|Publication number||US20060291591 A1|
|Application number||US 11/165,297|
|Publication date||Dec 28, 2006|
|Filing date||Jun 22, 2005|
|Priority date||Jun 22, 2005|
|Also published as||CN101238675A, CN101238675B, EP1908204A1, EP1908204B1, EP2182668A1, US8532232, US20100290512, WO2007002417A1|
|Publication number||11165297, 165297, US 2006/0291591 A1, US 2006/291591 A1, US 20060291591 A1, US 20060291591A1, US 2006291591 A1, US 2006291591A1, US-A1-20060291591, US-A1-2006291591, US2006/0291591A1, US2006/291591A1, US20060291591 A1, US20060291591A1, US2006291591 A1, US2006291591A1|
|Original Assignee||Kaushik Ghosh|
|Export Citation||BiBTeX, EndNote, RefMan|
|Patent Citations (8), Referenced by (10), Classifications (10), Legal Events (1)|
|External Links: USPTO, USPTO Assignment, Espacenet|
The present disclosure relates generally to wireless communication devices and, more specifically, to a method for estimating the bit error probability (BEP) in a wireless channel between a base station and a mobile station.
As mobile telecommunications evolves, increasing speeds of data transmission to mobile stations enables new types of services to be offered to mobile subscribers. Usage of these services, in turn, generates a demand for ever increasing data rates. The European Telecommunications Standards Institute (ETSI) introduced the General Packet Radio Service (GPRS) as an initial standard to increase data rates by providing packet-switched data to mobile stations based on the Global System for Mobile communications (GSM). Then as an enhancement to GSM data services, ETSI promulgated the Enhanced Data rates for GSM Evolution (EDGE) standard, with a packet-switched portion called Enhanced GPRS (EGPRS). Together, EDGE and EGPRS are described in the TIA/EIA-136-370 standard published by the Telecommunications Industry Association (TIA). Further enhancements to high-speed data transmission based on GSM include the GSM/EDGE radio access network (GERAN) standard specified by the 3rd Generation Partnership Project (3GPP). The TIA has described the GERAN enhancements in the TIA/EIA-136-370-A revision to its EGPRS-136 standard. For simplicity, the EDGE, EGPRS, TIA/EIA-136-370 and TIA/EIA-136-370-A standards are collectively referred to herein as the “EDGE standard.”
The physical layer dedicated to packet data traffic in the EDGE standard is called the Packet Data Channel (PDCH). The physical layer of the EDGE standard is specified in ETSI standard TS 145.008 (3GPP TS 45.008). Both signaling and traffic channels are transmitted over the PDCH. One of the signaling channels is the Packet Associated Control Channel (PACCH). The traffic channel transmitted over the PDCH is called the Packet Data Traffic Channel (PDTCH).
Unlike basic GSM, several of the higher-speed versions of GSM transmit data at multiple data rates. For example, data is transmitted at nine different data rates over the PDTCH. In a process called “link adaptation,” the data rate over the wireless channel is adjusted based on the channel condition. When the channel condition is good and the signal-to-noise ratio of the wireless channel is high, data can be transmitted at higher data rates. Conversely, when the channel condition is poor and the signal-to-noise ratio is low, data must be transmitted at slower data rates. Transmitting data using a particular modulation and coding scheme (MCS) at a data rate that is too high for the channel's signal-to-noise ratio can result in a loss of data. Link adaptation increases overall data throughput by using the highest data rate that can dependably be supported using a particular MCS at the signal-to-noise ratio that momentarily exists on the wireless channel. The EDGE standard requires the mobile station periodically to report the channel condition in the PACCH to the base station. The condition of the channel between the base station and the mobile station is expressed in terms of the bit error probability (BEP). The BEP is the expected value of the actual Bit Error Rate (BER) of a signal received by the mobile station over the wireless channel. The base station then transmits data in the PDTCH to the mobile station at the appropriate data rate depending on the channel condition as indicated in the PACCH.
