Statistical linear coding active interference elimination method for f-OFDM (Frequency-Orthogonal Frequency Division Multiplexing)
By dividing the active interference cancellation algorithm of f-OFDM into two parts: optimization solution and linear calculation, the linear encoding matrix is optimized using the real-number domain second-order cone planning tool, which solves the problems of high computing complexity and delay in the existing technology, and is suitable for scenarios with limited computing capabilities and delay-sensitive scenarios.
Patent Information
- Application Number
- CN202311388580.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-10-24
- Publication Date
- 2025-08-05
AI Technical Summary
The CCs optimization solution process of the active interference cancellation algorithm in the existing f-OFDM technology has a high computational complexity and high delay, which leads to unsuitable scenarios where computing power, limited battery life and delay sensitivity.
Active interference elimination is divided into two parts: optimization solution and linear calculation. The optimization solution part is performed outside the signal transmission processing process. The real-number domain second-order cone planning tool is used to solve the linear encoding matrix, and the linear calculation part is performed matrix multiplication and deployment of CCs within the signal reception processing process.
It greatly reduces the computational complexity and delay, and is suitable for scenarios where computing power is limited and delay-sensitive.
Smart Images
Figure CN120434090A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of active communication interference elimination, and in particular to a statistical linear coding active interference elimination method for f-OFDM. Background Art
[0002] Filtered orthogonal frequency division multiplexing (f-OFDM) is an adaptive air interface waveform modulation technology with variable subcarrier bandwidth and is an improved solution based on OFDM.
[0003] The basic concept of f-OFDM technology is to divide the OFDM carrier bandwidth into multiple subbands with different parameters and filter the subbands, while leaving as few isolation bands as possible between the subbands. For example, to achieve low-power, wide-coverage IoT services, a single-carrier waveform can be used in selected subbands; to achieve lower air interface latency, a smaller transmission slot length can be used; and to combat multipath channels, a smaller subcarrier spacing and a longer cyclic prefix can be used. The biggest difference between f-OFDM modulation systems and traditional OFDM systems is the addition of filters at the transmitter and receiver.
[0004] like Figure 1 As shown in Figure 1, the existing f-OFDM technology uses an active interference cancellation (AIC) algorithm. The AIC algorithm consists of two steps: optimizing cancellation subcarriers (CCs) and deploying CCs. The CCs optimization solution is further divided into three steps: optimization model design, optimization model conversion, and optimization model solution. Step 1. Optimization model design: The optimization model of the existing AIC algorithm for f-OFDM can be expressed as: st in, is the mapping matrix from data subcarrier to target suppression frequency point (DFP: desired frequency points), is the data subcarrier vector, B and E are the start and end numbers of the subband respectively, M is the number of CCs, and the superscript T represents the transpose operator.
[0005] is the mapping matrix from CCs to DFP; is the CCs vector.
[0006] α is the transmit power.
[0007] In the formula, CCs vector is directly used As the decision variable, the instantaneous composite spectrum power of the sub-band specific target suppression frequency band is As the cost function, the transmit power α of CCs is used as the constraint to design an optimization model.
[0008] Step 2. Optimize model conversion: The above optimization model is a standard quadratically constrained quadratic programming (QCQP) model. Through a series of transformations, the complex domain optimization model is transformed into the following standard real domain second-order cone programming (SOCP) optimization model: in, is a vector of CCs in the real field The new decision variables obtained by expanding one dimension are: For the complex vector The real vector obtained by reconstructing the real and imaginary parts, is the complex mapping matrix The real mapping matrix obtained by reconstructing the real and imaginary parts of the vector.
[0009] θ 2Q+1 It is a second-order cone of 2Q+1 dimensions; is the 2M-dimensional identity matrix; for The Cholesky decomposition matrix.
[0010] Step 3. Solve the optimization model: Use the SOCP optimization tool to solve the transformed optimization model and obtain the CCs vector
[0011] After obtaining CCs, they are deployed on the edge subcarriers of the target subband, completing all steps of the existing AIC algorithm.
