WO2017186309A1 - Measurement model optimization for channel prediction improvement in wireless networks - Google Patents

Measurement model optimization for channel prediction improvement in wireless networks Download PDF

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Publication number
WO2017186309A1
WO2017186309A1 PCT/EP2016/059667 EP2016059667W WO2017186309A1 WO 2017186309 A1 WO2017186309 A1 WO 2017186309A1 EP 2016059667 W EP2016059667 W EP 2016059667W WO 2017186309 A1 WO2017186309 A1 WO 2017186309A1
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Prior art keywords
ue
filter
channel
prediction
parameter
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PCT/EP2016/059667
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French (fr)
Inventor
Afef Feki
Mustapha Amara
Sami Mekki
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Huawei Technologies Co., Ltd.
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Priority to PCT/EP2016/059667 priority Critical patent/WO2017186309A1/en
Publication of WO2017186309A1 publication Critical patent/WO2017186309A1/en

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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W36/00Hand-off or reselection arrangements
    • H04W36/0005Control or signalling for completing the hand-off
    • H04W36/0083Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B17/00Monitoring; Testing
    • H04B17/30Monitoring; Testing of propagation channels
    • H04B17/373Predicting channel quality parameters

Abstract

A network apparatus, comprising a receiver adapted to receive/collect/acquire channel measurement information, a processor adapted to calculate a parameter update for updating a parameter of a filter of a UE the calculation comprises, determine a prediction performance based on the channel measurement information, and determine the parameter update for updating the parameter of the filter of the UE based on a measurement model of the filter and the prediction performance and a transmitter adapted to transmit and/or to provide the parameter update to the UE.

Description

MEASUREMENT MODEL OPTIMIZATION FOR CHANNEL PREDICTION IMPROVEMENT IN WIRELESS NETWORKS

BACKGROUND

The present invention, in some embodiments thereof, relates to improving channel prediction in wireless networks and, more specifically, but not exclusively, to improving channel prediction in wireless networks by optimizing a channel measurement information model.

Wireless networks, for example, universal terrestrial radio access network (UTRAN) monitor channel information of a communication link during communication between the network infrastructure, for example, a base station and a plurality of user equipment (UE) devices such as, for example, cellular phones, endpoints and the like. The channel information may be used for a plurality of applications, for example, defining a modulation and coding scheme (MCS) for the communication link, selecting a base station, assessing handover of the UE between base stations, managing radio resources and the like.

Channel measurement(s) such as, for example, a reference signal received power (RSRP), a reference signal received quality (RSRQ) and other measurement related to the channel are collected at the UE and are often processed at the UE using one or more filters to produce the channel information. The processing and/or filtering of the channel measurement(s) may be defined by respective standards, for example, as it is defined in the 3rd generation partnership project (3 GPP) for universal mobile telecommunications system (UMTS) and/or the long term evolution (LTE) standards.

The channel information may be transmitted by the UE to one or more network apparatuses, for example, the base stations and/or a radio network controller (RNC) which may make use of the channel information for one or more of the application presented herein above.

Due to inherent delays in the communication link, the channel information may be received and/or analyzed by the network apparatus in a significant delay from the time the channel information was collected. Thus, the channel information may no longer be relevant and/or reflect communication link characteristics that are out of date. To overcome the inherent delays, prediction models are employed by the network apparatuses that predict channel information based on previously received channel information. SUMMARY

According to an aspect of some embodiments of the present invention there is provided a network apparatus, comprising a receiver adapted to receive, collect and/or acquire channel measurement information, a processor adapted to calculate a parameter update for updating a parameter of a filter of a UE, the calculation comprises determine a prediction performance based on the channel measurement information, and determine the parameter update for updating the parameter of the filter of the UE based on a measurement model of the filter and the prediction performance and a transmitter adapted to transmit and/or to provide the parameter update to the UE.

The processor is adapted to determine the parameter update by evaluating a prediction error based on measurement prediction estimation.

The processor is adapted to calculate the parameter update according to an optimization and/or learning function determined according to an analysis of a plurality of previously determined prediction performances and respective obtained prediction quality.

The prediction performance is calculated based on a Doppler frequency shift and/or a signal to interference noise ratio, SINR, measured by the UE.

The parameter update is a value of a parameter calculated based on at least some variables selected from a group consisting of: a Doppler frequency shift, an SINR, a sampling period, a physical layer measurement model, a prediction method, a prediction horizon and/or an observation window.

The parameter update is calculated by extracting a value from a static precomputed table, in particular a look-up table, LUT.

The receiver is adapted to receive the channel measurement information in each of a plurality of iterations and the processor is adapted to calculate the parameter update and to instruct the transmitter to transmit the parameter update to the UE in each one of the plurality of iterations.

