Channel estimation in OFDM systems using neural network technology combined with a genetic algorithm

Severe time-dispersion of the channel is one of the limiting factors for high data rate acoustic communications in shallow water. To overcome the large time spread, the research community has devoted enormous effort in the design of high efficient, low complexity equalizers. The goal of these equalizers is to convert the time-dispersive channel into a unit-tap impulse function. In another research direction, code division multiple access (CDMA) and orthogonal frequency division multiplexing (OFDM) systems are designed to combat the multi-path effect. However, using OFDM or CDMA alone to combat the multi-path effect often forces system designers to reduce the data rate in such severe time-dispersive channels. In this paper, we propose to adopt a class of generalized equalizers to generate an effective channel with a desired length. These equalizers are referred to as channel-shortening equalizers. Then, other advanced modulation schemes, such as OFDM and CDMA, can be employed to overcome the remaining time-dispersion of the channel. Compared to the unit-tap equalization, channel-shortening equalizers can provide quality performance at low computation cost. Examples of current channel-shortening equalizers are based on minimum mean-squared error (MMSE) and maximum shortening signal-to-noise ratio (MSSNR) criteria. In this paper, these algorithms are compared in the context of underwater acoustic communications. Their performance, complexity, and implementation in acoustic communications are investigated. In the digital subscriber line (DSL) channel, where channel-shortening equalizers are often applied to, MSSNR algorithms perform better than MMSE algorithms. However, we show that the MMSE algorithm is more suitable for the underwater acoustic channels with lower complexity and robustness to noise. With the MMSE method, a channel-shortening equalizer with 20 taps can convert a typical 120-tap acoustic channel into a 33-tap shortened channel. Using data obtained from the Kauai experiment, June-July 2003, it shows that the mean of the effective signal-to-interference ratio (SIR) achieves 15.7 dB. For the same channel, the optimum unit-tap equalization needs more than 200 taps to achieve the same effective SIR

Multi carrier modulation is Orthogonal Frequency Division Multiplexing (OFDM), dividing the large band spectrum into small (narrow) band spectrum.OFDM offers flexibility in adaptation to time varying channel condition by adopting the parameters at each subcarrier accurately.Due to the multipath effect the OFDM system suffers Inter Symbol Interference (ISI).ISI can be reduced by introducing the guard band (cyclic prefix).Time-varying channel estimation is an important activity in next generation wireless communication.Channel estimation is required mainly for the significant signal processing operation in multiple input multiple output MIMO-OFDM systems.In this project DFT -based channel estimation based on block type arrangement is implemented in MAT Lab.The bit error rate (BER) of DFT (Discrete Fourier Transform)based channel estimation is less than the BER of LS (Least Square) and MMSE (Minimum Mean Square Error) based channel estimation with same signal to noise ratio (SNR).

In orthogonal frequency-division multiplexing (OFDM) systems, it is generally assumed that the channel response is static in an OFDM symbol period. However, the assumption does not hold in high-mobility environments. As a result, intercarrier interference (ICI) is induced and the system performance is degraded. A simple remedy for this problem is the application of the zero-forcing (ZF) and minimum mean square error (MMSE) equalizers. Unfortunately, the direct ZF method requires the inversion of an NxN ICI matrix, where N is the number of subcarriers. When N is large, the computational complexity can become prohibitively high. As for the direct MMSE method, in addition to an NxN matrix inverse, it requires an extra NxN matrix multiplication, making the required computational complexity higher compared to the direct ZF method. In this dissertation, we first propose a low-complexity ZF method to solve the problem in single-input-single-output (SISO) OFDM systems. The main idea is to explore the special structure inherent in the ICI matrix and to apply Newton's iteration for matrix inversion. With our formulation, fast Fourier transforms (FFTs) can be used in the iterative process, reducing the complexity from O(N^3) to O(Nlog_2 N) . Also, the required number of the iteration is typically one or two. We also analyze the convergence behavior of the proposed method and derive the theoretical output signal-to-interference-noise-ratio (SINR). For the MMSE method, we first reformulate the MMSE solution in a way that the extra matrix multiplication can be avoided. Similar to the ZF method, we then exploit the structure of the ICI matrix and apply Newton's iteration to reduce the complexity of the matrix inversion. For a multiple-input-multiple-output (MIMO) OFDM system, the required complexity of the ZF and MMSE methods becomes more intractable. We then manage to extend the proposed ZF and MMSE methods for SISO-OFDM systems to MIMO-OFDM systems. It turns out that the computational complexity can be reduced even more significantly. Simulation results show that the performance of the proposed methods is almost as good as that of the direct ZF and MMSE methods, while the required computational complexity is reduced dramatically. Finally, we explore the application of the proposed methods in mobility-induced ICI mitigation for OFDM multiple access (OFDMA) systems, and in carrier frequency offset (CFO) induced ICI mitigation for OFDMA uplink systems. As that in OFDM systems, the proposed methods can reduce the required computational complexity, effectively.

