WO2010028351A2 - Procédé et appareil permettant de prédire un indicateur de qualité de canal dans un système à accès par paquets en liaison descendante à vitesse élevée - Google Patents

Procédé et appareil permettant de prédire un indicateur de qualité de canal dans un système à accès par paquets en liaison descendante à vitesse élevée Download PDF

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Publication number
WO2010028351A2
WO2010028351A2 PCT/US2009/056206 US2009056206W WO2010028351A2 WO 2010028351 A2 WO2010028351 A2 WO 2010028351A2 US 2009056206 W US2009056206 W US 2009056206W WO 2010028351 A2 WO2010028351 A2 WO 2010028351A2
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Prior art keywords
cqi
processor
adaptive filtering
filtering algorithm
tap
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PCT/US2009/056206
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English (en)
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WO2010028351A3 (fr
Inventor
Tao CUI
Feng Lu
Vignesh Sethuraman
Subramanya P. Rao
Parvathanathan Subrahmanya
Anil Kumar Goteti
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Qualcomm Incorporated
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Publication date
Priority claimed from US12/554,202 external-priority patent/US20100135172A1/en
Application filed by Qualcomm Incorporated filed Critical Qualcomm Incorporated
Publication of WO2010028351A2 publication Critical patent/WO2010028351A2/fr
Publication of WO2010028351A3 publication Critical patent/WO2010028351A3/fr

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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
    • H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
    • H04L1/0023—Systems modifying transmission characteristics according to link quality, e.g. power backoff characterised by the signalling
    • H04L1/0026—Transmission of channel quality indication

Definitions

  • the present invention relates generally to wireless communication technologies, and more particularly to a system and method for predicting the quality of a channel supporting High Speed Downlink Packet Access for data communication.
  • High-Speed Downlink Packet Access is a part of the third generation (3G) mobile telephony communications protocol. It is considered by some to be an enhanced 3G mobile telephony communications protocol in the High-Speed Packet Access (HSPA) family, also coined 3.5G or 3G+.
  • HSDPA allows networks based on Universal Mobile Telecommunications Systems (UMTS) to have higher data transfer speeds and capacity. Communication over a HSDPA system occurs between a base station and a plurality of mobile user equipment (UE) stations, also referred to as mobile devices.
  • UE mobile user equipment
  • HSDPA High Speed Downlink Packet Access
  • a HSDPA system will obtain an indicator of the relative channel signal strength and arrange the data packets in a manner reflective of the relative channel signal strength. Accordingly, it is vital that the indicator of relative channel signal strength accurately reflect the channel signal strength at the time of transmission in order to effectively arrange the data packets. In most HSDPA systems, however, there is a delay between the time an indicator of relative channel signal strength is obtained and the time that data packets are actually transmitted. Changes in the channel signal strength during this delay period may negatively impact the efficiency of the data transmission. Accordingly, accurate predictions in channel signal strength values are desired.
  • systems and methods provide the ability to optimize the data transmission rate in a HSDPA system by applying a predictive filter to approximate the future value of Channel Quality Indicator (CQI) for a channel, based upon a stream of stale CQI values for the channel.
  • CQI Channel Quality Indicator
  • the approximated future value of CQI is used to schedule the transmission of data over the channel.
  • a root method may be used by the predictive filter to approximate a future value of CQI.
  • the root method algorithm provides an adaptive filter that is relatively low in complexity and requires relatively low memory resources as compared to other known approaches.
  • the predictive filter used to predict a future value of CQI may employ a stochastic gradient method.
  • the stochastic gradient method (also referred to as the gradient method) is similarly low in complexity and requires relatively low memory resources as compared to other approaches.
  • the various embodiment predictive filters may be implemented by either the base station or by mobile device processors to control and modify transmission parameters originating at either device.
  • FIG. 1 is a system component diagram of a 3G Mobile Telephone communication system illustrating various mobile devices in communication with a base station.
