An adaptive MLSE equalization method and system for Bluetooth low energy

Abstract

The application provides an adaptive MLSE equalization method and system for Bluetooth low energy, and belongs to the technical field of wireless communication; the method aims to solve the problems of fixed calculation complexity and huge power consumption of the existing MLSE equalization scheme applied to Bluetooth low energy; the method comprises the following steps: adopting MLSE equalization based on an equalizer complexity parameter to process the received signal; performing decoding performance checking on the decision symbol sequence to obtain a decoding performance index; calculating a channel quality factor according to the decoding performance index and the estimated channel condition; and adaptively updating the equalizer complexity parameter according to the channel quality factor for subsequent signal processing. The application also provides a corresponding equalization system. Through the closed-loop control of evaluation, decision, execution and feedback, the application can dynamically adjust the equalizer complexity according to the real-time channel condition.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to an adaptive maximum likelihood sequence estimation equalization method and system for Bluetooth Low Energy. Background Technology

[0002] Bluetooth Low Energy (BLE) technology is widely used in fields such as the Internet of Things (IoT), connected vehicles, and healthcare due to its low power consumption. However, in complex wireless communication environments, issues such as multipath propagation and frequency-selective fading in wireless channels can lead to signal distortion and inter-symbol interference (ISI), severely impacting communication reliability.

[0003] To combat channel distortion, Bluetooth receivers typically employ equalization techniques. Existing equalization schemes mainly include linear equalization, decision feedback equalization, and maximum likelihood sequence estimation. Linear equalization has a simple structure but is weak against severe inter-symbol interference (ISI). Decision feedback equalization outperforms linear equalization but suffers from error propagation problems and is sensitive to initial decision errors. Theoretically, maximum likelihood sequence estimation is the optimal sequence equalization scheme, as it fully utilizes the statistical information of the signal sequence to combat multipath effects and ISI.

[0004] However, the computational complexity of traditional maximum likelihood sequence estimation equalizers increases exponentially with channel memory length, resulting in complex hardware implementations and enormous power consumption. For applications like Bluetooth Low Energy, which have extremely strict power consumption constraints, the high power consumption of traditional maximum likelihood sequence estimation equalizers makes them unsuitable for direct application. Furthermore, existing feedback adjustment mechanisms mostly focus on adjusting macroscopic communication parameters such as data rate, lacking effective means for fine-tuning and dynamically adjusting the internal computational load of the equalizer. This leads to the equalizer operating at a fixed high complexity even under good channel conditions, resulting in unnecessary energy waste. Summary of the Invention

[0005] The purpose of this application is to provide an adaptive maximum likelihood sequence estimation equalization method and system for Bluetooth Low Energy (BLE), aiming to solve the problem that existing maximum likelihood sequence estimation equalization schemes are difficult to apply to BLE receivers due to their fixed computational complexity and huge power consumption. This application establishes a mechanism that can dynamically adjust the equalizer's computational complexity according to real-time channel conditions, thereby significantly reducing the system's average power consumption and computational resource consumption while ensuring communication reliability.

[0006] To address the aforementioned technical problems, this application provides an adaptive MLSE equalization method for a low-power Bluetooth receiver. The method includes: processing the received signal using MLSE equalization based on an equalizer complexity parameter to obtain a decision symbol sequence; performing decoding performance verification on the decision symbol sequence to obtain a decoding performance index; calculating a channel quality factor based on the decoding performance index and a channel condition estimated based on the received signal; and adaptively updating the equalizer complexity parameter based on the channel quality factor for subsequent processing of the received signal. Specifically, during initial processing, the equalizer complexity parameter is a preset value.

[0007] Optionally, the equalizer complexity parameter is the number of states of the Viterbi decoder.

[0008] Optionally, the channel conditions include signal-to-noise ratio and effective channel length.

[0009] Furthermore, the step of calculating the channel quality factor includes: performing a weighted summation of the normalized signal-to-noise ratio, the effective channel length, and the decoding performance index, and smoothing the summation result.

[0010] Optionally, the decoding performance metric is the cyclic redundancy check (CRC) success rate of historical data packets.