Link adaptation can most effectively be performed when the mobile station reports a BEP that most accurately estimates the actual BER. One way to estimate the BEP is to attempt to calculate the BER itself. A “re-encoding” method is based on determining the number of bit errors that are corrected in the decoding process. Error control decoding, such as that performed by a convolutional decoder, attempts to correct bit errors that are introduced in the wireless channel. Frames that are output from the block deinterleaver and the convolutional decoder of the mobile station are re-encoded and re-interleaved. The resulting re-encoded bits are then compared to the bits received by the block deinterleaver to determine the number of corrected bit errors. The re-encoding method, however, yields inaccurate results because it relies on the assumption that the error control decoding corrects all of the errors that have been introduced by the wireless channel. Therefore, the BEP obtained using the re-encoding method varies depending on the degree of redundancy employed by the various MCS schemes used to transmit the bits over the wireless channel. Even with a poor channel condition, a high redundancy level of the data allows the error control decoding to decode all of the bits correctly and thus yields a more accurate estimated BER. On the other hand, if the channel condition is poor and redundancy level of the data is low, the error control decoding is unable to correct all of the erroneous bits, and an inaccurate estimate of the BER results. Thus, a better channel quality is required to estimate the BER accurately using a lower redundancy MCS scheme, such as MCS9, than using a higher redundancy MCS scheme, such as MCS5.
A second way of estimating the BEP involves first measuring the signal-to-noise ratio of the radio frequency (RF) signal that carries the PDCH. The relationship between the measured signal-to-noise ratio and the BER of the PDCH received by the mobile station is empirically determined in a laboratory. The values of BER that vary as a function of the measured signal-to-noise ratio are then stored in a lookup table on the mobile station. This method requires the mobile station to have an estimator of the signal-to-noise ratio in the RF signal. The BEP is determined by using the estimated signal-to-noise ratio to look up the corresponding BER in the lookup table. The accuracy of the BEP in this method depends on the accuracy of the estimated signal-to-noise ratio of the RF signal. Where the channel condition is affected by signal interference and fading, an accurate determination of the signal-to-noise ratio of the RF signal can be difficult, and the BEP estimation is prone to inaccuracy.
A method is sought for accurately determining the bit error probability (BEP) without requiring a direct estimation of the signal-to-noise ratio of the RF signal and without re-encoding the output of the convolutional decoder of the mobile station. Moreover, a method is sought for determining the BEP that is not influenced by the degree of redundancy in the modulation and coding scheme (MCS) used to transmit the data over the wireless channel.
A distribution parameter mapping method estimates the bit error probability (BEP) of bits in a burst transmitted in a radio frequency (RF) signal from a base station to a mobile station using one of the nine modulation and coding schemes (MCSs) specified in the EDGE standard. The BEP estimated using the distribution parameter mapping method is not influenced by the degree of code redundancy in the particular MCS used to modulate data over the RF signal. The circuitry determines whether the multi-bit soft decisions that were equalized from demodulated I and Q samples of the burst most resemble a Gaussian distribution or a Rician distribution. The statistical parameters for the mean (μ) and the variance (σ) are determined for soft decisions having a Gaussian distribution. The statistical parameters A and σ are determined for soft decisions having a Rician distribution. The signal-to-noise ratio of the RF signal is represented by the ratio μ/σ for a Gaussian distribution of soft decisions and by the ratio A/σ for a Rician distribution of soft decisions. The BEP for a burst having a Gaussian distribution of soft decisions is determined by mapping the ratio μ/σ to an empirically determined BEP in a Gaussian lookup table stored in non-volatile memory on the mobile station. For a Rician distribution, the ratio A/σ is mapped to an empirically determined BEP in a Rician lookup table. The estimated BEPs for the four bursts of each radio block are then averaged, filtered and quantized into one of thirty-two levels according to the EDGE standard. The quantization level of the average BEP is then reported to the base station to permit the base station to transmit subsequent radio blocks using an MCS that is appropriate for the estimated BEP of the signal.
Circuitry in a mobile station that performs distribution parameter mapping to estimate the BEP includes an equalizer, a distribution analyzer, a BEP estimator, lookup tables, an averager, a filter and a non-linear quantizer. The equalizer removes intersymbol interference from demodulated I and Q samples received in bursts from a demodulator in the mobile station. For each burst, the equalizer outputs a distribution of multi-bit soft decisions that are subsequently processed by the mobile station into single-bit hard decisions that comprise frames of data. The distribution analyzer receives the distribution of multi-bit soft decisions from the equalizer and determines the type of distribution that the distribution of multi-bit soft decisions resembles. For example, the distribution of multi-bit soft decisions can resemble a Gaussian distribution or a Rician distribution. The distribution analyzer outputs a distribution type identifier.