  • FIG. 2 is a process flow diagram of an embodiment method that may be implemented on a transmitting device in a HSDPA communication system.
  • FIG. 3 is a component block diagram of a exemplary mobile device that may implement the embodiment predictive filters.
  • FIG. 4 is a component block diagram of an exemplary base station processing device that may implement the embodiment predictive filters.
  • the term user equipment may refer to any one or all of cellular telephones, personal data assistants (PDA's), palm-top computers, laptop computers, wireless electronic mail receivers (e.g., the Blackberry® and Treo® devices), multimedia Internet enabled cellular telephones (e.g., the Blackberry Storm®), and similar personal electronic devices that include a programmable processor and memory.
  • the mobile device is a cellular handset capable of high speed data communication over a cellular telephone network (e.g., a cellphone).
  • FIG. 1 is a system component diagram of a communication cell operating within a wireless telecommunications system that may employ the various embodiments disclosed herein.
  • a base station 101 services a cell 100 operating within a wireless telecommunications system.
  • any of a number of mobile devices 102-106 may be within range of base station 101 and relying on base station 101 to send and receive data transmissions and support their respective voice and data communications.
  • Each of the mobile devices 102-106 may be operating under different conditions that can affect the quality of the communication channel each mobile device establishes with the base station 101.
  • stationary users may use their mobile devices 102 and establish a communication link over a channel with the base station 101.
  • Other users may be mobile on foot as illustrated by the users of mobile devices 105 and 106.
  • a typical speed of users walking while employing their mobile device 105, 106 is around 3-5 kmh.
  • Other users may use their mobile devices while driving/riding in a car, as illustrated by mobile devices 103 and 104.
  • mobile devices used in cars move at speeds between 0 and 120 kmh.
  • the Universal Mobile Telecommunications System is one of the technologies standardized by the International Telecommunications Union Telecommunication Standardization Sector (ITU-T) for third generation (3G) networks.
  • High-Speed Downlink Packet Access was included by the 3rd Generation Partnership Project (3GPP) Release 5/6 to improve both the capacity and end-to-end performance of Wideband Code Division Multiple Access (WCDMA) systems, where a mobile device under optimal conditions can receive data at a rate up to 14 Mbps.
  • 3GPP 3rd Generation Partnership Project
  • WCDMA Wideband Code Division Multiple Access
  • each mobile device must periodically report a Channel Quality Indicator (CQI), indicating the downlink channel condition to Node B (base station).
  • CQI Channel Quality Indicator
  • the CQI is transmitted using a specific physical uplink channel, namely the High-Speed Dedicated Physical Control Channel (HS-DPCCH).
  • HS-DPCCH High-Speed Dedicated Physical Control Channel
  • the accuracy of the CQI is crucial for the system performance.
  • the CQI used by Node B in each subframe is presumed to be three (3) subframes stale.
  • the base station may determine how to schedule the transmission of data to each respective mobile device. For example, if a mobile device reports exceptionally high CQI values, indicating a strong signal channel link between the base station and the mobile device, the base station may elect to transmit data to the mobile device in large packet sizes, with minimal error correction and interleaving to achieve a high data rate. Thus, data throughput to mobile devices reporting high CQI values may be high.
  • the base station may elect to transmit data to the mobile device in small packet sizes using maximal error correction coding and interleaving schemes which will compensate for the weak link but result in a low data rate.
  • data throughput to the mobile device may be low.
  • Channel quality may be influenced by a variety of factors.
  • the relative position of the mobile device to the base station affects CQI, because as mobile devices move away from the base station, signal strength declines and the CQI value tend to decrease.
  • Geographic and atmospheric conditions in a particular location may also influence the CQI. For example, geographic features such as mountains, buildings, trees, etc. may cause interference between the mobile device and the base station. Such interference may degrade the signal strength of the communication channel between the mobile device and the base station, thus lowering the CQI.