[0011] In a preferred embodiment of this application, the step of determining the number of states based on the channel quality factor includes: comparing the channel quality factor with multiple preset thresholds, and using a hysteresis decision mechanism to map the channel quality factor to one of a preset set of state numbers.

[0012] Furthermore, the MLSE equalization includes a branch metric calculation step, and the branch metric calculation step utilizes the constant envelope characteristic of Gaussian frequency shift keying (GFSK) modulation to simplify the calculation based on Euclidean distance to the calculation based on the real part of the inner product.

[0013] Optionally, the method further includes: when the decoding performance index is detected to be lower than a preset performance threshold within a preset time window, forcibly increasing the value of the equalizer complexity parameter.

[0014] Furthermore, the detection that the decoding performance index is lower than the preset performance threshold within a preset time window specifically means detecting CRC check failures of a predetermined number of consecutive data packets.

[0015] This application also provides an adaptive MLSE equalization system for a low-power Bluetooth receiver. The system includes: an MLSE equalization module for processing a received signal using MLSE equalization based on an equalizer complexity parameter to obtain a decision symbol sequence; a feedback module for verifying the decoding performance of the decision symbol sequence to obtain a decoding performance index; a channel quality assessment module for calculating a channel quality factor based on the decoding performance index and channel conditions estimated from the received signal; and a complexity decision module for adaptively updating the equalizer complexity parameter based on the channel quality factor for use by the MLSE equalization module. During initial operation, the complexity decision module uses a preset value as the equalizer complexity parameter.

[0016] Compared with existing technologies, the technical solution provided in this application has the following beneficial effects: First, by dynamically adjusting the computational complexity of the equalizer, such as the number of states in the Viterbi decoder, based on real-time channel quality, this application enables the equalizer to operate with lower complexity when channel conditions are good, avoiding unnecessary computational overhead and significantly reducing the average power consumption of the system, thus enabling its successful application in power-sensitive low-power Bluetooth devices. Second, the adaptive mechanism established in this application can maintain excellent anti-interference performance and communication reliability by increasing the number of states under adverse channel conditions, while reducing complexity to save power consumption under good channel conditions, achieving an intelligent balance between performance and power consumption. Third, by utilizing the cyclic redundancy check (CRC) results to establish a feedback loop, especially the forced adjustment mechanism triggered when decoding performance continuously declines, the system can respond to changes in channel conditions in real time and quickly adapt to sudden deterioration of channel quality, enhancing communication robustness in complex wireless environments. Finally, by utilizing the inherent characteristics of Gaussian frequency shift keying (GFS) modulation to simplify branch metric calculations and other optimization methods, the inherent computational complexity of the algorithm is effectively reduced, helping to reduce hardware implementation area and cost. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram of the overall architecture of the Bluetooth receiver provided in the embodiments of this application;

[0019] Figure 2 This is a block diagram of the adaptive MLSE equalization system provided in the embodiments of this application;

[0020] Figure 3 A flowchart illustrating the adaptive MLSE equalization method provided in this application embodiment;

[0021] Figure 4 A flowchart illustrating the adaptive state number selection algorithm provided in an embodiment of this application;

[0022] Figure 5 This is a schematic diagram of the state transition of the Viterbi decoder provided in an embodiment of this application.

[0023] The main reference numerals in the attached figures are explained as follows: 10-RF front-end; 20-Mixer and filter module; 30-Demodulation module; 40-Adaptive MLSE equalization system; 100-Channel estimation module; 200-Channel quality assessment module; 300-State number decision module; 400-Branch metric calculation module; 500-Viterbi decoder module; 600-Path backtracking module; 700-CRC check feedback module; S10-Channel estimation step; S20-Channel quality factor Q calculation step; S30-Decision state number. Steps; S40 - Calculate branch metric; S50 - Viterbi decoding; S60 - Path backtracking output; S70 - CRC check and feedback; The input channel quality factor; - The number of output states; -First threshold; -Second threshold; -Third threshold; S0, S1, S2, S3 -State nodes. Detailed Implementation

[0024] To better understand the technical solutions, objectives, and advantages of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be noted that the specific embodiments described herein are only for explaining this application and are not intended to limit it.