According to an aspect of some embodiments of the present invention there is provided a method of a wireless network adapted to deliver updates of measurement model parameters to a wireless communication device, comprising receiving channel measurement information, determining a prediction performance based on the channel measurement information, determining a parameter update for updating a filter parameter of a filter of a UE based on a measurement model of the filter and the prediction performance and transmitting and/or providing the parameter update to the UE.

The prediction performance and/or the parameter update for a future calculation of an additional parameter update.

Optionally, one or more of the prediction performance and/or the parameter update are stored.

According to an aspect of some embodiments of the present invention there is provided a UE, comprising a receiver adapted to receive a parameter update from a network apparatus and a processor adapted to update a parameter of one or more filters based on the parameter update, the one or more filters are used to filter channel measurement information of a transmission received by the receiver.

Optionally, the UE comprises a transmitter adapted for transmitting the channel measurement information related to a transmission filtered using the one or more filters to a network apparatus. Wherein the parameter update is calculated according to the channel measurement information.

According to an aspect of some embodiments of the present invention there is provided a method for updating a filter of a UE, comprising wirelessly receiving, at a UE, a parameter update from a network apparatus and updating a filter of the UE based on the parameter update.

Optionally, the channel measurement information gathered based on filtering of the filter is transmitted to the network apparatus. Wherein the parameter update calculated according to the channel measurement information filter.

Unless otherwise defined, all technical and/or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of the invention, exemplary methods and/or materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting. BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Some embodiments of the invention are herein described, by way of example only, with reference to the accompanying drawings. With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments of the invention. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments of the invention may be practiced.

In the drawings:

FIG. 1 is a schematic illustration of an exemplary universal terrestrial radio access network (UTRAN);

FIG. 2 is a flowchart of an exemplary process for optimizing a channel measurement information model, according to some embodiments of the present invention;

FIG. 3 is a graph of an analysis of exemplary filtering parameters impact on network performance, according to some embodiments of the present invention;

FIG. 4 is a graph and a table of an exemplary pre-defined filtering parameters source for selecting pre-defined filtering parameters to optimize channel measurement information model, according to some embodiments of the present invention;

FIG. 5 is a schematic illustration of an optimization policy of an exemplary learning mechanism for selecting filtering parameters to optimize channel measurement information model, according to some embodiments of the present invention;

FIG. 6 is a schematic illustration of an exemplary UE, according to some embodiments of the present invention;

FIG. 7 is a flowchart of an exemplary process for updating channel information processing parameter(s) at a UE, according to some embodiments of the present invention;

FIG. 8 is a schematic illustration of an exemplary network apparatus, according to some embodiments of the present invention; and

FIG. 9 is a flowchart of an exemplary process for calculating updated parameter(s) used by a UE to produce channel information, according to some embodiments of the present invention. DETAILED DESCRIPTION

The present invention, in some embodiments thereof, relates to improving channel prediction in wireless networks and, more specifically, but not exclusively, to improving channel prediction in wireless networks by optimizing a channel measurement information model.

The present invention presents systems and methods for adjusting one or more parameters at a UE, i.e. a UE, such as, for example, a cellular phone, a cellular apparatus, an endpoint and the like. The parameters may be adjustment in order to improve channel prediction and hence improve network performance, for example, throughput, quality of link and the like of one or more radio access technology (RAT) networks, for example, as defined in the 3GPP for UMTS and/or LTE.

Channel information is created by the UE which collects channel measurements, for example, RSRP, RSRQ and the like and processes the channel measurements by applying, for example, one or more filters to produce the channel information. The channel information may be transmitted by the UE to one or more network apparatuses, for example, a base station, an RNC and the like. In some embodiments of the present invention, device-to-device (D2D) communication is employed where two or more UEs communicate with each other directly rather than through a base station. In a D2D network deployment the network apparatus may be utilized by another UE referred to herein as a transmitting UE. The transmitting UE may receive the channel information from one or more another UEs and perform as the network apparatus. The network apparatus(s) may use the channel information for channel (network) prediction to overcome inherent delays in the communication link between the UE and the network apparatus(s). The channel prediction may be used by the network apparatus(s) to predict network performance and take one or more actions accordingly, for example, define an MCS for the communication link with the UE, select a base station, asses and/or decide handover of the UE between base stations, manage radio resources and the like. The channel prediction is continuously monitored and analyzed at the network apparatus to determine performance of the channel prediction, i.e. determine how accurately the channel prediction predicts channel information compared to actual channel information subsequently received from the UE. The channel prediction may be performed in a closed feedback loop with the performance analysis such that one or more attributes and/or aspects of the channel prediction process may be adjusted to improve the channel prediction performance. Based on the analysis, the channel prediction may be improved by adjusting one or more parameters of the filters at the UE which are used to create the channel information. The channel prediction performance analysis may also be exposed (receive) the channel information to allow a more accurate analysis of the channel prediction performance and/or to properly calculate the filter(s)' parameters to be adjusted at the UE. The adjusted parameters may be selected from a standardized parameter space to maintain compliance with the specification, for example, 3 GPP. The adjusted parameter(s) may be transmitted to the UE which may update its filter(s) with the received adjusted parameter(s). By adjusting the parameter(s) of the UE filter(s), the channel information provided by the UE may better serve the channel prediction model at the network apparatus(s) to improve channel prediction and hence improve the network performance. The parameter(s) adjustment may be done using pre-defined parameter values selected to provide best channel prediction results and/or by a learning model that evolves and may dynamically calculate the parameter(s) value based on acquired experience.

