Multi-slot power distribution method based on genetic algorithm and digital signal processing

Abstract

The invention relates to the technical field of TFDMA coherent PON, digital signal processing, genetic algorithm, power distribution and the like, in particular to a multi-time-slot power distribution method based on a genetic algorithm and digital signal processing, which comprises DSP enabled power distribution and genetic algorithm enabled power distribution optimization. Compared with a traditional power distribution method, the method has the advantages that a plurality of possible optimal solutions are searched in parallel in a solution space by simulating natural selection and genetic mechanisms, so that the probability of finding a global optimal solution is greatly improved, and the fairness of resource distribution is also improved. Secondly, the method is high in adaptability, the method is oriented to a multi-user and multi-time-slot TFDMA coherent PON system, users served by each subcarrier in different time slots are different, and the environment is more complex. The method focuses on power distribution optimization of multiple subcarriers and multiple time slots in the TFDMA coherent PON system, the scheme is mature, the adaptive capacity is high, and the performance of the system can be improved on the whole.

Description

Technical Field

[0001] The invention relates to the technical fields of TFDMA coherent PON, digital signal processing, genetic algorithm and power allocation, and in particular to a multi-time slot power allocation method based on genetic algorithm and digital signal processing. Background Art

[0002] With the rapid development of Internet applications, the demand for high-speed and large-bandwidth optical access networks continues to increase. In particular, with the rise of emerging applications such as the Internet of Things, high-definition video streaming, and virtual reality, the requirements for the capacity and performance of optical access networks have become more urgent. Intensity modulation direct detection systems are widely used in PONs due to their low cost and low power consumption. However, due to their low spectral efficiency and sensitivity, intensity modulation direct detection systems cannot support future PONs with data rates of 200Gb / s and above. Therefore, coherent PON is gaining more and more attention as a promising ultra-high bandwidth PON transmission solution.

[0003] In addition to its advantages in spectrum efficiency and sensitivity, coherent PON is also flexible in implementing time-frequency division multiple access (TFDMA), which can support more ONUs in the network. In a TDFMA PON system, different subcarriers and different time slots can be assigned to different ONUs. In addition, in a flexible and dynamic scenario, ONUs can have different data rates according to business needs, and the system will allocate more bandwidth resources to ONUs with higher data rates. For example, two time slots in a subcarrier can be sent to the same ONU. In this rate-flexible TFDMA coherent PON system, optimization of system configuration, especially optical power allocation in the downlink, is critical to ensuring overall performance.

[0004] The March 2023 conference "Optical Fiber Communication Conference" disclosed an ultra-100G three-dimensional flexible coherent PON with time, frequency, and power allocation capabilities, and demonstrated downlink performance. Digital subcarriers with power allocation in both the time and frequency domains enable more flexible and adaptive access rates than traditional TDM and FDM. As a proof of concept, a three-dimensional flexible rate PON with four subcarriers was demonstrated downstream, achieving a peak data rate of 250Gb / s / λ on 20km of optical fiber. However, the system did not specifically demonstrate the power allocation of the PON, nor did it propose a corresponding power allocation optimization algorithm. Therefore, a flexible and intelligent power allocation method in a TFDMA coherent PON system is an issue worthy of study. Summary of the invention

[0005] The object of the present invention is to provide a multi-time slot power allocation method based on genetic algorithm and digital signal processing to solve the above technical problems.

[0006] To achieve the above object, the present invention provides the following technical solution: a multi-slot power allocation method based on genetic algorithm and digital signal processing, including DSP-enabled power allocation and genetic algorithm-enabled power allocation optimization.

[0007] Preferably, the power allocation part of the DSP empowerment includes:

[0008] Step 21: Generate digital subcarriers. In the TFDMA coherent PON system, the OLT uses DSP technology to generate a certain number of digital subcarriers. The subcarriers are independent of each other in the frequency domain and can be flexibly configured according to system requirements. Through digital signal processing algorithms, the frequency, phase, bandwidth and other parameters of each subcarrier can be accurately controlled to meet different transmission requirements.

