WO2007038018A2 - Elimination d'interference iterative au moyen de ponderations de retroaction mixtes ou de tailles de pas de stabilisation - Google Patents

Elimination d'interference iterative au moyen de ponderations de retroaction mixtes ou de tailles de pas de stabilisation Download PDF

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WO2007038018A2
WO2007038018A2 PCT/US2006/036018 US2006036018W WO2007038018A2 WO 2007038018 A2 WO2007038018 A2 WO 2007038018A2 US 2006036018 W US2006036018 W US 2006036018W WO 2007038018 A2 WO2007038018 A2 WO 2007038018A2
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symbol
interference
weight
signal
recited
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PCT/US2006/036018
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English (en)
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WO2007038018A3 (fr
Inventor
Tommy Guess
Michael L. Mccloud
Vijay Nagarajan
Singh Lamba Gagandeep
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Tensorcomm, Inc.
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Priority claimed from US11/233,636 external-priority patent/US8761321B2/en
Priority claimed from US11/451,688 external-priority patent/US7702048B2/en
Application filed by Tensorcomm, Inc. filed Critical Tensorcomm, Inc.
Publication of WO2007038018A2 publication Critical patent/WO2007038018A2/fr
Publication of WO2007038018A3 publication Critical patent/WO2007038018A3/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
    • H04B1/69—Spread spectrum techniques
    • H04B1/707—Spread spectrum techniques using direct sequence modulation
    • H04B1/7097—Interference-related aspects
    • H04B1/7103—Interference-related aspects the interference being multiple access interference
    • H04B1/7107—Subtractive interference cancellation
    • H04B1/71075—Parallel interference cancellation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
    • H04B1/69—Spread spectrum techniques
    • H04B1/707—Spread spectrum techniques using direct sequence modulation
    • H04B1/7097—Interference-related aspects
    • H04B1/711—Interference-related aspects the interference being multi-path interference
    • H04B1/7115—Constructive combining of multi-path signals, i.e. RAKE receivers
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B2201/00—Indexing scheme relating to details of transmission systems not covered by a single group of H04B3/00 - H04B13/00
    • H04B2201/69—Orthogonal indexing scheme relating to spread spectrum techniques in general
    • H04B2201/707—Orthogonal indexing scheme relating to spread spectrum techniques in general relating to direct sequence modulation
    • H04B2201/70702—Intercell-related aspects

Definitions

  • the present invention relates generally to iterative interference cancellation in received wireless communication signals and, more particularly, to cancellation of intra-cell interference and/or inter-cell interference in coded spread spectrum communication systems.
  • a communication resource is divided into code-space subchannels that are allocated to different users.
  • a plurality of subchannel signals received by a wireless terminal may correspond to different users and/or different subchannels allocated to a particular user.
  • a single transmitter broadcasts different messages to different receivers, such as a base station in a wireless communication system broadcasting to a plurality of mobile terminals
  • the channel resource is subdivided in order to distinguish between messages intended for each mobile.
  • each mobile terminal by knowing its allocated subchannel(s), may decode messages intended for it from the superposition of received signals.
  • a base station typically separates received signals into subchannels in order to differentiate between users.
  • received signals are superpositions of time- delayed and complex-scaled versions of the transmitted signals.
  • Multipath can cause several types of interference. Intra-channel interference occurs when the multipath time-delays cause subchannels to leak into other subchannels. For example, in a forward link, subchannels that are orthogonal at the transmitter may not be orthogonal at the receiver.
  • inter-channel interference caused by unwanted signals received from other base stations.
  • Each of these types of interference can degrade communications by causing a receiver to incorrectly decode received transmissions, thus increasing a receiver's error floor. Interference may also have other deleterious effects on communications. For example, interference may lower capacity in a communication system, decrease the region of coverage, and/or decrease maximum ⁇ ata rates, ⁇ or these reasons, a reduction in interference can improve reception of selected signals while addressing the aforementioned limitations due to interference.
  • interferences take the following form when code division multiplexing is employed for a communication link, either with code division multiple access (as used in CDMA 2000, WCDMA, and related standards) or with time division multiple access (as used in EV-DO and related standards).
  • code division multiple access as used in CDMA 2000, WCDMA, and related standards
  • time division multiple access as used in EV-DO and related standards.
  • a set of symbols is sent across a common time-frequency slot of the physical channel and separated using a set of distinct code waveforms, which are usually chosen to be orthogonal (or pseudo- orthogonal for reverse-link transmissions).
  • the code waveforms typically vary in time, and these variations are introduced by a pseudo-random spreading code (PN sequence).
  • PN sequence pseudo-random spreading code
  • the wireless transmission medium is characterized by a time-varying multipath profile that causes multiple time-delayed replicas of the transmitted waveform to be received, each replica having a distinct amplitude and phase due to path loss, absorption, and other propagation effects.
  • the received code set is no longer orthogonal.
  • the code space suffers from intra-channel interference within a base station as well as inter-channel interference arising from transmissions in adjacent cells.
