• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
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  • 优先权
    • 专利摘要:
    According to the present invention, after determining the fitness using the carrier band interference ratio transmitted from the mobile station by the first population, a second population is generated according to the fitness, and a predetermined bit is exchanged for each individual sequence of the generated second population. After generating the third population, the carrier-band interference ratio transmitted from the mobile station by the fourth population by generating a fourth population and transmitting the fourth population to the mobile station by replacing some bits of the most suitable population in the third population with opposite bits. If the condition of terminating the optimization of the transmission beam is satisfied by rearranging the object sequence using the, the beam optimization is completed, and the wireless communication base station can use the evolutionary algorithm to optimize the transmission beam to achieve efficient transmission beam optimization. The effect is expected.
    公开号:KR20040032697A
    申请号:KR1020020061930
    申请日:2002-10-10
    公开日:2004-04-17
    发明作者:권익승
    申请人:엘지전자 주식회사；
    IPC主号:
    专利说明:

    Method of evolutionary algorithm application for wireless communication system's beam forming
    [7] The present invention relates to a transmission beamforming of a wireless communication system, and more particularly, to a method of applying an evolutionary algorithm for beamforming of a wireless communication system for applying an evolutionary algorithm for optimizing a beam transmitted from a base station to a mobile station.
    [8] Modern communication systems are required to support a variety of applications. One such communication system is Code Division Multiple Access (CDMA), which conforms to the TIA / EIA / IS-95 Mobile Station-Base Station Compatibility Standard for Dual Mode Wideband Spread Spectrum Cellular Systems (hereinafter referred to as the IS-95 standard). Connection) system.
    [9] The CDMA system enables voice and data communication between users via a terrestrial link. In a CDMA system, communication between user stations is performed through one or more base stations. A subscriber station user transmits data to a base station on a reverse link. , Communicate with other users.
    [10] In addition, the base station may receive the data and route the data to another base station, which is transmitted on the forward link to the same base station or another base station. In this case, the forward link refers to transmission from the base station to the subscriber station, the reverse link refers to transmission from the subscriber station to the base station, and the forward link and the reverse link in the IS-95 system allocate and use separate frequencies.
    [11] The subscriber station will communicate with one or more base stations during communication and simultaneously communicate with multiple base stations during soft handoff. In this case, soft handoff refers to a process of establishing a link with a new base station before disconnecting the link with the old base station, and serves to minimize the disconnection of the call.
    [12] It is known that the carrier-to-interference ratio C / I of a user in a cellular system is a function of the location of that user in the service area. When the same frequency allocation is used, it can be reused in a cell to improve the overall efficiency.
    [13] In order to maximize the C / I, a reference signal having a different structure with respect to an antenna of a base station may be used to continuously transmit a channel impulse response reference signal at each antenna. The subscriber station transmits on the reverse link a signal representing its estimated channel impulse response corresponding to each transmit antenna-receive antenna pair. Once the channel impulse response of each transmit antenna-receive antenna pair is known, the base station optimally forms a beam for each subscriber station.
    [14] 1 is a block diagram showing the configuration of a transmission antenna of a conventional CDMA base station.
    [15] Referring to FIG. 1, data transmitted from a base station is formed in the form of a stream of video (I) and quadrature (Q) samples, which are provided as inputs to the complex similar noise (PN) spreader 110. The complex PN spreader 110 mixes its I and Q samples with the short PN code samples generated by the short PN code generator 140.
    [16] As a result, the PN spreading sample streams are filtered with a baseband finite impulse response (FIR) filter (120, 130) to produce baseband complex sample streams that are upconverted to subscriber stations for transmission.
    [17] At this time, the signals provided to the baseband FIR (120, 130) according to the equation (1).
    [18]
    [19] Where I is digital video samples, Q is digital quadrature-phase samples, PN I is video short PN sequence, PN Q is quadrature-phase PN sequence, and X I and X Q are on in-phase and quadrature phase channels, respectively. The signal to be modulated.