The systems and methods presented herein may significantly improve network performance by correlating the observed network performance and the channel information acquisition as opposed to existing solutions where the acquisition of the channel information at the UE is decoupled from the applicable use made with the acquired channel information at the network apparatus(s). While the existing methods may use biased and/or inaccurate channel information, the present invention is directed at analyzing the network performance which is based on channel prediction and updating channel information acquisition with adjusted parameters at the UE for better acquiring the channel information in order to improve the channel prediction accuracy and hence the network performance.

Moreover, the network communication, in particular cellular network communication may be dynamic and subject to various interferences, conditions changes and the like. By dynamically adjusting the channel information acquisition parameter(s) at the UE, the channel prediction may improve and thus significantly improve the network performance.

Furthermore, the systems and methods for analyzing the channel prediction, determining the adjusted parameter(s) and transmitting the adjusted parameters to be updated at the UE may be modular such that they may be combined with a variety of prediction methods and/or may be flexibly integrated into existing systems. Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not necessarily limited in its application to the details of construction and the arrangement of the components and/or methods set forth in the following description and/or illustrated in the drawings and/or the Examples. The invention is capable of other embodiments or of being practiced or carried out in various ways.

The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network.

The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer such as the user equipment (UE), as a standalone software package, partly on the user's computer and partly on a remote computer such as the network apparatus or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

Reference is now made to FIG. 1, which is a schematic illustration of an exemplary universal terrestrial radio access network (UTRAN). A UTRAN 100 includes a plurality of UEs 104 that connect over a wireless network, in particular a cellular network, for example, UMTS, LTE and the like to one or more network apparatuses 102, for example, a base state and/or an RNC connected to a core of the network 106. Each of the UEs 104 may be, for example, a cellular phone, a cellular apparatus, an endpoint and the like. In some embodiments of the present invention, where the UTRAN 100 and/or part thereof utilizes D2D network deployment, a transmitting UE (another UE) may perform as the network apparatus 102 for one or more UEs 104. In the D2D network deployment, the transmitting UE acting as the network apparatus 102 may communicate directly with the UE(s) 104 without going through the base station.

Reference is also made to FIG. 2, which is a flowchart of an exemplary process for optimizing a channel measurement information model, according to some embodiments of the present invention. A process 200 for optimizing a channel measurements information model comprises two parts 200 A and 200B. The first process part 200A is performed at a UE such as the UE 104. The second part is performed at a network apparatus such as the network apparatus 102.

As shown at 202, the process part 200A starts with the UE 104 collecting channel measurements which include one or more measurements of a channel (communication link) between the UE 104 and the network apparatus 102, for example, RSRP, RSRQ and the like. The channel measurements may indicate, for example, quality of the channel, signal strength, interference from other signals and/or background signals and the like. The collected channel measurements may be filtered and/or processed using one or more standards and/or specifications such as, for example, the 3GPP standard and the like.

It should be noted that while the methods and systems presented herein describe filtering and/or processing of the channel measurements per the 3GPP specification, other filtering and/or processing methods of the channel measurements may be applied per other communication channels, networks and/or specifications currently known and/or yet to be defined.

As shown at 204, an LI (layer 1) filter is applied to the channel measurements, for example, to average the collected instantaneous measurements over a pre-defined time interval. The pre-defined time interval (7) may be considered a filtering parameter which may be selected and/or adapted to affect the processing of the collected channel measurements by the LI filter and/or improve the outcome of the LI filter. Averaging the collected instantaneous measurements is one possible process applied at the LI to the channel measurements, other processing methods may be applied at the LI . Hence, other parameters (other than the time interval (7)) may be use at the LI filter.

As shown at 206, an L3 (layer 3) filter is applied to perform recursive filtering on the resulting channel data coming in from the LI filter. The recursive filtering is done using a filtering variable per the 3 GPP specification to create the channel information. The filtering variable (a) may be considered a filtering parameter that may be selected and/or adapted to affect the processing of the collected channel measurements by the L3 filter and/or improve the channel information coming out of the L3 filter. Recursive filtering is one possible process applied at the L3 to the channel measurements coming in from the LI filter, other processing methods may be applied at the L3. Hence, other parameters (other than the filtering variable (a)) may be use at the LI filter.

