Method and server for configuring parameters of a formation that moves proximally

Abstract

The present disclosure relates to a computer-implemented method for configuring values of a set of parameters of at least two movement formations. Each formation comprises a plurality of respective communication nodes, the set of parameters includes at least one pre-parameter, and the set of parameters further includes, for each formation, control parameters of a control algorithm and communication parameters for configuring a wireless link. The configuration method includes, for each pre-parameter, obtaining a range of acceptable performance values based on the corresponding pre-parameter value; exploring values of the control parameters and communication parameters of the formation that enable the acceptable performance values of each range to be achieved in an optimal manner; exploring an updated range of acceptable performance values for which it is possible to determine values of the control parameters and communication parameters that enable the achievement of the acceptable performance values of each updated range if it is not possible to achieve the acceptable performance values of each range; and determining updated values of at least one pre-parameter.

Description

Technical Field

[0001] The present disclosure relates to a communication system, and more specifically, to a method and a server for configuring a set of parameters of at least two formations of communication nodes moving in proximity to each other.

Background Art

[0002] In many applications, benefits can be obtained by constructing a formation composed of multiple vehicles. These multiple vehicles need to follow the same trajectory as a whole while cooperatively maintaining a desired geometric arrangement between the vehicles. Moving in a formation has many advantages over non-cooperative systems, including reducing system costs, enhancing system robustness and efficiency, and providing redundancy, reconfiguration ability, and structural flexibility, [Chen2005].

[0003] In particular, platooning is regarded as a very promising application where multiple vehicles (e.g., in 1D on one lane) are required to travel at a desired speed while maintaining a predetermined inter-vehicle distance. Platooning can bring great benefits in terms of power consumption, passenger comfort, traffic smoothness, etc., whether in the case of automobiles, trucks, robots, or airplanes. Other types of formation applications include satellite clusters, security, search and rescue, and swarms of drones for agriculture.

[0004] To maintain the geometric arrangement of the formation, the dynamics of the vehicles in the formation are typically handled using control algorithms that require the exchange of data between the communication nodes incorporated in the vehicles. For example, a platoon usually means that the leading vehicle transmits its acceleration and speed to its following vehicles, and each vehicle transmits its position and speed to its neighboring vehicles (see, e.g., [Sybis2019]). And, for example, by using a consensus algorithm, the vehicles can be maintained equidistant from each other.

[0005] Most formation applications rely on wireless links due to their mobility requirements. In communication over wireless links, errors not only arise from physical layer problems (such as additive noise, path loss, shadowing, fast fading, phase noise, etc.), but also tend to occur from upper layer problems (such as collisions caused by the hidden node effect in Carrier Sensing Multiple Access (CSMA) or distributed scheduling). Latency also occurs to some extent due to physical layer effects (such as transmission delay, packet duration, etc.), but mostly due to upper layer effects (such as scheduling policies, random access methods, etc.).

[0006] Since the control algorithm depends on the exchange of data between communication nodes, packet loss clearly has an adverse effect on the performance of the control algorithm. Packets can be lost due to insufficient communication resources. Due to the shortage of radio frequencies, wireless systems are designed to operate with limited bandwidth. Therefore, regardless of the transmission technology in use, communication nodes can only utilize a limited number of communication resources per unit of time (either at the physical level or at the logical level where the operator allocates only a subset of the available resources to a specific application).

[0007] When a single formation of vehicles moves while maintaining a predetermined geometric arrangement (usually 1D with a predetermined inter-vehicle distance and a predetermined speed) between the vehicles using a control algorithm (such as a consensus algorithm) and limited communication resources, it is important to ensure that the control algorithm can actually control the shape of the geometric arrangement and / or its speed at a sufficient convergence speed. The convergence speed of the control algorithm depends, among other things, on the packet loss rate (see, for example, [Sybis2019]).

[0008] On the other hand, it is necessary to be able to anticipate changes in the communication environment that may prevent the geometric arrangement of this formation from achieving sufficient convergence speed in the formation.

[0009] Such changes in the communication environment may occur, for example, when different formations approach each other and use the same communication resources to exchange data related to their respective control algorithms. Since those formations move within each other's wireless coverage, the communication nodes of those formations compete to access the same communication resources, resulting in increased mutual interference and increased packet loss. Examples of formations that may move within radio vicinity and compete to obtain the same communication resources include platoons of vehicles that cross or overtake each other on a highway.

SUMMARY OF THE INVENTION

PROBLEMS TO BE SOLVED BY THE INVENTION

[0010] The present disclosure aims to improve this situation. In particular, the present disclosure aims to overcome at least some of the limitations of the prior art described above by proposing a solution for configuring a set of parameters of at least two formations moving in proximity to each other.

MEANS FOR SOLVING THE PROBLEM

[0011] For this purpose, according to a first aspect, the present disclosure is a computer-implemented method of constructing values of a set of parameters for at least two movement formations, each formation having a plurality of individual communication nodes, the set of parameters having at least one pre-parameter, each pre-parameter corresponding to a formation parameter of each formation or a network parameter of the formation, the formation parameter of the formation defining the geometric arrangement of the communication nodes of the formation, the network parameter of the formation defining the communication resources allocated to the formation for exchanging data over a wireless link, each pre-parameter having a predetermined value, and a predetermined change in the communication environment of the formation being about to occur. The set of parameters, for each formation, · control parameters of a control algorithm used by the formation to maintain the geometric arrangement, and · communication parameters constituting a wireless link between the communication nodes of the formation, the wireless link being used to exchange data related to the control algorithm, further includes. The above configuration method is · for each pre-parameter, obtaining a range of acceptable performance values based on the value of the corresponding pre-parameter; · when considering that a change in the communication environment has occurred, exploring values of the control parameters and communication parameters of the formation that enable the achievement of acceptable performance values within each range; · when it is not possible to achieve the acceptable performance values of each range, exploring an updated range of acceptable performance values for which it is possible to obtain values of the control parameters and communication parameters that enable the achievement of acceptable performance values when considering that a change in the communication environment has occurred; · based on the values of the control parameters and communication parameters that enable the achievement of acceptable performance values in each updated range of acceptable performance values, obtaining the value of at least one updated pre-parameter; includes.

[0012] Thus, the proposed configuration method first considers, as input, at least one pre-parameter. The pre-parameter corresponds to a parameter having a value configured before a predetermined change in the communication environment of the formation is about to occur. For example, the expected change in the communication environment is due to the formations approaching each other, entering the wireless proximity, and potentially competing for the same allocated communication resources. According to another example, the expected change in the communication environment is due to, for example, at least one formation experiencing a predictable degradation in the quality of its wireless link while moving wirelessly close to at least one other formation. In the present disclosure, the value of each pre-parameter is maintained, if possible, when a change in the communication environment occurs. For example, it is possible to consider a pre-parameter corresponding to a formation parameter of each formation that describes the geometric arrangement of the formation (relative positions between communication nodes, speeds of communication nodes, etc.). In such a case, the value of the formation parameter (geometric arrangement) should be maintained without change, if possible. In some cases, it is also possible to consider a pre-parameter corresponding to a network parameter that describes the amount of communication resources allocated to one or more formations. In such a case, the value of the network parameter (amount of allocated communication resources) should be maintained without change, if possible.

[0013] The range of acceptable performance values is determined based on the value of the pre-parameter. The acceptable performance value is basically the value of a performance indicator that enables the value of the pre-parameter to be maintained. Before a change in the communication environment of the formation occurs (e.g., before the formations approach each other), the performance indicators have values within their respective acceptable ranges.

