Performance Analysis of Bit Loading Algorithms for fault tolerant Submarine Communication
Karunakara Rai B1, Shantharama Rai C2

1Karunakara Rai B, Dept. of ECE, Nitte Meenakshi institute of Technology, Banagalore.
2Shantharama Rai C, Dept. of ECE, AJ institute of engineering and technology, Managalore.
Manuscript received on July 01, 2019. | Revised Manuscript received on July 22, 2019. | Manuscript published on August 30, 2019. | PP: 4087-4093 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8660088619/2019©BEIESP | DOI: 10.35940/ijeat.F8660.088619
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Abstract: In this paper, performance metrics of rate adaptive and margin adaptive bit loading algorithms is simulated and analyzed to improve the data rate in undersea communication channel. In fault tolerant undersea communication, achieving higher data rate is a challenge. Bit loading algorithms have proven efficient with limited power budget. An optimal bit loading algorithm with low complexity and fast convergence for rate adaptive and margin adaptive problems with bit error rate (BER) constraints will increase the data rate and reliability of the system significantly. The simulation has been carried for submarine audio channel with a bandwidth of 0-20kHz considering ambient noise and thorps attenuation model for 16 subcarriers. Three different approaches such as Chow, Campello and Ant Colony Optimization are simulated for margin adaptive and rate adaptive criteria. Simulation results show that, for margin adaptive conditions Campello and ACO algorithms reduce energy by a factor of 71.50% and 13.05% with respect to Chow’s algorithm with an input of 90 bits. It was also observed that for rate adaptive conditions, Chow and ACO algorithms gave marginally increased bit rate compared to Campello algorithm with energy fixed at 8.35J. Campello and ACO algorithm execution time were reduced by a factor of 67% and 87.10% respectively as compared to Chow’s algorithm. 
Keywords: Bit Error Rate (BER), Discrete Multi Tone (DMT), Margin Adaptive (MA), Rate Adaptive (RA), Signal-to-Noise Ratio (SNR).