Distributed multi-user power distribution method of underwater wireless optical code division multiple access communication system

Abstract

The invention discloses a distributed multi-user power distribution method of an underwater wireless optical code division multiple access communication system, and belongs to the technical field of wireless optical communication. Comprising the following steps: aiming at a multi-user underwater wireless optical code division multiple access communication system, using a decorrelation detector to eliminate multiple access interference, aiming at optimizing the average bit error rate of the system, combining constraint conditions, adopting a distributed algorithm and utilizing a dichotomy iteration method to carry out multi-user power distribution, and compared with a centralized algorithm, the complexity is reduced. Compared with a traditional equal power distribution method, the power distribution method provided by the invention can effectively improve the overall performance of the system.

Description

Technical Field

[0001] The present invention belongs to the technical field of underwater wireless optical communication (UWOC), and relates to a distributed multi-user power allocation method of an underwater wireless optical code division multiple access (OCDMA) communication system. Background Art

[0002] Underwater wireless optical communication (UWOC) has the advantages of high speed, low latency and high confidentiality, and has become the research focus of underwater communication. In recent years, researchers have studied the key technologies in UWOC systems, including channel modeling, coding modulation technology and weak light signal detection technology. With the development of underwater wireless optical communication technology, the communication link of underwater wireless optical communication has gradually developed from simple point-to-point communication to multi-user networked communication.

[0003] In the communication network, it is necessary to select a suitable multiple access method to support multi-user communication. Among all multiple access schemes such as time division multiple access (TDMA), wavelength division multiple access (WDMA), space division multiple access (SDMA) and code division multiple access (CDMA), optical CDMA has a fully asynchronous random access capability and does not require a central network management protocol, which is very suitable for underwater environments. In addition, at the cost of a certain signal bandwidth, spread spectrum technology can improve the sensitivity of the UWOC system receiver, extend its transmission distance, and improve the system's anti-interference ability. CDMA using spread spectrum technology has more advantages than other multiple access schemes. The underwater wireless OCDMA system will be affected by the complex underwater environment. Due to factors such as the capacity of the power supply battery, the power provided to the transmitting device is limited. Through reasonable power allocation, the signal quality can be improved, interference can be reduced, the reliability of the system can be enhanced, and a longer transmission distance and a wider coverage can be achieved.

[0004] At present, the research on multi-user underwater wireless OCDMA communication network mainly focuses on system performance analysis, while the research on resource allocation needs to be deepened. The research on power allocation with the system average bit error rate as the optimization target can improve the overall performance of the system and has guiding significance for the resource optimization configuration of the actual multi-user underwater wireless OCDMA communication system. Summary of the invention

[0005] The purpose of the present invention is to use the average bit error rate of an underwater wireless OCDMA communication system as a measure of system performance, adopt a distributed algorithm to perform multi-user power allocation, and the power allocation method obtained by using the distributed algorithm has better bit error rate performance than the equal power allocation method.

[0006] The present invention is achieved through the following technical solutions.

[0007] A distributed multi-user power allocation method for an underwater wireless OCDMA communication system comprises the following steps:

[0008] 1) Establish the communication model of the multi-user underwater wireless OCDMA system, set the underwater channel conditions, and derive the average bit error rate expression of the system;

[0009] 2) Taking minimizing the average bit error rate of the system as the optimization goal, a mathematical model of the system for multi-user power allocation is established according to the system constraints;

[0010] 3) A distributed algorithm is used to use the binary iteration method to solve the optimal power allocation scheme for each transmitting source.

[0011] In the above technical solution, the present invention also has a further limited solution:

[0012] Further, in step 1), the communication model of the multi-user underwater wireless OCDMA system is established by the following steps:

[0013] 1a) Establish the signal transmission model of multi-user underwater wireless OCDMA system:

[0014] Assume that the system is a communication network with Q transmitters and K users. Each of the Q transmitters provides services for K users. In order to distinguish users, different users use different optical orthogonal codes (OOC) as spread spectrum codes for communication. In the i-th symbol period, the signal transmitted by the q-th transmitter can be expressed as:

[0015]

