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DATASET TITLE: Data for 'Compressed Sensing-Aided Multi-Dimensional Index Modulation'



This is the dataset of the accepted paper (Feburary, 2019):  S. Lu, I. A. Hemadeh, M. El-Hajjar and L. Hanzo, "Compressed Sensing-Aided Multi-Dimensional Index Modulation".

DOI: https://doi.org/10.5258/SOTON/D0820



This dataset contains the data used for producing Figures 7, 8, 9, 10, 11, 12, 13. Each folder is named according to the figure's number and the dataset - of each figure - is stored in one or two .dat file in that folder. There are THREE different plot types: BER plots, EXIT Charts, Maximum Achievable Rate plots.
All figures can be re-created by using GLE with the aid of .dat files of each figure, where each .dat file represents each curve in a specific figure. Explicitly, you should first write a .gle file using the .dat files of a specific figure, where you can decide the size, the color, or the caption of the figure depending on your requirement. Then you should run the .gle file by typing "gle filename.gle" in your terminal and the figure is re-created. 



Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/



Paper Abstract:

In this paper, we conceive a Compressed Sensing (CS) aided multi-dimensional Index Modulation
(IM) scheme, where the benefits of Space-Time Shift Keying (STSK), Orthogonal Frequency Division
Multiplexing (OFDM) relying on Frequency Domain (FD) IM and Spatial Modulation (SM) are ex-
plored. Explicitly, extra information bits are transmitted through the active indices of both the TAs
and subcarriers, while striking a flexible design trade-off between the throughput and the diversity
order. Furthermore, Compressed Sensing (CS) is invoked in both the transmitter and the receiver of
our multi-dimensional system for the sake of improving the system’s design flexibility, whilst reducing
the detector’s complexity. We first present the Maximum LikeLihood (ML) detector of the proposed
CS-aided multi-dimensional IM system for characterizing the best-case bound of the proposed system’s
performance. Specifically, an upper bound is derived for the Average Bit Error Probability (ABEP)
and it is observed that the derived theoretical upper bound becomes very tight with the ML detector
simulation curves as the Signal-to-Noise Ratio (SNR) increases. Then we propose a reduced-complexity
detector imposing only a modest Bit Error Ratio (BER) degradation, where we analyse the computational
complexities of both ML detector and reduced-complexity detector. Furthermore, a Soft-Input Soft-
Output (SISO) decoder is proposed for attaining a near-capacity performance, which is analyzed with
the aid of Extrinsic Information Transfer (EXIT) charts. The maximum achievable rate of the proposed
CS-aided multi-dimensional IM system relying both on ML detection and on our reduced-complexity
based detector is also evaluated using EXIT charts. Additionally, the Discrete-Input Continious-Output
Memoryless Channel (DCMC) capacity of the proposed CS-aided multi-dimensional IM scheme is
formulated.






Acknowledgements:

The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work.



Cheers!

Siyao "Olivia" Lu

25/02/2019