An orthogonal frequency division multiplexing (OFDM) is one of the effective techniques used in wireless communications. In OFDM systems, channel impairments due to multipath dispersive wireless channels can cause deep fades in wireless channels. The OFDM receiver also requires an accurate and computationally efficient channel state information when coherent detection is involved. Therefore, it needs a good robust estimation method of the channel in wireless communication for OFDM systems. And one of these channel estimation methods is minimum mean square error (MMSE) channel estimation. MMSE channel estimation one most used method in OFDM systems. In this work we enhanced robustness of MMSE channel estimation by using it in base of quasi-cyclic low density parity check (QC-LDPC) coded OFDM system.

In this paper, we derive the achievable rate of single-carrier frequency domain equalizer (SC-FDE) and orthogonal frequency division multiplexing (OFDM) systems, constrained on low resolution analog to digital converters (ADCs). We assume that there is a full knowledge of channels at both transmitter and receiver sides. We model low resolution ADCs with an additive uniform quantization noise. We consider an ultra-wide band millimeter wave (mm-Wave) system with a frequency selective channel. The channel is based on practical results at mm-Wave frequencies. Our numerical results show that SC-FDE systems with minimum mean square error (MMSE) equalizers outperform OFDM and SC-FDE systems with zero forcing (ZF) equalizers at high signal to noise ratio (SNR) regimes. Moreover, benefits of increasing the ADCs resolution are not significant due to exponentially increment in the power consumption of ADCs.

Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier modulation technique. It supports high data rates over wireless radio channels. Due to high peak-to-average power ratio, OFDM is sensitive to the non-linear effects of the power amplifier. So, many methods of PAPR reduction techniques have been proposed. Of these, the distortionless method of PAPR reduction is Partial Transmit Sequence (PTS) technique. But this method suffers from high computational complexity. Modified PTS technique has been proposed to address the issue of high computational complexity. Modified PTS technique adopts a neighbourhood search algorithm to find the optimum set of phase factors. In this paper, the performance the OFDM system by using modified PTS technique combined with interleaving and pulse shaping is evaluated by employing Least Square (LS) and Minimum Mean Square Error (MMSE) channel estimation methods. Mean Square Error (MSE) and Bit error rate (BER) are the performance measures that are employed to analyse the performance of the OFDM system. The simulation result shows that the performance of OFDM system improves with PAPR reduction.

An orthogonal frequency division multiplexing (OFDM) transmission system is known to exhibit superior performance compared to a single carrier system in a multipath environment. However, the communication quality degrades severely because of delayed waves exceeding the guard interval and the Doppler shift. To solve this problem, various systems have been proposed that use an adaptive array to suppress undesired signals. In this paper, we propose a system that uses the same signal in the guard interval inserted at the head and the guard interval at the tail of the OFDM effective symbol in an OFDM transmission system and operates a minimum mean square error (MMSE) adaptive array. To clearly show the effectiveness of the proposed system, we analyzed the basic performance by a computer simulation. These results demonstrated excellent operation as an MMSE adaptive array and superior undesired signal suppression. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 86(10): 1–9, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.10121