[0014] Thereafter, although before the communication environment changes (or shortly after the communication environment change starts), considering that the communication environment has changed, the configuration method searches for values of communication parameters that enable achievement of the allowable performance value (to configure a wireless link) and values of control parameters (to configure a formation control algorithm) within each range to be considered. This search can be performed, for example, by any multi-objective optimization method known to those skilled in the art.

[0015] If such values of the communication parameters and the control parameters exist, this means that the expected change in the communication environment does not prevent the maintenance of the values of the pre-parameters (e.g., the maintenance of the geometric configuration).

[0016] If such values of the communication parameters and the control parameters do not exist, this means that the expected change in the communication environment prevents the maintenance of the values of the pre-parameters (e.g., the maintenance of the geometric configuration).

[0017] In the latter case, the configuration method searches for an updated range of allowable performance values that enables achievement of the allowable performance value for the control parameters and the communication parameters within each range updated when considering that the communication environment has changed. Thus, the range is repeatedly updated by changing (i.e., decreasing) at least one of the ranges until an achievable allowable performance value is obtained for each updated range. Changing the range of the allowable performance value means including the decreased performance value that was not allowed in the original range in the updated range. Updating the range of the allowable performance value means allowing any performance value of the performance indicator to be considered.

[0018] When obtained for each range where the achievable allowable performance value is updated, based on the values of the control parameters and communication parameters that enable the achievement of the allowable performance value in each updated range, the value of the pre-parameter is updated. The constraints (i.e., allowable performance values) for at least one performance indicator are relaxed (e.g., by allowing a lower convergence speed for the formation control algorithm), so it may be necessary to change the values of one or more pre-parameters (e.g., by reducing the speed of the vehicles in the formation and / or increasing the distance between the vehicles).

[0019] The value of the updated pre-parameter can then be used, for example, by communicating the value of the updated pre-parameter to the formation, to configure the geometric arrangement of the formation and / or the communication resources allocated to the formation before the change in the communication environment actually occurs (or at least before the change in the communication environment reaches its maximum). Alternatively, or in combination, the values of the control parameters and communication parameters that enable the achievement of the allowable performance value in each updated range can be used, for example, by communicating these values to the communication nodes of the formation, to configure the control algorithm and the wireless links of the communication nodes of the formation as well.

[0020] In certain embodiments, the configuration method can further include one or more of the following features, which can be considered either alone or in any technically possible combination.

[0021] In certain embodiments, the range of the allowable performance value of the formation parameter corresponds to the range of the allowable control performance value of the control algorithm used by the above formation, and / or the range of the allowable performance value of the network parameter corresponds to the range of the allowable resource usage performance value for the use of the communication resources allocated by the formation.

[0022] In certain embodiments, the set of parameters has a plurality of pre-parameters including the formation parameters of each formation and at least one network parameter of all formations, and if the values of the control parameters of the formation and the values of the communication parameters that enable the achievement of the acceptable performance values for each range cannot be found, exploring the updated range includes reducing the acceptable control performance value of at least one formation.

[0023] In certain embodiments, exploring the updated range includes first reducing the acceptable control performance value and then, if necessary (i.e., if reducing only the acceptable control performance value is not sufficient), reducing the acceptable resource usage performance value.

[0024] In certain embodiments, the set of parameters has a plurality of pre-parameters including the formation parameters of each formation and at least one network parameter of all formations, and if the values of the control parameters of the formation and the values of the communication parameters that enable the achievement of the acceptable performance values for each range cannot be found, exploring the updated range includes reducing the acceptable resource usage performance value of the formation.

[0025] In certain embodiments, exploring the updated range includes first reducing the acceptable resource usage performance value and then, if necessary, reducing the acceptable control performance value.

[0026] In certain embodiments, exploring the updated range by reducing the acceptable control performance value of the formation includes using the same reduction factor for all or a plurality of formations.

[0027] In certain embodiments, the acceptable control performance value of the control algorithm represents the acceptable convergence speed value of this control algorithm.

[0028] In certain embodiments, the allowable resource usage performance value represents the global interference level generated by all formations when using the allocated communication resources.

[0029] In certain embodiments, the formation parameters of each formation are as follows, namely, · The distance between the communication nodes of the formation, · The shape of the geometric arrangement of the formation, · The speed of the communication nodes of the formation, represent at least one of the above.

[0030] In certain embodiments, the allocated communication resources defined by the network parameters are any of the following, namely, · At least one frequency bandwidth, · At least one spreading code or scrambling code, · At least one time pattern, among others.

[0031] In certain embodiments, the control parameter of a formation represents at least one weighting factor used to weight the data received from other communication nodes of this formation.

[0032] In certain embodiments, the communication parameters of a formation are as follows, namely, · Modulation method, · Channel coding method, · Multiple antenna method, · Transmission power, · Coverage distance, · Packet rate, represent at least one of the above.

[0033] In certain embodiments, the configuration method is performed by at least one communication node of the formation or by a configuration server separate from the formation.

[0034] According to a second aspect, the present disclosure relates to a computer program product comprising instructions that configure at least one processor to perform a configuration method according to any one of the embodiments of the present disclosure when executed by the at least one processor.

[0035] According to a third aspect, the present disclosure relates to a configuration server comprising at least one processor, at least one memory, and at least one communication module, wherein the at least one processor is configured to perform a configuration method according to any one of the embodiments of the present disclosure.

[0036] In certain embodiments, the configuration server is included in at least one communication node of the formation or is separate from the formation.

[0037] The present invention will be better understood by reading the following description. The following description is given by way of example only and is not in any way limiting and has been created with reference to the figures.

[0038] In these figures, the same reference numerals throughout the figures indicate the same or similar elements. For clarity, the illustrated elements are not to scale unless otherwise explicitly specified.

[0039] Also, the order of the steps represented in these figures is provided for illustrative purposes only and is not intended to limit the present disclosure, which can also be applied when the same steps are performed in a different order.

Brief Description of the Drawings

[0040]

Figure 1A

Figure 1B

Figure 2

Figure 3

Figure 4

Figure 5A

Figure 5B

Figure 6

Figure 7

Figure 8

Best Mode for Carrying Out the Invention

[0041] As described above, the present disclosure relates to the interaction of at least two mobile formations that time-share at least allocated communication resources common to the at least two mobile formations. In particular, the occurrence of changes in the communication environment of these formations is expected.

[0042] For example, a predicted change in the communication environment is due to formations approaching each other and coming to be within wireless proximity (e.g., at least one communication node of one formation being within the wireless coverage of at least one communication node of the other formation) and competing to obtain a common allocated communication resource. Thus, in such a case, the formations initially do not compete to obtain a common allocated communication resource (since they are not within wireless proximity of each other), but approach each other and soon come to be within wireless proximity and start to compete to obtain the same common allocated communication resource.

[0043] According to another example, a predicted change in the communication environment is due to, for example, at least one formation experiencing a predictable degradation in the quality of its wireless link while moving into wireless proximity with at least one other formation.

[0044] In the following, the change in the communication environment is considered non - limitingly to correspond to formations approaching each other, coming to be within wireless proximity, and competing to obtain a common allocated communication resource.

[0045] Figures 1A and 1B schematically illustrate an example of a change in the communication environment in the case of a first formation 10-1 and a second formation 10-2 corresponding to platoons of two intersecting vehicles. In formation 10-1, each vehicle is equipped with a communication node 11, and the communication node 11 is configured to exchange data regarding the control algorithms used by formation 10-1 to maintain the geometric arrangement (e.g., relative positions and speeds of the communication nodes) of formation 10-1 with other communication nodes 11 in formation 10-1. Similarly, each of the vehicles in formation 10-2 is also equipped with a communication node 11, and the communication node 11 is configured to exchange data regarding the control algorithms used by formation 10-2 to maintain the geometric arrangement of formation 10-2 with other communication nodes 11 in formation 10-2.

