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Problem statement: Channel capacity can be an essential parameter for wireless communication to improve the link quality. Until now, the channel capacity has been evaluated by utilizing Shannon capacity theory, but it presented many limitations and restrictions. The objectives of this study were to: (i) Expose, quantify, present and to give a clear and concise exposition of the mathematical principles of the proposed generic algorithm and (ii) Calculate and simulate the SNR, the BER and the channel capacity for wireless underwater transceiver and compare with the literature references. Approach: Contrary to the existing theory (Shannon capacity theory that required and needed of the exact channel state distribution and particularly the precisely estimation of cut-off threshold profile, so, the bad estimation allowed suspending transmission even for good channel conditions), the proposed generic algorithm was used to determine the channel capacity performance with varying block size input packet information (N was varied from 50-200) as a function of the optimum transmit power (from 3.5-7.0 w). This approach did not require the exact channel state distribution; but the channel was well modeled and the cut-off threshold profile had to be numerically solved. The iteration results of this algorithm were utilized to calculate and simulate the SNR, the BER and the channel capacity for wireless underwater transceiver performance. Results: The consultation performance of channel capacity wireless underwater communication, obtained with present approach, were significantly better in any transmission conditions (no suspending transmission) because the proposed algorithm was well suited to obtain results about channel capacity. In the other hand, the amount of transmitted data per resource depended on the channel conditions was performed. Blocking is no longer critical if data traffic without or with sensitivity has to be served. Data rate results were allowed of the users to be satisfied because of the wanted to achieve a certain guarantees. This was attributed to higher SNR, a lower BER and an optimal channel capacity value with the minimal transmission power. Conclusion: These results indicated that using this generic algorithm can enhance and optimize the link quality and the performance of wireless underwater communication

Generic Algorithm with Varying Block Sizes to Improve the Capacity of a Wireless Communications Networks