MIMO is a technique to increase data rate significantly with multiple antennas at both the transmitter and receiver. MIMO takes the advantage of random fading and multipath delay spread. MIMO systems will need to function reliably in interference limited environment in order to be effective. CDMA systems are designed to operate in an interference free environment and for this reason it is used in modern cellular systems. The combination of MIMO and CDMA can further improve the system transmission rate over the traditional CDMA system. Multiuser MIMO CDMA systems are considered where each user has multiple transmit antennas, different transmit antennas of the same user use the same spreading code. Matched filter method and decorrelating detector method are used to detect the signals with Gaussian Noise. In many wireless systems the ambient noise is known through experimental measurements to be decidedly non-Gaussian due to largely impulsive phenomena. The performance of many multiuser detectors can degrade substantially in the presence of such impulsive ambient noise. For combating Multi Access Interference and impulsive noise in CDMA communication systems, a technique based on mestimation is used. Performance comparison shows that mestimation has better performance under non-Gaussian noise than the other detection techniques.

Signal processing techniques incorporated with data compression processes enrich the signals and boost up storage efficiency and transmission reliability. Transmitting uncompressed original data consume wide bandwidth, which increases transmission time and leads to data hammering. These limitations enforce to look for strategic data compression techniques. Lossless compression techniques are requisite where it is important that the original and the decompressed data should be identical or where deviations from the original data would lead to catastrophic consequences, especially in biomedical signal analysis and diagnostics. For which, the input signal preprocessed with differential pulse code modulation (DPCM) reduces the interchannel dependencies to get the desired output. A whole array of unique compression techniques are being utilized in the compression process. The combination of (K‐means clustering, arithmetic encoding [AE], Huffman encoding [HE]) clustering and coding compression techniques are analyzed using electro cardiogram (ECG) and electroencephalogram (EEG) signals. The proposed method employs k‐means clustering combined with Huffman encoding (DiKHE) and k‐means clustering combined with arithmetic encoding (DiKAE) individually. Compression ratio (CR) is analyzed with these combinations of compression techniques for various cluster size K (K = 2,3,4,5,6). A maximum CR of 6.03144 and 4.54126 is obtained for ECG and EEG signals respectively. The compressions based on these techniques are efficient since the compressed signal is reconstructed perfectly as it matches exactly with the original signal.