  • the Rake receiver uses a channel-tracking algorithm to resolve the received signal energy onto various multipath delays. These delayed signals are then weighted by the associated complex channel gains (which may be normalized by path noise powers) and summed to form a single resolved signal, which exploits some of the path diversity available from the multipath channel. It is well known that the Rake receiver suffers from a significant interference floor, which is due to both self -interference from the base station of interest (or base stations, when the mobile is in a soft-handoff base station diversity mode) and multiple-access interference from all base stations in the coverage area. This interference limits the maximum data rates achievable by the mobiles within a cell and the number of mobiles that can be supported in the cell.
  • the optimal multi-user detector has the best performance, but is generally too computationally complex to implement. MUD complexity increases exponentially with respect to the total number of active subchannels across the cell of interest and the interfering cells as well as the constellation size(s) of the subchannels. This complexity is so prohibitive that even efficient implementations based on the Viterbi algorithm cannot make it manageable in current hardware structures.
  • Another approach is a properly designed linear receiver, which in many channel scenarios, is able to retain much of the optimal MUD performance, but with a complexity that is polynomial in the number of subchannels.
  • LMMSE linear minimum mean squared error
  • decorrelating or zero-forcing
  • PNA-LMMSE PN-averaged LMMSE
  • This receiver is generally inferior to the LMMSE approach, because it is amenable to adaptive (or partially adaptive) implementations.
  • the advantages of an adaptive implementation over a direct implementation include reduced complexity and the fact that the additive noise power (i.e., background RF radiation specific to the link environment, noise in the receiver's RF front end, and any processing noise such as noise due to quantization and imperfect filtering) does not have to be estimated.
  • an interference signal from the other subchannels is synthesized, followed by interference cancellation that subtracts the synthesized interference from each subchannel.
  • the interference-cancelled subchannels are then fed to a subsequent PIC stage. Ideally, within just a few stages (i.e., before the complexity grows too large), the performance rivals that of the full linear receiver using a matrix inverse.
  • PIC can be implemented in various modes depending on what types of symbol estimates are used for interference cancellation.
  • a soft-cancellation mode PIC does not exploit additional information inherent in the finite size of user constellations. That is, estimates of data symbols are not quantized to a constellation point when constructing interference signals.
  • the user constellations may be known (e.g., in an EV-DO link or in a WCDMA link without HSDPA users) or determined through a modulation classifier. In such cases, it is possible for PIC to be implemented in a hard-cancellation mode. That is, estimates of data symbols are quantized to constellation points (i.e., hard decisions) when constructing the interference signal.
  • PIC In a mixed-cancellation mode, PIC employs a soft decision on each symbol whose constellation is unknown, and either a soft or hard decision on each symbol whose constellation is known, depending on how close the soft estimate is to the hard decision.
  • Such a mixed-decision PIC typically outperforms both the soft-decision PIC and the hard-decision PIC. Moreover, it can also substantially outperform the optimal LMMSE receiver and promises even greater performance gains over PNA-LMMSE approaches currently under development for advanced receivers. The performance of soft-decision PIC is bounded by the optimal LMMSE.
  • embodiments of the present invention may provide a generalized interference-canceling receiver for canceling intra-channel and inter-channel interference in coded, multiple-access, spread-spectrum T/US2006/036018
  • Receiver embodiments may employ a designed and/or adapted soft-weighting subtractive cancellation with a stabilizing step-size and a mixed-decision symbol estimator.
  • Receiver embodiments may be designed, adapted, and implemented explicitly in software or programmed hardware, or implicitly in standard Rake-based hardware, either within the Rake (i.e., at the finger level) or outside the Rake (i.e., at the subchannel symbol level).
  • Embodiments of the invention may be employed in user equipment on the forward link and/or in a base station on the reverse link.
  • Some embodiments of the invention address the complexity of the LMMSE approach by using a low-complexity iterative algorithm. Some embodiments of the invention in soft-mode may be configured to achieve LMMSE performance (as contrasted to the lesser-performing PNA-LMMSE) using only quantities that are easily measured at the receiver. Some embodiments address the sub-optimality of the LMMSE and PNA-LMMSE approaches by using an appropriately designed mixed- decision mode and may even approach the performance of an optimal multi-user detector. In some embodiments, stabilizing step sizes may be used to enhance stability of various PIC approaches. Some embodiments may employ symbol-estimate weighting to control convergence of various PIC approaches.
  • Some embodiments of the invention address the limitation of various PIC approaches to binary and quaternary phase shift keying in mixed-decision mode by being configurable to any subchannel constellation. Some embodiments of the invention address the difficulty of efficiently implementing various PIC approaches in hardware by using a modified Rake architecture. Some embodiments of the invention address the so-called "ping- pong effect" (i.e., when the symbol error rate oscillates with iteration) in various PIC approaches by pre-processing with a de-biasing operation when making symbol estimates.
  • an iterative interference canceller comprises a weighting means configured for applying at least one symbol weight to the input symbol decisions, a stabilizing step size means configured for applying a stabilizing step size to an error signal, and a mixed-decision processing means.
  • the mixed-decision processing means may include, by way of example, but without limitation, a combination of hardware and software configured to produce soft and/or hard symbol estimates, and may be known as a decision device or a symbol estimator.
