GB2547018A

GB2547018A - Controlling direct data transmission between mobile devices in a wireless network

Info

Publication number: GB2547018A
Application number: GB1602050.5A
Authority: GB
Inventors: Ron Roy; Assouline Benny; Laifenfeld Moshe
Original assignee: TCL Communication Ltd
Current assignee: TCL Communication Ltd
Priority date: 2016-02-04
Filing date: 2016-02-04
Publication date: 2017-08-09
Anticipated expiration: 2036-02-04
Also published as: GB2547018B; CN108781442B; WO2017133432A1; GB201602050D0; CN108781442A

Abstract

In a method for controlling direct data transmission between a plurality of mobile devices in a wireless network, a master node or a plurality of the mobile devices in the network is selected for calculating an estimate of the average network load. The selected master node or the selected plurality of mobile devices calculates an estimate of the average network load. An optimal repetition factor, for direct communication between the mobile devices, is estimated based on the average network load. Resources are allocated based on the optimal repetition factor, and a message is transmitted from at least one of the mobile devices to at least another of the mobile devices using the allocated resources. A first load value may be calculated at one of the mobile devices which is indicative of an estimate of the average network load. This first load value may then be updated by a second load value indicative of an estimate of the average network load received from another mobile device. The first load value may then be updated by the second load value and the optimal repetition factor may then be estimated based on the updated load value.

Description

CONTROLLING DIRECT DATA TRANSMISSION BETWEEN MOBILE DEVICES IN A

WIRELESS NETWORK

TECHNICAL FIELD

[0001] This disclosure relates to methods and apparatus for controlling direct data transmission between mobile devices in a wireless network.

BACKGROUND

[0002] 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.

[0003] 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 UEs is facilitated by a PC5 interface at each UE.

[0004] 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.

[0005] 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).

[0006] 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.

[0007] Each UE operates in a half-duplex mode, were 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. The term “SA period” refers to the period over which resources allocated in a cell for sidelink control transmissions occur.

[0008] Figure 6 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.

[0009] 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.

[0010] 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 freeway of allocating resources.

[0011] 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.

[0012] 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 7 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.

[0013] 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.

[0014] The embodiments described below are not limited to implementations which solve any or all of the disadvantages of known systems.

SUMMARY

[0015] 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.

[0016] 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.

[0017] Thus, the mobile device transmits a message directly to another mobile device using resources allocated based on the average network load calculated by the master node or the mobile devices. Therefore, it is possible for the transmitting mobile device to allocate resources and transmit a message with a lower likelihood of collisions occurring. This may increase the average success rate of the message transmission.

[0018] 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 mobile device; wherein resources are allocated based on the updated optimal repetition factor.

[0019] Thus, each mobile device can allocate resources based on an integer value. This integer value is most likely different to the actual optimal repetition factor. However, the method allows the effective repetition factor across the mobile devices in the network to be about equal to the actual optimal repetition factor because the average of the selected integers is approximately equal to the optimal repetition factor. This may be particularly useful where the optimal repetition factor is estimated to at least one decimal place or where the optimal repetition factor is a non-integer value.

[0020] Preferably, the integer value is selected at each mobile device, and, preferably, the optimal repetition factor is selected at each mobile device.

[0021] 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.

[0022] This presents a simple way for each mobile device to select an integer value for the optimal repetition factor, where the effective repetition factor across the mobile devices in the network should be about equal to the optimal repetition factor that was calculated at the master node. Preferably, this process is executed at each mobile device.

[0023] The method may comprise 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.

[0024] In this way the mobile device is able to generate an accurate estimate of the average network load based on information received from other mobile devices in the network. This may be particularly useful for mobile devices that cannot connect with the master node. The mobile device can then generate the optimal repetition factor accordingly. This information may be received via a message transmitted over the control pool.

[0025] 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.

[0026] In this way, the mobile device is able to generate an accurate estimate of the average network load based on information received from other mobile devices in the network. The mobile device can then generate the optimal repetition factor and transmit data accordingly. The information regarding the average network load may be received via the control pool. This is particularly useful where a mobile device cannot connect with the master node or where the burden of allocating resources is delegated to the mobile devices. Therefore, it is possible for a mobile device to allocate resources itself and transmit a message with a lower likelihood of collisions occurring, even when the mobile device cannot connect with the master node or when the burden of allocating resources is delegated to the mobile devices. This may increase the average success rate of the message transmission.

[0027] 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.

[0028] 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.

[0029] 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

[0030] Embodiments of the invention will be described, by way of example, with reference to the following drawings, in which: [0031] Figure 1 shows a schematic network diagram; [0032] Figure 2 shows a flow diagram of the method for controlling direct data transmission between mobile devices (UEs) in a wireless network; [0033] Figure 3 shows a graph of average success rate as a function of load for a situation in which UEs receive the average load from the eNB or other master node and a fixed integer value is used for the repetition factor; [0034] Figure 4 shows a graph of average success rate as a function of load for a situation in which UEs receive the average load from the eNB or other master node, where the repetition factor is determined based on the method described with reference to Figure 2; [0035] Figure 5 shows a graph of the average success rate as a function of load for a situation in which the repetition factor is determined by the UEs; [0036] Figure 6 shows an example of a scheduling assignment (SA) period; and [0037] Figure 7 shows the expected success rate as a function of the network load.

