Methods and network entities for determining transmission parameters for transmission of data between a network node and a user equipment

Abstract

Disclosed is a method for determining transmission parameters for transmission of data between a network node (130) of a communication network (100) and a UE (140) of interest The method comprises obtaining a plurality of measurements of channel quality of a wireless communication channel between the network node (130) and one or more UEs (140, 145) including the UE of interest (140) and determining a statistical distribution of information on the channel quality of the wireless communication channel based on the plurality of measurements. The method further comprises convolving the determined statistical distribution of the information on channel quality with itself at least one time, and determining, from the convolved statistical distribution and on a required transmission reliability, one or more transmission parameters for a first transmission attempt of N number of admissible transmission attempts for transmission of data between the UE of interest (140) and the network node (130).

Description

METHODS AND NETWORK ENTITIES FOR DETERMINING TRANSMISSION PARAMETERS FOR TRANSMISSION OF DATA BETWEEN A NETWORK NODEAND A USER EQUIPMENTTECHNICAL FIELD

[0001] The present disclosure relates generally to methods and network entities for determining transmission parameters for transmission of data between a network node and a User Equipment (UE). The present disclosure further relates to computer programs and carriers corresponding to the above methods and entities.BACKGROUND

[0002] To meet the huge demand for higher bandwidth, higher data rates and higher network capacity, due to e.g., data centric applications, existing 4thGeneration (4G) wireless communication network technology, aka Long-Term Evolution (LTE) has emerged into 5thGeneration (5G) wireless communication technology, also called New Radio (NR) access. The following are requirements for 5G wireless communication networks:- Data rates of several tens of megabits per second should be supported for tens of thousands of users;- 1 gigabit per second is to be offered simultaneously to tens of workers on the same office floor;- Several hundreds of thousands of simultaneous connections are to be supported for massive sensor deployment;- Spectral efficiency should be significantly enhanced compared to 4G;- Coverage should be improved;- Signaling efficiency should be enhanced;- Latency should be reduced significantly compared to 4G, and- Providing services with high reliability requirements.

[0003] Further, as the communication demand of wireless communication devices, aka wireless devices, aka UEs, varies a lot depending on type of UE and type of communication that the UE is involved in, a number of different serviceclasses have been defined. The service classes comprise Time Critical Communication (TCC), Ultra reliable Low Latency Communication (URLLC), Mobile Broadband (MBB), massive Machine Type Communication (mMTC). For TCC, the time of delivery of the service is the most important. Typical use cases are real-time media, remote control, industrial control, and mobility automation. For URLLC, both the reliability to deliver data and the time of delivery is very important. For MBB, high data rate is most important but without any guarantees on latency. For mMTC the most important feature is to support a high connection density, i.e., be able to provide a high amount of UEs connection in a limited geographical area. mMTC is especially directed towards connection of Internet of Things (loT) devices.

[0004] As mentioned, communication for some service classes, such as TCC and URLLC, have stricter latency and reliability requirements than more conventional ones such as MBB. A typical reliability requirement for TCC is 99,9 % after all admissible transmission attempts. To be able to achieve the higher demands on latency and reliability, a more conservative choice of transmission parameters need to be selected for such communications than for MBB- communications, in other words, a more robust coding etc. A fundamental issue that needs to be handled is that channel quality, as measured for example in Signal to Interference and Noise Ratio (SINR) or supported Bit Per Resource Element (BPRE), fluctuates over time due to fading, mobility and / or varying interference levels. This means that even if the UE provides an accurate information on channel quality, in e.g., a Channel State Information (CSI) report (for DL cases) of SINR, the channel quality information is only correct for the point in time where the UE performed its measurements and will deviate from channel quality at the point of transmission of data. To provide high reliability, the link adaptation (LA) algorithms need to cater for such fluctuations: simply following the reported channel quality for the LA will at least sometimes not meet reliability requirements.

[0005] A baseline approach is to apply the lowest possible fixed backoff to the channel quality at each slot that can meet the throughput requirement achievinghigh reliability at the same time. A drawback with such approach is that it becomes very conservative and may hence lead to very low spectral efficiency.

[0006] A more advanced approach to handle such a problem is to statistically represent the fluctuations of the channel quality over time, for example the spread of SINR or the correlation of SINR over time.

[0007] A baseline for such a statistical approach, is to assume a distribution of the channel quality, such as Gaussian distribution, and fit the parameters of the distribution, in the Gaussian case, mean and co-variances, to the measured channel quality from e.g., the CSI reports. However, that approach might lead to poor estimates of the channel quality for strict reliability levels, such as the ones needed for TCC communications, due to mismatch between the assumed and actual distributions, in the Gaussian case especially when the variance of the channel quality is high.

[0008] Another approach is determining a non-parametric probability density function, essentially a histogram. If the wireless communication network supports retransmissions, this histogram needs to cover the effective channel quality after all allowed retransmissions. The problem with that approach, in particular when applied for downlink (DL), is that in order to determine transmission parameters that can provide a high required reliability performance, for example the reliability required for TCC communications, e.g., -99.9%, large databases, such as thousands or millions of samples of the channel quality per UE are required. This is in most cases not practical due to latency and memory constraints.

[0009] Consequently, there is a need of a method for determining transmission parameters for transmission of data between a network node and a UE that results in a transmission fulfilling reliability and latency requirements at the same time as it achieves a good throughput of data.SUMMARY

[0010] It is an object of embodiments of the invention to address at least some of the problems and issues outlined above. It is an object of embodiments of theinvention to provide a method for determining transmission parameters for transmission of data between a network node and a UE that results in a transmission fulfilling reliability requirements at the same time as it achieves a good throughput of data. Especially, it is an object of embodiments, to provide such a method that fulfills strict reliability requirements, such as the ones required in TCC or URLLC, which may be at 99,9 % or even 99,99 % after N admissible transmission attempts. It is possible to achieve at least of one of these objects by using methods and network nodes as defined in the attached independent claims.

[0011] According to one aspect, a method is provided for determining transmission parameters for transmission of data between a network node of a communication network and a UE of interest. The method comprises obtaining, over time, a plurality of measurements of channel quality of a wireless communication channel between the network node and one or more UEs including the UE of interest. The method further comprises determining a statistical distribution of information on the channel quality of the wireless communication channel based on the plurality of measurements of channel quality and convolving the determined statistical distribution of the information on channel quality with itself at least one time. The method further comprises determining, from the convolved statistical distribution of the information on channel quality and on a required transmission reliability for transmission of certain data between the UE of interest and the network node after N number of admissible transmission attempts, one or more transmission parameters for a first transmission attempt of the N number of admissible transmission attempts for transmission of the certain data between the UE of interest and the network node.