  • the stabilizing step size means may include, by way ot example, out wnnoui limitation, any combination of hardware and software configured to scale an error signal with a scaling factor that may be used for controlling convergence in an iterative canceller.
  • the stabilizing step size means may include a signal processor configured to calculate at least one stabilizing step size and a multiplier for scaling an error signal with the step size.
  • the weighting means may include, by way of example, but without limitation, a weight-calculation means configured for producing symbol weights, and a multiplier configured for multiplying symbol estimates by the weights.
  • the weight-calculation means may include, by way of example, but without limitation, any combination of hardware and software configured to calculate symbol weights from a function employing a merit of at least one input symbol decision.
  • the merit may comprise an average ratio of signal power to interference-plus-noise power (or a function thereof).
  • the merit may be a function of input symbol decisions and proximity of those input symbol decisions to a nearby constellation point.
  • the weight-calculation means may employ time-series averaging for calculating the proximity as a statistical average.
  • the weight-calculation means may include a signal processing means configured to perform statistical signal processing for estimating the average ratio of signal power to interference-plus-noise power. Such statistical signal processing may employ error-vector magnitude calculations.
  • Embodiments of the invention may be employed in any receiver configured to support one or more CDMA standards, such as (1) the "TIA/EIA-95-B Mobile Station-Base Station Compatibility Standard for Dual-Mode Wideband Spread Spectrum Cellular System” (the IS-95 standard), (2) the "TIA/EIA-98-C
  • Receivers and cancellation systems described herein may be employed in subscriber-side devices (e.g., cellular handsets, wireless modems, and consumer premises equipment) and/or server-side devices (e.g., cellular base stations, wireless access points, wireless routers, wireless relays, and repeaters). Chipsets for subscriber-side and/or server-side devices may be configured to perform at least some of the receiver and/or cancellation functionality of the embodiments described herein.
  • Figure 1 is a general schematic illustrating an iterative interference canceller.
  • Figure 2 is a block diagram illustrating a front-end processor for an iterative interference canceller.
  • FIG 3 is a general schematic illustrating an interference cancellation unit (ICU).
  • ICU interference cancellation unit
  • Figure 4 shows a weighting block in an ICU configured to separately process input symbol estimates corresponding to a plurality of base stations.
  • Figure 5 A is a block diagram illustrating part of an interference cancellation unit configured to synthesize constituent finger signals.
  • Figure 5B is a block diagram illustrating part of an interference cancellation unit configured to synthesize constituent user signals.
  • Figure 6A shows a cancellation block configured to perform interference cancellation on constituent signals, followed by Rake processing and despreading.
  • Figure 6B shows a cancellation block configured to cancel interference in constituent signals, preceded by Rake processing and despreading.
  • Figure 7 is a block diagram of the interference cancellation part of a subtractive canceller in which cancellation occurs prior to signal despreading.
  • Figure 8A is a block diagram illustrating post interference-cancellation signal despreading on constituent finger signals.
  • Figure 8B is a block diagram illustrating post interference-cancellation signal despreading on constituent user signals.
  • Figure 9A is a block diagram showing a method for implicitly despreading a signal in a subtractive canceller that performs interference-cancellation prior to signal despreading.
  • Figure 9B is a block diagram showing a method for explicitly despreading a signal in a subtractive canceller that performs interference-cancellation prior to signal despreading
  • Figure 10 is a block diagram of a subtractive canceller configured to perform interference cancellation prior to signal despreading.
  • Figure HA is a block diagram illustrating an embodiment for implicitly calculating a stabilizing step size.
  • Figure HB is a block diagram illustrating how linear functions, such as despreading and generating a difference signal, can be swapped in an alternative embodiment for calculating a stabilizing step size.
  • Figure HC is a block diagram illustrating another embodiment for implicitly calculating a stabilizing step size.
  • Figure 12 is a block diagram of a symbol-estimation block in an interference cancellation unit.
  • Figure 13 is a block diagram of a dual feedback algorithm configured for implementing an iterative interference canceller.
  • embodiments may take the form of programmable features executed by a common processor or discrete hardware unit.
  • the following formula represents an analog baseband signal received at a mobile from multiple base stations, each with its own multipath channel,
  • L (s) is the number of resolvable (or modeled) paths from base station (s) to the mobile; • ⁇ (s) / and ⁇ ⁇ s) l are the complex gain and delay, respectively, associated with the / -th path of base station (s) ;
  • K ⁇ s is the number of active users or subchannels in base station (s) that share a channel via code-division multiplexing; these users or subchannels are indexed from 0 to K (s) - 1 ; • u is) ⁇ k (t) is a code waveform (e.g., spreading waveform) of base station (s) used to carry the k th user's symbol for that base station (e.g., a chip waveform modulated by a user-specific Walsh code and covered with a base-station specific PN cover);
  • a code waveform e.g., spreading waveform
  • b (s) k is a complex symbol transmitted for the k th user or subchannel of base station (s) ;
  • w(t) is zero-mean complex additive noise that contains both thermal noise and any interference whose structure is not explicitly modeled (e.g., inter-channel interference from unmodeled base stations and/or intra-channel interference from unmodeled paths).
