Signal detection hardware system suitable for dual-mode communication chip of power system
By designing a signal detection hardware system suitable for dual-mode communication chips in power systems, and utilizing components such as SRAM delay units and complex multiplier units to work together, the problems of computational complexity and high power consumption in existing technologies are solved, achieving efficient and low-power STF detection.
Patent Information
- Application Number
- CN202511381771.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-25
- Publication Date
- 2025-12-09
AI Technical Summary
Existing OFDM signal detection methods are computationally complex, power-consuming, and require large areas in power systems. Traditional designs require storing a large number of old values, and the multiplier utilization is low, resulting in low STF detection efficiency.
Design a signal detection hardware system suitable for dual-mode communication chips in power systems, including an SRAM delay unit, a complex multiplier unit, a state machine controller, a moving autocorrelation value update unit, a moving energy update unit, and a peak detection and boundary judgment unit. Through the collaborative work of these units, the contribution value and energy value of the input signal are efficiently calculated, and the STF sequence and its end boundary are accurately determined.
It improves the efficiency of STF detection, reduces redundant calculations, lowers hardware resource consumption and power consumption, and enhances the real-time performance and accuracy of signal detection, making it suitable for low-power wireless communication scenarios in power systems.
Smart Images
Figure CN121098353A_ABST
Abstract
Description
Technical Field
[0001] The embodiments of the present invention relate to the field of power line carrier communication technology, and more specifically, the embodiments of the present invention relate to a signal detection hardware system suitable for dual-mode communication chips in power systems. Background Technology
[0002] In power systems, wireless communication technology is widely used in smart grids, power equipment monitoring, and other fields. Orthogonal Frequency Division Multiplexing (OFDM), as a highly efficient multi-carrier modulation technique, is used in power wireless communication protocols to address multipath fading and noise interference. However, the synchronization detection of OFDM signals is a key challenge, especially the detection of the Short Training Field (STF), which can be used to achieve frame synchronization and boundary localization.
[0003] However, existing OFDM signal detection methods suffer from computational complexity, high power consumption, and large area requirements in their hardware implementations. Traditional designs require storing large amounts of old values, resulting in low multiplier utilization and consequently, low efficiency in STF detection. Summary of the Invention
[0004] In this context, embodiments of the present invention aim to provide a signal detection hardware system suitable for dual-mode communication chips in power systems, which can improve the efficiency of STF detection.
[0005] In a first aspect of the present invention, a signal detection hardware system suitable for a dual-mode communication chip in a power system is provided, comprising an SRAM delay unit, a complex multiplier unit, a state machine controller, a moving autocorrelation value update unit, a moving energy update unit, and a peak detection and boundary judgment unit; the complex multiplier unit includes a first complex multiplier and a second complex multiplier, wherein: The SRAM delay unit is used to store historical sampling signals and receive input signals. The state machine controller is used to control the operation of the complex multiplier unit, the moving autocorrelation value update unit, and the moving energy update unit; The complex multiplier unit is used to calculate the new contribution value and new energy value of the input signal, as well as the old contribution value and old energy value of the historical sampled signal, based on the historical sampled signal and the input signal. The moving autocorrelation value update unit is used to determine the moving autocorrelation value based on the new contribution value and the old contribution value; The mobile energy update unit is used to determine the mobile energy value based on the new energy value and the old energy value; The peak detection and boundary determination unit is used to determine the peak value based on the moving autocorrelation value and the moving energy value; and to determine the detected STF sequence and the end boundary of the STF sequence based on the peak value.
[0006] In one embodiment of this implementation, the historical sampling signal comprises ten historical R sequences, which are ordered according to the sampling time. The input signal is an input R sequence, and the historical R sequences comprise historical real parts and historical imaginary parts. The input R sequence comprises real components and imaginary components.
