WO2017133433A1

WO2017133433A1 - Estimating success rate of direct data transmissions between mobile devices in wireless network

Info

Publication number: WO2017133433A1
Application number: PCT/CN2017/071249
Authority: WO
Inventors: Roy Ron; Moshe Laifenfeld; Olivier Marco; Efstathios KATRANARAS; Guillaume Vivier
Original assignee: Jrd Communication Inc.
Priority date: 2016-02-04
Filing date: 2017-01-16
Publication date: 2017-08-10
Also published as: CN109792616B; CN109792616A; GB2547019A; GB2547019B; GB201602051D0

Abstract

In a method for estimating success rate of direct data transmissions between mobile devices in a wireless network, a first group of mobile devices is selected from a plurality of mobile devices in the network. The mobile devices in the first group transmit at least one predefined test message to at least another mobile device in the network. Each mobile device to which a test message has been sent attempts to decode the test messages, and measures the number of successfully decoded messages. An estimate of a success rate is generated based on the number of successfully decoded messages.

Description

ESTIMATING SUCCESS RATE OF DIRECT DATA TRANSMISSIONS BETWEEN MOBILE DEVICES IN A WIRELESS NETWORK

TECHNICAL FIELD

This disclosure relates to methods and apparatus for estimating the success rate of direct data transmissions between a plurality of mobile devices in a wireless network.

BACKGROUND

Traditional wireless networks for mobile devices (UEs) rely on a cellular infrastructure which supports UE communications. In these traditional networks, even if communication is between a pair of UEs, communication is managed by network nodes. Here uplink (UL) and downlink (DL) transmissions are always made between a UE and a network node.

As an alternative to communication between UEs via network nodes, direct communication uses a direct link between UEs. Here, the only role of the network nodes, if any, is to establish the direct link and to assign resources. 3GPP LTE Release 13 considers this type of non-centralised communication， where the term ‘sidelink’ is used to refer to direct links between UEs. A direct link between a pair of UEs is facilitated by a PC5 interface at each UE.

In establishing a direct link between UEs two scenarios are considered, one in which a UE is “in coverage” and the other in which a UE is “out of coverage”. When a UE is “in coverage”， the UE is synchronised to a network node via the link between the base station and the UE (the Uu link) . In this case， the “in coverage” UEs can receive synchronisation signals as well as system information in order to access the PC5 link. On the other hand， when a UE is “out of coverage” the UE does not have this privilege and cannot use the Uu link to access the PC5 link. In this case, a UE requires a synchronisation source.

For direct communication between UEs there are two types of resource pool: the scheduling assignment (SA) pool, also referred to as the sidelink control pool, and the data pool. Messages are transmitted via the SA pool in order to indicate a transmission of data in the data pool. The data pool is for the transmission of data. Each resource pool is made up of a number of resources, or Physical Resource Blocks (PRB) .

Each data transmission is associated with a SA transmission which informs the receiving UE of the data parameters. The receiving UE is required to blindly decode the control transmission and, then, decode the data.

Each UE operates in a half-duplex mode, where each UE cannot receive and transmit on the same sub-frame. Therefore, for each SA period where the UE is transmitting, the UE cannot receive the SA and data transmissions occurring on the same sub-frame. Here the term “SA period” refers to the period over which resources allocated in a cell for SA transmissions occur.

Figure 1 illustrates an example of the SA period with its corresponding SA and data pools. The SA period is made up of a SA pool followed by a consecutive data pool. Each pool comprises a plurality of resource blocks, defined in the time and frequency domain. In another example, the SA resources and the data resources are frequency division multiplexed, where the SA and data resources are located on the same sub-frame in the time domain but in different frequency locations.

The blocks with corresponding hatching in the SA pool show that a particular SA transmission is repeated twice, while the blocks with corresponding hatching in the data pool show that a particular data transmission is repeated four times in the data pool. Therefore, a UE that is transmitting in a particular sub-frame has another opportunity to receive scheduling assignment and data messages from other UEs. This helps to overcome the issues of operating in half-duplex.

There are two modes for selecting resources for use by the transmitting UE: Mode 1 and Mode 2. In Mode 1 the SA and data resources are allocated by the network node. In this case, the UE sends a request to the network node over the Uu link, and the network node replies with a grant allocation using specific Downlink Control Information (DCI) . Thus, Mode 1 is a contention free way of allocating resources.

In Mode 2 the UE allocates resources without any assistance from the network node. In this case, for each SA period where a UE needs to transmit, it randomly selects resources. Thus, Mode 2 represents a contention based way of allocating resources. Therefore, collisions in resource selections are prone to occur. This is a particular problem where there are many UEs and in situations where data traffic is high.

The success rate of message transmissions, and therefore the performance of the network, is dependent on the number of available resources, the network load, and the resource selection strategy. To illustrate this point, Figure 2 shows the expected success rate for a scheduling assignment message as a function of the network load when transmitting ten messages per second. It can be seen that as the network load increases the success rate decreases. If the success rate can be calculated more accurately, then the network can be adjusted accordingly in order to improve the network performance.

There is a need to provide a high reliability of direct data transmissions between UEs in a wireless network. This is particularly important when considering vehicle-to-vehicle (V2V) transmissions, where the majority of the messages transmitted will be safety messages.

The success rate of SA transmissions is defined as the average number of PRBs used for SA transmissions that did not collide as a fraction of the total number of PRBs used for SA transmissions per SA period.

The embodiments described below are not limited to implementations which solve any or all of the disadvantages of known systems.

SUMMARY

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

According to an aspect of the invention there is provided a method of estimating the success rate of direct message transmissions between a plurality of mobile devices in a wireless network comprising: selecting a first group of mobile devices from the plurality of mobile devices in the network； transmitting at least one predefined test message from each one of the mobile devices in the first group to at least another mobile device in the network； at each mobile device to which a test message has been sent, attempting to decode the test message； measuring the number of successfully decoded messages； and estimating a success rate based on the number of successfully decoded messages.