  • a user terminal e.g., a handset
  • a user terminal is configured to detect only symbols transmitted from its serving base station (e.g., the symbols from base station (O)) or a subset thereof (e.g., symbols for the k th user of base station (O)). Interference can impede the determination of b (s) lc from y(t) .
  • additive noise w(t) present, but there may be intra-channel and inter-channel interference.
  • Intra-channel interference typically occurs when multiple users are served by a given base station (i.e., a serving base station). Even if the users' transmitted code waveforms are orthogonal, multipath in the transmission channel causes the codes to lose their orthogonality. Inter-channel interference is caused by transmissions from non-serving base stations whose signals contribute to the received baseband signal y(t) .
  • FIG. 1 is a block diagram of an iterative interference canceller (IIC), which is a low-complexity receiver configured to mitigate intra-channel and inter-channel interference.
  • the received baseband signal y(t) is input to a front-end processor 101, which produces initial symbol estimates for all symbols of the active users served by at least one base station.
  • the initial symbol estimates are coupled to a first interference cancellation unit (ICU) 102 configured to cancel a portion of the intra- channel and inter-channel interference that corrupts the symbol estimates.
  • the ICU 102 outputs a first set of updated symbol estimates, which are interference-cancelled symbol estimates.
  • the updated symbol estimates are coupled to a second ICU 103.
  • a plurality M of ICUs 102-104 illustrate an iterative process for performing interference cancellation in which the initial symbol estimates are updated M times.
  • FIG 2 is a block diagram of the front-end processor 101 shown in Figure 1.
  • Each of a plurality B of Rake-based receiver components 201-203 provides estimates of symbols transmitted from a corresponding base station.
  • the detailed block diagram depicted in Rake receiver 202 represents the functionality of each of the components 201-203.
  • Rake receiver 202, corresponding to an 5 th base station 202, includes a plurality L ⁇ of delay elements 210-211 configured to advance the received baseband signal y(t) in accordance with multipath-delay quantities ⁇ r ⁇ , ⁇ ' rl .
  • the advanced advanced delay elements 210-211 configured to advance the received baseband signal y(t) in accordance with multipath-delay quantities ⁇ r ⁇ , ⁇ ' rl .
  • Ia ⁇ is the Euclidean norm of the path-gain vector, 0 WJ ' ' ' a (s),L , -l F ' anc * the superscript T denotes the matrix transpose operator.
  • the combined signal is resolved onto the users' code waveforms by correlative despreading, which comprises multiplying 215-216 the combined signals by complex conjugates of each code waveform, followed by integrating 217-218 the resultant products.
  • correlative despreading comprises multiplying 215-216 the combined signals by complex conjugates of each code waveform, followed by integrating 217-218 the resultant products.
  • Equation 2 q ⁇ ⁇ p- J ⁇ J, u (t) ⁇ a m y ⁇ f + ⁇ ⁇ s) l ) dt .
  • Rake processing, combining, and despreading are linear operations, their order may be interchanged. Thus, alternative embodiments may be provided in which the order of the linear operations is changed to produce q ⁇ s) k .
  • a symbol estimator comprises scaling blocks 219-220 and function blocks 221-222, which are configured to refine the estimates q (s) ⁇ into front-end symbol estimates of the transmitted data symbols b ⁇ s) k .
  • Each of the functions depicted in Figure 2 may be configured to process discrete-time sequences. For example, time advances 210-211 (or delays) may be implemented as shifts by an integer number of samples in discrete-time sequences, and integration 217-218 may employ summation.
  • Figure 3 is a block diagram of an z >th ICU comprising four functional blocks.
  • a weighting module 301 calculates and applies soft weights to input symbol estimates.
  • a synthesizing module 302 processes weighted symbol estimates to synthesize constituent signals of an estimated received signal.
  • the estimated received signal y(t) is a sum of the constituent signals, each of which is synthesized from the weighted symbol estimates.
  • the synthesized constituents are processed in a canceller 303 (such as a subtraction module) configured to produce interference- cancelled signals having reduced intra-channel and inter-channel interferences.
  • the canceller 303 also includes a resolving module (not shown) configured to resolve the interference-cancelled signals onto user code waveforms to produce resolved signals.
  • a mixed-decision module 304 processes the resolved signals to produce updated symbol estimates.
  • FIG. 4 shows a weighting module (such as weighting module 301) configured to separately process input symbol estimates corresponding to a plurality B of base stations.
  • a plurality of scaling modules 401-403 scale the input symbol estimates.
  • Scaling module 402 depicts detailed functionality for processing signals from an exemplary 5 th base station. Similar details are typically present in each of the scaling modules 401-403.
  • the soft weights can be regarded as a confidence measure related to the accuracy of a decision, or symbol estimate. For example, a high confidence weight relates to a high certainty that a corresponding decision is accurate. A low confidence weight relates to a low certainty. Since the soft weights are used to scale decisions, low- valued weights reduce possible errors that may be introduced into a calculation that relies on symbol estimates.
  • the weights may be derived from at least one signal measurement, such as SINR.