[0012] According to another aspect, one or more network entities is provided, which is configured to operate in a communication network and configured for determining transmission parameters for transmission of data between a network node of the communication network and a UE of interest. The one or more network entities comprises processing circuitry and memory. Said memory contains instructions executable by said processing circuitry, whereby the one or more network entities is operative for obtaining, over time, a plurality ofmeasurements of channel quality of a wireless communication channel between the network node and one or more UEs including the UE of interest and determining a statistical distribution of information on the channel quality of the wireless communication channel based on the plurality of measurements of channel quality. The one or more network entities is further operative for convolving the determined statistical distribution of the information on channel quality with itself at least one time, and determining, from the convolved statistical distribution of the information on channel quality and on a required transmission reliability for transmission of certain data between the UE of interest and the network node after N number of admissible transmission attempts, one or more transmission parameters for a first transmission attempt of the N number of admissible transmission attempts for transmission of the certain data between the UE of interest 140 and the network node 130.

[0013] According to other aspects, computer programs and carriers are also provided, the details of which will be described in the claims and the detailed description.

[0014] Further possible features and benefits of this solution will become apparent from the detailed description below.BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The solution will now be described in more detail by means of exemplary embodiments and with reference to the accompanying drawings, in which:

[0016] Fig. 1 a is a schematic diagram of a wireless communication network in which the present invention may be used.

[0017] Fig. 1 b is a schematic diagram of a communication network in which the present invention may be used.

[0018] Fig. 2 is a flow chart illustrating a method for determining transmission parameters according to possible embodiments.

[0019] Fig. 3 is a flow chart illustrating another method for determining transmission parameters according to possible embodiments.

[0020] Fig. 4 is a diagram in a Cartesian coordinate system illustrating BPRE on the x-axis and CDF on the y-axis.

[0021] Fig. 5 is a diagram in a Cartesian coordinate system illustrating SINR on the x-axis and throughput on the y-axis.

[0022] Fig. 6 is a block diagram illustrating one or more network entities, according to further possible embodiments.DETAILED DESCRIPTION

[0023] Fig. 1 a shows a wireless communication network 100 comprising a radio access network (RAN) and a core network (CN). The RAN comprises one or more RAN nodes aka network nodes 130 that is in, or is adapted for, wireless communication with one or more wireless communication devices aka UEs 140, 145. The one or more network nodes 130 provides radio access in a cell 150 covering a geographical area. The one or more network nodes 130 is further connected to other RAN nodes and to CN nodes 160. The network node may also be connected to a cloud network 170. The cloud network may comprise one or more network entities realized as a group of network nodes, wherein functionality of the one or more network entities is spread out over the group of network nodes.

[0024] The wireless communication network 100 may be any kind of wireless communication network that can provide radio access to wireless devices.Example of such wireless communication networks are networks based on Global System for Mobile communication (GSM), Enhanced Data Rates for GSM Evolution (EDGE), Universal Mobile Telecommunications System (UMTS), Code Division Multiple Access 2000 (CDMA 2000), Long Term Evolution (LTE), LTE Advanced, Wireless Local Area Networks (WLAN), Worldwide Interoperability for Microwave Access (WiMAX), WiMAX Advanced, as well as fifth generation (5G) wireless communication networks based on technology such as New Radio (NR), and any possible future sixth generation (6G) wireless communication network.

[0025] The network node 130 may be any kind of network node that can provide wireless access to the wireless devices 140, 145 alone or in combination with another network node. Examples of network nodes 130 are a base station (BS), a radio BS, a base transceiver station, a BS controller, a network controller, a Node B (NB), an evolved Node B (eNB), a gNodeB (gNB), a Multi-cell / multicast Coordination Entity, a relay node, an access point (AP), a radio AP, a remote radio unit (RRU), a remote radio head (RRH) and a multi-standard BS (MSR BS).

[0026] The wireless devices 140, 145 may be any type of device capable of wirelessly communicating with a network node 130 using radio signals. For example, the wireless device 140, 145 may be a UE, a machine type UE or a UE capable of machine to machine (M2M) communication, a sensor, a tablet, a mobile terminal, a smart phone, a laptop embedded equipped (LEE), a laptop mounted equipment (LME), a USB dongle, a Customer Premises Equipment (CPE), an Internet of Things (loT) device, etc.

[0027] Fig. 1 b shows another example of a communication system 102 including a telecommunication network 103 that includes an access network 104, such as a radio access network (RAN), and a core network 106, which includes one or more core network nodes 108. The access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 103 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 103 that supports an ORAN specification, e.g., a specification published by the O-RAN Alliance, or any similar organization, and may operate alone or together with other nodes to implement one or morefunctionalities of any node in the telecommunication network 103, including one or more network nodes 110 and / or core network nodes 108.

[0028] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1 , F1 , W1 , E1 , E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment, in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes 110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 112a, 112b, 112c, and 112d, one or more of which may be generally referred to as UEs 112, to the core network 106 over one or more wireless connections. The indirect connection between network nodes 110 and UEs 112 may be performed via a hub 114, as illustrated between the hub 114 and the UEs 112c, 112d.

[0029] Fig. 2, with reference numerals referring to fig. 1 a, describes a method for determining transmission parameters for transmission of data between a network node 130 of a communication network 100 and a UE of interest 140. The method comprises obtaining 202, over time, a plurality of measurements of channel quality of a wireless communication channel between the network node 130 and one or more UEs 140, 145 including the UE of interest 140 The method further comprises determining 204 a statistical distribution of information on thechannel quality of the wireless communication channel based on the plurality of measurements of channel quality and convolving 206 the determined statistical distribution of the information on channel quality with itself at least one time. The method further comprises determining 210, from the convolved statistical distribution of the information on channel quality and on a required transmission reliability for transmission of certain data between the UE of interest 140 and the network node 130 after N number of admissible transmission attempts, one or more transmission parameters for a first transmission attempt of the N number of admissible transmission attempts for transmission of the certain data between the UE of interest 140 and the network node 130.

[0030] The method may be performed by a single network node, such as the network node 130, or by two or more network nodes in communication with each other. The method may be performed by one or more network entities. The method may be performed by a group of network nodes, wherein functionality for performing the method is spread out over the group of network nodes. The group of network nodes may be different physical, or virtual, nodes of the network. This alternative realization may be called a cloud-solution. The method may be performed by one or more network nodes in an Open RAN (O-RAN) such as the O-RAN network described in fig. 1 b.

