Source: http://www.google.com/patents/US6993064?dq=%22Meaning-based+advertising+and+document+relevance+determination%22
Timestamp: 2016-07-31 05:58:46
Document Index: 189651860

Matched Legal Cases: ['art 68', 'art 68', 'art 138', 'art 228', 'art 234', 'art 235']

Patent US6993064 - Multi-user receiving method and receiver - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsA receiver is provided, in which the receiver includes K signal extraction parts, a signal estimation part, K joint probability calculation parts and a multiplying part, wherein: an ith (1≦i≦K) signal extraction part extracts ith to Kth user signals; an ith joint probability calculation part calculates...http://www.google.com/patents/US6993064?utm_source=gb-gplus-sharePatent US6993064 - Multi-user receiving method and receiverAdvanced Patent SearchPublication numberUS6993064 B2Publication typeGrantApplication numberUS 10/028,357Publication dateJan 31, 2006Filing dateDec 28, 2001Priority dateDec 28, 2000Fee statusPaidAlso published asEP1220463A2, EP1220463A3, US20020126779Publication number028357, 10028357, US 6993064 B2, US 6993064B2, US-B2-6993064, US6993064 B2, US6993064B2InventorsSatoshi DennoOriginal AssigneeNtt Docomo, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (5), Non-Patent Citations (5), Classifications (18), Legal Events (3) External Links: USPTO, USPTO Assignment, EspacenetMulti-user receiving method and receiver
US 6993064 B2Abstract
A receiver is provided, in which the receiver includes K signal extraction parts, a signal estimation part, K joint probability calculation parts and a multiplying part, wherein: an ith (1≦i≦K) signal extraction part extracts ith to Kth user signals; an ith joint probability calculation part calculates a joint probability density function that any signal set in the ith to Kth user signals will be obtained if ith to Kth user signals estimated by the signal estimation part are assumed to be received; the multiplying part multiplies probability density functions calculated by the joint probability calculation parts; and the signal estimation part estimates first to Kth user signals which maximize the multiplied value, and outputs the first to Kth user signals.
An object of the present invention is to provide a receiving method and a receiving apparatus which can suppress transmission characteristics deterioration due to hard decision when a plurality of user signals are transmitted on the same communication channel so that communication quality can be improved.
In the following, embodiments of the present invention will be described. A system in which N users perform signal transmission by using the same channel at the same time and demodulates K user signals in the N user signals will be considered in the following. A receiver in the system forms multi-channels (K channels here) for separating the K user signals, and an output signal from each multi-channel is decided so that K user signals are demodulated accurately.
When the equation (1) is modified by the Bayes rule, P ( r 1 ( k ) r 1 ( k - 1 ) … r K ( 0 ) d 1 ( k ) d 1 ( k - 1 ) … d K ( k ) … d N ( 0 ) ) = P ( r 1 ( k ) r 1 ( k - 1 ) … r K ( 0 ) d 1 ( k ) d 1 ( k - 1 ) … d K ( k ) … d N - 1 ( 0 ) | d N ( k ) … d N ( 0 ) ) � P ( d N ( k ) … d N ( 0 ) ) = P ( r 1 ( k ) r 1 ( k - 1 ) … r K ( 0 ) d 1 ( k ) d 1 ( k - 1 ) … d K ( k ) … d K ( 0 ) | d K + 1 ( k ) … d K + 1 ( 0 ) ) � ∏ N - 1 m = K + 1 P ( d n ( k ) … d n | d n + 1 ( k ) … d N ( 0 ) ) = P ( r 1 ( k ) … r K + 1 ( 0 ) d 1 ( k ) … d K - 1 ( k ) | r k ( k ) … r k ( 0 ) d k ( k ) … d N ( 0 ) ) � P ( r k ( k ) … r k ( 0 ) | d K ( k ) … d N ( 0 ) ) ∏ N - 1 m = K + 1 P ( d n ( k ) … d n ( 0 ) | d n + 1 ( k ) … d N ( 0 ) ) = ∏ K m = 1 P ( r m ( k ) … r m ( 0 ) | r m + 1 ( k ) … r N ( 0 ) d m ( k ) … d N ( 0 ) ) � ∏ N - 1 m = K + 1 P ( d n ( k ) … d n ( 0 ) | d n + 1 ( k ) … d N ( 0 ) ) ( 2 ) is obtained.
