MXPA98006808A - System and method for measuring quality information of ca - Google Patents

Abstract

The present invention relates to a system and method for measuring channel quality in terms of signal-to-interference ratio for encoded signal transmissions over fading channels. A Viterbi encoder metric for the maximum likelihood path is used as a measure of channel quality. This Euclidean distance metric is filtered in order to smooth short-term variations. The averaged or filtered metric is a measure of reliable channel quality that remains consistent across different encoded modulation schemes at different mobile speeds. The filtered metric is mapped to the interference signal ratio per symbol, using a threshold scheme. The use of this implicit interference-to-interference ratio estimate is used for mobile-assisted transfer and data rate adaptation in the transmission

Description

SYSTEM AND METHOD FOR MEASURING CHANNEL QUALITY INFORMATION Background of the Invention 1 - Title of the Invention The present invention relates generally to the field of communication systems and more particularly to communication systems that measure the quality of channel information. 2. - Description of the Related Technique As the use of wireless communications continues to grow worldwide at a rapid pace, the need for efficient systems in the frequency spectrum that allow both the growing number of individual users and the new features and digital services such as facsimile, data transmission and various call handling features have increased. The current wireless data systems such as cellular digital data packet (CDPD = cellular digital packet data) and the multiple access data system with time division switched in circuit IS-130, support only fixed data rates that are insufficient for several applications. Because cellular systems are engineered to provide coverage at the cell border, the REF. 28083 ratio of signal to interference plus noise (SNR = signal to interference plus noise ratio) over a large proportion of a cell, is sufficient to support higher data rates. Adaptive data rate schemes that use bandwidth-efficient coded modulation are currently proposed to increase data throughput on fade channels found in cellular systems. Increased data throughput is achieved by using coding modulation schemes with efficient bandwidth with higher information speeds. However, a practical problem in using these schemes is to dynamically adjust the encoded modulation to adapt the channel conditions. Currently, there is a need to determine the quality of the channel, based on measurements or metrics of SNR or the frame error rate achievable (FER = frame error rate), for the channel variant in time. However, in cellular systems, there is no fast accurate method to measure either SNR or estimate FER. The difficulty in obtaining these metrics in a cellular system is due to varying signal intensity levels found in the cellular channel. The levels of intensity of time-varying signal (sometimes referred to as fading, are the result of the movement of the mobile station or cell phone with respect to the base station (also known as cell site.) Recent schemes propose a short-term prediction of the FER, but not of the SNR, using the metric for the second best trajectory by a Viterbi decoder.This metric is computationally very intense and reacts to short-term variations in fading conditions .Therefore, there is need in the field of wireless communication systems, by a method that accurately measures the channel quality in terms of the SNR It is also important to measure channel quality in terms of SNR or FER, with the purpose of mobile assisted transfer (MAHO = mobile assisted handoff.) However, RES measurements are usually too slow for the purpose of speed adaptation or transfusion. FER as a channel quality metric is slow because it can take a long time for the phone to count a sufficient number of frames error. Therefore, there is a need for a robust short-term channel quality indicator, which can be related to the FER. As a result, channel quality metrics such as symbol error rate, average bit error rate and received signal strength measurements have been proposed as an alternative. The IS-136 standard already specifies measurement procedures for both bit error rate and received signal strength. Nevertheless, these measures do not correlate well with FER, or SNR, which is widely accepted as the measurement of significant performance in wireless systems. Also, the received signal intensity measurements are often imprecise and unreliable. The present invention is directed to overcome or at least reduce the effects of one or more of the problems set forth above. SUMMARY OF THE INVENTION According to one aspect of the present invention, there is provided a system and method for determining the signal to interference ratio, which allows establishing a set of trajectory metrics