    [20] The signals output by the baseband FIRs 120, 130 are provided to two or more antenna transmission subsystems 170, with each antenna transmission subsystem having a single transmission antenna 176, a slot TDM timing generator. 150 generates timing signals corresponding to various time division multiplexing (TDM) transmission periods within each transmission slot.
    [21] Slot TDM timing generator 150 provides such an output signal to beamforming control processor 160, which uses the signal to transmit signals corresponding to different TDM periods in different signal beams.
    [22] The antenna transmission subsystem 170 has the necessary configuration for up-conversion, phase control, amplification, and transmission over one transmit antenna 176, where signals provided by baseband FIRs 120, 130 are phased. The mixing signal provided by the control oscillator 177 and the mixer 173 are mixed, and the phase controlled oscillators 171 and 177 receive amplitude and phase control signals from the beamforming control processor 160, which signals It is used to change the phase and amplitude of their output mixing signals.
    [23] In addition, the output signals of the mixers 172 and 173 are added by the adder 174, amplified by the amplifier 175, and transmitted through the transmission antenna 176.
    [24] 2 is a flowchart illustrating a transmission beam optimization process of a conventional wireless communication system base station.
    [25] Referring to FIG. 2, first, a subscriber station (mobile communication terminal) measures a C / I value and provides the measurement information to a serving base station (S201).
    [26] In this case, the C / I value is calculated by Equation 2.
    [27]
    [28] Where C / I is the carrier-to-interference ratio, Is the C / I value used by the subscriber station to generate the DRC signal to the base station, Is the C / I value measured by each finger demodulator and is the number of finger demodulators in use by the receiver.
    [29] The total C / I value is then mapped to a predetermined set of data rates, which are transmitted over the air to one or more serving base stations of the subscriber station.
    [30] When the base station receiving the C / I value from the subscriber station obtains the C / I value as the baseline, the transmission beam angle of the serving base station is increased by a predetermined amount of angular increment (S202), and the subscriber station The C / I of the received signal is measured again and provided to the serving base station (S203).
    [31] Then, the base station determines whether the C / I value has increased by increasing the beam angle (S204), and if so, increases the beam angle again (S202) and transmits, and receives the C / I value for the transmitted signal. The process is repeated (S203), and when the C / I value no longer increases or decreases, the most recent beam angle adjustment is canceled (S205).
    [32] In addition, it is checked whether the operation of continuously increasing the transmission beam angle is performed until step S205 (S206), and if it continues to increase, the beam is transmitted to the corresponding subscriber station at a predetermined transmission beam angle, and it was not a continuous increase. If the beam angle is reduced (S207), and the C / I for the signal whose beam angle is reduced is received (S208), and as a result, the C / I value is increased (S209). The process of decreasing the beam angle is repeated until the C / I value does not change or decreases.
    [33] In addition, if the C / I value does not increase or decreases, the beam angle decrease process is canceled (S210), and the beam transmission is performed.
    [34] As described above, when beamforming optimization of the selected subscriber station is ended, beamforming optimization is performed again for the next subscriber station using the steps S201 to S210.
    [35] As described above, in general, a base station of a wireless communication system calculates an initial C / I from a beam initially formed, and increases the beam scan angle by a predetermined angle in a direction in which the C / I value increases, while the previous C / I is increased. By repeating the process of comparing with the I value, the C / I value is no longer increased and the optimization process is terminated.
    [36] However, such an optimization algorithm compares a solution obtained through an iterative search process that linearly increases or decreases the beam directivity of a transmission antenna from a single initial value. As a result, when the initial value and the spatial separation of the entire area are large, the search time becomes long, and the convergence to the solutions of all regions is delayed.
    [37] In addition, the possibility of convergence to the sea to a limited area is also a problem that cannot be excluded.