[0012] The main problem with existing solutions is that CCs optimization must be performed for each transmitted data symbol in subband i. Since optimization is a nonlinear calculation process, its computational complexity and latency are significantly higher than those of a linear calculation process. This also makes the existing AIC algorithm unsuitable for scenarios with limited computing power and endurance, and those that are sensitive to latency. Summary of the Invention
[0013] This invention primarily addresses the problem that existing technologies require CC optimization for each transmitted data symbol in the target subband. This significantly increases computational complexity and latency compared to linear calculations, making existing AIC algorithms unsuitable for scenarios with limited computing power, limited endurance, and latency-sensitive environments. This invention provides a statistical linear coded active interference cancellation (SLCAIC) method for f-OFDM, which moves the optimization process outside the signal transmission process, requiring only a single linear coding matrix optimization. Each transmitted data symbol simply needs to be multiplied by the linear coding matrix to obtain CCs, significantly reducing computational complexity and latency.
[0014] The above technical problems of the present invention are mainly solved by the following technical solutions: A statistical linear coding active interference cancellation method for f-OFDM, which divides the active interference cancellation into an optimization solution part and a linear calculation part; The optimization solution is set outside the signal transmission processing flow, using the linear coding matrix as the decision variable, performing statistical processing, and designing an optimization model. After the optimization model is transformed, it is solved using a real-domain second-order cone programming optimization tool to obtain the linear coding matrix under the statistical model. The linear calculation part receives the data subcarriers, multiplies them with the linear coding matrix from the optimization solution part, and then deploys the calculated CCs.
[0015] By redesigning the AIC algorithm structure and moving the optimization process outside of the signal transmission process, the linear coding matrix optimization only needs to be performed once. Each transmitted data symbol is simply multiplied by the linear coding matrix to obtain the CCs. Matrix multiplication is a linear calculation, significantly reducing computational complexity and latency compared to nonlinear optimization.
[0016] Preferably, the optimization solution part specifically includes: Optimize model design to linearly encode matrix As the decision variable, the transmit power α of CCs is used as the constraint, the cost function of statistical processing is taken, and the optimization model is designed; Optimize model conversion and rewrite the linear encoding matrix Substituting the converted cost function into the real domain, the constrained optimization model in the form of second-order cone programming in the real domain is obtained after the real domain transformation; Solve the optimization model and use the real domain second-order cone programming optimization tool to solve the transformed real domain second-order cone programming form constraint optimization model to obtain the linear coding matrix under the statistical mode
[0017] As a preferred option, the designed optimization model is: st in, is the linear encoding matrix; is the data subcarrier vector; E(·) is the statistical expectation operator; is the mapping matrix from data subcarrier to DFP; is the mapping matrix from CCs to DFP.
[0018] The decision variables are derived from the vector of CCs Transformed into a linear encoding matrix The cost function also takes the instantaneous composite spectrum power of the target suppression band as Changes to statistical composite spectrum power Eliminates the uncertainty of random processes.
[0019] As a preference, the cost function is converted, and the converted cost function is: st in, tr(·) represents the trace operator of the matrix.
[0020] Since the designed optimization model formally takes statistical processing, random variables will also be eliminated during the operation The form of this algorithm is quite different from the existing AIC algorithm and is not a standard QCQP model. Therefore, the existing AIC optimization model conversion method cannot be directly applied to this algorithm, and a new conversion method needs to be designed.
[0021] As a preference, the linear encoding matrix Rewritten as a vector The matrix Rewritten as a vector Where, M is the number of CCs; I E is the ending subcarrier number of the target subband; I B is the starting subcarrier number of the target subband; Convert the trace operation to a matrix / vector multiplication operation: in in For I E -I B +1-M-dimensional identity matrix.
[0022] The optimization model is rewritten as st
[0023] Since SOCP can only solve optimization problems in the real domain, the above formula needs to be converted into an optimization model in the real domain first.
[0024] As a preference, the rewritten optimization model is converted into the real number domain as follows: st in, It means taking the real part of a complex value; Indicates taking the imaginary part of a complex value; The rewritten real domain optimization model is further rewritten as the standard QCQP model: st in
[0025] Since SOCP can only solve optimization problems in the real number domain, it needs to be converted into an optimization problem in the real number domain first.
[0026] As a preferred method, the obtained constraint optimization model in the form of real number domain second-order cone programming is: in, For a vector in the field of real numbers New decision variables obtained by expanding one dimension; 2M(I E -I S +1-M)+1-dimensional second-order cone; for The Cholesky decomposition matrix.