corresponding to a set of predetermined signal to interference ratios . A digital signal is received and a path metric is determined for the digital signal. The mapping of the path metric is provided at a corresponding signal-to-interference ratio in the set of predetermined signal-to-interference ratios. These and other features and advantages of the present invention will become apparent from the following detailed description, the accompanying drawings and the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS The advantages of this invention will be apparent upon reading the following detailed description and by reference to the drawings wherein: Figure 1 is a graphic representation of three cellular sites within a swarm; Figure 2 is a block diagram of both transmitters and receivers of mobile station and base station for the present invention; Figure 3 is a block diagram of a decoder system for the present invention; Figure 4 is a graph having a curve, with the vertical scale representing the average Viterbi decoder metric and the horizontal scale representing the even number (of blocks) of time slot; Figure 5 is a graph that has a curve, with the vertical scale representing the average Viterbi decoder metric and the horizontal scale representing the SNR; Figure 6 is a flow chart illustrating the steps that are performed during the process of determining the SNR using the lookup table and adjusting the coding modulation scheme employed by the system, - Figure 7 is a flowchart illustrating the steps performed during the process of determining the SNR using linear prediction and adjusting the coding modulation scheme used by the system; Figure 8 is a graph that has three curves, with the vertical scale representing the - and the horizontal scale represents the SNR; Figure 9 is a table of values for a strategy for conservative adaptation based on a metric average of the Viterbi algorithm; Figure 10 is a table of values for an aggressive adaptation strategy based on a metric average of the Viterbi algorithm; Figure 11 is a block diagram of both base station and mobile station transmitters and receivers for the implementation of an adaptive coding scheme; and Figure 12 is a block diagram of both transmitters and base station and mobile station receivers for the implementation of a mobile transfer scheme. DETAILED DESCRIPTION With reference to the drawings and initially Figure 1, a plurality of cells 2, 4 and 6 are illustrated in a telecommunications system. Consistent with the convention, each cell 2, 4 and 6 is illustrated as having a hexagonal cell boundary. Within each cell 2, 4 and 6, there are base stations 8, 10 and 12 which are located near the center of the corresponding cells 2, 4 and 6. Specifically, the base station 8 is located inside cell 2, the base station 10 is located inside the cell and base station 12 is located inside cell 6. Borders 14, 16 and 18 that separate cell 2, 4 and 6 in general, represent the points where assisted transfer occurs by mobile. As an example, when a mobile station 20 moves away from the base station 8 towards an adjacent base station 10, the SNR of the base station 8 will fall below a certain threshold level beyond the boundary 14 while at the same time , the SNR of the second base station 10 increases above this threshold, as the mobile station 20 crosses the border 14 in cell 4. The cellular systems are engineered to provide coverage of each base station to the cell boundary. In this manner, the SNR over a large portion of a cell 2 is sufficient to support higher data rates because the SNR of the base station 8 is greater than the minimum SNR required to support the data transfer at the border 14. Figure 2 is an exemplary implementation of an adaptive rate system that takes advantage of this support for higher data rates. Figure 2 is a block diagram for the schematic of the base station 8 and the mobile station 20 for the invention. The base station 8 consists of both an adaptive velocity base station transmitter 22 and an adaptive velocity base station receiver 24. Similarly, the mobile station 20 also consists of both an adaptive velocity 26 mobile station receiver and a transmitter. mobile adaptive speed 28. Each of the transmitter and receiver, corresponding already to the base station 8 or the mobile station 20, are in radio connection through a corresponding channel. In this way, the adaptive rate base station transmitter 22 is connected through a downlink radio channel 30 to the adaptive rate mobile receiver 26 and the adaptive rate mobile station transmitter 28 is connected through a channel uplink radio link 32 to the adaptive rate base station receiver 24. This implementation allows for increased throughput between the base station 8 and the mobile station 20 both on the downlink channel 30 and the uplink channel 32, due to the use of efficient coding modulation schemes in adaptive bandwidth.

