WO2011028982A2 - Symbol estimation methods and apparatuses - Google Patents
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- WO2011028982A2 WO2011028982A2 PCT/US2010/047781 US2010047781W WO2011028982A2 WO 2011028982 A2 WO2011028982 A2 WO 2011028982A2 US 2010047781 W US2010047781 W US 2010047781W WO 2011028982 A2 WO2011028982 A2 WO 2011028982A2
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- 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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
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- 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
-
- 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
- H04L25/03248—Arrangements for operating in conjunction with other apparatus
- H04L25/0328—Arrangements for operating in conjunction with other apparatus with interference cancellation circuitry
Definitions
- the present invention generally relates to wireless communication and, in particular, relates to refining estimation of received symbols.
- a symbol estimation method implemented at a receiver comprises calculating, based on an estimate of a communication channel and an initial estimate of a set of received symbols, a refined estimate of the set of received symbols, by performing, for each symbol in the set of received symbols, refinement operations.
- the refinement operations comprise parameterizing, for the symbol, a
- ISI intersymbol interference
- a symbol estimation system configured to calculate, based on an estimate of a communication channel and an initial estimate of a set of received symbols, a refined estimate of the set of received symbols.
- the system comprises an intersymbol interference (ISI) parameterization module configured to parameterize, for each symbol in the set of received symbols, a contribution to ISI by the remaining symbols in the set of received symbols, a refined estimation module configured to select a refined estimated value for each symbol in the set of received symbols, an optimization module configured to evaluate an optimization function using the estimate of the communication channel and one or more symbol values from the initial estimate of the set of received symbols, and a log-likelihood module configured to compute a log-likelihood value for the selected refined estimated value for each symbol in the set of received symbols based on, at least in part, the contribution of ISI by the remaining symbols in the set of received symbols and the estimate of the communication channel.
- ISI intersymbol interference
- a machine-readable medium comprising instructions for estimating symbols at a receiver.
- the instructions comprise code for calculating, based on an estimate of a communication channel and an initial estimate of a set of received symbols, a refined estimate of the set of received symbols, by performing, for each symbol in the set of received symbols, refinement operations comprising parameterizing, for the symbol, a contribution to intersymbol interference (ISI) by the remaining symbols in the set of received symbols, selecting a refined estimated value for the symbol by evaluating an optimization function using the estimate of the communication channel and one or more symbol values from the initial estimate of the set of received symbols, and computing a log- likelihood value for the selected refined estimated value for the symbol based on, at least in part, the contribution of ISI by the remaining symbols in the set of received symbols and the estimate of the communication channel.
- ISI intersymbol interference
- a symbol estimation apparatus comprising means for calculating, based on an estimate of a communication channel and an initial estimate of a set of received symbols, a refined estimate of the set of received symbols, by performing, for each symbol in the set of received symbols, refinement operations comprising means for parameterizing, for the symbol, a contribution to intersymbol interference (ISI) by the remaining symbols in the set of received symbols, means for selecting a refined estimated value for the symbol by evaluating an optimization function using the estimate of the communication channel and one or more symbol values from the initial estimate of the set of received symbols, and means for computing a log-likelihood value for the selected refined estimated value for the symbol based on, at least in part, the contribution of ISI by the remaining symbols in the set of received symbols and the estimate of the communication channel is disclosed.
- ISI intersymbol interference
- FIG. 1 illustrates an exemplary communication system in accordance with certain configurations of the present disclosure
- FIG. 2 is illustrates exemplary frame and burst formats in a GSM transmission, in accordance with certain configurations of the present disclosure
- FIG. 3 is a block diagram of a receiver, in accordance with certain configurations of the present disclosure.
- FIG. 4 is a block diagram of a multi-stream interference canceller block, in accordance with certain configurations of the present disclosure
- FIG. 5 illustrates a flow chart of a symbol estimation process, in accordance with certain configurations of the present disclosure
- FIG. 6 illustrates a flow chart of a multi-channel symbol estimation process, in accordance with certain configurations of the present disclosure
- FIG. 7 illustrates a flow chart of an iterative multi-channel symbol estimation process, in accordance with certain configurations of the present disclosure
- FIG. 8 is a chart illustrating frame error rate performance improvements achievable utilizing various aspects of the subject technology, in accordance with certain configurations of the present disclosure
- FIG. 9 is a chart illustrating symbol error rate performance improvements achievable utilizing various aspects of the subject technology, in accordance with certain configurations of the present disclosure.
