KR20090004022A - Multiuser detect device of ds-cdma system and method thereof - Google Patents

Abstract

A multi-user detector of a DS-CDMA system and a detection method thereof are provided to restore each user data in a receiver by removing the multi-user interference and error due to the same. A transmitter(10) spreads multi-user data or modulates a PN code so as to be discriminated in a DS-CDMA system, and then wirelessly transmits the modulated data. A matched filter(21) spreads the data of the transmitter or reversely spreads the PN code. A binary decision part(23) determines a plurality of samples outputted from the matched filter as 0 or 1 according to the predetermined reference output value. A genetic algorithm processor(25) performs an genetic algorithm for the multi-user detection to the output of the binary decision part.

Description

Multiuser Detect Device of DS-CDMA System and Method

1 is a block diagram schematically showing a multi-user detection apparatus of a DS-CDMA system according to the present invention.

Figure 2 is a block diagram showing a genetic algorithm processor of the multi-user detection apparatus of the DS-CDMA system according to the present invention.

3 is a schematic block diagram of a migration module of a multi-user detection apparatus of a DS-CDMA system according to the present invention;

Figure 4 is a schematic block diagram of a module using a random number generator of the multi-user detection apparatus of the DS-CDMA system according to the present invention.

5 is a flowchart schematically illustrating a multi-user detection method of a DS-CDMA system according to the present invention.

6 is a flowchart schematically illustrating a genetic processor algorithm in a multi-user detection method of a DS-CDMA system according to the present invention.

7 is a performance comparison graph using the multi-user detection apparatus and detection method of the S-CDMA system according to the present invention.

      <Brief description of reference numerals for the main parts of the drawings>

1: Multi-user detection device of DS-CDMA system

10: transmitter 20: receiver

21: matching filter section 23: binary determining section

25: genetic algorithm processor 25a, 25a ': memory

25b, 25b ': competitive object selection module 25d, 25d': migration module

25c, 25c ': Object Diversity Module

The present invention relates to a multi-user detection apparatus and a detection method of a DS-CDMA system using a genetic algorithm, and more particularly, to transmit data in a Direct Seqence-Code Division Multiple Access (DS-CDMA) system. The present invention relates to a multi-user detection apparatus and a detection method of a DS-CDMA system using a genetic algorithm that can improve the reliability of the detected signal when extracting a user signal from a receiver that receives a signal from a transmitter.

In general, DS-CDMA (Direct Seqence-Code Division Multiple Access) is a next-generation digital mobile communication method that adopts spectrum spread technology.

In addition, a plurality of users can share the same frequency band, and uses a unique pseudo noise code (PN Code) of the user communication channel to distinguish the user, the clock frequency (Clock Freqency) of the voice data frequency bandwidth of the transmitting side The frequency band is spread by multiplying the voice data by a PN code that is tens of times or more, and multiplying by the same PN code at the reception side, the bandwidth is returned to its original width and demodulated.

Here, multi-user interference (MAI) occurs between channels used by a plurality of users, and a genetic algorithm is used to remove them.

At this time, the genetic algorithm is an optimization algorithm based on Darwin's law of natural selection that 'highly suited individuals are likely to survive the next generation', and is a global search through implicit parallelism. Its excellent function and convergence speed make it suitable for pattern recognition, neural network learning, voice recognition, and robot control.

However, as the number of users increases, the performance of the receiver decreases due to multi-user interference (MAI), and the amount of computation increases to estimate the received data, causing an overload. When using a genetic algorithm, a local minimum The probability of staying at) increased, and there were problems such as early convergence and increased computational complexity.

The present invention has been made to solve the above problems, and an object of the present invention is to provide a multi-user detection device and a detection method of a DS-CDMA system that can remove the multi-user interference by connecting a genetic algorithm processor in parallel.

Another object of the present invention is to provide a multi-user detection apparatus and a detection method of a DS-CDMA system that can solve the early convergence problem, reduce the computational complexity and increase the computation speed.

Another object of the present invention is to provide a multi-user detection apparatus and a detection method of a DS-CDMA system using a competitive selection method in order to increase diversity and increase performance to obtain dominant individuals by connecting genetic algorithms in parallel. It aims to provide.

