WO2013131441A1 - 一种赋形颗粒度的估计方法及装置 - Google Patents

一种赋形颗粒度的估计方法及装置 Download PDF

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Publication number
WO2013131441A1
WO2013131441A1 PCT/CN2013/072021 CN2013072021W WO2013131441A1 WO 2013131441 A1 WO2013131441 A1 WO 2013131441A1 CN 2013072021 W CN2013072021 W CN 2013072021W WO 2013131441 A1 WO2013131441 A1 WO 2013131441A1
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Prior art keywords
granularity
shaped
shape
candidate
channel
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PCT/CN2013/072021
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English (en)
French (fr)
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李晓皎
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电信科学技术研究院
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Publication of WO2013131441A1 publication Critical patent/WO2013131441A1/zh

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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00Arrangements affording multiple use of the transmission path
    • H04L5/003Arrangements for allocating sub-channels of the transmission path
    • H04L5/0037Inter-user or inter-terminal allocation
    • H04L5/0039Frequency-contiguous, i.e. with no allocation of frequencies for one user or terminal between the frequencies allocated to another
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00Baseband systems
    • H04L25/02Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202Channel estimation
    • H04L25/022Channel estimation of frequency response
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00Baseband systems
    • H04L25/02Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202Channel estimation
    • H04L25/0224Channel estimation using sounding signals
    • H04L25/0228Channel estimation using sounding signals with direct estimation from sounding signals
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00Arrangements affording multiple use of the transmission path
    • H04L5/003Arrangements for allocating sub-channels of the transmission path
    • H04L5/0058Allocation criteria
    • H04L5/006Quality of the received signal, e.g. BER, SNR, water filling

Definitions

  • the present invention relates to the field of communications, and in particular, to a method and apparatus for estimating shaped granularity.
  • the base station when the base station performs downlink beamforming, the base station usually uses the ⁇ antenna for signal transmission.
  • the wireless channel between the transmitting end and the receiving end is expressed as: "' ⁇ 1 , ⁇ " is the number of transmitting antennas, and is the number of receiving antennas.
  • is the number of transmitting antennas, and is the number of receiving antennas.
  • different shaping vectors are used on different antennas, which are recorded as: — '' ⁇ ⁇ "' ⁇ , where ⁇ is the number of users.
  • a certain beam shape is emitted, so this operation is also called beamforming.
  • the channel frequency selectivity is caused by multipath propagation, so that the channel correlation matrices corresponding to different subcarriers are different, resulting in differences in the shaping vectors used by different subcarriers.
  • the base station does not set different shaping vectors for each subcarrier, but uses the same shape for data carried by multiple subcarriers on the same antenna.
  • the vector is shaped to reduce the computational complexity and storage space of the shaped vector.
  • the shaping vector is set in units of PRB (Physical Resource Block).
  • PRB Physical Resource Block
  • PRB is the unit of resource allocation in the system.
  • one PRB contains 12 frequency domains. Neighbor subcarriers.
  • the same shaping vector is used for the subcarriers in the same PRB, and the number of consecutive PRBs using the same shaping vector is called the shaped granularity.
  • the shape-grained particle size will have a great influence on the forming performance, and the reasonable definition of the shape-grained particle size can effectively improve the forming performance.
  • the size of the shaped granularity of the base station is implemented by the base station side algorithm, and there is no standardized regulation. Therefore, if the user equipment side does not use the corresponding shaped granularity estimation algorithm, it is impossible to accurately know the actual use of the base station. Shaped granularity.
  • the channel estimation granularity When the user equipment performs channel estimation, the channel estimation granularity is set.
  • the so-called channel estimation granularity means that the user equipment receives the pilot transmitted by the base station side within the PRB range corresponding to the channel estimation granularity, and uses nonlinear interpolation.
  • the method performs frequency domain interpolation between the pilot points, and then estimates the channel state of the uplink channel based on the interpolation result.
  • the user equipment cannot accurately know the shaped granularity actually used by the base station, if the predicted channel granularity of the user equipment is larger than the shaped granularity used by the base station side, or the shaped granularity Not channel Estimating the integer multiple of the granularity, a channel estimation granularity spans two shaped granularities, so that the pilots within the same channel estimation granularity will use two different shaping vectors. As a result, the user equipment cannot perform accurate frequency domain interpolation when performing channel estimation.
  • the user equipment will set the channel estimation granularity in the shaping mode to 1 , that is, use a PRB as the channel estimation granularity.
  • the channel estimation is frequency domain interpolation using the pilots within the same channel estimation granularity, when the shaped granularity is greater than 1, and the channel estimation granularity is 1, the available pilot number will be changed. Less, which leads to a decrease in channel estimation performance, which in turn affects detection performance.
  • Embodiments of the present invention provide a method and apparatus for estimating shaped granularity for solving channel estimation performance of a guaranteed system.
  • a method for estimating the shape of a particle comprising:
  • each adjacent PRB uses the same shaped vector, and the candidate shaped granularity corresponding to the arbitrary one of the measured subframes is determined according to the number of consecutive PRBs that use the same shaped vector.
  • An apparatus for estimating shape granularity comprising:
  • a first processing unit configured to calculate, according to a preset statistical duration, a corresponding candidate shaped granularity, where the statistical duration includes at least one measurement subframe;
  • a second processing unit configured to estimate a shaped granularity of the network side according to a preset manner according to the obtained shaped shape of the selected shape
  • the first processing unit calculates a corresponding candidate shaped granularity in any one of the measurement sub-frames included in the preset statistical duration, and is based on the downlink signal sent by the network side in any one of the measurement sub-frames. And a preset initial value of the channel estimation granularity, calculating a channel estimation value, and calculating, respectively, each of the shaping bandwidths based on the channel estimation value.
  • the channel correlation of adjacent subcarriers between PRBs on the set time domain symbols, and whether the adjacent PRBs use the same shaping vector based on the calculation result, and the number of consecutive PRBs according to the same shaping vector Determining each candidate shaped granularity corresponding to the any one of the measurement sub-frames.
  • the user equipment calculates the channel estimation value based on the downlink signal sent by the base station side and the preset initial value of the channel estimation granularity in each measurement subframe in the preset statistical duration, and then calculates the channel estimation value according to the channel estimation.
  • the value is used to calculate the channel correlation of adjacent subcarriers between the PRBs, and determine the number of consecutive PRBs using the same shaping vector according to the calculation result, thereby determining the candidate shaped granularity corresponding to the corresponding measurement subframe, and according to the preset
  • the method selects an estimated value of the shaped granularity used by the base station side from the obtained candidate shaped granularities, so that the user equipment can accurately compare the channel correlation of adjacent subcarriers between the PRBs.
  • the shape of the base station is used to estimate the granularity of the channel, so that the channel estimation granularity used by the base station can be adjusted according to the obtained shaped granularity, thereby effectively improving the channel estimation performance and signal receiving performance of the user equipment itself. In the application environment with high signal-to-noise ratio, due to the small influence of noise, the accuracy of the shape estimation of the above-mentioned scheme is higher, and the effect is more ideal.
  • FIG. 2 is a schematic diagram of PRB distribution in an embodiment of the present invention.
