# Research on Co-Channel Interference Cancellation for Underwater Acoustic MIMO Communications
## 3 Experimental Results
### Results and Analysis
Since the DA-DFE is widely used in underwater acoustic MIMO communication, we utilized DA-DFE as benchmark. Looking at the output SNR obtained by the DA-DFE based equalizer, one can see the output SNR obtained by RLS_DADFE is much higher than that obtained by IPNLMS_DADFE, as can be seen in Figure 4a,b. The reason for this is that the RLS-based equalizer achieves a lower output error than LMS-based equalizer achieves, but the cost of this better performance is a high computational complexity. One may also observe that the output SNR obtained by IPNLSM_DADFE shows obvious vibrations; the reason for this is that the channel is varied, as shown in Figure 3. The IPNLSM_DADFE cannot track channel variation. Since the RLS_DADFE and IPNLMS_DADFE contain the  
Figure 3: Channel estimation results. (**a**) Channel estimation without IC from transmitter 1 to hydrophone 6. (**b**) Channel estimation with IC from transmitter 1 to hydrophone 6. (**c**) Channel estimation without IC from transmitter 2 to hydrophone 6. (**d**) Channel estimation with IC from transmitter 2 to hydrophone 6. IC filters, and the filters are obtained via training sequences with specified criteria, the worst channel estimation results would not be introduced to the equalizers. As a result, the output SNR obtained by RLS_DADFE and IPNLMS_DADFE is higher than that obtained by CE_NoIC+DFE_NoIC, CE_NoIC+DFE_IC, and CE_IC+DFE_NoIC. The average output SNR from RLS_DADFE and IPNLMS_DADFE is 12.86 dB and 7.85 dB for transmitter 1, and 10.73 dB and 9.76 dB for transmitter2. Focusing on the proposed CE_IC+DFE_IC in Figure 5, it can be seen that the output SNR is the highest for both transmitter 1 and transmitter 2; for example, the average output SNR is 13.11 dB and 14.14 dB, respectively. Not only does the channel estimation perform interference cancellation by projecting the Col onto a specific subspace, the channel equalization utilizes IC filters to remove the interference shown in (19); thus, the SNR output is significantly improved. One can observe that the output SNR obtained by the proposed CE_IC+DFE_IC does not show significant vibration, because the channel estimation and Doppler compensation are performed block by block, and the filters for CE_IC+DFE_IC are updated periodically. The communication performance is also compared in decision-directed mode. The parameters are the same as they are in the training mode for all equalizers. To prevent error propagation in CE-DFE-based equalizers and DA-DFE-based equalizers, 25% training symbols were periodically inserted. In Figure 5, the results are similar to those in Figure 4. The obvious difference is that the average output SNR slightly drops for all the receivers. In the decision-directed mode,  
Figure 4: The output SNR in training mode. (**a**) The output SNR for TX1. (**b**) the output SNR for TX2. Figure 5: The output SNR in decision-directed mode. (**a**) The output SNR from transmitter 1. (**b**) The output SNR from transmitter 2.  
the input of the feedforward filter and IC filters is the decoded symbols; when the decoded symbols make an erroneous decision, the interference is incorrect, so the output SNR drops. In addition, for the CE-DFE-based equalizers, the decoded symbols are also used for channel estimation. Erroneous symbols will also lead to a worse channel estimation performance. The other difference is that the IPNLMS_DADFE output SNR obtained by IPNLMS_LMS receiver significantly drops in Figure 5a, and IPNLMS_LMS fails to decode the symbols from transmitter 1. The average output SNR obtained by CE_IC+DFE_IC is 10.41 dB and 12.4 dB for transmitter 1 and transmitter 2, respectively. The average output SNR obtained by RLS_DADFE is 9.2 dB and 11.8 dB for transmitter 1 and transmitter 2, respectively. The proposed CE_IC+DFE_IC receiver achieves an average gain of about 1 dB compared to the RLS_DADFE receiver.