  • Channel quality also varies over time for moving mobile devices. While the CQI for stationary users will rarely change during the course of voice or data communication session with the base station 101, the CQI will change during the course of a voice or data communication for moving users. Indeed, while the change in CQI for the mobile devices being used by walking users (e.g., mobile device 105 and 106) may be slight, the CQI may change rapidly for fast moving users, such as users in cars (e.g., mobile device 103 and 104). As a result, significant fading performance problems may exist for mobile users. The changing channel quality, and thus changing CQI values, may cause inefficiencies in data throughput in an HSDPA system.
  • a base station schedules the transmission of data packets in accordance with the received CQI values.
  • This scheduling process may modify packet size, error correction, interleaving, and other parameters affecting the data rate.
  • this scheduling delay by the time data is actually transmitted significant changes in the channel quality may have occurred which may degrade the transmission efficiency of the system. For example, data initially scheduled for transmission over a channel reporting a high CQI may now have a low channel quality, and as a result the data may be transmitted in a format unsuitable for the actual conditions and as a consequence data may not be accurately received. In contrast, data initially scheduled for transmission over a channel reporting a low CQI may now have much better channel quality. As a result, the system is unable to capitalize on the opportunity to send data using a higher data rate.
  • some advanced receivers employ offline processing for enhanced demodulation performance.
  • the data processing is performed offline and thus, may add to the delay in actually transmitting the data.
  • the measured CQI value is stale by the time the CQI report is transmitted back to the base station 101.
  • less data is sent than could be accurately sent over the communication channel.
  • too much data is sent over a degraded communication channel, resulting in error laden data packets that cannot be accurately decoded. In both cases, the optimal data throughput is not achieved.
  • FIR filter based predictors implementing the Least-Mean-Squares (LMS) and Recursive-Least-Squares (RLS) approaches, which are adaptive versions of the linear minimum mean-squared error (LMMSE) predictor.
  • LMS Least-Mean-Squares
  • RLS Recursive-Least-Squares
  • LMMSE linear minimum mean-squared error
  • the various embodiments implement adaptive filters that provide adaptive causal predictions of CQI.
  • the embodiment adaptive filters take into account recent historical CQI values to predict future CQI values.
  • the adaptive filters are a linear combination of the most recent CQI value and the existing filter states. While the convergence properties of adaptive infinite impulse response (HR) filters are largely unsolved, they can provide significantly better performance than their FIR counterparts having the same number of coefficients, indicating a smaller implementation complexity.
  • HR adaptive infinite impulse response
  • FIG. 2 is a process flow diagram illustrating a method 200 that may be implemented by the processor of a transmitting device (e.g., mobile device or base station) using any of the various embodiments disclosed herein.
  • the method 200 begins when the mobile device 102-106 and base station 101 establish a communication link with one another, step 201.
  • the CQI of the communication link is periodically determined, step 205.
  • the periodicity may be of such short duration that the CQI of the communication link may be said to be continuously determined.
  • the transmission device is the base station 101
  • a specific physical uplink channel such as the HS-DPCCH.
  • a historical record or sequence of CQI values for the communication link may be stored.
  • any of the various embodiment adaptive filter calculations may be implemented to predict an approximation of a future CQI value of the communication link between the base station 101 and the mobile device (e.g., 102-106), step 210.
  • the various embodiment adaptive filters are discussed in more detail below.
  • the predicted CQI value is applied to the scheduling of the data transmission over the communication link, step 215.
  • the scheduling of the data transmission may include the size of data packets, the amount of error correction coding, or the amount of interleaving.
  • the transmitting device may terminate the communication link. For example, in instances where the transmitting device is the base station 101, if the mobile device has already powered off or been handed off to another base station, the base station may take appropriate steps to terminate the communication link so that the channel may be used by another mobile device, step 225.