  • FIG. 3 is a flowchart of calculating a candidate shaped granularity in a measurement subframe in a measurement subframe according to an embodiment of the present invention
  • FIG. 4 is a channel correlation and setting of adjacent subcarriers between PRBs according to an embodiment of the present invention; Comparison of threshold values;
  • FIG. 5 is a schematic structural diagram of a function of a user equipment according to an embodiment of the present invention. detailed description
  • the method is as follows: the user equipment first obtains a channel estimation value based on the received downlink signal and a preset initial value of the channel estimation granularity (eg, an initial value of 1), and then based on the obtained channel estimation value, Estimating the shaped granularity of the base station side by counting the channel correlation on adjacent subcarriers between adjacent PRBs, since the instantaneous fading of the frequency domain channels through which the adjacent subcarriers pass is substantially uniform, if adjacent The subcarriers use the same shaping vector, and the channel correlation of the two subcarriers will be very high (for example, the channel correlation reaches the set channel correlation threshold), if the channel correlation does not reach the set In the case of a threshold, the user equipment can consider that two subcarriers use different shaping vectors
  • the user equipment may count the value of multiple shaped granularity, the user equipment eventually The value with the highest probability is determined as the value of the shaped granularity, and the shaped granularity is used as the channel estimation granularity, thereby improving the channel estimation performance.
  • the shape of the base station in a cell does not change frequently. Therefore, preferably, the user equipment can periodically update the measured shape granularity, or only measure when performing cell handover. And update the shaped particle size.
  • the user equipment before the user equipment starts the process of estimating the granularity, it is preferable to determine whether to enable the process of estimating the granularity according to the environmental measurement information. For example, the user equipment can perform the current CQI (Channel Quality Indicator, The channel shield indicates) information or noise is measured. When it is determined that the current CQI is higher than the expected threshold 1 or the current noise is less than the preset threshold 2, the user equipment turns on the shaped granularity estimation process. That is, step 200 is started.
  • CQI Channel Quality Indicator, The channel shield indicates
  • the shaped granularity may be initialized to a default value (eg, 1). Or, retain the previous measurement result, and wait until the next measurement of CQI or noise, until the time at which the shaping granularity estimation process can be started is reached.
  • the user equipment may calculate the corresponding candidate shaped granularity within a preset statistical duration, and according to the obtained shaped granularity, according to the preset
  • the method for estimating the shape of the network side is used for the network side; wherein, the preset statistical duration may be a single frame or a multiple frame; then, referring to FIG. 2 and FIG. 3, in the embodiment of the present invention, the user equipment is The detailed flow of calculating the corresponding candidate shape granularity in any measurement sub-frame included in the statistical duration is as follows:
  • Step 300 In any one measurement subframe, the user equipment calculates a channel estimation value based on a downlink signal sent by the base station side and a preset initial value of the channel estimation granularity.
  • First downlink signal S the user equipment receives a base station-side apparatus transmits user-specific demodulation pilot positions by each of receiving antennas may be referred to as Sl, ... ... Sk.
  • the user equipment calculates the receiving antennas according to the row signal S and the pilot sequence r configured in the upper layer.
  • the initial estimate of the channel frequency, recorded as Wherein, P can be used to indicate the first antenna
  • the channel estimate r of the upper pilot point is the channel estimate r of the upper pilot point.
  • the channel initial estimation frequency of each pilot point on each receiving antenna is performed within each channel estimation granularity (ie, within each PRB)
  • the domain is interpolated to obtain channel estimation values on the respective antennas in the entire shaped bandwidth of the current measurement subframe. For example, frequency domain interpolation is performed on p , and corresponding channel estimation values can be obtained.
  • the channel estimation values on each antenna are arranged in order, , where is the subcarrier index, / is the time domain symbol index, and the time domain symbol is the smallest unit in the time domain, as shown in FIG. 2 .
  • the channel estimation value obtained by the user equipment at this time is on each receiving antenna in the entire shaping bandwidth of the current measurement subframe. Channel estimate.
  • Step 310 The user equipment separately calculates channel correlations of the adjacent subcarriers in the set time domain symbols between the PRBs in the shaped bandwidth based on the obtained channel estimation values.
  • the user equipment may calculate the channel correlation of the adjacent subcarriers on the set time domain symbols in the shaping mode, where the set time domain symbols may be all time domain symbols, It can be a partial time domain symbol (for example, only the time domain symbol in which the pilot is located).
  • the channel correlation of adjacent subcarriers between the two PRBs can be calculated by the following formula; Wherein, indicating channel correlation of adjacent subcarriers between the nth PRB and the n+1th PRB on the jth time domain symbol, i represents an adjacent subcarrier index for calculating channel correlation, and j indicates The total number of time domain symbols,
  • ⁇ M m -1 n- ⁇ RB_lst ⁇ - ⁇ KB_end - V 3 ⁇ 4 is the total number of PRBs contained in the shaped bandwidth, which is the starting number of the PRB contained in the shaped bandwidth, and is the PRB included in the shaped bandwidth Termination number, since the channel correlation between the two PRBs is calculated, the last PRB will not be calculated.
  • Step 320 The user equipment determines, according to the calculation result of the channel correlation, whether each adjacent PRB uses the same shaping vector, and determines, according to the number of consecutive PRBs that use the same shaping vector, the corresponding ones of the foregoing one measurement subframes. Select shape granularity.
  • step 320 when step 320 is performed, the following methods may be used but are not limited to the following:
  • the user equipment separately calculates the average channel correlation of the adjacent subcarriers in each set time domain symbol between each PRB, and compares each average channel correlation with a set threshold respectively, if an average channel correlation reaches If the threshold is set, it is determined that the corresponding two adjacent PRBs use the same shaping vector. If an average channel correlation does not reach the set threshold, it is determined that the corresponding two adjacent PRBs do not use the same assignment.
  • the shape vector, and the number of consecutive PRBs that use the same shaped vector are determined based on the comparison result.
  • the user equipment averages the adjacent subcarriers between each two adjacent PRBs on each set time domain symbol, and draws n, U, where the first PRB and the n+1th PRB are between The average channel correlation of adjacent subcarriers is recorded corresponding to the index n of the nth PRB. Then, the user equipment uses the set threshold G (the G value can be determined by simulation according to experience), and sequentially records R ( n small and medium) The index of the PRB of G is obtained, and the indexes in the sequence ⁇ ', n' are arranged in order from the smallest value to the largest value.
  • G the G value can be determined by simulation according to experience
  • the phases in n' can reflect the number of consecutive PRBs.
  • the user equipment needs to further add before the first element in n'. , get the set n", and then calculate the interval of each element in n",
  • the user equipment may determine the candidate gradation granularity corresponding to the single measurement sub-frame in the manner described in step 300 - step 320. Then, the user equipment can determine the granularity of the candidate to be selected for each measurement subframe by using the methods described in step 300 to step 320.
  • the user equipment can estimate the shape granularity used by the network side according to a preset manner. Specifically, it can be used but is not limited to the following ways:
  • the first mode is: if the statistical duration includes only a single measurement subframe, the user equipment will have the highest probability of candidate shaped granularity estimation among the candidate shaped granularities corresponding to the obtained single measurement subframe.
  • the shaped particle size used for the network side.
  • the user equipment establishes a set t, and each element in t represents an appearance probability of a candidate shaped granularity, wherein the a-th element is denoted as m
  • m The number of occurrences of the a value multiplied by the weight of the a value
  • the total number of PRBs in the width which is the maximum possible value of the estimated shape granularity, a is the number of a values in m, such as
  • ⁇ a is the weight of a, which can be set according to prior experience.
  • N BF N BF _ N BF i
  • the probability of occurrence of the shape of the candidate shape is 3, the probability of occurrence of the shape of the candidate shape is 4, and the probability of occurrence of the shape of the candidate is 5 is 1.