[0031] The plurality of measurements of channel quality may be performed by the one or more UE on downlink signals transmitted by the network node, wherein the one or more UE sends the measurement results to the network node. For example, the network node, e.g., a gNodeB, sends a reference signal, such as Channel State Information Reference Signal CSI-RS or Channel State Information Interference Measurement CSI-IM and the respective UE of the one or more UEs measures channel quality on the reference signal, configures a report of the quality measurements and sends the report including the measurements back to the gNodeB. Alternatively, the plurality of measurements of channel quality may be performed by the network node on uplink signals sent by the one or more UEs. For example, the respective of the one or more UEs sends an uplink reference signal such as Sounding Reference Signal (SRS) to the network node, e.g., gNodeB andthe gNodeB performs the channel quality measurements of the uplink reference signal. Such a measurement could be combined with a DL-based measurement. The measurements of channel quality can be based on a Channel Quality Indicator (CQI) report. The channel quality may be measured as e.g., Signal to Noise Ratio (SNR), Signal to Interference and Noise Ratio (SINR), signal strength etc. “The UE of interest” is the UE which the transmission of certain data concerns, i.e. the transmissions for which we are to determine transmission parameters. Other UEs are other UEs in the cell. According to an embodiment, the plurality of measurements is all related to one and the same UE, i.e. the UE of interest. According to another embodiment, the measurements may be related to more than one UE, but one of the involved UEs should be the UE of interest.

[0032] The statistical distribution determined in step 204 may be e.g., a probability density function (PDF) or a histogram. The statistical distribution may be such a distribution that time point of each measurement does not influence the statistics. In other words, the statistical distribution of the information on channel quality does not take the time points of the measurements into consideration. The term convolving / convolution in step 206 is described for example at well-known to a person skilled in the art and described for example at https: / / en.wikipedia.org / wiki / Convolution.

[0033] The “information on channel quality” may be the actual measured channel quality, such as SNR or SINR or it may be a value that is related to channel quality but not directly measured, such as a spectral efficiency value on throughput / bandwidth, for example Bits Per Resource Element (BPRE).

[0034] “The one or more transmission parameters" determined in step 210 may be any type of parameter related to usage of transmission resources for transmission of the data. Examples of transmission resources are Modulation and Coding Scheme (MCS), transport block size, code rate, bandwidth, transmission power and rank transmission. The first transmission attempt may also be called the initial transmission attempt. The first transmission attempt may be followed by up to N-1 retransmission attempts. So, N is the number of admissible transmission attempts for trying to transmit the certain data including the first transmissionattempt and the N-1 admissible retransmission attempts. The first transmission attempt is the original or first in time transmission attempt of the allowed N transmission attempts. N can vary depending on each data transmission instance. For example, for different types of data belonging to different service classes, e.g. TCC or MBB, the number of admissible retransmissions vary. Normally, if the latency budget for the service can afford e.g., 4 admissible transmission attempts and the first transmission attempt is succeeded, no retransmission will be performed, if first transmission fails but the second transmission, i.e., first retransmission succeeds, no third or fourth transmission will take place, etc. In the retransmissions, the certain data (same data) may be transmitted in different versions compared to the version of the certain data transmitted in the first transmission attempt.

[0035] The required transmission reliability is achieved after all necessary retransmissions, if needed, when the same transmission parameters as used for the first transmission, i.e., the parameters determined in step 210, are used for all N-1 retransmissions. However, when it comes to the time of performing any of the retransmissions, it may be so that any of the one or more transmission parameters are changed compared to the transmission parameters determined and used for the first transmission attempt. An example of a required transmission reliability for TCC may be 99.9 %.

[0036] In this method, channel quality in two different points of time is assumed independent and identically distributed. This enables convolving the determined statistical distribution with itself. Such a convolution of the statistical distribution has proven to result in a good estimation of effective channel quality after all admissible transmission attempts particularly for statistical end / tail values. End / tail values are of most interest for e.g., TCC and URLCC since they are referring to reliability levels like 99%, 99.9%, 99.99% and even stricter reliability levels for some services. When using such convolution of the statistical distribution with itself, a much better estimation of the channel quality at statistical end values / extreme values is achieved than when using for example the standard Gaussian distribution. Also, a much lower number of measurements are requiredto determine statistical end / extreme values than when not using statistical distributions and just looking at a very high number of measurements. As a result, transmission parameters are determined in an efficient way and the determined transmission parameters result in a transmission including any admissible retransmissions fulfilling even rather strict reliability requirements at the same time as a good throughput of data can be achieved.

[0037] According to an embodiment, the convolving 206 of the determined statistical distribution of the information on channel quality with itself is performed N-1 number of times. When convolving the statistical distribution with itself N-1 number of times compared to only convolving the statistical distribution with itself one time, a better approximation of the statistical distribution is achieved, especially at the end / tail values with the same (rather few) amounts of measurements, compared to doing only one convolution. N-1 is the number of admissible retransmission attempts for the transmission of the certain data i.e. the number of admissible transmission attempts N minus the first original transmission attempt.

[0038] According to an embodiment, the method further comprises determining 208, from the convolved statistical distribution of the information on channel quality and on the required transmission reliability for transmission of the certain data between the UE of interest 140 and the network node 130 after the N number of admissible transmission attempts, a long-term estimation of the information on channel quality. Further, the determining 210 of one or more transmission parameters for the first transmission attempt is based on the long-term estimation of the information on channel quality.

[0039] According to another embodiment, the method further comprises determining 209 a short-term estimation of the information on channel quality for the first transmission attempt for transmission of the certain data based on the information on channel quality of the latest-in-time measurement for the UE of interest of the plurality of measurements and on an estimation of correlation between the information on channel quality of the latest-in-time measurement and the information on channel quality for the first transmission attempt. The estimatedcorrelation is a function of a time difference between the time of the latest-in-time measurement and the time of the first transmission attempt. Further, the determining 210 of the one or more transmission parameters for transmission of the certain data to the UE of interest at the first transmission attempt is also based on the determined short-term estimation of the information on channel quality. In other words, the one or more transmission parameters are determined 210 based on the short-term estimation and the long-term estimation of the information on channel quality. Such a determination 210 may be done in many different ways, based on the short-term and long-term estimation, as will be described below. By combining the short-term estimation and the long-term estimation, high initial reliability, i.e., reliability after the first transmission attempt is ensured. The "latestin-time measurement of the plurality of measurements" signifies the measurement that has been performed latest of the plurality of measurements.