  • SINR signal measurement
  • the weights y ⁇ k may be expressed by
  • SINR ⁇ is a ratio of average signal power to interference-plus-noise power of a k th user in base station (s) after the i th ICU
  • C k is a non-negative real constant that can be used to ensure some feedback of a symbol estimate, even if its SINR is small. Note that, as the SINR grows large, the weight tends toward unity, meaning that the estimate is very reliable.
  • the SENR (and thus, the soft weights) may be evaluated using techniques of statistical signal processing, including techniques based on an error- vector magnitude (EVM).
  • EVM error- vector magnitude
  • 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 SESfR values.
  • the weights may be expressed as a function of symbol estimates b ⁇ L , such as shown in the following equation
  • Equation 4 where Re ⁇ ⁇ returns the real part of the argument.
  • the statistical expectations E[ ] in the numerator and denominator can be estimated, for example, via time-series averaging.
  • slice ⁇ j A j represents the symbol estimate b ⁇ l ) k sliced (i.e., quantized) to the nearest constellation point from which the symbol b (s)>k was drawn. This approach is applicable for symbols with known constellations. For example, it is typical for a receiver to know the symbol constellation for a user of interest, but it may not know which constellations are assigned to other users.
  • the weights ⁇ [' s ] u are a function of a symbol estimate's h[ s ' ] ) k proximity to a given constellation point.
  • a symbol estimate b ⁇ )>k that is close to a constellation point is provided with a large weight indicative of a high confidence measure in the symbol estimate's accuracy.
  • the value b ⁇ l ) t is a hard-decision estimate of b (slL (i.e., it is quantized to the nearest constellation point)
  • both Equation 3 and Equation 4 may be used in a receiver to calculate soft weights.
  • Some embodiments of the invention may provide for subset selection to force one or more of the weights to zero.
  • Some embodiments may provide for canceling only a predetermined number P of strongest users (e.g., users having the largest weight values).
  • the number P may be fixed for all iterations, or it may vary with respect to iteration. In some embodiments, the number P may range from zero
  • the weights of user signals transmitted from at least one weakest base station are set to zero.
  • Figure 5 A is a block diagram of a synthesizing module (such as the synthesizing module 302) in which the constituent signals are associated with each Rake finger.
  • a synthesizing module such as the synthesizing module 302
  • Each of a plurality B of synthesizing modules 501-503 is assigned to one of a plurality B of base stations.
  • a block diagram for an exemplary synthesizing module 502 corresponding to a base station (s) depicts details that are common to all of the synthesizing modules 501-503.
  • Weighted symbol estimates Y ⁇ s ' ⁇ k b (s) ⁇ are modulated 510-511 onto corresponding code waveforms u (s ⁇ k (t) to produce a plurality K( S ) of coded waveforms, which are combined in combining module 512 to produce a synthesized
  • FIG. 5B is a block diagram of a synthesizing module (such as the synthesizing module 302) in which the constituent signals are associated with each user in the system.
  • Each synthesizing module 521-523 is configured to emulate multipath channels for all base stations.
  • Synthesizing module 522 includes a block diagram that is indicative of the functionality of each of the synthesizing module 521- 523
  • each modulated code waveform is processed by a bank of finger delay elements 532-533 and channel gain scaling elements 534-535 corresponding to the multipath channel of base station (s) .
  • the resulting emulated multipath components are combined in combining module 536 to produce an estimated received signal for a fc th user of base station (s) ,
  • Equation 8 % ⁇ k ⁇ ⁇ ⁇ rf ⁇ S Au (t - ⁇ ⁇ sU ) .
  • the left-hand sides of Equation 7 and Equation 9 are the same signal, whereas the right-hand sides are simply two different decompositions.
  • Figure 6 A shows a cancellation module 601 (such as the canceller 303 in
  • Figure 3 configured to perform interference cancellation 610 on constituent signals, followed by Rake processing and despreading 611 in a Rake-based receiver.
  • Figure 6B shows a cancellation module 602 configured to synthesize 621 a received signal from constituent components, followed by Rake processing and despreading 622, and interference cancellation 623.
  • Figure 7 is a block diagram of an interference canceller comprising a plurality
  • B of cancellers 701-703 configured to perform interference cancellation on a plurality J of constituent signals for each of a plurality B of base stations. Since the constituent signals may be either fingers or users, index j s ⁇ , 1, ... , / (s) - 1/ is expressed by for finger constituets
  • Canceller 702 includes a block diagram that represents the functionality of each of the cancellers 701-703.
  • the constituent signals corresponding to each base station are summed in a combining module 711 to produce a synthesized received
  • J O base station (s) .
  • a plurality B of these sums corresponding to different base stations are combined in combining module 721 to produce a synthesized receive signal
  • a stabilizing step size module 723 scales the residual signal by a complex stabilizing step size ⁇ [ ⁇ ] to produce a scaled residual signal ju [tl [y(t)- y [ ' ] (t)j.