According to another aspect of the invention there is provided a system for estimating the success rate of direct message transmissions between a plurality of mobile devices in a wireless network comprising: a selection module, at a master node in the network and/or at each of the plurality of mobile devices, arranged to select a first group of mobile devices from the plurality of mobile devices in the network； a transmission module, at the mobile devices, arranged to transmit at least one predefined test message to at least another mobile device in the network； a decoding module, at the mobile devices, arranged to attempt to decode the test messages； a measuring module, at the mobile devices, arranged to measure the number of successfully decoded messages； and an estimation module, at the master node, arranged to estimate a success rate based on the number of successfully decoded messages.

According to another aspect of the invention there is provided a mobile device in a wireless network comprising: a selection module arranged to select a first group of mobile devices from the plurality of mobile devices in the network； a transmission module arranged to transmit at least one predefined test message to at least another mobile device in the network； a decoding module arranged to attempt to decode the test messages； a measuring module arranged to measure the number of successfully decoded messages； and a connection interface arranged to transmit the number of successfully decoded messages to a master node in the network.

According to another aspect of the invention there is provided a method for controlling direct data transmission between a plurality of mobile devices in a wireless network comprising: selecting a master node or a plurality of the mobile devices in the network for calculating an estimate of the average network load； estimating a success rate using the method defined above, wherein the success rate is indicative of the average network load； calculating, at the selected master node or the selected plurality of mobile devices, an estimate of the average network load； estimating an optimal repetition factor, for direct communication between the mobile devices, based on the average network load； allocating resources based on the optimal repetition factor； and transmitting a message from at least one of the mobile devices to at least another of the mobile devices using the allocated resources.

The master node may be selected for calculating an estimate of the average network load where the plurality of mobile devices are able to establish a connection with the master node.

The master node may estimate the optimal repetition factor and notify at least one of the mobile devices of the optimal repetition factor.

The master node may notify at least one of the mobile devices of the average network load and the at least one mobile device estimates the optimal repetition factor based on the average network load.

The plurality of the mobile devices may be selected for calculating an estimate of the average network load where the plurality of mobile devices are not able to establish a connection with the master node.

The method may further comprise calculating, at at least one of the mobile devices, a first load value indicative of an estimate of the average network load； receiving, at the at least one mobile device, a second load value indicative of an estimate of the average network load from at least one other mobile device； updating, at the at least one mobile device, the first load value based on the second load value to generate an updated load value； estimating an optimal repetition factor based on the updated load value.

The method may further comprise: selecting an integer value, for each of the mobile devices, based on the optimal repetition factor, such that the average of the selected integers for the mobile devices is approximately equal to the optimal repetition factor calculated at the master node； and updating the optimal repetition factor with the selected integer, for each of the mobile devices； wherein resources are allocated based on the updated optimal repetition factor.

The method may further comprise: selecting a plurality of integer values, for each of the plurality mobile devices, based on the optimal repetition factor, such that the average of the selected integers, for the mobile devices, is approximately equal to the optimal repetition factor calculated at the master node； and storing a plurality of optimal repetition factors each defined by one of the plurality of selected integers, for each mobile device； and assigning each one of the optimal repetition factors to a different type of message； wherein resources are allocated based on the updated optimal repetition factor.

A high repetition factor may be assigned to a high priority message and a low repetition factor is assigned to a low priority message.

The method may comprise, for each mobile device: determining the floor value, the ceiling value and the fractional part of the optimal repetition factor； randomly selecting the ceiling value or the floor value, wherein the probability of selecting the ceiling value is equal to the fractional part of the optimal repetition factor； and updating the optimal repetition factor with the selected value； wherein allocating resources is based on the updated optimal repetition factor.

Allocating resources may comprise selecting the number of times that the message is to be sent, based on the optimal repetition factor.

Allocating resources may comprise selecting the number of resources on which the message is to be transmitted, based on the optimal repetition factor.

The mobile devices may be vehicle-based. The master node may comprise a road side unit. The master node may be a mobile device. The master node may be an evolved Node B (eNB) .

According to another aspect of the invention there is provided a method for controlling direct data transmission between a plurality of mobile devices in a wireless network comprising: estimating a success rate using the method above, wherein the success rate is indicative of the average network load； calculating, at at least one of the mobile devices, a first load value indicative of an estimate of the average network load； receiving, at the at least one mobile device, a second load value indicative of an estimate of the average network load from at least one other mobile device； updating, at the at least one mobile device, the first load value based on the second load value to generate an updated load value； estimating an optimal repetition factor based on the updated load value； allocating resources based on the optimal repetition factor； and transmitting a message at the at least one mobile device in the network directly to another mobile device in the network, using the allocated resources.

According to another aspect of the invention there is provided a system for controlling direct data transmission between a plurality of mobile devices in a wireless network comprising: a selection module, at a master node in the network and/or at each of the plurality of mobile devices, arranged to select a first group of mobile devices from the plurality of mobile devices in the network； a transmission module, at the mobile devices, arranged to transmit at least one predefined test message to at least another mobile device in the network； a decoding module, at the mobile devices, arranged to attempt to decode the test messages； a measuring module, at the mobile devices, arranged to measure the number of successfully decoded messages； an estimation module, at the master node, arranged to estimate a success rate based on the number of successfully decoded messages； an average load calculation module, at the master node and/or at least one of the mobile devices, arranged to calculate an estimate of the average network load based on the success rate； an optimal repetition factor estimation module, at the at least one mobile device, arranged to estimate an optimal repetition factor, for direct communication between the mobile devices, based on the average network load； a resource allocation module, at the at least one mobile device, arranged to allocate resources based on the optimal repetition factor； and a transmission module, at the at least one mobile device, arranged to transmit a message directly to another mobile device, using the allocated resources.

According to another aspect of the invention there is provided a mobile device in a wireless network comprising: a selection module arranged to select a first group of mobile devices from the plurality of mobile devices in the network； a transmission module arranged to transmit at least one predefined test message to at least another mobile device in the network； a decoding module arranged to attempt to decode the test messages； a measuring module arranged to measure the number of successfully decoded messages； a connection interface arranged to transmit the number of successfully decoded messages to a master node in the network； the connection interface arranged to receive an estimate of an average network load from the master node； an optimal repetition factor estimation module arranged to estimate an optimal repetition factor, for direct communication between mobile devices, based on the average network load； a resource allocation module arranged to allocate resources based on the optimal repetition factor； and a transmission module arranged to transmit a message from the mobile device directly to another mobile device in the network using the allocated resources.