As shown at 208, the channel information is compared to one or more reporting criterion(s) to decide whether or not the channel information should be transmitted to the network apparatus 102. For example, the RSRP may be checked under a respective reporting criterion such as, for example, a threshold value. In the event the reporting criterion(s) is met, i.e. the measured RSRP level is below the threshold value, the channel information may be transmitted by the UE 104 to the network apparatus 102. As another example, the channel information may be periodically reported by the UE 104 to the network apparatus 102. The reporting interval may be selected and/or adapted to increase or reduce the frequency for the UE 104 to report new channel information to the network apparatus 102.

As shown at 210 that is the first step of the process 200B, the channel information transmitted by the UE 104 is received at the network apparatus 102. The network apparatus may use the channel information (measurements) for one or more applicable operations, for example, define an MCS for the communication link with the UE, select a base station, asses and/or decide handover of the UE between base stations, manage radio resources and the likes.

As shown at 212, in parallel to using the channel information and/or measurements for the applicable application(s), the network apparatus 102 may use the channel information for channel prediction. The channel prediction may be used by the network apparatus 102 to select and/or apply the applicable operation(s) to overcome inherent delays between the time the channel measurements are taken at the UE 104 and the time the channel information is actually used by the network apparatus 102. The inherent delays may result, for example, from channel information processing time at the UE 104 and/or the transmission time between the UE 104 and the network apparatus 102. The channel prediction is performed by the network apparatus 102 using one or more prediction models (PM) based on the received channel information (measurements) to estimate one or more characteristics of the (communication) channel between the UE 104 and the network apparatus 102. For example, the network apparatus 102 may use a Doppler frequency shift and/or a signal to interference noise ratio (SINR) of the channel as measured by the UE 104. The prediction models may apply to long range prediction and/or short term prediction and may employ one or more prediction methods, for example, linear prediction, polynomial approximation and the likes. The network apparatus 102 may perform channel prediction in a closed feedback loop such that one or more aspects of the channel prediction model may be adjusted to improve the channel prediction performance based on evaluation of current channel prediction performance as described hereinafter at step 214. The channel prediction methods are beyond the scope of the present invention, however the optimization of the channel measurements model to improve the prediction performance is adapted per the selected channel prediction method, model and/or algorithm employed by the network apparatus 102.

As shown at 214, the network apparatus 102 evaluates performance of the channel prediction. The settings of the filtering parameters may have a significant impact on the channel prediction performance and/or accuracy since the channel prediction greatly depends on the actual channel information (measurements) received from the UE 104. Therefore by adapting the filters of the UE 104 to produce channel information which best fits the channel prediction method, model and/or algorithm used by the network apparatus 102, the channel prediction performance and/or accuracy may be significantly improved and hence performance of the channel between the network apparatus 102 and the UE 104 may be increased.

The performance of the channel prediction is evaluated by comparing the performance calculated for the current channel prediction with performance calculated for channel prediction through simulations using alternate filtering parameters. The simulations may apply to the channel prediction method (PM) used by the network apparatus 102 with different settings of the filtering parameters, for example, the time interval (7) of the LI filter, the filtering variable of the L3 filter and/or the reporting criterion(s). Multiple settings of the filtering parameters may be simulated to determine a set of filtering parameters that produces a lowest channel prediction error, for example, by calculating a mean square error (MSE). The MSE may not necessarily be a convex function and may be extracted from the prediction performance evaluation function.

The settings of the filtering parameters may include filtering parameters selected from which is the entire possible filtering parameters space. The selected filtering parameters may be selected based on one or more channel characteristics, for example, a Doppler frequency shift, a sampling period of the LI and/or L3 filters, a physical layer measurement model period, a prediction method, model and/or algorithm, a prediction horizon and/or an observation window. The parameter space T relates to the allowed set of possible values for the filtering parameters and/or reporting criteria as defined, for example, by the 3GPP specification (standard). Other values for the filtering parameters and/or reporting criteria may be considered but may not be standard compliant.

The network apparatus 102 may use the channel information currently used by the UE 104 for the evaluation of the channel prediction performance. Using the received channel information may allow the network apparatus 102 to better evaluate the performance of the channel prediction model since the input to the channel prediction model may be evaluated compared to the output of the channel prediction model.