Generally, since sending signals of each user are independent of one another, a joint probability density function of the last term in the equation (2) becomes the following equation (3). ∏ n = K + 1 N P ( d n ( k ) … d n ( 0 ) | d n - 1 ( k ) … d N ( 0 ) ) = ∏ l = 0 k ∏ n = K + 1 N P ( d n ( l ) ) ( 3 ) Normally, since incidence probability of each sending signal is uniform, contribution of the term of the equation (3) can be neglected in the equation (2). The sending signal series which maximizes the joint probability density function of the equation (1) in this condition, that is, maximizes a conditional probability density function can be estimated by MLSE (Maximum Likelihood Sequence Estimation). In this embodiment, the sending signal series which maximizes the conditional probability density function shown in the following equation (4) is estimated by the receiver according to the maximum likelihood sequence estimation. ∏ K m = 1 P ( r m ( k ) … r m ( 0 ) | r m + 1 ( k ) … r K ( 0 ) d m ( k ) … d N ( 0 ) ) ( 4 ) In a communication channel which can store finite time Lτ symbols, only signals included in a time window of Lτ before a time k contributes the conditional probability density function at the time k. Therefore, in this case, the equation (4) can be rewritten into the equation (5). ∏ K m = 1 P ( r m ( k ) … r m ( 0 ) | r m + 1 ( k ) … r K ( 0 ) d m ( k ) … d N ( 0 ) ) = ∏ K m = 1 P ( r m ( k ) | r m + 1 ( k ) … r K ( k ) d m ( k ) … d m ( k - L τ ) … d N ( k - L τ ) ) ( 5 ) The conditional probability density function P(rm(k)|rm+1(k) . . . rK(k)dm(k) . . . dm(k−Lτ) . . . dN(k−Lτ)) in the equation (5) indicates that a receiving signal of mth channel is estimated by channels after the (m+1)th channel and by sending candidate signals of channels after mth channel. In other words, the mth channel is independent of signals of channels before (m−1)th channel. This is the condition for performing MLSE by using likelihood function. Under this condition, most likely K user signals can be demodulated by estimating the sending signal series which maximizes the conditional probability density function represented by the equation (5). When the condition of MLSE is not satisfied, the receiver estimates sending probability of code according to the equation (3), and estimates the sending signal series which maximizes the probability represented by the equation (2) on the basis of the sending probability so that most likely K user signals can be demodulated.
When such estimation of the sending signal series is performed, joint probability density function on the sending signal series and the received signal is necessary. For example, joint probability density function is obtained from known sending signal and received signal, and the sending signal series can be estimated by using the joint probability density function. In addition, when distribution of noise applied in the communication channel is Gaussian distribution, a probability density function can be represented by P ( x k | y k ) = 1 2 πσ exp ( - | x k - a y k | 2 2 σ 2 ) , w h e r e i n ( 6.1 ) xk=ayk+nk (6.2) is satisfied, subscript k is time, a is impulse response of the communication channel, nk is noise and σ is dispersion of noise. When a receiver is warmed by a normal temperature such as in the case of ground mobile communication, the noise applied to a signal is decided by Gaussian noise which arises from an LNA (Low Noise Amplifier) in the receiver. Therefore, the equation (6.1) can be used as probability distribution of signals passed through a normal communication channel. In the receiver, the sending signal series can be estimated by calculating conditional probability density function of the equation (4) by using probability distribution which is directly measured or probability distribution obtained by the equation (6.1), and by regarding the conditional probability density function as likelihood function for the sending signal series.
The receiver is formed by circuits realized by arithmetic units. Especially, the receiver is formed by digital circuits in recent years. Since the arithmetic unit is formed mainly by product-sum operation units, it is necessary to perform rational-function-expanding in order to handle the function of the equation (6.1). Therefore, computing amount may be increased. Thus, there is a method to calculate logarithm of the equation (6.1) for decreasing the computing amount in the receiver. In this case, the equation (4) becomes log likelihood function Jk which is represented as follows. J k = ∑ m = l M log P ( r m ( k ) | r m + 1 ( k ) … r N ( k ) d m ( k ) … d m ( k - L τ ) … d K ( k - L τ ) ) = ∑ m = l M J k , m ( 7.1 ) J k , m = - | r k - ∑ n = m + 1 K d n ( k ) | 2 2 σ 2 ( 7.2 ) In the following, a first embodiment and a second embodiment will be described. In the first embodiment, the joint probability density function is calculated, and the sending signal series which maximizes the joint probability density function is estimated. In the second embodiment, the log likelihood function is calculated, and the sending signal series which maximizes the log likelihood function is estimated.