[0046] Hereinafter, when there is no need to distinguish, the formations are collectively referred to as 10, and when distinction is necessary, they are individually referred to as 10-i. Note that formation 10-i corresponds to the formation 10 with index i, where 1 ≦ i ≦ N, and N corresponds to the number of formations moving in proximity. Hereinafter, the case of mainly N = 2 (i.e., two formations) will be focused on. However, the present disclosure can also be applied to a number of formations 10 more than two.

[0047] In FIGS. 1A and 1B, the wireless coverage of formation 10-1 is indicated by RC-1, and the wireless coverage of formation 10-2 is indicated by RC-2. The wireless coverage of formation 10 corresponds to an area capable of receiving and decoding messages transmitted at a given packet delivery rate (PDR) by at least one communication node 11 of the formation. In FIG. 1A, formations 10-1 and 10-2 are approaching each other, but initially, they are positioned at a distance so that their respective wireless coverages RC-1 and RC-2 do not overlap. Thus, communication nodes 11 from different formations 10 do not compete for a common allocated communication resource. In FIG. 1B, formations 10-1 and 10-2 are in proximity such that their respective wireless coverages RC-1 and RC-2 overlap. Thus, communication nodes 11 from different formations compete for a common allocated communication resource, and for this reason, for example, each formation 10 experiences an increase in the packet collision level.

[0048] As described above, each formation 10 uses a control algorithm that controls the geometric arrangement of the formation, preferably a cooperative control algorithm such as a consensus algorithm or a distributed model predictive control (DMPC) algorithm. The communication nodes 11 of the formation 10 exchange data so that the cooperative control algorithm can control the geometric arrangement. In the case of a platoon of vehicles, the control algorithm is used to maintain a predetermined geometric arrangement (usually 1D with a predetermined distance and a predetermined speed between vehicles) between the vehicles while moving. It is important to ensure that the control algorithm can actually control the geometric arrangement at a sufficient convergence speed, for example, to avoid collisions between vehicles. Also, the convergence speed of the control algorithm depends particularly on packet loss (see, for example, [Sybis2019]). Therefore, as shown in FIG. 1B, when the wireless coverage RC-1 of the formation 10-1 overlaps with the wireless coverage RC-2 of the formation 10-2, the convergence speed of the control algorithm may become insufficient due to an increase in the mutual interference level (here, the mutual interference level of the formation corresponds to the interference level generated by other formations).

[0049] The present disclosure relates to a configuration method 20 for configuring a set of parameters of the formation 10 in response to an expected change in the communication environment of the formation 10. The formation 10 typically has a set of parameters configured using predetermined values before the communication environment change.

[0050] Some of the set of parameters are called "pre-parameters" and correspond to parameters whose values should be maintained during the communication environment change if possible. The set includes at least one pre-parameter.

[0051] There are mainly two types of pre-parameters.

[0052] The first possible type of pre-parameters is formation parameters that describe the geometric arrangement of formation 10. In the present disclosure, any formation parameters suitable for describing the geometric arrangement of formation 10 can be used. For example, the formation parameters of formation 10 can include at least one of the following characteristics of the geometric arrangement of formation 10. · The distance between communication nodes 11 of formation 10 (inter-vehicle distance, i.e., IVD), · The shape of the geometric arrangement of formation 10 (e.g., linear, square, etc.), · The speed of communication nodes 11 of formation 10 (e.g., maximum speed, minimum speed, or nominal speed).

[0053] When the pre-parameters include formation parameters, the pre-parameters include N formation parameters, one for each of the N formations 10. When the pre-parameters include formation parameters, this means that the geometric arrangement of formation 10 should remain unchanged during communication environment changes, if possible.

[0054] The second possible type of pre-parameters is network parameters that describe the communication resources allocated to a formation for data exchange over a wireless link. This includes communication resources that are also allocated to (i.e., common to) at least one of the other formations 10. The network parameters can use any format suitable for describing the communication resources allocated to a formation. For example, the allocated communication resources defined in the network parameters can be any of the following. · At least one frequency bandwidth (e.g., a frequency channel or one or more sub-channels, one or more sub-carriers in an orthogonal frequency division multiple access (OFDMA) system, etc.), · At least one spreading code or scrambling code (i.e., a code in a code division multiple access (CDMA) system), · At least one temporal pattern (e.g., one or more slots, etc.).

[0055] For example, it is possible to pre-define a pool of communication resources available for distribution to formation 10 (also known as a resource pool in the case of a 5G cellular network), and the network parameters of the formation define a subset of the pool that is first allocated to the formation before a communication environment change occurs. At least some of the allocated communication resources are assumed to be common to two or more formations 10, i.e., are allocated to two or more formations 10 simultaneously. In the present disclosure, it is possible to define one network parameter for each formation 10, and each network parameter defines the communication resources (from the pool of available communication resources) allocated to each formation 10 (which is at least partially shared with at least one other formation 10). However, it is also possible to consider fewer network parameters. For example, if the same communication resources are allocated to all formations 10, it is possible to consider a single network parameter (sometimes referred to as "global" below) for all formations 10. If the pre-parameters include at least one network parameter, this means that the amount of communication resources allocated to formation 10 should, if possible, remain unchanged during a communication environment change.

[0056] In the following, a single (global) network parameter is used, and thus, without limitation, it is considered that at least one pre-parameter includes at most one (global) network parameter.

[0057] In the present disclosure, it should be noted that the expression "parameter" can refer to either a single basic (scalar) parameter or a vector parameter or matrix parameter including a plurality of basic parameters. In other words, a parameter includes one or more basic parameters which may be of different types. For example, a formation parameter can include both IVD and speed. For example, a network parameter can include both one or more frequency bandwidths and one or more temporal patterns.

[0058] The values of the other parameters of the set (i.e., other than the pre-parameters) are first reconfigured when adapting to a communication environment change. These other parameters correspond to parameters that affect the convergence rate of the control algorithm used by the formation 10 and include the following for each formation 10. · Control parameters of the control algorithm used by the formation 10 to control the geometric arrangement, · Communication parameters for configuring the wireless links between the communication nodes 11 of the formation 10, which are used for the exchange of data related to the control algorithm.

[0059] In a control algorithm such as a consensus algorithm, for example, each communication node 11 uses control parameters corresponding to one or more weighting factors used to weight the data received from neighboring communication nodes 11. Such weighting factors affect the convergence rate of the control algorithm and can be adjusted when adapting to changes in the communication environment. For example, the continuous-time consensus algorithm can be summarized by the following equation.

Equation

[0060] Similarly, the configuration of the wireless link affects the communication performance, i.e., the characteristics of the set Θ i (t) (e.g., regarding PDR, etc.) on these wireless links, and affects the convergence speed of the control algorithm. Therefore, by changing the value of the communication parameter, the configuration of the wireless link can be adjusted when adapting to changes in the communication environment. Non-limiting examples of the basic communication parameters include the following. · Modulation method (e.g., BPSK, QPSK, 16QAM, etc.), · Channel coding method (e.g., the type of channel coder, the rate of the channel coder, etc.; the modulation method and the channel coding method are usually collectively called MCS), · Multi-antenna method (e.g., the number of layers, the number of antennas, etc.), · Transmission power, · Coverage distance, · Packet rate (e.g., the number of packets per second), · Maximum retransmission number, etc.

[0061] Figure 2 schematically shows the main steps of the configuration method 20 for constructing a set of formation parameters in response to the expected communication environment change.

[0062] The configuration method 20 is executed by the configuration server 30.

[0063] Figure 3 schematically shows an exemplary embodiment of the configuration server 30 suitable for the execution of the configuration method 20.

