A link adaptation method in a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) communication system
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 深圳市力合微电子股份有限公司
- Filing Date
- 2026-04-20
- Publication Date
- 2026-07-14
Smart Images
Figure CN122394742A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power line communication, and in particular to a link adaptation method in a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) communication system. Background Technology
[0002] Power line communication (PLC) is a communication method that uses existing power line networks to transmit signals. Its advantage lies in the fact that it eliminates the need for dedicated communication lines, significantly reducing deployment and maintenance costs. However, power lines are designed for transmitting electrical energy, and their channel environment is extremely harsh, exhibiting characteristics such as high noise, significant attenuation, and drastic impedance fluctuations, posing a serious challenge to communication reliability.
[0003] To overcome the aforementioned challenges, Orthogonal Frequency Division Multiplexing (OFDM) technology has become a core solution for broadband power line communication. OFDM effectively combats frequency-selective fading and multipath effects in power line channels by distributing high-speed data streams across multiple orthogonal subcarriers for parallel transmission. Building upon this, bitloading technology forms the cornerstone of modern adaptive modulation. Through real-time channel state monitoring, it dynamically allocates appropriate modulation order and power to each subcarrier—using higher-order modulation for high-quality subcarriers to increase data rate and lower-order modulation for low-quality subcarriers to ensure reliability—thereby maximizing system spectral efficiency while maintaining a low bit error rate. However, this refined frequency-domain adaptive mechanism faces new and more complex challenges when introducing Multiple-Input Multiple-Output (MIMO) technology to further enhance capacity.
[0004] Specifically, with the increasing demands for transmission rates, MIMO technology has been introduced into power line communication systems to unlock spatial multiplexing gains. However, in MIMO-OFDM systems, the effectiveness of the aforementioned bitloading mechanism is highly dependent on spatial configuration: increasing the number of data streams does not always lead to a linear increase in throughput. When channel conditions are poor or inter-stream interference is strong, blindly increasing the number of streams can worsen the signal-to-interference-plus-noise ratio (SINR) of each sub-data stream, forcing the system to adopt a lower modulation order than in single-stream mode, thus proving counterproductive. Therefore, to achieve truly optimal performance, it is necessary to further dynamically select the optimal number of data streams based on real-time channel state information, in addition to the frequency-domain adaptive nature of bitloading, to balance spatial multiplexing gains with the cost of inter-stream interference.
[0005] However, existing schemes for jointly and dynamically optimizing the number of data streams and modulation methods generally suffer from excessively high computational complexity. To evaluate system performance (such as equivalent signal-to-interference-plus-noise ratio) under different data stream assumptions, a large number of complex calculations are required. This enormous computational overhead makes it difficult for algorithms to meet the real-time requirements of power line communication, especially for computationally limited devices (such as concentrators and smart meters), and has become a major bottleneck restricting the full realization of MIMO system performance and its rapid adaptive capabilities.
[0006] Therefore, there is an urgent need in this field for a low-complexity, high-efficiency adaptive method for data stream number and modulation mode to solve the problem that traditional solutions cannot be effectively applied in real-world devices due to computational bottlenecks.
[0007] It should be noted that the information disclosed in the background section above is only for understanding the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention
[0008] The technical problem to be solved by this invention is "how to achieve joint adaptive optimization of the number of data streams and modulation scheme in a MIMO-OFDM communication system, while significantly reducing its computational complexity to adapt to communication devices with limited computing resources".
[0009] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows.
[0010] This invention provides a link adaptation method in a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) communication system, comprising the following steps: The channel state information of the channel is obtained, and the signal-to-noise ratio (SNR) matrix of at least one resource element (RU) is calculated based on the channel state information. For different data stream number assumptions, the SNR matrix is converted into the corresponding equalized SNR under each data stream number assumption through a preset transformation relationship, thereby avoiding the execution of physical equalizer operations involving matrix inversion for each data stream number assumption; Based on the equalized SNR and the preset signal-to-noise ratio threshold, determine the modulation scheme adopted by each resource unit under each data stream number assumption; Based on the determined modulation scheme, calculate the total number of bits that all resource units can transmit per channel under different data stream number assumptions, and select the data stream number assumption that maximizes the total number of bits as the final data stream number, while simultaneously determining the modulation scheme of each resource unit under this data stream number.