DETAILED DESCRIPTION

[0038] 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.

[0039] Figure 1 shows a schematic diagram of selected elements of an LTE network 1.

[0040] 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.

[0041] The eNB 3 comprises an average load calculation module 7, an optimal repetition factor estimation module 9, and a connection interface 11. The average load calculation module 7 is arranged to estimate the average network load by determining the number of UEs 5 in the network 1 that have a message in their transmission buffer. The average load calculation module 7 may be arranged to estimate the average network load by measuring other parameters, such as message transmission periodicity. The optimal repetition factor estimation module 9 is arranged to estimate an optimal repetition factor which indicates the number of times that a message should be transmitted or the number of resources on which a message should be transmitted. The connection interface 11 facilitates a connection between the eNB 3 and each of the UEs 5.

[0042] Each of the UEs 5, exemplified by UE-ι, also comprises an average load calculation module 13, an optimal repetition factor estimation module 15 and a connection interface 17. These modules fulfil similar functions to the corresponding modules in the eNB 3, as will be explained in more detail below.

[0043] UEi further comprises a repetition factor selection module 19, a resource allocation module 21 and a transmission module 23. The repetition factor selection module 19 selects a repetition factor based on the optimal repetition factor. The resource allocation module 21 allocates resources based on the selected repetition factor. The transmission module 23 is arranged to transmit messages directly to another UE 5 based on the allocated resources, via connections established using the connection interface 17.

[0044] Figure 2 shows a flow diagram of a method for controlling direct data transmission between the UEs 5 in the network 1.

[0045] In step 100, the eNB 3 estimates the average network load (L) using the average load calculation module 7. The average network load may be determined by measuring the average number of UEs 5 that have a message in their transmission buffer for sending via the sidelink 8. In other words, in step 100 the eNB 3 estimates the average network load by receiving an indication whether or not each UE 5 has a message in its transmission buffer. The average network load may be determined using another parameter, such as the message transmission periodicity.

[0046] 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.

[0047] 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.

[0048] In step 140, “mode 1” or “mode 2” is selected using a selection module (not shown) 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”.

[0049] 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 where 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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).

[0055] 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.

[0056] An example of the code for implementing step 280 is outlined below:

[0057] Here sa‘ is a subset of size m of the sidelink control pool (of size S) at the sidelink control period of time t. In addition, fi(x;m’) and f2(x;m’) are two one-dimensional, invertible, cumulative probability distributions of mean m’.

[0058] The load (L) is received as an input, and two parameters, Lmin and Lmax, are used to generate a random variable (with probability density function either ^ 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.

[0059] In this example:

[0060] [0061] [0062] 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.

[0063] 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.

[0064] In step 320, the UE 5 transmits the message in accordance with the resource allocation determined in step 300 using the transmission module 23.

[0065] 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.

[0066] 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. This calculation is based on an estimate of the number of vacant resources and/or collisions in the network. The UE 5 does this by measuring the Received Signal Strength Indicator (RSSI) and comparing it to a threshold. This same process may be carried at out a plurality of the UEs 5 in the network.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] In a specific example of step 240, the UE 5 measures the number of vacant resources, and if this number is higher than a given threshold, it will decrease its load value, and therefore increase its estimate of the optimal repetition factor. On the other hand, if the number of vacant resources is lower than another given threshold, it will increase its load value, and therefore decrease its estimate of the optimal repetition factor. In this example, the rate of increase and decrease is a function of the optimal repetition factor. In general, the procedure favours large increases when the optimal repetition factor is low and large decreases when the optimal repetition factor is high.

[0071] An example of the code for implementing this specific example of step 240 is outlined below:

[0072] Again, saf is a subset of size m of the sidelink control pool (of size S) at the sidelink control period of time t, and zi is the number of vacant resources in the sidelink control pool at the sidelink control period of time t. In addition, ^(χ,γίβΗ,βι.) is a function that receives the current estimated number of vacant resources and the previous average number of repetitions/punctures and returns the current average number of repetitions/punctures.

[0073] The getM subroutine is used to generate a random variable with an adjusted mean to the current average number of repetitions/punctures. Note that a partial measurement of the vacant slots can still be performed on sidelink control pools where transmission occurs, on sub-frames that are not transmitted on.

[0074] 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.

[0075] 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.

[0076] In “mode 2”, an individual UE 5 estimates the average network load based on local measurements and/or 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.

[0077] 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 3 or 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.

[0078] 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.

[0079] Figure 3 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.

