Source: https://patents.google.com/patent/US9602158B2/en
Timestamp: 2018-12-13 02:19:51
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Matched Legal Cases: ['Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 02776047', 'Application No. 02776047', 'Application No. 03770434', 'Application No. 03770434', 'Application No. 03770434']

US9602158B2 - Methods for estimation and interference suppression for signal processing - Google Patents
Methods for estimation and interference suppression for signal processing Download PDF
US9602158B2
US9602158B2 US14501640 US201414501640A US9602158B2 US 9602158 B2 US9602158 B2 US 9602158B2 US 14501640 US14501640 US 14501640 US 201414501640 A US201414501640 A US 201414501640A US 9602158 B2 US9602158 B2 US 9602158B2
US14501640
US20150016297A1 (en )
Gagandeep Singh Lamba
A receiver in a CDMA system comprises a front end processor that generates a combined signal per source. A symbol estimator processes the combined signal to produce symbol estimates. An S-Matrix Generation module refines these symbol estimates based on the sub channel symbol estimates. An interference canceller is configured for cancelling interference from at least one of the plurality of received signals for producing at least one interference-cancelled signal.
This application is a divisional of U.S. patent application Ser. No. 13/908,286, entitled “Methods for Estimation and Interference Suppression for Signal Processing,” and filed Jun. 3, 2013; which is a continuation of U.S. patent application Ser. No. 13/205,320, entitled “Methods for Estimation and Interference Suppression for Signal Processing,” and filed Aug. 8, 2011, now U.S. Pat. No. 8,457,263; which is a continuation of U.S. patent application Ser. No. 11/893,707, entitled “Methods for Estimation and Interference Cancellation for Signal Processing,” and filed Aug. 17, 2007, now U.S. Pat. No. 8,005,128; which (1) claims priority to U.S. Patent Application No. 60/838,262, entitled “Technique for estimating user and background noise powers in a code division multiple access system without signaling assistance and application of such to channel quality measurement with a linear receiver,” and filed on Aug. 17, 2006; (2) is a continuation-in-part of U.S. patent application Ser. No. 11/452,027, entitled “Iterative Interference Cancellation Using Mixed Feedback Weights and Stabilizing Step Sizes,” and filed Jun. 13, 2006, now U.S. Pat. No. 7,715,508; (3) is a continuation-in-part of U.S. patent application Ser. No. 11/432,580, entitled “Interference Cancellation in Variable Codelength Systems for Multi-Access Communication,” and filed May 11, 2006, now U.S. Pat. No. 7,697,595; (4) is a continuation-in-part of U.S. patent application Ser. No. 11/003,881, entitled “Systems and methods for serial cancellation,” and filed on Dec. 3, 2004, and published as U.S. Patent Application Publication Number 2005-0123080 A1; (5) is a continuation-in-part of U.S. patent application Ser. No. 10/686,829, entitled “Method and Apparatus for Channel Amplitude Estimation and Interference Vector Construction,” and filed on Oct. 15, 2003, now U.S. Pat. No. 7,580,448, which claims priority to U.S. Patent Application No. 60/418,187, entitled “Method for channel amplitude estimation and interference vector construction,” and filed Oct. 15, 2002; and (6) is a continuation-in-part of U.S. patent application Ser. No. 10/669,954, entitled “Method and Apparatus for Selectively Applying Interference Cancellation in Spread Spectrum Systems,” and filed on Sep. 23, 2003, now U.S. Pat. No. 7,787,518, which claims priority to U.S. Patent Application No. 60/412,550, entitled “Controller for interference cancellation in spread spectrum systems,” and filed Sep. 23, 2002. The entirety of each of the foregoing patents, patent applications, and patent application publications is incorporated by reference herein.
In one receiver embodiment, the symbol quality estimates are weights per subchannel that are applied to the symbol estimates.
In some receiver embodiments, dedicated finger hardware is not used. Rather, offline fingers running faster than real-time are used. Thus, the term “finger” is intended to refer to Rake-finger function, and not necessarily the Rake finger structure. The term “finger” in an equalizer context refers to the functionality of resolving the timing of individual paths, since the equalizer may use a different form of combining (from the Rake) of the different rays.
FIG. 12 shows a Decover block configured to operate in a system complying with the CDMA 2000 1×RTT standard.
FIG. 13 shows an SMG configured to operate in a system complying with the CDMA 2000 1×RTT standard.