[0025] Example 1

[0026] In one embodiment of this application, an adaptive maximum likelihood sequence estimation equalization method and system for a low-power Bluetooth receiver are described in detail. This technical solution aims to construct a closed-loop control system integrating evaluation, decision-making, execution, and feedback, which can dynamically adjust the computational complexity of the equalizer based on real-time channel quality, thereby significantly reducing the average power consumption of the system while ensuring communication reliability.

[0027] Reference Figure 1This diagram illustrates the overall architecture of the Bluetooth receiver used in the embodiments of this application. In a typical low-power Bluetooth receiver, the wireless signal received by the antenna is first amplified and preliminarily filtered by the RF front-end 10. Subsequently, the signal enters the mixing and filtering module 20, where it is down-converted to baseband or intermediate frequency and then subjected to channel selection filtering to remove out-of-band interference. The demodulation module 30 performs Gaussian frequency shift keying demodulation on the filtered signal, outputting an unequalized digital signal stream. Due to factors such as multipath propagation in the wireless channel, this signal stream typically contains inter-symbol interference. To eliminate this distortion, the demodulated signal is sent to the adaptive maximum likelihood sequence estimation equalization system 40 proposed in the embodiments of this application for processing. This system 40, as the core of the receiver's digital baseband processing, is responsible for recovering the original, distortion-free data sequence.

[0028] Combination Figure 2 and Figure 3 ,in Figure 2 This is a block diagram of the adaptive maximum likelihood sequence estimation equilibrium system 40 provided in an embodiment of this application. Figure 3 This is a flowchart illustrating the corresponding method. The system 40 mainly includes a channel estimation module 100, a channel quality assessment module 200, a state number decision module 300, a branch metric calculation module 400, a Viterbi decoder module 500, a path backtracking module 600, and a CRC check feedback module 700. These modules work together to achieve adaptive equalization processing of the received signal. The following will combine... Figure 3 The flowchart shown details the functions and interactions of each module.

[0029] The processing flow of this method begins with receiving a new Bluetooth Low Energy data packet. In the channel estimation step S10, the channel estimation module 100 estimates the current wireless channel characteristics using a known sequence (e.g., preamble or access address) within the data packet. As a specific implementation, the least squares method can be used to estimate the channel's impulse response by comparing the received preamble sequence with a locally stored standard preamble sequence. Based on the obtained channel impulse response The channel estimation module 100 further calculates two key channel condition parameters: effective channel length. And signal-to-noise ratio.

[0030] Among them, effective channel length This is used to quantify the multipath delay spread of a channel. In this embodiment, it can be defined as the minimum number of taps required for the energy accumulation of the channel impulse response to reach a certain percentage (e.g., 95%) of the total energy. Its calculation method can be expressed as:

[0031]

[0032] in, This represents the total number of taps in the channel impulse response. For example, if the effective channel length of the current channel is calculated... The value is 3.

[0033] The signal-to-noise ratio (SNR) measures the relative strength of the signal compared to the background noise level. Signal power can be obtained from the estimated energy of the channel impulse response, while noise power can be estimated by analyzing the residual of the received signal in the preamble portion. For example, the current channel estimation yields an SNR of 15 dB. The channel estimation module 100 calculates the channel impulse response... Effective channel length The output includes three results: signal-to-noise ratio, ... and signal-to-noise ratio.

[0034] Subsequently, in step S20 of calculating the channel quality factor Q, the channel quality assessment module 200 receives the effective channel length from the channel estimation module 100. In addition to the signal-to-noise ratio, it also receives decoding performance metrics from the CRC check feedback module 700, namely the cyclic redundancy check success rate of historical data packets. The Channel Quality Assessment Module 200 aims to integrate information from these three different dimensions into a single comprehensive indicator that can fully reflect the current communication link quality—the Channel Quality Factor. .