[0064] As shown in FIG. 3, the configuration server 30 includes one or more processors 31 and one or more memories 32. The one or more processors 31 can include, for example, a central processing unit (CPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), an application specific integrated circuit (ASIC), and the like. The one or more memories 32 can include any type of computer-readable volatile memory and computer-readable non-volatile memory (such as magnetic hard disks, solid state disks, optical disks, electronic memories, etc.). The one or more memories 32 can store a computer program product in the form of a set of program code instructions to be executed by the one or more processors 31 to perform all or part of the steps of the configuration method 20.

[0065] As shown in FIG. 3, the configuration server 30 also includes a communication module 33 that exchanges data with the formation 10.

[0066] In some cases, the configuration server 30 can be separated from the communication node 11 of the formation 10. For example, when data is exchanged directly with the formation 10, the communication module 33 implements at least one wireless communication protocol. When data is exchanged indirectly with the formation 10, that is, via one or more intermediate communication devices, the communication module 33 can implement at least one wireless communication protocol and / or at least one wired communication protocol to exchange data with the intermediate communication device. It should be noted that the configuration server 30 can be included in a single hardware device or in multiple separate hardware devices in a distributed computing architecture.

[0067] In some cases, as an alternative, the configuration server 30 can be included in one or more communication nodes 11 of the same formation 10 (in a distributed computing architecture). In such cases, the communication module 33 implements at least one wireless communication protocol, for example, the same wireless communication protocol used by the communication nodes 11 of formation 10 to exchange data related to the control algorithm. When the communication module 33 implements the same wireless communication protocol used by the communication node 11, the communication module 33 can also be used by the communication node 11 to exchange data related to the control algorithm with other communication nodes 11 (i.e., the communication device 11 can comprise a single communication module). Further, the configuration server 30 is configured to exchange data with at least one communication node 11 of each other formation 10, directly and / or indirectly via one or more intermediate communication devices separated from one or more other communication nodes 11 and / or formation 10.

[0068] Therefore, the configuration server 30 exchanges data with the formation 10. The data received from the formation 10 is any data required for the execution of the configuration method 20. For example, the received data can include the current values of the pre-parameters (before the communication environment change), the position and speed of the formation 10 (e.g., for determining when the communication environment change will occur), etc. If the configuration server 30 is included in one or more communication nodes 11 of the formation, this means that each formation 10 can directly or indirectly, temporarily or permanently, exchange data with any other formation. Depending on the nature of the inter-formation communication, the formation 10 can anticipate future communication environment changes to a greater or lesser extent. The data transmitted from the configuration server 30 to the formation includes the new values of the pre-parameters when the pre-parameters cannot be left unchanged. The data transmitted from the configuration server 30 to the formation can also include the new values of the control parameters and / or the communication parameters.

[0069] As shown in FIG. 2, the configuration method 20 includes step S20 of obtaining a range of allowable performance values for each pre-parameter based on the corresponding pre-parameter values.

[0070] Basically, the allowable performance value is the value of the performance indicator that enables the value of the pre-parameter configured before the communication environment change to be maintained during the communication environment change.

[0071] For example, the performance indicator of the formation parameters of the formation 10 can be any control performance indicator representing the performance of the control algorithm used by this formation 10. In a preferred embodiment, the control performance indicator represents the convergence speed of the above control algorithm, and the greater the convergence speed, the greater the control performance value.

[0072] For example, the performance indicator of the network parameters of all formations 10 can be any resource usage performance indicator that represents the performance of the use of communication resources allocated by the communication nodes 11 of all formations 10. In a preferred embodiment, the resource usage performance indicator represents the global interference level generated by all formations 10 when using the allocated communication resources. The global interference level corresponds to the interference level collectively generated by all formations 10 in the area where the formations 10 are located. The lower the global interference level, the higher the resource usage performance value. In the following, in some cases, the resource usage cost may be referred to. Basically, the resource usage cost is such that the higher the resource usage cost value, the lower the resource usage performance value (and the higher the global interference level). For example, the resource usage performance is the inverse or inversely proportional to the resource usage cost. Therefore, the resource usage cost can also represent the generated global interference level and can be expressed as the resource usage performance by considering its reciprocal or inverse. For example, the resource usage cost can correspond to the channel busy ratio (CBR) defined in the 3GPP (trademark) system, which represents the global interference level generated by the formation 10. Other examples include system load (SL), average received power, etc.

[0073] In the following, the values of the formation parameters before the communication environment change are denoted as p i form for their respective formation parameters p i form,t where 1 ≤ i ≤ N.

[0074] Regarding the formation parameters, the range of the allowable control performance value is the lower limit u i ctrl (e.g., convergence speed) for their respective control performance indicators u ictrl,t can be defined by. To obtain the range of the allowable control performance value, the function θ i form can be predefined as follows. [Numerical formula]

[0075] The function θ i form is assumed to change monotonically.

[0076] For example, the function θ i form can provide the slowest convergence rate that theoretically guarantees that the geometric arrangement is controlled fast enough to prevent a collision between the vehicle in the formation and an adjacent vehicle. For example, the convergence rate can be such that, given the speed of the vehicle, the probability that the IVD becomes zero is lower than a predetermined threshold (e.g., 10 -6 or less).

[0077] Also, given a value u i ctrl,a relating to the control performance, assume that the function i form for obtaining the value p i form,a of the formation parameter p [Numerical formula] is pre-determined as follows. [Numerical formula]

[0078] The function [Numerical formula] is assumed to change monotonically.

[0079] When the pre-parameters include network parameters, the network parameter p before the communication environment change net is denoted as p net,t .

[0080] For the (global) network parameters, the range of the allowable resource usage performance value can be defined by the upper limit c com (e.g., CBR) for the resource usage cost c com,t . To obtain the range of the allowable control performance value, the function θ net can be obtained in advance as follows. [Number]

[0081] Also, given the value c com,a for the resource usage cost, assume that the function net for obtaining the value p net,a of the network parameter p [Number] can be obtained in advance as follows. [Number]

[0082] Therefore, step S20 of obtaining the range of the allowable performance value of each pre-parameter is · Using the function θ i form to obtain the lower limit u i form of the control performance u i ctrl for each formation parameter p i ctrl,t based on the pre-value p i form of that formation parameter p i form,t ; and / or · The function θ netUsing it, the network parameter p net of the resource usage cost c com Regarding the upper limit c com,t For the network parameter p net of the previous value p net,t Based on, it can be obtained that It can be set as.

[0083] As shown in FIG. 2, the configuration method 20 includes a step S21 of searching for values of the control parameters and communication parameters of the formation 10 that enable the achievement of the allowable performance values of each range when considering a change in the communication environment.

[0084] As described above, the performance of the control algorithm depends on the communication performance on the wireless link. For example, the convergence speed of the consensus algorithm is related to the spectral radius of the matrix with the PDR values of various links between agents as elements and at least one control parameter [Pereira2011] (see also European Patent Application No. 21305452.1).

[0085] For each formation 10, let the communication parameters configured to adjust the communication performance on the wireless link be x i com (MCS, transmission power, packet rate, etc.). It is assumed non-limitingly that all communication nodes 11 of the same formation 10 use the same value of the communication parameter x i com For each formation 10, let the control parameters configured to adjust the control performance of the control algorithm be x i ctrl It is shown as.

[0086] Therefore, for each formation 10, x i com and / or x i ctrl The values of are the previous values of each pre-parameter (i.e., the value p of the formation parameter p i form of the value pi form,t and / or network parameter p net The value p of net,t ) can be optimized to be maintained without change. Since the pre-values of each pre-parameter can be left unchanged if they can achieve the performance value within each range of the allowable performance values, assuming a control performance indicator and a resource usage performance indicator, this means that this optimization is performed on the condition that the following is satisfied. · u i ctrl ≥ u i ctrl,t , that is, the values of the control parameter and the communication parameter enable the achievement of the control performance value within the allowable range, · c com ≤ c com,t (that is, -c com ≥ -c com,t , where -c com corresponds to the resource usage performance), that is, the values of the control parameter and the communication parameter enable the achievement of the resource usage performance value within the allowable range.