In this way, the information speed can be varied by transmitting a fixed symbol speed (as in IS-130 / IS-136) and changing the bandwidth efficiency (number of information bits per symbol) using a selection of coded modulation schemes. However, coding modulation schemes with different bandwidth efficiencies have different error rate performance for the same SNR per symbol. In each SNR, the coded modulation scheme is chosen that results in the highest performance with acceptable FER and re-transmission delay. Therefore, the detection of channel quality in terms of achievable SNR or FER is very important for this invention. Both SNR and FER as channel quality metrics can be derived from the cumulative Euclidean distance metric corresponding to a decoded received sequence. A block diagram of an encoder and decoder system for the invention is illustrated in Figure 3. Within the transmitter 34, the information sequence. { ak} 36 is encoded using a convolutional encoder 38 to provide a coded sequence. { bk} 40. The encoded sequence. { bk} 40 is then mapped through a symbol mapper 42 to a symbol. { sk} 44, either of an M-ary constellation such as an M-ary phase-shifted cipher (PSK = phase shift keying) or a modulation scheme with an M-ary quadrature amplitude (QAM = quadrature amplitude modulation), using either a direct division or gray division mapping technique. The pulse shaping is then carried out using transmission filters 46 which satisfy the Gibby Smith constraints (i.e. necessary and sufficient conditions for inter-zero-digit interference). The symbol . { sk} 44 is then transmitted through channel 48 to receiver 50. At receiver 50, the front end analog reception filters 52 are considered adjusted to the transmission filters 46 and the output. { rk} 54 is sampled at the optimal sampling times. The symbol received at the keBimo instant is given by rk = otksk + nk, where sk denotes the complex transmitted symbol. { sk} 44, otk represents the complex fade channel coefficient 64 and nk represents the complex additive white Gaussian interference (AWGN = additive white Gaussian noise) with variance N0. For this example, fading channel 64 is considered correlated, and can be represented by a model quantity. In this example, the Jake model for Rayleigh fading is used. The convolutional encoder 38 is chosen to optimize the needs of the system. Here, a trellis code has been selected, however many other codes may also be employed by this invention, without modifying the essence of the invention. Maximum probability decoding at receiver 50 can be carried out using a Viterbi algorithm circuit (also known as a maximum probability decoder) 56 to search for the best path through a trellis. An estimate of the coefficients of the complex fading channel 64 is considered available to the decoder (i.e. the convoluted encoder 58) of the receiver 50. The Viterbi algorithm circuit 56 associates an incremental Euclidean distance metric with each trellis branch transition and attempts to find the transmitted sequence. { sk} 44 which is closer in Euclidean distance to the received sequence. { rk} 54. The Viterbi 56 algorithm circuit processes each sequence of Possible data ^ ™ through both an encoder convolutional 58 as symbol mapper 60, to produce a decoded sequence of decoded sequence possible and ^ * J 62. The Viterbi algorithm circuit 56 then use the received sequence. { r ^} 54 and the estimated channel coefficient. { k} 64 in an incremental Euclidean distance metric computing circuit 66 that calculates the incremental Euclidean distance. The incremental Euclidean distance metric is then processed through a cumulative feedback loop 68 which produces the cumulative trajectory metric 72. Next, the cumulative trajectory metric 72 and the corresponding cumulative metrics to all possible transmitted sequences ^ ™ 70 is feed a minimum metric processor circuit 74 that outputs both the decoded data stream ^ m 76 and the minimum metric me for the block and ies? or. The cumulative trajectory metric corresponding to the . { £ > decoded sequence 62 is given by 5t * • _ where ak 64 is the fade channel coefficient estimated at the instant Jces? or and the trellis is considered to end in a known state after each N symbol. Thus, according to one aspect of the present invention, the Viterbi decoder is used to derive the channel quality information from the cumulative Euclidean distance metric, which corresponds to the trellis path decoded by each block. However, as noted previously, the Euclidean distance metric has large variations from one block to another in the presence of a fading channel. In this way, smoothing such as averaging of this variation is required to obtain a good estimate of the metric. A small cumulative Euclidean distance metric will indicate that the received sequence is very close to the decoded sequence. For well-designed trellis codes, this situation will only occur under good channel conditions with high SNR. Under poor channel conditions, the metric is much higher. In this way, a good estimate of the metric can be obtained in the same block of N symbols using the following relation M¿ = cxMi, 1 + (1 - a mi for o, greater than zero and less than 1.0, where the decoded trellis trajectory metric and i represents the filter coefficient that determines the variance of the estimate Figure 4 illustrates a graph that has 4 curves, with the vertical scale representing the average Viterbi decoder metric and the horizontal scale represents the number of blocks The solid line curves 80-86 represent the time evolution of the filtered Viterbi decoder metric for a trellis coded 8 PSK scheme and an equal filter coefficient 0.9 A time slot structure IS-130 / IS- 136 (N = 260 symbols) is considered and the trellis is terminated at the end of each pair of time slots. SNR ranges from 30 dB to 16 dB and are decremented in 2 dB steps after every 600 pairs of time slots. Each solid line curve represents a different combination of fd, the Doppler frequency, multiplied by T, the symbol duration. Therefore, the solid line curve parameters are as follows: a) fdT = 0.0002 for the solid line curve 80; (a) fdT = 0.0012 for the solid line curve 82; (a) fdT = 0.0034 for the solid line curve 84, and (a) fdT = 0.0069 for the solid line curve 86. From Figure 4, it is clear that there is direct one-to-one mapping between the average Euclidean distance metric M ± and the SNR. Maintains a uniform level when SNR is set and increases when SNR decreases.

