Method for configuring parameters for formations moving in close proximity and server

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

[0002] In many applications, it is beneficial to construct formations consisting of multiple vehicles. These vehicles must follow the same trajectory as a whole while coordinating to maintain a desired geometric arrangement among the vehicles. Moving in formations offers many advantages over non-cooperative systems, including reduced system costs, increased system robustness and efficiency, and redundancy, reconfigurability, and structural flexibility [Chen2005].

[0003] In particular, platooning is seen as a very promising application where multiple vehicles (for example, in a single lane) are required to travel at a desired speed while maintaining a predetermined distance between them. Whether it's cars, trucks, robots, or airplanes, platooning can bring significant benefits in terms of power consumption, passenger comfort, and traffic flow. Other types of formation applications include satellite clusters, security, search and rescue, and drone swarms for agriculture.

[0004] To maintain the geometric arrangement of a formation, the dynamics of the vehicles in the formation are typically handled using control algorithms that require data exchange between communication nodes built into the vehicles. For example, a platoon usually means that the lead vehicle transmits its acceleration and velocity to the following vehicles, and each vehicle transmits its position and velocity to its neighbors (see, e.g., [Sybis2019]). Then, for example, a consensus algorithm can be used to keep the vehicles equidistant from each other.

[0005] Most formation applications rely on wireless links due to their mobility requirements. Wireless communication is prone to errors not only from physical layer issues (additional noise, path loss, shadowing, fast fading, phase noise, etc.) but also from higher layer issues (collisions caused by hidden node effects in Carrier Sensing Multiple Access (CSMA) schemes or distributed scheduling). Latency, while partly caused by physical layer factors (transmission delay, packet duration, etc.), is mostly caused by higher layer factors (scheduling policies, random access schemes, etc.).

[0006] Since control algorithms rely on data exchange between communication nodes, the loss of data packets clearly has a negative impact on the performance of the control algorithms. Packets can be lost due to a lack of communication resources. Due to the scarcity of radio frequencies, wireless systems are designed to operate with limited bandwidth. Therefore, regardless of the transmission technology in use, communication nodes will only have access to a limited number of communication resources per unit of time (either at the physical level or at the logical level, where operators allocate only a subset of the total available resources to specific applications).

[0007] When a single formation of vehicles is moving while maintaining a predetermined geometric arrangement between vehicles (usually a 1D with a predetermined distance between vehicles and a predetermined speed) 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 rate. The convergence rate of the control algorithm depends, in particular, on the packet loss rate (see, e.g., [Sybis2019]).

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

[0009] Such changes in the communication environment can occur, for example, when different formations approach each other and use the same communication resources to exchange data related to their respective control algorithms. As these formations move within each other's radio coverage, their communication nodes compete for access to the same communication resources, increasing mutual interference and packet loss. An example of formations that may move within radio proximity and compete for the same communication resources is a platoon of vehicles crossing or overtaking each other on a highway. [Overview of the project] [Problems that the invention aims to solve]

[0010] This disclosure aims to improve this situation. In particular, this 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 for at least two formations moving in close proximity to each other. [Means for solving the problem]

[0011] For this purpose, according to a first aspect, the present disclosure relates to a computer implementation method for configuring a set of parameters for at least two moving formations, each formation having a plurality of separate communication nodes, the set of parameters having at least one pre-parameter, each pre-parameter corresponding to a formation parameter or network parameter of each formation, the formation parameter of a formation defining the geometric arrangement of the communication nodes of the formation, the network parameter of a 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 is about to occur. The set of parameters for each formation, • Control parameters of the control algorithm used by the formation to maintain the geometric arrangement, • Communication parameters that constitute a wireless link between communication nodes in a formation, where the wireless link is used to exchange data related to the control algorithm, and It also includes. The above configuration method is • For each pre-parameter, obtain the range of acceptable performance values ​​based on the corresponding pre-parameter value, • When considering changes in the communication environment, the goal is to find the values ​​of the control parameters and communication parameters for the formation that enable the achievement of acceptable performance values ​​within each range. • When it is not possible to achieve the permissible performance values ​​in each range, considering that a change in the communication environment has occurred, the updated range of permissible performance values ​​is searched for, which allows for the determination of control parameter and communication parameter values ​​that enable the achievement of the permissible performance values ​​in each updated range. • Determine the value of at least one updated pre-parameter based on the values ​​of control and communication parameters that enable the achievement of the acceptable performance value within each range of the updated acceptable performance value, Includes.