    [38] As described above, the present invention converges to the problem and local solution that takes a long search time due to spatial separation of the entire area in forming a beam in a wireless communication system base station by applying an evolution algorithm that is efficiently applied to solving an optimization problem. It is an object of the present invention to provide a method of applying an evolutionary algorithm for beamforming of a wireless communication system that can eliminate the possibility of the possibility and bring about more efficient beamforming.
    [1] 1 is a block diagram showing the configuration of a transmission antenna of a conventional CDMA base station.
    [2] 2 is a flow chart illustrating a transmission beam optimization process of a conventional wireless communication system base station.
    [3] 3 is a block diagram showing a configuration of a transmission system of a method for applying an evolutionary algorithm for beamforming in a wireless communication system according to an exemplary embodiment of the present invention.
    [4] 4 is a block diagram showing the configuration of a receiving system in a method of applying an evolutionary algorithm for beamforming in a wireless communication system according to an exemplary embodiment of the present invention.
    [5] 5 is a flowchart illustrating an operation procedure of a method of applying an evolutionary algorithm for beamforming in a wireless communication system according to an exemplary embodiment of the present invention.
    [6] FIG. 6 is a block diagram illustrating an embodiment of a combination configuration of individual strings applied to FIG. 5. FIG.
    [39] In the method for applying an evolutionary algorithm for beamforming in a wireless communication system according to the present invention, after determining a fitness using a carrier band interference ratio transmitted from a mobile station by a first population, generating a second population according to the fitness. Generating a third population by exchanging a predetermined bit in each individual string of the second population, and then changing a portion of bits of the most suitable population in the third population to opposite bits to generate a fourth population and transmitting the same to the mobile station. And, if the conditions for terminating the optimization of the transmission beam are satisfied by rearranging the object sequence using the carrier-to-interference ratio transmitted from the mobile station by the fourth population, completing beam optimization.
    [40] Evolutionary algorithm application method for beamforming in a wireless communication system according to the present invention,
    [41] Generating a first object group by forming a combination of object strings equal to the number of modules of the transmission array antennas of the wireless communication system base station, and transmitting a transmission beam according to the object string information to the mobile station;
    [42] Each of the transmitted individual strings are rearranged sequentially according to the carrier-band interference ratio received for the transmission beam, and the selected object strings having a carrier level interference ratio of a predetermined level or more are selected and copied, and the remaining entity strings are deleted. Selecting and reproducing the second object group by generating a new object string after the new object string;
    [43] A hybridization step of applying certain bits of the object string having the highest carrier-to-interference ratio in the second object group to any new object string, and applying certain bits between any new object strings to another new object string;
    [44] A mutation step of changing some bits of the individual string having the highest carrier-to-interference ratio of the population that has undergone the crossing step to opposite bits;
    [45] Transmitting the population subjected to the mutation step to a mobile station to receive carrier band interference ratio information and sequentially arrange the population to determine whether a termination condition is met;
    [46] If the determination result does not meet the termination condition, it is characterized in that it comprises a step of optimizing the transmission beam until the termination condition is satisfied by repeating the selection and reproduction step, the mating step, and the mutation step.
    [47] Preferably, the object sequence is generated by representing the phase and amplitude information consisting of arbitrary bits in binary to control the directivity and intensity of the transmission beam.
    [48] Preferably, the termination condition is,
    [49] A condition arbitrarily determined by an administrator in a wireless communication system, characterized in that it includes a maximum convergence value of the carrier-to-interference ratio, the selection regeneration step, and the number of repetition conditions of the crossing step and the mutation step.
    [50] A method of applying an evolution algorithm for beamforming in a wireless communication system according to the present invention configured as described above will be described in detail as follows.
    [51] First, Evolutionary Algorithms (EA) are mathematically modeled algorithms that can be applied to other real-world problems by mimicking the evolutionary process of living things in nature.The structure is relatively simple and the application range is very wide. It is widely used for solving problems.
    [52] Evolutionary algorithms can be divided into four types according to chromosomes, operators, etc., and have recently evolved into DNA computing algorithms.