  • the value of the candidate shaped particle size represented by the largest element in the set t is the estimated value of the shaped granularity used by the network side, which is recorded as
  • the second mode is: if the statistical duration includes multiple measurement subframes, the user equipment combines the obtained shaped granularity corresponding to each measurement subframe obtained, and in the merged candidate granularity
  • the candidate shaped granularity with the highest probability of occurrence is estimated as the shaped granularity of the network side (hereinafter referred to as method 1); or, the user equipment is in the candidate shaped granularity corresponding to each measured sub-frame,
  • the candidate shaped particle sizes with the highest probability of occurrence are screened out respectively, and the selected shaped particles with the highest number of occurrences in each selected shaped particle size are estimated as the shaped granularity of the network side (hereinafter Weigh method 2).
  • the specific implementation manner of the method 1 is as follows: tf, f - U f of each measurement subframe in the statistics period of the set ⁇ / _ record are established, and the number of measurement subframes in the set statistical duration is added, and the corresponding elements of the ⁇ are added.
  • the set t is expressed as:
  • the largest element index in the set t' is the estimated value of the shaped granularity used by the base station side.
  • N a
  • i'(a) max(i , ) 5 If there are multiple largest elements, that is, there are multiple candidate shaped granularities with the same probability of occurrence, then you can choose according to certain rules, for example, select the smallest
  • the specific implementation manner of the method 2 is as follows: First, the N corresponding to each measurement subframe in the statistical duration is calculated according to the first manner, and then the N value with the most occurrences is selected as the estimation of the shaped granularity used by the base station side. value. For example, assume that the N values corresponding to each measurement subframe determined after Method 2 are as shown in Table 3:
  • the above method is to average the channel correlations of adjacent subcarriers between the respective PRBs in the set time domain symbols in a measurement subframe, and then calculate the corresponding candidate shaped granularity. .
  • the user equipment in a measurement subframe, can also perform the candidate assignment t. t according to each time domain symbol that is set.
  • the user equipment determines the estimated value of the shaped granularity used by the base station side according to the first mode or the second mode, and details are not described herein again.
  • the user equipment after estimating the shaped granularity used by the base station side, sets the channel estimation granularity according to the shaped granularity, and preferably adjusts the channel estimation granularity to be used with the base station side.
  • the shape of the shaped particles is consistent. For example, after the measurement is completed, assuming that the estimated shaped granularity is m PRBs and there are N PRBs in the shaped bandwidth, the user equipment can set the channel estimated granularity cfc ⁇ according to the measured shaped granularity. Use the following formula:
  • the user equipment may periodically perform the estimation of the shaped granularity, or perform the estimation of the shaped granularity when the preset triggering condition is met; wherein, if the user equipment periodically If the estimation of the shaped granularity is performed, the measurement period set by the user equipment is greater than or equal to the above statistical duration; if the user equipment satisfies the preset triggering condition and then performs the estimation of the shaped granularity, the user equipment may The above measurement process is performed when it accesses or switches to a new cell, and the measurement is stopped after the shaped granularity is estimated.
  • the first method takes less time, and the latter method has higher accuracy.
  • a reasonable setting can also improve the estimation accuracy of the shaped particle size, preferably, but not limited to any of the following methods:
  • the granularity of the shape to be selected is N, and the shape of the shape to be selected is N+1.
  • the shape of the candidate shaped particle is M
  • the corresponding weight is set to 1
  • the weight of the other candidate shaped granularity is set to 0, where M and N are positive integers, and 0 ⁇ N ⁇ M.
  • M 4
  • the shape of the shape to be selected is 1.
  • the weight of the candidate shape is 2, the shape of the shape to be selected is 3, and the weight of the candidate shape is 4, the corresponding weight is set to 1, and the weight of the other candidate shape is set to 0. , set A... ⁇ 4 to 1 and other settings to 0.
  • 1 + ⁇
  • the other ⁇ is set to 1
  • the morphological granularity corresponding to the estimated result is added again to ⁇ , if the estimation results are inconsistent
  • ⁇ 1 corresponding to the shaped particle size characterized by the new estimation result is set to 1 + ⁇
  • the other is set to 1
  • the corresponding value is calculated each time the probability of occurrence of the selected shaped granularity is calculated.
  • a user equipment is also provided in the embodiment of the present invention. Since the principle of solving the problem is similar to the method in the embodiment of the present invention, the implementation of the device may refer to the implementation of the method, and the repetition is no longer Narration.
  • the user equipment includes:
  • the first processing unit 50 is configured to calculate, according to a preset statistical duration, a corresponding candidate shaped granularity, where the statistical duration includes at least one measurement subframe;
  • a second processing unit 51 configured to estimate a shaped granularity of the network side according to a preset manner according to the obtained shape-of-selection granularity
  • the first processing unit 50 calculates the corresponding candidate shaped granularity in any one of the measurement sub-frames included in the preset statistical duration, and uses the downlink signal and the channel sent by the network side in the any one measurement subframe. Estimating a preset initial value of the granularity, calculating a channel estimation value, and calculating a channel correlation of the adjacent subcarriers between the physical resource blocks PRB in the set time domain symbol according to the channel estimation value, respectively, And determining whether each adjacent PRB uses the same shaping vector based on the calculation result, and determining each candidate shaped granularity corresponding to any one of the measurement sub-frames according to the number of consecutive PRBs that use the same shaping vector.
  • the user equipment further includes a setting unit 52.
  • the channel estimation granularity is set based on the shaped granularity, which is specifically: Determining whether the shaping bandwidth is an integral multiple of the shaped granularity, and if so, setting the channel estimated granularity to be consistent with the shaped granularity; otherwise, Set the channel estimation granularity to 1.
  • the technical solution provided by the embodiment of the present invention has wide applicability.
  • the LTE system, the LTE-A system, and the like can be used to perform beamforming on the pilot at the base station side, and the user equipment cannot know the transmission mode of the shaped granularity. It can also be applied to both uplink and downlink communication systems, and can also be applied to both TDD and FDD duplex systems. Of course, it is also applicable to time domain channel estimation granularity.
  • embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware. Moreover, the present invention can be embodied in the form of a computer program product embodied on one or more computer-usable storage interfaces (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.
