Classifications

• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L25/00—Baseband systems
  • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
  • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
  • H04L25/03006—Arrangements for removing intersymbol interference
  • H04L25/03178—Arrangements involving sequence estimation techniques
  • H04L25/03305—Joint sequence estimation and interference removal

Definitions

• the present invention generally relates to communication signal processing, and particularly relates to interference cancellation.
• MIMO multiple-input-multiple-output
• the receiver may be understood as receiving a composite signal, including any number of component signals.
• One or more component signals generally are of interest to the receiver, but the recovery of any given signal of interest is complicated by interference caused by the remaining signals.
• a known approach to interference cancellation depends on the detection of interfering signals with the same processing complexity used to detect the desired signal(s). This approach applies full-complexity demodulation and decoding to the interfering signals, including soft value processing. Accurate determination of the interfering signals allows correspondingly accurate reconstruction of the interfering signals for interference cancellation. Full processing of interfering and desired signals, however, imposes a significant signal processing burden on the receiver and makes the approach not feasible or at least undesirable for low-complexity receivers.
• a simplification to full processing of interferer signals involves simplified interferer signal estimation through hard detection processing. With hard detection processing, interferer signals are estimated based on hard detection, e.g., hard-detecting interferer signal bits as 1 s or Os rather than as soft-valued likelihoods. Reconstruction of the interferer signals is simplified by hard detection, albeit with potentially significant decreases in estimation accuracy and corresponding interference cancellation performance than that provided by full processing of the interferer signals.
• the teachings herein disclose interference cancellation processing that uses hard decision logic for simplified estimation of interfering signals, in combination with soft scaling of the hard decisions for better interference cancellation performance, particularly in low signal quality conditions.
• the soft scaling may be understood as attenuating the amount of interference cancellation applied by a receiver, in dependence on the dynamically changing received signal quality at the receiver. More attenuation is applied at lower signal quality because the hard decisions are less reliable at lower signal qualities, while less (or no) attenuation is applied at higher signal qualities, reflecting the higher reliability of the hard decisions at higher signal qualities.
• Signal quality may be quantized into ranges, with a different value of soft scaling factor used for each range, or a soft scaling factor may be calculated for the continuum of measured signal quality.
• a method of interference cancellation comprises generating interfering symbol estimates for interfering symbols in a composite signal by making hard decisions at a bit or symbol level, and computing scaled interfering symbol estimates by soft scaling the interfering symbol estimates as a function of received signal quality.
• the method continues with obtaining a reduced-interference composite signal by combining the scaled interfering symbol estimates with the composite signal, and detecting symbols of interest from the reduced-interference composite signal.
• the method applies to a wide range of signal types and, as a non-limiting example, can be used in multi-stream MIMO (multiple-input-multiple-output) systems.
• the method can be implemented in a variety of wireless communication receiver types, e.g., in wireless communication base stations and/or mobile stations in cellular communication networks, such as Wideband-CDMA (WCDMA) networks or Long Term Evolution (LTE) networks.
• WCDMA Wideband-CDMA
• LTE Long Term Evolution
• a wireless communication receiver comprises one or more processing circuits, e.g., digital signal processing circuits such as one or more baseband processors, DSPs, microcontrollers, ASICs, or other digital processing circuitry that implements interference cancellation as taught herein via hardware, software, or any combination thereof.
• the one or more processing circuits are configured to generate interfering symbol estimates for interfering symbols in a composite signal by making hard decisions at a bit or symbol level, and compute scaled interfering symbol estimates by soft scaling the interfering symbol estimates as a function of received signal quality. Further, the one or more processing circuits are configured to obtain a reduced-interference composite signal by combining the scaled interfering symbol estimates with the composite signal and detect symbols of interest from the reduced- interference composite signal.
• Fig. 1 is block diagram of one embodiment of a wireless communication network, wherein the illustrated base station and/or mobile stations include a receiver configured according to the interference cancellation teachings presented herein.
• Fig. 2 is a block diagram of one embodiment of a mobile station receiver configured according to the interference cancellation teachings presented herein.
• Fig. 3 is a logic flow diagram of one embodiment of a method for carrying out the interference cancellation teachings presented herein.
• Fig. 4 is a logic flow diagram of another embodiment of a method for carrying out the interference cancellation teachings presented herein.
• Fig. 5 is a block diagram of another embodiment of a mobile station receiver configured according to the interference cancellation teachings presented herein.
• Fig. 6 is a block diagram of another embodiment of a mobile station receiver configured according to the interference cancellation teachings presented herein.