  • the scaled residual signal is combined with the constituent signals y ⁇ ⁇ in combining modules 712-714 to produce a set of interference-cancelled constituents represented by Equation 10 zg >y (t) ⁇ y ⁇ 1 (t) + ⁇ ⁇ (y(t) - y ll] (t)), where z ⁇ ⁇ )t] ⁇ t) is an interference-cancelled/ 11 constituent signal for base station (s) .
  • cancellation may be performed with only a subset of the constituent channels. In each base station, only those constituent signals being used for cancellation may be used to synthesize the estimated receive signal for
  • Embodiments of the invention may be configured for applications in which hardware limitations restrict the number of finger signals or user signals that can be used for interference cancellation (e.g., only the strongest constituents are used).
  • the interference-cancelled signals produced by the canceller shown in Figure 7 may be processed by a Rake despreader shown in Figure 8A, which is configured for processing finger inputs. Specifically, finger signals associated with each base station are input to corresponding fingers of a Rake despreading module tuned to that base station.
  • a Rake despreading module 802 tuned to an s th base station comprises a block diagram indicating the functionality of a plurality B of Rake despreading modules 801-803.
  • Interference cancelled signals are time-advanced 810-811 by an amount T ⁇ 1 .
  • a maximal ratio combining module scales 812-813 each time-advanced signal z ( E ⁇ , (t + ⁇ (s) , ) by ⁇ ( * ) / /
  • a resolving module comprising multipliers 815-816 and integrators
  • FIG. 8B is a block diagram of a Rake despreader configured for processing interference-cancelled signals relating to user inputs.
  • the input constituent signals are user signals.
  • a Rake despreading module 822 tuned to an 5 th base station comprises a block diagram indicating functional details that are common to a plurality B of Rake despreading modules 821-823.
  • Interference cancelled signals z[ ⁇ fc (t) corresponding to a k th user and 5 th base station are processed by a plurality L ⁇ of time-advance modules 831-832 corresponding to the multipath channel for the 5 th base station.
  • the resulting time- advanced signals ⁇ z ⁇ l k (t + ⁇ Wi/ ) j ⁇ are weighted by a plurality of weighting modules 833-834, and the weighted signals are combined in combiner 835.
  • a resolving module comprising multiplier 836 and integrator 837 resolves the combined signal onto the &" 1 user's code waveform to give Equation 12 .
  • Equation 11 The values of b£ ⁇ k snown m Equation 11 and Equation 12 are generally not the same value, since the value of m Equation 11 is produced by cancellation employing finger constituents, whereas b ⁇ L expressed by Equation 12 is produced by cancellation employing user constituents.
  • FIG 9A is a block diagram of a Rake despreader, such as Rake despreaders 611 and 622 shown in Figures 6A and 6B, respectively.
  • the Rake despreader comprises a plurality B of Rake despreading modules 901-903, each configured to process constituent signals from one of a plurality B of base stations.
  • An exemplary Rake despreader module 902 is a block diagram illustrating functionality of each of the Rake despreader modules 901-903.
  • Input constituent signals %j >y (0 for all values of j are subtracted 911 from the received signal y(t) to produce a difference signal, or error signal, representing the difference between the received signal and the synthesized estimates of signals received by the base stations.
  • the difference signal is
  • J O processed by a parallel bank of time advance modules 912-913 associated with the multipath channel for that base station, followed by maximal-ratio combining.
  • a maximal-ratio combining module is configured to perform weighting 914-915 and combining 916.
  • a resolving module comprising multipliers 917-918 and integrators 919-920 resolves the resulting combined signals onto code waveforms of the base station's users to give the difference signal vector, or error signal vector,
  • Equation 13 (O ⁇ a w z%(t + ⁇ w ) and zj ⁇ (t) was defined in Equation 10.
  • Rake despreading such as described with respect to the exemplary Rake despreading module 902, may also be accomplished explicitly by employing matrix multiplication to synthesize constituent signals of the received signal, such as represented by block 931 shown in Figure 9B.
  • a diagonal soft-weighting matrix may be defined as
  • Equation 14 I*' in which all of the users' soft weights are ordered first by base station and then by users within a base station.
  • the same indexing may also be used to express the column vector of symbol estimates input to an z -th ICU as
  • the values of q ll] represent the despread signals, such as described with respect to
  • Equation 13 The values of q l ' ] are represented by Equation 13, and R is a square matrix whose elements are correlations between the users' received code waveforms.
  • Equation 16 the functionality expressed by Equation 16 is implemented via the matrix- multiplication block 931 and a subtraction module 932.
  • FIG. 10 is a block diagram of an interference canceller, such as the interference-cancellation block 623 shown in Figure 6.
  • the resulting scaled difference signal is summed 1003 with a product 1002 of the weighted symbol
  • F I .
  • F is a block- diagonal matrix with a plurality B of diagonal blocks, wherein an s th diagonal block is a K (s) x K (s) block representing the users' transmit correlation matrix for base station
  • the stabilizing step size ⁇ b] may be used to enhance interference cancellation in each ICU and/or stabilize iterative interference cancellation.