    [53] In particular, the evolutionary algorithm, ie, the genetic algorithm, applied in the present invention, crossover, mutation, using the principle of superiority and mutation is important enough to be associated with the extinction of the organism. Among the new Generation Populations created through Mutation, etc., the Fitness (selection of genes to survive the law of superiority) is evaluated, and the most suitable individual is selected as the next generation. Algorithm to make a population of.
    [54] In general, the performance of genetic algorithms is determined by population size, breeding probability, and mutation probability. The smaller the population, the shorter the time needed to calculate the goodness of fit. However, as iteration progresses, the diversity between individuals is significantly reduced, resulting in the inability to find an optimal solution.
    [55] In addition, in the case of breeding and mutation probabilities, the larger the probability, the wider the search range for the unknown region is, which is effective in finding the optimal solution.
    [56] On the other hand, if the probability of crossing or mutation is small, the optimal solution can be found quickly, but the reliability of the optimal solution is significantly lowered.
    [57] In this way, the genetic algorithm is more efficient than the conventional calculation dependence method because it evaluates and uses the goodness of the values calculated from the previous generation.
    [58] Referring to the accompanying drawings, the present invention applying the above evolutionary algorithm (gene algorithm) is as follows.
    [59] 3 is a block diagram showing a configuration of a transmission system of a method for applying an evolutionary algorithm for beamforming in a wireless communication system according to an exemplary embodiment of the present invention.
    [60] Referring to FIG. 3, a transmission system at a base station having a plurality of antenna transmission modules includes a beamforming controller 306 for controlling the directivity and the intensity of a beam transmitted to the outside, and under the control of the beamforming controller 306. In the phase shifters 301a to 301n, the gain amplifiers 302a to 302n, the phase shifters 301a to 301n, and the gain amplifiers 302a to 302n for converting the phase and amplitude of the transmission beam. Frequency upconverters 303a to 303n for upconverting the frequency of the changed signal, amplifiers 304a to 304n for amplifying the signal to an output signal level, and transmission array antenna modules 305a to 305n for transmitting signals to the outside; It is configured to include.
    [61] The transmission system uses the maximum value of the C / I (carrier to interference ratio) obtained from the signal received from the mobile station in order to optimize the directivity and intensity of the beam radiated to the mobile station via the transmit array antenna modules 305a to 305n.
    [62] That is, the beamforming controller 306 receives the C / I value received from the outside and controls the phase shifters 301a to 301n and the gain amplifiers 302a to 302n to obtain an optimized signal. If the output is upconverted by the frequency upconverters 303a to 303n, amplified by the amplifiers 304a to 304n, and radiated through the transmission array antenna modules 305a to 305n, the maximum value of the C / I value is used. Optimization of the transmission beam is achieved by using an evolutionary algorithm.
    [63] In addition, the reception system for optimizing the transmission beam using the evolutionary algorithm by receiving the C / I value from the mobile station is as follows.
    [64] 4 is a block diagram illustrating a configuration of a reception system in a method of applying an evolutionary algorithm for beamforming in a wireless communication system according to an exemplary embodiment of the present invention.
    [65] Referring to FIG. 4, signals are received from the mobile stations by the reception array antenna modules 401a to 401n and converted into baseband signals through the low noise amplifier 402 and the frequency downconverter 403.
    [66] The received signal converted into the baseband signal is calculated through the complex PN processors 404a to 404n, and the C / I processor 405a to 405n calculates the C / I value corresponding to the final received array antenna module 406a to 406n. Will be done.
    [67] The beamforming optimization by the evolutionary algorithm is performed by using the calculated C / I values.
    [68] The beamforming optimization in the base station having the transmission system and the reception system as described above is applied to the evolutionary algorithm as follows.
    [69] 5 is a flowchart illustrating an operation procedure of a method for applying an evolutionary algorithm for beamforming in a wireless communication system according to an embodiment of the present invention, and FIG. 6 is a block diagram showing an embodiment of a combination configuration of individual sequences applied to FIG. 5. to be.