• Fig. 1 illustrates one embodiment of a base station 10 that transmits downlink signals to a mobile station 12, and receives uplink signals from the mobile station 12.
• the base station 10 includes a receiver 14 for processing uplink signals received from the mobile station 12.
• the mobile station 12 includes a receiver 16 for processing downlink signals received from the base station 10.
• the base station and mobile station are configured for operation in a Wideband-CDMA (WCDMA) network in one embodiment, and for Long Term Evolution (LTE) operation in another embodiment, but these should be understood as non-limiting examples.
• WCDMA Wideband-CDMA
• LTE Long Term Evolution
• the base station 10 may support other mobile stations, e.g., mobile stations 13 and 15.
• one or both of the receivers 14 and 16 are configured to carry out a method of interference cancellation that uses hard decision logic for simplified estimation of interfering signals, in combination with soft scaling of the hard decisions for better interference cancellation performance, particularly in low signal quality conditions.
• This implementation provides for an interference canceling (IC) receiver structure that is robust and has low incremental complexity compared to a non-IC receiver, while still providing performance improvements with respect to non-IC receivers, and with respect to IC receivers that perform interference cancellation using hard-detected interference estimates, but perform such cancellation only in high signal quality conditions.
• IC interference canceling
• interference cancellation is performed over the entire signal quality operating range (e.g., the entire signal-to-interference ratio or SIR range), without need for complex heuristic mechanisms for turning interference cancellation on and off.
• one of the advantages gained through these teachings is a significant complexity reduction compared to interference cancellation based on full soft-value processing (e.g., full expected value processing of interfering symbols).
• Such complexity reductions are, in particular, fully realized in the embodiment contemplated herein where the scaling factors used for soft scaling hard-detected interfering signal values are pre-computed.
• a receiver stores a look-up table of soft scaling values that are indexed as a function of measured received signal quality.
• the base station 10 comprises part of a wireless communication network 20, which also includes one or more additional core network (CN) entities 22 communicatively coupling the network 20 to one or more external networks 24, e.g., the Internet, PSTN, and/or other communication networks.
• CN core network
• the base station 10 communicatively couples mobile stations to each other and/or to other communication networks by transmitting downlink signals to them and receiving uplink signals from them.
• the base station 10 includes call control and processing circuits 40, interface circuits 42, the aforementioned receiver 14, which may comprise a portion of overall receiver circuitry in the base station 10, one or more transmitters 44, an antenna interface 46, and one or more transmit/receive antennas 48.
• the base station 10 comprises a multiple-input-multiple- output (MIMO) base station, and thus uses the antennas 48 for transmitting multiple streams to one or more users (mobile stations).
• the transmitter(s) 44 includes pre-coding circuits, which apply pre-coding matrices to the multiple streams being transmitted, corresponding to desired per-antenna transmit power weightings for the different MIMO streams.
• one or more of the mobile stations 12, 13, and 15 are configured for MIMO operation, although non-MIMO embodiments are contemplated herein.
• the mobile station 12 includes one or more transmit/receive antennas 50, antenna interface circuits 52 (e.g., a switch and/or duplexer), the aforementioned receiver 16, a transmitter 54, and additional processing circuits 56, which may include one or more microprocessors for controlling operation of the mobile station, and one or more interface circuits for user interaction with the mobile station.
• antenna interface circuits 52 e.g., a switch and/or duplexer
• additional processing circuits 56 which may include one or more microprocessors for controlling operation of the mobile station, and one or more interface circuits for user interaction with the mobile station.
• the receiver 16 comprises a receiver front-end 60 and one or more processing circuits 62, referred to herein as a "baseband processor.”
• the receiver front-end 60 processes antenna-received signals, e.g., a received composite signal including one or more component signals received on one or more of the mobile station's antennas.
• antenna-received signals e.g., a received composite signal including one or more component signals received on one or more of the mobile station's antennas.
• such processing includes filtering/gain control 64, down-conversion 66, and digitization 68.
• the baseband processor 62 therefore receives one or more streams of digital samples corresponding to the time-varying antenna-received signals.
• the digital sample stream(s) input to the baseband processor 62 comprise in-phase (I) and quadrature (Q) data streams for one or more signals.
• the input digital samples are a composite of more than one signal, one or more of which constitute “desired signals” and the remaining ones constituting "interfering signals.” More particularly, a given signal may be a desired signal but it still represents interference with respect to the detection of another desired signal.