- FIG. 10 is a block diagram illustrating a receiver apparatus, in accordance with certain configurations of the present disclosure.
- FIG. 11 is a block diagram illustrating a symbol estimation system, in accordance with certain configurations of the present disclosure.
- FIG. 12 is a block diagram illustrating a computer system with which certain aspects of the subject technology may be implemented in accordance with certain
- Receivers operating in accordance with certain wireless standards such as GERAN, often receive signals over a channel that may be characterized as a fading channel. Operation of a receiver often involves receiving a signal, extracting symbols from the received signal and demodulating the symbols to produce data bits. To help produce the data bits accurately, a receiver may also suppress (or remove) signal distortions caused by the communication channel, noise, interference from unwanted transmitters, and so on. Receivers are often designed by making assumptions about communication channels (e.g., assuming that a communication channel has a finite impulse response of a certain duration) and noise signal (e.g., assuming that noise has a white spectrum).
- a practitioner of the art may configure a receiver to suppress the signal distortions by performing channel equalization using, for example, maximum likelihood (ML) detection, decision feedback equalization (DFE), minimum least squares estimate (MLSE) and other well-known algorithms.
- ML maximum likelihood
- DFE decision feedback equalization
- MSE minimum least squares estimate
- ML estimator has the potential to offer theoretically best performance.
- an ML estimator may be computationally complex.
- configurations of the present disclosure provide alternate channel equalization techniques that cancel interference in the received signal by performing symbol estimation by recovering symbols from the received signals using an initial estimate of a linear estimator (e.g., channel impulse response) and iteratively using a matched filter and an interference cancellation technique to derive a "local maxima" optimal solution.
- a linear estimator e.g., channel impulse response
- the symbol estimation techniques may achieve performance close to an ML estimator, but at a much reduced computational complexity.
- the present disclosure provides interference cancellation techniques that provide improvement over traditional techniques under low signal conditions (low values of signal to noise ratios).
- the present disclosure provides symbol estimation methods and systems that improve receiver performance for binary as well as M-ary modulated signals.
- the modulation scheme is be 8PSK.
- the present disclosure provides signal reception techniques applicable to a multi-input multi-output (MIMO) channel.
- MIMO channel is characterized by having multiple receive antennas at a receiver configured to receive signals from multiple transmit antennas at a transmitter.
- GERAN GSM EDGE radio access network
- GSM Global Standard for Mobile communication (Groupe Mobil Special)
- ISI inter-symbol interference
- MDD minimum distance detector
- MIMO Multiple input multiple output
- MSIC multiple stream inter-symbol interference cancellation
- TDMA time domain multiple access
- FIG. 1 illustrates a communication system 100 in accordance with one aspect of the subject technology.
- the communication system 100 may, for example, be a wireless communication system based on the GSM standard.
- a receiver 102 receives a signal 104 transmitted by a base station 106 at an antenna 108 coupled to the receiver 102.
- the signal 104 may suffer from impediments such as co-channel interference (CCI), including a transmission 110 from another base station 112, and inter-symbol interference (ISI) comprising one or more reflections 114 of the signal 104.
- CCI co-channel interference
- ISI inter-symbol interference
- the receiver 102 processes the signal 104 to suppress effects of CCI and ISI and recover the data transmitted by the base station 106 by estimating received symbols.
- FIG. 2 shows exemplary frame and burst formats in GSM.
- the timeline for downlink transmission is divided into multiframes.
- each multiframe such as exemplary multiframe 202, includes 26 TDMA frames, which are labeled as TDMA frames 0 through 25.
- the traffic channels are sent in TDMA frames 0 through 11 and TDMA frames 13 through 24 of each multiframe, as identified by the letter "T” in FIG. 2.
- a control channel, identified by the letter “C,” is sent in TDMA frame 12.
- No data is sent in the idle TDMA frame 25 (identified by the letter "I”), which is used by the wireless devices to make measurements for neighbor base stations.
- Each TDMA frame such as exemplary TDMA frame 204, is further partitioned into eight time slots, which are labeled as time slots 0 through 7.
- Each active wireless device/user is assigned one time slot index for the duration of a call.