In order to achieve the above object, the present invention is a DS-CDMA system for transmitting a radio by modulating the multi-user data so as to be identified by a spreading or PN code; A matched filter unit for despreading the data of the transmitter with a spread or PN code, a binary determiner for determining a plurality of samples output from the matched filter unit as 0 or 1 depending on whether or not a predetermined reference output value is greater than or equal to; A receiver including a genetic algorithm processor for performing a genetic algorithm for multi-user detection as an output of the binary determination unit; It includes.

The genetic algorithm processor may be connected in parallel.

The genetic algorithm processor may further include a memory configured to store a population generated by mutating any one bit into an output of the binary determination unit; A competitive entity selection module that selects any two entities in the memory and selects an object to evolve to the next generation; An individual diversity module for crossing and mutating to said selected individual; A migration module for exchanging the individual outputted from the individual diversity module and outputting a dominant individual which is an individual having a high suitability compared with the individual existing in the individual diversity module; Characterized in that consists of.

In addition, the migration module is characterized by outputting a dominant individual by comparing the individual migrated from the migration module of the other genetic algorithm processor, the low suitability of the two parents, and the high suitability of the descendant.

The competitive entity selection module and the entity diversity module use a random number generator, but the competitive entity selection module selects an entity whose two randomly generated values are addresses of the memory using the random number generator, and the entity diversity module. The random number generator is characterized in that for selecting the cross and the mutation point to two randomly generated values.

Here, the individual diversity module is characterized in that it is possible to determine whether to perform crosses and mutations with the value generated by the random number generator.

In addition, in the individual diversity module, the crossing is characterized by applying a simple cross or a two-point cross or a uniform cross, and the mutation is characterized by applying a simple mutation or multiple mutations.

On the other hand, in the DS-CDMA system, a first step of modulating the data of the multi-user to be identifiable by the spreader or PN code in the transmitter for wireless transmission; A second step of despreading data of the transmitter with a spreading or PN code in a matching filter unit of a receiver; A third step of determining a plurality of samples output from the matched filter unit from the binary determiner to 0 or 1 depending on whether or not a predetermined reference output value is greater than or equal to; A fourth step of performing a genetic algorithm for multi-user detection in a genetic algorithm processor as an output of the binary determination unit; It includes.

The fourth step may include storing a population generated by mutating any one bit in a memory using an output of the binary determining unit; Selecting any two entities from among the entities stored in the memory to select an object to evolve to the next generation in a competitive entity selection module; Crossing and mutating in the individual diversity module with the selected individual; Exchanging an object outputted to the individual diversity module in a migration module, and outputting a dominant individual that is an object having a high degree of suitability compared with the individual existing in the individual diversity module; Characterized in that consists of.

The migrating of the individual may include outputting a dominant individual by comparing an individual migrated from a migration module of another genetic algorithm processor, a low suitability among two parent individuals, and a high suitability among descendants. .

In addition, the step of mating and mutating the individual is characterized in that the selection of the cross and the mutation point to two randomly generated values using a random number generator.

In addition, in the step of mating and mutating the individual, the crossing is characterized by applying a simple cross or a two-point cross or even cross, and the mutation is characterized by applying a simple mutation or multiple mutations.

The selecting of the object may include selecting an object whose two randomly generated values are addresses of the memory using a random number generator.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram schematically showing a multi-user detection device of a DS-CDMA system according to the present invention, Figure 2 is a block diagram showing a genetic algorithm processor of the multi-user detection device of a DS-CDMA system according to the present invention to be. As shown in the figure, the multi-user detection apparatus 1 of the DS-CDMA system according to the present invention comprises a DS-CDMA based transmitter 10 and a receiver 20.

Here, the transmitter 10 simultaneously transmits data to be transmitted by each user and uses a binary phase shift keying (BPSK) modulation scheme. The transmission symbol data is multiplied by the PN code and the transmission energy and propagated to the antenna.

At this time, the propagated transmission symbol data is combined in a free space, which is transmitted to the receiver 20.

To this end, DS-CDMA is an overview of DS-CDMA, a digital mobile communication system employing spread-spectrum technology, which allows multiple users to transmit, receive, and transmit signals while sharing time and frequency. Since the signal is spread over a wider bandwidth than that of the signal to be transmitted, and thus the power density of the signal is lowered, it is not easy to detect the presence or absence of the signal, thereby ensuring the confidentiality of the communication.