[0027] The optimization model is solved using the SOCP optimization tool to obtain the real vector Then, by combining the real and imaginary parts, we get the complex vector Then, by rearranging the elements, we get the linear coding matrix in the statistical mode At this point, the optimization solution part of the SLCAIC algorithm is completed.
[0028] Preferably, the linear calculation part includes a matrix multiplication process and a CCs deployment process in sequence; The matrix multiplication process described above will optimize the linear coding matrix obtained by solving the partial calculation Data subcarrier vectors related to the signal transmission process Multiply to get CCs vector The CCs deployment process deploys CCs on edge subcarriers of the target subband.
[0029] Matrix multiplication is a linear calculation, and compared with nonlinear calculations such as optimization solutions, its computational complexity and latency are greatly reduced.
[0030] The beneficial effects of the present invention are: By redesigning the AIC algorithm structure and moving the optimization process outside of the signal transmission process, the linear coding matrix optimization only needs to be performed once. Each transmitted data symbol is simply multiplied by the linear coding matrix to obtain the CCs. Matrix multiplication is a linear calculation, significantly reducing computational complexity and latency compared to nonlinear optimization. BRIEF DESCRIPTION OF THE DRAWINGS
[0031] Figure 1 This is a flow chart of active interference cancellation in the existing f-OFDM technology.
[0032] Figure 2 This is a flow chart of the statistical linear coding active interference elimination method for f-OFDM of the present invention. DETAILED DESCRIPTION
[0033] The technical solution of the present invention will be further specifically described below through embodiments and in conjunction with the accompanying drawings.
[0034] Example: A statistical linear coding active interference elimination method for F-OFDM in this embodiment is as follows: Figure 2 As shown, the active interference cancellation (AIC) algorithm is divided into two parts; the first part is the optimization solution part, and the second part is the linear calculation part.
[0035] The optimization solution part is set outside the signal transmission process, using the linear coding matrix As the decision variable, take the cost function of statistical processing and design the optimization model. After the optimization model is transformed, it is solved by the real domain second-order cone programming (SOCP) optimization tool to obtain the linear coding matrix under the statistical mode
[0036] The linear calculation part is still in the signal transmission process, and the received data subcarrier The received data subcarrier and the statistical linear coding matrix of the optimization solution part After multiplication, the calculated CCs are deployed.
[0037] The optimization solution of the first part is the linear coding matrix Optimization solution specifically includes three steps: optimization model design, optimization model transformation and optimization model solution.
[0038] 1) Optimize model design The designed optimization model is: st in, is the linear encoding matrix; is the data subcarrier vector; E(·) is the statistical expectation operator; is the mapping matrix from data subcarrier to DFP; is the mapping matrix from CCs to DFP.
[0039] The decision variables are derived from the vector of CCs Transformed into a linear encoding matrix The cost function also takes the instantaneous composite spectrum power of the target suppression band as Changes to statistical composite spectrum power Eliminates the uncertainty of random processes.
[0040] 2) Optimization model transformation Since the designed optimization model has statistical processing in form, random variables will also be eliminated during the operation process. The form of this algorithm is quite different from the existing AIC algorithm and is not a standard QCQP model. Therefore, the existing AIC optimization model conversion method cannot be directly applied to this algorithm. Instead, a new conversion method needs to be designed. The specific design is as follows: The linear encoding matrix Rewritten as a vector The matrix Rewritten as a vector Where, M is the number of CCs; I E is the ending subcarrier number of the target subband; I B is the starting subcarrier number of the target subband.
[0041] Next, convert the trace operation into a matrix / vector multiplication operation: in in For I E -I B +1-M-dimensional identity matrix.
[0042] At this point, the transformed cost function can be rewritten as: st Since SOCP can only solve optimization problems in the real domain, the above formula needs to be converted into an optimization problem in the real domain. The conversion method is as follows: It means taking the real part of a complex value; Represents the imaginary part of a complex value.
[0043] At this point, the transformed real domain optimization model is: st After derivation, the cost function of the above real domain optimization model can be transformed as follows in
[0044] Therefore, the above real number domain optimization model can be equivalently rewritten as st The optimization model in the above formula is already a standard QCQP model. After further model transformation and simplification, the following SOCP optimization model can be obtained: in, For a vector in the field of real numbers New decision variables obtained by expanding one dimension; 2M(I E -I S +1-M)+1-dimensional second-order cone; for The Cholesky decomposition matrix.
