Abstract
For Orthogonal Frequency Division Multiplexing (OFDM) and other communication systems, many estimating approaches have been developed to estimate the channel state information and lower the Bit Error Rate (BER). These estimating methods, however, are still subject to the influence of large peak powers compared to average powers. Reduced computational complexity is one of the most significant factors to consider while developing a new estimate algorithm. This study aims to provide a novel design of the Packet-Discrete Wavelet Transform (P-DWT) algorithm for channel estimation in wireless OFDM instead of the fast Fourier transform (FFT). It is presented to retrieve the code of a spread spectrum signal and transmitted data bits, and it is compared to particle swarm optimization PSO and least mean square (LMS) optimization. The suggested approach reduces the computing cost of DWT by recognizing the Packet Wavelet Transform (PWT) coefficients and local points, findings utilizing P-DWT channels generated from both models and measurements show that the proposed technique outperforms pilot-based channel estimation in terms of bit error rate under sparseness conditions BER. Moreover, as compared to typical semi-blind approaches, the estimation accuracy is enhanced while computing cost is reduced.
Highlights
Because Orthogonal Frequency Division Multiplexing (OFDM) is one of the strong multiplexing techniques wireless technologies of the future, it has recently been adopted as a modulation scheme to address the increasing demand for high data rate transmission in wireless communications
This work proposes a new method based on the Packet-Discrete Wavelet Transform (P-DWT) algorithm to solve the drawbacks of model estimate approaches for OFDM systems
The estimated at each level are up sampled by two, and processed through high pass filtering (HPF) and low pass filtering (LPF) synthesis before being additional, because reconstruction is the inverse of decomposition
Summary
In the meantime, [13] proposed the creation of a two-layer neural network model to predict the channel in a MIMO-OFDM system Another strategy, suggested in [14], uses fuzzy modeling to provide an accurate model for OFDM channel estimates. Aside from that, [16] proposes the usage of the PSO method, which employs a sub-model combination approach to fit the sample data optimally. Each of these strategies, has its own set of restrictions. This work proposes a new method based on the P-DWT algorithm to solve the drawbacks of model estimate approaches for OFDM systems. Other stochastic approaches, such as the DWT and P-DWT algorithms, can yield simple and efficient solutions in a faster calculation time and with a steadier convergence characteristic than the P-DWT methodology
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