Figure 5 shows a graph that has 4 curves, with the vertical scale representing the average long-term Viterbi decoder metric μ (the expected value of M ±) and the horizontal scale representing SNR. Again, as in Figure 4, the four curves 88-94 represent different doppler frequencies. From the figure , it is clear that the average metric μ does not depend on the mobile speed. As a result, the long-term cumulative metric average, μ, is the target metric for the present invention. In this way, once the Euclidean metric has been obtained, it can already be mapped to the Corresponding SNR of a search table or through linear prediction approach. The long-term cumulative metric μ and SNR satisfy the empirical relationship NE,? VR = 10lag1 () -? μ in dB, where EB is the average energy per symbol transmitted and N is the number of symbols per block. This behavior remains identical through the different coding modulation schemes. Therefore, the average Viterbi decoder metric provides a good indication of the SNR. In addition, the short-term average of the metric can be determined using the aforementioned relationship ML = o? Mi_1 + (l -o?) Mi. Figure 4 shows that the short-term average satisfies where the target metric, μ is obtained from NE,? V? = lOlog1 () - The thresholds 0low and? high depend on the standard deviation of J? Í; which in turn is a function of the filter parameter, or .. In this manner, the present invention incorporates two possible ways to determine SNR of the average metric M ^. Figure 6 is a flowchart describing in the steps performed either by the base station or the mobile station when determining the SNR of the average Mt metric using a lookup table. The process begins in step 96, where the cellular network determines the SNR range of interest. This SNR range is determined by the needs of the network at any given time. The next step 98 will generate a table of target values μn in descending order of SNR for the given range of interest. The arrangement in descending order is purely for example and is not a necessary or limiting aspect of the process. The objective values are determined by the following relationship: NEt, = ^ IC? Ffl.) for n = 1, 2, ... K, where K determines the desired granularity. In step 100, these values of μn against the corresponding value of SNR are then stored in a memory unit for later use when mapping the values measured from: ^ "m to the corresponding SNR values in the search table. Once the process of creating and storing the search table of μn against SNRn is complete, the system is then ready to receive and transmit data information. In step 102, the receiver obtains for this example a trellis coded signal and then decodes the received coded signal and outputs the trellis path metric mL in step 104. For this example, the system uses a minimum probability decoder Viterbi, to determine trellis trajectory metric -mL. Once the trellis trajectory metric is determined, the system then determines the average metric for the block in step 106 using the ratio Mi = a Mi_1 + (1 - a-ín ^). to decision stage 108, wherein a threshold detector circuit determines whether the value M, ^ m is less than the default threshold? lovl. If he The result of decision step 108 is a determination "YES" (YES), the process continues to step 110. In step 110, the system recognizes that the measured SNR is greater than SNR-L (the maximum SNR for the range of the search table). As a result, the system in step 110 trims the measured SNR to be equal to SNRX. Next, the system in step 102 provides the value SNR, SNRX. If the result of the determination step 108 is a determination "NO", the process continues on the contrary to the decision stage 114, wherein a second ? threshold detector circuit determines whether the value is greater than the pre-determined threshold? (high) If the result of the decision step 114 is a "YES" determination (YES), the process continues to step 116. In step 116, the system recognizes that the measured SNR is less than the SNRk (and the SNR minimum for the range of the search table). As a result, the system in step 116 trims the measured SNR to be equal to SNRk. Next, the system in step 112 provides the SNR, SNRk value to the transmitter. If on the other hand, the result of the determination step 114 is a "NO" determination, the process continues instead of the decision step 118 where a threshold detector circuit determines the threshold μn for the which the mm value is so much smaller than the threshold default 0 high and greater than the pre-determined threshold ^ low The system in step 120 adjusts the measured SNR equal to the