FIG. 1 is a first configuration example of a receiver which estimates the sending signal series which maximizes the joint probability density function according to the first embodiment. The receiver shown in FIG. 1 is in a base station in a wireless communication system for example. The receiver demodulates first to Kth (K≧2) user signals among N user signals which are transmitted on the same wireless channel. The receiver includes an input terminal 1, K stages of signal extractors 2-1–2-K, K stages of conditional probability estimators (Joint probability calculation) 3-1–3-K, a multiplier 4, a series estimator 5, and output terminals 6-1–6-K.
The input terminal 1 receives N user signals which are transmitted on the same wireless channel. Each signal extractor extracts predetermined user signals from the N user signals. More concretely, the signal extractor 2-i (ith signal extractor) extracts ith–Kth user signals.
The signal extractors 2-1–2-K can be realized by an orthogonal filter, a modified decorrelating detector and the like used for an adaptive array and CDMA communication. When using the adaptive array, desired signals can be received without receiving interference signal by directing null to the interference signal. When using the orthogonal filter, codes of predetermined signal group is output and coefficients of the filter can be decided such that codes of the other signal groups are orthogonalized. In addition, when using the modified decorrelating detector, predetermined number of user codes among all user codes are received by matched filters for users, and an inverse matrix of correlation matrix between the codes is operated on output signals of the matched filters. As a result, only finite signals are orthogonalized. Since only finite signals are orthogonalized, other signals can be received.
Each of the conditional probability estimator 3-1–3-K estimates the conditional probability density function on the basis of user signals extracted by the same stage signal extractor and user signals estimated by the series estimator 5. The conditional probability density function indicates probability that user signals extracted by the signal extractor will be obtained if the user signals estimated by the series estimator 5 are transmitted by transmitters (not shown in the figure). This probability can be obtained by the above-mentioned equation (2) or the equation (4). More concretely, the conditional probability estimator 3-i (ith conditional probability estimator) obtains probability (joint probability density function, conditional probability density function) that ith–Kth user signals extracted by the signal extractor 2-i are obtained if ith–Kth user signals (tentative decision data) estimated by the series estimator 5 are transmitted.
The multiplier 4 multiplies conditional probability density function obtained by the conditional probability estimators 3-1–3-K. The series estimator 5 estimates sending signal series of the users such that the multiplied value becomes maximum. That is, the series estimator 5 estimates sending signal series of the users such that the conditional probability density functions obtained by the conditional probability estimators 3-1–3-K become maximum.
By repeating this operation, when the multiplied value calculated by the multiplier 4 becomes maximum, the series estimator 5 determine each signal series estimated just before as first–Kth user sending signal series, and outputs them to the output terminals 6-1–6-K.
FIG. 2 shows a second configuration example of a receiver of the first embodiment. The receiver of this example adaptively estimates coefficients of the signal extractors on the basis of received signals and signal series from the series estimator. Compared with the receiver shown in FIG. 1, K stages of adaptive controllers 7-1–7-K are provided in the receiver shown in FIG. 2. Each adaptive controller adaptively estimates parameters of the same stage signal extractor on the basis of received signals and signal series from the series estimator according to variation of the communication channel states. The signal series from the series estimator 5 may be decided signal series as user sending signal series or may be not-decided signal series. The signal extractors 2-1–2-K weight received signals by using the estimated parameters.
FIG. 3 shows a third configuration example of a receiver of the first embodiment. The receiver of this example adaptively estimates coefficients of the signal extractors on the basis of only received signals. Compared with the receiver shown in FIG. 1, an adaptive controller (Blind Separator) 8 is provided in the receiver shown in FIG. 3. The adaptive controller 8 adaptively estimates parameters of the signal extractors 2-1–2-K on the basis of received signals according to variation of the communication channel states. The signal extractors 2-1–2-K weight received signals by using the estimated parameters.
In an environment in which transmission states vary every moment such as in mobile communications, received power of a signal of each user also varies. Therefore, demodulation having higher likelihood can be performed by determining which signals should be extracted from each of the signal extractors 2-1–2-K such that the conditional probability density function of the equation (4) becomes maximum. That is, communication quality can be further improved by determining user group state corresponding to signals output from the signal extractors such that the joint probability density function of the equation (1) becomes maximum.