[0012] Therefore, the proposed configuration method first considers at least one pre-parameter as input. The pre-parameter corresponds to a parameter whose value is 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 may result from the formations being close to each other, entering radio proximity, and potentially competing for the same allocated communication resources. In another example, the expected change in the communication environment may result from, for example, at least one formation experiencing a predictable degradation of the quality of its radio link while moving in radio proximity with at least one other formation. In this disclosure, the value of each pre-parameter is maintained where possible when a change in the communication environment occurs. For example, it is possible to consider pre-parameters corresponding to formation parameters for each formation that describe the geometric arrangement of the formations (relative positions between communication nodes, speed of communication nodes, etc.). In such cases, the value of the formation parameter (geometric arrangement) should be maintained without change where possible. In some cases, it is also possible to consider pre-parameters corresponding to network parameters that describe the amount of communication resources allocated to one or more formations. In such cases, the values ​​of network parameters (the amount of allocated communication resources) should be maintained without modification whenever possible.

[0013] The range of acceptable performance values ​​is determined based on the values ​​of the pre-parameters. Essentially, the acceptable performance values ​​are the values ​​of the performance indicators that allow the pre-parameter values ​​to be maintained. Before any changes occur in the formation's communication environment (for example, before the formations come into close proximity to each other), the performance indicators will have values ​​within their respective acceptable ranges.

[0014] Subsequently, taking into account that a change in the communication environment has occurred (or has occurred shortly after the change in the communication environment has begun), the configuration method searches for values ​​of communication parameters (which constitute the wireless link) and control parameters (which constitute the formation control algorithm) that enable the achievement of acceptable performance values ​​within each range considered. This search can be carried out, for example, by any multi-objective optimization method known to those skilled in the art.

[0015] If such values ​​exist for communication and control parameters, this means that anticipated changes in the communication environment will not prevent the maintenance of the pre-parameter values ​​(e.g., the maintenance of the geometric configuration).

[0016] If such values ​​for communication and control parameters do not exist, this means that anticipated changes in the communication environment will prevent the maintenance of the pre-parameter values ​​(e.g., the maintenance of the geometric configuration).

[0017] In the latter case, the configuration method searches for updated acceptable performance ranges that can determine the values ​​of control and communication parameters that enable the achievement of acceptable performance values ​​in each updated range, taking into account changes in the communication environment. Thus, the ranges are iteratively updated by changing (i.e., reducing) at least one of the ranges until an achievable acceptable performance value is obtained for each updated range. Changing the acceptable performance range means including reduced performance values ​​that were not allowed in the original range into the updated range. Updating the acceptable performance range means allowing any performance value of the performance indicator under consideration.

[0018] Once the achievable permissible performance values ​​are obtained for each updated range, the values ​​of the pre-parameters are updated based on the values ​​of the control and communication parameters that enable the achievement of the permissible performance values ​​in each updated range. Since the constraints on at least one performance indicator (i.e., permissible performance values) have been relaxed (by allowing reduced performance values ​​for at least one range, e.g., by allowing a slower convergence rate for the formation control algorithm), 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 vehicles).

[0019] The updated pre-parameter values ​​can then be used, for example, by communicating the updated pre-parameter values ​​to the formation, to configure the geometric arrangement of the formation and / or the communication resources allocated to the formation, before any changes to the communication environment actually occur (or at least before the changes to the communication environment become maximum). Alternatively, or in combination thereof, the control and communication parameter values ​​that enable the achievement of the updated allowable performance values ​​for each range can similarly 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 formation's communication nodes.