- User-specific data for each wireless device is sent in the time slot assigned to that wireless device and in TDMA frames used for the traffic channels.
- Each burst such as exemplary burst 206, includes two tail fields, two data fields, a training sequence (or midamble) field, and a guard period (GP).
- the number of bits in each field is shown inside the parentheses.
- GSM defines eight different training sequences that may be sent in the training sequence field.
- Each training sequence, such as midamble 208, contains 26 bits and is defined such that the first five bits are repeated and the second five bits are also repeated.
- Each training sequence is also defined such that the correlation of that sequence with a 16-bit truncated version of that sequence is equal to (a) sixteen for a time shift of zero, (b) zero for time shifts of ⁇ 1, ⁇ 2, ⁇ 3, ⁇ 4, and ⁇ 5, and (3) a zero or non-zero value for all other time shifts.
- FIG. 3 is a block diagram of a receiver 300, in accordance with certain aspects of the present disclosure.
- the receiver 300 comprises a short equalizer section 302, a channel estimator section 304, a long equalizer section 306, an interference canceller section 308, an interleaver section 310 and a channel decoder section 312.
- the receiver 300 depicted in FIG. 3 operates as disclosed in the commonly owned and co-pending patent application number 12/553,848 (Attorney Docket Number 091494), incorporated herein by reference in its entirety.
- the short equalizer section 302 is configured to generate a first set of equalized symbols by canceling CCI and ISI from a received burst of symbols (e.g., a midamble or a preamble).
- the short equalizer section 302 also generates a first estimate of the channel (e.g., impulse response coefficients) on which the received burst of symbols was received.
- the short equalizer section 302 uses a blind channel estimation algorithm to obtain the first estimate of the channel and calculate a first set of equalized symbols.
- the channel estimator section 304 is configured to use the first estimate of the channel and the first set of equalized symbols (input 322) to further estimate channel and further suppress ISI from the first set of equalized symbols and output the ISI-suppressed set of symbols (output 324).
- a long equalizer section 306 uses the ISI-suppressed set of symbols to further equalize the channel and suppress ISI and produce an estimate of symbols in the received set of symbol (output 326).
- the long equalizer section 306 also has the capability to re-estimate the channel using the ISI-suppressed set of symbols (also included in output 326).
- An interference canceller section 308 uses the re-estimated channel and the symbol estimates to refine the results to improve symbol decisions.
- the interference canceller section 308 produces symbol decisions and log-likelihood values associated with the symbol decisions (together shown as output 328).
- the values from the output 328 are used by further receiver sections such as a de- interleaver 310 to generate data samples 330, which are further decoded by a channel decoder 312 to produce demodulated data 332.
- the interference canceller (IC) section 308 operates to estimate symbols in the received signal by refining estimates of the symbols available and an estimate of the communication channel available to the IC section 308.
- the IC section 308 also calculates a log-likelihood for each estimated symbol and a log- likelihood ratio (LLR) for each estimated symbol.
- LLR log- likelihood ratio
- the log-likelihood and LLR values are fed to a de-interleaver stage 310, e.g., a Viterbi deinterleaver, to assist with de-interleaving.
- the symbol estimates are calculated using a theoretically optimal algorithm such as the maximum likelihood (ML) estimator.
- ML estimator requires searching for the best estimate for each symbol over a multi-variable search space, which can be computationally expensive, because the ML estimator often does not have the knowledge of values of any symbols.
- the symbol estimation process is simplified by parameterizing contribution to intersymbol interference from symbols ⁇ a. ⁇ for i ⁇ k when estimating a symbol a k .
- Estimation of symbol a k and corresponding log-likelihood are thus greatly simplified.
- Such a process is called multiple stream interface cancellation (MSIC) because, in general, the process works on multiple streams of input symbols.
- MSIC multiple stream interface cancellation
- the process is capable of being iteratively repeated by estimating each symbol in each iteration, until an iteration termination criterion is met.
- the iteration termination criterion is a measure of change to the values of the symbols ⁇ 3 ⁇ 4 ⁇ (e.g., sum of absolute square in value changes from one iteration to the next).
- An improvement in error rate (e.g., frame error rate or symbol error rate) calculation is also usable as the iteration termination criterion.
- D be a positive integer representing the number of symbol streams at the input of an MSIC section.
- d be a positive integer representing channel memory per stream.
