A digital signal processing method based on channel response-maximum ratio combining

Abstract

This invention relates to the field of wireless channel technology, specifically providing a digital signal processing method based on channel response-maximum ratio combining (CRF). The method includes: inserting training symbols into each frame of a multi-carrier signal; performing channel estimation on each training symbol to obtain the corresponding channel response estimate; estimating the SNR value of each training symbol; calculating normalized weighting coefficients based on the SNR values; and performing a weighted average based on the normalized weighting coefficients and channel response estimates of each training symbol to obtain the overall channel estimation result for the multi-carrier signal. This invention fully utilizes the time diversity characteristics inherent in continuous training symbol transmission, effectively suppressing estimation noise and improving system robustness without significantly increasing computational overhead. Furthermore, this method does not require prior acquisition of channel statistical characteristics, making it particularly suitable for dynamically changing wireless communication environments, achieving a good balance between accuracy, computational efficiency, and practicality.

Description

Technical Field

[0001] This invention relates to the field of wireless channel technology, and more specifically, to channel estimation and equalization techniques for orthogonal frequency division multiplexing (OFDM) / discrete multicarrier (DMT) signals, maximum ratio combining (MRC) techniques, and digital signal processing algorithms for the transceiver ends of communication systems. Background Technology

[0002] As wireless communication technology advances from 5G to 6G, the demand for long-distance, high-capacity data transmission is growing. The terahertz (THz) band is considered a disruptive solution for future networks, possessing unprecedented bandwidth that promises to meet the ever-increasing demands for ultra-high-speed, wide-coverage communication connections [Zhu M, Zhang J, Hua B, et al Ultra-wideband fiber-THz-fiber seamless integration communication system toward 6G: architecture, key techniques, and testbed implementation. Sci China InfSci, 2023, 66: 1-18.]. This band covers approximately 0.1–10 THz and has the potential to achieve data rates of hundreds of Gbps, supporting applications with extremely high bandwidth requirements such as augmented reality, holographic communication, and massive machine-type communications. However, to fully unleash the potential of THz communication, numerous technical challenges in high-frequency signal propagation must be overcome. Orthogonal Frequency Division Multiplexing (OFDM), due to its robustness against frequency-selective fading and inter-symbol interference, has become one of the key technologies for emerging THz communication systems. OFDM technology is mature and easily integrated with existing baseband processing architectures, making it widely considered an ideal choice for achieving high data rate communication in the THz band. However, achieving stable and reliable systems in the THz band remains challenging, with accurate and efficient channel estimation being a key bottleneck restricting system performance. THz channels exhibit propagation characteristics significantly different from microwave and millimeter-wave channels, such as high propagation loss, molecular absorption, and severe phase noise, all of which significantly degrade the performance of traditional channel estimation algorithms. Traditional channel estimation methods such as Least Squares (LS) are widely used due to their computational simplicity and suitability for real-time processing. However, under the prevalent low signal-to-noise ratio (SNR) conditions in THz communication, these methods are susceptible to significant noise amplification, severely reducing estimation accuracy. Furthermore, simply averaging the LS estimates obtained from multiple training symbols offers limited performance improvement due to the large differences in SNR among the training symbols. Therefore, there is an urgent need to develop novel channel estimation methods that overcome these problems while maintaining low complexity and high accuracy. Summary of the Invention

[0003] In view of this, the present invention proposes a digital signal processing method based on channel response-maximum ratio combining to solve the problem that the traditional LS algorithm is easily affected by noise interference, which leads to a significant increase in estimation error. At the same time, it breaks through the bottleneck that the stability of LS estimation will decrease significantly when the training symbol positions are unevenly distributed or the signal-to-noise ratios corresponding to different training symbols differ greatly.

[0004] To achieve the above objectives, this invention proposes a digital signal processing method based on channel response-maximum ratio combining, characterized by comprising:

[0005] Training symbols are inserted into each frame of the multicarrier signal, and channel estimation is performed on each training signal to obtain the corresponding channel response estimate.

