Patent ID: 12250071

DETAILED DESCRIPTION

To solve the above noted problems with conventional methods of link adaptation, disclosed herein are embodiments directed to using ML to obtain an optimized dynamic BLER target.

We now consider an exemplary scenario where there exists rapidly varying downlink (DL) inter-cell interference to describe the embodiments disclosed herein. The embodiments disclosed herein may be particularly beneficial in this exemplary scenario. Rapidly varying DL inter-cell interference may be rather common in LTE, NR, HSDPA or other wireless communication technologies with non-orthogonal DL transmissions. However, the exemplary scenario is in no way limiting and the embodiments disclosed herein may be applied to various alternative scenarios.

Let us now consider a UE running a common Internet application such as world-wide-web, video or social media service and receiving data in DL from a radio base station (RBS) for a relatively long time, for example, several seconds or minutes. While the number of active UEs in wireless networks is quite large, the majority of the active UE connections are short and devoted to multiple transmissions including a small number of data packets. This is because the majority of smartphone applications transmit small amounts of data in short bursts.

Accordingly, there is a high probability that the considered active UE in a cell and the majority of other active UEs in neighboring cells with Internet traffic are each active for short time periods. This results in rapidly varying resource allocation in the neighboring cells. Hence, the considered active UE will experience rapidly varying inter-cell interference.

In one embodiment, a UE receives Internet traffic data in DL from a RBS and the UE experiences rapidly varying inter-cell interference from the neighbor cells transmitting data to associated UEs in short bursts.

In this embodiment, link adaptation is deployed together with a dynamic BLER target set individually for each UE for a short period of time (e.g., sub-seconds). The BLER target is selected by a ML algorithm based on channel quality reports together with additional measurements such as neighbor cell activity, path gain to the serving cell, timing advance information and possibly other measurements, as shown inFIG.1.

It is also assumed that RBSs can report neighbor cell scheduling activity to each other via communication links. Examples of such communication links include a X2 interface in LTE, a Xn interface in NR or a combined lub-lur interface in HDSPA.

In some embodiments, dynamic BLER target is combined with a ML model for optimal BLER target selection. While it is known that a dynamic BLER target may yield a better transmission performance compared to a static BLER target in a scenario with rapidly varying inter-cell interference, it is difficult to find a simple mapping from channel quality measurements to an optimal BLER target. The difficulty lies in dynamic channel variations and missing information.

The advantage of selecting the BLER target with the ML model is that the ML model learns and predicts patterns in the experienced dynamic channel variations based on additional historical information, for example, neighbor cell scheduling activity during some historical period. This enables the reconstruction of information missing from the channel quality reports and the creation of a mapping of input time series to the optimal BLER target for the upcoming time period.

In some embodiments, the dynamic BLER target is used for a UE experiencing rapidly varying inter-cell interference from neighbor cells.FIG.1illustrates a procedure in which the ML model is used to select a close-to-optimal BLER target. This procedure is described in further detail below with reference toFIG.1.

In some embodiments, a range of possible BLER targets is chosen. For example, the range of possible BLER targets may be limited to a finite set: {BLER1, BLER2, . . . , BLERK}. The ML model selects one of the possible BLER targets as a close-to-optimal BLER target for the considered UE during the upcoming data transmission time period.

As shown inFIG.1, input measurements for the ML model are collected, fed into the ML model, and the ML model outputs are collected at each data transmission time period. The input measurements describe or are related to factors that influence the considered UE's radio environment, e.g., neighbor cell activity, variation at some historical time period. In some embodiments, the input measurements may include: (1) resource utilization in a number of neighbor cells; (2) channel quality reports (e.g., Channel Quality Indicator (CQI)); (3) path gain to the serving cell; (4) timing advance to the serving cell; and other relevant measurements. The resource utilization may be indicated by a vector with historical data (e.g., Neighbor cell 1 activity at times (t, t−1, . . . t−N), . . . , Neighbor cell M activity at times (t, t−1, . . . t−N)). The timing advance may provide an indication of the distance from the cell center for each UE.

The ML model uses the input measurements to predict the performance of the DL data transmissions to the considered UE in the upcoming data transmission time period for each of the BLER targets in the chosen set of BLER targets based on the current interference pattern. The ML model outputs the predicted performance for each of the BLER targets. For example, the predicted performance for each of the BLER targets may be indicated as Spectral Efficiency: {SE(BLER1), SE(BLER2), . . . , SE(BLERK)}. In some embodiments, a plurality of ML models may use the input measurements to predict the performance of the DL data transmissions to the considered UE and output the predicted performance.

As shown inFIG.1, the BLER value among a fixed set of chosen BLER targets with highest predicted performance is selected as the BLER target for the upcoming period.