[0009] Step 22: Determine the power ratio. According to the system design requirements and network conditions, determine the power ratio that should be allocated to each subcarrier, that is, the ratio of subcarrier power to total transmit power. These power ratios are based on comprehensive considerations of system capacity, noise level, and link loss factors. The sum of the power ratios of all subcarriers is 1.

[0010] Step 22.1: Calculate the total power of the generated subcarriers, where n is the number of subcarriers

[0011]

[0012] Step 22.2: Obtain the power allocation ratio of each subcarrier through genetic algorithm

[0013] ε=GA(P i ), i = 1, 2, ..., n

[0014] Step 23: Perform power control. After determining the power ratio of each subcarrier, use DSP technology to perform power control on each subcarrier, specifically by adjusting the amplitude or gain of the subcarrier.

[0015] Step 3.1: According to the power allocation ratio, obtain the new power of each subcarrier.

[0016] P i =P×ε i , i==1,2,...,n

[0017] Step 3.2: After normalizing the power of each subcarrier, set it to the new power;

[0018] Step 24: Subcarrier combination: combine the power-controlled subcarriers. The combined signal will contain the information of all subcarriers and will be transmitted according to the optimized power allocation scheme.

[0019]

[0020] Preferably, the steps of power allocation optimization enabled by genetic algorithm include:

[0021] Step 31: Initialization, setting the evolution generation counter to 0, and determining the maximum evolution generation of the algorithm, then randomly generating an initial group containing multiple individuals. In the optimization of TFDMA coherent PON power allocation, the individuals are the power ratios of subcarriers in different time slots. These individuals will be used as the initial solution set in the search space;

[0022] Step 32: Individual evaluation, by calculating the fitness value of each individual in the group to evaluate their quality. The fitness function varies according to the problem and is used to guide the search direction of the algorithm. In the optimization of TFDMA coherent PON power allocation, the fitness function is calculated based on the average value of the root mean square error of different time slots;

[0023] Step 33: Termination condition judgment: when the fitness of the optimal individual reaches a given threshold, or the fitness of the optimal individual and the fitness of the group no longer increase, or the number of iterations reaches a preset number of generations, the algorithm terminates and outputs the optimization result, which is the optimal power allocation ratio of the TFDMA coherent PON subcarriers in different time slots under the optimization target;

[0024] Step 34: Individual update, select, crossover and mutate individuals in the group to generate new individuals, and repeat steps 32 and 33 until the termination condition is reached and the optimization result is output. Crossover is to replace and recombine part of the structure of two parent individuals to generate new individuals. Crossover makes the search ability of genetic algorithm greatly improved.

[0025] Preferably, in step 34, selection is to select superior individuals and eliminate inferior individuals. The purpose of selection is to directly pass on the optimized individuals (or solutions) to the next generation or to generate new individuals through pairing and crossover and then pass on to the next generation. Mutation is to change certain genes of individuals in the group. The introduction of mutation allows the genetic algorithm to have random search capabilities and maintain the diversity of the group.

[0026] Preferably, DSP-enabled power allocation utilizes digital subcarrier modulation technology to generate independent subcarriers in the frequency domain, and combines the subcarriers after individually regulating the power of each subcarrier.

[0027] Preferably, the power allocation optimization enabled by genetic algorithm utilizes genetic algorithm to optimize the power allocation of multiple subcarriers and multiple time slots of TFDMA coherent PON system, and can converge quickly in complex environments.