[0006] Estimate the SNR value for each training symbol, and calculate the normalized weight coefficients based on the SNR values;

[0007] The overall channel estimation result of the multi-carrier signal is obtained by weighting the normalized weight coefficients and channel response estimates of each training symbol.

[0008] Furthermore, the multi-carrier signal is an OFDM signal or a DMT signal.

[0009] Furthermore, the number of training symbols inserted is determined based on the number of data symbols in the multi-carrier signal.

[0010] Furthermore, the channel estimation employs the LS algorithm.

[0011] Furthermore, the method for calculating the normalized weighting coefficients is as follows:

[0012]

[0013] Among them, w i SNR is the normalized weight coefficient of the i-th training symbol. i Let be the SNR value of the i-th training symbol, and n be the total number of training symbols.

[0014] Furthermore, the calculation method for the overall channel estimation result is as follows:

[0015]

[0016] Among them, H MRC For the overall channel estimation results, H LS,i w is the estimated channel response for the i-th training symbol. iis the normalized weight coefficient of the i-th training symbol, and n is the total number of training symbols.

[0017] The present invention also proposes the application of the above method in a photon-assisted millimeter-wave / terahertz wireless communication system, including: using the above method to perform digital signal processing on the oscilloscope's sampled data at the receiving end to recover the original transmitted symbols for evaluating the system's performance improvement.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0019] This invention achieves a synergistic complement between the low complexity of LS estimation and the noise resistance of diversity merging techniques by performing SNR-weighted averaging on the LS estimation results of multiple training symbols. This invention fully utilizes the time diversity characteristics inherent in continuous training symbol transmission to effectively suppress estimation noise and improve system robustness without significantly increasing computational overhead. Furthermore, this method does not require prior acquisition of channel statistical characteristics, making it particularly suitable for dynamically changing wireless communication environments. Therefore, the CR-MRC method proposed in this invention achieves a good balance between accuracy, computational efficiency, and practicality, providing a feasible approach for efficient channel estimation in next-generation wireless communication systems.

[0020] The parameters of the CR-MRC algorithm proposed in this invention, such as the number and distribution of training symbols, the channel estimation method for each training symbol, and the merging method for each channel estimation result, can be selected according to different systems, different channel characteristics, different transmission indicators, and other specific circumstances, and have good adjustability and applicability.

[0021] The method described in this invention has good versatility and is applicable to various application scenarios such as photon-assisted millimeter-wave terahertz systems, all-solid-state circuit millimeter-wave terahertz systems, polarization-multiplexed coherent transmission systems, and multimode / multi-core fiber optic transmission systems. At the same time, both OFDM and DMT signals can use the CR-MRC algorithm described in this invention to improve demodulation performance. Attached Figure Description

[0022] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. In the drawings:

[0023] Figure 1 This is a schematic diagram of the overall process of the method described in this invention;

[0024] Figure 2 This is a flowchart of OFDM modulation and demodulation using the method of the present invention in an embodiment of the present invention. Detailed Implementation

[0025] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0026] This embodiment proposes a digital signal processing method based on channel response-maximum ratio combining, such as... Figure 1 As shown, it includes:

[0027] Training symbols are extracted from the received frequency-domain OFDM signal, and the demodulation signal-to-noise ratio (SNR) of each training symbol is calculated to evaluate its channel estimation accuracy. Based on the demodulation SNR, the LS channel estimation results of multiple training symbols are weighted and averaged to obtain a more accurate channel estimation result. This result is then used for channel equalization and demodulation of the OFDM data symbols, improving the performance of the OFDM system.