[0020] In a particular embodiment, the configuration method may further include one or more of the following features, either individually or in any technically possible combination:

[0021] In a particular embodiment, the range of acceptable performance values ​​for the formation parameters corresponds to the range of acceptable control performance values ​​for the control algorithm used by the formation, and / or the range of acceptable performance values ​​for the network parameters corresponds to the range of acceptable resource usage performance values ​​for the use of communication resources allocated by the formation.

[0022] In a particular embodiment, the parameter set has a plurality of pre-parameters, including formation parameters for each formation and at least one network parameter for all formations, and if it is not possible to find values ​​for the control parameters and communication parameters of a formation that enable the achievement of the acceptable performance value for each range, searching for an updated range includes reducing the acceptable control performance value for at least one formation.

[0023] In certain embodiments, exploring the updated range involves first reducing the allowable control performance value, and then, if necessary (i.e., if reducing only the allowable control performance value is insufficient), reducing the allowable resource utilization performance value.

[0024] In a particular embodiment, the parameter set has a plurality of pre-parameters, including the formation parameters for each formation and at least one network parameter for all formations, and if it is not possible to find values ​​for the control parameters and communication parameters of a formation that enable the achievement of the acceptable performance value for each range, searching for an 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 allowable resource usage performance value, and then, if necessary, reducing the allowable control performance value.

[0026] In a particular embodiment, exploring the updated range by reducing the allowable control performance value of a formation includes using the same reduction coefficient for all or more of the formations.

[0027] In a particular embodiment, the allowable control performance value of the control algorithm represents the allowable convergence speed value of the control algorithm.

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

[0029] In a particular embodiment, the formation parameters for each formation are as follows: • Distance between communication nodes in the formation • The geometric arrangement of the formation, • Speed ​​of the formation's communication nodes, It represents at least one of the following.

[0030] In a particular embodiment, the allocated communication resources defined by the network parameters are the following: • At least one frequency bandwidth, • At least one spreading code or scrambled code, • At least one temporal pattern, It is one of the following.

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

[0032] In a particular embodiment, the communication parameters of the formation are as follows: Modulation method, Channel coding scheme, • Multi-antenna system, • Transmission power, • Coverage distance, Packet rate, It represents at least one of the following.

[0033] In a particular embodiment, the configuration method is either included in at least one communication node of the formation or performed by a configuration server separate from the formation.

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

[0035] In a third aspect, the 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 embodiment of the disclosure.

[0036] In certain embodiments, the configuration server is either 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 as an example and is not limited to the present invention, and is drawn with respect to the figures.

[0038] In these figures, the same reference numerals across the figures indicate the same or similar elements. For clarity, the elements shown are not at a uniform scale unless otherwise explicitly specified.

[0039] Furthermore, the order of steps shown in these diagrams is provided for illustrative purposes only and is not intended to limit this disclosure, and this disclosure may also apply when the same steps are performed in a different order. [Brief explanation of the drawing]

[0040] [Figure 1A]This is a schematic diagram of two formations corresponding to separate platoons of vehicles. [Figure 1B] This is a schematic diagram of two formations corresponding to separate platoons of vehicles. [Figure 2] This diagram illustrates the main steps in configuring the set of parameters for a formation. [Figure 3] This is a schematic diagram of the server configuration. [Figure 4] This figure shows an example of a Pareto frontier in a multi-objective optimization problem. [Figure 5A] This figure shows examples of various selection policies for choosing points on the Pareto frontier. [Figure 5B] This figure shows examples of various selection policies for choosing points on the Pareto frontier. [Figure 6] This plot illustrates an example where the control performance constraints are excessively strict. [Figure 7] This figure illustrates an example where both the control performance constraints and the resource usage performance constraints are excessively strict. [Figure 8] This figure summarizes the behavior of an example embodiment of the configuration method shown in Figure 2. [Modes for carrying out the invention]

[0041] As described above, this disclosure relates to the interaction of at least two mobile formations that share at least allocated communication resources common to the two mobile formations for a certain period of time. In particular, changes in the communication environment of these formations are expected to occur.