[0007] In one embodiment of this implementation, the first complex multiplier is used to calculate the new contribution value of the input signal and the old contribution value of the historical sampled signal based on the historical sampled signal and the input signal; The second complex multiplier is used to calculate the new energy value of the input signal and the old energy value of the historical sampled signal based on the historical sampled signal and the input signal.
[0008] In one embodiment of this implementation, the first complex multiplier calculates the new contribution value of the input signal and the old contribution value of the historical sampled signal based on the historical sampled signal and the input signal in the following specific manner: Obtain the historical real part of each historical R sequence from the historical sampled signal; Obtain the real part vector of the input signal; The old contribution value of the historical sampling signal is obtained by conjugating the historical real part of each historical R sequence. The real part vector and the historical real part of each historical R sequence are conjugate to obtain the new contribution value of the input signal.
[0009] In one embodiment of this implementation, the second complex multiplier calculates the new energy value of the input signal and the old energy value of the historical sampled signal based on the historical sampled signal and the input signal in the following specific manner: Obtain the target historical R sequence from the historical sampled signals whose sampling time is closest to the time of receiving the input signal; Obtain the real part and imaginary part of the target history R sequence; Based on the real part and the imaginary part of the target history, the old energy value of the historical sampled signal is determined; Based on the real component and the imaginary component, a new energy value of the input signal is determined.
[0010] In one embodiment of this implementation, the second complex multiplier determines the old energy value of the historical sampled signal based on the real part and the imaginary part of the target history in the following specific manner: The old energy value of the historical sampling signal is obtained by adding the square of the real part of the target history to the square of the imaginary part of the target history. The second complex multiplier determines the new energy value of the input signal based on the real and imaginary components in the following specific manner: The square of the real component is added to the square of the imaginary component to obtain the new energy value of the input signal.
[0011] In one embodiment of this implementation, the moving autocorrelation value update unit determines the moving autocorrelation value based on the new contribution value and the old contribution value in the following specific way: The difference between the new contribution value and the old contribution value is determined as the moving autocorrelation value.
[0012] In one embodiment of this implementation, the mobile energy update unit determines the mobile energy value based on the new energy value and the old energy value in the following specific way: The difference between the new energy value and the old energy value is determined as the moving energy value.
[0013] In one embodiment of this implementation, the peak detection and boundary judgment unit determines the peak value based on the moving autocorrelation value and the moving energy value in the following specific way: The approximate square root of the moving autocorrelation value is determined as the autocorrelation value amplitude; The peak value is determined based on the autocorrelation value amplitude, the mobile energy value, and a preset threshold.
[0014] In one embodiment of this implementation, the peak detection and boundary determination method based on the autocorrelation value amplitude, the moving energy value, and a preset threshold specifically involves: The product of the mobile energy value and the preset threshold is calculated; If the autocorrelation value is greater than the product, then the input signal is determined to be the peak value.
[0015] According to an embodiment of the present invention, a signal detection hardware system for dual-mode communication chips in power systems utilizes an SRAM delay unit to store historical sampled signals and receive new inputs, providing basic data for subsequent calculations. A state machine controller precisely regulates the operation of each unit, ensuring orderly process flow. A complex multiplier unit efficiently calculates the new and old contribution values and energy values of the input signal, providing key parameters for subsequent processing. Moving autocorrelation update units and moving energy update units quickly determine the moving autocorrelation value and moving energy value based on these parameters, reducing redundant calculations. Finally, a peak detection and boundary judgment unit accurately determines the peak value based on the above results, thereby determining the STF sequence and its termination boundary. All units in the entire system work collaboratively, resulting in a compact and efficient process that effectively improves the efficiency of STF detection. Attached Figure Description
[0016] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein: Figure 1 This is a schematic diagram of a signal detection hardware system for a dual-mode communication chip in a power system, provided as an embodiment of the present invention.
[0017] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation
[0018] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are given merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of this disclosure to those skilled in the art.
[0019] Those skilled in the art will recognize that embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.
[0020] According to an embodiment of the present invention, a signal detection hardware system suitable for dual-mode communication chips in power systems is proposed.