  • computer-usable storage interfaces including but not limited to disk storage, CD-ROM, optical storage, etc.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

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Abstract

本申请公开了一种赋形颗粒度的估计方法及装置,该方法为:在预设的统计时长中的各测量子帧内,用户设备基于基站侧发送的下行信号以及信道估计颗粒度的预设初始值,计算信道估计值,再基于该信道估计值计算各PRB之间相邻子载波的信道相关性,以及根据计算结果确定使用相同赋形向量的连续PRB的数目,从而确定相应测量子帧对应的待选赋形颗粒度,并按照预设方式从获得的各待选赋形颗粒度中选取出基站侧釆用的赋形颗粒度的估计值,这样,用户设备便可以基于各PRB之间相邻子载波的信道相关性,准确地对基站侧釆用的赋形颗粒度进行估计,从而可以根据获得的赋形颗粒度对信道估计颗粒度进行调整,进而有效提高了信道估计性能及信号接收性能。

Description

一种赋形颗粒度的估计方法及装置
本申请要求在 2012年 3月 8 日提交中国专利局、 申请号为 201210060314.1、 发明名 称为"一种赋形颗粒度的估计方法及装置 "的中国专利申请的优先权, 其全部内容通过引用 结合在本申请中。 技术领域
本发明涉及通信领域, 特别涉及一种赋形颗粒度的估计方法和装置。
'景技术
参阅图 1所示, 基站在进行下行波束赋形时, 通常会釆用^ ^根天线进行信号发射,
'ku ) k = ] · · · Κ κ
发射端和接收端之间的无线信道表示为: " ' ― 1 , Λ "为发送端天线数目, 为接收端天线数目。 对于每一个用户信号, 基站通过子载波进行发送时在不同的天线 上釆用不同的赋形向量, 记为: — ' ' α ~ " ' α , 其中 Μ为用户数目。 赋形后的信号以一定的波束形状发射出去, 因此, 这一操作又称为波束赋形。
实际信道环境中, 由于多径传播导致信道频率选择性, 使得不同子载波对应的信道相 关矩阵不同, 从而导致不同子载波釆用的赋形向量存在差别。 在实际系统实现时, 出于实 现复杂度的考虑, 基站并不会针对每一个子载波分别设置不同的赋形向量, 而是将多个子 载波在同一天线上承载的数据釆用同一个赋形向量进行赋形, 以降低赋形向量的计算复杂 度和存储空间。赋形向量以 PRB ( Physical Resource Block,物理资源块)为单位设置, PRB 是系统中进行资源分配的单位, 在 LTE ( Long Term Evolution; 长期演进) 系统中, 一个 PRB包含 12个频域上相邻的子载波。 同一个 PRB内的子载波釆用相同的赋形向量, 而连 续的釆用相同赋形向量的 PRB的数目称为赋形颗粒度。赋形颗粒度对赋形性能会有较大影 响, 合理定义赋形颗粒度的大小能够有效提高赋形性能。
目前,基站设置赋形颗粒度的大小是通过基站侧算法实现的, 没有标准化规定, 因此, 用户设备侧如果不釆用相应的赋形颗粒度估计算法, 是无法准确获知基站实际釆用的赋形 颗粒度的。
而用户设备在进行信道估计时, 要设置信道估计颗粒度, 所谓信道估计颗粒度, 是指 用户设备在信道估计颗粒度对应的 PRB范围内接收基站侧发送的导频,并釆用非线性插值 方式在各导频点之间进行频域插值, 再基于插值结果对上行信道的信道状态进行预估。
现有技术下, 由于用户设备无法准确获知基站实际釆用的赋形颗粒度, 因此, 如果用 户设备预估的信道估计颗粒度大于基站侧釆用的赋形颗粒度, 或者, 赋形颗粒度不是信道 估计颗粒度的整数倍, 则会出现一个信道估计颗粒度跨越两个赋形颗粒度的情况, 这样, 同一个信道估计颗粒度内的导频将会釆用了两种不同的赋形向量, 从而导致用户设备在进 行信道估计时无法进行准确的频域插值。
为了避免这一情况的发生, 通常情况下, 用户设备都会将赋形模式下的信道估计颗粒 度固定设置为 1 , 即以一个 PRB作为信道估计颗粒度。 但是, 由于信道估计是釆用同一个 信道估计颗粒度内的导频进行频域插值, 因此, 当赋形颗粒度大于 1 , 而信道估计颗粒度 为 1时, 将会导致可用的导频数变少, 从而令信道估计性能下降, 进而影响检测性能。 发明内容
本发明实施例提供一种赋形颗粒度的估计方法及装置, 用以解决保证系统的信道估计 性能。
本发明实施例提供的具体技术方案如下:
一种赋形颗粒度的估计方法, 包括:
在预设的统计时长内计算相应的待选赋形颗粒度, 所述统计时长包含至少一个测量子 帧;
根据获得的待选赋形颗粒度, 按照预设方式估计网络侧釆用的赋形颗粒度; 其中, 在预设的统计时长包含的任意一个测量子帧内计算相应的待选赋形颗粒度, 包 括:
在所述任意一个测量子帧内, 基于网络侧发送的下行信号以及信道估计颗粒度的预设 初始值, 计算信道估计值;
基于所述信道估计值,分别计算赋形带宽内各 PRB之间相邻子载波在设定时域符号上 的信道相关性;
基于计算结果判断各相邻 PRB是否釆用相同的赋形向量,并根据釆用相同赋形向量的 连续 PRB的数目确定所述任意一个测量子帧对应的各待选赋形颗粒度。
一种赋形颗粒度的估计装置, 包括:
第一处理单元, 用于在预设的统计时长内计算相应的待选赋形颗粒度, 所述统计时长 包含至少一个测量子帧;
第二处理单元, 用于根据获得的待选赋形颗粒度, 按照预设方式估计网络侧釆用的赋 形颗粒度;
其中, 所述第一处理单元在预设的统计时长包含的任意一个测量子帧内计算相应的待 选赋形颗粒度时, 在所述任意一个测量子帧内, 基于网络侧发送的下行信号以及信道估计 颗粒度的预设初始值, 计算信道估计值, 并基于所述信道估计值, 分别计算赋形带宽内各 PRB之间相邻子载波在设定时域符号上的信道相关性,以及基于计算结果判断各相邻 PRB 是否釆用相同的赋形向量,并根据釆用相同赋形向量的连续 PRB的数目确定所述任意一个 测量子帧对应的各待选赋形颗粒度。
本发明实施例中, 在预设的统计时长中的各测量子帧内, 用户设备基于基站侧发送的 下行信号以及信道估计颗粒度的预设初始值, 计算信道估计值, 再基于该信道估计值计算 各 PRB 之间相邻子载波的信道相关性, 以及根据计算结果确定使用相同赋形向量的连续 PRB的数目, 从而确定相应测量子帧对应的待选赋形颗粒度, 并按照预设方式从获得的各 待选赋形颗粒度中选取出基站侧釆用的赋形颗粒度的估计值, 这样, 用户设备便可以基于 各 PRB之间相邻子载波的信道相关性, 准确地对基站侧釆用的赋形颗粒度进行估计,从而 可以根据获得的赋形颗粒度对自身使用的信道估计颗粒度进行调整, 进而有效提高了用户 设备自身的信道估计性能及信号接收性能。 在高信噪比的应用环境下, 由于受到噪声的影 响较小, 釆用上述方案进行赋形颗粒度估计准确性更高, 实现效果更为理想。 附图说明
图 1为现有技术下波束赋形示意图;
图 2为本发明实施例中 PRB分布示意图
图 3为本发明实施例中用户设备在任意一个测量子帧内计算待选赋形颗粒度流程图; 图 4为本发明实施例中各 PRB之间相邻子载波的信道相关性与设定门限值比较示意 图;