[0011] In some embodiments, obtaining channel state information of the channel includes: receiving training sequence (TF) symbols; performing frequency domain transformation on the TF symbols to obtain frequency domain received data; and performing least squares (LS) estimation based on the frequency domain received data and the known TF frequency domain transmitted data to obtain channel estimation results.
[0012] In some embodiments, calculating the SNR matrix based on channel state information includes: calculating the channel noise power based on the channel estimation results; and calculating the SNR matrix using the channel estimation results and the channel noise power.
[0013] In some embodiments, calculating channel noise power includes subtracting channel estimation results of different orthogonal frequency division multiplexing (OFDM) symbols on the same subcarrier to estimate noise.
[0014] In some embodiments, calculating channel noise power includes: performing noise reduction filtering on the channel estimation result to obtain a filtered channel estimation result; and subtracting the channel estimation result from the filtered channel estimation result to estimate the noise.
[0015] In some embodiments, noise reduction filtering includes Wiener filtering or transform domain filtering.
[0016] In some embodiments, the SNR matrix is calculated based on subcarriers that are not subject to single-frequency interference.
[0017] In some embodiments, the communication system is a power line communication (PLC) system.
[0018] In some embodiments, a communication device is also provided, including: a processor and a memory; the memory stores a computer program; when the processor executes the computer program, it implements the link adaptation method of the present invention.
[0019] In some embodiments, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the link adaptation method of the present invention.
[0020] The present invention has the following beneficial effects: First, this invention avoids performing physical equalizer operations involving matrix inversion for each data stream number assumption by "converting the signal-to-noise ratio (SNR) matrix into the equivalent equalizer SNR under each data stream number assumption through a pre-derived conversion relationship." This bypasses the traditional physical equalizer operation involving complex matrix inversion for each data stream number assumption, significantly reducing the computational load in determining the optimal data stream number and lowering the algorithm's implementation complexity. Second, by "determining the equalizer SNR and a pre-defined SNR threshold for each data stream number assumption," this invention further... The modulation scheme adopted by the resource unit quickly and accurately maps the converted physical layer index (signal-to-noise ratio after equalization) to the configuration parameters (modulation scheme) that can be executed by the communication protocol, laying the foundation for performance evaluation. Furthermore, by "calculating the total number of bits that all resource units can transmit per channel under different data stream number assumptions based on the determined modulation scheme, and selecting the data stream number assumption that maximizes the total number of bits as the final data stream number, and simultaneously determining the modulation scheme of each resource unit under this data stream number", a global performance comparison is performed on all possible data stream number assumptions, thereby selecting the optimal data stream number and corresponding modulation scheme combination that maximizes the system spectral efficiency, thus achieving the performance optimization goal.
[0021] In summary, through the synergistic effect of the above-mentioned technical features, this invention significantly reduces the computational complexity of jointly adaptively optimizing the number of data streams and modulation methods while ensuring system throughput performance, enabling this invention to be efficiently applied in communication devices with limited computing resources.
[0022] In some embodiments, the present invention also clarifies a specific and efficient implementation method for "obtaining channel state information of the channel", namely, obtaining "channel estimation result" by "receiving training sequence symbols", "performing frequency domain transformation" and "performing least squares estimation". This method utilizes standard training sequences in the communication system, is simple to implement, and the estimation accuracy meets the requirements of subsequent calculations.
[0023] In some embodiments, the present invention further defines a specific path for calculating the signal-to-noise ratio matrix from the channel estimation result, namely, by calculating the channel noise power in combination with the channel estimation result. This method has a clear physical meaning and provides accurate input for subsequent conversion.