    [70] 5 and 6, the transmission system initially generates an object string of each antenna module representing binary and phase information of beams to be transmitted by each transmission array antenna module 305a to 305n, and the object string. Are formed into one individual group and transmitted to the mobile station (S501).
    [71] That is, as shown in FIG. 6, a phase search bit string consisting of a predetermined bit and an object string consisting of a gain search bit string are generated to form one entity group.
    [72] In this case, the number of bits of the bit string is determined by selecting a radiation area of a transmission beam in advance, and the maximum angle of the radiation area. Is determined by Equation 3, and the minimum phase search unit Is determined by Equation 4, and the number of bits is determined by setting the maximum change value of the amplitude in the same manner.
    [73] When the initial population is transmitted as described above, C / I is received from the mobile station (S502).
    [74] The larger the received C / I value, the greater the suitability. It is determined that the individual stations are sequentially rearranged according to the C / I corresponding to each individual string in order of high suitability or low order. At this time, each entity string has its own C / I value information.
    [75] After redeployment as described above, the selection and regeneration steps are performed (S503).
    [76] In the selecting and reproducing step, it is determined that an object sequence having a C / I value of a predetermined value or more in the rearranged population is a high dominance, and is copied back to an object string forming the same bit sequence, and the object string having a low suitability is deleted. Creates a new population by newly creating another bit string.
    [77] This is in accordance with the general rule of dominance, and selects and uses a highly suitable individual string determined to be dominant, and deletes and generates a newly generated individual string having low suitability determined to be recessive. High and low allow judgment according to an arbitrary level determined by the user's judgment.
    [78] As described above, although the individual population of the regenerated new population is more likely to be a high-fit population than the previous population, it forms a higher-fit population and is limited to a local area limited to a certain region. In order to prevent convergence, the goodness-of-fit is subjected to a crossing step in which predetermined bits are exchanged between each entity sequence (S504).
    [79] The hybridization step is to strictly align certain bits of the object string having a high degree of suitability with other object strings. For example, it is assumed that an object string having a bit string of '110011' is a high-quality dominant trait. Selecting to swap the first two bits by selection means replacing two bits with '11' in front of another bit. In other words, it is possible to increase the probability of becoming close to dominant by changing the low-fit individual string into '11XXXX' shape.
    [80] In addition, if a part of the bit string of the dominant dominant trait having high suitability is applied to another individual string in the above manner, there is a problem in that convergence to a local hazard confined to a certain region increases. Therefore, in order to prevent this, a method of excluding convergence to a local solution may be applied by setting the user-defined constant bits to be applied to each other even among individual objects having a low fit. In other words, it expands the search area to the whole area.
    [81] The population that has undergone the crossing step as described above has a higher probability of having a higher fitness, and in order to further increase the possibility of optimization in all areas, a mutation step of inverting some bits of the population having the highest fitness is performed (S505). ). In other words, even if the object sequence having a high degree of fit, the other part can be considered in order to optimize the other area in consideration of the probability of having a greater degree of goodness of fit in another area.
    [82] As described above, new populations generated after the selection, reproduction, mating, and mutation steps are transmitted back to the mobile station through the transmit array antenna modules 305a to 305b (S506).
    [83] The mobile station then transmits the C / I value for the received transmission beam back to the base station, and the base station receives the C / I value for the new population (S507).
    [84] Then, after rearranging the individual strings sequentially according to the received C / I values, it is determined whether the optimization is finished by comparing the fitness of the previous population.
    [85] That is, it is determined whether or not the transmission result of the new population meets the condition for terminating optimization (S508), and if it is satisfied, the beam optimization is completed (S509), and if it does not match, it is repeated from the selection and reproduction step (S503) again. It is to perform the optimization process.
    [86] In this case, the terminating condition may be variously given according to the user's setting, and basically sets satisfactory C / I value information to confirm whether the C / I value is optimized until reaching a certain level. In order to prevent a problem in which the repetition process can be continued indefinitely in the system, information on the number of times of repeating the predetermined number of times is set.