图 5为本发明实施例中用户设备功能结构示意图。 具体实施方式
在设置赋形颗粒度时, 最理想的情况是, 信道估计颗粒度和赋形颗粒度一致, 这就需 要设计一种新的赋形颗粒度的估计方法。 本发明实施例中, 该方法如下: 用户设备先基于 接收的下行信号以及信道估计颗粒度的预设初始初始值(如, 初始值为 1 )获取信道估计 值,再基于获得的信道估计值,通过统计相邻 PRB之间相邻子载波上的信道相关性估计基 站侧釆用的赋形颗粒度, 由于相邻子载波经过的频域信道的瞬时衰落基本上是一致的, 因 此如果相邻子载波釆用了相同的赋形向量, 这两个子载波的信道相关性将会很高 (如, 信 道相关性达到设定的信道相关性门限值 ), 如果出现了信道相关性未达到设定门限值的情 况, 则用户设备可以认为两个子载波使用了不同的赋形向量。 由于同一个 PRB上的子载波 釆用的赋形向量相同,因此只需要统计各个 PRB之间相邻子载波的信道相关性就可以确定 各 PRB是否釆用了相同的赋形向量, 从而将釆用相同赋形向量的连续 PRB的数目即为赋 形颗粒度。 釆用这种方式, 用户设备可能会统计出多个赋形颗粒度的取值, 用户设备最终 会将概率最大的值确定为赋形颗粒度的取值 , 并该赋形颗粒度作为信道估计颗粒度 , 从而 提高信道估计性能。
通常情况下, 基站在一个小区内的赋形颗粒度是不会经常变化的, 因此, 较佳的, 用 户设备可以周期性地更新测量的赋形颗粒度, 或者, 只在进行小区切换时测量并更新赋形 颗粒度。
下面结合附图对本发明优选的实施方式进行详细说明。
本发明实施例中, 用户设备开启赋形颗粒度估计流程之前, 较佳的, 可以根据环境测 量信息判断是否开启赋形颗粒度估计流程,如,用户设备可以对当前的 CQI( Channel Quality Indicator, 信道盾量指示)信息或噪声进行测量, 在确定当前的 CQI高于预计的门限值 1 , 或者, 当前的噪声小于预设的门限值 2时, 用户设备开启赋形颗粒度估计流程, 即开始执 行步骤 200。 此外, 若用户设备确定当前的 CQI低于预设的门限值 1 , 或者, 当前的噪声 小于预设的门限值 2时, 可以将赋形颗粒度初始化为一个默认值(如, 1 ), 或者, 保留前 一次测量结果, 等到下一次测量 CQI或噪声时再进行判断, 直到达到可以开启赋形颗粒度 估计流程的时刻。
本发明实施例中, 在开启赋形颗粒度估计流程后, 用户设备可以在预设的统计时长内 计算相应的待选赋形颗粒度, 并根据获得的待选赋形颗粒度, 按照预设方式估计网络侧釆 用的赋形颗粒度; 其中, 预设的统计时长可以是单帧, 也可以是多帧; 那么, 参阅图 2和 图 3所示, 本发明实施例中, 用户设备在统计时长包含的任意一个测量子帧内计算相应的 待选赋形颗粒度的详细流程如下:
步骤 300: 在任意一个测量子帧内, 用户设备基于基站侧发送的下行信号以及信道估 计颗粒度的预设初始值, 计算信道估计值。
具体为:
首先, 用户设备通过每一根接收天线在用户设备专属解调导频位置接收基站侧发送的 下行信号 S, 可以记为 Sl、 ... ... Sk
其次, 用户设备下根据行信号 S 以及高层配置的导频序列 r, 计算出各接收天线上导 频点的信道初始估计值, 记为
Figure imgf000007_0001
其中, 可以釆用 P表示第 个天线
上导频点的信道估计值 r
最后, 根据信道估计颗粒度的预设初始值(如, 1 ), 在每一个信道估计颗粒度内 (即 在每个 PRB内)分别针对每一个接收天线上导频点的信道初始估计进行频域插值,从而获 得当前测量子帧内整个赋形带宽上各个天线上的信道估计值,例如,对 p进行频域插值, 可以获得相应的信道估计值
Figure imgf000007_0002
将各天线上的信道估计值按顺序排列得 ',
Figure imgf000007_0003
, 其中, 为子载波索引, /为时域符号索引, 时域符号是时域上的最小单 位, 具体如图 2所示。
另一方面, 由于用户设备是以测量子帧为单位接收基站侧发送的下行信号的, 因此, 用户设备此时获得的信道估计值是当前测量子帧内整个赋形带宽上各个接收天线上的信 道估计值。
步骤 310: 用户设备基于获得的信道估计值, 分别计算赋形带宽内各块 PRB之间相邻 子载波在设定时域符号上的信道相关性。
参阅图 2所示, 实际应用中, 任意两个相信的 PRB之间, 会存在多个相对应的时域符 号, 相邻子载波在每两个相应的时域符号上均存在一定的信道相关性, 本实施例中, 用户 设备可以在赋形模式下计算相邻子载波在设定的各个时域符号上的信道相关性, 其中, 设 定的时域符号可以是全部时域符号, 也可以是部分时域符号 (如, 只选取导频所在的时域 符号)。 那么, 在任意一个时域符号 j上, 两个 PRB之间相邻子载波的信道相关性可以釆 用以下公式计算;
Figure imgf000008_0001
其中, 表示第 j个时域符号上第 n个 PRB和第 n+1个 PRB之间相邻子载波的 信道相关性, i表示用于计算信道相关性的相邻子载波索引, j表示设定的时域符号总数目,
Π Mm -1 n- ^RB_lst^ - ^^KB_end - V ¾为赋形带宽内包含的 PRB总 数目, 为赋形带宽内包含的 PRB的起始编号, 为赋形带宽内包含的 PRB 的终止编号, 由于是计算两个 PRB之间的信道相关性, 因此, 最后一个 PRB将无法计算
NBF —1
信道相关性, 因此, n的取值截止于 。
步骤 320: 用户设备基于信道相关性的计算结果判断各相邻 PRB是否釆用相同的赋形 向量,并根据釆用相同赋形向量的连续 PRB的数目确定上述任意一个测量子帧对应的各待 选赋形颗粒度。
本实施例中, 在执行步骤 320时, 可以釆用但并不局限于以下方式:
用户设备分别计算各 PRB之间相邻子载波在全部设定时域符号上的平均信道相关性, 并分别将每一个平均信道相关性与设定阈值进行比较, 若某一个平均信道相关性达到设定 阈值, 则确定对应的两个相邻 PRB釆用了相同的赋形向量,若某一个平均信道相关性未达 到设定阈值, 则确定对应的两个相邻 PRB未釆用相同的赋形向量, 以及根据比较结果确定 釆用相同的赋形向量的连续 PRB的数目。
例如, 用户设备分别对每两个相邻 PRB 之间相邻子载波在各个设定时域符号上的 进行平均, 画 , n二 U ,其中, 第 个 PRB和第 n + 1个 PRB之间相邻子载波的平均信道相关性对应第 n个 PRB的索引 n进 行记录, 那么, 用户设备釆用设定门限值 G(G值可以根据经验通过仿真来确定), 依次记 录 R(n 中小于 G的 PRB的索引, 得到序列 η' , n'中的各索引按照取值从小到大的顺序排 列, 由于 n'中记录的是未达到 G的 PRB的索引, 因此, n'中各相邻元素的差值能够体现 出连续 PRB 的数目, 为了便于计算, 用户设备需要进一步地在 n'中第一个元素前添加
Figure imgf000008_0002
, 得到集合 n", 然后, 计算 n"中各元素间隔,
,^lr.£, m(I) = n \I + l)-n \I I = \ ..,N„,,_\ Nn、、 丄
得到序列 m, Jh ' ' n 为 n"中的 L素个 数, I为