• the IC receiver circuit 70 is configured to perform interference cancellation for each of one or more desired signals included in a received composite signal.
• the illustrated embodiment of the baseband processor 62 includes an interference-canceling (IC) receiver circuit 70, which includes or is associated with a signal quality processor 72, a scaling processor 74, and memory 76.
• Memory 76 may comprise more than one device and/or more than one type of memory.
• the baseband processor 62 may include or otherwise have access to volatile memory (cache and/or off-chip) for working computations and data, and have access to one or more types of non-volatile memory for storing computer program instructions, configuration data, look-up tables, etc., such as FLASH and/or EEPROM memory.
• the baseband processor 62 also may include additional processing circuits 78, such as receiver/transmitter, control and signaling circuits.
• the baseband processor 62 in one or more embodiments comprises a general or special-purpose microprocessor or digital signal processor.
• the received signal processing of interest herein may be performed at least in part using hardware-based circuits.
• Fig. 3 illustrates one embodiment of a method 100 of interference cancellation as contemplated herein, and it should be understood that the mobile station receiver 16 and/or the base station receiver 14 are configured to carry out this method, or variations of it, in one or more embodiments. It should also be understood that the illustrated order of processing is not intended as a limitation. One or more processing steps may be performed in a different order, or performed concurrently, or performed as part of ongoing foreground and/or background processing operations. As such, the illustrated processing may be performed as part of a larger set of ongoing receiver/device processing, and may be looped or iterated as needed or desired.
• the illustrated processing includes generating interfering symbol estimates for interfering symbols in a composite signal by making hard decisions at a bit or symbol level (Block 102), and computing scaled interfering symbol estimates by soft scaling the interfering symbol estimates as a function of received signal quality (Block 104).
• the method 100 continues with obtaining a reduced-interference composite signal by combining the scaled interfering symbol estimates with the composite signal (Block 106), and detecting symbols of interest from the reduced-interference composite signal (Block 108).
• computing scaled interfering symbol estimates by soft scaling the interfering symbol estimates as a function of received signal quality comprises determining a soft scaling factor as a function of the received signal quality, and scaling the interfering symbol estimates by the soft scaling factor.
• Such embodiments also include determining the received signal quality based on determining an average symbol quality after combining and equalization processing of the symbols of interest, or determining the received signal quality based on determining an average log-likelihood-ratio for the symbols of interest after demodulation.
• received signal quality may be determined dynamically, on an ongoing basis, and may be based on determining the received signal quality as a signal-to-noise ratio (SNR) or a signal-to-noise-plus-interference ratio (SINR). These measurements can be made using the signal(s) of interest, and generally are performed in any case for other reasons, such as for channel quality reporting, etc. Further, making hard decisions at a bit or symbol level (for the interfering signals) generally comprises making hard symbol decisions for the interfering symbols or demodulating the interfering symbols to obtain soft bit values and making hard decisions on the soft bit values.
• SNR signal-to-noise ratio
• SINR signal-to-noise-plus-interference ratio
• computing scaled interfering symbol estimates by soft scaling the interfering symbol estimates as a function of received signal quality comprises, in one or more embodiments, selecting a pre-computed soft scaling factor as a function of the received signal quality. Such selection comprises, for example, accessing a look-up table that is indexed as a function of received signal quality.
• the look-up table comprises a data structure containing different values for the soft scaling factor corresponding to different values or ranges of received signal quality.
• look-up table While such processing may be practiced with one look-up table, it is also contemplated herein to store different look-up tables for different modulation and coding schemes, and access a particular one of the different look-up tables in dependence on the modulation and coding scheme associated with the symbols of interest.