In some embodiments, the procedure for using ML model to select a close-to-optimal BLER target comprises: (1) collect input measurements for a current data transmission period; (2) feed the collected input measurements into the ML model and obtain the predicted performance for the possible BLER targets in a chosen set of BLER targets for the next data transmission period; and (3) select the BLER target with highest predicted performance. In some embodiments, the obtained predicted performance for the possible BLER targets may indicated as SE(BLER1), SE(BLER2), . . . , SE(BLERK). In some embodiments, selecting the BLER target with the highest predicted performance may be shown as BLERtarget=argmaxkSE(BLERk).

As shown inFIG.1, the selected BLER target is passed into DL link adaptation. The selected BLER target is used for link adaptation during the next update period to select close-to-optimal transport block sizes. In some embodiments, transport block sizes are selected at shorter time periods (e.g., several ms or shorter) than the BLER target (e.g., 10-1000 ms).

In some embodiments, the ML model for the BLER target selection is obtained based on supervised learning. Supervised learning is a way to build a mathematical model by estimating the relation between a number of known input and known output examples.

In some embodiments, a procedure of supervised learning starts by collecting the input and output sample pairs from a target environment. Then, a suitable function with possibly random parameters is chosen as an initial model. This is followed by a “training” procedure where the collected input samples are fed into the function and its parameters are gradually adjusted to produce outputs that are as close as possible to the desired output samples according to a chosen loss metric, e.g., mean squared error. The model is considered to be sufficiently well trained when the model produces outputs that are close enough to the desired output set for a given test set of inputs that have not been used for training.

Some non-limiting examples of functions used for supervised learning include artificial neural networks and decision trees.

Some exemplary ML model configurations for BLER target selection are now described. Let us consider an embodiment in which dynamic BLER target is used for a UE experiencing rapidly varying inter-cell interference from neighbor cells and the ML model for BLER target selection comprises the structure shown inFIG.1and further described in related description. It is assumed that a finite set of possible BLER targets is chosen {BLER1, BLER2, . . . , BLERK}. Accordingly, the ML model (or a plurality of ML models) predicts spectral efficiency values {SE(BLER1), SE(BLER2), . . . , SE(BLERK)} in the upcoming data transmission period for each of the BLER targets in the set.

Some possible ML model configurations for BLER target selection in this embodiment includes, but is not limited to, the following:

1. A plurality of ML models with a single output for spectral efficiency. As shown inFIG.2, a set of ML models is trained, where the set includes one ML model for each BLER target. Each ML model has the same inputs as described above and one output for spectral efficiency for the corresponding BLER target.

2. One ML model with multiple outputs for spectral efficiency. As shown inFIG.3, one common ML model is trained, with multiple outputs, where each output predicts spectral efficiency for one BLER target in the set.

3. One model with multiple outputs for BLER target selection. As shown inFIG.4, one common ML model is trained, with multiple outputs, where each output predicts a probability for each BLER target in the set for being the optimal BLER target.

In some embodiments, software packages for ML may be used to implement the ML model disclosed herein. For example, software packages provided by Python, Tensorflow, Keras, Scikit-learn, deeplearning4j, Pytorch, Caffe, MXnet, and Theano may be used to implement the ML model disclosed herein.

The performance of embodiments disclosed herein has been evaluated using computer simulations. Specifically, the ML model performance in simulations for DL link adaptation has been evaluated and is explained in further detail below.

A simulator for DL link adaptation for LTE or NR has been used to generate input and output data sets for the ML model training. The chosen simulation scenario is modeling a UE510with large amount of DL traffic from a first cell505where the UE510experiences rapidly varying inter-cell interference from neighbor cells515a-etransmitting data to UEs in short bursts. It is assumed that the domain of possible BLER targets is limited to a finite set {BLER1, BLER2, . . . , BLERK}.

As shown inFIG.5, the first cell505is modeled in detail with basic DL link adaptation operating in a fading radio channel. The first cell505has a number of neighbor cells (“interferers”)515a-eplaced in a grid as shown inFIG.5. Each neighbor515a-ecell transmits an interfering signal with a certain probability at each transmission period causing further dips in the signal quality in the first cell505. The network500shown inFIG.5may be an LTE and/or a NR network according to some embodiments.

The UE510with a large amount of DL traffic is randomly placed in the cell505and data transmissions are simulated for a predetermined time period (e.g., 2-4 seconds). In a single simulation experiment, one data input and output sample is generated by logging the required model input and output measurements as time series. The simulation experiment is repeated a large number of times (e.g., 100000-1000000 times). A new random position for the UE510is chosen for each simulation experiment.