    [87] Therefore, if the C / I value is high enough to be satisfied or if the above process is repeated a certain number of times, the transmission system does not go through the iteration loop anymore and completes the optimization.
    [88] Using the optimized amplitude and phase information, the transmission system forms and transmits a transmission beam to the mobile station.
    [89] Such transmission beam optimization is generally performed when a call is set up with a mobile station or when a transmission beam needs to be changed due to a change in a call, etc., and serves to provide the best possible call quality to the mobile station.
    [90] As described above, the method for applying an evolutionary algorithm for beamforming in a wireless communication system according to the present invention includes a mobile station in a wireless communication system base station using an evolutionary algorithm (genetic algorithm), which is one of optimization algorithms generally applied in nature. By efficiently optimizing the transmission beam transmitted by the mobile station, it improves the call quality to the mobile station, solves the problems caused by convergence to the local solution according to the conventional optimization method or long time optimization search time, and transmits efficiently. There is an effect that enables optimization of the beam.
    权利要求:
    Claims (4)
    [1" claim-type="Currently amended] Determining a goodness of fit using the carrier-band interference ratio transmitted from the mobile station by the first population, and then generating a second population according to the goodness of fit;
    After generating a third population by exchanging a predetermined bit in each individual string of the generated second population, the third population is generated by replacing some bits of the most suitable population in the third population with opposite bits to generate a fourth population. Transmitting; and
    Completing beam optimization if the condition for terminating the optimization of the transmission beam by rearranging the object sequence using the carrier to interference ratio transmitted from the mobile station by the fourth population is satisfied;
    Evolution algorithm application method for beamforming of a wireless communication system comprising a.
    [2" claim-type="Currently amended] Generating a first object group by forming a combination of object strings equal to the number of modules of the transmission array antennas of the wireless communication system base station, and transmitting a transmission beam according to the object string information to the mobile station;
    Each of the transmitted individual strings are rearranged sequentially according to the carrier-band interference ratio received for the transmission beam, and the selected object strings having a carrier level interference ratio of a predetermined level or more are selected and copied, and the remaining entity strings are deleted. Selecting and reproducing the second object group by generating a new object string after the new object string;
    A hybridization step of applying certain bits of the object string having the highest carrier-to-interference ratio in the first object group to any new object string, and applying certain bits between any new object strings to another new object string;
    A mutation step of changing some bits of the individual string having the highest carrier-to-interference ratio of the population that has undergone the crossing step to opposite bits;
    Transmitting the population subjected to the mutation step to a mobile station to receive carrier band interference ratio information and sequentially arrange the population to determine whether a termination condition is met;
    If it is determined that the termination condition is not met, repeating the selection and reproduction, the mating step, and the mutation step to optimize the transmission beam until the termination condition is satisfied. Application of Evolutionary Algorithm for Beamforming of Communication Systems.
    [3" claim-type="Currently amended] The method according to claim 2, wherein the object string is generated by expressing a phase and amplitude information consisting of arbitrary bits in binary for controlling the directivity and the intensity of the transmission beam. Way.
    [4" claim-type="Currently amended] The method of claim 2, wherein the termination condition,
    A condition that is arbitrarily determined by a manager in a wireless communication system includes the maximum convergence value of the carrier-to-interference ratio, the selective regeneration step, and the number of repetition conditions of the mating step and the mutation step. Evolutionary Algorithm Application Method.
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    同族专利:
    公开号 | 公开日
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    法律状态:
    2002-10-10|Application filed by 엘지전자 주식회사
    2002-10-10|Priority to KR1020020061930A
    2004-04-17|Publication of KR20040032697A
    优先权:
    申请号 | 申请日 | 专利标题
    KR1020020061930A|KR20040032697A|2002-10-10|2002-10-10|Method of evolutionary algorithm application for wireless communication system's beam forming|
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