NBF = 5 NBF = 24
n"中各元素的序号; 如, 参阅图 4所示, - st , m-md , 即当前测 量子帧中整个赋形带宽内存在 20个 PRB, 则 η'={9,13,16,20} , η" ={4,9,13,16,20,24} , 则 m={5,4,3,4,4} , m中的各元素即为在当前测量子帧内估计出的待选赋形颗粒度。
若统计时长仅包含单个测量子帧, 则用户设备可以釆用步骤 300 -步骤 320记载的方 式确定出该单个测量子帧对应的待选赋形颗粒度, 若统计时长包含多个测量子帧, 则用户 设备可以釆用步骤 300 -步骤 320记载的方式分别确定出每一个测量子帧对应的待选颗粒 度。
在预设的统计时长内获得相应的各待选赋形颗粒度后, 用户设备便可以按照预设方式 从中估计出网络侧釆用的赋形颗粒度。 具体可以釆用但不限于以下几种方式:
第一种方式为: 若统计时长仅包含单个测量子帧, 则用户设备在获得的该单个测量子 帧对应的各待选赋形颗粒度中, 将出现概率最高的待选赋形颗粒度估计为网络侧釆用的赋 形颗粒度。
具体为: 仍以上述序列 m为例, 基于序列 m, 用户设备建立集合 t, t中的每一个元素 分别表示一种待选赋形颗粒度的出现概率, 其中, 第 a个元素记为 m中 a值的出现次数乘 以 a值的权重
Figure imgf000009_0001
为赋形带
Nm
宽内的 PRB总数目, 即为估计的赋形颗粒度的最大可能值, a 为 m中 a值的个数, 如
J fm o
果 m中没有某一 a值, 则相应的 a 为 0, ^a为 a的加权值, 可以根据先验经验设置,
NBF = NBF _ NBF i 如果没有先验信息则设为 1。 例如, 参阅图 4所示, m - end m - t =20, m={5,4,3,4,4} , 由于 m 中只有 3、 4、 5 这三种待选赋形颗粒度的取值, 因此, a = 1 6 … , 20时, N: =0, __ N: ^,如无先验 息,则 A ^, 那么, t={0,0,l,3,l,0,0,...,0}。 即待选赋形颗粒度为 3的出现概率为 1 , 待选赋形颗粒度为 4 的出现概率为 3 , 而待选赋形颗粒度为 5的出现概率为 1。 那么, 集合 t中最大元素表示的 待选赋形颗粒度的取值即为 网络侧釆用 的赋形颗粒度的估计值, 记为
A^ = a i(a) = max( 5 如果有多个最大元素, 即存在多个出现概率相同的待选赋形颗粒 度, 则可按一定规则进行选择, 例如, 选取最小的元素索引 a, 即选取取值最小的待选赋 、 N = min(a t(a) = max( ) ,. e r -, ,, , , ^
形颗粒度, 记为 、 " ', 按照图 2的例子, Ν=4。
第二种方式为: 若统计时长包含多个测量子帧, 则用户设备将获得的各测量子帧对应 的待选赋形颗粒度进行合并, 并在合并后的各待选赋形颗粒度中, 将出现概率最高的待选 赋形颗粒度估计为网络侧釆用的赋形颗粒度 (以下称方法 1 ); 或者, 用户设备在每一个测 量子帧对应的待选赋形颗粒度中, 分别筛选出出现概率最高的待选赋形颗粒度, 再将筛选 出的各待选赋形颗粒度中出现次数最多的待选赋形颗粒度估计为网络侧釆用的赋形颗粒 度(以下称方法 2 )。 方法 1 的具体实现方式为: 建立集合^/ _ 记录统计时长内各测量子帧的 tf ,f - U f , 为设定的统计时长内的测量子帧数目, 将各 ^对应元素相加得 到集合 t,, 用公式表示为:
Nf
则集合 t'中的最大元素索引即为基站侧釆用的赋形颗粒度的估计值
N = a|i'(a) = max(i,) 5 如果有多个最大元素, 即存在多个出现概率相同的待选赋形 颗粒度, 则可按一定规则进行选择, 例如, 选取最小的元素索引 a, 即选取取值最小的待 选赋形颗粒度, ^ = 111111(^(^ = 1113^0)。 例如, 假设预设的统计时长内包含的测量子 帧数目为 5 , 则各测量子帧对应的集合 f 如表 1所示:
表 1
Figure imgf000011_0002
则 t'的各元素取值如表 2所示: 表 2
Figure imgf000011_0003
那么, = a i '(a) =丽、 , =4
方法 2的具体实现方式为: 可以先按照上述第一种方式分别计算出统计时长内各测量 子帧对应的 N,再选取出现次数最多的 N值作为基站侧釆用的赋形颗粒度的估计值。例如, 假设釆用方法 2后确定的各测量子帧对应的 N值如表 3表所示:
表 3
Figure imgf000011_0001
则用户设备选取出现次数最多的 N值作为最终确定的赋形颗粒度的估计值, 即 N=4。 另一方面,上述方法是在一个测量子帧中,将各 PRB之间相邻子载波在设定的各时域 符号上的信道相关性先进行平均, 再计算相应的待选赋形颗粒度。 实际应用中, 参阅图 2 所示, 在一个测量子帧中, 用户设备还可以按照设定的每一个时域符号, 分别进行待选赋 t . t .
形颗粒度的计算, 获得各时域符号对应的集合 ] , 再将各时域符号对应的集合 按元素 a) = » ), a = i,...,
进行叠加之后得到 t, 即 '=1 , 然后, 用户设备再按照上述 第一种方式或第二种方式确定基站侧釆用的赋形颗粒度的估计值, 在此不再赘述。
基于上述实施例, 用户设备在估计出基站侧釆用的赋形颗粒度后, 按照该赋形颗粒度 设置信道估计颗粒度, 较佳的, 将信道估计颗粒度对应调整为与基站侧釆用的赋形颗粒度 一致。 例如, 测量结束后, 假定估计的赋形颗粒度为 m个 PRB, 而赋形带宽中存在 N个 PRB, 则用户设备根据测量到的赋形颗粒度设置信道估计颗粒度 cfc ^时, 可以釆用以 下公式:
m, if mod( V, m) =
m chest 一
1, else
进一步的, 在后续流程中, 用户设备可以周期性地进行赋形颗粒度的估计, 或者, 在 满足预设的触发性条件时, 再进行赋形颗粒度的估计; 其中, 若用户设备周期性地进行赋 形颗粒度的估计, 则用户设备设置的测量周期要大于等于上述统计时长; 若用户设备在满 足预设的触发性性条件时, 再进行赋形颗粒度的估计, 则用户设备可以在自身接入或切换 至新小区时执行上述测量过程,估计出赋形颗粒度后停止测量。釆用前一种方式耗时较短, 釆用后一种方式准确性较高。 此外, 合理的设置 也能够提高赋形颗粒度的估计准确性, 较佳的, 釆用但不限于 以下任意一种方法:
1 )若根据预设配置信息确定基站侧釆用的赋形颗粒度不超过 M, 则将待选赋形颗粒 度为 N, 待选赋形颗粒度为 N+1 ... ... , 待选赋形颗粒度为 M时各自对应的权重设置为 1 , 其他待选赋形颗粒度对应的权重设置为 0, 其中 M和 N为正整数, 且 0 < N < M。 比如 M = 4 , 则根据预设配置信息确定基站侧釆用的赋形颗粒度不超过 4, 将待选赋形颗粒度为 1、待选赋形颗粒度为 2、待选赋形颗粒度为 3和待选赋形颗粒度为 4时各自对应的权重设 置为 1 , 其他待选赋形颗粒度对应的权重设置为 0 , 即将 A…^4设置为 1 , 其他 设置 为 0。