[0021] It should be noted that the number of any elements in the accompanying drawings is for illustrative purposes only and not as a limitation, and any naming is for distinction only and has no limiting meaning.
[0022] The principles and spirit of the present invention will be explained in detail below with reference to several representative embodiments.
[0023] Exemplary System The following is for reference. Figure 1 , Figure 1 This is a schematic diagram of a signal detection hardware system for a dual-mode communication chip in a power system, provided according to an embodiment of the present invention. The system includes an SRAM delay unit 101, a complex multiplier unit 102, a state machine controller 103, a moving autocorrelation update unit 104, a moving energy update unit 105, and a peak detection and boundary judgment unit 106. The complex multiplier unit 102 includes a first complex multiplier and a second complex multiplier, wherein: The SRAM delay unit 101 is used to store historical sampling signals and receive input signals.
[0024] In this embodiment of the application, the historical sampling signal includes ten historical R sequences, which are ordered according to the sampling time. The input signal is an input R sequence, and the historical R sequence includes a historical real part and a historical imaginary part. The input R sequence includes a real component and an imaginary component.
[0025] In this embodiment, the SRAM delay unit 101 is written and used via address cyclic mode, with a depth of 640, meeting the delay requirements of 10 R sequences, each R sequence having a length of 64. Its width is the input sampling data bit width, and it can be reused with other system modules (such as a deinterleaving module) to reduce the overall chip area.
[0026] In this embodiment, since reading the corresponding 10 sampling points of 10 segments of R sequence data requires 10 clock cycles, it would be too time-consuming to read them during the calculation. Therefore, one sampling point needs to be read from SRAM in advance and latched into registers to form a real part history register array (length 10) and an imaginary part history register array (length 10). Simultaneously, a real part increment array (length 10) and an imaginary part increment array (length 10) corresponding to each newly added data point in the R segment are generated. These can be generated by shifting from the real part history array and the imaginary part history array, with the last element being the current input point. This avoids extra storage of the increment arrays and reduces register usage.
[0027] The state machine controller 102 is used to control the operation of the complex multiplier unit, the moving autocorrelation value update unit, and the moving energy update unit.
[0028] In this embodiment, the state machine controller 102 controls the calculation process, which is divided into three states: idle, update, and summation.
[0029] The complex multiplier unit 103 is used to calculate the new contribution value and new energy value of the input signal, as well as the old contribution value and old energy value of the historical sampled signal, based on the historical sampled signal and the input signal.
[0030] In this embodiment of the application, the first complex multiplier is used to calculate the new contribution value of the input signal and the old contribution value of the historical sampled signal based on the historical sampled signal and the input signal.
[0031] As an optional implementation, the first complex multiplier calculates the new contribution value of the input signal and the old contribution value of the historical sampled signal based on the historical sampled signal and the input signal in the following specific manner: Obtain the historical real part of each historical R sequence from the historical sampled signal; Obtain the real part vector of the input signal; The old contribution value of the historical sampling signal is obtained by conjugating the historical real part of each historical R sequence. The real part vector and the historical real part of each historical R sequence are conjugate to obtain the new contribution value of the input signal.
[0032] This implementation method precisely extracts the historical real part of each historical R sequence from the historical sampled signal and obtains the real part vector of the input signal, laying a solid data foundation for subsequent calculations. A conjugation operation is performed on the historical real part to obtain the old contribution value, and the conjugation of the real part vector and the historical real part is processed to obtain the new contribution value. This processing method fully utilizes the characteristics of conjugation operations, enabling a more accurate characterization of the correlation and differences between signals. Compared to traditional methods, it avoids complex and error-prone computational steps, simplifies the calculation process while ensuring the accuracy of the calculation results, and improves the calculation speed. This allows the system to respond to signal changes more quickly, providing strong support for subsequent contribution-value-based detection and analysis.