Channel estimation is a critical component in orthogonal frequency division multiplexing (OFDM) systems for ensuring reliable wireless communication. In this study, we propose a fast super-resolution convolutional neural network (FSRCNN) model for channel estimation, designed to reduce computational complexity while maintaining high estimation accuracy. The proposed FSRCNN model incorporates modifications such as replacing linear interpolation with zero padding and leveraging a new fast CNN architecture to estimate channel coefficients. Our numerical experiments and simulations demonstrate that the FSRCNN model significantly outperforms traditional methods, such as least square (LS) and linear minimum mean square error (LMMSE), in terms of mean square error (MSE) across various signal-to-noise ratios (SNRs). Specifically, the FSRCNN model achieves MSE values comparable to MMSE estimation, particularly at higher SNRs, while maintaining lower computational complexity. At an SNR of 20 dB, the FSRCNN model shows a notable improvement in MSE performance compared to the ChannelNet and LS methods. The proposed model also demonstrates robust performance across different SNR levels, with optimal results observed when the training SNR is close to the operating SNR. These findings validate the effectiveness of the FSRCNN model in providing a low-complexity, high-accuracy alternative for channel estimation, making it suitable for real-time applications and devices with limited computational resources. This advancement holds significant promise for enhancing the reliability and efficiency of current and future wireless communication networks.

A Krylov subspace based low-rank channel estimation in OFDM systems

This paper deals with channel estimation for orthogonal frequency-division multiplexing (OFDM) systems with transmit diversity. Space time coded OFDM systems, which can provide transmit diversity, require perfect channel estimation to improve communication quality. In actual OFDM systems, training sequences are usually used for channel estimation. The authors propose a training based channel estimation strategy suitable for space time coded OFDM systems. This novel strategy provides enhanced performance, high spectrum efficiency and relatively low computation complexity.

In this paper, we investigate and compare the performance of single- and multi-carrier modulation schemes for indoor visible light communication (VLC). Particularly, the performances of single carrier frequency domain equalization (SCFDE), orthogonal frequency division multiplexing (OFDM) and on-off keying (OOK) with minimum mean square error equalization (MMSE) are analyzed in order to mitigate the effect of multipath distortion of the indoor optical channel where nonlinearity distortion of light emitting diode (LED) transfer function is taken into account. Our results indicate that SCFDE system, in contrast to OFDM system, does not suffer from high peak to average power ratio (PAPR) and can outperform OFDM and OOK systems. We further investigate the impact of LED bias point on the performance of OFDM systems and show that biasing LED with the optimum value can significantly enhance the performance of the system. Bit-interleaved coded modulation (BICM) is also considered for OFDM and SCFDE systems to further compensate signal degradation due to inter-symbol interference (ISI) and LED nonlinearity.

Channel estimation and synchronization are considered the most challenging issues in Orthogonal Frequency Division Multiplexing (OFDM) system. OFDM is highly affected by synchronization errors that cause reduction in subcarriers orthogonality, leading to significant performance degradation. The synchronization errors cause two issues: Symbol Time Offset (STO), which produces inter symbol interference (ISI) and Carrier Frequency Offset (CFO), which results in inter carrier interference (ICI). The aim of the research is to simulate Comb type pilot based channel estimation for OFDM system showing the effect of pilot numbers on the channel estimation performance and propose a modified estimation method for STO with less number of pilot to mitigate ISI effect. Channel estimation is simulated for different pilot number with 16QAM modulation in Ped-A Rayleigh channel. The STO effect assumed less than the cyclic prefix length to minimize ISI and maintain the orthogonality between subcarriers. The simulation results show that the high number of pilots are needed to achieve good channel estimation in case no synchronization used while similar results are achieved with smaller number of pilots if the propose modification on the synchronization method are used aligned with the channel estimation. The proposed system is evaluated in terms of BER and SNR.

