All Projects → rnissel → Pruned-DFT-s-FBMC_Python

rnissel / Pruned-DFT-s-FBMC_Python

Licence: GPL-3.0 License
Simulates pruned DFT spread FBMC and compares the performance to OFDM, SC-FDMA and conventional FBMC. The included classes (QAM, DoublySelectiveChannel, OFDM, FBMC) can be reused in other projects.

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Pruned DFT spread FBMC

Pruned DFT spread FBMC is a novel modulation scheme with the remarkable properties of a low PAPR, low latency transmissions and a high spectral efficiency. It is closely related to FBMC, OFDM and SC-FDMA and I first proposed it in my PhD thesis, see Chapter 6. A more detailed description can be found in R. Nissel and M. Rupp, “Pruned DFT Spread FBMC: Low PAPR, Low Latency, High Spectral Efficiency”, IEEE Transactions on Communications, 2018.

The Python script simulates a pruned DFT spread FBMC transmission over a doubly-selective channel (time-variant multipath propagation) and compares the performance to OFDM, SC-FDMA and FBMC.

Furthermore, the included classes (QAM, DoublySelectiveChannel, OFDM, FBMC) can also be reused in future projects.

  • A Matlab code of pruned DFT spread FBMC with much more features can also be found on GitHub.

Usage

Just run Simulation.py in Python 3.

Requires the packages: numpy, scipy(sparse), matplotlib, time and mpl_toolkits.mplot3d.

Simulation Results*

* for "nr_rep = 1000"

Pruned DFT spread FBMC has the same PAPR as SC-FDMA:


Pruned DFT spread FBMC outperforms SC-FDMA in doubly-selective channels:

Note that pruned DFT spread FBMC does not require a CP and thus has a higher data rate than conventional SC-FDMA.


Pruned DFT spread FBMC has superior spectral properties, comparable to FBMC:


Pruned DFT spread FBMC dramatically reduces the ramp-up and ramp-down period of FBMC:

Please Cite Our Paper

@ARTICLE{Nissel2018,
	author  = {R. Nissel and M. Rupp},
	journal = {IEEE Transactions on Communications},
	title   = {Pruned {DFT} Spread {FBMC}: Low {PAPR},Low Latency, High Spectral Efficiency},
	year    = {2018},
	volume  = {},
	number  = {},
	pages   = {}, 
	doi     = {10.1109/TCOMM.2018.2837130},
	ISSN    = {},
	month   = {},
}

References

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