[0035] To enable unified calculation of parameters with different dimensions, the signal-to-noise ratio (SNR) and effective channel length must first be normalized, mapping them to the range of 0 to 1. For example, the normalized value of the SNR... The normalized value of the effective channel length can be obtained by linear mapping through a preset signal-to-noise ratio range. A similar approach can be used. Accordingly, by adjusting the normalized signal-to-noise ratio... Normalized effective channel length and historical cyclic redundancy check success rate By performing a weighted summation, the channel quality factor can be obtained. The calculation formula is as follows:

[0036]

[0037] in, , , These are preset weighting factors, summing to 1, representing the importance of signal-to-noise ratio, channel multipath severity, and historical decoding success rate in the comprehensive evaluation. In this embodiment, the following settings can be configured: , , It should be noted that the formula uses... This is because the longer the channel length, the worse the channel quality; therefore, its contribution to the quality factor should be negative. Assuming the current normalized signal-to-noise ratio is 0.8, the normalized channel length is 0.2, and the historical cyclic redundancy check success rate is 0.9, then the calculated... Value .

[0038] To avoid channel quality factor Due to the frequent switching of equalizer complexity caused by instantaneous fluctuations, the channel quality assessment module 200 will also adjust the calculated... The values ​​are smoothed, for example by using a moving average or a first-order low-pass filter, to obtain a more stable average channel quality factor. .Should It will be passed to the state number decision module 300.

[0039] Number of states to enter decision state In step S30, the state number decision module 300 determines the state number based on the input average channel quality factor. An equalizer complexity parameter is adaptively selected. In this application, this parameter is specifically the core parameter required by the maximum likelihood sequence estimation algorithm (i.e., the Viterbi algorithm)—the number of decoder states. Understandably, the more states there are, the better the performance, but the computational complexity and power consumption also increase exponentially.

[0040] Reference Figure 4 The algorithm for state number decision-making is shown in detail. The state number decision-making module 300 internally pre-sets a set of discrete state numbers (e.g., {2, 4, 8, 16}) and a set of corresponding multi-level thresholds (e.g., first threshold T1=0.85, second threshold T2=0.65, third threshold T3=0.45). The decision logic is as follows: When... When the number of states exceeds the first threshold T1, it indicates that the channel quality is excellent. In this case, the lowest number of states is selected, which is the number of output states. It is 2; when If the threshold is not greater than T1 but greater than the second threshold T2, it indicates that the channel quality is good, and therefore, select... ;when If the threshold is not greater than T2 but greater than the third threshold T3, it indicates that the channel quality is average, and therefore, select... ;like If the value is not greater than T3, it indicates poor channel quality, and the highest number of states should be selected in this case. .

[0041] To further enhance the stability of decision-making and avoid [further issues]... To address the frequent state number switching caused by fluctuations around the threshold, this embodiment introduces a hysteresis decision mechanism. Specifically, when the system is preparing to adjust from a lower state number to a higher state number (e.g., switching from state 4 to state 8), its decision threshold is temporarily lowered by a preset value (e.g., 0.05); conversely, when preparing to adjust downwards, the decision threshold is temporarily increased by a preset value (e.g., 0.05). This mechanism forms a "decision hysteresis loop," ensuring that state number switching requires a more significant change in channel quality to trigger, thereby improving system stability. Assuming smoothing... The value is 0.7. Based on the aforementioned threshold, since... The state number decision module 300 will output The number of states determined by this decision This will be used as a core parameter for subsequent processing modules.

[0042] In step S40, the branch metric calculation module 400 calculates the state number based on the state number output by the state number decision module 300. (4 in this example) and the channel impulse response output by the channel estimation module 100. The branch metric is calculated for each valid state transition of the Viterbi decoder at each time step. The branch metric, which forms the basis of the Viterbi algorithm, measures the difference or similarity between the actual received signal and the expected received signal under the assumption of a particular state transition.

[0043] Traditional branch metric calculations are typically based on the square of the Euclidean distance, and the formula is as follows: ,in These are the actual received signal samples. It is assumed that the previous state was The current input bits are The desired received signal sample. This calculation involves complex multiplication and subtraction, and is computationally intensive.