corresponding SNRn by the mapped value of m in the search table. As a result, the system in step 112 provides the value SNR, SNRjj to the transmitter. Figure 7 is a flow chart describing the steps performed either by the base station or the mobile station to determine the SNR of the average metric ML using a linear prediction process. The process begins at step 126 where the cellular network determines the SNR range of interest. Similar to the search table approach previously, this range of SNR is first established by the needs of the network at any given time. However, the use of a linear prediction approach, instead of direct mapping of a lookup table, allows the receiver to react more quickly to SNR changes within the cell. In step 126, a table of target values μn in descending order of SNR is generated for the particular range of interest. Again, the arrangement in descending order is purely exemplary and is not a necessary or limiting aspect of the process. The objective values are determined by the following relationship: for n = 1, 2, ... K, where K determines the desired granularity. In step 128, these values of μn against the corresponding value of SNR are then stored in a first memory unit for later use when mapping the Measured values of: m? ^ m to SNR values corresponding in the search table. Once the process to create and store the search table of μn against SNRn is completed, the system is then ready to receive and transmit data information. In step 130, the receiver obtains an encoded signal, a trellis code for example, and then decodes the received encoded signal and outputs the trellis m ± path metric in step 132. Again, for this example, the system uses a Viterbi minimum probability decoder to determine the trellis path metric. Once the trellis trajectory metric is determined, the system then determines the average metric for the block in step 134 using the ratio M ± = aWi-? + (1 - a) • Then, in step 136, the values of a linear predictor of optimum pestel order h (for 1 = a 0, 1, ..., - p) are generated and stored in a second unit of memory for later use. Next, in step 138, the process proceeds and determines whether the value Future of ^ m of the previous values of ^^ ™ using the relationship The process continues to decision stage 140, A where a threshold detector circuit determines whether the value "" - ' is less than the default threshold 0low. If the result of decision step 140 is a determination "YES" (Yes), the process continues to step 142. The system in step 142, cuts the measured SNR to be equal to SNR !. Next, the system in step 144 provides the SNR, SNR1 to the transmitter. If the result of the determination step 140 is a determination "NO", the process continues instead of the decision stage 146, wherein a detection circuit of threshold determines whether the value m ^^ m is greater than the pre-determined threshold flálto • If the result of the decision stage 146 is a determination "YES" (YES), the process continues to stage 148. The system in stage 148 cuts the SNR measured to be equal to the SNRk. Next, the system in step 144 provides the value of SNR, SNRk to the transmitter. If on the other hand, the result of the determination step 146 is a "NO" determination, the process continues instead of the decision step 150 where a threshold detector circuit determines if the value "" "" is both smaller than the predetermined threshold 0aLto Y greater than the pre-determined threshold -fcagp. The system in step 152 adjusts the measured SNR equal to the corresponding SNRn for μ the mapped value of - «" - • in the search table.

As a result, the system in step 144 provides the value of SNR, SNRn to the transmitter. This linear prediction approach helps the receiver using the current value and the past p-l values of the average metric to predict the D blocks of channel quality metrics in the future. In this way, this allows the receiver to react quickly to changes in SNR. While SNR is the preferred performance measure in the present invention, it is well known that performance is often measured in terms of FER for the forward and reverse links. At a fixed SNR, the FER can often be different at different mobile speeds. In order to obtain an indication of RES, the SNR should be noted at the average FER under some broad mobility range. At each SNR value, define the weighted sum and where ? i ± = 1, ^ is the FER at speed, v ± the coefficient, wi represents the weight assigned to the FER velocity and ^ ™ "denotes the weighted average of FER. that technique, it is possible to use the average FER metric to determine the SNR that in turn can be mapped to m m.