According to another aspect of the invention there is provided a mobile device in a wireless network comprising: a selection module arranged to select a first group of mobile devices from the plurality of mobile devices in the network； a transmission module arranged to transmit at least one predefined test message to at least another mobile device in the network； a decoding module arranged to attempt to decode the test messages； a measuring module arranged to measure the number of successfully decoded messages； a connection interface arranged to transmit the number of successfully decoded messages to a master node in the network； the connection interface arranged to receive an estimate of an average network load from the master node； an average load calculation module arranged to calculate a first load value based on the average network load； a connection interface arranged to receive a second load value indicative of an estimate of the average network load from at least one other mobile device； wherein the average load calculation module is further arranged to update the first load value based on the second load value to generate an updated load value； an optimal repetition factor estimation module arranged to estimate an optimal repetition factor based on the updated load value； a resource allocation module arranged to allocate resources based on the optimal repetition factor； and a transmission module arranged to transmit a message from the mobile device to another mobile device in the network, using the allocated resources.

According to an aspect of the invention there is provided a method for controlling direct data transmission between a plurality of mobile devices in a wireless network comprising: selecting a master node or a plurality of the mobile devices in the network for calculating an estimate of the average network load； calculating, at the selected master node or the selected plurality of mobile devices, an estimate of the average network load； estimating an optimal repetition factor, for direct communication between the mobile devices, based on the average network load； allocating resources based on the optimal repetition factor； and transmitting a message from at least one of the mobile devices to at least another of the mobile devices using the allocated resources.

According to another aspect of the invention, there is provided a method for controlling direct data transmission between a plurality of mobile devices in a wireless network comprising: calculating, at at least one of the mobile devices, a first load value indicative of an estimate of the average network load based on signals received from at least one other mobile device in the network； receiving, at the at least one mobile device, a second load value indicative of an estimate of the average network load from at least one other mobile device； updating, at the at least one mobile device, the first load value based on the second load value to generate an updated load value； estimating an optimal repetition factor based on the updated load value； allocating resources based on the optimal repetition factor； and transmitting a message at the at least one mobile device in the network directly to another mobile device in the network, using the allocated resources.

According to another aspect of the invention there is provided a system for controlling direct data transmission between a plurality of mobile devices in a wireless network comprising: an average load calculation module, at the master node and/or at least one of the mobile devices, arranged to calculate an estimate of the average network load； an optimal repetition factor estimation module, at the at least one mobile device, arranged to estimate an optimal repetition factor, for direct communication between the mobile devices, based on the average network load； a resource allocation module, at the at least one mobile device, arranged to allocate resources based on the optimal repetition factor； and a transmission module, at the at least one mobile device, arranged to transmit a message directly to another mobile device, using the allocated resources.

According to another aspect of the invention there is provided a mobile device in a wireless network comprising: a connection interface arranged to receive an estimate of an average network load from a master node in the wireless network and/or an average load calculation module arranged to calculate an estimate of the average network load； an optimal repetition factor estimation module arranged to estimate an optimal repetition factor, for direct communication between mobile devices, based on the average network load； a resource allocation module arranged to allocate resources based on the optimal repetition factor； and a transmission module arranged to transmit a message from the mobile device directly to another mobile device in the network using the allocated resources.

According to another aspect of the invention there is provided a mobile device in a wireless network comprising: an average load calculation module arranged to calculate a first load value indicative of an estimate of the average network load based on signals received from at least one other mobile device in the network； a connection interface arranged to receive a second load value indicative of an estimate of the average network load from at least one other mobile device； wherein the average load calculation module is further arranged to update the first load value based on the second load value to generate an updated load value； an optimal repetition factor estimation module arranged to estimate an optimal repetition factor based on the updated load value； a resource allocation module arranged to allocate resources based on the optimal repetition factor； and a transmission module arranged to transmit a message from the mobile device to another mobile device in the network, using the allocated resources.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will be described, by way of example, with reference to the following drawings, in which:

Figure 1 shows an example of a scheduling assignment (SA) period；

Figure 2 shows the expected success rate as a function of the network load；

Figure 3 shows a schematic network diagram；

Figure 4 shows a flow diagram of a method of estimating the success rate of direct message transmissions between UEs；

Figure 5 shows a flow diagram of a method for controlling direct data transmission between mobile devices (UEs) in a wireless network；

Figure 6 shows a graph of average success rate as a function of load for a situation in which UEs receive the average load from an eNB or other master node and a fixed integer value is used for the repetition factor；

Figure 7 shows a graph of average success rate as a function of load for a situation in which UEs receive the average load from an eNB or other master node, where the repetition factor is determined based on the method described with reference to Figures 4 and 5； and

Figure 8 shows a graph of the average success rate as a function of load for a situation in which the repetition factor is determined by UEs.

DETAILED DESCRIPTION

Embodiments of the present invention are described below by way of example only. These examples represent the best ways of putting the invention into practice that are currently known to the Applicant although they are not the only ways in which this could be achieved. The description sets forth the functions of the example and the sequence of steps for constructing and operating the example. However, the same or equivalent functions and sequences may be accomplished by different examples.

Described below is a method that uses test messages sent between UEs to estimate the success rate of message transmissions. This allows a more accurate estimate of success rate to be calculated. In one example, this estimate can be used to allocate resources in order to improve network performance. However, the estimate of success rate can be used in other ways.

One possible impact of transmitting test messages is that, if the amount of test message traffic is too high, network performance may be adversely affected. In order to help to prevent this, the method selects an appropriate number of UEs for transmitting the test messages. The selection process may occur at a master node in the network. Alternatively, the UEs may handle the selection process themselves. This may be particularly useful when the UEs cannot connect to the master node, or where it is desired to delegate this processing task to the UEs rather than to the master node.

Figure 3 shows a schematic diagram of selected elements of an LTE network 1. The network 1 comprises an evolved NodeB (eNB) 3 which is connected to a number of mobile devices (UEs) 5 via Uu links 6. In addition, the UEs 5 are connected to one another via sidelinks 8.