[0028] Preferably, digital signal processing is used to perform independent power control on subcarriers to achieve flexible and intelligent power allocation, and a genetic algorithm is used to optimize the power allocation of multiple subcarriers and multiple time slots in a TFDMA coherent PON system to improve the performance of the TFDMA coherent PON system.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] Compared with the traditional power allocation method, the multi-slot power allocation method of TFDMA coherent PON based on genetic algorithm and digital signal processing adopts a flexible and intelligent allocation method, fully considering the capacity, noise, loss and other factors of each subcarrier link, adding various factors to the constraints of the genetic algorithm and the expression of the fitness function, and using the genetic algorithm to perform heuristic search on the power allocation ratio. The power allocation ratio required by the system is obtained through the algorithm and modified in the relevant digital signal processing. The genetic algorithm simulates natural selection and genetic mechanisms, and searches for multiple possible optimal solutions in the solution space in parallel, thereby greatly improving the probability of finding the global optimal solution.

[0031] Secondly, the implementation logic of the traditional power allocation method is also significantly different from this technology. The traditional power allocation method is to improve the overall performance of the system by allocating more power to the links with good performance, at the expense of the links with poor performance. The method proposed in this patent is to balance the performance between the links, allocate the power of the links with excess performance to the links with poor performance, improve the shortcomings of the system transmission, and ultimately improve the overall performance of the system and improve the functionality of power allocation.

[0032] In addition, this method has strong adaptability. Traditional methods usually rely on fixed mathematical models and parameter settings, while this method is aimed at multi-user multi-time slot TFDMA coherent PON systems. Each subcarrier serves different users in different time slots, and the environment is more complex. This method can converge quickly in complex situations and achieve optimal power allocation for multi-time slots and multi-subcarriers.

[0033] Finally, this method has the convenience of multi-parameter optimization design. Traditional methods can often only perform optimization design of a single parameter, while genetic algorithms can easily handle optimization problems of multiple parameters at the same time, thereby achieving simultaneous optimization of multiple parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of a multi-slot power allocation method based on genetic algorithm and digital signal processing in the present invention;

[0035] Figure 2 This is a schematic diagram of the time-frequency division multiplexing scenario of the present invention;

[0036] Figure 3 This is a schematic diagram of the correspondence between the ONU, time slot, and subcarrier of the patent of the present invention;

[0037] Figure 4 The simulation process and network topology diagram of the patent of this invention;

[0038] Figure 5 It is a diagram of the iterative process of the genetic algorithm of the present invention;

[0039] Figure 6 (a) The spectrum diagram of average power distribution and (b) the spectrum diagram after power distribution optimization of the patent of this invention;

[0040] Figure 7 This is a BER comparison diagram before and after the ONU power allocation of the patent of this invention; DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the implementation regulations described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] like Figure 1 As shown, the present invention provides a multi-slot power allocation method based on genetic algorithm and digital signal processing, specifically, a multi-slot subcarrier power allocation method in a coherent point-to-multipoint (PTMP) time-frequency division multiple access (TFDMA) passive optical network (PON) based on digital signal processing (DSP) and genetic algorithm (GA).

[0043] Example 1

[0044] The present invention implements a multi-slot power allocation method for TFDMA coherent PON based on genetic algorithm and digital signal processing, including DSP-enabled power allocation and genetic algorithm optimization. A TFDMA coherent PON instance with 4 subcarriers and 4 time slots is provided to serve 13 ONUs, wherein it is assumed that subcarrier 1 is sent to ONU1 in all 4 time slots, and the other 3 subcarriers are allocated to different ONUs in the 4 time slots.

[0045] The power allocation part of the DSP empowerment includes:

[0046] Step 1: Generate digital subcarriers. In the TFDMA coherent PON system, the OLT uses DSP technology to generate four digital subcarriers. These subcarriers are independent of each other in the frequency domain and can be flexibly configured according to system requirements. Through digital signal processing algorithms, the frequency, phase, bandwidth and other parameters of each subcarrier can be accurately controlled to meet different transmission requirements.

[0047] Step 2: Determine the power ratio. According to the system design requirements and network conditions, determine the power ratio that should be allocated to each subcarrier, that is, the ratio of subcarrier power to total transmit power. These power ratios are based on comprehensive consideration of factors such as system capacity, noise level, and link loss. The sum of the power ratios of all subcarriers is 1 to ensure the rational use of total power.