As an example of a system for speed adaptation implemented using SNR measurements as a channel quality indicator. Be C?, C7,, CQ representing in ascending order of bandwidth efficiency, the different schemes of operation mode Q for the transmitter. These different schemes can be implemented by using a fixed symbol rate and changing the trellis encoder and symbol mapper to pack a variable number of information bits per symbol. The upper bound in achievable performance WC, XJ-FER (C, .SNR)) for each in some SMR is given by? Mm * - m? M? Mm? ^ - m in where R (Cj) is the data rate that corresponds to Cj in bits / seconds. Current performance may be lower because it also depends on higher layers of recovery that may occur during retransmission. Figure 8 illustrates a graph that has three curved FERs, with the vertical scale representing m m and the horizontal scale representing SNR. The curves 154, 156 and 158 represent three hypothetical coding modulation schemes. For each coding modulation scheme, FER, 'ji, i ^^ ™ is the average FER, averaged over speeds of mobiles. As an example, associated with curve 156 is the adaptation point A 160. If the SNR falls below this point, the transmitter must change its mode from scheme Cj to scheme Cj.xy and start the operation at curve 154 in Bj_x 155, corresponding to the scheme Cj_x on which Cj has lower performance than Cj-i- The filtered Viterbi decoder metric can be used as an SNR indicator at the mode adaptation point. For the block decoded iesl or, be "" * "* ^ depending on the selection of the filter parameter. ? ALio and Shortcut are the thresholds that depend on the filter parameter, or .. Then, the adaptation rule for data transmission is as follows: after the lousy block the transmitter currently operates with the change of operating mode Cj to where r = 1, 2, ...., Q - j. For each, the highest permissible value of r maximizes performance by allowing operation at a higher speed in bits per symbol. Finally, the filtering of the metric can be applied through the encoded modulation schemes since the average metric μ is independent of the mobile speed or the coding modulation scheme. In this way, there is no need to readjust the channel quality measurement after adaptation. Applying current data to this example, Figure 9 shows a table of values for an adaptation strategy conservatively, based on a metric average Viterbi algorithm. In Figure 9, Cx, C2 and C3 represent three coded modulation schemes where the selection of x results in the lowest data rate and C3 results in the highest data rate. Here, μx, μ2 and μ3 are the target metrics corresponding to the FER adaptation points "^^" for the three modulations encoded respectively. The 0th and Ahajo thresholds are defined in such a way that fla-Lto is greater than 1.0 and 0bajomenor less than 1.0. Additionally, Figure 10 shows a table of values for an adaptive strategy in an aggressive way, based on a metric average of the Viterbi algorithm. A block diagram of an adaptive velocity system for the invention is illustrated in Figure 11. The diagram shows the possible implementation of the system either at the base station or at the mobile station. The system operates in the following way. Initially, the system organizes the information to be transmitted in a transmission data stream 162. The transmission data stream 162 is then fed to the transmitter 164 of the system. Within the transmitter 164, the transmission data stream 162 is encoded and modulated by the adaptive channel encoder and modulator 166. The coding and modulation employed by the adaptive channel modulator and encoder 166 are controlled by the unit for modulation decision and encoded 168. The modulation and coding addition unit 168 determines the correct coding and modulation scheme in response to the received SNR estimate 184 from the receiver 162. Initially, the modulated and encoded decision unit 168 chooses a predetermined scheme that is it feeds the adaptive channel modulator and encoder 166. The adaptive channel modulator and encoder 166 then encodes and modulates the transmission data stream 162 to a pre-determined scheme and transmits the information through a channel 170 (possibly noise and fading) ) to the receiver 172. After the information is received on the receiver 172, it is fed to a demodulator and channel decoder 174 which produces two outputs. The first output of the demodulator and channel decoder 174 is a value of the Viterbi decoder metric 176 for the received information signal. The second output of the demodulator and channel decoder 174 is the received data stream 186 which will be the same as the information sent by the transmission data stream 162 a large fraction of the time. Next, the value of the Viterbi decoder 166 metric is averaged by an averaging / aggregate circuit 178 that produces an average movement value for the Viterbi 180 encoder metric. The average movement value for the Viterbi 180 decoder metric then it is mapped when estimating SNR 184 by a mapping circuit 182. The resulting SNR estimate 184 is fed back to the modulation decision unit and encoded 168 to determine the coding and modulation scheme that is used corresponding to the SNR estimate 184. The new scheme value for the modulation and encoding decision unit 168 is fed to the adaptive channel modulator and encoder 166 which switches to the new modulation and coding scheme for the transmission data stream 162 and transmits the information about the channel 170. A block diagram of a system that uses the SNR to perform power control is illustrated in the Fi Figure 12. The diagram shows the possible implementation of the system in either the base station or the mobile station. The system operates in the following way. Initially, the system organizes the information to be transmitted in a transmission data stream 188. The transmission data stream 