(I.C.I. ), and inter-antenna interference (I.A.I.) [9; 10; 11]. Those reference signals that are received have been influenced by various measurements of interference (known as pilots); this estimates the channel (C.E.) and the complicated and consecutive equalization (E.Q.) of the received signal (Rx). Mostly, the channel is dispersed in multiple types of broadband communication [12; 13; 14; 15]. In ref. [4], it was found that the improved channel estimation of C.P. information could be designed to be utilized through Kalman filters. However, this technique can be employed only for orthogonal waveforms. In order to improve the performance of Channel Estimation by reusing pilots' knowledge from C.P., this paper utilized the localization of symbol-time in orthogonal waveforms [16; 17]. It can also be referred to by correctly localizing the pilot energy up to the terminated point of the block. It needs to possess maximum energy while it is copied to the C.P. Identification of symbols in the data of non-orthogonal multi-carriers and acquires the various Channel Estimation and C.Q. modules independent of the usual practice. The earlier works on Channel Estimation [18] were calculated with L.S. then LMMSE estimators that assumed that I.S.I. and I.C.I. manage additional correlation noise with the white noise of non-orthogonal waveforms. Even though the non-iterative techniques have been discussed in [19], they can acquire the implementation of Channel Estimation with increased errors and poor SNR. It is directly applicable to MIMO interference with little interference and non-orthogonal waveforms and has no solution for channel estimation. Channel Estimation (C.E.) utilizes C.S.I. to transmit and receive the wireless signal. In a wireless communication medium, C.S.I. causes power degradation, fading, and wave scattering as a function of transmission distance. The channel state information (C.S.I.) is determined by comparing the pilot signals used during the transmitter to the received pilot signals. In contrast, the SNR reflects the resilience of the bit stream about the channel noise. The concept of C.E. is evolving based on the objective of the correlation of signal at the center point integrated with suppression of the M.A.I. This signal quality impacts receiver BER performance [20]. In C.E., two schemes are employed for estimating signals, namely, (i) pilot-assisted scheme, which trains the allocation of symbols to bandwidth; and (ii) blinding using statistical features. At the receiving end, channel estimation inserts pilot signals into the transmitted signal, resulting in signal overhead, which a minimum number of pilot symbols can reduce. Currently, for uplink MU-MIMO communication, countless MIMO detectors are used. In addition, an optimal maximum likelihood detector and a low-complexity environment are also developed. The channel estimate is crucial in OFDM systems. Enhancing the system's bit error rate performance is utilized to boost the channel capacity of an OFDM system. Based on a comb-shaped pilot array, channel estimation is performed on the pilots, and a Rayleigh fading channel is considered. A performance measure of the M.S.E. of the channel estimator fusing DFT-based procedure obtains the best results as contrasted with the consolidation of LS-based strategies. The comparative analysis is carried out by varying parameters, such as the number of subcarriers, channel taps, cyclic prefix length, and pilot frequency. This article recommends a channel estimation method incorporating differential evolution and L.S. with DFT. The proposed ICHE-DA is included in the central part of the OFDM-MIMO block. We consider a two-user interference channel for MIMO-OFDM systems. We demonstrate the basic idea of a user-based transmitter and receiver using DFT-LS-WIENER. Further, we focus on interference from frequency-selective channels. By doing so, the main struggle is to evaluate the accuracy of covariance matrices, which may need accurate information on interference parameters.