The eNB 3 comprises a connection interface 11, a test message module 12, a selection module 2, a success rate estimation module 10, and a resource allocation module 4. The connection interface 11 facilitates a connection between the eNB 3 and each of the UEs 5. The test message module 12 is arranged to define test messages and the parameters of these test messages.

The selection module 2 is arranged to select a first group of UEs 5 from the plurality of UEs 5 in the network 1. The first group of UEs 5 are selected for transmitting the test messages defined by the test message module 12.

The success rate estimation module 10 is arranged to calculate an estimate of the success rate of direct transmissions between UEs 5 based on information received via the connection interface 11. The resource allocation module 4 is arranged to allocate resources for data transmission between UEs based on the success rate.

The eNB 3 also comprises an average load calculation module 7 and an optimal repetition factor estimation module 9. These modules will be described in more detail below.

Each of the UEs 5, exemplified by UE₁, comprises a connection interface 17, a transmission module 23, a selection module 18, a decoding module 14, a measuring module 16 and a success rate estimation module 20. The connection interface 17 facilitates a connection between the UE 5 and the eNB 3. The connection interface 17 also facilitates direct connections between UEs 5.

The transmission module 23 is arranged to transmit test messages to other UEs 5 via the scheduling assignment resource pool. The selection module 18 is arranged allow the UE 5 to select itself for transmitting the test messages. The decoding module 14 is arranged to decode test messages received from other UEs 5. The measuring module 16 counts the number of successfully decoded test messages. The success rate estimation module 20 is arranged to calculate an estimate of the success rate based on number of successfully decoded messages.

The UEs 5 also comprise an average load calculation module 13, an optimal repetition factor estimation module 15, a resource allocation module 21 and a repetition factor selection module 19. These modules will be described in more detail below.

Figure 4 shows a flow diagram of a method of estimating the success rate of direct message transmissions between the UEs 5 in the network 1. Steps 400-404 define a method of selecting UEs 5 for transmitting a test message, which uses the eNB 3 for the selection process. Steps 406-414 define a method where the UEs 5 manage the selection process themselves, without assistance from the eNB 3. Steps 416-426 define a method of using test messages to estimate average success rate, and allocating resources accordingly to improve network performance.

In step 400, the eNB 3, analyses the status of the available UEs 5 using signals received over the connection interface 11. The eNB 3 uses these signals to determine the transmission load at each UE 5, and therefore the ability of each UE 5 to transmit. The eNB 3 may also determine the location, direction and speed of travel of each UE 5.

In step 402, a first group of UEs 5 is selected using the selection module 2. The UEs 5 in the first group are assigned with the task of transmitting test messages. The other UEs 5 in the network 1 will not transmit test messages； however, these UEs 5 may receive test messages from the UEs 5 in the first group. Each UE 5 in the first group may be selected as being in the first group for only a predetermined time period.

Each UE 5 may be selected for the first group based on its transmission load. For instance, a particular UE 5 may already have a high transmission load and thus may not be selected for the first group.

The first group of UEs 5 may be selected based on each UEs 5 location, direction and/or speed of travel. Whichever characteristic the eNB 3 uses for selection, the first group of UEs 5 is selected to ensure an even distribution of transmitting UEs 5 through the network. This limited number of UEs 5 is selected, such that network performance is not adversely affected by the transmission of test messages.

In step 404, the connection interface 11 at the eNB 3 is used to send test message transmission parameters to each of the UEs 5 in the first group. The test message transmission parameters instruct the UEs 5 to transmit test messages according to a particular regime. These parameters are used to ensure that only a limited number of test messages are transmitted over a particular time period, such that network performance is not adversely affected by the test messages being transmitted. These parameters are determined and stored at the test message module 12.

The test message transmission parameters may include transmission periodicity which defines the time period between test messages being sent from each mobile device. The test message transmission parameters may also include transmission offset, which defines the time offset between periodic test messages being sent from the mobile devices.

The UEs 5 in the first group may be instructed to transmit a predefined total number of test messages in a series of test messages. In another example, the first group is instructed to transmit the test messages at random, using a predefined transmission rate.

Steps 406-414 define a method of selecting UEs 5 in which the UEs 5 manage the selection process without assistance from the eNB 3. In step 406, an arbitrary selection of the UEs 5 transmit test messages, using the transmission module 23. These UEs 5 are configured to transmit test messages at arbitrary intervals. These test messages are received by other UEs 5 in the network 1.

In step 408, each UE 5 measures the number of test messages that it has received during the scheduling assignment period. This process is executed by the measuring module 16 at each one of the UEs 5. Then, in step 410 the selection module 18 compares this number against a threshold value.

If the number of test messages received at a particular UE 5 is less than the threshold value, then the method proceeds to step 414 where UE 5 selects itself for the first group using its selection module 18. However, if this is not the case then the method proceeds to step 412, where the UE 5 decides not to select itself for the first group. As in step 402 each UE 5 in the first group may be selected for the first group for only a predetermined time period.

This process of self-selection by the UEs 5 allows an appropriate number of UEs 5 to be selected for test message transmission without assistance from the eNB 3. The value of the threshold is used to control the desired distribution of transmitting UEs 5 in the network 1.

Each UE 5 may define its own parameters for test message transmission. Alternatively, each UE 5 may decide to transmit or not to transmit a test message on an ad-hoc basis per SA period.

Steps 416-426 define a method for transmitting test messages using the first group of selected UEs 5 in order to calculate an estimate of the average success rate of message transmissions. Resources may then be allocated based on the average success rate in order to optimise the performance of the network.

In step 416, each one of UEs 5 in the first group transmits a series of predefined test messages, each using their respective transmission module 23. The test messages may be transmitted in accordance with the test message parameters received from the eNB 3. Alternatively, each UE 5 may transmit test messages in accordance with test message parameters defined by the UEs. This may be particularly useful for “out of coverage” UEs 5.

Each test message may include information indicative of the UE from which the test message was transmitted. For instance, each test message may comprise an identification number indicative of the transmitting UE. In addition, each test message may include information indicative of the distance of the transmitting UE away from the receiving UE. For instance, each test message may comprise the location of the transmitting UE which can be compared to the location of the receiving UE.

