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Massive multiple input multiple output (MIMO) technology stands as a cornerstone in the evolution of 5G networks. Using an abundance of degrees of freedom, MIMO systems exploit complex spatial characteristics to improve performance. Despite its potential, designing effective 5G MIMO communication systems faces major challenges, primarily in system modeling and channel estimation. Dynamic and complex propagation environments present significant obstacles in accurately representing system behaviour and estimating channel parameters. In order to estimate the channel in the wireless network an improved deep learning based Residual recurrent neural network (ResRNN) is implemented in this paper. Firstly, the least square estimator is used to estimate or calculate the historic channel responses of a pilot block. With these collective responses of the channel, we train the responses using the new deep learning based ResRNN, in which the weight of channel parameters are selected optimally using Improved pelican optimization algorithm (IPOA). Using the IPOA-based ResRNN hybrid precoder the present channel responses can be evaluated or estimated. By varying the length of pilot sequence and by varying the size of antennas at the transmitter and receiver, the metrics such as the bit error rate (BER), mean square error and throughput for (signal to noise ratio) SNR are evaluated. Simulation results show the developed method outperforms the conventional methods by attaining a BER of 10–5 with SNR is 30 dB wireless channel access. The performance of the proposed IPOA-DERNN method is compared with the conventional channel estimation methods. [ABSTRACT FROM AUTHOR]

In this work, two machine learning (ML)‐based structures for joint detection‐channel estimation in OFDM systems are proposed and extensively characterized. Both ML architectures, namely Deep Neural Network (DNN) and Extreme Learning Machine (ELM) are developed for provide improved data detection performance and compared with the conventional matched filter (MF) detector equipped with the minimum mean square error (MMSE) and least square (LS) channel estimators. The bit‐error‐rate (BER) performance vs computational complexity trade‐off is analyzed, demonstrating the superiority of the proposed DNN‐OFDM and ELM‐OFDM detectors methodologies. [ABSTRACT FROM AUTHOR]

The performance of OFDM system depends on the how efficient channel estimation is, which directly impactsits bit error rate (BER). In this research work, the novel technique of pseudo-pilot aided OFDM system is usedits channel estimation with least square and Recursive Least Squares approach. In proposed technique, for channel estimation Pseudo-pilot technique is used in OFDM system over AWGN fading channels. A hybridization technique has been implemented to alleviate the error rate in the signals. The hybrid optimization approach is implemented on the estimated output for the optimum solution. The combination of the two optimization techniques is particle swarm optimization (PSO) and Moth Flame Optimization (MFO) method that is used for optimization of the performance rate. To get more optimum solutions (reducing BER) OFDM system is trained with neural network. The performance of the proposed techniques and the weighted scheme are compared and verified using computerized simulation carried out using Matrix Laboratory software. The hybrid approach is able to achieve low BER of the network. The BER of PSO is 1.004 × 10−7 whereas for hybrid optimization (PSO + MFO) BER is 6.275 × 10−8 at 18 dB SNR. After training of the whole system is done with BPNN which will further reduce the BER while increasing SNR. By using BPNN, BER will further reduce to 1.243 × 10−8 at 18 dB SNR. It means hybrid optimization is done to optimize the performance of the channel and reduce the BERs, which will help to increase the channel estimation process as well as channel capacity and further reduces the losses while transmission from one end to the other end. [ABSTRACT FROM AUTHOR]

In this paper, two techniques of channel estimation are studied. For both, a statistical estimation of the channel in the time domain is discussed. Assuming that the transmission is performed on a flat fading channel, the two estimation methods MMSE and LS are tested and compared. A reduction of the complexity of the MMSE method by simplifying the estimation matrix is put to the test to find a compromise between complexity and performance. The results presented are the error rate of symbol SER and the quadratic mean of the error MSE as a function of the signal-to-noise ratio SNR. [ABSTRACT FROM AUTHOR]

This paper describes the performance of LTE advanced uplink transmission in a flat Rayleigh channel. The uplink is simulated using a modified version of the Vienna uplink link level matlab code simulator. This modified version supports two transmission antennas instead of one. Moreover, it includes two extra processes; layer mapping and precoding. In addition, the demodulation reference signal is presented and employed to allow channel estimation. In this paper, the structure of the LTE advanced system is described. Furthermore, we present generation of the demodulation reference signal. Four combinations of two distinct channel estimation and two signal detection methods are used to provide the simulation results of performance evaluation in term of the BER and throughput curves for selected scenarios.

This paper describes the performance of LTE advanced uplink transmission in a at Rayleigh channel. The uplink is simulated using a modified version of the Vienna uplink link level matlab code simulator. This modified version supports two transmission antennas instead of one. Moreover, it includes two extra processes; layer mapping and precoding. In addition, the demodulation reference signal is presented and employed to allow channel estimation. In this paper, the structure of the LTE advanced system is described. Furthermore, we present generation of the demodulation reference signal. Four combinations of two distinct channel estimation and two signal detection methods are used to provide the simulation results of performance evaluation in term of the BER and throughput curves for selected scenarios. [ABSTRACT FROM AUTHOR]