[0040] According to an embodiment, when the estimated correlation is at least 0.9, more preferably at least 0.95, on a scale between 0 and 1 where 0 means no correlation and 1 total correlation, the short-term estimation of the information on channel quality for the first transmission attempt is determined 209 to the same as the information on channel quality of the latest-in-time measurement. This may be especially applicable when the time difference between the time of the latest-in-time measurement and the time of the first transmission attempt is short, or when the environmental conditions do not vary a lot over time.

[0041] According to another embodiment, the short-term estimation of the information on channel quality for the first transmission attempt is determined 209 as a function of the information on channel quality of the latest-in-time measurement for the UE of interest, the estimation of the correlation and an average value of the information on channel quality of the obtained plurality of measurements of channel quality. The average value of the information on channel quality can be taken from the determined 204 statistical distribution. The average value of the information on channel quality can be taken from the M latest-in-time measurements of the plurality of measurements. According to an alternative of this embodiment, the short-term estimation of the information onchannel quality for the first transmission attempt is determined 209 as a linear combination of the information on channel quality of the latest-in-time measurement for the UE of interest, the estimation of the correlation and an average value of the information on channel quality of the obtained plurality of measurements of channel quality.

[0042] According to yet another embodiment, the short-term estimation of the information on channel quality for the first transmission attempt is determined 209 as a function of the information on channel quality of the latest-intime measurement for the UE of interest, the estimation of the correlation and an Xthpercentile value of the information on channel quality of the obtained plurality of measurements of channel quality. The Xth percentile value of the information on channel quality can be taken from the determined 204 statistical distribution. X in Xth percentile may be e.g., 5-30 %, for example 10 %. The Xth percentile value can be taken from the M latest-in-time measurements of the plurality of measurements. According to an alternative of this embodiment, the function is a linear combination.

[0043] According to yet another embodiment, the determining 210 of one or more transmission parameters for the first transmission attempt is based on the lowest value of the determined 209 short-term estimation of the information on channel quality and the determined 208 long-term estimation of the information on channel quality.

[0044] According to another embodiment, the determining 210 of one or more transmission parameters for the first transmission attempt is based on a weighted average value of the determined 209 short-term estimation of the information on channel quality and the determined 208 long-term estimation of the information on channel quality.

[0045] According to yet another embodiment, the method further comprises triggering transmission 212 of the certain data between the network node and the UE of interest 140 using the determined 210 one or more transmission parameters at least at the first transmission attempt of the N number of admissibletransmission attempts. If the first transmission attempt is successful, no retransmission attempts will be performed. For the retransmission attempts, the transmission parameters may be set at the same values as for the first transmission or the transmission parameters may be set to different values. In one embodiment, in the retransmission attempts, the same Transport Block (TB) size is used as for the first transmission, but the modulation scheme may change, for example a lower modulation scheme may be used compared to the first transmission. In that case, the code rate for the retransmission attempts could for example be increased to configure the TBs. “Triggering transmission” means, in case it is downlink communication, that the one or more network entities instructs the network node 130 to transmit the certain data using the determined transmission parameters. In case it is uplink transmission, triggering transmission means that the one or more network entities instructs the network node 130 to instruct the UE of interest 140 to transmit the certain data using the determined transmission parameters.

[0046] According to still another embodiment, the method further comprises transforming 203 the measurements of channel quality into the information on channel quality. The information on channel quality may be set in discrete values. According to embodiments, for DL transmissions, the CSI reports configured by the UE of interest are simple quantized values giving an indication about the current SNR conditions, such as CQI-values. The CQI values are then converted into e.g. SNR or a spectral efficiency value, e.g., BPRE. The whole analysis could be carried out in SNR dB domain (converting reports to SNR values) or in the rate or spectral efficiency domain (rate = Iog2(1+SNR)). Both are very similar, but in some implementations rate may slightly better. The main idea in the former is to find a proxy for SNR after combining all transmission attempts with reliability by combining all the SNR from all the transmission attempts. The main idea in the latter is to calculate the total spectral efficiency or rate or BPRE after all transmission attempts with reliability R = (log2(1+SNR_tx1 ) + log2(1 +SNR_Tx2) ... ). When referring to combining SNR or summing spectral efficiency, the convolved statistical distribution is equivalent to the statistical distribution of the combined SNR or summed spectral efficiency.

[0047] According to an embodiment, two sub-processes are used for determining transmission parameters for the first transmission attempt for transmission of a certain data between a UE of interest 140 and a network nodel 30. The first sub-process is based on analysis aiming to meet a requested transmission reliability, e.g., set in BLER, after N admissible transmission attempts, e.g., Hybrid Automatic Repeat Request (HARQ) transmission attempts. This is performed by estimating a first non-parametric pdf / histogram of SINR, derived from CSI reports from at least the UE of interest, in case of DL communication. Thereafter, the pdf / histogram of the effective SINR or effective spectral efficiency, e.g., BPRE after all transmission attempts is approximated by convolving the first histogram with itself, once per admissible retransmission attempt. This allows the first sub-process to estimate the tail / very low percentiles, which is important for high reliability services, of the pdf of effective SINR after retransmissions based on only a small buffer of measurements. Performing this operation, the first sub-process essentially disregards correlation over time. The second subprocess is based on an analysis of transmission reliability, e.g., BLER, for the first transmission attempt for the UE of interest to ensure high initial reliability, i.e., reliability after the 1sttransmission attempt. The analysis considers correlation of the channel quality at the time instant for the first transmission and the time instant that the UE performed the latest CSI report.

[0048] The first sub-process is important since based on this a rough estimation of the aggregated channel quality after all N admissible transmission attempts can be achieved. Also, it requires statistics of the channel quality only from a smalltime window assuming random and independent SINR at transmission attempts, i.e., no autocorrelation. It should be pointed out that the first sub-process could be standalone as well as combined with the second sub-process.

[0049] The outputs of the first and second sub-processes are applied to determine the transmission parameters for the first transmission attempt, the transmission parameters may include an MCS scheme. The application of the outputs of the first and second subprocesses may be applied as a function with arguments described as f(BPREtotaliNtxBPREltx), where BPREtotaliNtxis the outputof the first sub-process and BPREltxis the output of the second sub-process. The result of the applied function BPRELA is used for determining the transmission parameters.