[0044] This application's embodiments optimize the branch metric calculation for Gaussian frequency shift keying modulation (GFS) used in Bluetooth Low Energy (BLE) by leveraging its constant envelope characteristic. Since the amplitude of the GFS signal is approximately constant, the calculation of the squared Euclidean distance is simplified. After expansion, constant terms and terms unrelated to the received signal can be ignored, ultimately simplifying the calculation to the real part of the inner product of the actual received signal and the desired signal. The simplified branch metric calculation formula is:

[0045]

[0046] in This represents the conjugate of the desired signal. This simplification converts complex multiplication into real multiplication and addition, significantly reducing computational complexity. Furthermore, as an optional implementation, in low-power modes with even more stringent power requirements, integer arithmetic can be used instead of floating-point operations. For example, the received and desired signals can be quantized as integers, and hardware-friendly bit shift operations can be used instead of multiplication. The branch metric calculation module 400 provides the branch metric values ​​calculated for all possible state transitions to the Viterbi decoder module 500.

[0047] In Viterbi decoding step S50, the Viterbi decoder module 500 uses the branch metric value and state number provided by the branch metric calculation module 400. This is the core part of executing the Viterbi algorithm. (Refer to...) Figure 5 , its For example, the state transition relationship in a Viterbi mesh diagram is shown, where S0, S1, S2, and S3 are four state nodes. At each time step... For each target state, the Viterbi decoder module 500 performs an "add-compare-select" operation. Specifically, it examines all predecessor states that can transition to the target state, adds the path metric of the predecessor state to the corresponding branch metric, and obtains a new set of path metrics. Then, it compares these new path metrics and selects the smallest one as the current target state at time step [time]. The path metric is calculated, and the preceding state that produces the minimum path metric is recorded as the surviving path. This process is repeated for all states at each time step.

[0048] In the path backtracking output step S60, the path backtracking module 600 is responsible for recovering the optimal sequence of transmitted symbols from the surviving path information maintained by the Viterbi decoder module 500. To ensure the reliability of the decision, backtracking does not begin immediately, but waits for the Viterbi algorithm to process a sufficient number of symbols so that all surviving paths have a high probability of converging to the same starting segment. This waiting length is called the backtracking depth. In this embodiment, the backtracking depth It is set to be related to channel characteristics, specifically the effective channel length. 5 times, that is In this example, Therefore, backtracking depth After processing the first... After a symbol, the path backtracking module 600 will start from time 1. Starting from the state with the minimum path metric, backtrack in reverse along the surviving path pointers recorded in the Viterbi decoder module 500. Step, get the moment The decision symbols. By sliding the backtracking window, module 600 can continuously output the symbol sequence after the decision, which constitutes the recovered protocol data unit.

[0049] Finally, in the CRC check and feedback step S70, the CRC check feedback module 700 performs cyclic redundancy check on the protocol data units output by the path backtracking module 600. Cyclic redundancy check is a standard mechanism in the Bluetooth Low Energy protocol used to ensure data integrity. Its check result (success or failure) has a dual function. First, the result is used to update the historical cyclic redundancy check success rate. For example, a moving average of the success rates for the most recent 100 data groups can be maintained. Updated The channel quality factor will be used to calculate the channel quality factor when the next data packet arrives, in step S20. This forms a long-term, smooth feedback loop, enabling the complexity of the entire system to adapt to the slow changes in channel quality.

[0050] Secondly, the CRC check result is also used in a rapid adjustment mechanism to cope with sudden deterioration of channel quality. The CRC check feedback module 700 monitors consecutive decoding failure events. In this embodiment, when the cyclic redundancy check of three consecutive data packets fails, the system determines that the channel may have experienced rapid and severe fading. At this time, in order to quickly restore communication, a forced adjustment will be triggered. This forced adjustment bypasses the normal decision-making of the state number decision module 300 and directly changes the state number used to process the next data packet. The value can be doubled (e.g., forcibly increased from 4 to 8), but not exceeding a preset maximum value (e.g., 16). This rapid intervention mechanism ensures the system's robustness in the face of channel mutations.

[0051] Through the coordinated work of the above modules, the system in this embodiment can dynamically optimize its internal computational complexity packet by packet based on the channel conditions and historical decoding performance of each data packet, thereby achieving a significant reduction in power consumption while ensuring communication quality.