[0080] In Figure 3 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.

[0081] 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.

[0082] 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.

[0083] Figure 4 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.

[0084] Figure 5 shows a graph of the average success rate as a function of load for the case where the average load and repetition factor is determined by the UEs.

[0085] 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 4 and 5, it can be seen that, the method described above performs close to the theoretical optimum performance in “mode 1” and in “mode 2”.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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 (mTx) and the optimal repetition factor of the non-TX group (mn0n-Tx) diverge, and therefore cannot be satisfied simultaneously.

[0090] It has been found that the penalty for using mn0n-Tx for the TX group is significant for loads smaller than 0.3, while the penalty for using mTX for the non-TX group is not that high. Therefore, for this region it may be preferably to use mTX as a reference. At the higher load range the degradation in performance for the non-TX group is more significant. However, since mTX 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] It will be appreciated that the methods described above apply to any other wireless technologies without losing the effect sought.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[00100] 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

1. 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.

2. A method according to claim 1 wherein the master node is 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.

3. A method according to claim 2 wherein the master node estimates the optimal repetition factor and notifies at least one of the mobile devices of the optimal repetition factor.

4. A method according to claim 2 wherein the master node notifies 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.

5. A method according to claim 1 wherein the plurality of the mobile devices are 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.

6. A method according to any of the preceding claims further 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.

7. A method according to claim 6 wherein the first load value is calculated by estimating the number of vacant resources and/or collisions in the network, based on signals received from at least one other mobile device in the network.

8. A method according to any of claim 7 wherein estimating the number of vacant resources and/or collisions in the network comprises: measuring a Received Signal Strength Indicator (RSSI) value at the first mobile device; and comparing the RSSI to a threshold.

9. A method according to claim 8 wherein the threshold is derived from a value of the local Signal-to-Noise Ratio (SNR).

10. A method according to any of claims 6-9 further comprising estimating, at the at least one mobile device, the number of vacant resources in the network; increasing the first load value, if the number of vacant resources is lower than a given threshold; and decreasing the first load value, if the number of vacant resources is higher than a given threshold.

11. A method according to any of the preceding claims wherein the method further comprises: 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.

12. A method according to any of claim 11 wherein the method further comprises: 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.

13. A method according to claim 12 wherein a high repetition factor is assigned to a high priority message and a low repetition factor is assigned to a low priority message.

14. A method according to any of the preceding claims wherein the method comprises, 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.

15. A method according to any of the preceding claims wherein allocating resources comprises selecting the number of times that the message is to be sent, based on the optimal repetition factor.

16. A method according to any of the preceding claims wherein allocating resources comprises selecting the number of resources on which the message is to be transmitted, based on the optimal repetition factor.

17. A method according to any of the preceding claims wherein each mobile device comprises a transmission buffer; and the method further comprises: receiving an indication from each mobile device of whether each individual mobile device has a message in its transmission buffer; and calculating the average number of devices in the network that have a message in their transmission buffer, wherein the estimate of the average load at the network average network-load is based on the calculated average.

18. A method according to any of the preceding claims wherein the mobile devices are vehicle-based.

19. A method according to any of the preceding claims wherein the master node comprises a road side unit.

20. A method according to any of the preceding claims wherein the master node is a mobile device.

21. A method according to any of the preceding claims wherein the master node is an evolved Node B (eNB).

22. 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.

23. A method according to claim 22 wherein the method further comprises: selecting an integer value, for each mobile device, 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 mobile device; wherein resources are allocated based on the updated optimal repetition factor.

24. A method according to claim 22 or 23 wherein the method comprises, 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.

25. A method according to any of claims 22-24 wherein the first load value is calculated by estimating the number of vacant resources and/or collisions in the network, based on signals received from at least one other mobile device in the network.

26. A method according to any of claim 25 wherein estimating the number of vacant resources and/or collisions in the network comprises: measuring a Received Signal Strength Indicator (RSSI) value at the first mobile device; and comparing the RSSI to a threshold.

27. A method according to claim 26 wherein the threshold is derived from a value of the local Signal-to-Noise Ratio (SNR).

28. A method according to any of claims 22-27 further comprising: estimating, at the at least one mobile device, the number of vacant resources in the network; increasing the estimate of the first load value, if the number of vacant resources is lower than a given threshold; and decreasing the first load value, if the number of vacant resources is higher than a given threshold.

29. A method according to any of claims 22-28 wherein allocating resources comprises selecting the number of times that the message is to be sent, based on the optimal repetition factor.

30. A method according to any of the claims 22-29 wherein allocating resources comprises selecting the number of resources on which the message is to be transmitted, based on the optimal repetition factor.

31. 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.

32. 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.

33. 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.

34. A method substantially as herein described with reference to the accompanying drawings.

35. A system substantially as herein described with reference to the accompanying drawings.

36. A mobile device substantially as herein described with reference to the accompanying drawings.