The BEP estimator receives the distribution of multi-bit soft decisions from the equalizer, as well as the distribution type identifier from the distribution analyzer. The BEP estimator calculates various statistical parameters of the distribution of multi-bit soft decisions, depending on the type of distribution. When the soft decisions have a Gaussian distribution, the BEP estimator calculates the statistical parameters for the mean (μ) and the variance (σ). When the soft decisions have a Rician distribution, the BEP estimator calculates the statistical parameters A and σ. The BEP estimator also calculates the ratio μ/σ for a Guassian distribution and the ratio A/σ for a Rician distribution. The ratios μ/σ and A/σ correlate to the signal-to-noise ratios of the I and Q samples.
The BEP estimator estimates the BEP of a burst containing a Gaussian distribution of multi-bit soft decisions by mapping the ratio μ/σ to an empirically determined BEP in a Guassian lookup table stored on the mobile station. The BEP of a burst containing a Rician distribution of multi-bit soft decisions is estimated by mapping the ratio A/σ to an empirically determined BEP in a Rician lookup table stored on the mobile station.
The averager then averages the estimated BEPs from four bursts and generates a MEAN_BEP. The filter filters the MEAN_BEP and outputs a filtered MEAN_BEP. The non-linear quantizer quantizes the filtered MEAN_BEP into one of thirty-two levels and outputs a value (MEAN_BEP_0 through MEAN_BEP_31) that represents the BEP of the four bursts on a logarithmic scale.
Other embodiments and advantages are described in the detailed description below. This summary does not purport to define the invention. The invention is defined by the claims.
The accompanying drawings, where like numerals indicate like components, illustrate embodiments of the invention.
Reference will now be made in detail to some embodiments of the invention, examples of which are illustrated in the accompanying drawings.
The BEP determined by circuitry 20 is an indication of the channel condition of the PDCH transmitted over input RF signal 22. The EDGE physical layer specification (ETSI standard TS 145.008; 3GPP standard TS 45.008) provides that the mobile station periodically reports the channel condition of the PDCH to the base station in the Packet Associated Control Channel (PACCH). The base station polls the mobile station for the channel condition. The PACCH is transmitted back to the base station over an output RF signal 30. The mobile station uses the BEP to obtain the channel condition that is reported to the base station. The channel condition is expressed as one of thirty-two BEP levels. The base station then transmits data in the PDTCH over the PDCH back to the mobile station at the appropriate data rate depending on the BEP level indicated in the PACCH.
Depending on the BEP level, data is transmitted at nine different data rates in the EDGE standard.
The first four MCSs have different coding schemes that provide for nearly no coding (MSC4) to highly redundant coding (MSC1). The code rate listed in
The highest five MCSs support higher data rates because 8-PSK signals are able to carry three bits per modulated symbol instead of one bit per symbol with GMSK modulation. Thus, the data rates of the MCSs employing 8-PSK are approximately three times as fast. Signal propagation using 8-PSK is diminished, however, in comparison to GMSK. The coverage area achieved with signals employing the higher data rates of 8-PSK modulation is therefore smaller.
In one mode of link adaptation, the mobile station reports the BEP level based on the mean BEP for each of the eight timeslots in a temporary block flow (TBF). The method of
Digital baseband processor 27 receives the I and Q samples 26 from the RF receiver 25 and outputs frames containing single-bit hard decisions 31. The single-bit hard decisions 31 are output by a convolutional decoder 32, such as a Viterbi decoder. The frames are processed as data or are analyzed as speech in a voice decoder. Circuitry 20 estimates the signal-to-noise ratio of the PDCH transmitted over input RF signal 22 without re-encoding the output of convolutional decoder 32. Circuitry 20 instead analyzes multi-bit soft decisions 33 that are generated as part of the digital baseband layer-1 processing to estimate the signal-to-noise ratio of the PDCH.
In a step 34, a modulation detector 35 receives the I and Q samples 26 from RF receiver 25 and determines the type of modulation scheme by which data was modulated over the carrier signal on input RF signal 22. According to the EDGE standard, the modulation scheme is either GMSK or 8-PSK. A detection algorithm is used to differentiate I and Q samples modulated with either GMSK or 8-PSK based on the different phase characteristics of the GMSK and 8-PSK modulations. One detection method, for example, first assumes that the data is modulated with GMSK and then performs a □-by-4 rotation. A signal-to-noise ratio is then estimated for this GMSK hypothesis. A rotation is then performed assuming that the data is modulated with 8-PSK, and the signal-to-noise ratio is again estimated. The method determines that the modulation scheme corresponds to the modulation hypothesis for which the signal-to-noise ratio was the greatest.