[0028] The key part of this invention is the CR-MRC algorithm, the principle of which is as follows:

[0029] In OFDM systems, accurate channel estimation is crucial for reliable signal recovery. Traditional least-squares estimators are widely used due to their low computational complexity, and their basic form can be expressed as:

[0030]

[0031] Where Y represents the received training symbol, X represents the known transmitted training symbol, H represents the actual channel response, and N represents additive white Gaussian noise (AWGN). However, the LS estimator performs poorly under low signal-to-noise ratio (SNR) conditions because it fails to utilize the statistical characteristics of noise, resulting in a large channel estimation error. Furthermore, the SNR corresponding to different training symbols may vary significantly; directly averaging the LS estimates of multiple training symbols often only yields very limited performance improvements. The CR-MRC algorithm fully exploits the SNR diversity gain among multiple training symbols, thereby improving the accuracy of channel estimation and system robustness. This algorithm mainly includes the following three steps:

[0032] Step 1: LS channel estimation with multiple training symbols:

[0033] Multiple training symbols are inserted into each OFDM frame. The number of training symbols can be determined based on the number of data symbols. This improves the accuracy of channel estimation while ensuring low overhead. In an OFDM data frame, training symbols can be distributed at equal intervals or inserted at non-equal intervals using different methods, offering high flexibility.

[0034] Perform independent LS estimation for each training symbol to obtain the corresponding channel response estimate. That is, for the i-th training symbol, calculate its LS estimate H. LS,i , where i = 1, 2, ..., n, and n represents the number of training symbols inserted. This step ensures that training data collected at different time locations can reflect the channel characteristics at their corresponding times.

[0035] Step 2: Calculation of weighting coefficients based on demodulated SNR:

[0036] To fully utilize the channel information carried by each training symbol, weighting is required based on their respective SNR levels. Specifically, the SNR value (SNR) for each training symbol is first estimated. i Then calculate its normalized weighting coefficient w. i ,

[0037]

[0038] Among them, w i SNR is the normalized weight coefficient of the i-th training symbol. i Let be the SNR value of the i-th training symbol, and n be the total number of training symbols.

[0039] This weighting strategy can effectively enhance the contribution of high-quality estimates to the final channel estimation result and suppress noise interference caused by low SNR.

[0040] Step 3: Merge the largest ratios:

[0041] All weighted LS estimates are combined to obtain the final channel estimate H. MRC Its expression is:

[0042]

[0043] Among them, H MRC For the overall channel estimation results, H LS,i w is the estimated channel response for the i-th training symbol. i is the normalized weight coefficient of the i-th training symbol, and n is the total number of training symbols.

[0044] This maximum ratio merging strategy effectively improves the robustness and accuracy of the estimation while preserving the multipath diversity gain.

[0045] The CR-MRC algorithm utilizes the SNR diversity characteristics among multiple training symbols to adaptively weight and fuse multiple channel estimation results under different signal-to-noise ratio conditions. Compared to the direct averaging method, this algorithm can significantly improve the estimation accuracy and system anti-interference capability in low SNR environments, and is particularly suitable for OFDM systems in complex channel environments.

[0046] The technical solution described in this embodiment is applicable to various communication systems, such as photon-assisted millimeter-wave terahertz wireless communication systems, all-solid-state circuit millimeter-wave terahertz wireless communication systems, polarization-multiplexed coherent transmission systems, multimode / multi-core fiber optic transmission systems, etc., and has good versatility. For example, in a photon-assisted millimeter-wave / terahertz wireless communication system, the OFDM electrical signal undergoes electro-optical conversion in an I / Q modulator, and beats with another optical signal in a photodetector (PD) to generate a millimeter-wave / terahertz signal. This signal is then transmitted in free space through an antenna and lens. After the receiving antenna receives the high-frequency electrical signal, it is down-converted to an intermediate frequency by a low-noise amplifier and mixer. The signal is then sampled by an oscilloscope, and the sampled data undergoes digital signal processing at the receiving end, including synchronization, CR-MRC channel estimation, and back-end equalization, to recover the original transmitted symbol and evaluate the system's performance improvement.

[0047] As a preferred embodiment, the parameters of the CR-MRC algorithm used in this embodiment, such as the number and distribution of training symbols, the channel estimation method for each training symbol, and the merging method for each channel estimation result, can be selected according to different systems, different channel characteristics, different transmission indicators, and other specific circumstances, and have high adjustability and applicability.