[0024] In some embodiments, the present invention also provides two different specific implementation methods for "calculating channel noise power": estimating noise by "subtracting the channel estimation results of different orthogonal frequency division multiplexing symbols on the same subcarrier", which is based on the slowly varying characteristics of the channel and has a small computational load; estimating noise by subtracting after "noise reduction filtering", which helps to suppress noise in the channel estimation stage and may obtain a more accurate noise power estimate, thereby improving the accuracy of the signal-to-noise ratio matrix.
[0025] In some embodiments, the present invention further avoids the negative impact of interfered subcarriers on the accuracy of average signal-to-noise ratio estimation by limiting "the calculated signal-to-noise ratio matrix is based on subcarriers that are not subject to single-frequency interference", thereby improving the reliability and robustness of the signal-to-noise ratio matrix and all subsequent decision results.
[0026] Other beneficial effects of the present invention will be further described below. Attached Figure Description
[0027] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is the basic process for confirming the data flow and load balancing method in the Bitloading mode of this invention; Figure 2 This is a specific implementation process for confirming the number of data streams and modulation method in the Bitloading mode of the present invention. Detailed Implementation
[0028] The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary and not intended to limit the scope and application of the present invention.
[0029] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of the present invention, "a plurality of" means two or more, unless otherwise explicitly specified.
[0030] This invention provides a link adaptation method in a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) communication system.
[0031] In some embodiments, this invention addresses the high correlation and potentially significant frequency selectivity of power line channels by directly converting the equalized SNR values under different data stream number assumptions using theoretical formulas. The specific modulation scheme is then determined based on the equalized SNR values, and finally, an appropriate data stream number is determined based on the obtained modulation scheme. The core innovation lies in avoiding the computationally intensive equalization process by directly converting the equalized SNR values under the corresponding data stream number assumptions using theoretical formulas. This process is suitable for software implementation.
[0032] In some embodiments, the link adaptation method includes the following steps: Step 1: Use the frequency domain transform of the training field (TF) symbols in the power line communication system to perform least squares (LS) estimation to obtain a coarse channel estimate for the current channel. Since the power line protocol's PREAmble (preamble) or training sequence is generally transmitted repeatedly on multiple identical OFDM symbols, the noise estimation module performs least squares estimation on all OFDM symbols. For PREAM or TF frequency domain The symbol of the first Data received in the frequency domain of each subcarrier. For the known first If data is transmitted in the frequency domain of the nth subcarrier, then different transmit and receive antennas will pair with the nth subcarrier. The symbol of the first Least squares estimation of subcarriers for: Step 2: Subtract the least squares estimates of different subcarriers from the LS results obtained in Step 1 for adjacent OFDM symbols to obtain the noise of different subcarriers. Calculate the noise corresponding to different transmit / receive antenna pairs using the frequency response of the same subcarrier in different OFDM symbols. It should be noted that this operation is possible because the time-varying characteristics of power line communication channels are generally poor. Therefore, for adjacent OFDM symbols, the frequency domain channel response of the same subcarrier can be considered identical. If MIMO transmission is involved, the above operation needs to be performed on each transmit / receive antenna pair.
[0033] Using the noise described above and the frequency response obtained in step one, the average SNR linearity value in the current RU is obtained. The specific calculation method is as follows: For power line MIMO systems, each transmit / receive antenna pair calculates the aforementioned SNR value. That is, for a system with a number of transmit ports... The number of receiving ports is With the above configuration, each RU can obtain a dimension of [dimensional value]. The SNR matrix.
[0034] In some embodiments, the numerator of the SNR calculation in step two, i.e., the frequency response, can be obtained using filtering and noise reduction methods. That is, the frequency response after noise removal can be obtained by applying filtering methods, including but not limited to Wiener filtering or transform-domain filtering, to the least squares result obtained in step one.