[0016] Among them, P qk represents the power provided by the qth transmitting source to the kth user, d k (i) represents the i-th binary bit data transmitted by the k-th user, c k (l) represents the lth code chip of the spreading code of the kth user, L C is the spreading code length, g(t) represents the chip duration T c Rectangular pulse inside;

[0017] 1b) Assuming a channel model affected by the absorption and scattering of seawater, the path loss from the qth transmitting source to the kth user can be expressed as:

[0018]

[0019] Among them, A r represents the aperture area of ​​the receiver, η t and η r Represent the transmission efficiency and receiving efficiency respectively, θ qk and θ0 represent the inclination angle and divergence angle of the light beam, respectively. qkrepresents the transmission distance from the qth transmitting source to the kth user, a1, c1, a2 and c2 are the parameters of the double exponential function, and their values ​​are obtained by fitting the channel response curve obtained by Monte Carlo simulation of the selected wavelength and seawater turbidity;

[0020] 1c) Establish the signal transmission model of multi-user underwater wireless OCDMA system:

[0021] The signal emitted by the transmitting source in 1a) reaches the photoelectric detector of user k after passing through the underwater channel set in 1b). The electrical signal obtained after photoelectric conversion can be expressed as:

[0022]

[0023] Where ρ represents the photodetector responsivity, h qk represents the channel loss between the qth transmitting source and the kth user, n k (t) is the noise term at the kth user receiving end, with a mean of 0 and a variance of σ k 2 Gaussian distribution of

[0024] After low-pass filtering, matched filtering and sampling, the spread spectrum codes of all users are used for correlation operations. The symbol number i is omitted in the subsequent analysis, and the signal sequence of user k can be obtained at the receiving end:

[0025]

[0026] in, is the correlation matrix of the spreading code, a ij (i=1, 2, ..., K, j=1, 2, ...K) is the normalized correlation coefficient of the spreading code between user i and user j, w is the code weight, c k =[c k (1) c k (2)…c k (L C )] is the spreading code sequence of the kth user, n k =[n k (1) n k (2)…n k (L C )] is based on the chip time T c For the period n k (t) discrete signal after sampling, T is the transpose operation;

[0027] Take the solution correlation matrix G = R -1 , the decorrelation vector of the kth user is set to the kth row of the decorrelation matrix G, with G kIt means that the signal processed by the decorrelation module at the receiving end of user k is expressed as:

[0028]

[0029] in, represents the correlated discrete noise vector.

[0030] Further, in step 1), the average bit error rate expression of the system is derived by the following steps:

[0031] 1d) According to the receive-end signal expression after decorrelation in 1c), the average power of the received signal of user k can be obtained:

[0032]

[0033] The average noise power at the receiving end of user k can be expressed as:

[0034]

[0035] in, is the equivalent noise variance after decorrelation;

[0036]

[0037] According to OOK modulation, the bit error rate of the kth user can be expressed as:

[0038]

[0039] Where Q(·) represents the Q function,

[0040] The average bit error rate of the system is expressed as:

[0041]

[0042] Further, in step 2), a mathematical model of the system for multi-user power allocation is established by the following steps:

[0043] The optimization target is the average bit error rate of the system. The maximum power of each transmitting source is set. The sum of the power allocated to each user by each transmitting source does not exceed the maximum power of each transmitting source, and the power allocated to each user is greater than 0. According to the optimization target and constraints, the optimal mathematical model of user power allocation of the system is obtained.

[0044] Further, in step 3), the optimal power allocation scheme for each transmitting source is obtained by the following steps:

[0045] 3a) Only one transmitting source is considered in each optimization, and a mathematical model of distributed multi-user power allocation is derived when each transmitting source works alone;

[0046] 3b) Considering the qth transmitting source, according to the mathematical model of distributed multi-user power allocation derived in step 3a), the KKT condition is used to solve the optimal power allocation method, and the power expression allocated from the qth transmitting source to the kth user is derived;

[0047] 3c) Based on the power expression derived in 3b), combined with the power constraint conditions, list the equations that the power satisfies, and use the bisection method to iteratively solve the optimal power allocation solution for the qth emission source;

[0048] 3d) Update the value of q, the value range of q is q=1, 2, ..., Q, repeat steps 3a) to 3c) until q completes the traversal of all values ​​in the value range, and solves the optimal power allocation scheme for each transmitting source.