  • a quality metric of a canceller's output b_ may be derived as follows. If it is known (or approximated) that the additive noise w(t) in Equation 1 is Gaussian, then the despread outputs q , conditional on the transmitted symbols
  • E q ua t ion 22 £ are jointly complex normal random variables with mean R& and covariance r m R
  • Equation 23 and Equation 24 may be used in a sequence of ICUs and Equation 24 may be used in the last ICU of the sequence.
  • F contains the users' correlation matrices at the transmitter for each base station as its block diagonal, it will approximately equal identity, as the users' code waveforms are typically designed to be mutually orthogonal (or quasi-orthogonal for the reverse link). Any non-orthogonality is due to the finite duration of the pulse-shaping filters that approximate their infinite-duration theoretical counterparts. In this case, Equation 23 becomes
  • FIG. HA illustrates a method and apparatus for calculating a stabilizing step size.
  • a Rake receiver 1100 comprises a first Rake, maximal ratio combining, and despreading unit 1101 to process a received signal y(t) for producing an output despread signal vector q .
  • a second Rake, maximal ratio combiner, and despreader unit 1102 processes a synthesized receive signal with weighted symbol estimates corresponding to an z 'th iteration, and represented by
  • a combiner 1103 calculates the difference between the outputs of 1101 and
  • a difference signal y(t) - yf s ] (t) may be produced prior to despreading, such as shown by block 1110 in Figure HB.
  • the norm-square of ⁇ [ ⁇ ] (i.e., L ⁇ 1 ' 1 ) is evaluated 1104 to generate the
  • Equation 24 becomes
  • the signal ⁇ ' is generated by a Rake, maximal ratio combining, and despreading unit 1120 and multiplied 1121 by r w to produce vector T b] ⁇ ⁇ ' ] .
  • a synthesis module
  • a synthesized received signal is generated 1124 from the vector ( ⁇ [I] ) ⁇ ⁇ and processed with received signal y(t) by an adder 1125 to produce a difference signal.
  • a Rake/combiner/despreader 1126 processes the difference signal to generate the vector q - R( ⁇ [I] J b .
  • the stabilizing step size may be derived from the multipath channel gains
  • Figure 12 is a block diagram of a symbol-estimation block comprising a plurality B of mixed-decision modules 1201-1203 configured to process signals received from B base stations.
  • Mixed-decision module 1202 shows functionality that is common to all of the mixed-decision modules 1201-1203. De-biasing modules
  • the mixed-decision module 1202 includes symbol-estimation modules 1212-1213 configured to perform symbol estimation on de-biased input symbol estimates whose constellations are known at the receiver.
  • the map ⁇ (i) t may be a mixed-decision map, which is a combination of soft and hard decisions.
  • a soft-decision map is provided by a function ⁇ ⁇ k (x) that is a continuous function whose output ranges over the complex plane. Common examples, include, but are not limited to, Equation 32
  • the slicer quantizes its argument x to the nearest constellation symbol according to some metric (e.g., Euclidean distance).
  • some metric e.g., Euclidean distance
  • a mixed-decision map ⁇ TM * 4 ed (x) produces an output that is a soft decision or a hard decision, such as f ⁇ JSt ⁇ if SINR ⁇ > C(fVk
  • the mixed-decision map ⁇ ( TM ⁇ d (x) produces a hard decision if the SINR of a k th user of base station (s) exceeds a threshold c (s)Jc . Otherwise, a soft decision is performed.
  • the SINR may be estimated with a time-averaged error-vector measurement (EVM). Time-averaging may cause a block of symbols to share the same SINR estimate.
  • EVM error-vector measurement
  • An alternative mixed-decision map ⁇ ( TM* A ed (x) may act on individual symbols,
  • any distance metric may be used (e.g., for some p > 0) and the radii c (sU (b) over the set of constellation points b are chosen such that the hard-decision regions are non-overlapping.
  • Alternative embodiments of the invention may employ different partitions of the constellation space. For example, edge constellation points may be given unbounded hard-decision regions.
  • Both the average SINR and instantaneous approaches are applicable to any known constellation; they need not be restricted to BPSK, QPSK, or even QAM. Either of these mixed-decision approaches may be performed with the additional constraint that the receiver knows only the constellation employed for a subset of the active codes. Such situations may arise in EV-DO and HSDPA networks. In such cases, the receiver may use soft decisions for codes employing an unknown modulation. Those skilled in the art will understand that a modulation classification of these codes may be performed, which may be particularly useful in systems wherein all interfering codes share the same unknown constellation.
  • Algorithm 1 The following algorithm, which is illustrated in Figure 13, demonstrates one embodiment for performing ⁇ C. Algorithm 1:
  • Equation 19 F is in Equation 19
  • F is I or as in Equation 21
  • r tl] is in Equation 14 with elements defined in Equation 3-Equation 5 ⁇ [ ⁇ is defined in Equation 23-Equation 27
  • Equation 28 maps each argument to a complex number to implement de-biasing as in Equation 28 -Equation 30Equation and then symbol estimation as in Equation 32-Equation 36
  • Index i -1, 0, 1, ... , M - 1 , where M is the number of times to iterate the succeeding update equation
  • Figure 13 shows an internal feedback loop comprising operations 1308, 1301, 1302, 1306, and an external feedback loop comprising operations 1308, 1301, 1303, and 1304.