• look-up table for QPSK is given in the form of (SIR (dB), a ) as (-14.0000, 0.0103), (-13.0000, 0.0130), (-12.0000, 0.0165), (-11.0000, 0.0209), (-10.0000, 0.0267), (-9.0000, 0.0340), (-8.0000, 0.0434), (-7.0000, 0.0553), (-6.0000, 0.0713), (- 5.0000, 0.0910), (-4.0000, 0.1 163), (-3.1000, 0.1476), (-2.1000, 0.1865), (-1.1000, 0.2331 ), (-0.2000, 0.2888), (0.8000, 0.3511 ), (1.7000, 0.4196), (2.5000, 0.4922), (3.4000, 0.5670), (4.2000, 0.6409), (5.0000, 0.7145), (5.8000, 0.7827), (6.6000, 0.8443), (7.4000, 0.8960), (8.2000, 0.9376), and (9
• an example look-up table for 64QAM is (- 14.0000, 0.0213), (-13.0000, 0.0262), (-12.0000, 0.0324), (-1 1.0000, 0.0405), (-10.0000, 0.0500), (-9.0000, 0.0617), (-8.0000, 0.0757), (-7.0000, 0.0926), (-6.0000, 0.1 132), (- 5.0000, 0.1373), (-4.0000, 0.1651 ), (-3.1000, 0.1980), (-2.1000, 0.2356), (-1.1000, 0.2776), (-0.2000, 0.3219), (0.8000, 0.3695), (1.7000, 0.4208), (2.5000, 0.4746), (3.4000, 0.5255), (4.2000, 0.5799), (5.0000, 0.6326), (5.8000, 0.6809), (6.6000, 0.7282), (7.4000, 0.7724), (8.2000, 0.8142), (9.0000, 0.8493), (9.9000, 0.8781
• the scaling factor may be defined as a scalar value that takes on discrete or continuous values between zero and one, as a function of the received signal quality. Further, in one or more embodiments, the scaling factor is set to unity if the received signal quality is above a defined quality threshold. In other words, a scaling factor of "1" may be used during times of high received signal quality. Of course, such operation is optional. As another option, but one which offers potentially significant operational advantages, the receiver 16 (or 14) may determine combining weights at least in part based on the scaling factor, where the combining weights are used in combining signal values for the symbols of interest.
• the receiver 16 may be configured to determine combining weights based on impairment correlation estimations that include correlation terms relating to inter-signal interference between a desired signal and one or more interfering signals. These impairment correlation terms, which capture the correlation of such inter-signal interference, may be scaled or otherwise compensated using the scaling factor, to reflect the reduction of interference in the reduced-interference composite signal from which desired signal symbols are detected.
• the method may be carried out in a variety of IC receiver structures, including parallel IC (PIC) receivers and successive IC (SIC) receivers.
• PIC parallel IC
• SIC successive IC
• a SIC receiver including two or more successive interference cancellation stages, at least one stage is configured to perform soft-scaling based interference cancellation for a composite signal input to that stage.
• soft scaling is used in the one stage, for hard decisions determined for the one interferer.
• a PIC receiver includes two or more parallel interference cancellation stages, where at least one stage is configured to perform soft-scaling based interference cancellation for a composite signal input to that stage.
• the soft-scaling of hard-detected interfering signals as taught herein may be applied to one or more signals included in a received composite signal.
• the modulated symbols representing the signals for other users (e.g., other mobile stations) or representing data streams not of interest in a MIMO context can be produced with low computational overhead by applying hard decisions at the bit level or at the symbol level.
• These hard- detected interfering symbols are then scaled to match the expected signal level corresponding to the given SIR or soft value quality.
• the scaling factor(s) may be pre-computed and stored in a look-up table.
• the individual bit soft values reflect the reliability of each bit.
• the expected modulated symbol may be computed as
• ⁇ m is the log-likelihood ratio (LLR) for bit m and b l m is the bit value for constellation point /, bit position m.
• QAM is merely an example of a symmetrical modulation constellation where the LLR magnitudes indicate bit reliability, but the teachings herein are not limited to QAM.
• a simplified LLR generation routine is often used for Max-Log-MAP Turbo decoders, where the LLR values have magnitudes not reflecting the SIR of the propagation environment.
• the soft scaling of hard-detected interfering symbols contemplated herein can be based on the use of a soft scaling factor that derives from or otherwise reflects the relationship between expected symbol magnitudes and received signal quality, and the corresponding reliability of hard detection.
• the receiver contemplated herein avoids excessive (incorrect) interference cancellation when the quality or reliability of hard detection is low.
• a soft scaling factor a may be calculated by evaluating the average power of the expected symbol values and the relevant modulation constellation, e.g., a QAM constellation. Because, as explained previously, the expected symbol power depends on the reception quality, characterized e.g., by the post-combining SINR value, the resulting value of a is a function of e.g. the prevailing SINR value.
• the scaling factor thus may be expressed as
• the receiver need not perform full soft-value processing of the interfering signal(s) to be suppressed, e.g., need not carry out full expected value processing for the interfering symbols.
• the soft scaling factor a may be pre- computed for a number of SIR operating points and stored in receiver memory, such as in a look-up table. (See Fig. 2, for an example look-up table stored in memory 76, included in or accessible to the IC receiver circuit 70 within the baseband processor 62.)