[0049] The present invention has the following advantages:

[0050] The present invention proposes a distributed algorithm for power allocation for a multi-user underwater wireless OCDMA communication system. The algorithm considers the power allocation of each transmitting source to each user separately, and adopts a binary iteration method to solve the power allocation scheme of each transmitting source one by one. Compared with the centralized consideration of all transmitting sources, the algorithm reduces the complexity. At the same time, compared with the equal power allocation method, the algorithm has better bit error rate performance. The method has guiding significance for the resource optimization configuration of an actual multi-user underwater wireless OCDMA communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a distributed algorithm flow chart;

[0052] Figure 2 It is the comparison result of the average bit error rate performance of the system using the equal power allocation algorithm and the distributed algorithm. DETAILED DESCRIPTION

[0053] To make the purpose, technical solution and advantages of the present invention more clear, the following is a further detailed description in conjunction with the accompanying drawings and specific implementation methods. This embodiment is only intended to illustrate the principle of the present invention and does not represent any limitation to the present invention.

[0054] The present invention is a distributed multi-user power allocation method based on an underwater wireless optical code division multiple access (OCDMA) communication system. The specific implementation method and application examples are described as follows:

[0055] Step 1), establish the communication model of the multi-user underwater wireless OCDMA system through the following steps:

[0056] 1a) Establish the signal transmission model of multi-user underwater wireless OCDMA system:

[0057] The system is a communication network with Q transmitters and K users. Each of the Q transmitters provides services for K users. In order to distinguish users, different users use optical orthogonal codes (OOC) as spread spectrum codes for communication. In the i-th symbol period, the signal transmitted by the q-th transmitter can be expressed as:

[0058]

[0059] Among them, P qk represents the power provided by the qth transmitting source to the kth user, d k (i) represents the i-th binary bit data transmitted by the k-th user, c k (l) represents the lth code chip of the spreading code of the kth user, L C is the spreading code length, g(t) represents the chip duration T c Rectangular pulse inside;

[0060] 1b) Assuming a channel model affected by the absorption and scattering of seawater, the path loss from the qth transmitting source to the kth user can be expressed as:

[0061]

[0062] Among them, A r represents the aperture area of ​​the receiver, η t and η r Represent the transmission efficiency and receiving efficiency respectively, θ qk and θ0 represent the inclination angle and divergence angle of the light beam, respectively. qk represents the transmission distance from the qth transmitting source to the kth user, a1, c1, a2 and c2 are the parameters of the double exponential function, and their values ​​are obtained by fitting the channel response curve obtained by Monte Carlo simulation of the selected wavelength and seawater turbidity;

[0063] 1c) Establish the signal transmission model of multi-user underwater wireless OCDMA system:

[0064] The signal emitted by the transmitting source in 1a) reaches the photoelectric detector of user k after passing through the underwater channel set in 1b). The electrical signal obtained after photoelectric conversion can be expressed as:

[0065]

[0066] Where ρ represents the photodetector responsivity, h qk represents the channel loss between the qth transmitting source and the kth user, n k (t) is the noise term at the kth user’s receiving end. The noise characteristics of each user are the same, all with a mean of 0 and a variance of σ n2 Gaussian distribution of

[0067] The noise considered in the system includes background noise, dark current noise and thermal noise. Among them, the noise variance of background radiation can be calculated as Where q represents the electron charge, P BG represents the background noise power, B is the electronic bandwidth, represents the response coefficient of the photodetector, η and h represent the quantum efficiency and Planck constant of the photodetector, respectively. represents the light source frequency, c and λ represent the speed of light and the wavelength of the light source respectively; the dark current noise variance can be calculated as Among them, I DC represents the dark current; the thermal noise variance can be calculated as Among them, T e represents the equivalent temperature, F represents the system noise index, R L represents the load resistance. Each noise is independent of each other and can be regarded as additive Gaussian white noise. Therefore, the total noise can be modeled as having a mean of 0 and a variance of Gaussian white noise.