[0052] Example 2

[0053] As an alternative implementation, another embodiment of this application describes a variation of the technical solution, which prioritizes ensuring communication performance and reliability in harsh or highly fluctuating channel environments. Compared to the pursuit of a balance between performance and power consumption in Embodiment 1, this embodiment adjusts parameter configuration to make the equalization system more inclined to choose higher computational complexity in exchange for stronger anti-interference capabilities.

[0054] The overall system architecture and method flow of this embodiment are basically the same as those of Embodiment 1, and can also be referred to accordingly. Figures 1 to 5 The main difference lies in the parameter settings within the channel quality assessment module 200 and the state number decision module 300.

[0055] In the channel quality assessment module 200, the channel quality factor is calculated. The weighting factors were reconfigured. The weights in Example 1 were... In this embodiment, the weights are adjusted to As can be seen, the signal-to-noise ratio weights The weight of the effective channel length, representing the severity of multipath propagation, is reduced. This is significantly improved. The intention behind this adjustment is to reduce the channel delay spread (due to...) when the design objective is to cope with complex multipath environments. The reflection (of signal-to-noise ratio) has become a more critical influencing factor than the signal-to-noise ratio. Therefore, improving... The weighting in the comprehensive evaluation can make the system more sensitive to changes in multipath effects.

[0056] Accordingly, in the state number decision module 300, the average channel quality factor is used to... Mapping to state number The multi-level thresholds have also been adjusted. In Example 1, the threshold sequence was {0.85, 0.65, 0.45}, while in this example, the threshold sequence has been increased overall to {0.90, 0.75, 0.55}. This means that the system has higher requirements for channel quality, only requiring extremely high channel quality (…). The system will only select the lowest 2-state mode when the channel quality is low. Within a wider range of channel quality, the system will tend to select 4-state, 8-state, or even 16-state modes.

[0057] To illustrate the impact of this parameter configuration, consider the same input scenario as in Example 1: assuming that the average channel quality factor obtained after channel estimation and evaluation... It is also 0.7. In Example 1, because The number of states selected by the system The value is 4. However, in Example 2, a new threshold sequence is used for judgment, because... If the value is less than the second threshold of 0.75, the decision-making process will continue downwards, ultimately based on... Given a given decision interval, the system will select a higher state number, for example... .

[0058] The difference in these decision outcomes directly leads to different system behaviors. This means that the Viterbi decoder module 500 will operate on a trellis diagram with eight states, which is far more computationally intensive than the four-state case. While this will consume more computational resources and power, the increased number of states allows for more accurate modeling of channel memory, thereby providing stronger inter-symbol interference suppression capabilities and effectively reducing the bit error rate under harsh channel conditions.

[0059] It is understood that this embodiment constructs a "performance-first" configuration mode by adjusting the weight of channel quality assessment and the threshold of state number decision. This configuration exhibits better robustness in wireless environments with large or generally poor channel conditions (such as factory automation and vehicle-to-everything (V2X) communication scenarios), effectively reducing data packet loss rate and ensuring connection stability. The trade-off is that the system's average power consumption will be slightly higher than the balanced configuration in Embodiment 1. This configurability demonstrates the flexibility of the technical solution in this application, allowing for trade-offs and customization between performance and power consumption according to different application requirements and operating scenarios.

[0060] Example 3

[0061] Alternatively, in another embodiment, the technical solution of this application can also be implemented in a variant for ultra-low power applications where cost and computing resources are extremely sensitive. In such scenarios, the channel quality evaluation model can be further simplified, sacrificing some evaluation accuracy in exchange for lower computational overhead and faster processing speed.

[0062] The overall system architecture and method flow of this embodiment are largely the same as those of Embodiment 1, with the core change being the simplification of the function of the channel quality assessment module 200. In Embodiments 1 and 2, the channel quality factor... The calculation integrates three dimensions: signal-to-noise ratio, effective channel length, and historical cyclic redundancy check success rate. Among these, the effective channel length... The calculation requires the estimated channel impulse response. Energy accumulation and comparison still require a certain amount of computation.

[0063] To minimize the complexity of the evaluation process, in this embodiment, the channel quality evaluation module 200 omits the evaluation of the effective channel length. The calculation and considerations. Therefore, the channel quality factor. The calculation formula is simplified to rely solely on the signal-to-noise ratio and the historical cyclic redundancy check success rate. The simplified calculation formula is as follows:

[0064]

[0065] As the evaluation dimensions are reduced, the weighting factors also need to be adjusted accordingly, for example, they can be set to... And their sum is still 1.