[0035] In some embodiments, the method for calculating channel noise in step two can be implemented in other ways. For example, one implementation is to subtract adjacent subcarriers of the same OFDM symbol to obtain the noise. However, it should be noted that the subcarrier spacing specified by commonly used power line communication protocols is generally large (for example, the subcarrier spacing specified by the carrier communication protocol used by China Southern Power Grid is 24.414 kHz). When the channel has rich multipath propagation, resulting in severe frequency selectivity, the frequency response of adjacent subcarriers of the same symbol may also have large differences, which will lead to the noise calculated in this way being larger than the true value. Another way to estimate the noise is to apply noise reduction filtering (such as Wiener filtering or transform-domain filtering) to the least squares result obtained in step one, and then subtract the noise reduction filtering result from the least squares result to obtain the noise.
[0036] In some embodiments, due to the presence of power line communication channel interference (e.g., single-frequency interference), when calculating the average SNR of the RU in step two, subcarriers affected by single-frequency interference can be excluded from the calculation to avoid the extreme values caused by single-frequency interference affecting the accuracy of the overall average SNR.
[0037] Step 3: Using the SNR matrix calculated in Step 2, based on different data flow number assumptions (e.g., based on the actual power line network layout and configuration, assuming the data flow number is 1 flow, 2 flows, etc.), and based on the preset transformation relationship, obtain the balanced SNR of the current RU under the current data flow number assumption.
[0038] In some embodiments, the preset transformation relationship can be a preset mathematical formula, the theoretical basis of which is derived as follows: Assume the following... The MIMO settings, namely: Generally, the thermal noise of each link in a receiver mainly originates from individual electronic components within that link (such as low-noise amplifiers and mixers), and these noise sources are statistically uncorrelated. Most MIMO communication standards (such as LTE and Wi-Fi) and academic research assume independent noise by default. Therefore, we can assume that the noise at different receivers is independent, meaning that the off-diagonal elements of the noise covariance matrix are 0. That is, the dimension... noise covariance matrix It can be represented as: in , , Let be the noise power at each receiver. Then the inverse of the above noise covariance matrix is: Based on the above assumption of noise independence, the whitening process of the above receiving model can be simplified as follows: In the above In the expression, in step two of claim 1, the SNR matrix estimated by TF is the square of each of its elements, that is: In the derivation, the linear SNR mentioned above is the SNR matrix obtained using a single subcarrier in a MIMO-OFDM system. In practical systems, we generally prefer to obtain the average performance of multiple adjacent subcarriers. In this case, the above expression can be generalized to the average SNR matrix of multiple adjacent subcarriers.
[0039] The expression for MMSE equilibrium can be represented as: Through derivation, the balanced SNR and the following matrix can be obtained. It relates to the diagonal elements, that is: The above dimensions can be obtained. matrix It can be represented as: Dimension matrix The diagonal elements can be represented as and The denominator is... It can be represented as: Then matrix The diagonal elements can be represented as: and The SNR of the two balanced data streams can be expressed as follows: for The derivation of the equalized SNR in the scenario only requires the above and In the expression, the superscript of the summation symbol can be changed from 2 to 1.
[0040] The above assumes that the sending end uses two data streams. The following assumes that the sending end uses only one data stream, as follows: SIMO settings: In the above model, the noise at each receiver is independent. Due to this assumption of noise independence, the whitening process of the above receiver model can be simplified as follows: in , , This represents the noise power at each receiver.
[0041] The expression for MMSE equilibrium can be represented as: To calculate the SNR after equalization, we can calculate the total power of the useful signal and noise as follows: Simultaneously, the useful signal power can be obtained as: The linear SNR after equilibrium can then be expressed as: When When setting up SIMO, use TF estimation. The relationship between the individual SNR and the balanced SNR is as follows: Typically, the average equalized SNR of several subcarriers in a MIMO-OFDM system (e.g., a RU in a power line communication system) is calculated to achieve functions such as link adaptation and performance evaluation. If the equalized SNR of each subcarrier in the RU is calculated first using the channel matrix, and then the SNRs are averaged, this involves numerous computationally intensive operations such as matrix inversion. However, if the average SNR matrix of the RU is calculated first, and then the equalized SNR value is obtained directly using the aforementioned conversion formula, most of the computationally intensive matrix operations can be avoided.