[0068] After low-pass filtering, matched filtering and sampling, the spread spectrum codes of all users are used for correlation operations. The symbol number i is omitted in the subsequent analysis, and the signal sequence of user k can be obtained at the receiving end:

[0069]

[0070] in, is the correlation matrix of the spreading code, a ij (i=1, 2, ..., K, j=1, 2, ...K) is the normalized correlation coefficient of the spreading code between user i and user j, w is the code weight, c k =[c k (1) c k (2)…c k (L C )] is the spreading code sequence of the kth user, n k =[n k (1) n k (2)…n k (L C )] is based on the chip time T c For the period n k (t) discrete signal after sampling, T is the transpose operation;

[0071] Take the solution correlation matrix G = R -1 , the decorrelation vector of the kth user is set to the kth row of the decorrelation matrix G, with G kIt means that the signal processed by the decorrelation module at the receiving end of user k is expressed as:

[0072]

[0073] in, represents the correlated discrete noise vector.

[0074] Further, in step 1), the average bit error rate expression of the system is derived by the following steps:

[0075] 1d) According to the receive-end signal expression after decorrelation in 1c), the average power of the received signal of user k can be obtained:

[0076]

[0077] The average noise power at the receiving end of user k can be expressed as:

[0078]

[0079] in, is the equivalent noise variance after decorrelation;

[0080] Then the signal-to-noise ratio of user k at the receiving end is expressed as:

[0081]

[0082] According to OOK modulation, the bit error rate of the kth user can be expressed as:

[0083]

[0084] Where Q(·) represents the Q function,

[0085] The average bit error rate of the system is expressed as:

[0086]

[0087] Step 2) is to establish a mathematical model of the system for multi-user power allocation through the following steps:

[0088] The optimization goal is the average bit error rate of the system. The maximum power of each transmitting source is set. The sum of the power allocated to each user by each transmitting source does not exceed the maximum power of each transmitting source, and the power allocated to each user is greater than 0. According to the optimization goal and constraints, the optimal mathematical model of the system for user power allocation is obtained. The optimization problem can be expressed as follows:

[0089]

[0090]

[0091] In step 3), the optimal power allocation scheme for each transmitting source is obtained by the following steps:

[0092] 3a) Only one transmitting source is considered in each optimization, and the mathematical model of distributed multi-user power allocation when each transmitting source works alone is derived. The mathematical model of multi-user power allocation of the qth transmitting source can be expressed as:

[0093]

[0094]

[0095] 3b) Considering the qth transmitting source, analyzing the mathematical model of multi-user power allocation obtained in step 3a), it is concluded that the optimization problem is a convex problem, and the equation is listed according to the KKT condition:

[0096]

[0097] Using derivatives, we can analytically solve the problem:

[0098]

[0099] Where μ is the Lagrange multiplier;

[0100] 3c) Based on the power expression derived in 3b), combined with the power constraints, list the equations that the power satisfies:

[0101]

[0102] Use the bisection method to iteratively solve μ in the above formula:

[0103] a) Initialize various parameters: the minimum value of μ min = 0 and the maximum value of μ Total power of the transmitting source P q , algorithm accuracy ε, and number of iterations n = 0;

[0104] b) Use Calculate the P at this time qk , using P qk Calculate the total power at this time Compare P Z and the total power of the transmitting source P q , if P Z Greater than P q , then μ min =μ (n) ;P Z Less than P q , then μ max =μ (n) ;

[0105] c) Update the number of iterations n = n + 1;

[0106] d) Repeat b) to c) until |μ max -μ min |≤ε;

[0107] 3d) Update the value of q, the value range of q is q=1, 2, ..., Q, repeat steps 3a) to 3c) until q completes traversal of all values ​​in the value range, and solves the optimal power allocation scheme for each transmitting source.

[0108] The correctness and advantages of the present invention can be further illustrated by comparing the following theoretical results:

[0109] In the method of the present invention, simulation verification is carried out through MATLAB to prove the effectiveness of the method of the present invention.