In addition, in the process of despreading the signal received by the receiver 20, in order to modulate the original data signal, the code used at the time of spreading must be known. .

In the direct sequence (DS) method of spreading the frequency, the frequency bandwidth may be increased by multiplying a spread code having a short period by the digital data to be transmitted.

Here, it is preferable to use a PN code as a gold sequence. In the transmitter 10, one PN code of two PN codes moves by one bit, and a gold sequence can be obtained by XOR operation. This is because the product between sequences approaches 1, and the product between other gold sequences approaches 0.

Meanwhile, the receiver 20 adds AWGN (Addition White Gaussian Noise) by summing signals of N users in a transmitting antenna, and when the number of users increases, multiple user interference (MAI) increases. ) Increases.

Therefore, the received signal passes through the matched filter 21. The output of the matched filter 21 multiplied by the Gold Sequence by the data bit period is the signal of the multi-user and the number of users. The signal generated by the multi-user interference (MAI) according to the increase and the signal multiplied by the weight of the matching filter unit 21 AWGN.

That is, the matched filter unit 21 includes a multiplier, an integrator, and a switch to despread the received multi-user received signal and restore each message signal of the multi-user.

In addition, the data of each user passing through the matched filter unit 21 passes through the binary determiner 23 and is output as 1 for a value above a predetermined criterion and 0 for a value below a certain criterion.

Here, in order to remove AWGN, each user's signal received from the matched filter unit 21 is passed through the AWGN matrix to detect each user's signal. As the number of users increases, that is, the user goes down the AWGN matrix, It has a structure in which signal components are summed one by one.

In other words, the first user's signal is output only by its own signal through the product of the first row of the AWGN matrix, while the second user's signal is output in the form of the sum of interference signals caused by the first user's signal, and the signal of the Nth user. Is the sum of all interference signals from the first user to the N-1 th user.

Accordingly, the genetic algorithm processor 25 is further provided on the receiver 20 side to remove the user interference signal MAI.

The genetic algorithm processor 25 receives an array of zeros or ones representing binary data of each user received from the binary determiner 23, and minimizes a user interference signal (MAI) to multi-user. It is made to be able to detect.

Here, multi-user detection is easier as the probability that the i-th transmission data and the i-th reception data are the same data is as easy as possible so that the maximum number of transmission data can be estimated. Therefore, it is preferable to determine and use the fitness function accordingly.

In addition, two genetic algorithm processors 25 are provided in parallel, and in order to generate an initial individual, the values output from the binary determining unit 23 are muted to configure the memories 25a and 25a '.

The reason is that the output of the matched filter unit 21 input to the binary determining unit 23 is close to the optimum solution, so that any one bit of the object is mutated to create a population close to the optimal solution. ) Can save you time looking for solutions.

Thus, various populations are generated using mutations from each individual whose data of each of the multi-users output from the binary determining unit 23 is an array expressed in bits, and are defined as initial individuals.

In addition, through the competitive object selection module (25b, 25b ') that selects any two objects from the memory (25a, 25a') stored in the initial object to evolve to the next generation, the arbitrary two objects When selecting, the random number generator module is used to randomly generate random numbers and to select an object having the generated random numbers as addresses of the memories 25a and 25a '.

That is, the competitive object selection module 25b, 25b 'selects the addresses of the memories 25a, 25a' with three bits, and generates a string of three bits to select any two entities.

The competitive entity selection module 25b, 25b 'is composed of two blocks, each of which is executed at the same time, and transmits and receives a series of signals to synchronize data transmission between the memories 25a, 25a' and the random number generator module. It is desirable to be connected by the Hand Shaking Protocol.

In addition, two objects are selected from the memory 25a and 25a 'to compare their goodness-of-fit, and the dominant object is the parent object, the recessive object is the recessive object, and the recessive object is replaced when the population is updated. In order to increase the speed of computation, we create two parent objects in one clock and pass them to the next module to increase the computation speed.

In addition, the competitive entity selection module 25b, 25b 'is preferably designed to converge at the optimal solution as quickly as possible by increasing the selection pressure rather than making a random selection.

Then, when a target to be evolved to the next generation is selected in the competitive object selection modules 25b and 25b ', the individual selected as the individual diversity module 25c and 25c' that performs the mating and mutation process is input.