[0050] According to an embodiment, which is shown in fig. 3, the following step-wise procedure may be applied for determining transmission parameters for DL transmission of data from the network node to the UE of interest: Obtain 302, over time, a plurality of measurements of channel quality from measurement reports, e.g. CSI reports, received from the UE of interest. Obtain 304 a database of values of the channel quality based on the obtained measurements. Obtain 306 statistics for the values of the channel quality in the database. Perform 308 the 1stsub-process: Analysis of the effective channel quality after N transmission attempts based on the obtained statistics. Perform 310 the 2ndsub-process: Analysis of the effective channel quality for the first transmission attempt. Determining 312 an effective channel quality based on the output from the 1stand 2ndsub-process. Use the determined effective channel quality to determine 314 DL transmission parameters for the first transmission attempt. Observe that the 2ndsub-process can be optional. A similar procedure is applicable to UL transmission. For UL transmission, the network node obtains measurements of channel quality by determining channel quality on reference signals received from the UE.

[0051] Differences to prior art statistical approaches outline comprises: for the first sub-process, applying convolution of the histogram of the channel quality in order to find statistics for effective SINR or effective BPRE after all N transmission attempts using a limited number of samples from the database of the channel quality, thereby not relying of a parametric PDF (such as Gaussian), or a very long trace of measurements as in prior art; the combination of the first and second subprocesses, where the second sub-process considers the autocorrelation of the channel quality at the transmission time and the time that the CSI report was configured. The first subprocess considers BLER after all retransmissions.

[0052] According to another embodiment, a first sub-process and possibly also a second sub-process are used for determining transmission parameters for the first transmission attempt for transmission of a certain data between a UE ofinterest 140 and a network node130. The first sub-process disregards the timecorrelation among the transmission attempts, and it is applied in order to find an approximation for the long-term statistics of the total channel quality after retransmissions. This is done using convolution of a histogram of the channel quality with itself up to N-1 times. The resulting histogram after convolution is used to determine transmission parameters to meet reliability R given N transmission attempts. Based on this, a rough estimation of the aggregated channel quality after all the transmission attempts can be achieved considering memory constraints and without assuming any well-known distribution for the aggregated channel quality. The second sub-process performs an analysis for the first transmission attempt to ensure high initial reliability, i.e., reliability after the 1sttransmission attempt, which considers autocorrelation of the channel quality at the transmission time and the time instant that the UE configured the CSI report. A function that takes the outputs of the two sub-processes into consideration could be applied to determine the DL transmission parameters.

[0053] According to embodiments, possible details of the steps of the method for determining transmission parameters for DL transmission of data from a network node to a UE as described with relation to fig. 3 is described.

[0054] The obtaining 304 of a database of channel quality may be performed by mapping value(s) of each CSI report to a proxy value of channel quality, e.g. BPRE, hereinafter called “information on channel quality”, the information on channel quality thus corresponding to an estimated spectral efficiency. Therefore, a database comprising channel quality values of the M latest CSI reports that the UE reported to the network node could be transformed into a database comprising the M latest information on channel quality, e.g., the M latest BPRE values. In one embodiment, the database is populated with lowest possible CSIs as initialization, and then acts as a First-Input First-Output (FIFO) queue. The database is updated as new CSI reports are received but not necessarily each time a UE is scheduled.

[0055] The obtaining 306 of statistics for the channel quality or information on channel quality may include include obtaining a statistical distribution of the channel quality values or of the information on channel quality, such as an empirical PDF, thestatistical distribution may be for a single transmission attempt, so disregarding correlation over time. This statistical distribution is then used in the first sub-process. Further, the obtaining 306 of statistics for the channel quality or information on channel quality may comprise determining an estimate of a correlation over time of the channel quality / information on channel quality: let for example p be an estimated auto-correlation of the channel quality / information on channel quality with a lag corresponding to typical delay between a transmission attempt and the latest reported channel quality measurement. The estimated correlation / auto-correlation is then used in the second sub-process. The obtaining 306 of statistics may be performed regularly or upon update of the database, but not necessarily each time a UE is scheduled.

[0056] The performing 308 of the 1stsub-process, i.e. the analysis of the effective channel quality after N transmission attempts based on the obtained statistics disregards the time-correlation among the transmission attempts. It is an approach to find an estimate of a long-term channel quality value / information on channel quality, which in the following is illustrated by BPRE and represented as BPREtotai Ntx. The approach according to an embodiment is to determine an estimate of the total channel quality after N transmission attempts, which is used to find transmission parameters, e.g. transport format, that meet a required transmission reliability, e.g., 99.9%, using channel quality values of the M latest reports and with very general assumptions of the actual distribution of channel quality. Based on the values in the database, the statistical distribution, e.g., the pdf, of the channel quality values / information on channel quality at the transmission time could be obtained (as per the step above). By assuming independent transmission attempts, convolution on the pdf could be applied to obtain the pdf of the aggregated channel quality after all N transmission attempts. The resulting convoluted pdf represents an estimate of the total BPRE that could be achieved after N transmission attempts. Having estimated the pdf after N transmission attempts, the convoluted pdf could be used to pick the BPRE level with reliability R. This approach does not assume a parametrized distribution to be used to fit the aggregated channel quality, which could otherwise create a mismatch with the actual distribution of the channel quality.

[0057] The performing 310 the 2ndsub-process, i.e., the determining of the information on channel quality for the first transmission attempt, which is optional, may rely on all or some of the following components: the latest measurement of channel quality for the channel quality between the UE and the network node, the estimated autocorrelation between the time of the first transmission and the time of the latest measurement, and the Xth percentile of the channel quality / channel quality information calculated from samples included into a small-time window, for example a time window of the database. The determining of the information on channel quality for the first transmission attempt may also be called determining a short-term information on channel quality. The information on channel quality for the first transmission attempt is illustrated by BPRE and represented as BPREltx est.

[0058] In one embodiment, the BPRE from the latest report (BPREiatest) could be used as the information on channel quality for the first transmission attempt:BPREltx est= BPREiatest.