  • the various embodiments implement a special first order adaptive HR filter calculation to predict CQI in HSDPA. While previous applications of adaptive HR filters have been mainly directed toward system identification, adaptive HR filtering has rarely been applied to parameter tracking and prediction. While the adaptive HR filtering convergence proofs may be borrowed from their system identification applications, the convergence rate is still undetermined. In addition, the convergence proofs supplied from the system identification application still fail to provide insight as to how to choose the parameters in the algorithm.
  • the steady state mean squared error is derived as a function of the single parameter alpha ( ⁇ ).
  • MSE steady state mean squared error
  • an exact gradient descent algorithm may be derived as well as two pseudolinear regression algorithms.
  • the proposed algorithms are shown to converge using contraction mapping.
  • the pseudolinear regression algorithms do not converge to the same optimal equilibrium point as does the exact gradient descent algorithm.
  • the various embodiments specify the conditions under which the convergent point of the pseudolinear regression algorithms is close to that of the exact gradient descent algorithm. Further, the various embodiments consider the relationship between MSE minimization and mutual information maximization. It has been determined that the former can be considered to be an approximation of the latter.
  • the proposed algorithm can be readily extended to the case in which the process is complex. It may be assumed that the process to be predicted is the same as the observed process, not only for notational simplicity but also for the application of CQI. The obtained results can be readily extended to the case when the two processes are different.
  • 2 ⁇ , whose solution is where d(n) [ ⁇ K), ... ⁇ K + M - l)] r and
  • Equation (1) is a causal LMMSE predictor.
  • n ⁇ + ⁇ the equation becomes a causal Wiener- Kolmogorov filter.
  • LMS and RLS can be considered to be adaptive versions of the LMMSE predictor.
  • ⁇ ( ⁇ ) demnotes the power spectral density of ⁇ u(n) ⁇ , i.e.,
  • first order adaptive HR predictors (I - a ⁇ n))u ⁇ n) + a ⁇ n)u ⁇ n), (6) where ⁇ (n) is an HR filter coefficient at time n. Note that equation (6) is a first order adaptive HR filter.
  • This result provide a first order adaptive HR predictor.
  • ⁇ (n) fl(n - K) + (1 - ⁇ (n)) ⁇ (n - K).
  • ⁇ >0 is a stepsize. This equation may be used as a gradient based algorithm.
  • the predictive filter to approximate a future value of CQI may employ a pseudo-linear regression algorithm which is referred to herein as the root method.
  • the root method algorithm provides an adaptive filter that is relatively low in complexity, and that requires relatively low memory resources as compared to other known method using the LMS or RLS approach.
  • f ⁇ a(n)) E ⁇
  • a(i), u(i) are also implicit functions of a(n).
  • Minimizing equation (17) over a(n) should also take into account this dependence.
  • This algorithm is referred to as the root method algorithm.
  • the predictive filter to approximate a future value of CQI may employ a stochastic gradient method.
  • the stochastic gradient method (also referred herein as the gradient method) is similarly low in complexity and requires relatively low memory resources as compared to other known method using the LMS or RLS approach.
  • ⁇ g ⁇ (20(0) - 4q(0) + 2p(0)) ⁇ (n) + Iq(K) - 2 V (K) - 20(0) + 2q(0) (20)
  • a( ⁇ + 1) [a(n) - 2 ⁇ ((fi(n) - u ⁇ n)) ⁇ u ⁇ n - K) - u ⁇ n - (21) where [-] Q denotes mapping to the interval [0,1] and // is a stepsize for a(n) update.
  • the stochastic gradient algorithm may be derived by differentiating equation (7) directly with respect to a(n), which gives the gradient ⁇ (n + K) — u(n + K)(u(n) — u(n)).
  • a(n) the time in the gradient may be shifted K ,and then a(n) may be updated using
  • a(n + 1) [a(n) - 2 ⁇ ((fl(n) - u(n))(un - K) - u(n - K)))] 2 0 (22)
  • ⁇ may be chosen according to the average level of the absolute value of the gradient.