2 )在进行小区接入或切换时, 将所有待选赋形颗粒度对应的权重设置为 1 , 即 设 置为 1 , 在接入或切换完成后的赋形颗粒度估计过程中, 每估计一次赋形颗粒度, 将估计 结果表征的赋形颗粒度对应的权重增加设置变量 , 例如, 在接入或切换后的第一个估计 周期内, 根据估计结果获知基站侧釆用的赋形颗粒度为 2 , 则在第二个估计周期内设置
Ρι = 1 + Δ , 其他 Α设为 1 , 之后, 每个估计周期结束后, 若估计结果与上一次一致, 则将估计结果表征的赋形颗粒度对应的 再次累加 Δ ,若估计结果不一致, 则将新的估计 结果表征的赋形颗粒度对应的^1设置为 1 + Δ , 其他 设置为 1 , 并在每次计算待选赋形 颗粒度的出现概率时相应的 的取值。
基于同一发明构思, 本发明实施例中还提供了一种用户设备, 由于该设备解决问题的 原理与本发明实施例的方法相似, 因此这些设备的实施可以参见方法的实施, 重复之处不 再赘述。
基于上述实施例, 参阅图 5所示, 本发明实施例中, 用户设备包括:
第一处理单元 50 , 用于在预设的统计时长内计算相应的待选赋形颗粒度, 该统计时长 包含至少一个测量子帧;
第二处理单元 51 , 用于根据获得的待选赋形颗粒度, 按照预设方式估计网络侧釆用的 赋形颗粒度;
其中, 第一处理单元 50在预设的统计时长包含的任意一个测量子帧内计算相应的待 选赋形颗粒度时, 在该任意一个测量子帧内, 基于网络侧发送的下行信号以及信道估计颗 粒度的预设初始值, 计算信道估计值, 并基于该信道估计值, 分别计算赋形带宽内各物理 资源块 PRB之间相邻子载波在设定时域符号上的信道相关性,以及基于计算结果判断各相 邻 PRB是否釆用相同的赋形向量, 并根据釆用相同赋形向量的连续 PRB的数目确定上述 任意一个测量子帧对应的各待选赋形颗粒度。
如图 5所示, 用户设备内进一步包括设置单元 52 , 用在第二处理单元 51估计出网络 侧釆用的赋形颗粒度后, 基于该赋形颗粒度设置信道估计颗粒度 , 具体为: 判断赋形带宽 是否为赋形颗粒度的整倍数, 若是, 则将信道估计颗粒度设置为与赋形颗粒度一致, 否则, 将信道估计颗粒度设置为 1。
本发明实施例提供的技术方案具有广泛的适用性, 可以同时应用 LTE系统、 LTE-A系 统等在基站侧对导频进行了波束赋形, 且用户设备无法获知赋形颗粒度的传输模式, 以及 可以同时应用于上下行通信系统, 也可以同时应用于 TDD和 FDD双工系统, 当然, 也同 样适用于时域信道估计颗粒度。
本领域内的技术人员应明白, 本发明的实施例可提供为方法、 系统、 或计算机程序产 品。 因此, 本发明可釆用完全硬件实施例、 完全软件实施例、 或结合软件和硬件方面的实 施例的形式。 而且, 本发明可釆用在一个或多个其中包含有计算机可用程序代码的计算机 可用存储介盾 (包括但不限于磁盘存储器、 CD-ROM、 光学存储器等)上实施的计算机程 序产品的形式。
本发明是参照根据本发明实施例的方法、 设备(系统)、 和计算机程序产品的流程图 和 /或方框图来描述的。 应理解可由计算机程序指令实现流程图和 /或方框图中的每一流 程和 /或方框、 以及流程图和 /或方框图中的流程和 /或方框的结合。 可提供这些计算机 程序指令到通用计算机、 专用计算机、 嵌入式处理机或其他可编程数据处理设备的处理器 以产生一个机器, 使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用 于实现在流程图一个流程或多个流程和 /或方框图一个方框或多个方框中指定的功能的 装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方 式工作的计算机可读存储器中, 使得存储在该计算机可读存储器中的指令产生包括指令装 置的制造品, 该指令装置实现在流程图一个流程或多个流程和 /或方框图一个方框或多个 方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上, 使得在计算机 或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理, 从而在计算机或其他 可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和 /或方框图一个 方框或多个方框中指定的功能的步骤。
尽管已描述了本发明的优选实施例, 但本领域内的技术人员一旦得知了基本创造性概 念, 则可对这些实施例作出另外的变更和修改。 所以, 所附权利要求意欲解释为包括优选 实施例以及落入本发明范围的所有变更和修改。
显然, 本领域的技术人员可以对本发明进行各种改动和变型而不脱离本发明的精神和 范围。这样,倘若本发明的这些修改和变型属于本发明权利要求及其等同技术的范围之内, 则本发明也意图包含这些改动和变型在内。

Claims

权 利 要 求
1、 一种赋形颗粒度的估计方法, 其特征在于, 包括:
在预设的统计时长内计算相应的待选赋形颗粒度, 所述统计时长包含至少一个测量子 帧;
根据获得的待选赋形颗粒度, 按照预设方式估计网络侧釆用的赋形颗粒度; 其中, 在预设的统计时长包含的任意一个测量子帧内计算相应的待选赋形颗粒度, 包 括:
在所述任意一个测量子帧内, 基于网络侧发送的下行信号以及信道估计颗粒度的预设 初始值, 计算信道估计值;
基于所述信道估计值,分别计算赋形带宽内各物理资源块 PRB之间相邻子载波在设定 时域符号上的信道相关性;
基于计算结果判断各相邻 PRB是否釆用相同的赋形向量,并根据釆用相同赋形向量的 连续 PRB的数目确定所述任意一个测量子帧对应的各待选赋形颗粒度。
2、 如权利要求 1 所述的方法, 其特征在于, 在预设的统计时长内计算相应的待选赋 形颗粒度之前, 对当前的信道盾量指示 CQI信息或噪声进行测量, 确定测量的 CQI 达 到预设的第一门限值, 或者, 确定测量的噪声低于预设的第二门限值时, 开始在预设的统 计时长计算相应的待选赋形颗粒度。
3、 如权利要求 1 所述的方法, 其特征在于, 在所述任意一个测量子帧内, 基于网络 侧发送的下行信号以及信道估计颗粒度的预设初始值, 计算信道估计值, 包括:
通过每一根接收天线在导频位置接收网络侧发送的下行信号;
根据接收的下行信号以及高层配置的导频序列, 计算获得各接收天线上导频点的信道 初始估计值;
基于信道估计颗粒度的预设初始值, 在每一个信道估计颗粒度内分别针对每一个接收 天线上导频点的信道初始估计值进行频域插值, 获得当前测量子帧内赋形带宽上各个接收 天线的信道估计值。
4、 如权利要求 1所述的方法, 其特征在于, 基于计算结果判断各相邻 PRB是否釆用 相同的赋形向量,并根据釆用相同赋形向量的连续 PRB的数目确定所述任意一个测量子帧 对应的各待选赋形颗粒度 , 包括:
分别计算各 PRB之间相邻子载波在全部设定时域符号上的平均信道相关性,并分别将 每一个平均信道相关性与设定阈值进行比较, 若某一个平均信道相关性达到设定阈值, 则 确定对应的两个相邻 PRB釆用了相同的赋形向量,若某一个平均信道相关性未达到设定阈 值, 则确定对应的两个相邻 PRB未釆用相同的赋形向量, 以及根据比较结果确定釆用相同 的赋形向量的连续 PRB的数目, 并将釆用相同的赋形向量的连续 PRB的数目确定为所述 任意一个子帧对应的待选赋形赋形颗粒度。