[0033] The second complex multiplier is used to calculate the new energy value of the input signal and the old energy value of the historical sampled signal based on the historical sampled signal and the input signal.
[0034] As an optional implementation, the second complex multiplier calculates the new energy value of the input signal and the old energy value of the historical sampled signal based on the historical sampled signal and the input signal in the following specific manner: Obtain the target historical R sequence from the historical sampled signals whose sampling time is closest to the time of receiving the input signal; Obtain the real part and imaginary part of the target history R sequence; Based on the real part and the imaginary part of the target history, the old energy value of the historical sampled signal is determined; Based on the real component and the imaginary component, a new energy value of the input signal is determined.
[0035] This implementation method, by precisely selecting the target historical R sequence from historical sampled signals that is closest to the reception time of the input signal, ensures the timeliness and relevance of the selected historical data, enabling the calculated old energy value to more accurately reflect the historical energy state of the current signal. The calculation of the old energy value using the real and imaginary parts of the target historical R sequence, and the determination of the new energy value using the real and imaginary components of the input signal itself, fully utilizes the mathematical characteristics of the signal, simplifies the calculation process, and reduces unnecessary computational steps. Simultaneously, the precise data selection and scientific calculation method guarantee the accuracy of the energy value calculation, providing a reliable basis for subsequent signal detection, analysis, and judgment based on energy values, and helping the system to identify signal characteristics more quickly and accurately.
[0036] Specifically, the second complex multiplier determines the old energy value of the historical sampled signal based on the real part and the imaginary part of the target history as follows: The old energy value of the historical sampling signal is obtained by adding the square of the real part of the target history to the square of the imaginary part of the target history.
[0037] Furthermore, the second complex multiplier determines the new energy value of the input signal based on the real and imaginary components in the following specific manner: The square of the real component is added to the square of the imaginary component to obtain the new energy value of the input signal.
[0038] This implementation method determines the old energy value of the historical sampled signal by squaring and adding the real and imaginary parts of the target signal respectively. The new energy value is obtained by processing the real and imaginary parts of the input signal in the same way. This calculation method strictly follows the mathematical principles of the complex number energy definition, enabling precise quantification of signal energy. The squaring and adding operation effectively avoids the cancellation of positive and negative real and imaginary parts, ensuring that the energy value accurately reflects the signal strength. Moreover, the calculation process is simple and clear, requiring no complex transformations or multiple iterations, reducing computational load and time overhead, allowing the system to quickly obtain energy values. Based on such accurate and efficient energy calculation, subsequent signal detection and analysis can obtain more reliable data support, thereby improving the performance and stability of signal detection in the dual-mode communication chip of the entire power system.
[0039] The moving autocorrelation value update unit 104 is used to determine the moving autocorrelation value based on the new contribution value and the old contribution value.
[0040] In this embodiment of the application, the moving autocorrelation value update unit 104 determines the moving autocorrelation value based on the new contribution value and the old contribution value in the following specific way: The difference between the new contribution value and the old contribution value is determined as the moving autocorrelation value.
[0041] This implementation method determines the moving autocorrelation value by calculating the difference between the new and old contribution values. This approach can sensitively capture changes in the signal over time. Since the new and old contribution values reflect the characteristics of the signal at different times, their difference directly reflects the dynamic changes in signal characteristics over time. This helps to promptly detect abnormal fluctuations or specific patterns in the signal, providing crucial information for subsequent accurate signal feature detection. Furthermore, this calculation method is simple and direct, involving only one subtraction operation, significantly reducing computational complexity, hardware resource consumption, and computation time. This allows the system to respond quickly and update the moving autocorrelation value in real time, improving the efficiency and real-time performance of the entire signal detection system. It is particularly suitable for dual-mode communication scenarios in power systems with high real-time requirements.
[0042] The mobile energy update unit 105 is used to determine the mobile energy value based on the new energy value and the old energy value.