188 is then fed to the transmitter 190 of the system. Within the transmitter 190, the transmission data stream 188 is encoded and modulated by a channel modulator and encoder 192. The transmit power level in the channel modulator and encoder 192 is controlled by the power control algorithm circuit 202. The power control algorithm circuit 202 can determine the power control level in response to the received SNR estimate 210 from the receiver 196. Additionally, the algorithm circuit for power control 212 can also determine the level of control of power in response to the signal strength and bit error rate estimate 200 from the receiver 196. Initially, the algorithm circuit for power control 212 is set to a pre-determined value which is fed to the modulator and encoder of channel 192. The channel modulator and encoder 192 then encodes and modulates the transmission data stream 188 using predetermined codes and schematic. a modulation and transmits the information at a pre-determined power level through a channel 194 (possibly with noise and fading) to the receiver 196. After the information is received at the receiver 196, it is fed to a demodulator and channel decoder 198 that produces three outputs. The first output of the demodulator and channel decoder 198 is a value of the metric of the Viterbi decoder 202 for the received information signal. The second output comprises estimates of the signal strength and bit error rates 200. The third output of the demodulator and channel decoder 198 is the received data stream 218 which should be the same as the information sent by the data stream of the data. transmission 188. Next, the value of the Viterbi decoder 202 metric is averaged by an averaged / aggregate circuit 204 that produces an average value for the Viterbi 206 decoder metric. The average value for the Viterbi 206 decoder metric is then mapped to the estimate of SNR 210 by a mapping circuit 208. The resulting SNR estimate 210 is fed back into the algorithm circuit for power control 212, to determine a power control value corresponding to the SNR 210 estimate. The new value for power control of the algorithm circuit for power control 212 is fed to the modulator and channel encoder 192 for use in subsequent transmissions of the current of data 188 on channel 194 to the receiver.

Additionally, the mobile assisted transfer decision circuit 214 also processes the SNR 210 estimate and the bit error rate and signal intensity 200 estimates. The SNR value is below a predetermined threshold, the unit for decision mobile-assisted transfer station 214 sends a message to the transfer processor 216, to transfer the mobile station to a new base station. In conclusion, the following summary will easily allow a person with skill in the specialty to practice the invention. The first part of the invention is an apparatus for adaptively changing the modulation schemes of a transmission data stream based on the measured SNR of a channel. The adaptive modulation schemes are implemented in a transmitter by an adaptive channel modulator and encoder. A modulated and coded decision unit is connected to the transmitter adaptive channel modulator and encoder, to determine the correct coding and modulation scheme based on the information received in the receiver. Then, a receiver channel demodulator and decoder is placed in radio connection with the transmitter adaptive channel decoder and demodulator through the channel. This adaptive transmitter channel demodulator and decoder produces a path metric value that is averaged by an averaging circuit to produce an average path metric value. This averaged path metric value is then mapped through a mapping device to an estimated value of SNR. The estimated SNR value is then fed to the modulation and transmission coding decision unit to determine if the coding and modulation scheme should be changed in response to the estimated value of SNR. It will be noted that the receiver channel decoder and decoder can be implemented in various forms, however, in this exemplary implementation a Viterbi decoder is employed. The second part of the invention relates to an apparatus for implementing mobile-assisted transfer in the measured SNR of a channel. The mobile-assisted transfer is implemented in a transmitter by a modulator and channel encoder. A receiver channel demodulator and decoder is in radio connection with the transmitter channel demodulator and decoder through a channel. The receiver channel demodulator and decoder produces a path metric value in response to the information received by the receiver that is averaged by an averaging circuit to produce an average path metric value. This averaged path metric value is then mapped through a mapping device to an estimated value of SNR. An algorithm circuit for power control is connected to the modulator and transmitter channel encoder which varies the power level of the transmitter in response to the estimated value of SNR. Finally, the estimated value of SNR is fed to a unit for mobile assisted transfer decision, which determines whether the mobile station will perform a transfer operation based on the estimated value of SNR. As in the first part of the invention, it will again be noted that the modulator and the receiver channel decoder can be implemented in various forms, however in this exemplary implementation, a Viterbi decoder is employed. Additionally, this second part of the invention can already be implemented in the mobile station or the base station. Please note that while the specification of this invention is described in relation to certain implementations or modalities, many details are set for the purpose of illustration. In this way, the foregoing merely illustrates the principles of the invention. For example, this invention may have other specific forms without departing from its spirit or essential