[0048] Step 2.1: Calculate the total power of the generated subcarriers

[0049] P=P1+P2+P3+P4

[0050] Step 2.2: Obtain the power allocation ratio of each subcarrier through genetic algorithm

[0051] ε=GA(P1,P2,P3,P4)Step 3: Perform power control. After determining the power ratio of each subcarrier, use DSP technology to perform power control on each subcarrier. This can be achieved by adjusting the amplitude or gain of the subcarrier. The DSP algorithm can monitor and adjust the power of the subcarriers in real time to ensure that they are allocated according to the preset ratio.

[0052] Step 3.1: Obtain the new power of each subcarrier according to the power allocation ratio

[0053] P i =P×ε i , i=1,2,3,4

[0054] Step 3.2: After normalizing the power of each subcarrier, set it to the new power.

[0055] Step 4: Subcarrier combination. The subcarriers that have undergone power control are combined. The combined signal will contain the information of all subcarriers and will be transmitted according to the optimized power allocation scheme.

[0056] W=W1+W2+W3+W4

[0057] The power allocation optimization part of the genetic algorithm includes:

[0058] Step 1: Initialization. Set the evolution generation counter to 0, and determine the maximum evolution generation of the algorithm to be 100. Then, randomly generate an initial group of 50 individuals. In the optimization of TFDMA coherent PON power allocation, individuals are the power ratios of subcarriers in different time slots. These individuals will be used as the initial solution set in the search space, where the power ratio of OUN1 in 4 time slots is fixed, so each individual is a set of 13 parameter ratios.

[0059] Step 2: Individual evaluation. The fitness value of each individual in the group is calculated to evaluate their quality. The fitness function varies according to the problem and is used to guide the search direction of the algorithm. In the optimization of TFDMA coherent PON power allocation, the fitness function is calculated based on the average value of the root mean square error in different time slots. The bit error rate of the subcarrier in different time slots is simulated by the simulator. The distance, noise, loss, bandwidth, modulation format and coding parameters of the link are added to the simulator, which can be expressed by the following formula:

[0060] BER i =f(L, noise, B, M, R)

[0061] Where BER is the bit error rate of the link, L is the distance of the link, noise is the noise of the system, B is the bandwidth, M is the modulation format, and R is the code rate of the system probability shaping. The above parameters jointly determine the BER of the system. The specific BER value varies according to the actual parameters. In this case, the link distance is shown in the figure of the specification, the additional noise is set to the same, the bandwidth of each subcarrier is 10G, the modulation format is 16QAM, and there is no probability shaping. Some variables can even be used as variables that affect the fitness function, as well as as input variables or conditional constraints of the genetic algorithm. Through the establishment of the fitness function, the genetic algorithm can fully consider the conditions of the channel for optimization, so that the optimization results are close to reality. The optimization objective can be expressed by the following formula:

[0062] min BER=GA(P)

[0063] Step 3: Termination condition judgment. When the fitness of the optimal individual reaches a given threshold, or the fitness of the optimal individual and the group fitness no longer increase, or the number of iterations reaches a preset number of generations, the algorithm terminates and outputs the optimization result. The optimization result is the optimal TFDMA coherent PON subcarrier power allocation ratio in different time slots under the optimization goal.

[0064] Step 4: Individual update. Perform selection, crossover and mutation operations on individuals in the group to generate new individuals, and repeat steps 2 and 3 until the termination condition is reached and the optimization result is output. Crossover is to replace and recombine part of the structure of two parent individuals to generate new individuals. Crossover greatly improves the search ability of genetic algorithms.

[0065] In step 4, selection is to select superior individuals and eliminate inferior individuals. The purpose of selection is to directly pass on the optimized individuals (or solutions) to the next generation or to generate new individuals through pairing and crossover and then pass on to the next generation. Mutation is to change certain genes of individuals in the population. The introduction of mutation gives the genetic algorithm the ability of random search and allows the genetic algorithm to maintain population diversity.