[0048] Both OFDM and DMT signals are applicable to the CR-MRC scheme proposed in this embodiment; OFDM will be used as an example for the scheme description. Figure 2 As shown, the input bitstream is mapped to complex symbols. These symbols are then converted from serial to parallel and processed using M-point Discrete Fourier Transform Spread Spectrum (DFT-s) to reduce the peak-to-average power ratio, thereby improving the efficiency of the power amplifier. Each OFDM frame contains T payload symbols and inserts S training symbols (which can be equally spaced or non-equally spaced in different ways) for accurate channel estimation and equalization. Next, an N-point Inverse Fast Fourier Transform (IFFT) is performed, and a cyclic prefix is ​​added to mitigate inter-symbol interference and inter-subcarrier interference. After the OFDM signal is converted from parallel to serial, it is resampled to match the sampling rate of the arbitrary waveform generator. Finally, pilot tones are inserted at the edges of the signal spectrum to assist in frequency synchronization and signal recovery at the receiver.

[0049] In the digital signal processing flow at the receiver, the intermediate frequency (IF) signal is first down-converted to baseband via digital mixing, and then frequency offset estimation and carrier phase recovery are performed using pilot signals. Subsequent steps include resampling, frame synchronization, and cyclic prefix removal. Then, the frequency domain OFDM symbols are recovered using an N-point Fast Fourier Transform (FFT). Embedded training symbols are extracted for LS channel response estimation, and these initial channel responses are further optimized using the CR-MRC algorithm proposed in this invention. To further suppress frequency domain noise, intra-symbol frequency averaging is used to smooth the estimation results. The equalized symbols are then spread using an M-point inverse discrete Fourier transform to recover the original symbols, which are then input into a decision-guided minimum mean square equalizer with adaptive taps to compensate for residual linear distortion. Finally, the end-to-end link performance of the system is evaluated by calculating the bit error rate and signal-to-noise ratio (SNR).

[0050] Compared to the channel response estimated by a single training symbol and the arithmetic average of individual channel responses, this embodiment fully utilizes the time diversity and SNR diversity characteristics inherent in continuous training symbol transmission to effectively suppress estimation noise and improve system robustness without significantly increasing computational overhead. Furthermore, this framework does not require prior acquisition of channel statistical characteristics, making it particularly suitable for dynamically changing wireless communication environments. Therefore, the CR-MRC method proposed in this embodiment achieves a good balance between accuracy, computational efficiency, and practicality, providing a feasible approach for efficient channel estimation in next-generation OFDM communication systems.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A digital signal processing method based on channel response-maximum ratio combining, characterized in that, include: Training symbols are inserted into each frame of the multi-carrier signal, and channel estimation is performed on each training symbol to obtain the corresponding channel response estimate. Estimate the SNR value for each training symbol, and calculate the normalized weight coefficients based on the SNR values; The overall channel estimation result of the multi-carrier signal is obtained by weighting the normalized weight coefficients and channel response estimates of each training symbol. The normalized weighting coefficients are calculated as follows: ， Among them, w i SNR is the normalized weight coefficient of the i-th training symbol. i Let be the SNR value of the i-th training symbol, and n be the total number of training symbols; The calculation method for the overall channel estimation result is as follows: ， Among them, H MRC For the overall channel estimation results, H LS,i w is the estimated channel response for the i-th training symbol. i is the normalized weight coefficient of the i-th training symbol, and n is the total number of training symbols.

2. The method according to claim 1, characterized in that, The multicarrier signal is an OFDM signal or a DMT signal.

3. The method according to claim 1, characterized in that, The number of training symbols to be inserted is determined based on the number of data symbols in the multi-carrier signal.

4. The method according to claim 1, characterized in that, The channel estimation uses the LS algorithm.

5. An application of the method according to any one of claims 1-4 in a photonic-assisted millimeter-wave / terahertz wireless communication system, characterized in that, include: The sampling data from the oscilloscope is processed by the method described in any one of claims 1-4 to recover the original transmitted symbols, which is used to evaluate the performance improvement of the system.

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