[0059] According to another embodiment, a linear combination of the BPRE of the latest report (BPREiatest), the autocorrelation (p) and the average BPRE over the M older samples from a small-time window (BPREmean) could be applied to determine the information on channel quality for the first transmission attempt:

[0060] According to another embodiment, a linear combination of the BPRE of the latest report (BPREiatest), the autocorrelation (p) and an Xth percentile of the BPRE values based on the M older samples from a small-time window (BPRExth per) could be applied to determine the information on channel quality for the first transmission attempt:

[0061] According to yet another embodiment, a combination of the BPRE of the latest report (BPREiatest), the autocorrelation (p), the Xth percentile of the BPRE values based on the M older samples from a small-time window (BPRExth per) and the average BPRE (BPREmean) could be applied to determine the information on channel quality for the first transmission attempt:* BPREmean, where BPREltxest= biased BPRE estimate at the first transmission attempt, p = estimated auto-correlation between the BPRE value at the transmission attempt and the latest BPRE (BPREtatest), BPRExth per= Xth percentile of the latest M BPRE values, and BPREmean= average BPRE over the latest M BPRE samples.

[0062] The step of determining 312 an effective channel quality / information on channel quality BPRELAbased on the output of the first sub-process, i.e. the determined long-term information on channel quality (BPREtotai Ntx) and the output of the second sub-process, i.e. the determined short-term information on channel quality, aka the information on channel quality for the first transmission attempt (BPREltx est) may be performed by applying a function with arguments of the outputs of the first and second sub-process:where LA in BPRELAstands for Link Adaption.

[0063] In the embodiments where only the first sub-process is used, BPRELA= BPREtotai Ntx. In the embodiments where both the first and sub-process are used, any different way in which the two results are used in such a function is covered. According to one approach, the lowest value of BPREltxand BPREtotai Ntxis selected as the effective channel quality / information on channel quality. According to another approach, a weighted or non-weighted average of BPREltxand BPREtotai Ntxis determined and selected as the effective channel quality / information on channel quality.

[0064] In step 314, the determination of the DL transmission parameters, the DL transmission parameters will be determined based on the effective channel quality / information on channel quality BPRELA. For example, an MCS scheme is selected out of a number of different MCS schemes based on the BPRELA.

[0065] Fig. 4 shows results of a test with the proposed convolution approach according to the first sub-process compared to a Gaussian distribution approach,when the number of allowed transmission attempts N = 2. Both the convolution approach as well as the Gaussian approach is made on 200 samples of channel quality, here represented by BPRE. As a reference, an offline process has been used, where 100.000 samples of BPRE have been gathered beforehand, in order to determine the histogram of the total BPRE after all the transmission attempts. It contains samples of the actual channel quality; it does not assume any kind of distribution and it takes time correlation into account. The Gaussian distribution is provided as a baseline scheme assuming that the total channel quality or supported BPRE follows Gaussian distribution, which is more realistic implementation-wise, since it relies only on 200 samples to calculate the mean and the variance. However, the Gaussian distribution intersects the required transmission reliability target, here represented by residual BLER (rBLER) target set to 10-3, at much lower BPRE level compared to the offline case, leading to pessimistic selection of the transmission parameters due to the high variance of the channel quality and hence degrades the spectral efficiency performance. It is notable that the convolution approach according presented embodiments is following the curve of the offline case much better, especially at low BLER values, while considering the memory / delay constraint.

[0066] Fig. 5 shows a comparison of embodiments of the presented invention when combining the disclosed first and second sub-process to a prior art method with a fixed backoff, for 0, 1 and 2 HARQ transmission attempts, respectively. By combining these two sub-processes, the embodiments of the present invention have demonstrated excellent performance in evaluations to the prior art. As seen, the throughput of data in Mpbs over a Physical Downlink Shared Channel (PDSCH) becomes significantly higher with the present invention. Note that a wellfunctioning Link Adaptation is crucial for good system performance.

[0067] Fig. 6, in conjunction with fig. 1 a, describes one or more network entities 600 configured to operate in a communication network 100 and configured for determining transmission parameters for transmission of data between a network node 130 of the communication network 100 and a UE 140 of interest. The one or more network entities 600 comprises processing circuitry 603 and memory 604.Said memory contains instructions executable by said processing circuitry, whereby the one or more network entities 600 is operative for obtaining, over time, a plurality of measurements of channel quality of a wireless communication channel between the network node 130 and one or more UEs 140, 145 including the UE of interest 140 and determining a statistical distribution of information on the channel quality of the wireless communication channel based on the plurality of measurements of channel quality. The one or more network entities is further operative for convolving the determined statistical distribution of the information on channel quality with itself at least one time, and determining, from the convolved statistical distribution of the information on channel quality and on a required transmission reliability for transmission of certain data between the UE of interest 140 and the network node 130 after N number of admissible transmission attempts, one or more transmission parameters for a first transmission attempt of the N number of admissible transmission attempts for transmission of the certain data between the UE of interest 140 and the network node 130.

[0068] The one or more network entities 600 may be realized at or in the network node 130 or any other RAN node or CN node 160. Alternatively, the one or more network entities 600 may be realized at or in any of the O-RAN nodes shown in fig 1 b. Alternatively, the one or more network entities 600 may be realized as a group of network nodes, wherein functionality of the one or more network entities 600 is spread out over the group of network nodes in the communication network of fig. 1 a or fig. 1 b. The group of network nodes may be different physical, or virtual, nodes of the network spread out for example in a network cloud 170 as shown in fig. 1a.

[0069] According to an embodiment, the one or more network entities 600 is operative for performing the convolving of the determined statistical distribution of the information on channel quality with itself N-1 number of times.

[0070] According to an embodiment, the one or more network entities 600 is operative for determining, from the convolved statistical distribution of the information on channel quality and on the required transmission reliability for transmission of the certain data between the UE of interest 140 and the networknode 130 after the N number of admissible transmission attempts for transmission of the certain data, a long-term estimation of the information on channel quality. Further, the determining of one or more transmission parameters for a first transmission attempt is based on the long-term estimation of the information on channel quality.

[0071] According to another embodiment, the one or more network entities 600 is further operative for determining a short-term estimation of the information on channel quality for the first transmission attempt for transmission of the certain data based on the information on channel quality of the latest-in-time measurement for the UE of interest of the plurality of measurements and on an estimation of correlation between the information on channel quality of the latestin-time measurement and the information on channel quality for the first transmission attempt. The correlation is a function of a time difference between the time of the latest-in-time measurement and the time of the first transmission attempt. Further, the determining of the one or more transmission parameters for transmission of the certain data to the UE of interest at the first transmission attempt is also based on the determined short-term estimation of the information on channel quality.