FIG. 1 shows an embodiment of the invention. The front end processor 120 receives a signal from one or more antennae (two are shown in the figure, as an example), using an RF front end 110, which performs operations such as down conversion, A/D conversion (not shown), and then the front end resolves the signal into the constituent multipaths from various sources such as base stations. As is understood in the art, a single stream of received baseband data may be composed of multiple constituent signals all arriving from different sources, and within each source, typically composed of rays arriving at different times. This resolution may use a searcher finger and finger management, which is well known in the art. The front end processor may also include functionality to select which subset of the sectors and paths it is receiving should be used for the purposes of generating symbol estimates. More details about how the front-end processor may perform this function are described in U.S. patent application Ser. No. 10/669,954, entitled “Method and apparatus for selectively applying interference cancellation in spread spectrum systems,” and filed on Sep. 23, 2003, now U.S. Pat. No. 7,787,518, the entire contents of which are hereby incorporated by reference. The front-end also applies the appropriate receive filter, and typically performs a dispreading operation using the codes of the source signal being assigned to the symbol estimator. The front-end processor creates a single stream of data per transmitting source that can then be used to recover symbol estimates for that source.
A symbol estimator module 130 operates on the signals and sectors thus resolved and generates symbol estimates for multiple user subchannels that are part of the transmission. The estimation may use either a Rake like structure where multipaths are “raked” together to generate a single stream of data per transmitting source, or an equalizer such as a linear LMMSE equalizer or a non linear equalizer such as a Decision feedback equalizer. Estimation usually involves performing operations that would be performed in normal demodulation operations, but extended to multiple sources and subchannels, for the purposes of this invention. For example, in an HSDPA system or DO system, operations such as despreading and decovering may normally only be performed for the active sector, but for the purposes of interference cancellation, may be performed on all sectors and signals considered ‘strong’. Estimation for CDMA may be performed using a Fast Walsh transform (sometimes referred to as a Fast Hadamard Transform) in order to efficiently produce multiple symbol estimates, while estimation for OFDM systems may be performed using a FFT (Fast Fourier Transform). Symbol estimates are then refined in a subsequent symbol quality estimator module 140 where the raw symbol estimates undergo further processing to generate signal metrics for multiple user subchannels. Such a step of refinement is needed since the raw symbol estimates are often colored by the noise and interference that are inherent in the symbol estimates. Such refinement may include filtering the symbol estimates, thresholding, weighting, debiasing of the estimates, and performing soft, hard or mixed decisions, or some combination of these operations. Some of the steps of refinement may take into account information about the standard (such as all traffic subchannels in an EV-DO transmission being transmitted at the same amplitude, OVSF codes in an HSDPA transmission being of the same amplitude) or information available to the receiver through signaling about user subchannels that may or may not be present in the signal. After refinement, the signal metrics generated by the symbol quality estimator are combined with the symbol estimates in a processor 150 and then used in a signal post-processor 160 to help recover the information in a desired subchannel. Such processing may include interference cancellation in order to mitigate interference, the generation of channel quality information (CQI) for providing feedback to a transmitter about the quality of the received data, or decoding processes that perform error decoding using the symbol estimates and the signal quality metric thus generated.
FIG. 4 illustrates a receiver in accordance with one embodiment of the invention, where the post-processor comprises an Interference Canceller 412 and the front end comprises a searcher 432, an RF front end 436, and a Rake 416. An RF front end 436 (which typically comprises an AGC, an ADC, and a receive filter) processes a received signal from one or more receive antennae to produce baseband IQ data. The IQ data is provided to a searcher block 432 to identify multi paths used to update the Rake 416.
The Rake 416 illustrates details of one exemplary embodiment. The Rake module 416 comprises a set of Rake fingers 420 and a plurality of Signal Quality Estimate Blocks 440 and 442. However, other embodiments of a Rake may be used without departing from the spirit and scope of the invention. For example, a single finger may be employed for processing all of the multi paths in a Time Division Multiplexed (TDM) mode. Furthermore, different Rake embodiments may be used for estimating interference vectors. A Processing and Control Unit 428 may be employed for handling switchovers between different Rake modes, and also may perform finger management functions. Alternatively, a separate Rake module may be provided for estimation in addition to a Rake module employed for decoding.
Signals from fingers assigned to a single symbol estimator are added together at the chip level. This scheme of combining energies from different fingers from a single source is a form of Maximal ratio combining, and is well known in the art. In some embodiments, alternative scaling techniques may be used to weight each finger's data. Alternative scaling techniques may employ any combination of information about the signal, interference, noise energy present in the path tracked by each finger, and cross correlations between fingers when multiple receive antennas are employed. For example, signal quality estimators 440 and 442 shown in FIG. 4 may be employed for generating finger weights.