Here, the individual diversity module (25c, 25c ') is a module designed to perform two genetic operators step by step, Crossover is a simple cross-over method of the crossover method of the object for the generality of various problems (1-Point Crossover) ), Two-point crossover, and uniform crossover can be used.

In addition, two parent objects and one inferior object are received from the competitive object selection modules 25b and 25b ', and the intersection is performed. For example, in the case of a simple intersection, a random number generator generates a random random number, Accordingly, the intersection point is determined based on the random number, and each bit index of the object is compared based on the intersection point (1-Point) and exchanged when the index is small. Will be done.

In addition, in the case of receiving mutations from two competing entities and one recessive entity from the competitive entity selection modules 25b and 25b ′, single mutations and multiple mutations are performed. It is desirable to design a genetic operator in the form of a combination of crossover and mutations, which are designed to generate point mutations, and for easy problems, have a small area and can evolve quickly.

In addition, the selection is terminated in the competitive entity selection module 25b and 25b 'and the random rate generator compares the random rate with the mutation rate. If the random probability is greater than the mutation rate, the bit is' 1'. Generate a '0' if any probability is less than the mutation.

Through this iterative process, each individual is created, and the final descendant is generated by performing an XOR operation on each bit of the object received from the individual and the object diversity module 25c or 25c '.

In addition, the migration modules 25d and 25d 'are provided for selecting an object to be finally replaced to update the population, that is, an object having the best suitability.

In addition, in order to select the dominant individual to be finally replaced, by comparing the high-fit of the descendants, the low-fit of the current two parents and the migrated from the genetic algorithm connected in parallel, Select the most dominant of them and use them for the population.

In addition, as the comparison population increases, the genetic algorithm according to the present invention quickly arrives at an optimal solution. It is preferable to form a hardware structure so that the entity having the most suitable fitness is updated in the memories 25a and 25a '.

In addition, the migration modules 25d and 25d 'are configured to function as interfaces for exchanging data between sub-processors, so that two objects created after one genetic operator are performed. Among them, progeny individuals having excellent traits are selected and migrated, and through this, individuals received from other processors are transferred to competitive individual selection modules 25b and 25b 'and used as comparative data when selecting dominant individuals.

In particular, the migration module 25d, 25d 'does not unconditionally use an object moved from another processor, but compares it with a recessive object inside its processor and makes use of a good one, thereby allowing diversity and speed of movement. Can be increased.

Therefore, if the dominant entity is output from the migration modules 25d and 25d 'and satisfies the goodness of fit, a signal with improved reliability can be detected, and thus multi-user detection is completed.

3 is a schematic block diagram of a migration module of a multi-user detection apparatus of a DS-CDMA system according to the present invention. As shown in the figure, the migration module 25d according to the present invention selects a dominant object out of two descendant objects, defines it as a migration object, exports it to another migration module 25d ', and vice versa. (25d ') selects the dominant of two descendant entities, defines them as migration entities, and exports them to migration module 25d.

Each delivered individual is then compared with the Worst Data determined in the competitive population selection modules 25b, 25b ', and the dominant trait is compared with the migrant, and the final dominant trait is the bad individual ( Worst Data).

In addition, as an important variable of the migration method, the timing of migration, the amount of objects to be migrated, the selection of objects to be migrated, and the type of topology can be changed.

In addition, the migration time of the migration individuals is made for each generation, and the selection of the migration objects is performed by comparing the migrated ones with the high-fitting ones of the two descendant individuals, the low-fitting ones of the parental ones, and outputting the dominant ones. Only one progeny of the two progeny generated survives and the superior trait can replace the parent, thereby preventing rapid spread of the solution and converging to the optimal solution.

4 is a schematic block diagram of a module using a random number generator of a multi-user detection apparatus of a DS-CDMA system according to the present invention. As shown in the figure, a module using a random number generator comprises a competitive entity selection module 25b and an entity diversity module 25c.

Here, a random number generator is used to determine the competitive selection of the individual from the arrangement of the population and the possibility of occurrence of the crossing, and at the same time to determine the location of the crossing and mutation.

Thus, the competitive entity selection module 25b is used to select the address of the memory 25a with three bits and does not use a random number generator, making the probability '1' for the intersection occurring in the entity diversity module 25c.