Reference is now made to FIG. 3, which is a graph of an analysis of exemplary filtering parameters impact on network performance, according to some embodiments of the present invention. A three dimensional (3D) graph 300 shows a 3D distribution of a relative throughput gain presented as percentage (%) at axis Z with respect to an LI filter time interval (7) in seconds at axis Y and an L3 filtering parameter (a) at axis X. The graph 300 depicts a relative throughput results for a moving UE 104 with a Doppler frequency shift of 100 Hz and an SINR γ = lOdB. The Doppler frequency shift and/or the SINR are measured by the UE 104 and transmitted to the network apparatus 102. The relative throughput gain is calculated compared to a worst-case set of the filtering parameters. The graph 300 demonstrates how channel performance may be improved by enhancing the channel prediction; where the channel prediction is improved through an optimization of the channel information model of a UE such as the UE 104, for example, by optimizing the filtering parameters at the UE 104.

As seen, a reference point 302 represents a relative throughput gain of 11.79% achieved by the prediction method using the reference filtering parameters T and a applied at LI and L3 filters of the UE 104. The reference filtering parameters used at the UE 104 are set to:

T= 200ms (0.2s) and a = Q)6 = ^ .

As seen, an optimal point 304 represents a relative throughput gain of 18.71% achieved with optimal filtering parameters T and a applied at LI and L3 filters (respectively) of the UE 104. The optimal filtering parameters are set to: T= 300ms (0.3s) and a = (j) = ± .

The optimal filtering parameters present a relative throughput gain of ~7% over the reference filtering parameters and a relative throughput gain of almost 20% over the worst case filtering parameters.

As discussed before, the optimal filtering parameters for identifying the optimal set of the optimal point 304 may be identified through simulation of multiple sets of filtering parameters combinations to identify a set which provides the optimal performance for the given channel characteristics.

The simulations may be done offline to identify the optimal set of filtering parameters that presents best channel prediction for a plurality of scenarios based and/or channel characteristic(s).

Reference is now made to FIG. 4, which is a graph and a table of an exemplary pre-defined filtering parameters source for selecting pre-defined filtering parameters to optimize channel measurement information model, according to some embodiments of the present invention. The network apparatus 102 may retrieve the sets of filtering parameters from one or more sources, for example, a list, a table, a database and the likes. An exemplary look up table (LUT) 402 holds a plurality of entries each storing a set of filtering parameters. The sets of filtering parameters constituting the LUT 402 may be explored and/or constructed off-line through an exhaustive optimization process for identifying a filtering parameters set presenting a minimum MSE computed over the standardized parameter space for a plurality of various scenarios and/or different channel characteristics. The various scenarios and/or channel characteristics may include, for example, the Doppler frequency shift, the SINR, a special correlation, the sampling period of the LI and/or L3 filters, the physical layer measurement model period, the channel prediction method, model and/or algorithm, the prediction horizon and/or the observation window. The selected set of filtering parameters may be the optimal set presenting the minimum MSE for the current channel characteristic(s) as measured by the UE 104.

The exemplary LUT 402 shows the sets of filtering parameters as a function of the Doppler frequency. For example, considering the example of a moving UE 104 with a Doppler frequency shift of fd = 30 Hz, the channel prediction error is computed over the possible set of filtering parameters (T and a). Consequently, the set comprising T* = 200 ms and a* = 1/8 is deduced to be the optimal parameter set for this Doppler frequency shift and then stored in the LUT 402. The optimal parameter set comprising the filtering parameters T* = 200 ms and a* = 1/8 is selected following a simulation performed off-line as shown in graph 404. The graph 404 presents the MSE at axis Y computed through simulation of five L3 filter values over several LI averaging time period values at axis X for a channel having a Doppler frequency shift of fa = 30 Hz. As evident from the graph 404, the set comprising sampling T* = 200 ms and a* = 1/8 presents the lowest channel prediction MSE and is therefore stored in the appropriate entry in the LUT 402.

Optionally, the network apparatus 102 employs a learning machine to select the set of filtering parameters. The learning mechanism may employ an online learning algorithm allowing selection of the optimal set of filtering parameters values based on analysis of previously chosen filtering parameters sets compared to the channel prediction performance and/or accuracy obtained using the previously chosen filtering parameters sets. For example, an upper bound confidence (UBC) learning algorithm as described in the art may be employed to choose the set of filtering parameters from the space of possible filtering parameters . The UBC method allows achieving a tradeoff between exploration and exploitation. Exploration refers to trial of different filtering parameter values with respect to corresponding obtained channel prediction accuracy and/or performance. Exploitation refers to the knowledge gathered from previous selections of the sets of filtering parameters and the channel prediction accuracy and/or performance obtained using theses sets.

The online learning algorithm may be executed by a network apparatus such as the network apparatus 102 where the accuracy and/or performance of the channel prediction may be evaluated. The selection of the filtering parameters may be realized in a proactive manner. At each instant of time t , the measurement model optimization function executed by the network apparatus 102 evaluates whether the filtering parameters should be updated.

An exemplary Upper Confidence Bound (UCB) online learning algorithm may be employed in this context. A set of possible values for the L3 filtering parameters (a) is presented in equation 1 below.