[0066] DSP-enabled power allocation uses digital subcarrier modulation technology to generate independent subcarriers in the frequency domain, and then combines the subcarriers after individually adjusting the power of each subcarrier, thereby improving the intelligence and flexibility of power allocation.

[0067] Genetic algorithm-enabled power allocation optimization uses genetic algorithms to optimize the power allocation of multi-subcarriers and multi-timeslots in TFDMA coherent PON systems. It can converge quickly in complex environments, and its random search capability can also avoid falling into local optimality.

[0068] The present invention uses digital signal processing to perform independent power control on subcarriers to achieve flexible and intelligent power allocation, and uses genetic algorithms to optimize the power allocation of multiple subcarriers and multiple time slots in a TFDMA coherent PON system, thereby greatly improving the performance of the TFDMA coherent PON system.

[0069] The specific manifestations are:

[0070] Traditional power allocation methods are often implemented manually based on the staff's experience. According to the link performance and actual needs, the ratio between each subcarrier is manually updated. It is usually necessary to repeatedly debug and update the power allocation coefficient to approach the optimal power allocation coefficient and achieve the required power allocation effect. The traditional manual power allocation adjustment method is inefficient and does not fully consider various factors such as system capacity, noise level, link loss, etc. It is difficult to achieve global optimality and is easy to fall into the trap of local optimality.

[0071] Compared with the traditional power allocation method, the multi-slot power allocation method of TFDMA coherent PON based on genetic algorithm and digital signal processing adopts a flexible and intelligent allocation method, fully considering the capacity, noise, loss and other factors of each subcarrier link, adding various factors to the constraints of the genetic algorithm and the expression of the fitness function, and using the genetic algorithm to perform heuristic search on the power allocation ratio. The power allocation ratio required by the system is obtained through the algorithm and modified in the relevant digital signal processing. The genetic algorithm simulates natural selection and genetic mechanisms, and searches for multiple possible optimal solutions in the solution space in parallel, thereby greatly improving the probability of finding the global optimal solution.

[0072] Secondly, the implementation logic of the traditional power allocation method is also significantly different from that of this technical patent. The traditional power allocation method is to improve the overall performance of the system by allocating more power to the links with good performance, at the expense of the links with poor performance. The method proposed by this patent is to balance the performance between the links, allocate the power of the links with excess performance to the links with poor performance, improve the shortcomings of the system transmission, and ultimately improve the overall performance of the system and improve the functionality of power allocation.

[0073] In addition, this method has strong adaptability. Traditional methods usually rely on fixed mathematical models and parameter settings, while this method is aimed at multi-user multi-time slot TFDMA coherent PON systems. Each subcarrier serves different users in different time slots, and the environment is more complex. This method can converge quickly in complex situations and achieve optimal power allocation for multi-time slots and multi-subcarriers.

[0074] Finally, this method has the convenience of multi-parameter optimization design. Traditional methods can often only perform optimization design of a single parameter, while genetic algorithms can easily handle optimization problems of multiple parameters at the same time, thereby achieving simultaneous optimization of multiple parameters.

[0075] This method focuses on optimizing the power allocation of multiple subcarriers and multiple time slots in the TFDMA coherent PON system. The solution is mature, has strong adaptability, and can improve the overall system performance.

[0076] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-slot power allocation method based on genetic algorithm and digital signal processing, characterized in that: Including DSP-enabled power allocation and genetic algorithm-enabled power allocation optimization.