[0042] The correctness of the above conversion formula is verified through a simulation platform, as shown in Table 1. Table 1 shows the equalized SNR conversion value calculated using the SNR matrix and the equalized SNR measurement value calculated directly using the equalization formula under different scenarios. It can be seen that the equalized SNR values calculated by the two methods have a very small difference in the dB domain. However, the computational complexity of the method using the above conversion formula is much lower than that of the method of obtaining the equalized SNR of each subcarrier using the equalization formula and then averaging it.
[0043] Table 1. Verification table of SNR converted and measured values after equalization It should be noted that the "preset transformation relationship" can also include various implementation methods such as lookup tables (LUTs) and machine learning models.
[0044] Step 4: Based on the equalized SNR under different data stream number assumptions of the current RU obtained in Step 3, and the pre-set SNR thresholds for different modulation schemes, the modulation schemes for different data stream number assumptions of the current RU are obtained. It should be noted that the pre-set SNR thresholds for different modulation schemes are obtained through a link-level simulation platform under an AWGN channel. Because the entire communication system algorithm is implemented on this simulation platform, the thresholds obtained through simulation can reflect at what SNR level a solution can be achieved.
[0045] Step 5: After obtaining the modulation schemes of all RUs under all data stream assumptions, calculate the total number of bits used per channel use for all RUs under different data stream number assumptions. Finally, select the data stream number corresponding to the larger total number of bits as the determined data stream number. Once this data stream number is determined, the modulation scheme for each RU has been obtained in Step 4. Here, "per channel use" refers to the basic time-frequency resource unit for the system to perform one independent transmission using the channel. In simpler terms, it's one opportunity to send one symbol.
[0046] It should be noted that the method described in this invention is not limited to power line communication systems, but is also applicable to other communication systems (such as wireless communication systems).
[0047] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. For those skilled in the art, several simple deductions or substitutions can be made without departing from the concept of the invention, and all such modifications or substitutions should be considered within the scope of protection of the present invention.
[0048] In this embodiment, reference Figure 1 The overall flow of this method is shown below. Figure 2 To understand the specific implementation process shown, Figure 1 The steps and data flow of the method of the present invention are shown: starting from obtaining channel state information, the process involves calculating the signal-to-noise ratio matrix, obtaining the equalized signal-to-noise ratio based on a preset transformation relationship, determining the modulation method, and finally completing the complete process of selecting the number of data streams and determining the modulation method. Figure 2 Then in Figure 1 Building upon this foundation, the processing of training sequences, noise estimation, and specific implementation details for power line communication scenarios are further refined. Data transmission is considered using 411 data subcarriers (from subcarrier index 80 to carrier index 490) in the State Grid HPLC band 0 (2-12 MHz). For ease of description, it is assumed that... The MIMO system divides the frequency band into 5 RUs, with a total of TFs. One OFDM symbol. The specific implementation method is as follows: Step 1: Time-domain data of different symbols of the TF in power line carrier communication Perform Fourier transform to receive data in the frequency domain The least-squares (LS) results of the frequency domain channel response for different OFDM symbols of the TF are obtained by dividing the known frequency domain transmitted data by the frequency domain received data. Note the above. For size The matrix.
[0049] Step 2: Calculate the noise of the subcarrier using the least-squares (LS) results of the same subcarrier for different OFDM symbols in the frequency domain response described above. The above subtraction represents the dimension as follows: Subtract the elements of the matrix one by one.
[0050] Step 3: Use the least squares result calculated in Step 1 and the noise matrix calculated in step two Further averaging is performed on each RU to obtain the dimension as follows: The SNR matrix. In this step, a total of 5 dimensions are obtained. The SNR matrix.