The test messages may be sent in a sequence, where each test message includes information indicative of the total number of messages in the sequence along with the index of each individual message. The index indicates the order of a particular test message within the test message sequence.

The test messages are sent over the scheduling assignment control pool. In one example, each test message is distinct from the other scheduling assignment messages. In another example, each test message may be included in a normal scheduling assignment message. Each test message may comprise a test message identifier, such as a test message identification code. This allows the UEs 5 to filter the test messages from other messages.

Once the UEs 5 in the first group have begun transmitting test messages, these may be received at other UEs 5 in the network 1. The receiving UEs 5 may be those in the first group or any other UE 5 in the network 1. In step 420, each UE 5 to which a test message has been transmitted attempts to decode the received test message (s) using its decoding module 14.

In step 422, each UE 5 measures the number of successfully decoded test messages using the measuring module 16. The measuring module 16 measures the total number of successfully decoded messages over a predefined time period. In this example, the SA period is used.

In step 422 the number of successfully decoded test messages is transmitted to the eNB 3. This information may be accompanied by information indicative of the geographical location of the UE 5.

The number of successfully decoded test messages is included in a report sent from each of the UEs 5 to the eNB 3. This report is sent periodically, so as not to overload the eNB 3 with reporting traffic. The report indicates the number of successfully decoded messages compared against the total number of messages sent from a particular UE 5 from the first group. The report may also include the distance of each transmitting UE 5 away from the receiving UE 5.

In order to limit the quantity of information sent to the eNB 3, the UE 5 may only transmit a selected number of results. The UE 5 may decide to limit the results to those regarding the closest transmitting UEs 5 and/or those results having a success rate over a particular threshold.

In step 424, the eNB 3 receives the number of successfully decoded messages from the UEs 5 over the connection interface 17. Then, the success rate estimation module 10 estimates an average success rate based on this information. Where the number of successfully decoded messages is received along with the UEs 5 geographical information. The eNB 3 is able to generate an estimate of average success rate per geographical location. The success rate estimation module 10 uses a look-up-table to determine average success rate from the number of successfully decoded messages.

In step 426, the eNB 3 uses the resource allocation module 4 to allocate resources based on the average success rate. Here the resource allocation module 4 allocates resources in an attempt to increase the average success rate, and thus improve the performance of the network 1.

One way of allocating resources, is for the eNB 3 to increase or decrease the message transmission rate assigned to the UEs 5. For example, if the average success rate is high then it may be possible to improve network performance by increasing the message transmission rate. On the other hand, if the average success rate is low then the message transmission rate may be decreased.

Another way of improving network performance would be to reroute some direct message transmissions through the eNB 3 via the Uu link, rather than using the direct link between UEs 5. This may be appropriate if the average success rate of message transmission between UEs 5 is low. On the other hand, the number of messages sent directly between UEs 5 could be increased if the average success rate is low.

In another example, the eNB 3 may instruct the UEs 5 to increase the number of message transmissions or the number of resources on which a message is transmitted, in response to a high success rate. Alternatively, the eNB 3 may instruct the UEs 5 to decrease these variables, in response to a low success rate.

In a further example, the resource allocation module 4 may update pool partitioning in order to improve network performance. Any other system level algorithm may be used to improve network performance based on the success rate.

In another example, the success rate estimation module 20 at each of the UEs 5 estimates a success rate based on the number of successfully decoded test messages received at the UE 5. Then, the UEs 5 allocates resources using their resource allocation modules 21. Resources may be allocated as described above in reference to the eNB 3.

In the example described above, test messages are sent using the scheduling assignment pool. These test messages are used to determine an average success rate for the scheduling assignment pool. It would be possible to apply the techniques described in the method above to messages sent using the data pool. However, in this case deriving an average success rate would be more complex since the data pool typically contains a larger number of PRBs. Therefore, collisions and partial overlaps are more difficult to detect using the data pool.

In the method described above, an estimate of success rate for the scheduling assignment pool is estimated. This success rate is assumed to be indicative of the success rate of the data pool, and resources can be allocated accordingly.

A possible alternative method for estimating success rate is based on using vacant SA resources as an indicator of success rate. In this alternative method, the UEs 5 attempt to identify vacant SA resources by sensing energy on each resource. The energy detected is compared with a predefined threshold, where a detected energy level below the threshold would indicate that a particular resource is vacant. However, the issue with this method is that detecting energy levels may be more difficult and, potentially, less accurate than measuring the number of successfully decoded messages as in the method described above.

Figure 5 shows a flow diagram of a method for controlling direct data transmission between the UEs 5 in the network 1. This method relates to one example of controlling data transmissions based on the estimated success rate； however, operation of the network may be controlled in other ways in response to the estimated success rate, examples of which are given above.

In step 100, the eNB 3 estimates the average network load (L) using the average load calculation module 7. Here the success rate value estimated using the method described above is used to estimate the average network load.

In step 120, the optimal repetition factor estimation module 9 calculates an estimate of an optimal repetition factor (m*) based on the average network load. The optimal repetition factor indicates how the UEs 5 should allocate resources by indicating the number of times that a message should be transmitted or the number of resources on which a message should be transmitted.

In practice, message repetitions and the number of resources will be defined by integer values. However, the optimal repetition factor may be calculated as a non-integer value in step 120. For instance, the optimal repetition factor may be calculated to at least one decimal place by the optimal repetition factor estimation module.

In step 140， “mode 1” or “mode 2”is selected using a selection module at the eNB 3 and/or the UE 5. In this step it may be determined whether a connection has been established between the eNB 3 and a particular UE 5 using the connection interfaces 11, 17. If the UE 5 is connected to the eNB 3, via the Uu link 6, the UE 5 is able to receive the optimal repetition factor from the eNB 3, and hence the UE 5 is able to receive an indication of the network load from the eNB 3. In this situation the UE 5 is described as being “in coverage” .

If, in step 140, it is determined that a connection has been established between the eNB 3 and the UE 5， “mode 1” may be selected. Alternatively， even ehere a connection is established， “mode 2” may be selected where it is decided that the burden of estimating average network load and allocating resources is to be delegated to the UEs 5.