In a step 36, the I and Q samples 26 are then demodulated. Depending on the modulation scheme identified in step 34, the I and Q samples 26 are demodulated by either a GMSK demodulator 37 or an 8-PSK demodulator 38. A GMSK demodulator 37 demodulates I and Q samples 26 that were modulated with MCS1 through MCS4, which employ GMSK. An 8-PSK demodulator 38 demodulates I and Q samples 26 that were modulated with MCS5 through MCS9, which employ 8-PSK. In the embodiment of
The demodulated I and Q samples 41 output by GMSK demodulator 37 and the demodulated I and Q samples 42 output by 8-PSK demodulator 38 constitute symbols in baseband. Depending on the modulation scheme, a demodulated sample can have various number of bits, for example, 1, 2 or 10. The demodulated samples represent positive and negative numbers in GMSK and real and imaginary numbers in 8-PSK. There are one in-phase sample and one quadrature sample per symbol bit. In GMSK, there are 116 symbols in each of the four bursts of a radio block. In 8-PSK, there are 348 symbols (3×116) per burst.
In a step 39, an equalizer 40 equalizes demodulated I and Q samples 41 and 42 and outputs the multi-bit soft decisions 33. Thus, each I and Q sample bit is assigned a multi-bit soft decision value. The multi-bit soft decisions 33 constitute symbols for which inter-symbol interference has been removed. Inter-symbol interference results when one symbol is temporally modulated on top of another symbol. In one example, each of the multi-bit soft decisions 33 is a 16-bit 2's complement signed digital value.
Circuitry 20 estimates the BEP based on the multi-bit soft decisions 33. The multi-bit soft decisions 33 are also further processed by digital baseband processor 27 to obtain the single-bit hard decisions 31 that are included in the frames that contain voice and data information. A quantizer 41 quantizes the multi-bit soft decisions 33 into a lesser number of levels than the number of digital states available from the number of bits of the multi-bit soft decisions 33. A block deinterleaver 42 receives quantized symbols 43 from quantizer 41 and output deinterleaved symbols 44. The convolutional decoder 32 than decodes the deinterleaved symbols 44 and outputs the single-bit hard decisions 31.
Returning to the distribution parameter mapping method of estimating the BEP, circuitry 20 next determines the type of statistical distribution of the multi-bit soft decisions 33. In a step 45, a distribution analyzer 46 determines the type of statistical distribution to which the soft decisions 33 of each burst correspond. Distribution analyzer 46 then outputs a corresponding distribution type identifier 47. For example, the distribution of the values of the multi-bit soft decisions 33 may resemble one of the following distribution types: a Gaussian distribution, a Rice (Rician) distribution, a Rayleigh distribution, a Poisson distribution or a Laplace distribution. The distribution of the multi-bit soft decisions 33 typically resembles either a Gaussian or a Rician distribution. In a static channel where the signal-to-noise ratio is not significantly improving or deteriorating, the distribution of the multi-bit soft decisions 33 typically resembles a Gaussian distribution. On the other hand, if there is a line of sight path between the base station and the mobile station, the wireless channel is usually described by the Rician fading model, and the distribution of the multi-bit soft decisions 33 typically resembles a Rician distribution. Distribution analyzer 46 uses well-known algorithms to determine the statistical distribution type that the distribution of the multi-bit soft decisions 33 most closely resembles. For example, the type of distribution can be recognized by the maximum value of the distribution, the location of the maximum value within the distribution, and the spread of the distribution.
A BEP estimator 48 receives the soft decisions 33 for each burst that are output by equalizer 40. In addition, BEP estimator 47 receives distribution type identifier 47. In a decision step 49, BEP estimator 48 determines which statistical parameters to calculate. If the distribution type identifier 47 indicates that the soft decisions 33 resemble a Gaussian distribution, BEP estimator 48 proceeds to a step 50 and calculates the statistical parameters μ (mu) and σ (sigma). If the distribution type identifier 47 indicates that the soft decisions 33 resemble a Rician distribution, BEP estimator 48 proceeds to a step 51 and calculates the statistical parameters A and σ.