[0051] Step 4: Make three assumptions about the number of data streams sent (i.e., send 2 streams, send 1 stream, and only use...). The first column of the channel matrix is used for transmission, and one stream is transmitted using only one channel. (The second column of the channel matrix is used for transmission). For each of the three assumptions regarding the number of transmitted data streams, the SNR information from step three is theoretically converted to an equalized SNR. When the assumption is 2 streams transmitted, a 5 (RU) SNR is obtained. 2 (streams) = 10 equalized SNR values; assuming 1 stream is sent, 5 (RU) will be obtained. 1 (stream) = 5 equalized SNR values.
[0052] For example, suppose the balanced SNR value is obtained by transformation, and the balanced SNR value for each RU of each data stream is... Assume two streams are sent: [-0.2dB 1dB 2dB 1.9dB 2dB][-0.16dB 2.2dB 2.2dB 2.3dB 0dB] Assume sending one stream using column 1: [0dB 2dB 12dB 8.8dB 8.2dB] Assuming a single stream is sent using column 2: [1dB 2.2dB 11.2dB 9.2dB 7.8dB] Step 5: Based on the modulation scheme threshold table obtained from the pre-simulation (i.e., the constellation diagram of the modulation scheme can only be correctly solved when the equalized SNR exceeds the threshold; assuming the threshold table is [0.15dB (BPSK), 1.88dB (QPSK), 6.48dB (16QAM), 10.56dB (64QAM)]), and combined with the equalized SNR value from Step 4, obtain the modulation scheme under different transmitted data stream assumptions: Assuming two streams are sent: [No transmission (0 bit) BPSK (1 bit) QPSK (2 bits) QPSK (2 bits) QPSK (2 bits)] [No transmission (0 bit) QPSK (2 bits) QPSK (2 bits) QPSK (2 bits) No transmission (0 bit)], the number of bits that can be transmitted per channel is: (0+1+2+2+2)+(0+2+2+2+0)=13 bits; Assuming a single stream is sent using column 1: [No transmission (0 bit) QPSK (2 bits) 64QAM (6 bits) 16QAM (4 bits) 16QAM (4 bits)], the number of bits that can be transmitted per channel is: 0 + 2 + 6 + 4 + 4 = 16 bits; Assuming a single stream is sent using column 2: [BPSK (1 bit) QPSK (2 bits) 64QAM (6 bits) 16QAM (4 bits) 16QAM (4 bits)], the number of bits that can be transmitted per channel is: 1+2+6+4+4=17 bits.
[0053] Therefore, it can be determined that the appropriate number of data streams is 1, and the second column is used for transmission. The modulation schemes that should be used for the 5 RUs are BPSK, QPSK, 64QAM, 16QAM, and 16QAM, respectively.
[0054] The technical solution provided by this invention has the following advantages compared with the prior art: 1. Significantly reduced computational complexity and improved real-time performance: The core effect lies in establishing a theoretical conversion relationship between candidate data stream number assumptions and the equalized signal-to-noise ratio (SNR). This avoids complex iterative calculations or high-dimensional matrix decompositions (such as matrix inversion) for each assumption when calculating the equalized SNR. This greatly reduces the computational overhead of the algorithm, making it easy to implement in hardware or software on power line communication chips with limited computing resources, thus meeting the requirements of real-time system adaptation.
[0055] 2. Achieving precise joint optimization of data stream number and modulation scheme to maximize system throughput: Based on the accurate equalized SNR obtained by the above low-complexity method, this invention achieves joint optimization of data stream number and modulation scheme. This effectively solves the "high stream number, low benefit" problem caused by inter-stream interference in traditional schemes, ensuring that the total system throughput always approaches the actual capacity limit under the current channel conditions.