FIG. 6 is a detailed schematic of the Symbol estimation block 424 shown in FIG. 4, which in this embodiment performs its function by decovering. PN-stripped, phase-stripped MRC data from the Rake 416 is processed by a demultiplexer 604, which separates the data/preamble chips, Mac chips, and pilot chips. The data/preamble chips are processed by a Data/Preamble Discriminator 608 to distinguish between Data and Preamble chips. Data chips are processed by a Serial to Parallel converter (S/P) 612. The symbol estimator performs its function by Walsh decovering (multiplication by the appropriate Hadamard code and summing up the chips) by a Walsh decover block 616 to produce decovered symbols α1 through α16. Any of the Walsh-decover blocks, such as blocks 616, 620, and 624 may be configured to perform a Fast Walsh Transform or a Fast Hadamard Transform.
Preamble chips are processed by an S/P block 615 and Walsh decovered by Walsh decover block 620 to yield α1 1 through α1 32. MAC chips are processed through an S/P block 618 and Walsh decovered by Walsh decover block 624 to yield α11 1 through α11 64 Outputs of the Decover block 424 α1 through α16, α1 1 through α1 32, and α11 1 through α11 64 are input to the canceller 412, which uses the decovered data in an SMG 404. Alternatively, the decovered data may be bypassed to the switch 452.
A plurality K(s) of symbol estimates {{circumflex over (b)}(s),k [i]}k=0 K (s) −1 of transmitted symbols {{circumflex over (b)}(s),k [i]}k=0 K (s) −1 produced by an ith symbol estimator is input to scaling module 802. The symbol estimates are multiplied 810-811 by corresponding complex weights {γ(s),k [i]}k=0 K (s) −1 to produce weighted symbol estimates {γ(s),k [i]}k=0 K (s) −1. The magnitude of weight γ(s),k [i] may be calculated with respect to a merit of the corresponding symbol estimate {circumflex over (b)}(s),k [i].
The weights may be a function on the amplitude or power of the symbol estimates, or may be a function of its SINR (Signal to Interference Plus Noise ratio). The SINR (and thus, the soft weights) may be evaluated using techniques of statistical signal processing, including techniques based on an error-vector magnitude (EVM). An estimation of the noise floor may be performed. Alternatively, a pilot-assisted estimate of the broadband interference-plus-noise floor, together with a user specific estimate of the signal-plus-interference-plus-noise floor, may be used to estimate the SINR values.
r _ a ⁡ [ n ] ⁢ ∑ d = - 1 1 ⁢ ⁢ ∑ k = 1 N b ⁢ ⁢ H a , k ⁡ [ d ] ⁢ S k ⁡ [ n - d ] ⁢ WP k 1 / 2 ⁢ b _ k ⁡ [ n - d ] + z _ a ⁡ [ n ] Equation ⁢ ⁢ 1
Nb is the number of base station identified by the receiver (i.e., those base stations whose. channel gains and spreading sequences are tracked 904);
b k[n] is a column vector that contains the transmitted data vector from base station k in the n-th symbol interval;
Pk 1/2 is a matrix of user amplitudes for base station k;
W=[w 1 w 2 . . . w N c ] is an Nc×Nc matrix whose columns are the common spreading sequences (e.g., Walsh sequences) employed for the channels on all base stations;
Sk[n] is a diagonal matrix that contains base station k's scrambling sequence (e.g., PN cover) during the n-th symbol interval down its main diagonal; for the purpose of power estimation, these are modeled as independent and identically distributed (i.i.d.) complex Bernoulli random variables;
Ha,k[d] is the matrix model for the multipath spreading channel linking the k-th transmitter to the a-th antenna of the mobile at delay d, where d=0 corresponds to the current symbol, d=1 to the postcursor symbol, and d=−1 to the precursor symbol (note that the transmitted data symbols are numbered such that all base stations transmit symbol n within one symbol period);
z a[n] is an i.i.d. sequence of additive noise vectors for the processing on the a-th antenna chain with mean zero and covariance σ2I where σ2 accounts for all received power not explicitly modeled (RF noise, unidentified interference, etc.).
If multilevel codes are part of the CDMA network such that some users have shorter spreading sequences than others (e.g., WCDMA/HSDPA), then the terms just described hold with the following modifications
W=[w 1 w 2 . . . w N c ] is an Nc×Nc matrix whose columns are the common surrogate spreading sequences (e.g., Walsh sequences) that are assumed as-if-employed for the channels on all base stations;
b k[n] is a column vector that contains the surrogate symbols in a transmitted data vector from base station k in the n-th symbol interval;
While the surrogate symbols and sequences are not the actual symbols and sequences employed by some of the users, the estimated powers will still be accurate by virtue of the structure of OVSF (orthogonal variable spreading factor) codes as employed in such CDMA systems. Moreover, the LMMSE and time-averaged LMMSE receivers (which may be employed using parameters determined in this invention) are unchanged even if this surrogate approach is taken. More details are covered in U.S. patent application Ser. No. 11/432,580, entitled “Interference cancellation in variable codelength systems for multi-access communication,” and filed on 11 May 2006, now U.S. Pat. No. 7,697,595, the entire contents of which are hereby incorporated by reference.