[0087] As shown in FIG. 2, without changing the pre-value of each pre-parameter, the value of x that enables the achievement of the allowable performance value of each range i com The value of [Number] and x i ctrl The value of [Number] can be found (reference numeral S210 in FIG. 2), each control parameter x i ctrl is configured using the corresponding value [Number] and each communication parameter x i com is the corresponding value

Number

[0088] On the other hand, without changing the pre - values of each pre - parameter, an x that enables the achievement of the allowable performance value for each range i com value of

Number

Number

[0089] Control parameter x i ctrl and communication parameter x i com When such a value is found, the configuration method 20 includes step S23 of updating the value of each pre-parameter for which the range of the acceptable performance value has been changed during step S22. In the present disclosure, all ranges obtained after step S22 are referred to as "updated ranges", but the updated ranges are not necessarily all changed with respect to the original ranges, and in some cases, it is sufficient to change only one range of the acceptable performance value or only one type of range of the acceptable performance value (for example, only the range corresponding to the control performance value or only the range corresponding to the resource usage performance value). It should be noted that this may be the case.

[0090] In step S23, the value of the control parameter that enables the achievement of the acceptable performance value for each updated range

Number

Number

Number

Number

Number

Number

[0091] In FIG. 2, the configuration method 20 includes step S24 of configuring the formation 10 using the obtained values by the configuration server 30 distributing the obtained values for a set of parameters to the formation 10. This configuration step S24 is executed before the communication environment change starts or at least before this change reaches its maximum.

[0092] Next, a detailed example of different steps of the configuration method 20 is shown. The configuration method 20 is preferably used to configure a set of parameters of two or more formations 10 moving close to each other. First, however, for the purpose of introducing some concepts and notations, an explanation is provided assuming a single formation 10 which is more convenient.

[0093] {For a single formation} Control parameter x i ctrl And communication parameter x i com Searching for the values of is to maximize the control performance u i ctrl As well as minimize the resource usage cost c com That is, the resource usage performance -c com Maximize) the control parameter x i ctrl And communication parameter x i com Can be regarded as optimizing.

Number

[0094] However, the two performance indicators u i ctrl (x i ctrl 、x i com ) and c com (x i com ) are interdependent (for example, allocating more communication resources will most likely improve the control performance), and there is no unique optimal solution to this problem as described above.

[0095] Therefore, here we are dealing with multi-objective optimization. In the case of multi-objective optimization, when changing one variable, improving one performance indicator (here u i ctrl or -c com ) will necessarily degrade other performance indicators. It is common to search for the Pareto frontier (or Pareto front) corresponding to all points (u i ctrl 、-c com ) (see Figure 4).

[0096] The communication parameter x i com may exist on a support that is restricted (e.g., maximum transmission power) and / or discrete (e.g., MCS, packet rate). The same applies to the control parameter x i ctrl . The optimization is performed in the so-called decision (variable) space

Number

[0097] Therefore, when dealing with multi-objective optimization, if there is a unique "optimal" point among the points on the Pareto frontier, it is necessary to define the criteria for selecting this optimal point. Hereinafter, the criteria for selecting points on the Pareto frontier are referred to as "selection policies".

[0098] For example, the selection policy can be to search for a solution that minimizes the resource usage cost c com as follows.

Number

[0099] control performance u i ctrl Under the equality constraints on the resource usage cost c com while minimizing it, it should be noted that a solution on the Pareto frontier is not necessarily obtained. Therefore, it is more preferable to focus on inequalities. In the above formula, the optimization aims to minimize the resource usage cost while ensuring the allowable control performance value. In other words, this selection policy prioritizes the minimization of the resource usage cost (hereinafter referred to as the "communication-priority selection policy"). This method is shown in Figure 5A.

[0100] As another method, it is possible to prioritize maximizing the control performance while ensuring the allowable resource usage cost value (hereinafter referred to as the "control-priority selection policy").

Number

[0101] The control-priority selection policy method is shown in Figure 5B. There are other optimal solutions on the Pareto frontier that can be obtained by considering other selection policies between the optimal point represented in Figure 5A (communication-priority selection policy) and the optimal point represented in Figure 5B (control-priority selection policy).

[0102] First, assume that the segments on the Pareto frontier are not empty. The selection of a single point on the Pareto frontier can be done as follows.

Number

Number

Number

[0103] In the above formula, setting the parameter λ = 1 simply gives the communication priority selection policy, and setting λ = 0 simply gives the control priority selection policy. For any value in between, another trade-off between the two constraints is realized.

[0104] For example, as shown in Figure 6, due to a change in the communication environment (causing a transmission failure), the constraint u i ctrl ≥ u i ctrl,t may not be achievable (reference numeral S211 in Figure 2). In such a case, one thing that can be done is to make the pre-values of the formation parameters changeable. This is to, as follows, the resource usage cost constraint c com,tThis can be achieved by maximizing the control performance without changing it.

Number

[0105] This corresponds to updating the range of acceptable control performance values by enabling any control performance value given the communication cost constraint. Subsequently, the value of the formation parameter is within the updated range c com ≦c com,t (not changed during step S22) and u i ctrl >0 (changed during step S22), values that enable the achievement of the acceptable performance value as follows

Number

Number

Number

[0106] Figure 7 shows another example where there is no solution because the resource usage cost constraint c com,t is too strict. In this example, simply relaxing the constraint u i ctrl,t on the control performance is not sufficient. The only solution is to also relax the constraint c com,t on the resource usage cost. When dealing with the communication priority selection policy, the solution is to then also decrease the resource usage cost constraint so as to reach the first non-zero control performance value, as follows.

Number

[0107] This corresponds to updating both the range of acceptable control performance values and the range of acceptable resource usage cost values by basically allowing any control performance value and any resource usage cost value in this example. In the example of FIG. 7, the solution

Number

Number

Number

Number

[0108] More generally, when it is not possible to find values of the control parameters and communication parameters that enable the achievement of performance values within the original range of acceptable performance values, the following four main situations can be considered. · The control performance constraint u i ctrl,t cannot be achieved, but the resource usage cost constraint c com,t can be achieved, · The resource usage cost constraint c com,t cannot be achieved, but the control performance constraint u i ctrl,t can be achieved, · Neither the resource usage cost constraint c com,t nor the control performance constraint u i ctrl,t can be achieved, · The resource usage cost constraint c com,t and the control performance constraint ui ctrl,t Both can be achieved, but not simultaneously.

[0109] Therefore, during step S22, according to a predetermined criterion, the control performance constraint u i ctrl,t is reduced or the resource usage cost constraint c com,t is increased, or both are required. Hereinafter, the criterion applied to selectively lower the range of the allowable performance value is called the "priority policy". For example, in the case of a communication priority policy, the range of the allowable control performance value is first changed (lowered). In the case of a control priority policy, the range of the allowable resource usage performance value is first changed (lowered).

[0110] This can be generalized as the following formula.

Number

Number

Number

Number

[0111] {General multi-objective problem} A general multi-objective problem can be expressed as the following formula.

Number

Mathematics

Mathematics

Mathematics

Mathematics

[0112] Since F(x) is a vector in at least two (m > 1) dimensions, there is no total order relation. That is, there is no higher (or equal) value for all pairs of the vector F(x). When F(x) is a vector, it is necessary to define what is meant by max(F(x)), and we will examine the Pareto meaning of this maximum value. If a certain special vector is regarded as the maximum value, the maximum value of such a vector function is one for which one of the components cannot be increased without decreasing another component. In this sense, generally, there are many solutions. That is, the set of such solutions

Mathematics

Mathematics

Mathematics

Mathematics

[0113] Using the following formula, the constraints can be considered more clearly.