• a receiver e.g., base station receiver 14 and/or mobile station receiver 16 from Fig.
• the mobile station 12 can be pre-configured with the appropriate scaling factor values, based on determined relationships between hard-detection reliability and received signal quality.
• the mobile station 12 can be configured with different scaling factor values for different ranges or values of received signal quality. Such configuration may be done at manufacture or during configuration by a system operator or other vendor, for example.
• a different value of the scaling factor a can be stored for different SINRs or LLR magnitudes. For example, a given range of SINRs or LLR magnitudes can be quantized into a given number of regions, and a different value of the scaling factor ⁇ can be stored for each such region.
• the receiver determines SINR or LLR magnitude for symbols of interest — which is processing it would do as part of desired signal processing — and then selects the appropriate value of the scaling factor ⁇ to use for soft-scaling the hard-detected interfering symbols.
• each modulation scheme may have a dedicated table of scaling factor values. That is, the receiver may store a different table of scaling factor values for each modulation scheme that the receiver supports. This approach yields the best scaling accuracy, but some embodiments use the same set of soft scaling values for more than one modulation scheme. Indeed, one or more embodiments contemplated herein trade off scaling accuracy for implementation convenience, and simply use one table of scaling values for all modulation schemes.
• the post-combining/equalization symbol SIR and LLR quality relationship is essentially channel-independent, arbitrary single-user (e.g. AWGN) simulations may be used advantageously to build the tables.
• AWGN arbitrary single-user simulations
• the average symbol SINR after combining/equalization or the average LLR magnitude after demodulation may be used as the look-up parameters to fetch the appropriate scaling value.
• the soft scaling correction in the receiver thus only requires scaling the interfering symbol estimates with a predetermined scalar factor, which may be V ⁇ ⁇ .
• the receiver may store different values for ⁇ , where the ⁇ values represent power.
• the receiver uses V ⁇ ⁇ for the actual scaling operation, to obtain the scaled interfering symbol estimates used for interference cancellation.
• the receiver could simply store scaling factor values in the form of Va .
• the receiver would compute ⁇ for use in channel quality reporting, such that channel quality reporting, which depends on signal/interference power measurements, reflects the effects of the scaled interference cancellation.
• a flow chart of such processing is given in Fig. 4, which should be understood as a more detailed example of the method 100 illustrated in Fig. 3.
• the processing of Fig. 4 refers to "user 1 " and "user 2" signals, which may be different user signals, different data streams, etc.
• the user 2 signal is a desired signal
• the processing given in Fig. 4 represents the hard-detection of user 1 symbols as interfering symbols with respect to user 2.
• Processing begins with receiving a block of data (Block 1 10), which includes user 1 and user 2 symbols. Processing continues with demodulation of the user 1 symbols (Block 1 12), and determining the appropriate soft scaling factor value to use for scaling the hard decisions (Block 114).
• the processing of Block 1 14 includes estimating symbol SINR and using the estimated symbol SINR to fetch or otherwise determine the appropriate value of the scaling factor ⁇ (or, equivalently, Va ). Alternatively, the processing of Block 1 14 includes estimating the average LLR magnitude and correspondingly fetching or otherwise determining the appropriate value of the scaling factor.
• Processing continues with applying hard decisions at the symbol or bit level to the user 1 symbols (Block 1 16), and correspondingly scaling those hard decisions by the scaling factor (e.g., by multiplying the hard decision values with the scalar-valued Va ) (Block 1 18).
• This scaling generates scaled interfering symbol estimates for user 1
• processing thus continues with obtaining a reduced-interference composite signal by combining the scaled interfering symbol estimates with the composite signal (i.e., the combined user 1 and user 2 composite signal).
• the receiver may regenerate the user 1 signal by applying channel estimates to the scaled interfering symbol estimates (Block 120), and then subtract the regenerated user 1 signal from the composite signal (Block 122).
• Fig. 5 illustrates an embodiment of the baseband processor 62, as introduced in Fig.
• the baseband processor 62 in this configuration includes a weighting circuit 130, a demodulator 132, a decoder 134, a hard decision processor 136, a scaling circuit 138 (which may comprise or otherwise be part of the earlier-described scaling processor 74), a channel processor 140, a combining circuit 142, a weighting circuit 144, a combining weight processor 146, a demodulator 148, and a decoder 150.
• the interfering symbols for user 1 are hard-detected at the symbol level, after application of user 1 weights (w1 ) by the weighting circuit 130. That is, hard detector 136 provides hard decisions on the user 1 symbols.