Each simulation experiment is repeated for each of the BLER targets in the set of BLER targets with the UE510placed at the same random position and experiencing the same interference pattern. Accordingly, one round of simulation experiments produces a set of transmission performance measurements, e.g. Spectral Efficiency: {SE(BLER1), SE(BLER2), . . . , SE(BLERK)} corresponding to the ML model output, as shown inFIG.6. The experiment inputs for the simulation includes deployment, traffic model, and random seed. For each of the experiments with BLER(1), BLER(2), up to BLER(N), the model inputs include CQI, neighbor cell activity, TA, and path gain and the model outputs for each respective experiment is the spectral efficiency, e.g., spectral efficiency of BLER(1), spectral efficiency of BLER(2), etc.

Given the data obtained from the simulation experiments, the generated input and output data sets are used to train a ML model (or a plurality of ML models) using a supervised learning procedure. Finally, the ML model performance is evaluated in terms of the prediction accuracy.

With respect to the parameters for the simulation experiments, the simulated scenario models a cell with a mix of high and low loads, where all load values occur equally often. That is, the load is approximately uniformly distributed, as indicated by the histogram shown inFIG.7of Physical Resource Block (PRB) utilization in the cell505. In some embodiments, other distributions may be used for the load depending on, for example, how the model is going to be used. For example, the model may be used for urban or suburban scenarios.

The finite set of possible BLER targets are provided by the set {0.05, 0.1, 0.2, 0.3, 0.5, 0.7, 0.9}. A 3-layer neural network with multiple outputs (which may also be referred to as a neural network with two hidden layers), as shown inFIG.3, has been used as the ML model and each output is a predicted spectral efficiency for each corresponding BLER target.

The inputs for ML model were provided as follows: (1) mean and standard deviation for PRB utilization for three neighbor cells; mean signal to noise ratio (SINR); distance to the serving eNodeB; and pathgain to the serving cell.

Finally, the ML model had been trained on 100,000 simulated input and output samples.

FIGS.8and9show the observed distributions for spectral efficiency for the UE experiencing rapidly varying inter-cell interference with three different link adaptation algorithms. The first algorithm802uses dynamic BLER target selected with the ML model, the second algorithm804uses a fixed 10% BLER target and the third algorithm806, hereafter referred to as the “genie” algorithm, is a full-tree search algorithm setting the optimal BLER target from the given finite set at each transmission instant. This is in contrast to dynamic BLER, which has a comparatively sparse BLER target selection.FIG.8shows box plots for the distributions andFIG.9shows CDF plots.FIG.8shows observed distributions for spectral efficiency for three link adaptation algorithms: dynamic BLER selected with the ML model (802), fixed 10% BLER (804), and the best possible dynamic BLER target from the considered finite set (806).FIG.9shows the observed CDF for spectral efficiency for three link adaptation algorithms: dynamic BLER selected with the ML model (802), fixed 10% BLER (804), and the best possible dynamic BLER target from the considered finite set (806).

The full-tree “genie” search algorithm806shows the highest possible potential of replacing static BLER target by a dynamic one, but cannot be implemented in the reality. It requires knowing all possible spectral efficiency outcomes for all chosen BLER target values, which is only possible in simulation experiments. In reality, only one spectral efficiency outcome corresponding to the chosen BLER value is known.

By comparing the estimated mean values from the box plots inFIG.8, it can be observed that the available potential for setting BLER target dynamically with the “genie” full-tree search algorithm806may be up to 40% spectral efficiency gain on average. Deploying dynamic BLER target set by a ML model802is almost as good as the “genie” algorithm806and yields up to 36% spectral efficiency gain on average.

By further inspecting the CDF plots inFIG.9, it can be seen that dynamic BLER target selected with the ML model802yields higher spectral efficiency than fixed 10% BLER target804in most load cases. Up to 30% gains in spectral efficiency can be achieved for certain loads and UE positions. The CDF is formed with samples where both neighbor cell load and UE positions are varied. Also, performance of dynamic BLER target selected with the ML model is very close to the best optimal one achieved by the “genie” algorithm806.

FIG.10is a flow chart illustrating a process1000, according to some embodiments, that is performed in a communication network for dynamically selecting a link adaptation policy, LAP. Process1000may begin with step s1002in which a machine learning, ML, model is generated, wherein generating the ML model comprises providing training data to an ML algorithm. In step s1004, a first transmission point, TRP, transmits first data to a user equipment, UE, using a first LAP, wherein the first TRP serves at least a first cell. In step s1006, a channel quality report transmitted by the UE is received, wherein the channel quality report comprises channel quality information indicating a quality of a channel between the UE and the first TRP. In step s1008, additional information is obtained, wherein the additional information comprises: neighbor cell information about a second cell served by a second TRP, distance information indicating a distance between the UE and the first TRP (e.g., a timing advance, TA, indicator transmitted by the UE), and/or gain information indicating a radio propagation gain between the UE and the serving node (e.g., an average gain). In step s1010, a LAP is selected from a set of predefined LAPs using the channel quality information, the additional information, and the ML model. In some embodiments, the set of predefined LAPs comprising the first LAP and a second LAP. In step s1012, the first TRP transmits second data to the UE using the selected LAP.