[0045] 3) Optimization model solution The final optimization model is solved using the SOCP optimization tool to obtain the real vector Then, by combining the real and imaginary parts, we get the complex vector Then, by rearranging the elements, we get the linear coding matrix in the statistical mode At this point, the first part of the SLCAIC algorithm ends.
[0046] The linear calculation part of the second part specifically includes two steps: matrix multiplication and deployment of CCs.
[0047] 1) The linear encoding matrix With data subcarrier vector Multiply to get CCs vector
[0048] 2) CCs are deployed on the edge subcarriers of the target subband, and the SLCAIC algorithm ends here.
[0049] This embodiment redesigns the AIC algorithm structure, moving the optimization process outside of the signal transmission process. This requires only a single linear coding matrix optimization. Each transmitted data symbol is simply multiplied by the linear coding matrix to obtain CCs. Matrix multiplication is a linear calculation, significantly reducing computational complexity and latency compared to nonlinear optimization.
[0050] It should be understood that the embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope limited by the appended claims of the application.
Claims
1. A statistical linear coding active interference cancellation method for f-OFDM, characterized in that: Split active interference elimination into optimization solution part and linear calculation part; The optimization solution is set outside the signal transmission processing flow, using the linear coding matrix as the decision variable and the cost function of statistical processing to design an optimization model. After the optimization model is transformed, it is solved using the real-domain second-order cone programming optimization tool to obtain the linear coding matrix under the statistical mode. The linear calculation part receives the data subcarriers, multiplies them with the linear coding matrix from the optimization solution part, and then deploys the calculated CCs.
2. The method for statistical linear coding active interference cancellation of f-OFDM according to claim 1, characterized in that: The optimization solution part specifically includes: Optimize model design to linearly encode matrix As the decision variable, the transmit power α of CCs is used as the constraint, the cost function of statistical processing is taken, and the optimization model is designed; Optimize model conversion and rewrite the linear encoding matrix Substituting the converted cost function into the complex / real domain, the constrained optimization model in the real domain second-order cone programming form is obtained; Solve the optimization model and use the real domain second-order cone programming optimization tool to solve the transformed real domain second-order cone programming form constraint optimization model to obtain the linear coding matrix under the statistical mode 3. The method for statistical linear coding active interference cancellation of f-OFDM according to claim 1 or 2, characterized in that: The designed optimization model is: st in, is the linear encoding matrix; is the data subcarrier vector; E(·) is the statistical expectation operator; is the mapping matrix from data subcarrier to DFP; is the mapping matrix from CCs to DFP.
4. The method for statistical linear coding active interference cancellation of f-OFDM according to claim 3, characterized in that: Convert the cost function to: st in, tr(·) represents the trace operator of the matrix.
5. The method for statistical linear coding active interference cancellation of f-OFDM according to claim 4, characterized in that: The linear encoding matrix Rewritten as a vector The matrix Rewritten as a vector Where, M is the number of CCs; I E is the ending subcarrier number of the target subband; I B is the starting subcarrier number of the target subband; Convert the trace operation to a matrix / vector multiplication operation: in in For I E -I B +1-M-dimensional identity matrix; The optimization model is rewritten as st 6. The method for statistical linear coding active interference cancellation of f-OFDM according to claim 4 or 5, characterized in that: The rewritten optimization model is converted into the real number domain and expressed as: st in, It means taking the real part of a complex value; Indicates taking the imaginary part of a complex value; The rewritten real domain optimization model is further rewritten as the standard QCQP model: st in 7. The method for statistical linear coding active interference cancellation of f-OFDM according to claim 6, characterized in that: The obtained constrained optimization model in the form of second-order cone programming in the real field is: in, For a vector in the field of real numbers New decision variables obtained by expanding one dimension; 2M(I E -I S +1-M)+1-dimensional second-order cone; for The Cholesky decomposition matrix.
8. The method for statistical linear coding active interference cancellation of f-OFDM according to claim 1 or 7, characterized in that: The linear calculation part includes the matrix multiplication process and the CCs deployment process in sequence; The matrix multiplication process described above will optimize the linear coding matrix obtained by solving the partial calculation Data subcarrier vectors related to the signal transmission process Multiply to get CCs vector The CCs deployment process deploys CCs on edge subcarriers of the target subband.