  • ⁇ may be chosen according to the average gradient square:
  • d( ⁇ i) 2 ((fi(n) - u(n))u(n + K) - (fi(n - K) - u(n - K))u(n ⁇ ). (23) Let d(n) be the average value of ⁇ d(n) ⁇ update to time n, i.e.,
  • ⁇ does not need to be updated every subframe, rather only once in ⁇ subframes, where ⁇ >0 is the interval between two ⁇ updates.
  • an n-tap minimum mean-squared error (MMSE) filter may be obtained for time n.
  • the HR predictor can be considered to be a variable length MMSE filter even though it appears to only contain one tap and there is a single parameter to control the MSE. This may possibly explain why an adaptive HR predictor is better than a finite impulse response predictor given the same number of coefficients.
  • equation (28) may be minimized numerically and the MSE of IIR may be compared using the exact gradient method, denoted as ""1IR Optimal” with that of LMMSE in equation (26).
  • the pseudolinear regression root method may respond slowly to the time variations of the processes because the new contribution to the correlation coefficient in equation (19) decays as Vn, while the gradient method may oscillate too much.
  • ⁇ (k), p(k), and q(k) may be estimated using a finite sliding window rather than an infinite window.
  • another HR filter may be used to update p(k), p(k), and q(k), where the coefficient of this HR filter could either be fixed to a constant value or be adjusted adaptively.
  • the root method can also be combined with the gradient method by running the gradient method first using a large stepsize and then applying the root method to take advantage of the fast start of the gradient method and the smooth dynamic of the root method.
  • the gradient in equation (20) may be estimated using a short window, which includes equation (21) as a special case using a window of size 1.
  • a(n) may also be updated using a window of size W, i.e., equation (21) is replaced by
  • Minimizing the MSE has been considered in a single subframe.
  • minimizing a general cost function may be considered, for example, the weighted least squares error function
  • C(w n ) EIL 0 A ⁇ 1 Iu(O - fi(01 2 (30) where 0 ⁇ ⁇ ⁇ 1 is an exponential weighting factor or forget factor, effectively limiting the number of input samples based on which cost function is minimized or the memory of the algorithm. All proposed algorithms can be readily generalized to this case.
  • the embodiment algorithms may be implemented in a variety of devices.
  • the mobile device 102-106 determines the quality of the communication channel in the form of CQI between the mobile device and the base station 101 and reports the CQI back to the base station 101 over a physical uplink channel.
  • the embodiment CQI prediction calculations may be performed by the individual mobile devices 102-106.
  • each mobile device 102-106 may transmit the calculated predicted CQI value back to the base station 101 in place of the conventional CQI value.
  • the calculated predicted CQI values may be received by the base station 101 and used to schedule the transmission of data to each of the respective mobile devices 102-106.
  • the base station 101 may continue to receive conventional CQI values from each of the mobile device 102-106 that have established a communication link with the base station 101.
  • the base station may implement any of the embodiment CQI prediction algorithms to calculate the predicted CQI values.
  • the calculated predicted CQI values may be used to schedule the transmission of data to each of the respective mobile devices 102-106.
  • the increased processing power and performance of the computer operating at the bases station 101 may be leveraged.
  • both processing power and battery power of each individual mobile device 102-106 may be conserved.
  • the portable computing devices may include a processor 191 coupled to internal memory 192.
  • the processor 191 may also be coupled to a display device 11.
  • the portable computing device 100 may have an antenna 194 for sending and receiving electromagnetic radiation which is connected to a wireless data link and/or cellular telephone transceiver 193, coupled to the processor 191. Determination of a CQI may be completed by the processor 191, or in a module of the transceiver 193.
  • Portable computing devices 102-106 typically include some form of input device such as a key pad 13, or miniature keyboard and menu selection keys or rocker switches 12 which serve as pointing devices. Alternatively, some portable computing devices may employ touchscreen technology, wherein virtual keypads may be employed on the display surface 11.