2. The multi-slot power allocation method based on genetic algorithm and digital signal processing according to claim 1, characterized in that: The power allocation part of the DSP enabling process includes: Step 21: Generate digital subcarriers. In the TFDMA coherent PON system, the OLT uses DSP technology to generate a certain number of digital subcarriers. The subcarriers are independent of each other in the frequency domain and can be flexibly configured according to system requirements. Through digital signal processing algorithms, the frequency, phase, bandwidth and other parameters of each subcarrier can be accurately controlled to meet different transmission requirements. Step 22: Determine the power ratio. According to the system design requirements and network conditions, determine the power ratio that should be allocated to each subcarrier, that is, the ratio of subcarrier power to total transmit power. These power ratios are based on comprehensive considerations of system capacity, noise level, and link loss factors. The sum of the power ratios of all subcarriers is 1. Step 22.1: Calculate the total power of the generated subcarriers, where n is the number of subcarriers Step 22.2: Obtain the power allocation ratio of each subcarrier through genetic algorithm ε=GA(P i ),i,1,2,...,n Step 23: Perform power control. After determining the power ratio of each subcarrier, use DSP technology to perform power control on each subcarrier, specifically by adjusting the amplitude or gain of the subcarrier. Step 3.1: According to the power allocation ratio, obtain the new power of each subcarrier. P i =P×ε i ,i==1,2,...,n Step 3.2: After normalizing the power of each subcarrier, set it to the new power; Step 24: Subcarrier combination: combine the power-controlled subcarriers. The combined signal will contain the information of all subcarriers and will be transmitted according to the optimized power allocation scheme.

3. The multi-slot power allocation method based on genetic algorithm and digital signal processing according to claim 1, characterized in that: The power allocation optimization part of the genetic algorithm includes: Step 31: Initialization, setting the evolution generation counter to 0, and determining the maximum evolution generation of the algorithm, then randomly generating an initial group containing multiple individuals. In the optimization of TFDMA coherent PON power allocation, the individuals are the power ratios of subcarriers in different time slots. These individuals will be used as the initial solution set in the search space; Step 32: Individual evaluation, by calculating the fitness value of each individual in the group to evaluate their quality. The fitness function varies according to the problem and is used to guide the search direction of the algorithm. In the optimization of TFDMA coherent PON power allocation, the fitness function is calculated based on the average value of the root mean square error of different time slots; Step 33: Termination condition judgment: when the fitness of the optimal individual reaches a given threshold, or the fitness of the optimal individual and the fitness of the group no longer increase, or the number of iterations reaches a preset number of generations, the algorithm terminates and outputs the optimization result, which is the optimal power allocation ratio of the TFDMA coherent PON subcarriers in different time slots under the optimization target; Step 34: Individual update, select, crossover and mutate individuals in the group to generate new individuals, and repeat steps 32 and 33 until the termination condition is reached and the optimization result is output. Crossover is to replace and recombine part of the structure of two parent individuals to generate new individuals. Crossover makes the search ability of genetic algorithm greatly improved.

4. The multi-slot power allocation method based on genetic algorithm and digital signal processing according to claim 3 is characterized in that: In step 34, selection is to select superior individuals and eliminate inferior individuals. The purpose of selection is to directly pass on the optimized individuals (or solutions) to the next generation or to generate new individuals through pairing and crossover and then pass on to the next generation. Mutation is to change certain genes of individuals in the group. The introduction of mutation allows the genetic algorithm to have random search capabilities and maintain the diversity of the group.

5. The multi-slot power allocation method based on genetic algorithm and digital signal processing according to claim 1, characterized in that: DSP-enabled power allocation uses digital subcarrier modulation technology to generate independent subcarriers in the frequency domain, and then combines the subcarriers after individually adjusting the power of each subcarrier.

6. The multi-slot power allocation method based on genetic algorithm and digital signal processing according to claim 1, characterized in that: Power allocation optimization enabled by genetic algorithms uses genetic algorithms to optimize the power allocation of multiple subcarriers and multiple time slots in TFDMA coherent PON systems, and can converge quickly in complex environments.

7. The multi-slot power allocation method based on genetic algorithm and digital signal processing according to claim 1, characterized in that: Digital signal processing is used to perform independent power control on subcarriers to achieve flexible and intelligent power allocation. At the same time, genetic algorithms are used to optimize the power allocation of multiple subcarriers and multiple time slots in the TFDMA coherent PON system to improve the performance of the TFDMA coherent PON system.

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