If the UE 5 is not connected to the eNB 3, via the Uu link 6, the UE 5 is not able to receive the optimal repetition factor from the eNB 3, and hence the UE 5 is unable to receive an indication of the network load from the eNB 3. In this situation the UE 5 is described as being “out of coverage” . In this case， “mode 2” is selected.

If “mode 1” is selected the method proceeds to step 160 where the eNB 3 transmits the optimal repetition factor to the UE 5 using the connection interfaces 11, 17. In step 260, the optimal repetition factor is stored at the UE 5 using storage means associated with the optimal repetition factor estimation module 15.

As explained above the UE 5 will allocate resources based on an integer value for the repetition factor. For example, the UE 5 will not repeat a message 2.4 times or to transmit a message over 1.3 resources because the number of repetitions and resources is discrete. Therefore, the UE 5 will need to select an integer value for the optimal repetition factor.

In step 280, the UE 5 selects an integer value for a repetition factor (m) based on the non-integer value of the optimal repetition factor (m*) using the repetition factor selection module 19. In order to do this, the UE 5 determines the floor of m*, the ceiling of m*and its fractional part. The UE 5 determines the floor by mapping m*to the largest lower integer. The UE 5 determines the ceiling by mapping m*to the smallest higher integer. The UE 5 subtracts the floor from m*to determine the fractional part. For example, if m*is 2.4 the UE 5 calculates the floor as 2, the ceiling as 3 and the fractional part as 0.4.

Next, the repetition factor selection module 19 selects, at random, either the floor or the ceiling as the repetition factor (m) . The probability of selecting the ceiling of m*is equal to the fractional part of m*. If the ceiling is not selected, the floor is selected for m instead. For example, if m*is 2.4 the UE 5 will select m to be equal to 3, 40％of the time, and the UE 5 will select m to be equal to 2, 60％of the time. Therefore, when this algorithm is applied across all UEs 5, that are trying to communicate with one another, the resulting average repetition factor across the UEs 5 will be equal to the optimal repetition factor m* (in this example 2.4) .

It will be appreciated that the same result would occur if the UEs 5 were configured to select the floor of m*to be equal to m at random, where the probability of the selecting the floor of m*is equal to 1 minus the fractional part and selecting the ceiling for m if the floor is not selected.

An example of the code for implementing step 280 is outlined below:

Here sa^tis a subset of size m of the sidelink control pool (of size S) at the sidelink control period of time t. In addition, f₁(x；m’) and f₂(x；m’) are two one-dimensional, invertible, cumulative probability distributions of mean m’.

The load (L) is received as an input, and two parameters, L_min and L_max, are used to generate a random variable (with probability density function either f₁ or f₂) that is used to select the repetition factor. This random variable can take any non-negative value. Values less than 1 correspond to the puncturing or dropping messages, and values larger than one correspond to the repeating messages.

In this example:

In step 300, the UE allocates resources for transmission based on its selected value for the repetition factor (m) using the resource allocation module 21. Here, when a UE 5 transmits to other UEs 5, it repeats the message a number of times based on the repetition factor. For example, if m is equal to 2, the UE 5 may decide to send the message twice, and if the m is equal to 1 the UE may decide to send the message only once.

In another example, the UE 5 may decide to select a number of resources on which to transmit the message based on the repetition factor. For example, if m is equal to 2, the UE 5 may select two resources on which to send the message, and if m is equal to 1, the UE 5 may select only one resource on which to send the message.

In step 320, the UE 5 transmits the message in accordance with the resource allocation determined in step 300 using the transmission module 23.

If in step 140， “mode 2” is selected and the method proceeds to step 180. As explained previously， “mode 2” may be selected if it is determined that a connection has not been established between the eNB 3 and the UE 5. “Mode 2” may also be selected if, for instance, the task of estimating average network load is delegated to the UEs 5.

In step 180, the UE 5 itself generates a first load value which is a first estimate of the average network load (L) using the average load calculation module 13. Here the success rate value estimated using the method described above is used to estimate the average network load.

In step 200, the UE 5 receives load values from other UEs 5 in the network which are estimates of the average network load (L) calculated at the other UEs 5 in the network. These estimates are calculated by each UE 5 individually based on each UEs 5 particular perception of the network, in the same manner as described with reference to step 180.

The UEs 5 share their estimates amongst one another, and in step 220 each UE 5 updates their load value, using the average load calculation module 13, to take into account other received estimates of the average network load.

In step 240, each UE 5 calculates an optimal repetition factor (m*) using the optimal repetition factor estimation module 15. This calculation is based on the updated estimate of the average network load. This optimal repetition factor is used in steps 260-320 as explained above.

In summary, the method described with reference to steps 100-320 above allows the UEs 5 to estimate the average network load and to allocate resources for transmission based on the average load on the physical resources at the network 1. This can help to reduce collisions and to maximise the average success rate.

In “mode 1” an estimate of the average load is provided by the eNB 3, which has a better understanding of the network 1 load than an individual UE 5. The UEs 5 then transmit data in accordance with resource allocation based on the estimate of the average load provided by the eNB 3.

In “mode 2” ， an individual UE 5 estimates the average network load based on messages received from other UEs 5 via the control resource pool. In addition, an individual UE 5 can share its estimate of the average network load to other UEs 5, using the PC5 link and sidelink control message. Thus, the UEs 5 are able to communicate with one another in order to estimate an accurate convergent value for average network load, rather than using the eNB 3. This represents a distributed method of allocating resources.

In the method described above, the eNB 3 establishes a connection with a UE 5 and provides it with the optimal repetition factor. In an alternative example, a remote device, for example a road side, unit may establish a connection with a UE 5 and provide it with an optimal repetition factor. In another example, any mobile device (UE) may provide the other UEs 5 with an optimal repetition factor. In a further example, a leading UE 5 in a platoon or in a group of vehicles travelling at a similar speed on a highway may establish a connection with other UEs 5 and provide them with an optimal repetition factor for the area. It would， in fact， be possible for any ‘master node’， such as an eNB 3or another type of base station, to provide the UEs 5 with an optimal repetition factor so long as the ‘master node’ has a broad enough perception of the network to determine an accurate average network load.