Number

Number

Number

[0114] Therefore, for a general multi-objective optimization problem, if there are two or more solutions

Number

[0115] Here, the function

Number

Number

Number

[0116] We will consider the following general method. This method encompasses all optimization methods based on the following max min optimization method. [Number]

[0117] The function min is, here, a vector function of dimension m’ ≤ m (where m ≥ N and N is the number of formations) [Number] and is the minimum value over each component of [Number] is a linear combination (using a matrix A of size m’ × m) of the individual functions f t,i parameterized by the constraint f i (x). [Number]

[0118] [{In the case of multiple formations}] Even in the case of multiple formations 10, we will face a multi-objective optimization problem. When a communication priority selection policy is considered, a typical method is to minimize the resource usage cost c com of all formations 10 under the control performance inequality constraints of each formation. [Number] That is, the allowable range corresponds to u i ctrl ≥ u i ctrl,t , i = 1...N and c com ≤ c com,t .

[0119] The above optimization focuses on minimizing the resource usage cost. Similar to the case of a single formation, it may be appropriate to instead focus on maximizing the control performance, for example, as follows.

Number

Number

[0120] Another possible and advantageous approach is to establish a trade-off between two types of constraints in the same methodology as for a single formation, as follows.

Number

[0121] If some or all of the formations 10 cannot achieve the control performance within their respective ranges of acceptable control performance values, and / or the formations 10 cannot achieve the resource usage cost values within the range of acceptable resource usage cost values (reference numeral S211 in FIG. 2), it is possible to again rely on updating the acceptable ranges. Here too, two situations can be satisfied depending on whether all the constraint regions (acceptable ranges) overlap with the Pareto frontier (but do not overlap with each other). If the range of acceptable resource usage cost overlaps with the Pareto frontier, the solution is, for example, to maximize the achievable control performance, as follows.

Number

[0122] As an example, it is appropriate to ensure that, as follows, all the formations 10 undergo a proportional decrease in control performance.

Number

[0123] Therefore, the allowable range c of the resource usage cost com ≦c com,t is not changed, but the allowable range of the control performance value is changed by allowing any control performance value u i ctrl ≧0. Since the allowable range of the control performance value is first changed (decreased), a communication-priority policy is adopted here. When the allowable range of the control performance value is changed, a control-priority selection policy is used under a fair (proportional) control performance degradation constraint.

[0124] The optimization can

Number

Number

[0125] Instead of using a fair (proportional) control performance degradation constraint, it is possible to use, for example, a fairness constraint as follows to use the fair degradation of the formation parameters instead.

Number

[0126] Overall, the problem can be rewritten as follows.

Number

Number

[0127] Some of the equations may not be achievable on the Pareto frontier. From that perspective, it is more preferable to rely on inequality constraints as follows.

Number

[0128] These two above cases reduce N degrees of freedom to one degree of freedom, so they can be considered as max - min problems as follows.

Number

Number

[0129] This method can be further extended when the range of the allowable resource usage cost value does not overlap with the Pareto frontier. In that case, not only is it necessary to operate at a control performance level below the original control performance constraint u i ctrl,t but it is also necessary to operate at a communication cost above the original communication cost constraint c com,t . A general problem can be solved as follows.

Number

[0130] As a possible solution, it can be considered to require all performance values (control and resource usage) to be proportionally reduced. That is,

Number

Number

[0131] Generally speaking, the problem is to find a parameter x such that the performance value satisfies the following constraints (range of acceptable performance values) according to any multi-objective optimization algorithm that gives the solution when there is a unique solution on the Pareto frontier. i ctrl , x i com can be generalized as searching for.

Number

[0132] Therefore, during step S20, the range of acceptable performance values can be obtained by finding the values u i ctrl,t , i = 1...N and c com,t .

[0133] During step S21, the control parameter x i ctrl and the communication parameter x i com are optimized to satisfy the control performance constraints and the resource usage cost constraints according to a given selection policy (communication priority, control priority, fairness or proportional reduction, etc.) as follows.

Number

Number

Number

[0134] If it is not possible to achieve the performance value within the tolerance range (reference numeral S211 in FIG. 2), during step S22, in the updated tolerance range, in order to attempt to find values of the control parameter and the communication parameter that enable achievement of the performance value, as in the following equation, the tolerance range can be updated (by changing at least one tolerance range).

Number

Number

Number

Number

Number

Number

[0135] When a solution is found, the values of the control parameters and communication parameters that enable the achievement of the performance value within the updated tolerance range are, as follows, the performance value u achieved for the selected solution i ctrl,a , i = 1...N and / or c com,a are provided.

Number

[0136] Next, during step S23, the values of the formation parameters and / or network parameters can be updated as follows.

Number

[0137] The above behavior is summarized in FIG. 8.

[0138] {Achievement of a fair solution} From the perspective of the application, solutions that guarantee a proportional decrease of the formation parameters with respect to their prior values provide a certain kind of fairness among all formations 10 and, although not limited thereto, seem to be very suitable. It may be possible to solve this optimization problem as follows using the well-known max-min optimization technique.

Number

Number

Number

[0139] {Separation of Formation Parameters into Two Groups} In some cases, the formation parameter is p, a parameter that is not updated for some reason. i form,2 and p, a function of the effective control performance that can be updated. i form,1 It can be separated into two groups. That is, p i form = [p i form,1 , p i form,2 . p i form,2 should be noted that it may depend on the resulting p i form,1 . This means that

Number

Number

[0140] Function

Number

[0141] {Generalization of the proposed optimization algorithm} Next, some detailed examples of steps S21 and S22 in the framework of the above section {General multi-objective problem} are provided. A first example of a function that can be used to transform a multi-objective optimization problem into a single-objective optimization problem is presented. The allowable performance range of up to N + 1 ranges defined by each performance target value, that is, · Control performance constraint:

Number

[0142] Next, possible examples of the function h i , i = 1...N and the matrix A (refer to the above equation (1)) are shown.

[0143] For example, the constraints (range of allowable performance values) can be expressed as follows.

Number

[0144] For example, for h i the following functions can be considered. · Case 1: Relative objective function with respect to the basic target value (formation and network):

Number

Number

Number

Number

[0145] For example, the following definitions can be considered for matrix A. · Case 1 (α i can be made equal to 1 for any i = 1... N + 1):

Number

Number

Number

[0146] function h i These examples of the function h and the matrix A can be used during step S21 by considering the original range of the acceptable performance values and a given selection policy (communication priority i.e., minimization of resource usage cost, control priority i.e., maximization of control performance, fairness policy, etc.) for selecting a solution (if any) on the Pareto frontier.

[0147] If no solution exists on the Pareto frontier when considering the initial range of the acceptable performance values, it is necessary to relax some of the constraints on the performance values. During step S22, a different function h i and matrix A can be used, and it is also possible to use a different selection policy than during step S21.

[0148] Next, three different examples of step S22 are detailed for different priority policies (priorities regarding the range of acceptable performance values) and different selection policies (selection of a solution on the Pareto frontier).

[0149] {Step S22 with a communication - priority - based priority policy} Even if the matrix A corresponds to case 1 during step S21, consider case 2 or 3 of the matrix A for step S22.

[0150] During step S21, when considering the communication priority selection policy, it should be noted that usually (in order to maximize the resource usage performance), λ = 1 is set. However, in this example, the priority policy is used. This priority policy means that the range of the initial allowable resource usage performance value remains unchanged, and only all or part of the range of the allowable control performance value is changed (decreased). Since the range of the allowable resource usage performance value remains unchanged, in this example, the control priority selection policy is applied, and the control performance is maximized within the changed (decreased) range of the allowable control performance value. Therefore, initially λ = 0 is set, and the changed range of the allowable control performance value can allow any positive value for the control performance.