FIG. 4 shows a fourth configuration example of a receiver in the first embodiment. The receiver switches user signals output from the signal extractors according to dynamic signal variation. Compared with the receiver shown in FIG. 1, a state estimator 9 and switch circuits 10-1–10-K−1 are provided in the receiver of FIG. 4. The state estimator 9 determines which user signals are to be extracted from the signal extractors 2-1–2-K such that the conditional probability density function obtained by the conditional probability estimators 3-1–3-K becomes maximum every predetermined time or every time when discrete communication such as packet communication starts. The signal extractors 2-1–2-K outputs predetermined user signals according to this determination. The switch circuits 10-1–10-K−1 switch output signals such that signals input to the conditional probability estimators 3-1–3-K from the signal extractors 2-1–2-K and signal series input to the conditional probability estimators 3-1–3-K from the series estimator 5 have the same user group. For example, the switch circuit 10-1 switch output signals such that signals input to the conditional probability estimator 3-2 from the signal extractor 2-2 and signal series input to the conditional probability estimator 3-2 from the series estimator 5 have the same user group.
The signal extractors may switch output user signals in addition to adaptively estimating coefficients of the signal extractors. FIG. 5 shows a fifth configuration example of the receiver of the first embodiment. The receiver adaptively estimates coefficients of the signal extractors on the basis of received signals and signal series from the series estimator, and user signals output from the signal extractors are switched. Compared with the receiver shown in FIG. 1, K stages of adaptive controllers 7-1–7-K, a state estimator 9 and switch circuits 10-1–10-K−1 are provided to the receiver shown in FIG. 5. The operations of the adaptive controllers 7-1–7-K, the state estimator 9 and switch circuits 10-1–10-K−1 are the same as those of FIG. 2 and FIG. 4.
FIG. 6 shows a sixth configuration example of the receiver of the first embodiment. The receiver adaptively estimates coefficients of the signal extractors on the basis of only received signals, and user signals output from the signal extractors are switched. Compared with the receiver shown in FIG. 1, an adaptive controller (Blind Separator) 8, a state estimator 9 and switch circuits 10-1–10-K−1 are provided to the receiver shown in FIG. 5. The operations of the adaptive controller 8, a state estimator 9 and switch circuits 10-1–10-K−1 are the same as those of FIG. 3 and FIG. 4.
Next, the second embodiment will be described in which log likelihood function is calculated and sending signal series which maximizes the log likelihood function is estimated. FIG. 7 shows a first configuration example of a receiver which estimates the sending signal series which maximizes the log likelihood function according to the second embodiment. Like the receiver in the first embodiment, the receiver is in a base station in a wireless communication system for example. The receiver demodulates first to Kth (K≧2) user signals among N user signals transmitted on the same wireless channels. The receiver includes an input terminal 21, K stages of signal extractors 22-1–22-K, K stages of likelihood estimators (metric generator) 23-1–23-K, an adder 24, a series estimator 25 and output terminals 26-1–26-K.
N user signals transmitted on the same wireless channel are input to the input terminal 21. Like the signal extractors in the first embodiment, the signal extractors 22-1–22-K extract only predetermined user signals from the N user signals input to the input terminal 21.
The adder 24 adds the log likelihood functions obtained by the likelihood estimators 23-1–23-K. The series estimator 25 estimates sending signal series for each user such that the added value becomes maximum, that is, such that, the log likelihood functions obtained by the likelihood estimators 23-1–23-K become maximum.
By repeating this operation, when the added value calculated by the adder 24 becomes maximum, the series estimator 25 determine each signal series estimated just before as first–Kth user sending signal series, and outputs the signals to the output terminals 26-1–26-K.
FIG. 8 shows a second configuration example of a receiver of the second embodiment. The receiver of this example adaptively estimates coefficients of the signal extractors on the basis of received signals and signal series from the series estimator. Compared with the receiver shown in FIG. 7, K stages of adaptive controllers 27-1–27-K are provided in the receiver shown in FIG. 8. Each adaptive controller adaptively estimates parameters of the same stage signal extractor on the basis of received signals and signal series from the series estimator 25 according to variation of the communication channel states, like the adaptive controllers 7-1–7-K of the first embodiment. The signal series from the series estimator 25 may be decided signal series as user sending signal series or may be not-decided signal series. The signal extractors 22-1–22-K weight received signals by using the estimated parameters.