• the scaling circuit 138 soft- scales the hard-detected interfering symbol values, e.g., by multiplying them by Va , where the value of a is fetched from a look-up table or is otherwise determined in dependence on the received signal quality.
• the "received signal quality" in this sense may be the most recent measure of received signal quality, such that the actual soft scaling value used is appropriate for the current or prevailing received signal quality.
• the scaled interfering symbols are then combined with the composite signal, such as by passing them through the channel processor 140 and then subtracting the channel- adjusted, scaled interfering symbols from the composite signal, via the combining circuit 142.
• the channel processor 140 applies the estimated propagation channel coefficients to the scaled interfering symbol estimates.
• the channel processor 140 includes a channel estimation circuit, or is associated with one, and thus has access to the complex channel coefficients estimated for the propagation path(s) between the receiver and transmitter
• the combining circuit 142 outputs a reduced-interference composite signal, which represents the incoming composite signal with the reconstructed interfering symbols subtracted from it.
• Weighting circuit 144 applies user 2 combining weights to the reduced- interference composite signal.
• the baseband processor 62 compensates or otherwise adjusts its combining/equalization weight computation process to account for the extent of interfering cancellation, such that its interference model is matched to the actual cancellation that is carried out.
• the baseband processor 62 accounts for interference cancellation by scaling the interference power or covariance term for the interfering signal by a scaling factor that is equal to the value a as used for soft scaling the hard-detected interfering symbols, or is determined from another function of the same a .
• a receiver could be configured to apply a scaling factor heuristically determined as a monotonic function of a .
• the weight generator 146 which includes or is associated with an impairment correlation processor that provides interference power or covariance terms for the interfering signal(s) accounts for the soft scaling that is applied to the user 1 signal.
• Fig. 6 illustrates another embodiment of the baseband processor 62. The primary difference between Figs. 5 and 6 is that Fig. 5 illustrated hard detection at the symbol level for the interfering user 1 signal, while Fig. 6 illustrates hard detection at the bit level for the interfering user 1 signal.
• the hard decision processor 136 is positioned after the demodulator 132, for making hard decisions on the demodulated bit soft values obtained by demodulating the user 1 symbols. These hard bit decisions are re-modulated via a modulator 152, which simply may "map" groups of hard-detected bits into corresponding modulation symbols, according to the modulation constellation in use. These modulation symbols are then scaled as in Fig. 5, according to the value of the scaling factor appropriate for the current received signal quality. The balance of the processing is as described for Fig. 5.
• circuit elements of Figs. 5 and 6 may represent physical circuit elements. That is, at least some of the illustrated circuit elements, e.g., demodulator 132, hard decision processor 136, scaling circuit 138, etc., may be implemented as dedicated circuitry (pure hardware). However, it also should be understood that one or more of the circuit elements represented in these diagrams may be "functional" circuits, implemented in a general or special-purpose signal processor or microprocessor through the execution of stored computer program instructions. In this sense, execution of the stored computer program instructions by the baseband processor 62 specially configures it to carry out the method operations disclosed herein.
• the teachings presented herein may also be applied to the post-decoding approach, where the soft values obtained at the output of a decoder are used to re-generate the interfering user signals.
• Corresponding functional results may be tabulated as was described earlier herein, and stored in look-up tables.
• the teachings presented herein implement an IC receiver structure that is robust and has low incremental complexity compared to a non-IC receiver.
• the performance is improved and the whole SIR range of the receiver is handled without requiring heuristic mechanisms for turning the IC feature on and off, and it should be understood that the disclosed scaling of hard-detected values represents a significant complexity decrease to achieve a robust interference cancellation performance as compared to interference cancellation based on full soft-value processing of the interfering signals.
• the look-up table embodiments presented herein are particularly simple to implement, but, of course, the teachings herein are not limited to look-up table embodiments.
• the network 20 is a Wideband-CDMA (WCDMA) network
• the base station 10 is configured as a WCDMA base station.
• the base station 10 is configured as a Long Term Evolution (LTE) base station.
• the mobile stations 12, 13, and 15 are correspondingly configured, and thus may be WCDMA or LTE devices, or may be compatible with more than one standard/protocol.
• WCDMA Wideband-CDMA
• LTE Long Term Evolution
• Non-limiting examples of a mobile station include cellular radiotelephones, smart-phones and PDAs, palmtop/laptop computers, network interface cards, etc.
• a mobile station includes cellular radiotelephones, smart-phones and PDAs, palmtop/laptop computers, network interface cards, etc.

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