  • the output of the external feedback loop is q - R r bl b , which is multiplicatively scaled 1305 by ⁇ [ ⁇ ] .
  • the scaled output is combined 1306 with the
  • embodiments of the invention are described with respect to forward- link channels, embodiments may be configured to operate in reverse-link channels.
  • reverse link different users' transmissions experience different multipath channels, which requires appropriate modifications to Rake processing and signal synthesis.
  • a front-end processor may incorporate one Rake for every user in every base station rather than a single Rake per base station.
  • a separate multipath channel emulator may be employed for imparting multipath delays and gains to each user's signal. Accordingly, the number of constituent finger signals will equal the sum over the number of multipath fingers per user per base station, rather than the sum over the number of multipath fingers per base station.
  • ASICs Application Specific Integrated Circuits
  • FPGAs Field Programmable Gate Arrays
  • DSPs Digital Signal Processors
  • Software and/or firmware implementations of the invention may be implemented via any combination of programming languages, including Java, C, C++, MatlabTM, Verilog, VHDL, and/or processor specific machine and assembly languages.
  • Computer programs i.e., software and/or firmware implementing the method of this invention may be distributed to users on a distribution medium such as a SIM card, a USB memory interface, or other computer-readable memory adapted for interfacing with a consumer wireless terminal.
  • a distribution medium such as a SIM card, a USB memory interface, or other computer-readable memory adapted for interfacing with a consumer wireless terminal.
  • computer programs may be distributed to users via wired or wireless network interfaces. From there, they will often be copied to a hard disk or a similar intermediate storage medium.
  • the programs When the programs are to be run, they may be loaded either from their distribution medium or their intermediate storage medium into the execution memory of a wireless terminal, configuring an onboard digital computer system (e.g., a microprocessor) to act in accordance with the method of this invention. All these operations are well known to those skilled in the art of computer systems.
  • modules may be provided through the use of dedicated hardware, as well as hardware capable of executing software in association with appropriate software.
  • the functions may be performed by a single dedicated processor, by a shared processor, or by a plurality of individual processors, some of which may be shared.
  • explicit use of the term "processor” or “module” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor DSP hardware, read-only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. Other hardware, conventional and/or custom, may also be included.
  • the function of any component or device described herein may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the implementer as more specifically understood from the context.

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Abstract

L'invention porte sur un récepteur configuré pour éliminer les interférences intracellulaires et intercellulaires dans des transmissions codées, à accès multiple, par étalement du spectre qui se propagent via des voies de communication sélectives de fréquence vers plusieurs antennes de réception. Le récepteur utilise la pondération itérative par estimée de symbole, l'annulation soustractive avec une taille de pas de stabilisation, et des estimées de symbole par modélisation de décision mixte. Les récepteurs selon certains modes de réalisation peuvent être conçus, adaptés et mis en oeuvre explicitement dans des logiciels ou dans du matériel programmé, ou implicitement dans du matériel standard RAKE soit dans le RAKE (e.g. au niveau du doigt de contact) soit à l'extérieur du RAKE (e.g. au niveau de l'utilisateur ou du symbole de sous-canal).
PCT/US2006/036018 2005-09-23 2006-09-15 Elimination d'interference iterative au moyen de ponderations de retroaction mixtes ou de tailles de pas de stabilisation WO2007038018A2 (fr)

Applications Claiming Priority (6)

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US11/233,636 US8761321B2 (en) 2005-04-07 2005-09-23 Optimal feedback weighting for soft-decision cancellers
US11/233,636 2005-09-23
US73620405P 2005-11-15 2005-11-15
US60/736,204 2005-11-15
US11/451,688 2006-06-13
US11/451,688 US7702048B2 (en) 2005-11-15 2006-06-13 Iterative interference cancellation using mixed feedback weights and stabilizing step sizes

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6714585B1 (en) * 1999-06-25 2004-03-30 Ericsson Inc. Rake combining methods and apparatus using weighting factors derived from knowledge of spreading spectrum signal characteristics
US20040196892A1 (en) * 1999-10-19 2004-10-07 Interdigital Technology Corporation Parallel interference cancellation receiver for multiuser detection CDMA signals
US20050163196A1 (en) * 1999-08-31 2005-07-28 Currivan Bruce J. Cancellation of burst noise in a communication system with application to S-CDMA
US20050180364A1 (en) * 2002-09-20 2005-08-18 Vijay Nagarajan Construction of projection operators for interference cancellation

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6714585B1 (en) * 1999-06-25 2004-03-30 Ericsson Inc. Rake combining methods and apparatus using weighting factors derived from knowledge of spreading spectrum signal characteristics
US20050163196A1 (en) * 1999-08-31 2005-07-28 Currivan Bruce J. Cancellation of burst noise in a communication system with application to S-CDMA
US20040196892A1 (en) * 1999-10-19 2004-10-07 Interdigital Technology Corporation Parallel interference cancellation receiver for multiuser detection CDMA signals
US20050180364A1 (en) * 2002-09-20 2005-08-18 Vijay Nagarajan Construction of projection operators for interference cancellation

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