5、 如权利要求 1 _ 4任一项所述的方法, 其特征在于, 根据获得的待选赋形颗粒度, 按照预设方式估计网络侧釆用的赋形颗粒度, 包括 :
若预设的统计时长内包括一个测量子帧, 则将所述一个测量子帧对应的各待选赋形颗 粒度中出现概率最高的待选赋形颗粒度估计为网络侧釆用的赋形颗粒度;
若预设的统计时长内包括一个以上测量子帧, 则将各测量子帧对应的待选赋形颗粒度 进行合并, 并在合并后的各待选赋形颗粒度中, 将出现概率最高的待选赋形颗粒度估计为 网络侧釆用的赋形颗粒度; 或者, 在每一个测量子帧对应的待选赋形颗粒度中, 分别筛选 出出现概率最高的待选赋形颗粒度, 再将筛选出的各待选赋形颗粒度中出现次数最多的待 选赋形颗粒度估计为网络侧釆用的赋形颗粒度。
6、 如权利要求 5 所述的方法, 其特征在于, 计算任意一个待选赋形颗粒度的出现概 率, 包括:
将所述任意一个待选赋形颗粒度的出现次数与所述任意一个待选赋形颗粒度对应的 权重相乘, 计算结果即为所述任意一个待选赋形颗粒度的出现概率。
7、 如权利要求 6所述的方法, 其特征在于, 设置各待选赋形颗粒度的权重时, 包括: 若根据预设配置信息确定网络侧釆用的赋形颗粒度不超过 Μ, 则将待选赋形颗粒度为
Ν, 待选赋形颗粒度为 N+1 ... ... , 待选赋形颗粒度为 Μ时各自对应的权重设置为 1 , 其他 待选赋形颗粒度对应的权重设置为 0, 其中 Μ和 Ν为正整数, JL 0 < N < M;
或者,
在进行小区接入或切换时, 将所有待选赋形颗粒度对应的权重设置为 1 , 在接入或切 换完成后的赋形颗粒度估计过程中, 每估计一次赋形颗粒度, 将估计结果表征的赋形颗粒 度对应的权重增加设置变量, 其他赋形颗粒度对应的权重设置为 1。
8、 如权利要求 5 所述的方法, 其特征在于, 估计出网络侧釆用的赋形颗粒度后, 基 于所述赋形颗粒度设置信道估计颗粒度 , 包括:
判断赋形带宽是否为所述赋形颗粒度的整倍数, 若是, 则将所述信道估计颗粒度设置 为与所述赋形颗粒度一致, 否则, 将所述信道估计颗粒度设置为 1。
9、 如权利要求 5 所述的方法, 其特征在于, 按照设定周期执行赋形颗粒估计流程, 或者, 在满足预设的触发条件时, 执行赋形颗粒度估计流程。
10、 一种赋形颗粒度的估计装置, 其特征在于, 包括: 第一处理单元, 用于在预设的统计时长内计算相应的待选赋形颗粒度, 所述统计时长 包含至少一个测量子帧;
第二处理单元, 用于根据获得的待选赋形颗粒度, 按照预设方式估计网络侧釆用的赋 形颗粒度;
其中, 所述第一处理单元在预设的统计时长包含的任意一个测量子帧内计算相应的待 选赋形颗粒度时, 在所述任意一个测量子帧内, 基于网络侧发送的下行信号以及信道估计 颗粒度的预设初始值, 计算信道估计值, 并基于所述信道估计值, 分别计算赋形带宽内各 物理资源块 PRB之间相邻子载波在设定时域符号上的信道相关性,以及基于计算结果判断 各相邻 PRB是否釆用相同的赋形向量, 并根据釆用相同赋形向量的连续 PRB的数目确定 所述任意一个测量子帧对应的各待选赋形颗粒度。
11、 如权利要求 10所述的装置, 其特征在于, 所述第一处理单元具体用于: 在预设的统计时长内计算相应的待选赋形颗粒度之前,对当前的信道盾量指示 CQI信 息或噪声进行测量, 确定测量的 CQI达到预设的第一门限值, 或者, 确定测量的噪声低于 预设的第二门限值时, 开始在预设的统计时长计算相应的待选赋形颗粒度。
12、 如权利要求 10所述的装置, 其特征在于, 所述第一处理单元具体用于: 在所述任意一个测量子帧内, 基于网络侧发送的下行信号以及信道估计颗粒度的预设 初始值,计算信道估计值时,通过每一根接收天线在导频位置接收网络侧发送的下行信号, 并根据接收的下行信号以及高层配置的导频序列, 计算获得各接收天线上导频点的信道初 始估计值, 以及基于信道估计颗粒度的预设初始值, 在每一个信道估计颗粒度内分别针对 每一个接收天线上导频点的信道初始估计值进行频域插值, 获得当前测量子帧内赋形带宽 上各个接收天线的信道估计值。
13、 如权利要求 10所述的装置, 其特征在于, 所述第一处理单元具体用于: 分别计算各 PRB之间相邻子载波在全部设定时域符号上的平均信道相关性,并分别将 每一个平均信道相关性与设定阈值进行比较, 若某一个平均信道相关性达到设定阈值, 则 确定对应的两个相邻 PRB釆用了相同的赋形向量,若某一个平均信道相关性未达到设定阈 值, 则确定对应的两个相邻 PRB未釆用相同的赋形向量, 以及根据比较结果确定釆用相同 的赋形向量的连续 PRB的数目, 并将釆用相同的赋形向量的连续 PRB的数目确定为所述 任意一个子帧对应的待选赋形赋形颗粒度。
14、 如权利要求 10 - 13 任一项所述的装置, 其特征在于, 所述第二处理单元具体用 于:
若预设的统计时长内包括一个测量子帧, 则将所述一个测量子帧对应的各待选赋形颗 粒度中出现概率最高的待选赋形颗粒度估计为网络侧釆用的赋形颗粒度;
若预设的统计时长内包括一个以上测量子帧, 则将各测量子帧对应的待选赋形颗粒度 进行合并, 并在合并后的各待选赋形颗粒度中, 将出现概率最高的待选赋形颗粒度估计为 网络侧釆用的赋形颗粒度; 或者, 在每一个测量子帧对应的待选赋形颗粒度中, 分别筛选 出出现概率最高的待选赋形颗粒度, 再将筛选出的各待选赋形颗粒度中出现次数最多的待 选赋形颗粒度估计为网络侧釆用的赋形颗粒度。
15、 如权利要求 14所述的装置, 其特征在于, 所述第二处理单元具体用于: 计算任意一个待选赋形颗粒度的出现概率时, 将所述任意一个待选赋形颗粒度的出现 次数与所述任意一个待选赋形颗粒度对应的权重相乘, 计算结果即为所述任意一个待选赋 形颗粒度的出现概率。
16、 如权利要求 15所述的装置, 其特征在于, 所述第二处理单元具体用于: 若根据预设配置信息确定网络侧釆用的赋形颗粒度不超过 M, 则将待选赋形颗粒度为 N, 待选赋形颗粒度为 N+1 ... ... , 待选赋形颗粒度为 M时各自对应的权重设置为 1 , 其他 待选赋形颗粒度对应的权重设置为 0, 其中 M和 N为正整数, 且 0 < N < M; 或者, 在进 行小区接入或切换时, 将所有待选赋形颗粒度对应的权重设置为 1 , 以及在接入或切换完 成后的赋形颗粒度估计过程中, 每估计一次赋形颗粒度, 将估计结果表征的赋形颗粒度对 应的权重增加设置变量, 其他赋形颗粒度对应的权重设置为 1。
17、 如权利要求 14所述的装置, 其特征在于, 所述装置还包括:
设置单元, 用于在所述第二处理单元估计出网络侧釆用的赋形颗粒度后, 基于所述赋 形颗粒度设置信道估计颗粒度, 包括: 判断赋形带宽是否为所述赋形颗粒度的整倍数, 若 是, 则将所述信道估计颗粒度设置为与所述赋形颗粒度一致, 否则, 将所述信道估计颗粒 度设置为 1。
18、 如权利要求 14 所述的装置, 其特征在于, 所述第一处理单元和第二处理单元按 照设定周期执行赋形颗粒估计流程, 或者, 在满足预设的触发条件时, 执行赋形颗粒度估 计流程。
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