FIG. 9 shows a third configuration example of a receiver of the second embodiment. The receiver of this example adaptively estimates coefficients of the signal extractors on the basis of only received signals. Compared with the receiver shown in FIG. 7, an adaptive controller (Blind Separator) 28 is provided in the receiver shown in FIG. 9. Like the adaptive controller 8 of the first embodiment, the adaptive controller 28 adaptively estimates parameters of the signal extractors 22-1–22-K on the basis of received signals according to variation of the communication channel states. The signal extractors 22-1–22-K weight received signals by using the estimated parameters.
Like the first embodiment, in an environment in which transmission states vary every moment, received power of a signal of each user also varies. Therefore, communication quality can be further improved by determining which signals should be extracted from the signal extractors 22-1–22-K such that the log likelihood function of the equation (4) becomes maximum. That is, communication quality can be further improved by determining user group state corresponding to signals output from the signal extractors such that the joint probability density function of the equation (1) becomes maximum.
FIG. 10 shows a fourth configuration example of a receiver in the second embodiment. The receiver switches user signals output from the signal extractors according to dynamic signal variation. Compared with the receiver shown in FIG. 7, a state estimator 29 and switch circuits 30-1–30-K−1 are provided in the receiver of FIG. 10. Like the state estimator 9 in the first embodiment, the state estimator 29 determines which user signals are to be extracted from the signal extractors 22-1–22-K such that the log likelihood functions obtained by the likelihood estimators 23-1–23-K becomes maximum every predetermined time or every time when discrete communication such as packet communication starts. The signal extractors 22-1–22-K outputs predetermined user signals according to this determination. Like the switch circuits 10-1–10-K−1 in the first embodiment, the switch circuits 30-1–30-K−1 switch output signals such that signals input to the likelihood estimators 23-1–23-K from the signal extractors 22-1–22-K and signal series input to the likelihood estimators 23-1–23-K from the series estimator 25 have the same user group.
User signals output from the signal extractors may be switched in addition to adaptively estimating coefficients of the signal extractors. FIG. 11 shows a fifth configuration example of the receiver of the second embodiment. The receiver adaptively estimates coefficients of the signal extractors on the basis of received signals and signal series from the series estimator, and user signals output from the signal extractors are switched. Compared with the receiver shown in FIG. 7, K stages of adaptive controller 27-1–27-K, a state estimator 29 and switch circuits 30-1–30-K−1 are provided in the receiver shown in FIG. 11. The operations of the adaptive controller 27-1–27-K, a state estimator 29 and switch circuits 30-1–30-K−1 are the same as those of FIG. 9 and FIG. 10.
FIG. 12 shows a sixth configuration example of the receiver of the second embodiment. The receiver adaptively estimates coefficients of the signal extractors on the basis of only received signals, and user signals output from the signal extractors are switched. Compared with the receiver shown in FIG. 7, an adaptive controller (Blind Separator) 28, a state estimator 29 and switch circuits 30-1–30-K−1 are provided in the receiver shown in FIG. 12. The operations of the adaptive controller 28, a state estimator 29 and switch circuits 30-1–30-K−1 are the same as those of FIG. 9 and FIG. 10.
FIG. 13 shows a first configuration example of the signal extractor. The signal extractor shown in the figure is a configuration in the case of using four element phased array antennas. The signal extractor includes feeding points 51-1–51-4 of the array antennas, coefficient memory elements 52-1–52-4, multipliers 53-1–53-4, an adder 54 and an output terminal 55. Since this signal extractor have the same configuration as that of a normal field array, this signal extractor operates such that only signals of a predetermined direction are received and signals of other directions are canceled.
FIG. 14 shows a second configuration example of the signal extractor in the case of estimating coefficients adaptively. Like the signal extractor shown in FIG. 13, the signal extractor shown in FIG. 14 is a configuration in the case of using four element phased array antennas. The signal extractor includes feeding points 51-1–51-4 of array antennas, multipliers 53-1–53-4, an adder 54, an output terminal 55, input signal output terminals 56, coefficient input terminals 57, and an output terminal 58 to the adaptive controller. The input signal (received signal) is output to the adaptive controller from the input signal output terminal 56. Weight coefficients from the adaptive controller are input from the coefficient input terminal 57. The signal extractor performs adaptive beam forming by using the weight coefficients and outputs signals.