- N represent data length for the data underlying an input signal burst.
- h l be a lx (d + 1) row vector representing equivalent channel impulse response for the h input stream at the input of MSIC (1 ⁇ i ⁇ D).
- A be a (d + 1, N - d) matrix representing input symbols from alphabet c k , where 0 ⁇ k ⁇ M-l and where N is a positive integer representing length of the received data.
- the input alphabet set [c h ⁇ may be equal to ⁇ -1, +1 ⁇ .
- N may be equal to 26, corresponding to midamble 208.
- a decision regarding value of a symbol a k at time k as being equal to one of the members of the alphabet set may be computed using information about symbol a k contained in d+l received symbols (d is the channel memory) and using D streams at the input of the MSIC section.
- the relationship can be written as: a k-l l k-d
- Equation (2) is a matrix form as:
- equation (3 a) can be rewritten as below.
- z3 ⁇ 4 m - - - ' a u k T - - a u k+d ' -!h m >)
- equation (4) implies that to estimate likelihood of a symbol a k being c n , a large number of calculations may have to be performed over all possible states X i . In practice, this may be prohibitively expensive to implement with regard to computational resources and computational time.
- Equation (5) above presents a simpler expression to evaluate, because the number of unknown variables is greatly reduced.
- Equation (7) above is capable of being interpreted as a simplified log-likelihood expression for symbol a k being equal to c n , by taking into consideration contribution of the m th symbol stream (out of D possible symbol streams).
- the parameterized symbol matrix used in equation (5) represents contribution from previous estimates of ⁇ 3 ⁇ 4 ⁇ , with term corresponding to a k set to zero:
- the received signal comprises phase shift key (PSK) modulated symbols. In such configurations, all symbols have the same magnitude. Equation (10) can be simplified as:
- equation (13a) produces results close to results produced by a maximum likelihood detector.
- the received signal comprises PSK modulation
- the magnitude of each symbol c n is constant.
- FIG. 4 is a block diagram of an MSIC operation 400 performed by the interference canceller 308, according to certain aspects of subject technology.
- the input h 412 represents an initial estimate of the communication channel and the input a 414 represents an initial estimate of a set of received symbols (including symbol a k ).
- the input stream values Z 402 are shown separated both temporally and spatially in section 416.
- the superscript index corresponds to a stream index (the index having integer values from 1 to D, including both).
- the stream index represents a stream to which the sample is associated.
- the subscript index represents temporal value of the sample. Therefore, in the depicted example, at a time k, a given stream is shown to have values up to the time index k+d (d being the channel memory).
- each section 404 depicts the calculation of a portion of an estimate of symbol a k , at the instant k, that is generated by subtracting contribution from a set of stream samples from input Z 402, filtered through the estimated channel filter h 412.
- the output of each section 404 collectively shown as elements 406, therefore represents a portion of estimate of the symbol a k , scaled by a corresponding channel impulse response coefficient, plus noise n k .
- the noise n k represents contribution from channel noise and from computational inaccuracies from any previous computational sections. No particular assumptions are made about statistics of the noise n k .
- Each output 406 is then multiplied by a complex conjugate of the estimated filter coefficient (multipliers 408). In one aspect, the multiplication helps match the total power of the interference-cancelled symbol values with the input symbol values.
- the results of all the multiplications 408 are added in a sum section 410 to produce a refined estimate of the symbol a k .
- FIG. 5 illustrates a flow chart of operations of a process 500 of symbol estimation, in accordance with certain configurations of the present disclosure.
- the process 500 of FIG. 5 includes an operation 502 of parameterizing, for the symbol, a contribution of intersymbol interference (ISI) by the remaining symbols in the set of received symbols.
- the process 500 also includes an operation 504 of selecting a refined estimated value for the symbol by evaluating an optimization function using the estimate of the communication channel and one or more symbol values from the initial estimate of the set of received symbols.
- the process 500 further includes an operation 506 of computing a log-likelihood value for the refined estimated value for the symbol based on, at least in part, the contribution of ISI by the remaining symbols in the set of received symbols and the estimate of the communication channel.
- the process 500 further includes an operation (not shown in FIG. 5) of iteratively improving the refined estimate of the set of received symbols by using an output refined estimate of the set of received symbols of an iteration as the initial estimate of the set of received symbols for a next iteration, until an iteration termination criterion is met.
