Time Markov Chain (DTMC) model  is invoked for the theoretical analysis of the proposed system, where we conceive an algorithm to generate all possible states of the CR transmitter. Both the  throughput and delay of the CGBN-HARQ scheme is analysed by deriving a range of closed-form formulas,  which are validated by  simulation results. The occupation  of the channel by the PU and the reliability of the CU's channel significantly affect  both the achievable throughput and the delay  of the CGBN-HARQ scheme. 
Finally, our studies show  that the number of packets transmitted within a time-slot should be adapted according to the  communication channel  for attaining the maximum throughput and the lowest average transmission delay. 



* Project:
The financial support of the EPSRC projects EP/Noo4558/1 and EP/L018659/1, as well as of the European Research Council’s Advanced Fellow Grant under the Beam-Me-Up project and of the Royal Society’s Wolfson Research Merit Award is gratefully acknowledged.





* This DOI contains the datasets of Figures 13, 14, 15, 16, 17, 18, 19 and 20 of the aforementioned paper. Each folder is named according to its content, where the dataset of each cruve - of each figure - is stored in a .dat file. To regenerate the results please use the command "gle Figure_Name.gle" (Graphics Layout Engine -GLE- should be installed on your machine)