[0151] If no solution is found by also allowing any positive value for the control performance of the control algorithm of formation 10, the range of the allowable resource usage performance value is also changed (decreased). For example, any resource usage performance value can be further allowed. Then, λ = 1 is set, and the communication priority selection policy is applied to search for a solution on the Pareto frontier that maximizes the resource usage performance (i.e., minimizes the resource usage cost).

[0152] Therefore, in this example, initially the range of the allowable control performance value is decreased, and only when no solution is found by decreasing only the range of the allowable control performance value, the range of the allowable resource usage performance value is then decreased. Therefore, by performing at most two optimization phases, a solution can be found during step S22.

[0153] {Step S22 with the priority policy of control priority} Even if matrix A corresponds to case 1 during step S21, consider case 2 or 3 of matrix A for step S22.

[0154] During step S21, when considering the control priority selection policy, it should be noted that usually (in order to maximize the control performance), λ = 0 is set.

[0155] However, in this example, a priority policy is used. This means that the range of the first allowable control performance value is not changed, and only the range of the allowable resource usage performance value is changed (decreased). Since the range of the allowable control performance value is not changed, in this example, a communication priority selection policy is applied, and the resource usage performance is maximized within the changed (decreased) range of the allowable resource usage performance value. Therefore, first, λ = 1 is set, and the changed range of the allowable resource usage performance value can allow any positive value for the resource usage performance.

[0156] If no solution is found by allowing any value for the resource usage performance of formation 10, the range of the allowable control performance value is also changed (decreased). For example, any control performance value can be further allowed. Then, λ = 0 is set, and a control priority selection policy is applied to search for a solution on the Pareto frontier that maximizes the control performance.

[0157] Therefore, in this example, first the range of the allowable resource usage performance value is decreased, and only when no solution is found by decreasing only the range of the allowable resource usage performance value, the range of the allowable control performance value is then decreased. Therefore, by performing at most two optimization phases, a solution can be found during step S22.

[0158] {Step S22 without priority regarding constraints} Function h i Even when corresponding to case 3 or 4 in step S21, in step S22, it is preferable to consider case 1 or 2 of function h i During step S22, cases 1, 2, or 3 of matrix A are possible. When it is completely fair, that is, when there is no priority regarding constraints, in step S22, it is possible to consider matrix A corresponding to the identity matrix of size N + 1. Therefore, the goal is for all ranges of the allowable performance values, that is, the target value u i ctrl,t , i = 1...N + 1, and c com,tRather, it is to search for a solution that tends to apply the same reduction factor. Given that matrix A is the identity matrix N+1 and function h i Considering Case 1 of i , max min tends to find a solution that maximizes the reduction factor ρ when allowing any performance value of control performance and resource usage performance.

Equation

[0159] Therefore, in this example, all ranges of the allowable performance values are reduced simultaneously. Therefore, the solution can be found during step S22 by performing a single optimization phase.

[0160] Next, optional non-limiting embodiments of some aspects of the present disclosure will be described.

[0161] {Optimization Using a Reference Performance Model} In the optimization stage (steps S21 and S22), it is more preferable to rely on an analytical model that evaluates control performance. The problem is that the control algorithms actually used are generally complex, and thus it is difficult in some cases to obtain an analytical representation of their control performance. The solution is then to rely on a control performance model related to a simple control algorithm and use a different control algorithm for the actual control of formation 10. The goal is, in that case, to be able to handle differences in control algorithms, for example, by applying a margin (e.g., one whose effectiveness has been confirmed from simulations) to control performance constraint u i ctrl,t That is, it is possible to handle differences in control algorithms by applying a margin (for example, one whose effectiveness has been confirmed from simulations) to control performance constraint u.

[0162] {Individual Optimization of Communication Parameters and Control Parameters} As described above, the control performance of the control algorithm for each formation 10 depends on both communication parameters and control parameters according to the following types of relationships.

Equation

[0163] In addition to what has already been introduced, the control performance depends on the communication performance. Therefore, in order to perform optimization, a method for calculating the communication performance function

Number

Number

[0164] Similarly, from the communication performance function and the control parameters, the control performance function u i ctrlThere may also be a need for a method to calculate (·). Similar to the case of the communication performance function, several techniques such as Monte Carlo simulation can be used for that purpose. It is easier to rely on analytical expressions such as the lower bound of the convergence rate of the first-order static consensus algorithm in [Pereira2011]. In this case, due to the homogeneity within the platoon (e.g., the same PDR for all vehicles), the PDR matrix can be inferred from the PDR profile, a given topology in the formation, and a given number of vehicles (and in some cases the correlation matrix from the correlation characteristics), and then a (symmetric) matrix can be constructed and finally its spectral radius can be calculated.

[0165] Another solution is to perform a Monte Carlo simulation of the control algorithm to obtain the relationship between the communication performance profile and the control performance. This makes it possible to consider not only those with an analytical version (e.g., the lower bound regarding the true performance as described above), but also all control algorithms, scenario types, and control performance indicator types (e.g., the probability of an emergency brake).

[0166] {Use of individual transmission models for multiple formations} The proposed technique is applicable to all kinds of formations 10. However, it is easier to implement in the case of a homogeneous formation, that is, a formation organized according to a regular geometric arrangement where all communication nodes 11 can use the same communication parameters and control parameters (except for the leading vehicle which may have different parameters from those of the following vehicles in some cases). In the homogeneous case, the number of parameters to be optimized is reduced (divided approximately by N), and the communication performance can be evaluated depending on a simplified but accurate model.

[0167] When dealing with multiple platoons, the interacting formations 10 are generally homogeneous individually but not homogeneous overall. Clearly, designing a communication model suitable for such a situation is quite complex. Here, a method is proposed that allows the use of a simple homogeneous model even in non-homogeneous situations. Assume that the homogeneous communication model is available as a LUT (which can be obtained from system-level simulations or simplified (quasi-)analytical models).

[0168] Communication performance can be measured in terms of PDR and depends on the distance between the transmitter and the receiver (since it is considered homogeneous, PDR does not depend on the positions of specific transmitters and receivers). It is also possible to consider the correlation between these PDRs, but at the cost of a significant increase in complexity.

[0169] Regarding communication parameters, here, consider that PDR depends at least on coverage and packet rate. Here, coverage is related to transmission power, average path gain power, MCS frame error rate (FER) vs. signal to noise ratio (SNR), and in some cases the reference FER value of the detection threshold, and / or half-duplex errors, corresponding to coverage without shadowing and packet collisions. If the transmission power is constant (e.g., at the maximum possible), coverage is not required. The vehicle spacing or the distance to coverage can be normalized.

[0170] For the non-limiting examples considered here, consider coverage Cov and packet rate PR as basic communication parameters. That is, x com =(Cov,PR). Also, consider PDR (the profile of PDR) as communication performance. That is, the homogeneous model (and thus the homogeneous model of one formation with homogeneous in-formation parameters) is the following function.

Equation

Number

Number

Number

[0171] This PDR should represent a PDR without half-duplex errors. To obtain the final PDR, consider PDR(d; Cov, PR) × (1 - HDER(PR)). Here, HDER(PR) is a function of the half-duplex error rate that does not depend on the distance or coverage communication basic parameters.

[0172] To obtain a heterogeneous model from a homogeneous model, the following assumptions can be made. · When d is the distance normalized by the coverage distance (i.e., divided by the coverage distance), each formation is considered to have the same PDR model PDR(d; Cov Glob , PR Glob ). · Cov Glob and PR Glob are calculated as a given function of the values of x i com =(Cov i , PR i ) for all i, and in some cases the interval Δ i (IVD) (and in some cases those values averaged over additional non-platoon vehicles).