- FIG. 6 is a flow chart illustrating a process 600 of interference cancellation, in accordance with certain aspects of the subject technology may be implemented.
- the process 600 also includes operation 604 at which a first stream for symbol estimates a) ⁇ k) , where 0 ⁇ i ⁇ d is processed,
- the process 600 also includes additional D-l operations similar to process 604 such that at the last operation 606, symbol estimates af(k) , where 0 ⁇ i ⁇ d are processed.
- a d ' (k) z k ' +d - h 0 'a(k + d) - h[d(k + d - 1) - h d l _ x d(k + 1)
- the process 600 further includes operation 608 at which parameterized
- the process 600 further comprises an operation 610 of calculating log-likelihood for each refined symbol estimate.
- the operation 610 is performed using, for example, equations (10) or (1 1), if PSK modulation is employed.
- the process 600 further includes operation 612 at which an estimate a k is calculated for a symbol a k .
- the estimate (also called hard decision) is calculated by evaluating an expression such as given in equations (13a) or (13b). Once a hard decision a k is made for the value of symbol a k , this calculated value a k is used for subsequent symbol estimation, including, for example, value in the matrix shown in equation (1).
- optimization function F() is represented as
- the optimization function is a minimum distance detector, as represented by equation (15b).
- the function is a hyperbolic tangent function
- equation (15c) the function given in equation (15c) is suitable when the input symbols can take on one of two possible values only, as is well known in the art.
- (x) tanh( ) (15c)
- FIG. 7 depicts a block diagram illustrating an iterative implementation 700 of symbol estimation, in accordance with certain configurations of the present disclosure.
- the first iteration comprises an MSIC section 702, followed by an optimization function section 704 that generates a symbol decision by evaluating an optimization function as discussed with respect to equations (15a) - (15c) above.
- the symbol decisions 706 from the function section 704 are used as input to the MSIC section 710 for the next iteration.
- the MSIC section 710 is followed by the optimization function section 708.
- Output symbol decisions 706 from the function section are used as input to the next MSIC section, and so on.
- the implementation 700 is terminated after L iterations.
- Each iteration (e.g., sections 702 and 704) is also referred to as a parallel hierarchical interference cancellation (PHIC) stage.
- the value L for the last MSIC iteration can either be fixed a priori, or can be decided during run time, by evaluating an iteration termination criterion. For example, in certain configurations, at the end of each iteration (e.g., sections 708, 710), a determination is made regarding improvement achieved by the new symbol estimates. The improvement is evaluated in terms of magnitude of changes to estimates a k (e.g. L ls L 2 or L ⁇ norm).
- the improvement is evaluated in terms of FER or SER as a result of the new symbol estimates, and whether the improvement over the previous estimates was above a pre-determined threshold. For example, in certain configurations, iterations is terminated if the improvement in a next iteration corresponds to less than 0.2 dB SNR.
- FIG. 8 is a chart 800 illustrating exemplary performance achievable in accordance with certain configurations of the subject technology.
- Chart 800 depicts the frame error rate over a range of signal energy to noise energy ratios (Eb/No) for exemplary receiver systems operating on GSM TU50 communication channel.
- Eb/No signal energy to noise energy ratios
- FIG. 9 is a chart 900 illustrating exemplary performance achievable in accordance with certain configurations of the subject technology.
- Chart 900 depicts the symbol error rate over a range of signal energy to noise energy ratios (Eb/No) for exemplary receiver systems operating on an EDGE HT100 communication channel using 8PSK modulation.
- Eb/No signal energy to noise energy ratios
- performance in a second iteration 904 and a third iteration 906 improves over performance after a first iteration 902 by several dB Eb/No.
- performance improvement by successive iterations of symbol estimates gives marginally diminishing improvements.
- FIG. 10 is a block diagram that illustrates exemplary receiver apparatus 1000 in accordance with certain configurations of the subject technology.
- means 1002, 1004 and 1006 are in communication with each other via a communication means 1008.
- FIG. 11 is a block diagram that illustrates exemplary receiver system 1100 in accordance with certain configurations of the subject technology.
- the receiver system 1100 comprises an ISI parameterization module 1102 configured for parameterizing, for the symbol, a contribution of intersymbol interference (IS) by the remaining symbols in the set of received symbols.