[0056] 3. Achieving Fine-grained Adaptive Configuration of Spectrum Resources: By calculating the SNR matrix at the resource unit (RU) level and mapping the modulation scheme to each RU, fine-grained configuration of spectrum resources is achieved. This scheme enables each selected spatial stream to use a matching highest-order modulation at an acceptable level of interference, thereby achieving a dynamic balance between inter-stream interference suppression and spatial multiplexing gain, and improving spectrum utilization efficiency.
[0057] 4. Strong architectural versatility and easy deployment: The method principle of this invention does not depend on specific operating frequencies or communication protocol details, avoiding the development of independent algorithms for different frequency bands or systems, reducing the deployment complexity and maintenance costs of the system, and has good versatility and portability.
[0058] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0059] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0060] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0061] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0062] The background section of this invention may include background information about the problems or environment in which the invention is being developed, and is not necessarily a description of prior art. Therefore, the content included in the background section does not constitute an admission of prior art by the applicant.
[0063] The above description provides a further detailed explanation of the present invention in conjunction with specific / preferred embodiments, and it should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various substitutions or modifications can be made to these described embodiments without departing from the concept of the present invention, and all such substitutions or modifications should be considered within the scope of protection of the present invention. In the description of this specification, the reference to terms such as "an embodiment," "some embodiments," "preferred embodiment," "example," "specific example," or "some examples," etc., indicates that the specific features, structures, materials, or characteristics described in connection with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification and the features of different embodiments or examples. Although the embodiments of the present invention and their advantages have been described in detail, it should be understood that various changes, substitutions, and modifications can be made herein without departing from the scope of protection of the patent application.
Claims
1. A link adaptation method in a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) communication system, characterized in that, Includes the following steps: The channel state information of the channel is obtained, and the signal-to-noise ratio (SNR) matrix of at least one resource element (RU) is calculated based on the channel state information. For different data stream number assumptions, the SNR matrix is converted into the corresponding equalized SNR under each data stream number assumption through a preset transformation relationship, thereby avoiding the execution of physical equalizer operations involving matrix inversion for each data stream number assumption; Based on the equalized SNR and the preset signal-to-noise ratio threshold, the modulation method adopted by each resource unit under each data stream number assumption is determined; Based on the determined modulation scheme, calculate the total number of bits that all resource units can transmit per channel use under different data stream number assumptions, and select the data stream number assumption that maximizes the total number of bits as the final data stream number, and at the same time determine the modulation scheme of each resource unit under this data stream number.
2. The link adaptive method according to claim 1, characterized in that, The process of obtaining channel state information includes: receiving training sequence (TF) symbols; performing frequency domain transformation on the TF symbols to obtain frequency domain received data; and performing least squares (LS) estimation based on the frequency domain received data and known TF frequency domain transmitted data to obtain channel estimation results.
3. The link adaptive method according to claim 2, characterized in that, The SNR matrix is calculated based on the channel state information, including: calculating the channel noise power based on the channel estimation result; and calculating the SNR matrix using the channel estimation result and the channel noise power.
4. The link adaptive method according to claim 3, characterized in that, Calculating channel noise power includes subtracting the channel estimation results of different orthogonal frequency division multiplexing (OFDM) symbols on the same subcarrier to estimate the noise.
5. The link adaptive method according to claim 3, characterized in that, Calculating channel noise power includes: performing noise reduction filtering on the channel estimation result to obtain a filtered channel estimation result; and subtracting the channel estimation result from the filtered channel estimation result to estimate the noise.
6. The link adaptive method according to claim 5, characterized in that, The noise reduction filtering includes Wiener filtering or transform domain filtering.
7. The link adaptive method according to any one of claims 3 to 6, characterized in that, The SNR matrix is calculated based on subcarriers that are not subject to single-frequency interference.
8. The link adaptive method according to claim 1, characterized in that, The communication system is a power line communication (PLC) system.
9. A communication device, characterized in that, include: A processor and a memory; the memory stores a computer program; when the processor executes the computer program, it implements the link adaptation method as described in any one of claims 1 to 8.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the link adaptation method as described in any one of claims 1 to 8.