r[n]=H 0 [n]P 1/2 b[n]+H 1 [n]P 1/2 b[n+1]+H −1 [n]P 1/2 b[n−1]+z[n] Equation 2
b[n] is the column vector obtained by stacking the base station symbol vectors, b 1 [n], . . . , b N b [n] into a single vector;
P1/2=diag {P1 1/2 . . . Pb 1/2} is a diagonal matrix with the user powers from each base station down the main diagonal;
H d ⁡ [ n ] = [ H 1 , 1 ⁡ [ d ] ⁢ S 1 ⁡ [ n - d ] ⁢ W … H 1 , N b ⁡ [ d ] ⁢ S N b ⁡ [ n - d ] ⁢ W ⋮ ⋱ ⋮ H A , N b ⁡ [ d ] ⁢ S N b ⁡ [ n - d ] ⁢ W … H A , N b ⁡ [ d ] ⁢ S N b ⁡ [ n - d ] ⁢ W ]
is the effective channel over all antennas (1 to A) and all base stations (1 to Nb) at time n and delay d which is estimated 906.
q[n]=R 0 [n]P 1/2 b[n]+L −1 [n]P 1/2 b[n−1]+L 1 [n]P 1/2 b[n+1]+u[n] Equation 3
where R0[n]=H0*[n]H0[n] is the instantaneous correlation matrix for symbol n (with the superscript * indicating the complex-conjugate transpose operation), L−1[n]=H0*[n]H−1[n] is the postcursor transfer matrix, L1[n]=H0*[n]H1[n] is the precursor transfer matrix, and u[n] is a zero mean additive noise vector with covariance σ2R0[n].
⁢ ( a ) ⁢ ⁢ E ⁢ { q _ ⁡ [ n ] | { S 1 ⁡ [ n - d ] , … ⁢ , S N b ⁡ [ n - d ] } d = - 1 1 } = O _ ⁢ ( b ) ⁢ ⁢ Q ⁡ [ n ] = ⁢ E ⁢ { q _ ⁡ [ n ] ⁢ q * _ ⁡ [ n ] | { S 1 ⁡ [ n - d ] , … ⁢ , S N b ⁡ [ n - d ] } d = - 1 1 } = ⁢ R 0 ⁡ [ n ] ⁢ PR 0 ⁡ [ n ] + L - 1 ⁡ [ n ] ⁢ PL - 1 * ⁡ [ n ] + ⁢ L 1 ⁡ [ n ] ⁢ PL 1 * ⁡ [ n ] + σ 2 ⁢ R 0 * ⁡ [ n ] Equation ⁢ ⁢ 4
Q=E{Q[n]} Equation 5
The elements of this matrix may be estimated with an empirical average over multiple symbol intervals, e.g.
Q ~ = 1 N ⁢ ∑ n = 1 N ⁢ ⁢ q _ ⁡ [ n ] ⁢ q _ * ⁡ [ n ] ,
or any other of the various types of moving average or autoregressive estimators. It is only the diagonal elements of this matrix that are of primary interest; these are the RAKE output powers 1001. If an equalizer is used at the front end instead of a Rake, those would also yield a similar set of powers. The key step is to represent them analytically in terms of the quantities that are to be estimated namely the sub channel powers and the background noise power. The k-th RAKE output power, where k=1, 2, . . . , K with K=NbNc (i.e., the total number of subchannels in the system), is expressible as
Q kk = ∑ j = 1 K ⁢ ⁢ ( E ⁢ {  ( R 0 ) kj ⁡ [ n ]  2 } + E ⁢ {  ( L - 1 ) kj ⁡ [ n ]  2 } + E ⁢ {  ( L 1 ) kj ⁡ [ n ]  2 } ) ⁢ p j + σ 2 ⁢ E ⁢ { ( R 0 ) kk ⁡ [ n ] } Equation ⁢ ⁢ 6
(a) A kj =E{|(R 0)kj [n]| 2 }+E{|(L −1)kj [n]| 2 }+E{|(L 1)kj [n]| 2}
(b) c k =E{(R 0)kk [n]} Equation 7
[ Q 11 Q 22 ⋮ Q KK ] = [ A 11 A 12 … A 1 ⁢ ⁢ K c 1 A 21 A 22 … A 2 ⁢ ⁢ K c 2 ⋮ ⋮ ⋱ ⋮ ⋮ A K ⁢ ⁢ 1 A K ⁢ ⁢ 2 … A KK c K ] ⁡ [ p 1 p 2 ⋮ p K σ 2 ] Equation ⁢ ⁢ 8