- the receiver system 1100 further comprises a Refined Estimation module 1104 configured for selecting a refined estimate value for the symbol by evaluating an optimization function using the estimate of the communication channel and one or more symbol values from the initial estimate of the set of received symbols.
- the receiver system 1100 further comprises an optimization function module 1106 configured for providing symbol estimates by evaluating an optimization function.
- the receiver system 1100 further comprises a log-likelihood module 1108 configured for computing a log-likelihood value for the refined estimate value for the symbol, based on, at least in part, the contribution of ISI by the remaining symbols in the set of received symbols.
- a log-likelihood module 1108 configured for computing a log-likelihood value for the refined estimate value for the symbol, based on, at least in part, the contribution of ISI by the remaining symbols in the set of received symbols.
- the modules 1102, 1104, 1106 and 1108 are in communication via a communication module 1110.
- FIG. 12 is a block diagram that illustrates a computer system 1200 upon which an aspect may be implemented.
- Computer system 1200 includes a bus 1202 or other
- Computer system 1200 also includes a memory 1206, such as a random access memory (“RAM”) or other dynamic storage device, coupled to bus 1202 for storing information and instructions to be executed by processor 1204.
- RAM random access memory
- Memory 1206 can also be used for storing temporary variable or other intermediate information during execution of instructions to be executed by processor 1204.
- Computer system 1200 further includes a data storage device 1210, such as a magnetic disk or optical disk, coupled to bus 1202 for storing information and instructions.
- Computer system 1200 may be coupled via I/O module 1208 to a display device (not illustrated), such as a cathode ray tube ("CRT") or liquid crystal display (“LCD”) for displaying information to a computer user.
- a display device such as a cathode ray tube ("CRT") or liquid crystal display (“LCD”) for displaying information to a computer user.
- An input device such as, for example, a keyboard or a mouse may also be coupled to computer system 1200 via I/O module 1208 for communicating information and command selections to processor 1204.
- interference suppression is performed by a computer system 1200 in response to processor 1204 executing one or more sequences of one or more instructions contained in memory 1206. Such instructions may be read into memory 1206 from another machine -readable medium, such as data storage device 1210. Execution of the sequences of instructions contained in main memory 1206 causes processor 1204 to perform the process steps described herein.
- circuitry may also be employed to execute the sequences of instructions contained in memory 1206.
- hard-wired circuitry may be used in place of or in combination with software instructions to implement various aspects.
- aspects are not limited to any specific combination of hardware circuitry and software.
- machine-readable medium refers to any medium that participates in providing instructions to a processor for execution. Such a medium may take many forms, including, but not limited to, non- volatile media, volatile media, and
- Non- volatile media include, for example, optical or magnetic disks, such as a data storage device.
- Volatile media include dynamic memory.
- Transmission media include coaxial cables, copper wire, and fiber optics, including the wires that comprise a bus connecting processors and memory sections. Transmission media can also take the form of acoustic or light waves, such as those generated during radio frequency and infrared data communications.
- Machine-readable media include, for example, floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH EPROM, any other memory chip or cartridge, a carrier wave, or any other medium from which a computer can read.
- the present disclosure provides a symbol estimation method that is computationally more efficient compared to traditional approaches.
- contribution to intersymbol interference by other symbols is parameterized for estimating a given symbol from a received signal.
- the parameterization advantageously reduces the space of unknown variables over which to perform search in estimating a log- likelihood value for a symbol decision.
- performance can be improved by iteratively refining estimates of symbols.
Abstract
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JP2012528079A JP5619900B2 (en) | 2009-09-03 | 2010-09-03 | Symbol estimation method and apparatus |
KR1020127008668A KR101520362B1 (en) | 2009-09-03 | 2010-09-03 | Symbol estimation methods and apparatuses |
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JP2013504265A (en) | 2013-02-04 |
WO2011028982A3 (en) | 2011-04-28 |
US20110051859A1 (en) | 2011-03-03 |
JP5619900B2 (en) | 2014-11-05 |
KR101520362B1 (en) | 2015-05-14 |
US8831149B2 (en) | 2014-09-09 |
CN102484624A (en) | 2012-05-30 |
KR20120053524A (en) | 2012-05-25 |
EP2474138A2 (en) | 2012-07-11 |
CN102484624B (en) | 2015-04-22 |
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