In some embodiments, the selected LAP indicates a block error rate (BLER) target and transmitting the second data to the UE using the selected LAP comprises transmitting the second data to the UE using the BLER target.

In some embodiments, transmitting the second data to the UE using the BLER target comprises selecting a transport block size, TBS, based on the BLER target and transmitting the second data to the UE using the selected TBS.

In some embodiments, the additional information further comprises neighbor cell information about a third cell served by a third TRP.

In some embodiments, the neighbor cell information about the second cell and/or the third cell comprises Physical Resource Block, PRB, utilization.

In some embodiments, the distance information indicating a distance between the UE and the first TRP comprises a timing advance, TA, indicator transmitted by the UE.

FIG.11is a block diagram of TRP1100according to some embodiments. In some embodiments, the TRP1100may be a base station or a component of a base station. In some embodiments, a base station may comprise one or more TRPs. As shown inFIG.11, TRP1100may comprise: a processing circuit (PC)1102, which may include one or more processors (P)1155(e.g., a general purpose microprocessor and/or one or more other processors, such as an application specific integrated circuit (ASIC), field-programmable gate arrays (FPGAs), and the like) which processors may be co-located or distributed across different locations; a network interface1148comprising a transmitter (Tx)1145and a receiver (Rx)1147for enabling TRP1100to transmit data to and receive data from other nodes connected to a network110(e.g., an Internet Protocol (IP) network) to which network interface1148is connected; circuitry1103(e.g., radio transceiver circuitry comprising an Rx1105and a Tx1106) coupled to an antenna system1104for wireless communication with UEs); and local storage unit (a.k.a., “data storage system”)1108, which may include one or more non-volatile storage devices and/or one or more volatile storage devices (e.g., random access memory (RAM)). In embodiments where PC1102includes a programmable processor, a computer program product (CPP)1141may be provided. CPP1141includes a computer readable medium (CRM)1142storing a computer program (CP)1143comprising computer readable instructions (CRI)1144. CRM1142may be a non-transitory computer readable medium, such as, but not limited, to magnetic media (e.g., a hard disk), optical media, memory devices (e.g., random access memory, flash memory), and the like. In some embodiments, the CRI1144of computer program1143is configured such that when executed by data processing apparatus1102, the CRI causes TRP1100to perform steps described herein (e.g., steps described herein with reference to the flow charts and/or message flow diagrams). In other embodiments, TRP1100may be configured to perform steps described herein without the need for code. That is, for example, PC1102may consist merely of one or more ASICs. Hence, the features of the embodiments described herein may be implemented in hardware and/or software.

FIG.12is a diagram showing functional units of TRP1100according to some embodiments. As shown inFIG.12, TRP1100includes a generating unit1202for generating a machine learning, ML, model, wherein generating the ML model comprises providing training data to an ML algorithm; a first transmitting unit1204for transmitting first data to a user equipment, UE, using a first LAP, wherein the first TRP serves at least a first cell; a receiving unit1206for receiving a channel quality report transmitted by the UE, the channel quality report comprising channel quality information indicating a quality of a channel between the UE and the first TRP; an obtaining unit1208for obtaining additional information, wherein the additional information comprises: neighbor cell information about a second cell served by a second TRP, distance information indicating a distance between the UE and the first TRP (e.g., a timing advance, TA, indicator transmitted by the UE), and/or gain information indicating a radio propagation gain between the UE and the serving node (e.g., an average gain); a using unit1210for using the channel quality information, the additional information, and the ML model to select a LAP from a set of predefined LAPs, the set of predefined LAPs comprising the first LAP and a second LAP; and a second transmitting unit1212for transmitting second data to the UE using the selected LAP.

Also, while various embodiments of the present disclosure are described herein, it should be understood that they have been presented by way of example only, and not limitation. Thus, the breadth and scope of the present disclosure should not be limited by any of the above-described exemplary embodiments. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the disclosure unless otherwise indicated herein or otherwise clearly contradicted by context.

Additionally, while the processes described above and illustrated in the drawings are shown as a sequence of steps, this was done solely for the sake of illustration. Accordingly, it is contemplated that some steps may be added, some steps may be omitted, the order of the steps may be re-arranged, and some steps may be performed in parallel.