In addition, it is preferable to use two 32-bit random number generators at the intersection, and to make a 7-bit overlapping structure by shifting the 68-bit random number generator by one bit in order to reduce the area, instead of creating 32 32-bit random numbers in mutation. Do.

In addition, in order to improve the problem of period and initial value, it is preferable to use the most significant bit and the least significant bit of the 4-bit counter as the random number generator input by putting them in the most significant input and the least significant input of the random number generator.

For example, if two random numbers generated by the random number generator are x1 and x2, when the object selection is made in the competitive object selection module 25b, the object of which x1 is a memory address and the object of which x2 is a memory address are selected. will be.

In addition, if any two numbers generated by the random number generator are y1 and y2, when selecting the intersection point and the mutation point in the individual diversity module 25c, the point y1 is selected as the intersection point, and the point y2 is selected as the mutation point. To cross and mutate.

5 is a flowchart schematically illustrating a multi-user detection method of a DS-CDMA system according to the present invention. As shown in the figure, the multi-user detection method of the DS-CDMA system according to the present invention starts as follows.

First, the transmitter spreads the multi-user data for wireless transmission with a PN code and transmits the multi-user data to the receiver (S10).

Then, the receiver despreads the data of each user received from the transmitter to the PN code through the matching filter unit (S20).

In addition, the matched filter unit transmits data of multiple users to the binary determination unit, and converts the data to '1' for data above a certain level and to '0' for data below a certain level to form a series of bit arrays. Let (S30).

In addition, the binary decision unit transfers the bit array consisting of 0 or 1 to the genetic algorithm processor so that the multi-user interference (MAI) can be eliminated as much as possible to increase the multi-user estimate. Accordingly, the genetic algorithm processor detects a signal of each multi-user and ends (S40).

6 is a flowchart schematically illustrating a gene processor algorithm in a multi-user detection method of a DS-CDMA system according to the present invention. As shown in the figure, a detailed look at the gene processor algorithm according to the present invention.

The bit array output from the binary determining unit is mutated by 1 bit and stored in the memory. The address stored in the memory is determined as an address according to the number generated by the random number generator (S41).

Each initial population stored in memory is then sent to a competitive entity selection module, where random numbers are generated to select the entity with the random number as the address of the memory, generating two random numbers, and selecting two ( S42).

In addition, the two selected entities in the competitive entity selection module are input to the entity diversity module, and cross and mutate based on the random number generated in the random number generator (S43).

In addition, crossover and mutated individuals are moved to a migration module, where migration occurs between each migration module, which compares the low-fit of the migrated and parent individuals with the high-fit of the progeny. The dominant entity is output (S44).

Thus, the steps S42, S43, and S44 are repeatedly driven until a solution that satisfies the goodness-of-fit function is output by comparing the individual objects, or until it approaches a predetermined number of times, and satisfies the goodness-of-fit function. When the set number of times is reached, the output of the migrant individual is output to the dominant individual to terminate the genetic algorithm according to the present invention, and the operation is terminated (S46).

7 is a performance comparison graph using a multi-user detection apparatus and detection method of a DS-CDMA system according to the present invention. As shown in the figure, it is a graph simulating the performance of each detection apparatus.

Here, CMUD (Conventional Multi-User Detector) is a multi-user detection device according to the prior art, SGAMUD (Survival Based Genetic Algorithm Multi-User Detector) is a detection device using a single genetic algorithm processor, SPGAMUD (Survival Based Parallel Genetic Algorithm) Multi-User Detector) represents a detection apparatus according to the present invention.

Then, simulation is performed by Matlab to compare the BER (Bit Error Rate) curves of the respective detection devices.

At this time, the SPGAMUD using the multi-user detection device and detection method of the DS-CDMA system according to the present invention shows a performance gain of about 0.55 dB over the conventional multi-user detection device CMUD at a bit error rate (BER) of 10 ^-3 . , as compared to the user detection apparatus of the applied single genetic algorithm SGAMUD 10 ^- because it appears more than 0.3 dB at a bit error rate of the ^third performance gain, found to be retained in an increased performance over a multi-user detection apparatus according to the prior art; have.

Although the preferred embodiments of the present invention have been described above by way of example, the scope of the present invention is not limited to such specific embodiments, and those skilled in the art are appropriate within the scope described in the claims of the present invention. It will be possible to change.