FIG. 15 is a first configuration example of the adaptive controller which uses the signal extractor shown in FIG. 14. The adaptive controller includes weight coefficient output terminals 61, signal input terminals 62, a signal extractor output signal input terminal 63, an adder 64, multipliers 65-1–65-K, input terminals 66-1–66-K+1 for tentative decision data from the series estimator, a subtractor 67, and an adaptive algorithm part 68. The adaptive controller generates replicas of output signals of the signal extractor by assigning weight to tentative decision data from the series estimator and adding them. Then, the adaptive controller controls weight coefficients by the adaptive algorithm part 68 such that mean value of difference power between the replicas and output signals of the signal extractors becomes minimum. Accordingly, the weight coefficients are controlled such that the deference between the replica and the output signal of the signal extractor becomes minimum. Therefore, only signals on the tentative decision data input terminals 66-1–66-K+1 are output from the signal extractor.
When assuming that a received signal of the feeding element of the array antenna is ui(k) (i is a number corresponding to a feeding element), and an output signal of the signal extractor is rk, ri(k)=j=1 Netv*i,j (k)uj(k) (8) is satisfied, wherein vi,j (k) represents the weight coefficient for multipliers in the signal extractor, N.sub.el represents the number of antenna elements. At this time, when di(k) is tentative decision data from the tentative decision data input terminal 66−K+1, the difference between the replica and the output signal of the signal extractor can be represented as follows. e i ( k ) = d i ( k ) - y i ( k ) - ∑ j = i + 1 K w i , j * ( k ) d j ( k ) , ( 9 ) wherein di (k) is the tentative decision data of an ith user at time k. At this time, the weight coefficients are updated by the following equation (10). ( ⋮ v i , j ( k ) ⋮ w i , j ( k ) ⋮ ) = ( ⋮ v i , j ( k - 1 ) ⋮ w i , j ( k - 1 ) ⋮ ) + μ e i * ( k ) ( ⋮ u 1 ( k ) ⋮ d 1 ( k ) ⋮ ) i = 1 , … K , i 〈 l 〈 K , ( 10 ) wherein * indicates complex conjugate and μ indicates a constant of 0<μ<1 called a step size parameter.
In addition, as an configuration example of the adaptive controller of a blind type which is used in the receiver shown in FIG. 3 and FIG. 9, there is one applying an ultra-resolution arriving direction estimation method. In the following, MUSIC method which is a representative ultra-resolution arriving direction estimation method will be described. In the MUSIC method, a correlation matrix R of input signal vector of the array antenna is obtained, and, then, eigenvalue resolution is performed for the mean value as shown in the equations (11) and (12). R = E [ U k U k H ] = E [ ( ⋮ u i ( k ) ⋮ ) ( … u i * ( k ) … ) ] , ( 11 ) R=UΓUHΓ=diag(λ1λ2 . . . λel) (12),
When assuming that eigenvector corresponding to each eigenvalue λi is ui, each weight coefficient can be set as v i = ∑ j = i K u i . ( 14 ) FIG. 16 shows a configuration example of the likelihood estimator. The likelihood estimator shown in the figure includes an input terminal 71, an arithmetic circuit 72 for calculating square value of a real part, an arithmetic circuit 73 for calculating square value of an imaginary part, an adder 74 and a scalar output terminal 75. Generally, in a communication system, since a received signal is represented by equalization low band system, each signal can be represented by a complex number. Therefore, the likelihood estimator calculates the square value of an envelope of the complex number.
FIG. 17 shows a configuration example of the switch circuit. The switch circuit shown in the figure outputs i-1 signals for i input signals. The switch circuit includes input terminals 81-1–81-i, a body 82 of the switch circuit, nodes 83-1–83-i, 84-1–84-i−1, a switch control terminal 85, and output terminals 86-1–86-i−1. In the example shown in FIG. 17, a signal from the input terminal 81-2 is disconnected and other signals are output.
When assuming that sk(m)=[di(k), . . . , dj(k)] represents a user vector state at time k, the state estimator which controls the switch circuit in FIG. 17 estimates m defined in a following equation (15). log P ( s k ( m ) , r 1 ( k ) ⋯ r K ( k ) ) P ( s k ( m ′ ) , r 1 ( k ) ⋯ r K ( k ) ) ≥ 0 for ∀ m ′ ∈ R K ! ( 15 ) Since sk(m) is generated by permuting K user data, m can take any of values the number of which is m = K != ∏ i = 1 K i . ( 16 ) P(.) is defined by the equation (1), and represents the output signal of the multiplier 4 shown in FIG. 1. At this time, the switch circuit shown in FIG. 17 allows only the top i−1 user vectors among i input user vectors to pass through. Which user vector in the i−1 user vectors in the user vectors sk(m) corresponds to which user depends on m.