[0110] The simulation scenario is designed as the transmitting source is at sea level and the user is underwater. The Z axis represents the direction perpendicular to the sea level, the positive direction is perpendicular to the sea level downward, and the position of Z=0 is defined as the sea level. The X axis and Y axis represent the horizontal direction. The origin is set to a point at sea level, and the coordinates are expressed as (0, 0, 0). The simulation uses an equal-weight symmetric OOC code with a code length of 38, a code weight of 3, and an autocorrelation side peak and a cross-correlation value less than or equal to 1. It can support up to 6 users, assuming that the maximum transmission power of each transmitting source is the same. Other main simulation parameters are shown in the following table:

[0111] parameter value parameter value parameter value parameter value parameter value η 0.8 <![CDATA[c1]]> 0.178 <![CDATA[η r ]]> 0.9 <![CDATA[T e ]]> 290K K <![CDATA[1.4×10 -23 J / K]]> q <![CDATA[1.602×10 -19 C]]> <![CDATA[c2]]> 0.0602 <![CDATA[θ0]]> 10° <![CDATA[R L ]]> 100Ω <![CDATA[A r ]]> <![CDATA[0.008m 2 ]]> <![CDATA[a1]]> 0.001045 λ 532nm <![CDATA[P BD ]]> <![CDATA[6.34×10 -11 In]]> F 4 c <![CDATA[2.26×10 8 m / s]]> <![CDATA[a2]]> 0.002134 <![CDATA[η t ]]> 0.9 B 5MHZ <![CDATA[I DC ]]> <![CDATA[1.226×10 -9 A]]>

[0112] Table 1 Simulation parameter settings

[0113] Two scenarios were simulated: single-source multi-user and multi-source multi-user:

[0114] For an underwater wireless OCDMA system consisting of 1 source node and 3 users, the source coordinates are set to (2.5, 2.5, 0), and the 3 user coordinates are (2.5, 2.5, 10), (5, 3.75, 10), and (5, 5, 10). The performance of the equal power allocation method and the step-by-step power allocation method proposed in this invention are simulated and compared. The performance of the two algorithms is shown in Figure 2. Figure 2 As shown, compared with the equal power allocation method, the system using the step-by-step power allocation method has a better bit error rate performance. In the case of a single transmitting source, the method of the present invention can theoretically make the system average bit error rate performance optimal.

[0115] For an underwater wireless OCDMA system consisting of 3 source nodes and 3 users, the source coordinates are set to (2.5, 2.5, 0), (5, 3.75, 0) and (5, 5, 0), and the 3 user coordinates are (2.5, 2.5, 10), (5, 3.75, 10) and (5, 5, 10). The performance of the equal power allocation method and the power allocation method proposed in this invention are simulated and compared. The performance of the two algorithms is shown in the figure. Figure 2 As shown in the figure, when there are multiple transmitting sources, the system using the step-by-step power allocation method still has better bit error rate performance than the equal power allocation method.

[0116] The present invention is not limited to the above-mentioned embodiments. On the basis of the technical solution disclosed in the present invention, technicians in this field can make some substitutions and deformations to some technical features therein according to the disclosed technical content without creative labor, and these substitutions and deformations are all within the protection scope of the present invention.

Claims

1. A distributed multi-user power allocation method for an underwater wireless optical code division multiple access (OCDMA) communication system, characterized in that: The method comprises the following steps: 1) Establish the communication model of the multi-user underwater wireless OCDMA system, set the underwater channel conditions, and derive the average bit error rate expression of the system; 2) Taking minimizing the average bit error rate of the system as the optimization goal, a mathematical model of the system for multi-user power allocation is established according to the system constraints; 3) A distributed algorithm is used to use the binary iteration method to solve the optimal power allocation scheme for each transmitting source.