  • the processor 191 may further be connected to a wired network interface 194, such as a universal serial bus (USB) or Fire Wire® connector socket, in order to connect the processor 191 to an external device or external local area network.
  • the processor 191 may also be coupled to a speaker 18 and microphone 19 through a vocoder 199.
  • the processor 191 may be any programmable microprocessor, microcomputer, or multiple processor chip or chips that can be configured by software instructions (applications) to perform a variety of functions, including the functions of the various embodiments described above.
  • multiple processors 191 may be provided, such as where one processor is dedicated to wireless communication functions and another processor is dedicated to running other applications.
  • the processor may also be included as part of a communication chipset.
  • software applications may be stored in the internal memory 192 before they are accessed and loaded into the processor 191.
  • the processor 191 may include internal memory sufficient to store the application software instructions.
  • the term memory refers to all memory accessible by the processor 191, including internal memory 192 and memory within the processor 191 itself.
  • Application data files are typically stored in the memory 92.
  • the memory 192 may be a volatile or nonvolatile memory, such as flash memory, or a mixture of both.
  • the embodiments described above may be implemented on any of a variety of stationary computing devices, such as at a base station 101.
  • An example of which is the server 300 illustrated in FIG. 4.
  • Such a server 300 typically includes a processor 361, coupled to volatile memory 362, and to a large capacity nonvolatile memory, such as a disk drive 363.
  • the server 300 may also include a floppy disc drive and/or a compact disc (CD) drive 366, coupled to the processor 361.
  • the server 300 may also include a number of connector ports 364, coupled to the processor 361 for establishing data connections with network circuits 365.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • a general-purpose processor may be a microprocessor, but, in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine.
  • a processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Alternatively, some steps or methods may be performed by circuitry that is specific to a given function.
  • the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium.
  • the steps of a method or algorithm disclosed herein may be embodied in a processor-executable software module executed which may reside on a computer-readable medium.
  • Computer- readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another.
  • a storage media may be any available media that may be accessed by a computer.
  • such computer-readable media may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to carry or store desired program code in the form of instructions or data structures and that may be accessed by a computer.
  • any connection is properly termed a computer-readable medium.
  • the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave
  • DSL digital subscriber line
  • wireless technologies such as infrared, radio, and microwave
  • Disk and disc includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media. Additionally, the operations of a method or algorithm may reside as one or any combination or set of codes and/or instructions on a machine readable medium and/or computer-readable medium, which may be incorporated into a computer program product.

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  • Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

L’invention concerne différents modes de réalisation qui prédisent l’indicateur de qualité de canal (CQI) dans un accès par paquets en liaison descendante à vitesse élevée (HSDPA). La précision du CQI est cruciale pour la performance du HSDPA. Dans certains systèmes HSDPA, le CQI peut correspondre à trois (3) sous-trames. En conséquence, la prédiction de valeurs CQI est requise pour planifier efficacement des données à transmettre par le biais du canal de communication. Divers modes de réalisation concernent des filtres adaptatifs de premier ordre (IIR), qui sont beaucoup moins complexes que leurs homologues à réponse impulsionnelle finie (FIR) et assurent une précision similaire. En minimisant l’erreur quadratique moyenne (MSE), il est possible d’utiliser un algorithme de descente de gradient exact ainsi que les deux algorithmes de régression pseudo-linéaires du mode de réalisation.
PCT/US2009/056206 2008-09-08 2009-09-08 Procédé et appareil permettant de prédire un indicateur de qualité de canal dans un système à accès par paquets en liaison descendante à vitesse élevée WO2010028351A2 (fr)

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US9527008P 2008-09-08 2008-09-08
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US9784808P 2008-09-17 2008-09-17
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US12/554,202 2009-09-04
US12/554,202 US20100135172A1 (en) 2008-09-08 2009-09-04 Method and apparatus for predicting channel quality indicator in a high speed downlink packet access system

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