Equation 1 :

r 1 1 1 1 )

a e {1; 2 ; 4 ; 6 ; 8 i The UCB learning algorithm may employ a decision function (DF) for calculating a channel prediction performance for each of the values of

{ l l l l Ί

1; - ; - ; - ; - j. The decision function (DF) is presented in equation 2 below.

Equation 2:

c x var(^ t)

DFi,^u + — ^

ni,t

Where is the channel prediction accuracy and/or performance value corresponding to the prediction mean square error ^∑¾=1(½ — ¾)2 when using filter parameter at at a time t, where is the actual measured value and ¾ is the predicted value.

var^i t) is the variance of the mean reward when using at until the time t. c is a constant that tunes the exploration probability.

ni t is the number of iterations where at have been chosen up to the time t (t <

T) where T is a finite evaluation horizon.

Reference is now made to FIG. 5, which is a schematic illustration of an optimization policy of an exemplary learning mechanism for selecting filtering parameters to optimize channel measurement information model, according to some embodiments of the present invention. A schematic illustration 500 presents a progress of a learning mechanism executed on a network apparatus such as the network apparatus 102 providing updated sets of filtering parameters to a UE such as the UE 104 at a plurality of points in time. Each of the X represents the currently used (instantaneous) filtering parameter(s) value(s) where in the current example the filtering parameters refer to the L3 filtering parameter (a). As can be seen during a first period of time that may be designated as an initialization period 502, the learning mechanism may provide the UE 104 with different sets of values of the filtering parameters such as the L3 filtering parameter and evaluate the channel prediction obtained with each of the different sets. Gradually the learning mechanism converges to an optimal set of filtering parameters 504 used at the UE 104 that provides the best channel prediction accuracy and/or performance. However, during and/or after identifying the optimal set of the filtering parameters 504, the learning mechanism may still explore other sets of filtering parameters 506 to evaluate possibilities of sets that may provide better channel prediction accuracy and/or performance. In case explored set(s) of filtering parameters 506 present better channel prediction accuracy and/or performance than the selected optimal set of filtering parameters 504, the explored set(s) of filtering parameters 506 may be considered as the optimal set of filtering parameters 504.

Reference is made once again to FIG. 2. As shown at 216, the set of filtering parameters that presents the lowest prediction error is selected. The set comprising one or more updated filtering parameters, for example, the time interval (7) of the LI filter, the filtering parameter (a) of the L3 filter and/or the reporting criterion(s) may be transmitted by the network apparatus 102 to the UE 104. The UE 104 may update the respective filter(s) and/or the reporting mechanism accordingly with the received updated filtering parameter(s).

The process 200 may be repeated to provide updated filtering parameters values to the UE 104 to compensate for changes in the channel prediction performance and/or accuracy. The changes in the channel prediction performance and/or accuracy may result from changes in one or more aspects of the channel, for example, a change in the interference level, a change in the location of the UE 104, a handover to a different cell/network apparatus such as the network apparatus 102 and the like.

Reference is now made to FIG. 6, which is a schematic illustration of an exemplary UE, according to some embodiments of the present invention. A UE such as the UE 104 includes a receiver 602 for receiving updated filtering parameters from a network apparatus such as the network apparatus 102 and a processing unit 604 for applying the updated filtering parameters in one or more filters of the UE 104. Optionally, the UE 104 includes a transmitter 606 for transmitting channel information (measurements) to the network apparatus 102. The processing unit 604 may include one or more processors (homogenous or heterogeneous), which may be arranged for parallel processing, as clusters and/or as one or more multi core processing units. The processing unit 604 may execute program instructions from one or more storage devices, for example, a volatile memory, a non-volatile memory, a hard drive and/or the like.

Reference is also made to FIG. 7, which is a flowchart of an exemplary process for updating channel information processing parameter(s) at a UE, according to some embodiments of the present invention. A process 700 for updating one or more channel information processing parameters of the UE 104 may be executed by the processing unit 604 of the UE 104. The process 700 corresponds to the process 200A presented hereinabove. It should be noted that the process 900 may be used by the network apparatus 102 for updating any parameter the UE 104 may use for producing the channel information, either the parameters are currently known and/or to be defined in the future.

As shown at 702, the process 700 starts with collecting channel measurements that include one or more measurements of a channel (communication link) between the UE 104 and the network apparatus 102, for example, RSRP, RSRQ and the like. The channel measurements may indicate, for example, quality of the channel, signal strength, interference from other signals and/or background signals and the like. The channel measurements may be collected by the processing unit 604 from the receiver 602 according to one or more standards and/or specifications such as, for example, the LTE standard and the like.

As shown at 704, the collected channel measurement(s) may be processed, for example, filtered using LI and/or L3 filters as described in 204 and/or 206 to produce channel information.