[0043] In this embodiment of the application, the mobile energy update unit 105 determines the mobile energy value based on the new energy value and the old energy value in the following specific way: The difference between the new energy value and the old energy value is determined as the moving energy value.
[0044] In this implementation, from the perspective of signal monitoring accuracy, the new energy value represents the current energy state of the input signal, while the old energy value reflects the historical signal energy situation. The difference between the two can accurately capture the dynamic changes of signal energy over time. This helps to quickly detect sudden changes in signal energy, such as the sudden appearance of interference signals or the attenuation of useful signals, providing strong support for timely identification of signal anomalies and ensuring communication quality. In terms of computational efficiency, this method only requires one subtraction operation, making the calculation process simple and efficient, greatly reducing the complexity of hardware implementation and the consumption of computing resources. This allows the system to update the moving energy value in real time with lower power consumption and faster speed, meeting the stringent requirements of power system dual-mode communication for real-time performance and reliability, and effectively improving the performance and stability of the entire signal detection system.
[0045] The peak detection and boundary judgment unit 106 is used to determine the peak value based on the moving autocorrelation value and the moving energy value; and to determine the detected STF sequence and the end boundary of the STF sequence based on the peak value.
[0046] In this embodiment of the application, the peak detection and boundary judgment unit 106 determines the peak value based on the moving autocorrelation value and the moving energy value in the following specific way: The approximate square root of the moving autocorrelation value is determined as the autocorrelation value amplitude; The peak value is determined based on the autocorrelation value amplitude, the mobile energy value, and a preset threshold.
[0047] This implementation method allows the square root operation to balance numerical values to some extent, making the autocorrelation value amplitude more accurately reflect the strength of the signal's own correlation, thus laying a solid foundation for accurate peak determination. Secondly, determining the peak value by combining the autocorrelation value amplitude, the moving energy value, and a preset threshold significantly improves the accuracy and reliability of peak detection. The moving energy value provides a reference for the overall energy level of the signal, corroborating the autocorrelation value amplitude, and combined with the preset threshold, effectively eliminates false peaks and avoids misjudgment.
[0048] Specifically, the peak detection and boundary judgment unit 106 determines the peak value based on the autocorrelation value amplitude, the moving energy value, and the preset threshold in the following manner: The product of the mobile energy value and the preset threshold is calculated; If the autocorrelation value is greater than the product, then the input signal is determined to be the peak value.
[0049] In this implementation, the moving energy value reflects the overall energy status of the signal. Combined with the product obtained from a preset threshold, it effectively adapts to signal scenarios with different energy levels, avoiding misjudgments or missed judgments due to signal energy fluctuations. When the autocorrelation value amplitude is greater than this product, it is determined to be a peak value. This judgment method fully considers the signal's own correlation and energy characteristics, greatly improving the accuracy of peak detection.
[0050] In this embodiment, the peak position is recorded, and then the peak value is refreshed by comparing the autocorrelation value divided by the power value (if it is larger, multiplication is used in hardware). If no refresh count meets the set condition, it is confirmed that an STF sequence and the output STF end boundary have been detected.
[0051] For example, the system works as follows: The input signal enters in complex form (real and imaginary components). SRAM stores 640 historical data points, including 10 R-sequences, each 64 units long. One sampling point in advance, the system reads data from SRAM for the next calculation point, forming a real-part history array and an imaginary-part history array, each 10 units long. These arrays are then combined with the current input point to generate a real-part increment array and an imaginary-part increment array. Autocorrelation value updates do not directly store old contribution values; instead, they are calculated in real-time from the real-part and imaginary-part history arrays. A state machine uses two complex multipliers to process addition / subtraction operations in parallel over 45 cycles (two multiplications per cycle: one to calculate the complex product of the new contribution value and the other to calculate the complex product of the old contribution value). Moving energy calculation uses two complex multipliers within one cycle to calculate both the old and new energy values, obtaining the moving energy value by adding the new energy value and subtracting the old energy value. The autocorrelation amplitude is then calculated (using an approximate square root), and a threshold comparison (autocorrelation value > moving energy value × threshold) is used to detect peak values. Then search for consecutive descending points. If the consecutive descending points meet the set number of points, it is confirmed as an STF boundary.