As described above, the present invention having the configuration as described above can eliminate the multi-user interference as much as possible, thereby eliminating errors due to the multi-user interference, thereby restoring the data of each multi-user in the receiver and reducing the amount of computation. It can increase computational speed, eliminate elements that can overload the system, increase receiver performance, enable accurate user estimation, and more.

Claims (13)

A transmitter for wirelessly modulating data of multiple users in a DS-CDMA system so as to be identifiable by spreading or a PN code; A matched filter unit for despreading the data of the transmitter with a spread or PN code, a binary determiner for determining a plurality of samples output from the matched filter unit as 0 or 1 depending on whether or not a predetermined reference output value is greater than or equal to; A receiver including a genetic algorithm processor for performing a genetic algorithm for multi-user detection with the output of the binary determination unit; Multi-user detection device of the DS-CDMA system comprising a. The method of claim 1, The genetic algorithm processor is a multi-user detection device of the DS-CDMA system, characterized in that connected in parallel. The method of claim 1, The genetic algorithm processor A memory for storing a population generated by mutating any one bit as an output of the binary determining unit; A competitive entity selection module that selects any two entities in the memory and selects an object to evolve to the next generation; An individual diversity module for crossing and mutating to said selected individual; A migration module for exchanging the individual outputted from the individual diversity module and outputting a dominant individual which is an individual having a high suitability compared with the individual existing in the individual diversity module; Multi-user detection device of the DS-CDMA system, characterized in that consisting of. The method of claim 3, The migration module compares an individual migrated from a migration module of another genetic algorithm processor, a low suitability among two parent individuals, and a high suitability among descendants, and outputs a dominant individual. Multi user detection device. The method of claim 3, The competitive entity selection module and the entity diversity module use a random number generator, but the competitive entity selection module selects an entity whose two randomly generated values are addresses of the memory using the random number generator, and the entity diversity module selects the entity. A multi-user detection device of a DS-CDMA system, characterized in that for selecting a cross point and a mutation point by two randomly generated values using a random number generator. The method of claim 3, The individual diversity module is a multi-user detection device of the DS-CDMA system, characterized in that it is possible to determine whether to perform cross-breeding and mutation based on the value generated by the random number generator. The method of claim 3, In the individual diversity module, the crossing is a simple cross or a two-point cross or a uniform cross, and the mutation is a multi-user detection device of the DS-CDMA system, characterized in that for applying a simple mutation or multiple mutations. A first step of modulating data of multiple users in a DS-CDMA system so as to be identifiable by a spreader or a PN code at a transmitter and transmitting wirelessly; A second step of spreading the data of the transmitter by a matched filter part of the receiver by a spread or PN code; A third step of determining a plurality of samples output from the matched filter unit from the binary determiner to 0 or 1 depending on whether or not a predetermined reference output value is greater than or equal to; A fourth step of performing a genetic algorithm for multi-user detection in a genetic algorithm processor as an output of the binary determination unit; Multi-user detection method of the DS-CDMA system comprising a. The method of claim 8, The fourth step is Storing a population created by mutating any one bit in a memory by using the output of the binary determining unit; Selecting any two entities from among the entities stored in the memory to select an object to evolve to the next generation in a competitive entity selection module; Crossing and mutating in the individual diversity module with the selected individual; Exchanging an object outputted to the individual diversity module in a migration module, and outputting a dominant individual that is an object having a high degree of suitability compared with the individual existing in the individual diversity module; Multi-user detection method of the DS-CDMA system, characterized in that consisting of. The method of claim 9, Migrating the subject A method for detecting a multi-user in a DS-CDMA system, comprising: outputting a dominant object by comparing an object migrated from a migration module of another genetic algorithm processor, an object of low suitability among two parents, and an object of high suitability among descendants . The method of claim 9, The step of crossing and mutating the individual is a multi-user detection method of a DS-CDMA system, characterized in that the random number generator using a random number generator to select the crossing and mutation points. The method of claim 9, In the step of mating and mutating the individual, the crossing is a simple cross or a two-point cross or a uniform cross, and the mutation is a simple user or multiple mutation detection method of the DS-CDMA system, characterized in that for applying multiple mutations. The method of claim 9, Selecting the object A method for multi-user detection of a DS-CDMA system, comprising selecting an entity whose two randomly generated values are addresses of the memory using a random number generator.

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