[0072] According to another embodiment, when the estimation of correlation is at least 0.9, more preferably at least 0.95, on a scale between 0 and 1 where 0 is no correlation and 1 total correlation, the one or more network entities is operative for determining the short-term estimation of the information on channel quality for the first transmission attempt to the same as the information on channel quality of the latest-in-time measurement.

[0073] According to yet another embodiment, the one or more network entities 600 is operative for the determining of the short-term estimation of the information on channel quality for the first transmission attempt as a function of the information on channel quality of the latest-in-time measurement for the UE of interest, the estimation of the correlation and an average value of the information on channel quality of the obtained plurality of measurements of channel quality.

[0074] According to yet another embodiment, the one or more network entities 600 is operative for the determining of the short-term estimation of the information on channel quality for the first transmission attempt as a function of the information on channel quality of the latest-in-time measurement for the UE of interest, the estimation of the correlation and an Xthpercentile value of the information on channel quality of the obtained plurality of measurements of channel quality.

[0075] According to yet another embodiment, the one or more network entities 600 is operative for the determining of one or more transmission parameters for the first transmission attempt based on the lowest value of the determined shortterm estimation of the information on channel quality and the determined long-term estimation of the information on channel quality.

[0076] According to yet another embodiment, the one or more network entities 600 is operative for the determining of one or more transmission parameters for the first transmission attempt based on a weighted average value of the determined short-term estimation of the information on channel quality and the determined long-term estimation of the information on channel quality.

[0077] According to still another embodiment, the one or more network entities 600 is further operative for triggering transmission of the certain data between the network node 130 and the UE of interest 140 using the determined one or more transmission parameters at least at the first transmission attempt of the N number of admissible transmission attempts.

[0078] According to still another embodiment, the one or more network entities 600 is further operative for transforming the measurements of channel quality into the information on channel quality.

[0079] According to other embodiments, the one or more network entities 600 may further comprise a communication unit 602 which comprises conventional means for communication within network nodes and / or with network nodes of the communication network 100. The instructions executable by said processing circuitry 603 may be arranged as a computer program 605 stored e.g. in saidmemory 604. The processing circuitry 603 and the memory 604 may be arranged in a sub-arrangement 601 . The sub-arrangement 601 may be a micro-processor and adequate software and storage therefore, a Programmable Logic Device, PLD, or other electronic component(s) / processing circuit(s) configured to perform the methods mentioned above. The processing circuitry 603 may comprise one or more programmable processor, application-specific integrated circuits, field programmable gate arrays or combinations of these adapted to execute instructions.

[0080] The computer program 605 may be arranged such that when its instructions are run in the processing circuitry 603, the instructions cause the one or more network entities 600 to perform the steps described in any of the described embodiments of the one or more network entities 600 and its method. The computer program 605 may be carried by a computer program product connectable to the processing circuitry 603. The computer program product may be the memory 604, or at least arranged in the memory. The computer program product may be called a computer-readable storage medium 606. The memory 604 may be realized as for example a Random-access memory (RAM), Read-Only Memory (ROM) or an Electrical Erasable Programmable ROM (EEPROM). In some embodiments, a carrier may contain the computer program 605. The carrier may be one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or computer readable storage medium. The computer-readable storage medium 606 may be e.g., a CD, DVD or flash memory, from which the program could be downloaded into the memory 604. Alternatively, the computer program 605 may be stored on a server or any other entity to which the one or more network entities 600 has access via the communication unit 602. The computer program 605 may then be downloaded from the server into the memory 604.

[0081] Although the description above contains a plurality of specificities, these should not be construed as limiting the scope of the concept described herein but as merely providing illustrations of some exemplifying embodiments of the described concept. It will be appreciated that the scope of the presently describedconcept fully encompasses other embodiments which may become obvious to those skilled in the art, and that the scope of the presently described concept is accordingly not to be limited. Reference to an element in the singular is not intended to mean "one and only one" unless explicitly so stated, but rather "one or more." All structural and functional equivalents to the elements of the abovedescribed embodiments that are known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed hereby. Moreover, it is not necessary for an apparatus or method to address each and every problem sought to be solved by the presently described concept, for it to be encompassed hereby. In the exemplary figures, a broken line generally signifies that the feature within the broken line is optional.

Claims

CLAIMS1 . A method for determining transmission parameters for transmission of data between a network node (130) of a communication network (100) and a User Equipment, UE, (140) of interest, the method comprising: obtaining (202), over time, a plurality of measurements of channel quality of a wireless communication channel between the network node (130) and one or more UEs (140, 145) including the UE of interest (140), determining (204) a statistical distribution of information on the channel quality of the wireless communication channel based on the plurality of measurements of channel quality, convolving (206) the determined statistical distribution of the information on channel quality with itself at least one time, and determining (210), from the convolved statistical distribution of the information on channel quality and on a required transmission reliability for transmission of certain data between the UE of interest (140) and the network node (130) after N number of admissible transmission attempts, one or more transmission parameters for a first transmission attempt of the N number of admissible transmission attempts for transmission of the certain data between the UE of interest (140) and the network node (130).

2. Method according to claim 1 , wherein the convolving (206) of the determined statistical distribution of the information on channel quality with itself is performed N-1 number of times.

3. Method according to claim 1 or 2, further comprising: determining (208), from the convolved statistical distribution of the information on channel quality and on the required transmission reliability for transmission of the certain data between the UE of interest (140) and the network node (130) after the N number of admissible transmission attempts for transmission of the certain data, a long-term estimation of the information on channel quality, andwherein the determining (210) of one or more transmission parameters for a first transmission attempt is based on the long-term estimation of the information on channel quality.

4. Method according to claim 3, further comprising: determining (209) a short-term estimation of the information on channel quality for the first transmission attempt for transmission of the certain data based on the information on channel quality of the latest-in-time measurement for the UE of interest of the plurality of measurements and on an estimation of correlation between the information on channel quality of the latestin-time measurement and the information on channel quality for the first transmission attempt, which correlation is a function of a time difference between the time of the latest-in-time measurement and the time of the first transmission attempt, wherein the determining (210) of the one or more transmission parameters for transmission of the certain data to the UE of interest at the first transmission attempt is also based on the determined short-term estimation of the information on channel quality.

5. Method according to claim 4, wherein when the estimated correlation is at least 0.9, more preferably at least 0.95, on a scale between 0 and 1 where 0 is no correlation and 1 total correlation, the short-term estimation of the information on channel quality for the first transmission attempt is determined (209) to the same as the information on channel quality of the latest-in-time measurement.