[0052] The detection logic for STF sequences in the protocol is as follows: The STF sequence consists of 10 repeating short sequences. Each sequence is modulated using scrambling symbols ([-1, -1, -1, -1, 1, 1, -1, -1, -1, 1]) to form either a positive or negative repeating short sequence. The system detects the sequence by calculating the autocorrelation value of paired short sequences. Specifically, the 10 short sequences are paired into 45 possible combinations. The conjugate complex product of each pair is calculated, and the autocorrelation contribution values of all pairs are accumulated. Simultaneously, autocorrelation calculations with different delays (delay orders from 1 to 9) are supported to capture the periodic repetition characteristics of the sequence, ensuring accurate identification of the STF start and end boundaries even in noisy environments.
[0053] The system disclosed in this application can achieve an input rate of up to 25 / 24MHz, a system clock of 50MHz, and a 46-cycle interval (less than the input cycle) to ensure real-time performance. Furthermore, by multiplexing SRAM with other modules (such as deinterleaving), the baseband chip area is further reduced.
[0054] Two complex multipliers are used to perform a large number of autocorrelation and mobile energy calculations. Addition / subtraction are processed in parallel each cycle, resulting in high computational efficiency. The time-division multiplexing multiplication accounts for 46 cycles. At the module operating frequency of 50MHz, it supports input rates of 25 / 24MHz, which meets the protocol requirements. Data is stored in SRAM, avoiding register arrays and reducing power consumption and area; SRAM is reusable, further optimizing system resources. Accurate STF boundary detection is achieved through threshold comparison and descent point search, which is robust and suitable for power noise environments; Overall hardware resources are optimized, making it suitable for low-power wireless communication chips.
[0055] According to an embodiment of the present invention, a signal detection hardware system for dual-mode communication chips in power systems utilizes an SRAM delay unit to store historical sampled signals and receive new inputs, providing basic data for subsequent calculations. A state machine controller precisely regulates the operation of each unit, ensuring orderly process flow. A complex multiplier unit efficiently calculates the new and old contribution values and energy values of the input signal, providing key parameters for subsequent processing. Moving autocorrelation update units and moving energy update units quickly determine the moving autocorrelation value and moving energy value based on these parameters, reducing redundant calculations. Finally, a peak detection and boundary judgment unit accurately determines the peak value based on the above results, thereby determining the STF sequence and its termination boundary. All units in the entire system work collaboratively, resulting in a compact and efficient process that effectively improves the efficiency of STF detection. In the description of this invention, it should be noted that the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.
[0056] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0057] In the several embodiments provided by this invention, it should be understood that the disclosed system can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and other division methods may be used in actual implementation. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces. Indirect couplings or communication connections between systems or units may be electrical, mechanical, or other forms.
[0058] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0059] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0060] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0061] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
[0062] Furthermore, although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.
Claims
1. A signal detection hardware system suitable for dual-mode communication chips in power systems, characterized in that, The system includes an SRAM delay unit, a complex multiplier unit, a state machine controller, a moving autocorrelation update unit, a moving energy update unit, and a peak detection and boundary judgment unit; the complex multiplier unit includes a first complex multiplier and a second complex multiplier, wherein: The SRAM delay unit is used to store historical sampling signals and receive input signals. The state machine controller is used to control the operation of the complex multiplier unit, the moving autocorrelation value update unit, and the moving energy update unit; The complex multiplier unit is used to calculate the new contribution value and new energy value of the input signal, as well as the old contribution value and old energy value of the historical sampled signal, based on the historical sampled signal and the input signal. The moving autocorrelation value update unit is used to determine the moving autocorrelation value based on the new contribution value and the old contribution value; The mobile energy update unit is used to determine the mobile energy value based on the new energy value and the old energy value; The peak detection and boundary determination unit is used to determine the peak value based on the moving autocorrelation value and the moving energy value; and to determine the detected STF sequence and the end boundary of the STF sequence based on the peak value.