The left-hand column vector must be estimated, as previously described. The rectangular matrix just to the right of the equal sign may be estimated similarly (as previously discussed), or it may be calculated exactly (to be described shortly). The far-right column vector contains the unknowns that need to be estimated. Notice that there are K+1 unknowns, but only K independent equations, so there is not a unique solution for the unknowns. To remedy this there are several preferred embodiments of the invention. One is to assume that the background noise is weak enough that σ2 can be safely ignored, such as in interference-limited scenarios; this leads to K equations and K unknowns. Another preferred embodiment is to take advantage of any base-station pilots. The power of each base-station pilot signal may be accurately estimated with a coherent estimator 1005. First consider only the estimated power of the pilot of the first base station. In other words, let {tilde over (p)}1 pilot be the coherent estimate of p1. The matrix equation in Equation 8 may be updated in one of two ways, both of which are now described. The first way is to let the estimate of {circumflex over (p)}1 be given by {circumflex over (p)}1={circumflex over (p)}1 pilot, and then take that part of the right-hand side of Equation 8 that depends on p1 to the left-hand side; this leads to
[ Q 11 Q 22 ⋮ Q KK ] - p ~ 1 pilot ⁡ [ A 11 A 21 ⋮ Q K ⁢ ⁢ 1 ] = [ A 12 A 13 … A 1 ⁢ K c 1 A 22 A 23 … A 2 ⁢ K c 2 ⋮ ⋮ ⋱ ⋮ ⋮ A K ⁢ ⁢ 2 A K ⁢ ⁢ 3 … A KK c K ] ⁡ [ p 2 p 3 ⋮ p K σ 2 ] Equation ⁢ ⁢ 9
[ Q 11 Q 22 ⋮ Q KK p ~ 1 pilot ] = [ A 11 A 12 … A 1 ⁢ K c 1 A 21 A 22 … A 2 ⁢ K c 2 ⋮ ⋮ ⋱ ⋮ ⋮ A K ⁢ ⁢ 1 1 A K ⁢ ⁢ 2 0 … … A KK 0 c K 0 ] ⁡ [ p 1 p 2 ⋮ p K σ 2 ] Equation ⁢ ⁢ 10
y=Xθ Equation 11
{circumflex over (θ)}=arg min θ {∥y−Xθ∥ 2:θk≧0 for all k} Equation 12
where ∥x∥2=Σnxn 2 is the square of the 2-norm of the real-valued vector x. Since the objective function is strictly convex (i.e., since X has full column rank) and the constraint set is a convex set, there is a unique solution. Any exact solution or lower-complexity approximate solution to this problem is part of the invention 1004. Moreover, the invention need not be restricted to this particular objective function. For example, other convex functions such as g (x)=Σnαnxn s where s>0 and αn>0 for all n, are included. The constraint set may also take on different forms to include other known constraints. Given that background noise is always present in a receiver, a tighter lower bound than zero may be set to prevent the estimator from making the estimate of σ2 too small. Similarly, if it is known that the power of a subchannel cannot exceed some value (e.g., the power of the pilot from its originating base station), a corresponding upper bound can be used to further restrict the constraint set.