[0173] Here, x i com =(Cov i , PR i ), ∀i = 1...N (the interval Δ i is not considered here) for CovGlob and PR Glob describes two possible ways to obtain it. · Method 1:

Number

Number

[0174] These two methods are linear (the total system load is the sum of the system loads of each formation) and result in the same system load, as shown in the following equation.

Number

[0175] Method 1 is accurate when all formations 10 have the same communication parameters, but overestimates the PDR for very different communication parameters.

[0176] On the other hand, Method 2 is consistent for very different communication parameters (when two clearly different control performances of two platoons of the same size are involved), but is not accurate for homogeneous formations with the same communication parameters.

[0177] {Use of two control performance functions} When there is no simple formula for the control performance of a given control algorithm, another control performance indicator that is considered to be close to the performance of the control algorithm used can be used. In this case, a given control performance indicator can be used for the optimization in steps S21 and S22 (due to computational complexity reasons), and the obtained communication parameters (and possibly control parameters) can be used to use another control performance indicator that is more realistic (for example, this indicator can be calculated by Monte Carlo simulation). Then, this control performance indicator is fixed for u icom , that is, use it with fixed communication parameters to optimize the control parameters related to this control performance indicator, that is, it is possible to maximize the control performance indicator separately for each formation over its own control parameters. The function for updating the formation parameters (step S23) should use this second type of control performance indicator, rather than the indicator used during optimization (steps S21 and S22).

[0178] It should be emphasized that the present disclosure is not limited to the above exemplary embodiments. Variations of the above exemplary embodiments are also within the scope of the present invention.

[0179] For example, the above exemplary embodiments are provided by focusing on a single (global) network parameter. However, as described above, in some cases it is also possible to consider two or more network parameters, for example, one network parameter for each formation 10. The same method described above for handling multiple formation parameters can be similarly applied to the handling of multiple network parameters.

[0180] {References} [Chen2005] Yang Quan Chen and Zhongmin Wang, "Formation control: a review and a new consideration", 2005 IEEE / RSJ International Conference on Intelligent Robots and Systems, 2005, pp. 3181-3186, doi: 10.1109 / IROS.2005.1545539 [Sybis2019] M. Sybis et al., "Communication Aspects of a Modified Cooperative Adaptive Cruise Control Algorithm", in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 12, pp. 4513-4523, Dec. 2019, doi: 10.1109 / TITS.2018.2886883 [Pereira2011] S. Silva Pereira and A. Pages-Zamora, “Consensus in Correlated Random Wireless Sensor Networks”, IEEE Transactions on Signal Processing, vol. 59, no 12, pp. 6279-6284, Dec. 2011, doi: 10.1109 / TSP.2011.2166552 [Zhang2007] Q. Zhang and H. Li, "MOEA / D: A Multiobjective Evolutionary Algorithm Based on Decomposition," in IEEE Transactions on Evolutionary Computation, Vol. 11, no. 6, pp. 712-731, Dec. 2007, doi: 10.1109 / TEVC.2007.892759 [Gonzales2019] Manuel Gonzalez-Martin, Miguel Sepulcre, Rafael Molina-Masegosa and Javier Gozalvez, "Analytical Models of the Performance of C-V2X Mode 4 Vehicular Communications", IEEE Transactions on Vehicular Technology, Vol. 68, Issue 2, Feb. 2019.

Claims

1. A computer-implemented method for configuring values of a set of parameters of at least two moving formations, each said formation comprising a plurality of distinct communication nodes, said set of parameters including at least one pre-parameter, said pre-parameter corresponding to a formation parameter of said formation or a network parameter of said formation, said formation parameter of said formation defining a geometric arrangement of said communication nodes of said formation, said network parameter of said formation defining communication resources allocated to said formation for exchanging data over a wireless link, said pre-parameter having a predetermined value and a predetermined change in the communication environment of said formation being about to occur, said set of parameters for said formation control parameters of a control algorithm used by said formation to maintain said geometric arrangement, and communication parameters constituting said wireless link between said communication nodes of said formation, said wireless link being used for exchanging data related to said control algorithm, further comprising, said computer-implemented method comprising obtaining a range of acceptable performance values based on the value of the corresponding said pre-parameter for said pre-parameter; exploring values of said control parameters and said communication parameters of said formation that enable achievement of said acceptable performance values within said range when considering that a change in the communication environment of said formation has occurred; if achievement of said acceptable performance values within said range is not possible, exploring an updated range of said acceptable performance values for which it is possible to determine values of said control parameters and said communication parameters that enable achievement of said acceptable performance values when considering that a change in the communication environment has occurred; Determining an updated value of the at least one pre-parameter based on values of the control parameter and the communication parameter that enable achievement of the allowable performance value within an updated range of the allowable performance value; A computer-implemented method, including. **Claim 2** The range of the allowable performance value of the formation parameter corresponds to a range of an allowable control performance value of the control algorithm used by the formation, and / or the range of the allowable performance value of the network parameter corresponds to a range of an allowable resource usage performance value for use of communication resources allocated by the formation. The computer-implemented method according to claim 1. **Claim 3** The set of parameters has a plurality of pre-parameters including one formation parameter for each formation and at least one network parameter for all formations. If it is not possible to find values of the control parameter and the communication parameter of the formation that enable achievement of the allowable performance value within the range, exploring the updated range includes reducing the allowable control performance value of at least one of the formations. The computer-implemented method according to claim 2. **Claim 4** Exploring the updated range includes first reducing the allowable control performance value and then, if necessary, reducing the allowable resource usage performance value. The computer-implemented method according to claim 3. **Claim 5** The set of parameters has a plurality of pre-parameters including one formation parameter for each formation and at least one network parameter for all formations, and when it is not possible to find the values of the control parameters and the communication parameters of the formation that enable the achievement of the allowable performance value within the range, exploring the updated range includes reducing the allowable resource usage performance value of the formation, the computer-implemented method according to claim 2.

6. Exploring the updated range includes first reducing the allowable resource usage performance value and then, if necessary, reducing the allowable control performance value, the computer-implemented method according to claim 5.

7. Exploring the updated range by reducing the allowable control performance value of the formation includes using the same reduction factor for all or a plurality of the formations, the computer-implemented method according to any one of claims 3, 4, and 6.

8. The allowable control performance value of the control algorithm represents the allowable convergence speed value of the control algorithm, the computer-implemented method according to any one of claims 2 to 6.

9. The allowable resource usage performance value represents the global interference level generated by all formations when using the allocated communication resources, the computer-implemented method according to any one of claims 2 to 6.

10. The formation parameters of the formation are the distance between communication nodes of the formation, the shape of the geometric arrangement of the formation, the speed of communication nodes of the formation, and represents at least one of them, the computer-implemented method according to any one of claims 1 to 6.

11. The allocated communication resources included in the network parameters are at least one frequency bandwidth, and at least one spreading code or scrambling code, and at least one temporal pattern, and are any one of the above. The computer-implemented method according to any one of claims 1 to 6.

12. Executed by at least one of the communication nodes of the formation or by a configuration server separated from the formation. The computer-implemented method according to any one of claims 1 to 6.

13. When executed by at least one processor, a computer program product including instructions for configuring the at least one processor to execute the computer-implemented method according to any one of claims 1 to 6.

14. A configuration server including at least one processor, at least one memory, and at least one communication module, wherein the at least one processor is configured to execute the computer-implemented method according to any one of claims 1 to 6.

15. The configuration server according to claim 14, which is included in at least one communication node of the formation or is separated from the formation.

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