Channel estimation (CE) is a challenging problem and one of the key technologies in Orthogonal Frequency Division Multiplexing (OFDM) systems. The Linear Minimum Mean Square Error (LMMSE) proposed for OFDM systems has excellent Mean Square Error (MSE) performance. However, the conventional method requires the statistical knowledge of the channel information in advance and the complicated inverse operation of large dimension matrices. In this case, an enhanced fast LMMSE channel estimation using Fast Fourier Transform (FFT) operation and the most significant taps (MST) algorithm for OFDM systems is proposed. This method has the advantage of low computational complexity and no requirement on the detailed channel information. In addition, simulation results demonstrate that the proposed algorithm outperforms other LMMSE methods in terms of bit error rate (BER) without the loss of MSE performance.

In this paper, we study low complexity transceiver for double space time transmit diversity (D-STTD) and orthogonal frequency division multiplexing (OFDM) system with antenna shuffling. We utilize a simple antenna shuffling method based on the criterion of minimum mean square error (MMSE). We propose an algorithm to reduce the amount of feedback by exploiting the fact that the channel frequency responses across OFDM subcarriers are correlated. In the proposed algorithm, subcarriers are clustered in blocks, which are allocated the same shuffling pattern with the largest number of the shuffling patterns in the cluster. This way, the signaling overhead can be reduced in comparison with each subcarrier based feedback. Furthermore, the weighted soft decision Viterbi decoding is applied to mitigate noise enhancement inherent to ZF and MMSE linear detectors and improve error rate performance. Simulation results are presented to show the effectiveness of the proposed techniques in frequency-selective MIMO channels.

OFDM(Orthogonal Frequency Division Multiplexing) 시스템에서 직교 불균형 문제는 송수신기의 front-end에서 발생하며, 성상도에 영향을 주게 되어 BER(Bit Error Rate)을 증가시킨다. 또한, 위상 잡음은 송수신시 국부 발진기에서 발생되는 잡음으로 각 부반송파의 직교성을 깨뜨림으로써 시스템 성능을 크게 저하시킨다. 기존 방식인 PNS(Phase Noise Suppression) 알고리즘은 이러한 위상 잡음을 효과적으로 제거하는 방법이지만 직교 불균형 이동시에 적용되면 오히려 성능이 감소된다. 본 논문에서는 OFDM 시스템의 수신기에서 하향 변환 시 발생하는 직교 불균형과 위상 잡음의 영향을 분석하고, 수신기 FFT(Fast Fourier Transform) 후단에서 파일럿 심볼을 사용하여 CPE를 먼저 제거하고 직교 불균형과 위상 잡음의 성분을 검출하여 등화기의 판정 기준으로 사용하여 보상하는 방법을 제시하였다. 또, 다른 기존 방식들은 FFT 후단에서 추정하고 피드백 시키거나 프리엠블과 같은 시퀀스를 사용하는 방식이지만, 본 논문에서는 FFT 후단에서 MMSE 등화기만을 사용하여 제거하므로 기존의 방법보다 복잡도가 줄어든다. 기존의 위상 잡음 제거 방식에 ICI(Inter Carrier Interference) 제거 기능을 추가하고 직교 불균형 성분을 추출하여 MMSE(Minimum Mean Square Error) 과정 중에 적응 forgetting factor를 적용하면 성능 개선과 직교 불균형 성분의 영향이 줄어들며 성능이 개선됨을 보인다. In the OFDM system, IQ imbalance problem happens at the RF front-end of transceiver, which degrades the BER(bit error rate) performance because it affects the constellation in the received signal. Also, phase noise is generated in the local oscillator of transceivers and it destroys the orthogonality between the subcarriers. Conventional PNS algorithm is effective for phase noise suppression, but it is not useful anymore when there are jointly IQ(In-phase and Quadrature) imbalance and phase noise. Therefore, in this paper, we analyze the effect of IQ imbalance and phase noise generated in the down-conversion of the receiver. Then, we estimate and compensate the IQ imbalance and phase noise at the same time. Compared with the conventional method that IQ imbalance after IFFT is estimated and compensated in front of FFT via the feedback, this proposed method extracts and compensates effect of IQ imbalance after FFT stage. In case IQ imbalance and phase noise exist at the same time, we can decrease complexity because it is needless to use elimination of IQ imbalance in time domain and training sequences and preambles. Also, this method shows that it reduces the ICI and CPE component using adaptive forgetting factor of MMSE after FFT.