In the method, it is assumed that the UE 5 establishes a single optimum repetition factor at a time. However, it would be possible for each UE 5 to establish more than one repetition factor, and for the UE 5 to assign different repetition factors to different types/classes of message. For example, the UE 5 may assign a higher repetition factor to high priority messages and a lower repetition factor to low priority messages.

Figure 6 shows a graph of average success rate as a function of load for a situation in which UEs 5 receive the average load from the eNB 3 or other master node and a fixed integer value is used for the repetition factor.

In Figure 6 two practical examples are given, which are shown by the solid line labelled m＝1 and the dotted line m＝2, and one theoretical example is given, which is shown by the thick dotted line. The theoretical example represents the theoretical maximum average success rate as a function of load.

The solid line labelled m＝1 shows how the average success rate varies with load where the repetition factor (m) is equal to 1. As can be seen, when m＝1 the average success rate is close to the theoretical maximum for loads over 0.4. However, the performance is sub-optimal at loads less than 0.4.

The solid line labelled m＝2 shows how the average success rate varies with load with the repetition factor (m) is equal to 2. As can be seen, when m＝2 the average success rate is close to the theoretical maximum for loads less than 0.4. However, the performance is sub-optimal at loads over 0.4.

Figure 7 shows a graph of average success rate as a function of load for a situation in which UEs 5 receive the average load from the eNB 3 or other master node, where the repetition factor is determined based on the method described above. The thick dotted line represents the theoretical maximum average success rate. The solid line shows the performance of the average success rate versus load where the method described above is used. It can be seen that, this method performs close to the theoretical maximum average success rate.

Figure 8 shows a graph of the average success rate as a function of load for case where the average load and repetition factor is determined by the UEs.

The dotted line labelled m＝1 shows how the average success rate varies with load where the repetition factor (m) is equal to 1. The dotted line labelled m＝2 shows how the average success rate varies with load where the repetition factor (m) is equal to 2. The thick dotted line represents the theoretical maximum average success rate as a function of load. The solid line shows how the average success rate varies with load when the “mode 2” method is implemented. With reference to Figures 7 and 8, it can be seen that, the method described above performs close to the theoretical optimum performance in “mode 1” and in “mode 2” .

In implementing the above method two constraints are considered. The first constraint is to limit a UE 5 to transmit only on contiguous physical resource blocks (PRBs) within the same sub-frame, in order to reduce the Peak-to-Average Power Ratio (PAPR) . It has been found that for a sidelink control message transmitted over the sidelink control pool of 8 sub-frames or more, this constraint does not result in any noticeable degradation in performance.

The second constraint is to limit the UE 5 to half-duplex. In other words, a UE 5 that is transmitting on a certain sub-frame cannot receive anything at that same sub-frame. Conversely, a UE 5 can receive on any sub-frame on which that UE is not transmitting. This constraint divides UEs 5 into two main groups: the TX group and the non-TX group. The UEs 5 in the TX group have a PC5 message to transmit at that particular sidelink control period, whilst the UEs in the non-TX group do not.

For UEs 5 that are in the TX group, it has been found that for high loads, i.e. for loads greater than 1, performance degradation is minor and equals exactly a constant factor of (1-1/Ssf) , where Ssf is the size of the resource pool in sub-frames. For high loads, the method where the UEs 5 get the average load from the eNB or other master node and the method where the UEs themselves estimate the average load will work similarly (in terms of performance, fairness and dynamics) for the UEs 5 in the TX group and for the UEs 5 in the non-TX group.

The situation for low loads, i.e. for loads less than one, the situation may be more complex. Here the degradation in performance due to half duplex can be significant, since the optimal repetition factor of the TX group (m_TX) and the optimal repetition factor of the non-TX group (m_non-TX) diverge, and therefore cannot be satisfied simultaneously.

It has been found that the penalty for using m_non-TX for the TX group is significant for loads smaller than 0.3, while the penalty for using m_TX for the non-TX group is not that high. Therefore, for this region it may be preferably to use m_TX as a reference. At the higher load range the degradation in performance for the non-TX group is more significant. However, since m_TX enjoys a better performance for the TX group, this repetition factor may be used as reference for the entire range too. This sacrifices of performance of the non-TX group performance for the benefit of the TX group.

If PAPR is an issue then the UEs 5 may be configured to transmit on a single PRB per sub-frame. For loads less than 1, it is suggested to sacrifice the performance of the non-TX group of UEs 5 in favour to the TX group, meaning that the number of repetitions will be smaller. If PAPR is not an issue, namely a UE can transmit on multiple (non-contiguous) PRBs within the same sub-frame, then optimality for both the TX group and non-TX group can be satisfied simultaneously.

Those skilled in the art will appreciate that methods according to the embodiments may be carried out by software computer programs, hardware, or a combination of software and hardware.

These methods are provided by way of example only. The disclosure of this application is not restricted by the specific combination of steps shown in the figures, and described herein, but includes any appropriate subsets or combinations of steps performed in any appropriate order. Sections of the method may be performed in parallel.

The term ′user equipment′ (UE) is used herein to refer to any device with processing and telecommunication capability such that it can perform the methods according to the embodiments of the present invention. Those skilled in the art will realize that such processing and telecommunication capabilities can be incorporated into many different devices and therefore the term ′user equipment′ includes mobile telephones, personal digital assistants, PCs and many other devices.

It will be appreciated that the methods described above apply to any other wireless technologies without losing the effect sought.

Any range or device value given herein may be extended or altered without losing the effect sought, as will be apparent to the skilled person.

It will be understood that the benefits and advantages described above may relate to one embodiment or may relate to several embodiments. The embodiments are not limited to those that solve any or all of the stated problems or those that have any or all of the stated benefits and advantages.

Any reference to ′an′ item refers to one or more of those items. The term ′comprising′ is used herein to mean including the method blocks or elements identified, but that such blocks or elements do not comprise an exclusive list and a method or apparatus may contain additional blocks or elements.