As shown at 706, which is a decision point corresponding to step 208 of the process 200A, the produced channel information may be compared to one or more reporting criterion(s) to decide whether or not the channel information should be transmitted to the network apparatus 102. For example, the RSRP level of the channel between the UE 104 and the network apparatus 102 may be checked against a respective reporting criterion.

As shown at 708, in the event the reporting criterion(s) is met, the channel information may be transmitted by the UE 104 using the transmitter 606 to the network apparatus 102.

As shown at 710, the UE 104 may receive, through the receiver 602, one or more updated parameters to be applied to the processing of the channel measurements. The updated parameter(s) may include, for example, a time interval (7) for the LI filter, a filtering parameter (a) for the L3 filter and/or a reporting criterion(s).

As shown at 712, the updated parameters may be applied to one or more processing elements of the UE 104 that process the collected channel measurements to produce the channel information. For example, the LI filter and/or the L3 filter may be updated with the received updated filtering parameters, the time interval (7) and the filtering parameter (a) respectively. The process 700 may be repeated to update the channel measurement(s) processing with one or more additional updated parameters received from the network apparatus 102.

Reference is now made to FIG. 8, which is a schematic illustration of an exemplary network apparatus, according to some embodiments of the present invention. A network apparatus such as the network apparatus 102 includes a receiver 802 for receiving channel information from one or more UEs such as the UE 104, a processing unit 804 for calculating updated parameters processing channel information at the UE 104 and a transmitter 806 for transmitting the updated parameters to the UE 104. The processing unit 804 may include one or more processors (homogenous or heterogeneous), which may be arranged for parallel processing, as clusters and/or as one or more multi core processing units. The processing unit 804 may execute program instructions from one or more storage devices, for example, a volatile memory, a nonvolatile memory, a hard drive and the like.

Reference is now made to FIG. 9, which is a flowchart of an exemplary process for calculating updated parameter(s) used by a UE to produce channel information, according to some embodiments of the present invention. A process 900 for updated parameter(s) for channel information processing at the UE 104 may be executed by the processing unit 804 of the network apparatus 102. The process 900 corresponds to the process 200B presented hereinabove. It should be noted that the process 900 may be used by the network apparatus 102 for updating any parameter the UE 104 may use for producing the channel information, either the parameters are currently known and/or to be defined in the future.

As shown at 902 which corresponds to step 210 of the process 200B, the process 900 starts with receiving channel information transmitted from the UE 104. The channel information may be received through the receiver 802. The channel information may be used by the network apparatus for one or more applicable operations, for example, define a MCS for the communication link with the UE, select a base station, asses and/or decide handover of the UE between base stations, manage radio resources and the likes.

As shown at 904 which corresponds to step 212 of the process 200B, channel prediction is performed by, for example, the processing unit 804 of the network apparatus 102. The processing unit 804 may employ one or more prediction models (PM) based on the received channel information (measurements) to estimate one or more characteristics of the (communication) channel between the UE 104 and the network apparatus 102. The channel prediction aims to overcome delays and/or latencies between the time the channel measurements are collected at the UE 104 and the time the channel information is used at the network apparatus 102.

As shown at 906 which corresponds to step 214 of the process 200B, performance and/or accuracy of channel prediction are evaluated.

The performance of the channel prediction is evaluated by comparing the performance calculated for the current channel prediction with performance calculated for channel prediction using alternate parameters. The simulations may apply to the channel prediction method (PM), used by the network apparatus 102, different settings of the parameters, for example, the averaging time interval (7) of the LI filter at the UE 104, the filtering parameter (a) of L3 filter at the UE 104 and/or the reporting criterion(s) at the UE 104. The performance of the channel prediction may also be evaluated in real-time by the network apparatus 102, for example, using the online learning algorithm. The network apparatus 102 may evaluate channel prediction performance with respect to the parameters the UE 104 used to produce the channel information used by the evaluated channel prediction. The network apparatus 102 may further explore one or more parameters settings to identify a parameter set which when used by the channel prediction model presents an optimal channel prediction performance.

As shown at 908, multiple settings of the parameters may be simulated to determine an optimal set of parameters that produces a lowest channel prediction error, for example, by calculating an MSE. The optimal set of parameters may include, for example, an optimal combination of one or more of, the LI filter time interval (7), the L3 filtering parameter (a) and/or a reporting criterion(s) for the UE 104.

As shown at 910, the optimal set of updated parameters may be transmitted using the transmitter 806 to the UE 104 which in turn may update its channel information processing mechanism with the updated parameters.

The process 900 may be repeated to update one or more additional updated parameters to be transmitted to the UE 104 to compensate for degradation in the channel prediction due to, for example, change in interference level(s), change of the propagation environment, change of the location and/or speed of the UE 104 and the likes. The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

It is expected that during the life of a patent maturing from this application many relevant channel information aspects, channel information filtering/processing and/or channel prediction models will be developed and the scope of the term channel information, channel information filtering and/or processing and channel prediction respectively are intended to include all such new technologies a priori.