2. The distributed multi-user power allocation method for underwater wireless OCDMA communication system according to claim 1, characterized in that: In the step 1), the communication model of the multi-user underwater wireless OCDMA system is established by the following steps: 1a) Establish the signal transmission model of multi-user underwater wireless OCDMA system: Assume that the system is a communication network with Q transmitters and K users. Each of the Q transmitters provides services for K users. Different users use different optical orthogonal codes (OOCs) as spreading codes for communication. In the i-th symbol period, the signal transmitted by the q-th transmitter can be expressed as: Among them, P qk represents the power provided by the qth transmitting source to the kth user, d k (i) represents the i-th binary bit data transmitted by the k-th user, c k (l) represents the lth chip of the spreading code of the kth user, L C is the spreading code length, g(t) represents the chip duration T c Rectangular pulse inside; 1b) Assuming a channel model affected by the absorption and scattering of seawater, the path loss from the qth transmitting source to the kth user can be expressed as: Among them, A r represents the aperture area of ​​the receiver, η t and η r Represent the transmission efficiency and receiving efficiency respectively, θ qk and θ0 represent the inclination angle and divergence angle of the light beam, respectively. qk represents the transmission distance from the qth transmitting source to the kth user, a1, c1, a2 and c2 are the parameters of the double exponential function, and their values ​​are obtained by fitting the channel response curve obtained by Monte Carlo simulation of the selected wavelength and seawater turbidity; 1c) Establish the signal transmission model of multi-user underwater wireless OCDMA system: The signal emitted by the transmitting source in 1a) reaches the photoelectric detector of user k after passing through the underwater channel set in 1b). The electrical signal obtained after photoelectric conversion can be expressed as: Where ρ represents the photodetector responsivity, h qk represents the channel loss between the qth transmitting source and the kth user, n k (t) is the noise term at the kth user receiving end, with a mean of 0 and a variance of σ k 2 Gaussian distribution of After low-pass filtering, matched filtering and sampling, the spread spectrum codes of all users are used for correlation operations. The symbol number i is omitted in the subsequent analysis, and the signal sequence of user k can be obtained at the receiving end: in, is the correlation matrix of the spreading code, a ij (i=1, 2, ..., K, j=1, 2, ...K) is the normalized correlation coefficient of the spreading code between user i and user j, w is the code weight, c k =[c k (1) c k (2) … c k (L C )] is the spreading code sequence of the kth user, n k =[n k (1) n k (2) … n k (L C )] is based on the chip time T c For the period n k (t) discrete signal after sampling, T is the transposition operation; Take the solution correlation matrix G = R -1 , the decorrelation vector of the kth user is set to the kth row of the decorrelation matrix G, with G k It means that the signal processed by the decorrelation module at the receiving end of user k is expressed as: in, represents the correlated discrete noise vector.

3. The distributed multi-user power allocation method of the underwater wireless OCDMA communication system according to claim 1, characterized in that: In step 1), the average bit error rate expression of the derivation system is obtained by the following steps: 1d) According to the receive-end signal expression after decorrelation in 1c), the average power of the received signal of user k can be obtained: The average noise power at the receiving end of user k can be expressed as: in, is the equivalent noise variance after decorrelation; Then the signal-to-noise ratio of user k at the receiving end is expressed as: According to OOK modulation, the bit error rate of the kth user can be expressed as: Where Q(·) represents the Q function, The average bit error rate of the system is expressed as:

4. The distributed multi-user power allocation method for underwater wireless OCDMA communication system according to claim 1, characterized in that: In step 2), a mathematical model of the system for multi-user power allocation is established by the following steps: The optimization target is the average bit error rate of the system. The maximum power of each transmitting source is set. The sum of the power allocated to each user by each transmitting source does not exceed the maximum power of each transmitting source, and the power allocated to each user is greater than 0. According to the optimization target and constraints, the optimal mathematical model of user power allocation of the system is obtained.

5. The distributed multi-user power allocation method for underwater wireless OCDMA communication system according to claim 1, characterized in that: In step 3), the optimal power allocation scheme for each transmitting source is obtained by the following steps: 3a) Only one transmitting source is considered in each optimization, and a mathematical model of distributed multi-user power allocation is derived when each transmitting source works alone; 3b) Considering the qth transmitting source, according to the mathematical model of distributed multi-user power allocation derived in step 3a), the KKT condition is used to solve the optimal power allocation scheme, and the power expression allocated from the qth transmitting source to the kth user is derived; 3c) Based on the power expression derived in 3b), combined with the power constraint conditions, list the equations that the power satisfies, and use the bisection method to iteratively solve the optimal power allocation solution for the qth emission source; 3d) Update the value of q, the value range of q is q=1, 2, ..., Q, repeat steps 3a) to 3c) until q completes the traversal of all values ​​in the value range, and solves the optimal power allocation scheme for each transmitting source.

Images