The steps of the methods described herein may be carried out in any suitable order, or simultaneously where appropriate. Additionally, individual blocks may be deleted from any of the methods without departing from the spirit and scope of the subject matter described herein. Aspects of any of the examples described above may be combined with aspects of any of the other examples described to form further examples without losing the effect sought.

It will be understood that the above description of a preferred embodiment is given by way of example only and that various modifications may be made by those skilled in the art. Although various embodiments have been described above with a certain degree of particularity, or with reference to one or more individual embodiments, those skilled in the art could make numerous alterations to the disclosed embodiments without departing from the scope of this invention.

Claims

A method of estimating the success rate of direct message transmissions between a plurality of mobile devices in a wireless network comprising:

selecting a first group of mobile devices from the plurality of mobile devices in the network；

transmitting at least one predefined test message from each one of the mobile devices in the first group to at least another mobile device in the network；

at each mobile device to which a test message has been sent, attempting to decode the test message；

measuring the number of successfully decoded messages； and

estimating a success rate based on the number of successfully decoded messages.
A method according to claim 1 further comprising:

allocating resources for transmitting messages directly between the mobile devices based on the success rate in order to optimise the success rate.
A method according to claim 2 wherein allocating resources comprises at least one of:

increasing/decreasing the transmission rate of direct message transmissions between mobile devices；

updating pool partitioning；

transmitting messages via an evolved Node B (eNB) rather than via a direct link between the mobile devices or vice versa；

increasing/decreasing the number of message transmissions； and

increasing/decreasing the number of message transmission resources.
A method according to any of the preceding claims wherein a master node in the network selects the first group of mobile devices.
A method according to claim 4 further comprising:

selecting transmission parameters, at the master node； and

transmitting the transmission parameters to the mobile devices in the first group.
A method according to any of claim 4 or 5 wherein the master node selects the first group based on at least one of characteristic of the mobile devices.
A method according to claim 6 wherein the characteristic is selected from at least one of the mobile devices’ ability to transmit, location, direction and speed.
A method according to any of the preceding claims wherein the mobile devices are vehicle-based.
A method according to any of claims 4-8 wherein the master node comprises a road side unit.
A method according to any of claims 4-8 wherein the master node is a mobile device.
A method according to claims 4-8 wherein the master node is an evolved Node B (eNB) .
A method according to any of claims 1-3 wherein a plurality of the mobile devices in the network select the first group of mobile devices.
A method according to claim 12 wherein the plurality of the mobile devices select the first group of mobile devices by:

transmitting a predefined test message from a plurality of the mobile devices in the network；

receiving the test messages at a plurality of the mobile devices in the network；

counting, at each mobile device, the number of test messages received within a pre-defined time period；

comparing the number of test messages against a threshold； and

if the number of test messages is less than the threshold, selecting the mobile device as being in the first group.
A method according to any of the preceding claims wherein the predefined test message comprises at least one of:

the location of the transmitting mobile device；

an identifier of the transmitting mobile device；

the total number of test messages in a sequence of test messages from the transmitting mobile device； and

an index of the order of the test message in a sequence of test messages.
A method according to any of the preceding claims further comprising:

measuring the number of successfully decoded messages at each mobile device；

transmitting the number of successfully decoded messages to a master node； and

estimating the success rate, at the master node, based on the number of successfully decoded messages.
A method according to claim 15 wherein the number of successfully decoded messages is associated with geographical information corresponding to the transmitting mobile device； and

the method further comprises:

determining a success rate for a geographical area at the master node.
A method according to any of the preceding claims wherein the test message is included within a scheduling assignment message for indicating parameters associated with a subsequent data transmission.
A method according to any of the preceding claims wherein each mobile device is selected for the first group for a predefined time period.
A method according to any of the preceding claims wherein the test message comprises a test message identifier.
A method for controlling direct data transmission between a plurality of mobile devices in a wireless network comprising:

selecting a master node or a plurality of the mobile devices in the network for calculating an estimate of the average network load；

estimating a success rate using the method defined in any of claims 1 to 19, wherein the success rate is indicative of the average network load；

calculating, at the selected master node or the selected plurality of mobile devices, an estimate of the average network load；

estimating an optimal repetition factor, for direct communication between the mobile devices, based on the average network load；

allocating resources based on the optimal repetition factor； and

transmitting a message from at least one of the mobile devices to at least another of the mobile devices using the allocated resources.
A method for controlling direct data transmission between a plurality of mobile devices in a wireless network comprising:

estimating a success rate using the method defined in any of claims 1 to 20, wherein the success rate is indicative of the average network load；

calculating, at at least one of the mobile devices, a first load value indicative of an estimate of the average network load；

receiving, at the at least one mobile device, a second load value indicative of an estimate of the average network load from at least one other mobile device；

updating, at the at least one mobile device, the first load value based on the second load value to generate an updated load value；

estimating an optimal repetition factor based on the updated load value；

allocating resources based on the optimal repetition factor； and

transmitting a message at the at least one mobile device in the network directly to another mobile device in the network, using the allocated resources.
A system for estimating the success rate of direct message transmissions between a plurality of mobile devices in a wireless network comprising:

a selection module, at a master node in the network and/or at each of the plurality of mobile devices, arranged to select a first group of mobile devices from the plurality of mobile devices in the network；

a transmission module, at the mobile devices, arranged to transmit at least one predefined test message to at least another mobile device in the network；

a decoding module, at the mobile devices, arranged to attempt to decode the test messages；

a measuring module, at the mobile devices, arranged to measure the number of successfully decoded messages； and

an estimation module, at the master node, arranged to estimate a success rate based on the number of successfully decoded messages.
A mobile device in a wireless network comprising:

a selection module arranged to select a first group of mobile devices from the plurality of mobile devices in the network；

a transmission module arranged to transmit at least one predefined test message to at least another mobile device in the network；

a decoding module arranged to attempt to decode the test messages；

a measuring module arranged to measure the number of successfully decoded messages； and

a connection interface arranged to transmit the number of successfully decoded messages to a master node in the network.
A method substantially as herein described with reference to the accompanying drawings.
A system substantially as herein described with reference to the accompanying drawings.
A mobile device substantially as herein described with reference to the accompanying drawings.