FIG. 19 shows a third configuration example of the signal extractor. The signal extractor shown in FIG. 19 includes signal input terminals 101-1–101-N, feedforward filters 102-1–102-N each formed by a tapped delay line, an adder 103 and a signal output terminal 104. Unlike the signal extractor shown in FIG. 14, since this signal extractor can perform operation on time axis, the signal extractor can not only form a single space beam but also a space-time beam. Therefore, there is a merit that signal extraction can be performed for multi-path waves occurring in high speed communication by forming beams effective for the multi-path waves.
FIG. 20 shows a configuration example of the tapped delay line used for the signal extractor shown in FIG. 19. The tapped delay line shown in FIG. 20 includes a signal input terminal 111, delay elements 112-1–112-3, multipliers 113-1–113-4, weight coefficient input terminals 114-1–114-4 for the multipliers, an adder 115 and a signal output terminal 116.
FIG. 21 shows a fourth configuration example of the signal extractor. This signal extractor is an example for the case coefficients of the signal extractor in FIG. 19 are adaptively estimated. The signal extractor shown in FIG. 21 includes input terminals 121-1–121-N, feedforward filters 122-1–122-N each formed by the tapped delay line shown in FIG. 20, input signal output terminals 123 to the adaptive controller, coefficient input terminals 124 from the adaptive controller, an adder 125, a signal output terminal 126, and a signal output terminal 127 to the adaptive controller.
FIG. 22 shows a second configuration example of the adaptive controller, which is an example when using the signal extractor shown in FIG. 21. The adaptive controller shown in FIG. 22 includes a coefficient output terminal 131 to the signal extractor, signal input terminals 132 from the signal extractor, an adder 134, a subtracter 135, feedback filters 136-1–136-K each formed by the tapped delay line shown in FIG. 20, tentative decision data input terminals 137-1–137-K+1 from the series estimator and an adaptive algorithm part 138.
FIG. 26 shows a third configuration example of the adaptive controller for using the signal extractor shown in FIG. 24 and FIG. 25. The adaptive controller in FIG. 26 includes a coefficient output terminal 221 to the signal extractor, a signal input terminal 222 from the signal extractor, an adder 224, a subtracter 225, multipliers 226-1–226-K, tentative decision data input terminals 227-1–227-K+1 from the series estimator, and an adaptive algorithm part 228. If w* i,j(k) dj(k) in the equation (9) is regarded as a signal from the multiplier 226 1, and Ui(k) in the equation (10) is regarded as data vector to shift register of the tapped delay line of the orthogonal filter 213, the adaptive controller can perform coefficient estimation by using the equation (10).
FIG. 27 shows a seventh configuration example of the signal extractor in which a modified decorrelating detector is applied. The signal extractor shown in FIG. 27 includes a signal input terminal 231, matched filters 232-1–232-i for despreading, a inner product calculation part 234 for calculating inner product, a correlation calculation part 235 for calculating correlation matrix between codes and outputting predetermined vectors forming the inverse matrix, and a signal output terminal 236. The signal extractor calculates correlation matrix between 1−i codes, and performs inner product operation for predetermined vectors of an inverse matrix of the correlation matrix and the matched filter. As a result, terms on correlation between 1−i codes for desired signals can be canceled. Therefore, like the antennas, only several user group signals can be extracted.
FIG. 28 is a configuration example of the inner product calculation part used in the signal extractor of FIG. 27, The inner product calculation part shown in FIG. 28 includes signal input terminals 241-1–241-4, multipliers 242-1–242-4, an adder 243, and an output terminal 244.
FIG. 29 shows a eighth configuration example of the signal extractor which is for CDMA communication. The signal extractor shown in the figure includes tentative decision data input terminals 251-i–251-K from the series estimator, matched filters 252-i–252-K, input terminals 253-i–253-K for i-K carrier phase, multipliers 254-i–254-K, an adder 255, a signal input terminal 256, a subtracter 257, and a signal output terminal 258.
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