As used herein the term "about" refers to ± 10 %.

The terms "comprises", "comprising", "includes", "including", "having" and their conjugates mean "including but not limited to". This term encompasses the terms "consisting of and "consisting essentially of.

The phrase "consisting essentially of means that the composition or method may include additional ingredients and/or steps, but only if the additional ingredients and/or steps do not materially alter the basic and novel characteristics of the claimed composition or method.

As used herein, the singular form "a", "an" and "the" include plural references unless the context clearly dictates otherwise. For example, the term "a compound" or "at least one compound" may include a plurality of compounds, including mixtures thereof.

The word "exemplary" is used herein to mean "serving as an example, instance or illustration". Any embodiment described as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments and/or to exclude the incorporation of features from other embodiments.

The word "optionally" is used herein to mean "is provided in some embodiments and not provided in other embodiments". Any particular embodiment of the invention may include a plurality of "optional" features unless such features conflict. Throughout this application, various embodiments of this invention may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to include any cited numeral (fractional or integral) within the indicated range. The phrases "ranging/ranges between" a first indicate number and a second indicate number and "ranging/ranges from" a first indicate number "to" a second indicate number are used herein interchangeably and are meant to include the first and second indicated numbers and all the fractional and integral numerals there between.

It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination or as suitable in any other described embodiment of the invention. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment is inoperative without those elements.

All publications, patents and patent applications mentioned in this specification are herein incorporated in their entirety by reference into the specification, to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention. To the extent that section headings are used, they should not be construed as necessarily limiting.

Claims

1. A network apparatus, comprising:
a receiver adapted to receive/collect/acquire channel measurement information; a processor adapted to:
calculate a parameter update for updating a parameter of a filter of a user equipment, UE, the calculation comprises:
determine a prediction performance based on the channel measurement information, and
determine the parameter update for updating the parameter of the filter of the UE based on a measurement model of the filter and the prediction performance; and
a transmitter adapted to transmit and/or to provide the parameter update to the
UE.
2. The network apparatus of claim 1, wherein the processor is adapted to determine the parameter update by evaluating a prediction error based on measurement prediction estimation.
3. The network apparatus of any of the previous claims, wherein the processor is adapted to calculate the parameter update according to an optimization and/or learning function determined according to an analysis of a plurality of previously determined prediction performances and respective obtained prediction quality.
4. The network apparatus of any of the previous claims, wherein the prediction performance is calculated based on a Doppler frequency shift and/or a signal to interference noise ratio, SINR, measured by the UE.
5. The network apparatus of any of the previous claims, wherein the parameter update is a value of a parameter calculated based on at least some variables selected from a group consisting of: a Doppler frequency shift, an SINR, a sampling period, a physical layer measurement model, a prediction method, a prediction horizon, and an observation window.
6. The network apparatus of any of the previous claims, wherein the parameter update is calculated by extracting a value from a static precomputed table, in particular a look-up table, LUT.
7. The network apparatus of any of the previous claims, wherein the receiver is adapted to receive the channel measurement information in each of a plurality of iterations and the processor is adapted to calculate the parameter update and to instruct the transmitter to transmit the parameter update to the UE in each one of the plurality of iterations.
8. A method of a wireless network adapted to deliver updates of measurement model parameters to a wireless communication device, comprising:
receiving channel measurement information;
determining a prediction performance based on the channel measurement information;
determining a parameter update for updating a filter parameter of a filter of a UE based on a measurement model of the filter and the prediction performance; and transmitting and/or providing the parameter update to the UE.
9. The method of claim 8, further comprising storing the prediction performance and/or the parameter update for a future calculation of an additional parameter update.
10. The method of claim 8, further comprising storing at least one of the at least prediction performance and/or the parameter update.
11. A UE, comprising:
a receiver adapted to receive a parameter update from a network apparatus; and a processor adapted to update a parameter of at least one filter based on the parameter update, the at least one filter is used to filter channel measurement information of a transmission received by the receiver.
12. The UE of Claim 11, further comprising a transmitter adapted for:
transmitting the channel measurement information related to a transmission filtered using the at least one filter to a network apparatus; wherein the parameter update is calculated according to the channel measurement information.
13. A method for updating a filter of a UE, comprising:
wirelessly receiving, at a UE, a parameter update from a network apparatus; and
updating a filter of the UE based on the parameter update.
14. The method of claim 13, further comprising transmitting channel measurement information gathered based on filtering of the filter to the network apparatus, and
wherein the parameter update calculated according to the channel measurement information filter.
PCT/EP2016/059667 2016-04-29 2016-04-29 Measurement model optimization for channel prediction improvement in wireless networks WO2017186309A1 (en)

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