characteristics. The structures described are illustrative and not restrictive. For those with skill in the specialty, the invention is susceptible to further implementations or modalities and certain of the details described in this application may be varied considerably, without departing from the basic principles of the invention. In this way it will be appreciated by those skilled in the art that they will be able to design various structures which, although not explicitly described or illustrated here, incorporate the principles of the invention and thus are within their spirit and scope. The scope of the invention is indicated by the appended claims. It is noted that in relation to this date, the best method known to the applicant to carry out the aforementioned invention, is that which is clear from the present description of the invention. Having described the invention as above, property is claimed as contained in the following:

Claims (1)

1. CLAIMS 1. - A method for determining the signal to interference ratio, characterized in that it comprises the steps of: establishing a set of trajectory metrics corresponding to a set of pre-determined interference-to-interference ratios; receive a digital signal; determine a path metric for the digital signal; and mapping the path metric to the corresponding signal to interference ratio in the set of pre-determined signal to interference ratios. 2. - The method according to claim 1, characterized in that the digital signal is a coded signal. 3. The method according to claim 1, characterized in that the digital signal is a trellis coded signal. 4. - The method according to claim 1, wherein the step of determining a path metric for the digital signal further comprises the steps of: establishing a set of values of signal to interference ratio corresponding to an average set of short-term metric values, the short-term average of the metric values is defined as Mi / μ; determining a decoded path metric from the received digital signal using a decoder, the decoded path metric is defined as m; averaging my and storing in a second memory unit, the average encoded path metric, the average encoded path metric is defined as μ; and determine an estimated Euclidean distance metric. 5. - The method according to claim 4, characterized in that the step to determine the estimated Euclidean distance metric is performed using the following equation: Mi = aMi. + (1 -ct) m¿ where the estimated Euclidean distance metric is defined as Mi and OI is a predetermined filter coefficient that is greater than 0 and less than 1.0. 6. - The method according to claim 5, characterized in that it includes the steps of: determining a standard ML decision; determine the average metric thresholds defined as daLto and Down based on the standard deviation of Mi t determine a value for M ^ / μ, by dividing the value of Mi by the value of μ; map the value of Mi / μ to a minimum value of the corresponding signal to interference ratio if Mi / μ is less than Below map the M ± / μ value to a maximum value of the corresponding signal interference ratio if M. μ is greater than 0aLto »map the value of M μ to the corresponding signal to interference ratio. The method according to claim 4, characterized in that the decoder is a Viterbi decoder for the maximum probability path. 8. - A system for determining the signal to interference ratio, characterized in that it comprises means for establishing a set of trajectory metrics corresponding to a set of pre-determined relations of signal to interference; means for receiving a digital signal; means for determining a path metric for the digital signal; and means for mapping the trajectory metric to the corresponding signal to interference ratio in the set of pre-determined signal to interference ratios. 9. - The system according to claim 1, characterized in that the digital signal is a coded signal. 10. The system according to claim 1, characterized in that the digital signal is a trellis coded signal. 11. The system according to claim 8, wherein the step of determining a metric for digital signal path further comprises: means for establishing a set of values of signal to interference ratio corresponding to an average set of predetermined short-term metric values, the short-term average of the metric values is defined as M / μ; means for determining a decoded path metric from the received digital signal using a decoder, the decoded path metric is defined as my; means for averaging mi and means for storing in a second memory unit, the average decoded path metric, the average decoded path metric is defined as μ; and means to determine an estimated Euclidean metric distance. 12. - The system according to claim 5, characterized in that the means to determine the estimated Euclidean distance metric are performed using the following equation: ML = uMi_1 + (1 -a) mi where the estimated Euclidean distance metric is define as M ± ya is a predetermined filter coefficient that is greater than 0 and less than 1.0. 13. - The system according to claim 12, characterized in that it includes the steps of: means for determining a standard deviation of Mi; means for determining the average metric thresholds defined as A At and Below based on the standard deviation of M, means for determining a value for M ± / μ by dividing the value of A ^ by the value of μ; means to map the value of M ± / μ to a minimum value of the corresponding interference signal ratio in the lookup table if M ± / μ is less than Down; means for mapping the value Mi / μ to a maximum value of the corresponding ratio of interference signal in the lookup table if M / μ is greater than full; and means for mapping the value of M ± / μ to the corresponding signal to interference ratio. 14. - The system according to claim 4, characterized in that the decoder is a Viterbi decoder for the maximum probability path.