[0066] Under this simplified model, the system's operation becomes as follows: the channel estimation module 100 only needs to estimate the signal-to-noise ratio (SNR) when processing each data packet, without needing to calculate the effective channel length. The channel quality assessment module 200 receives the normalized SNR. and the historical cyclic redundancy check success rate from the CRC check feedback module 700 Then, the channel quality factor is quickly calculated using the simplified formula described above. The subsequent smoothing, state number decision, branch metric calculation, and other steps are consistent with those in Example 1.

[0067] The benefits of this simplification are obvious. First, it significantly reduces the computational complexity of the channel assessment process by eliminating the need for traversing the channel impulse response vector and calculating energy accumulation. This translates to valuable savings in processing cycles and power consumption for receivers implemented with low-frequency microcontrollers or minimal hardware logic.

[0068] However, this simplification also brings certain performance trade-offs. The system no longer directly considers the multipath delay spread of the channel, which means that its judgment of the channel type may not be accurate enough. For example, in two channels with similar signal-to-noise ratios and cyclic redundancy check success rates, but one is a flat fading channel (… The other is a long-delay multipath channel (small), In the case of a large number of cases, the evaluation model in this embodiment may give the same result. The value is chosen to select the same number of states, but in reality, long-delay multipath channels require a higher number of states to achieve effective equalization.

[0069] Nevertheless, in many channel environments dominated primarily by additive white Gaussian noise rather than severe multipath propagation, the signal-to-noise ratio (SNR) is the most crucial factor determining communication quality. In these scenarios, the simplified evaluation scheme of this embodiment still achieves good adaptive performance, effectively adjusting the equalizer complexity according to changes in the SNR.

[0070] Therefore, the adaptive equalization framework proposed in this application has strong scalability and adaptability. The composition of the channel quality factor is not fixed and can be tailored according to application requirements and hardware resource constraints. The scheme of this embodiment is a broader technical concept, that is, the channel quality factor can be determined by at least one of the signal-to-noise ratio and the cyclic redundancy check success rate, providing a specific implementation method, thereby providing a stronger layer and broader coverage for the protection scope of this application.

[0071] Example 4

[0072] This embodiment demonstrates another specific implementation of the feedback adjustment mechanism in the technical solution of this application, aiming to provide a fast response strategy different from the "continuous failure counting" in Embodiment 1. This variation is mainly reflected in the fast adjustment logic inside the CRC check feedback module 700, which changes from monitoring continuous events to monitoring statistical performance within a time window, thereby supporting the broader technical concept of "degradation of decoding performance triggering forced adjustment".

[0073] The overall system architecture and method flow of this embodiment are consistent with those of Embodiment 1. The difference lies in the judgment criteria and triggering conditions of the CRC check feedback module 700 for decoding performance degradation. In Embodiment 1, the triggering condition for the fast adjustment mechanism is "detection of cyclic redundancy check failure for three consecutive data packets". This mechanism is very effective for situations where the channel suddenly experiences deep fading, leading to continuous packet loss.

[0074] In this embodiment, the CRC check feedback module 700 maintains a long-term historical cyclic redundancy check success rate for calculating the channel quality factor. In addition, the success rate of cyclic redundancy check (CRC) is monitored within a short window. This short window can be defined as the most recently received... Data groups, for example The module calculates and updates the success rate of the 10 most recent data groups in real time.

[0075] Meanwhile, the system presets an emergency performance threshold, such as 50%. The fast adjustment logic of the CRC check feedback module 700 accordingly becomes: after processing each data packet and obtaining its cyclic redundancy check result, it updates the success rate within a short-term window. If this short-term success rate is lower than the preset emergency threshold (i.e., the success rate of the most recent 10 packets is lower than 50%), the system considers the communication link to be continuously unstable in the near future, and even without three or more consecutive failures, the overall performance has deteriorated to an unacceptable level.