[ A 11 A 12 … A 1 ⁢ K c 1 A 21 A 22 … A 2 ⁢ K c 2 ⋮ ⋮ ⋱ ⋮ ⋮ A K ⁢ ⁢ 1 A K ⁢ ⁢ 2 … A KK c K ]
 W ij  = 1 N c
for au elements or the matrix W of common spreading sequences. The scrambling sequences (i.e., PN covers) which make up the diagonal elements of each Sk [n] are taken to be i.i.d. complex Bernoulli random variables that are normalized to have unit variance. It is helpful to arrange the indices by base station and then partition the matrix by base station in the form
[ A 11 A 12 … A 1 ⁢ N b c _ 1 A 21 A 22 … A 2 ⁢ N b c _ 2 ⋮ ⋮ ⋱ ⋮ ⋮ A N b ⁢ 1 A N b ⁢ 2 … A N b ⁢ N b c _ N b ] Equation ⁢ ⁢ 13
where each Abb′ an Nc×Nc matrix whose row sub-indices correspond base station b and whose column sub-indices correspond to base station b′, and each c b is an Nc×1 vector whose sub-indices correspond to base station b. Now make the definitions
(a) X bb′ =H b*[0]H b′[0]
(b) Y bb′ =H b*[0]H b′[−1]
(c) Z bb′ =H b*[0]H b′[1] Equation 14
(a) x b=[(X bb)11(X bb)22 . . . (X bb)N c N c ]T
(b) Δb=diag(|(X bb)11|2,|(X bb)22|2, . . . ,|(X bb)N c N c |2) Equation 15
where the superscript T denote the matrix transpose operation. Then the (k, j) element of Abb is given by
( A bb ) kj = 1 N c 2 ⁢ 1 _ T ⁢ ( N c 2 ⁡ ( x _ b ∘ w _ k ∘ w _ _ j ) ⁢ ( x _ b ∘ w _ j ∘ w _ _ k ) T + X bb ∘ X _ bb + Y bb ∘ Y _ bb + Z bb ∘ Z _ bb - Δ b ) ⁢ 1 _ Equation ⁢ ⁢ 16
where the ∘ operator denotes the Hadamard (i.e., element-wise) product, 1 is the all-ones Nc×1 vector, and the overbar notation indicates taking the complex conjugate of every element of the vector or matrix. The off-diagonal blocks are defined by
A bb′={1 T(X bb′ ∘X bb′ +Y bb′ ∘Y bb′ ∘Z bb′ ∘Z bb′)1}E, Equation 17
c _ b = trace ⁡ ( x bb ) N c ⁢ 1 _ Equation ⁢ ⁢ 18
where i[n]=L−1[n]P1/2b[n−1]+L1[n]P1/2b[n+1]+u[n] is the interference due to background noise and inter-symbol interference (ISI); it has covariance
R ii [n]=L −1 [n]PL −1 *[n]+L 1 [n]PL* 1 *[n]+σ 2 R 0 *[n] Equation 20
q k ⁡ [ n ] = ( R 0 ) kk ⁡ [ n ] ⁢ p k 1 2 ⁢ b k ⁡ [ n ] Signal ⁢ ⁢ of ⁢ ⁢ Interest + ∑ k ′ ≠ k ⁢ ( R 0 ) kk ′ ⁡ [ n ] ⁢ p k ′ 1 2 ⁢ b k ′ ⁡ [ n ] Intra ⁢ - ⁢ cell ⁢ ⁢ and ⁢ ⁢ Inter ⁢ - ⁢ Cell Interference + i k ⁡ [ n ] Background ⁢ ⁢ Noise and ⁢ ⁢ ISI Equation ⁢ ⁢ 21
SINR k ⁡ [ n ] =  ( R 0 ) kk ⁡ [ n ]  2 ⁢ p k ∑ k ′ ≠ k ⁢  ( R 0 ) kk ′ ⁡ [ n ]  2 ⁢ p k ′ + ( R ii ) kk ⁡ [ n ] = N k ⁡ [ n ] D k ⁡ [ n ] Equation ⁢ ⁢ 22
The constituent numerator and denominator may each be averaged to obtain, respectively, the means E{Nk[n]} and E{Dk[n]}; their ratio is the average noise power divided by the average interference-plus-noise power.
The instantaneous SINR may be averaged to obtain an estimate of E{SINRk[n]}, the mean Rake SINR.
The instantaneous symbol error rate (SER) may be approximated for the signal constellation being used (or an assumed constellation if it is unknown) and then averaged to estimate the mean SER, E{SERk[n]}; for example, a QPSK constellation has SERk[n]=1−(1−Q(SINRk[n]))2, while formulas for other constellations are commonly known to those familiar with the art.
A bound on the maximum supportable data rate η may be obtained by averaging the instantaneous throughput log(1+SINRk[n]) to obtain an estimate of average throughput η=E{log(1+SINRk[n])} bits per second per Hertz.