6. Method according to claim 4, wherein the short-term estimation of the information on channel quality for the first transmission attempt is determined (209) as a function of the information on channel quality of the latest-in-time measurement for the UE of interest, the estimation of the correlation and an average value of the information on channel quality of the obtained plurality of measurements of channel quality.

7. Method according to claim 4 or 6, wherein the short-term estimation of the information on channel quality for the first transmission attempt is determined(209) as a function of the information on channel quality of the latest-in-time measurement for the UE of interest, the estimation of the correlation and an Xthpercentile value of the information on channel quality of the obtained plurality of measurements of channel quality.

8. Method according to any of claims 4-7, wherein the determining (210) of one or more transmission parameters for the first transmission attempt is based on the lowest value of the determined (209) short-term estimation of the information on channel quality and the determined (208) long-term estimation of the information on channel quality.

9. Method according to any of claims 4-7, wherein the determining (210) of one or more transmission parameters for the first transmission attempt is based on a weighted average value of the determined (209) short-term estimation of the information on channel quality and the determined (208) long-term estimation of the information on channel quality.

10. Method according to any of the preceding claims, further comprising: triggering transmission (212) of the certain data between the network node and the UE of interest (140) using the determined (210) one or more transmission parameters at least at the first transmission attempt of the N number of admissible transmission attempts.11 . Method according to any of the preceding claims, further comprising transforming (203) the measurements of channel quality into the information on channel quality.

12. One or more network entities (600) configured to operate in a communication network (100) and configured for determining transmission parameters for transmission of data between a network node (130) of the communication network (100) and a User Equipment, UE, (140) of interest, the one or more network entities (600) comprising processing circuitry (603) and memory (604), said memory containing instructions executable by said processing circuitry, whereby the one or more network entities (600) is operative for:obtaining, over time, a plurality of measurements of channel quality of a wireless communication channel between the network node (130) and one or more UEs (140, 145) including the UE of interest (140), determining a statistical distribution of information on the channel quality of the wireless communication channel based on the plurality of measurements of channel quality, convolving the determined statistical distribution of the information on channel quality with itself at least one time, and determining, from the convolved statistical distribution of the information on channel quality and on a required transmission reliability for transmission of certain data between the UE of interest (140) and the network node (130) after N number of admissible transmission attempts, one or more transmission parameters for a first transmission attempt of the N number of admissible transmission attempts for transmission of the certain data between the UE of interest (140) and the network node (130).

13. One or more network entities (600) according to claim 12, operative for performing the convolving of the determined statistical distribution of the information on channel quality with itself N-1 number of times.

14. One or more network entities (600) according to claim 12 or 13, further being operative for: determining, from the convolved statistical distribution of the information on channel quality and on the required transmission reliability for transmission of the certain data between the UE of interest (140) and the network node (130) after the N number of admissible transmission attempts for transmission of the certain data, a long-term estimation of the information on channel quality, wherein the determining of one or more transmission parameters for a first transmission attempt is based on the long-term estimation of the information on channel quality.

15. One or more network entities (600) according to claim 14, further being operative for:determining a short-term estimation of the information on channel quality for the first transmission attempt for transmission of the certain data based on the information on channel quality of the latest-in-time measurement for the UE of interest of the plurality of measurements and on an estimation of correlation between the information on channel quality of the latest-in-time measurement and the information on channel quality for the first transmission attempt, which correlation is a function of a time difference between the time of the latest-in-time measurement and the time of the first transmission attempt, wherein the determining of the one or more transmission parameters for transmission of the certain data to the UE of interest at the first transmission attempt is also based on the determined short-term estimation of the information on channel quality.

16. One or more network entities (600) according to claim 15, wherein when the estimation of correlation is at least 0.9, more preferably at least 0.95, on a scale between 0 and 1 where 0 is no correlation and 1 total correlation, the one or more network entities is operative for determining the short-term estimation of the information on channel quality for the first transmission attempt to the same as the information on channel quality of the latest-in-time measurement.

17. One or more network entities (600) according to claim 15, operative for the determining of the short-term estimation of the information on channel quality for the first transmission attempt as a function of the information on channel quality of the latest-in-time measurement for the UE of interest, the estimation of the correlation and an average value of the information on channel quality of the obtained plurality of measurements of channel quality.

18. One or more network entities (600) according to claim 15 or 17, operative for the determining of the short-term estimation of the information on channel quality for the first transmission attempt as a function of the information on channel quality of the latest-in-time measurement for the UE of interest, the estimation of the correlation and an Xthpercentile value of the information on channel quality of the obtained plurality of measurements of channel quality.

19. One or more network entities (600) according to any of claims 15-18, operative for the determining of one or more transmission parameters for the first transmission attempt based on the lowest value of the determined short-term estimation of the information on channel quality and the determined long-term estimation of the information on channel quality.

20. One or more network entities (600) according to any of claims 15-18, operative for the determining of one or more transmission parameters for the first transmission attempt based on a weighted average value of the determined shortterm estimation of the information on channel quality and the determined long-term estimation of the information on channel quality.21 . One or more network entities (600) according to any of claims 12-20, further being operative for: triggering transmission of the certain data between the network node and the UE of interest (140) using the determined one or more transmission parameters at least at the first transmission attempt of the N number of admissible transmission attempts.

22. One or more network entities (600) according to any of claims 12-21 , further being operative for transforming the measurements of channel quality into the information on channel quality.

23. A computer program (605) comprising instructions, which, when executed by at least one processing circuitry (603) of one or more network entities (600) of a communication network, configured for determining transmission parameters for transmission of data between a network node (130) and a UE of interest (140), causes the one or more network entities (600) to perform the following steps: obtaining, over time, a plurality of measurements of channel quality of a wireless communication channel between the network node (130) and one or more UEs (140, 145) including the UE of interest (140),determining a statistical distribution of information on the channel quality of the wireless communication channel based on the plurality of measurements of channel quality, convolving the determined statistical distribution of the information on channel quality with itself at least one time, and determining, from the convolved statistical distribution of the information on channel quality and on a required transmission reliability for transmission of certain data between the UE of interest (140) and the network node (130) after N number of admissible transmission attempts, one or more transmission parameters for a first transmission attempt of the N number of admissible transmission attempts for transmission of the certain data between the UE of interest (140) and the network node (130).

24. A carrier containing the computer program (605) according to claim 23, wherein the carrier is one of an electronic signal, an optical signal, a radio signal, an electric signal or a computer readable storage medium (606).

Images