Dear Reader,

* This is the dataset of the accepted paper (January, 2018):  Abbas Ahmed, Panagiotis Botsinis, SeungHwan Won, Lie-Liang Yang  and Lajos Hanzo
, "EXIT Chart-Aided Convergence Analysis of Recursive Soft m-Sequence Initial Acquisition in Nakagami-m Fading Channels" IEEE Transactions on Vehicular Technology




* Paper Abstract:
A delay of less than one millisecond is required by low-latency 5G wireless communication systems for supporting the ‘tactile’ Internet. Hence, conventional initial synchronisation cannot be readily employed because of its potentially excessive delay. In this paper, an EXtrinsic Information Transfer (EXIT) Chart assisted approach is used for the convergence analysis of m-sequences using Recursive Soft Sequence Estimation (RSSE) in the context of Nakagami-m fading channels. Explicitly, the novelty of our work is based on employing a new type of EXIT Charts operating without using interleavers. This is a challenge, because the original EXIT charts rely on the employment of long, high-delay interleavers for ensuring that the inputs to the decoders become uncorrelated. We then evaluate the performance of various classes of m-sequences with the aid of the proposed EXIT charts and demonstrate that the m-sequences generated
by the lower-order polynomials maximise the mutual information more promptly with the aid of our RSSE scheme than those, which belong to a higher-order polynomial.



* Project:
The research is supported by the Fundamental Research Grant Scheme funded by Malaysia’s Ministry of Higher Education (FRGS/1/2015/TK04/USMC/02/2) and by the European Research Council’s Advance Fellow Grant Beam-me-up.



* This dataset contains the data used for producing Figures 3, 4, 5, 6, 7 and 8 of the ACCEPTED paper "EXIT Chart-Aided Convergence Analysis of Recursive Soft m-Sequence Initial Acquisition in Nakagami-m Fading Channels".  There is THREE different plot types: Number of received chips,R.L, Number of Iterations,L, A priori Information IA /Extrinsic Information IE and SNR in (dB), which are represented as follows:
      -  Mutual Information (MI) versus the number of received chips for the GP g1 (D) = 1 + D2 + D5 , and for various SNR values.
      -  Mutual information (MI) versus the number of decoding iterations when SNR= 0 dB and the Nakagami Fading channel has ml = 3.0.
      -  EXIT chart for the GPs of g1 (D) = 1 + D^2 + D^5 (blue), g2(D) =1 + D + D^2 + D^4 + D^5 (red) and g3 (D) = 1 + D + D^3 + D^4 + D^13 (black) over Nakagami channels, when ml = 3.0 at SNR=−3 dB. 
      -  EXIT chart for the GPs of g1 (D) = 1 + D^2 + D^5 (blue), g2(D) = 1+D +D^2 +D^4 +D^5 (red) and g3(D) = 1+D +D^3 +D^4 +D^13 (black) over Nakagami channels, when ml = 3.0 at SNR=−2 dB and SNR= 0 dB.
      -  Erroneous loading probability Pe , versus the SNR, performance for various numbers of chips invoked into the proposed recursive SISO detector, when transmitting the said polynomials over Nakagami Fading channels when ml = 3.0.
      -  Erroneous loading probability Pe versus the SNR performance for various numbers of chips invoked into the proposed recursive SISO decoder, when transmitting the generator polynomials of g1(D =1+D^2+D^5,g2(D) = 1+D+D^2+D^4+D^5  
         and g3(D) = 1+D+D^3+D^4+D^13 over AWGN channe.

To regenerate the results please use the matlab figure command "matlab.fig" (Matlab should be installed on your machine)

Enjoy!
Abbas Ahmed
2/01/2018