In the following example of step 50, the statistical parameters μ and σ are calculated from soft decisions whose distribution is found to resemble a Gaussian distribution in decision step 49. Thus, the distribution of the soft decisions resembles the Gaussian probability density function (PDF) 52 shown in
Returning to the next step in
In a decision step 61, circuitry 20 determines whether the BEP value 60 of each of the four bursts in the radio block has been determined. If four BEP values have not yet been determined, BEP estimator 48 determines the BEP for the next distribution of 116 soft decisions on the next GMSK burst. Where the burst has been modulated with 8-PSK, BEP estimator 48 determines the BEP for a distribution comprising 348 soft decisions per burst.
Returning to step 51, the statistical parameters A and σ are calculated from the sample distribution of soft decisions listed above assuming that the distribution is found to resemble a Rician distribution in decision step 49. Thus, in this example, the sample distribution is found to resemble the Rician probability density function (PDF) 62 shown in
In a step 67, the BEP is then determined by mapping the quotient A/σ to a BEP value in a lookup table. For the sample Rician distribution, the quotient A/σ is 1.035. The relationship between the quotient A/σ and the BER for channels whose data resembles a Rician distribution is also empirically determined in a laboratory. The results of the empirical determination are then stored in a Rician lookup table 68 in processor-readable medium 59. Rician lookup table 68 is then used to estimate the BEP based on the quotient A/σ. Where the quotient A/σ of the sample Rician distribution equals 1.035 in this example, BEP value 60 is determined to be 0.079.
In a step 69, an averager 70 calculates the average of four BEP values 60 when circuitry 20 determines in decision step 61 that the BEP of each of the four bursts in a radio block has been determined. Averager 70 outputs a signal MEAN_BEP 71 that represents the average of the four BEP values 60.
In a step 72, a filter 73 receives and filters the MEAN_BEP 71. Filter 73 is a digital low pass filter, such as an infinite impulse response (IIR) filter. Filter 73 outputs a filtered MEAN_BEP 74.
In a step 75, a non-linear quantizer 76 quantizes the filtered MEAN_BEP 74 into one of thirty-two non-linear levels or intervals. Non-linear quanitizer 76 outputs one of thirty-two values MEAN_BEP_0 through MEAN_BEP_31 (77) that represents the average, filtered BEP on a logarithmic scale. The quantized MEAN_BEP 77 is then received by an RF transmitter 78 on RF analog chip 29. In one embodiment, most of the circuitry of digital baseband processor 27 is part of a digital signal processor (DSP) 79, including distribution analyzer 46, BEP estimator 48, averager 70, filter 73 and non-linear quantizer 76.
In a step 80, the quantized MEAN_BEP 77 (MEAN_BEP_0—MEAN_BEP_31) of the level of the average BEP is transmitted back to the base station in PACCH over output RF signal 30. The base station then transmits subsequent radio blocks using an MCS that is chosen based on the quantized MEAN_BEP 77. For example, the base station chooses the MCS with the fastest data rate that can be supported under the channel condition described by the quantized MEAN_BEP 77.
Although the present invention has been described in connection with certain specific embodiments for instructional purposes, the present invention is not limited thereto. Most of the circuitry of digital baseband processor 27 is described above as being part of DSP 79. In other embodiments, some components of circuitry 20 are implemented as sets of instructions operating on a processor separate from DSP 79. For example, the separate processor can be an ARM processor. The instructions are stored on processor-readable medium 59, and the separate processor reads the instructions from processor-readable medium 59 before performing the instructions. Thus, processor-readable medium 59 stores not only Gaussian lookup table 58 and Rician lookup table 68, but also program instructions. In this case, processor-readable medium 59 is a type of non-volatile memory, such as read only memory (ROM). In one embodiment, for example, each of equalizer 40, distribution analyzer 46, BEP estimator 48, averager 70, filter 73 and non-linear quantizer 76 is implemented as a set of instructions operating on the separate processor.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Accordingly, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principle and novel features disclosed herein.
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|U.S. Classification||375/340, 375/332|
|International Classification||H04L27/06, H04L27/22|
|Cooperative Classification||H04L1/0026, H04L1/203, H04L1/206, H04L1/0009, H04L1/0003|
|Aug 22, 2005||AS||Assignment|
Owner name: QUALCOMM INCORPORATED, CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:GHOSH, KAUSHIK;REEL/FRAME:016657/0593
Effective date: 20050802