In a preferred embodiment, and as pictured in FIG. 11, a suitable channel quality estimator of any linear receiver can be obtained in a manner analogous to that described for the Rake receiver. The output of a general linear receiver may be expressed as
q linear [n]=Σ m=−M M G[n,m]r[n−m] Equation 23
q linear [n]=Σ l=−M−1 M+1 F[n,l]P 1/2 b[n−l]+v[n] Equation 24
F[n,l] is the effective channel-plus-receiver 1101 filter and has matrix values defined by
F ⁡ [ n , m ] = ∑ d = - 1 1 ⁢ G ⁡ [ n , m + d ] ⁢ H d ⁡ [ n + m - d ] ⁢ Ψ [ - M - d , M - d ] ⁡ [ m ] where Ψ [ - M - d , M - d ] ⁡ [ m ] = { 1 , - M - d ≤ m ≤ M - d 0 , else
v[n] is a vector of filtered noise with correlation matrix 1102
R vv ⁡ [ n ] = σ 2 ⁢ ∑ m = - M M ⁢ G ⁡ [ n , m ] ⁢ G * [ n · m ]
q linear ⁡ [ n ] = F ⁡ [ n , 0 ] ⁢ P 1 2 ⁢ b ⁡ [ n - l ] + ∑ 1 ≠ 0 ⁢ F ⁡ [ n , l ] ⁢ P 1 / 2 ⁢ b ⁡ [ n - l ] + v ⁡ [ n ] ⁢ ⁢ i _ ⁡ [ n ] ⁢ : ⁢ ⁢ Background ⁢ ⁢ Noise ⁢ ⁢ and ⁢ ⁢ ISI Equation ⁢ ⁢ 25
R ii ⁡ [ n ] = ∑ 1 ≠ 0 ⁢ F ⁡ [ n , 1 ] ⁢ PF * [ n , l ] ISI ⁢ ⁢ Covariance + R vv ⁡ [ n ] Background ⁢ ⁢ Noise Covariance Equation ⁢ ⁢ 26
q k linear ⁡ [ n ] = ( F ⁡ [ n , 0 ] ) kk ⁡ [ n ] ⁢ p k 1 / 2 ⁢ b k ⁡ [ n ] Signal ⁢ ⁢ of ⁢ ⁢ Interest + ∑ k ′ ≠ k ⁢ ( F ⁡ [ n , 0 ] ) kk ′ ⁢ p k ′ 1 / 2 ⁢ b k ′ ⁡ [ n ] Intra ⁢ - ⁢ Cell ⁢ ⁢ and ⁢ ⁢ Inner ⁢ - ⁢ Cell Interference + i k ⁡ [ n ] Background ⁢ ⁢ Noise and ⁢ ⁢ ISI Equation ⁢ ⁢ 27
SINR k ⁡ [ n ] =  ( F ⁡ [ n , 0 ] ) kk ⁡ [ n ]  2 ⁢ p k ∑ k ′ ≠ k ⁢  ( F ⁡ [ n , 0 ] ) kk ′ ⁡ [ n ]  2 ⁢ p k ′ + ( R ii ) kk ⁡ [ n ] = N k ⁡ [ n ] D k ⁡ [ n ] Equation ⁢ ⁢ 28
and as shown in FIG. 11 (numerator 1103 and denominator consisting of the sum 1107 of intra-cell and inter-cell interference power 1104, ISI power 1105, and background noise power 1106). In this manner, estimates of the output SINRs and achievable rates may be obtained for any linear receiver. In a preferred embodiment, one or more of the matrices which define the interference correlation matrix in Equation 26 may be removed from the calculation to simplify the resulting computation. Any of the uses for the instantaneous SINR that were described in the context of the RAKE are equally applicable to an arbitrary linear receiver.
FIG. 12 shows a Symbol estimator (Decover) block 424 configured to operate in a system complying with the CDMA 2000 1×RTT standard or the WCDMA/HSPA standard. Signals output from the Rake 416 are coupled to Fast Walsh Transform (FWT) 1208 after passing through a serial to parallel block 1204. The FWT 1208 produces a vector of Walsh energies denoted by α, which is provided to the SMG 404.
In weighing, the symbol estimates received are weighed by some figure of merit of those symbol estimates, such as SINR, or signal strength, as described earlier in the specification. The weights, α are computed and applied as shown in FIG. 8.
In one embodiment, information from different paths may be weighted in the ratio of their strengths or signal quality estimates, and then combined. Another embodiment may estimate the interference vector on a per-finger basis. In SMGOne, cancellation of interference from multipaths is performed using interference estimates derived from only the strongest path originating from a sector. This technique assumes that the transmitted symbols from the sector are identical across all paths. Thus, the strongest path provides the best sign and amplitude estimates for all paths from that sector. Each path experiences an independent fading profile, and individual channel estimates (phase estimates derived from the pilot) may be used to properly reconstruct the interference for each path prior to interference cancellation. The estimated interference vector from the strongest multi paths may be used to cancel out interference from other multi paths of the strongest path.
Interference cancellation may be performed either through projection or subtraction. Projection-based cancellation may require more operations than subtraction based methods. But projection-based methods may be less sensitive to estimation errors, and they are invariant to scaling errors. Thus, the Control Unit 328 may be configured to switch between subtraction and projection depending on reliability of the estimated interference vector. If the reliability exceeds a predetermined dynamic/static threshold, then subtraction may be used. Alternatively, a projection operation may be performed. A projection may be preferred over subtraction when the path strengths are small or when the fading coefficients are highly uncorrelated over time. Embodiments of this invention may be realized by either subtraction based or projection based cancellation modules, as well as having a configurable canceller that switches between the methods depending on the estimation quality.