[0076] Once this situation is detected, the CRC check feedback module 700 will trigger a forced adjustment mechanism similar to that in Embodiment 1. For example, it will send an instruction to the state number decision module 300 to force an increase in complexity, so that when processing the next data packet, the number of states of the equalizer will increase. The value is forcibly doubled (but not exceeding the maximum value), or the branch metric calculation module 400 is forcibly switched to a higher precision calculation mode.

[0077] This "success rate threshold monitoring" mechanism has different sensitivity characteristics compared to the "continuous failure counting" mechanism. It is more sensitive to occasional, non-continuous packet loss patterns. For example, if channel conditions deteriorate, causing the decoding results of data packets to exhibit a "success-failure-success-failure-failure-success-failure…" pattern, this pattern may never trigger the "three consecutive failures" condition. However, in this case, the short-term success rate will drop rapidly, and when it falls below the 50% threshold, the mechanism in this embodiment can intervene in time to attempt to stabilize the connection by increasing the complexity of the equalizer.

[0078] This embodiment provides an effective method for detecting and responding to continuous channel degradation, enhancing the system's adaptability and robustness under different packet loss modes. It and the mechanism in Embodiment 1 can be considered as two complementary fast feedback strategies, which can be selected and used, or even combined (i.e., triggering adjustment when either condition is met), depending on the expected channel fading characteristics in practical applications. This embodiment provides a specific implementation method and support for the technical feature of "forcibly increasing the value of the equalizer complexity parameter when the decoding performance index is detected to be lower than a preset performance threshold within a preset time window."

[0079] The above description is merely a preferred embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An adaptive MLSE equalization method for a low-power Bluetooth receiver, characterized in that, include: The received signal is processed using MLSE equalization based on the equalizer complexity parameter to obtain a decision symbol sequence; The decoding performance of the decision symbol sequence is verified to obtain decoding performance indicators; The channel quality factor is calculated based on the decoding performance indicators and the channel conditions estimated based on the received signal. The equalizer complexity parameter is adaptively updated based on the channel quality factor for subsequent processing of the received signal. During the initial processing, the equalizer complexity parameter is a preset value.

2. The method according to claim 1, characterized in that, The equalizer complexity parameter is the number of states of the Viterbi decoder.

3. The method according to claim 1 or claim 2, characterized in that, The channel conditions include the signal-to-noise ratio and the effective channel length.

4. The method according to claim 3, characterized in that, Calculating the channel quality factor includes: The normalized signal-to-noise ratio, the effective channel length, and the decoding performance index are weighted and summed, and the summation result is smoothed.

5. The method according to any one of claims 1 to 4, characterized in that, The decoding performance metric is the success rate of cyclic redundancy check (CRC) for historical data packets.

6. The method according to claim 2, characterized in that, Determining the number of states based on the channel quality factor includes: The channel quality factor is compared with multiple preset thresholds, and a hysteresis decision mechanism is used to map the channel quality factor to one of a preset set of states.

7. The method according to any one of claims 1 to 6, characterized in that, The MLSE equalization includes a branch metric calculation step, which utilizes the constant envelope characteristic of Gaussian frequency shift keying (GFSK) modulation to simplify the calculation based on Euclidean distance to a calculation based on the real part of the inner product.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: When the decoding performance metric is detected to be lower than a preset performance threshold within a preset time window, the value of the equalizer complexity parameter is forcibly increased.

9. The method according to claim 8, characterized in that, The detection of a decoding performance index falling below a preset performance threshold within a preset time window specifically refers to the detection of CRC check failures in a predetermined number of consecutive data packets.

10. An adaptive MLSE equalization system for a low-power Bluetooth receiver, characterized in that, include: The MLSE equalization module is used to process the received signal using MLSE equalization based on an equalizer complexity parameter to obtain a decision symbol sequence. The feedback module is used to verify the decoding performance of the decision symbol sequence in order to obtain a decoding performance index. The channel quality assessment module is used to calculate the channel quality factor based on the decoding performance indicators and the channel conditions estimated based on the received signals. The complexity decision module is used to adaptively update the equalizer complexity parameter according to the channel quality factor for use by the MLSE equalization module. In the initial operation, the complexity decision module uses a preset value as the complexity parameter of the equalizer.

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