2. The signal detection hardware system for dual-mode communication chips in power systems according to claim 1, characterized in that, The historical sampling signal comprises ten historical R sequences, which are ordered according to the sampling time. The input signal is an input R sequence, and the historical R sequences comprise historical real parts and historical imaginary parts. The input R sequence comprises real components and imaginary components.
3. The signal detection hardware system for dual-mode communication chips in power systems according to claim 2, characterized in that, The first complex multiplier is used to calculate the new contribution value of the input signal and the old contribution value of the historical sampled signal based on the historical sampled signal and the input signal; The second complex multiplier is used to calculate the new energy value of the input signal and the old energy value of the historical sampled signal based on the historical sampled signal and the input signal.
4. The signal detection hardware system for dual-mode communication chips in power systems according to claim 3, characterized in that, The first complex multiplier calculates the new contribution value of the input signal and the old contribution value of the historical sampled signal based on the historical sampled signal and the input signal in the following specific manner: Obtain the historical real part of each historical R sequence from the historical sampled signal; Obtain the real part vector of the input signal; The old contribution value of the historical sampling signal is obtained by conjugating the historical real part of each historical R sequence. The real part vector and the historical real part of each historical R sequence are conjugate to obtain the new contribution value of the input signal.
5. The signal detection hardware system for dual-mode communication chips in power systems according to claim 4, characterized in that, The second complex multiplier calculates the new energy value of the input signal and the old energy value of the historical sampled signal based on the historical sampled signal and the input signal in the following specific manner: Obtain the target historical R sequence from the historical sampled signals whose sampling time is closest to the time of receiving the input signal; Obtain the real part and imaginary part of the target history R sequence; Based on the real part and the imaginary part of the target history, the old energy value of the historical sampled signal is determined; Based on the real component and the imaginary component, a new energy value of the input signal is determined.
6. The signal detection hardware system for dual-mode communication chips in power systems according to claim 5, characterized in that, The second complex multiplier determines the old energy value of the historical sampled signal based on the real part and the imaginary part of the target history in the following specific way: The old energy value of the historical sampling signal is obtained by adding the square of the real part of the target history to the square of the imaginary part of the target history. The second complex multiplier determines the new energy value of the input signal based on the real and imaginary components in the following specific manner: The square of the real component is added to the square of the imaginary component to obtain the new energy value of the input signal.
7. The signal detection hardware system for dual-mode communication chips in power systems according to claim 6, characterized in that, The method by which the moving autocorrelation value update unit determines the moving autocorrelation value based on the new contribution value and the old contribution value is as follows: The difference between the new contribution value and the old contribution value is determined as the moving autocorrelation value.
8. The signal detection hardware system for dual-mode communication chips in power systems according to claim 7, characterized in that, The mobile energy update unit determines the mobile energy value based on the new energy value and the old energy value in the following specific way: The difference between the new energy value and the old energy value is determined as the moving energy value.
9. The signal detection hardware system for dual-mode communication chips in power systems according to claim 8, characterized in that, The peak detection and boundary judgment unit determines the peak value based on the moving autocorrelation value and the moving energy value in the following specific way: The approximate square root of the moving autocorrelation value is determined as the autocorrelation value amplitude; The peak value is determined based on the autocorrelation value amplitude, the mobile energy value, and a preset threshold.
10. The signal detection hardware system for dual-mode communication chips in power systems according to claim 9, characterized in that, The peak detection and boundary judgment unit determines the peak value based on the autocorrelation value amplitude, the moving energy value, and the preset threshold in the following specific manner: The product of the mobile energy value and the preset threshold is calculated; If the autocorrelation value is greater than the product, then the input signal is determined to be the peak value.