Design and Implementation of M-FSK for a Software-Defined Underwater Acoustic Modem

By Ryan Balloun
Slide 1: Title slide for M-FSK Software-Defined Underwater Acoustic Modem research by Ryan Balloun under Dr. George Sklivanitis

Slide-1

Research by Ryan Balloun, REU Scholar from University of Rochester, under the guidance of Dr. George Sklivanitis

NSF REU in Sensing and Smart Systems – FAU 2021

Marine and Environment: Cognitive Wireless Radios for Maritime Robotics

Slide 2: Simulating the Underwater Acoustic Channel showing three types of signal loss and their effects

Slide-2

Simulating the Underwater Acoustic Channel

Three types of signal loss:

  1. Spreading
  2. Scattering
  3. Absorption as heat

Weaker signal, as well as "echoes" at a receiver node from delayed paths

Slide 3: Underwater Wireless Communications Challenges showing frequency-dependent effects and signal clarity graph across distances

Slide-3

Underwater Wireless Communications Challenges

Both absorption and added ambient noise are frequency – dependent

→ optimal transmission band dependent on distance

Interference stacks up later in received signal with more echoes

Signal Clarity across Distance Chart

The chart shows SNR in dB (y-axis ranging from -250 to 50) versus frequency in kHz (x-axis from 10^1 to 10^2) for three distances:

  • 0.1km (highest SNR curve)
  • 0.5km (middle SNR curve)
  • 1.2km (lowest SNR curve)

Notable points marked: X=8, Y=9.102 and X=34, Y=9.259

Given maximum SNR = 12.3 at 20 kHz for 1.2 km

Cumulative Error over Signal Transmission Chart

Shows cumulative error (0-70 on y-axis) versus number of bits received (0-1200 on x-axis), demonstrating increasing error rates with more transmitted bits.

Slide 4: Building a Reliable Low-Complexity Underwater Wireless Link with comparison of 8-FSK and BFSK modulated signals

Slide-4

Building a Reliable Low-Complexity Underwater Wireless Link

Higher data rates require more bandwidth but very limited band to use underwater

Idea: more symbols available to convey info at cost of higher error and more work to clean up signal

8-FSK Modulated Signal

Time domain plot showing amplitude (V) ranging from -1.5 to 1.5 over time period 0 to 1.2 seconds, displaying complex waveform with multiple frequency components.

BFSK Modulated Signal

Time domain plot showing amplitude (V) ranging from -1.5 to 1.5 over time period 0 to 1.2 seconds, displaying simpler binary frequency shift keying waveform.

Slide 5: GNU Software-Defined Radio Simulation showing system implementation

Slide-5

GNU Software-Defined Radio Simulation

This slide shows the GNU Software-Defined Radio implementation used for simulating the underwater acoustic modem system.

Slide 6: Experimental Results & Bellhop showing data accuracy charts for BFSK and 4-FSK modulation schemes

Slide-6

Experimental Results & Bellhop

Data Accuracy in a Horizontal Link (BFSK)

Chart showing % Bits Wrong (0-30% on y-axis) versus Data Rate in bps (0-18000 on x-axis). Notable points marked: X=1200, Y=12.24 and X=4800, Y=14.72

Data Accuracy over Differing Data Rates (4-FSK)

Chart showing % Symbols Wrong (0-45% on y-axis) versus Data Rate in bps (0-10000 on x-axis). Notable point marked: X=800, Y=15.68

Comparable Performance to BFSK

Test conditions: TX/RX depth 15 m, seafloor depth 25 m, RX range of 250 m, f reference = 25000 Hz, n bits = 10e6

Slide 7: Experimental Results comparison between MATLAB and GNU Radio showing SNR performance charts

Slide-7

Experimental Results & Bellhop

Estimation differences between MATLAB and GNU Radio

MATLAB yielded higher accuracy for same signal quality (SNR)

Effect of non-white Gaussian Noise for Different Data Rates (MATLAB)

Chart showing % Bits Wrong (0-35% on y-axis) versus Signal to Noise Ratio in dB (-10 to 50 on x-axis) for three data rates:

  • 4.8 kHz
  • 9.6 kHz
  • 19.2 kHz

Effect of non-white Gaussian Noise for Different Data Rates (GNU Radio)

Chart showing % Bits Wrong (0-45% on y-axis) versus Signal to Noise Ratio in dB (-20 to 50 on x-axis) for:

  • 4.8 kHz
  • 9.6 kHz
  • GNU Radio Ideal

SNR Vs. P(BER) for M-ary Frequency Shift Keying

Chart showing Probability of Bit Error Rate

Slide 8: Future Work outlining hardware implementation, backscatter model, and chirped basis waveform analysis

Slide-8

Future Work

  • Hardware Implementation of Modem with PYNQ FPGA
  • Backscatter / Relay Receiver Model
  • Analysis of Chirped Basis Waveform
Slide 9: References listing multiple academic sources and online resources about acoustic communication and FSK modulation

Slide-9

References

Warty, C. and Richard Wai Yu. "Resource allocation using ASK, FSK and PSK modulation techniques with varying M." 2011 Aerospace Conference (2011): 1-7.

Stojanovic, Milica, and Pierre-Philippe J. Beaujean. "Acoustic communication." Springer Handbook of Ocean Engineering. Springer, Cham, 2016. 359-386.

Stojanovic, Milica. "On the relationship between capacity and distance in an underwater acoustic communication channel." ACM SIGMOBILE Mobile Computing and Communications Review 11.4 (2007): 34-43.

https://www.dip.ee.uct.ac.za/~nicolls/lectures/eee482f/13_fsk_2up.pdf

https://www.gaussianwaves.com/2016/10/modeling-a-frequency-selective-multipath-fading-channel-using-tdl-filters/

https://wiki.gnuradio.org/index.php/Simulation_example:_FSK

https://www.gaussianwaves.com/2014/07/power-delay-profile/

https://oalib-acoustics.org/AcousticsToolbox/Bellhop-2010-1.pdf

Last slide: Contains plain text stating 'End of presentation. Click the right arrow to return to beginning of slide show.'

End of Presentation

Click the right arrow to return to the beginning of the slide show.

For a downloadable version of this presentation, email: I-SENSE@FAU.

Additional Information
The Institute for Sensing and Embedded Network Systems Engineering (I-SENSE) was established in early 2015 to coordinate university-wide activities in the Sensing and Smart Systems pillar of FAU’s Strategic Plan for the Race to Excellence.
Address
Florida Atlantic University
777 Glades Road
Boca Raton, FL 33431
i-sense@fau.edu