All paths input to the SMG should be multi paths from a common signal source (Base station sector or Node-B, for example). For example, in CDMA2000 and in HSDPA/WCDMA, the control unit distinguishes multi paths from other base station soft handoff paths and assigns the paths to the SMG. The control unit assigns the other active paths from the base station in soft handoff to a second vector estimation block, if available.
The estimation and cancellation embodiments described herein may be adapted to systems employing transmit and receive diversity. When multiple transmit and receive antennas are employed, it is more likely that Rake fingers are locked to stronger multipaths. Thus, better interference estimation may be performed using SMG and SMGOne schemes. In one embodiment, the control unit may switch between SMG and SMGOne schemes based on multi path and interference profiles. Alternatively, maximal ratio combining schemes may be employed with receive diversity, as is well known in the art.
It is clear that the methods described herein may be realized in hardware or software, and there are several modifications that can be made to the order of operations and structural flow of the processing. Those skilled in the art should recognize that method and apparatus embodiments described herein may be implemented in a variety of ways, including implementations in hardware, software, firmware, or various combinations thereof. Examples of such hardware may include Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), general-purpose processors, Digital Signal Processors (DSPs), and/or other circuitry. Software and/or firmware implementations of the invention may be implemented via any combination of programming languages, including Java, C, C++, Matlab™, Verilog, YHDL, and/or processor specific machine and assembly languages.
a front-end processor configured to track one or more active base stations, estimate powers for one or more base station pilots, and estimate one or more multipath channels from the one or more base stations to one or more mobile stations;
an estimation module configured to relate unknown user and noise powers to estimates of powers for the one or more base station pilots based on an average correlation matrix for the one or more multipath channels;
a quality estimator configured to generate one or more quality estimates for the one or more multipath channels based on the estimates of powers; and
an interference canceller configured to generate an interference-cancelled signal from the one or more multipath channels based on the one or more quality estimates.
2. The system of claim 1, wherein the front-end processor is further configured to receive signals from a plurality of receive antennas.
3. The system of claim 1, wherein the front-end processor is further configured to operate with surrogate user sequences in networks that use multilevel codes.
4. The system of claim 1, wherein the estimation module is further configured to estimate elements of the average correlation matrix based on an empirical average over multiple symbol intervals.
the front-end processor is further configured to provide the estimates of the one or more base station pilot powers to the estimation module; and
the estimation module is further configured to use the estimates of the one or more base station pilot powers as final estimates of one or more subchannels.
6. The system of claim 1, wherein the estimation module is further configured to calculate elements of the average correlation matrix based on the estimates of the one or more base station pilot powers and a system of constraints.
tracking one or more active base stations;
estimating power for one or more base station pilots;
estimating one or more multipath channels from the one or more base stations to one or more mobile station;
relating unknown user and noise powers to estimates of power for the one or more base station pilots based on an average correlation matrix for the one or more multipath channels;
generating one or more quality estimates for the one or more multipath channels based on the estimates of powers; and
generating an interference-cancelled signal from the one or more multipath channels based on the one or more quality estimates.
8. The method of claim 7, wherein said tracking comprises receiving signals from a plurality of receive antennas.
9. The method of claim 7, further comprising operating with surrogate user sequences in networks that use multilevel codes.
10. The method of claim 7, further comprising estimating elements of the average correlation matrix based on an empirical average over multiple symbol intervals.
11. The method of claim 7, further comprising using the estimates for the one or more base station pilot powers as the final estimates of corresponding subchannels.
12. The method of claim 7, further comprising calculating elements of the average correlation matrix based on the estimates for the one or more base station pilot powers and a system of constraints.
13. The method of claim 7, further comprising providing the estimates to a multi user receiver.
14. A non-transitory computer-readable storage medium comprising a computer program that, in response to being executed, results in a receiver:
15. The non-transitory computer-readable storage medium of claim 14, wherein execution of the computer program further results in the receiver processing signals received from a plurality of receive antennas.
16. The non-transitory computer-readable storage medium of claim 14, wherein execution of the computer program further results in the receiver operating with surrogate user sequences in networks that use multilevel codes.
17. The non-transitory computer-readable storage medium of claim 14, wherein execution of the computer program further results in the receiver estimating elements of the average correlation matrix based on an empirical average over multiple symbol intervals.
18. The non-transitory computer-readable storage medium of claim 14, wherein execution of the computer program further results in the receiver using the estimates of one or more base station pilot powers as the final estimates of corresponding subchannels.
19. The non-transitory computer-readable storage medium of claim 14, wherein execution of the computer program further results in the receiver calculating elements of the average correlation matrix based on the estimates for the one or more base station pilot powers and a system of constraints.
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