[0042] For example, an expected change in the communication environment would result in formations moving closer to each other and being within radio proximity (e.g., at least one communication node of one formation being within the radio coverage of at least one communication node of the other formation), leading to competition for common allocated communication resources. In such a case, the formations would initially not be competing for common allocated communication resources (because they are not within radio proximity to each other), but as they move closer to each other, they would soon be within radio proximity and begin competing for the same common allocated communication resources.

[0043] Another example suggests that anticipated changes in the communication environment could result from, for instance, at least one formation experiencing a predictable degradation in the quality of its wireless link while moving in wireless proximity to at least one other formation.

[0044] In the following, we will consider, in a non-limiting manner, that changes in the communication environment correspond to formations moving closer to each other, existing within wireless proximity, and competing for commonly allocated communication resources.

[0045] Figures 1A and 1B schematically illustrate examples of changes 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, which is configured to exchange data with other communication nodes 11 in formation 10-1 regarding the control algorithm used by formation 10-1 to maintain the geometric arrangement of formation 10-1 (e.g., relative position and velocity of the communication nodes). Similarly, each vehicle in formation 10-2 is also equipped with a communication node 11, which is configured to exchange data with other communication nodes 11 in formation 10-2 regarding the control algorithm used by formation 10-2 to maintain the geometric arrangement of formation 10-2.

[0046] In the following, formations are referred to collectively as 10 when they do not need to be distinguished, and individually as 10-i when they do need to be distinguished. Formation 10-i corresponds to formation 10 at index i, where 1 ≤ i ≤ N, and N corresponds to the number of adjacent formations. The following mainly focuses on the case where N = 2 (i.e., 2 formations). However, this disclosure can also be applied to more than 2 formations 10.

[0047] In Figures 1A and 1B, the radio coverage of formation 10-1 is indicated by RC-1, and the radio coverage of formation 10-2 is indicated by RC-2. The radio coverage of formation 10 corresponds to the area where messages transmitted at a given packet delivery rate (PDR) by at least one communication node 11 of the formation can be received and decoded. In Figure 1A, formations 10-1 and 10-2 are approaching each other, but initially they are spaced apart so that their respective radio coverages RC-1 and RC-2 do not overlap. Therefore, communication nodes 11 from different formations 10 do not compete for a common allocated communication resource. In Figure 1B, formations 10-1 and 10-2 are close enough that their respective radio coverages RC-1 and RC-2 overlap. Therefore, communication nodes 11 from different formations compete for common, allocated communication resources, and thus, for example, each formation 10 experiences an increase in packet collision levels.

[0048] As described above, each formation 10 uses a control algorithm to control 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 formation 10 exchange data so that the cooperative control algorithm can control the geometric arrangement. In the case of a vehicle platoon, the control algorithm is used to maintain a predetermined geometric arrangement between the vehicles while they are moving (usually a 1D having a predetermined distance and a predetermined speed between the vehicles). It is important that the control algorithm can actually control the geometric arrangement with a convergence speed sufficient to avoid collisions between vehicles, for example. The convergence speed of the control algorithm also depends, in particular, on packet loss (see, e.g., [Sybis2019]). Therefore, as shown in Figure 1B, when the wireless coverage RC-1 of formation 10-1 and the wireless coverage RC-2 of formation 10-2 overlap, the convergence speed of the control algorithm may become insufficient due to the increase in the level of mutual interference (here, the level of mutual interference of formations corresponds to the level of interference generated by other formations).

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

[0050] Some of the parameters in a set are called "pre-parameters," and their values, where possible, correspond to parameters whose values ​​should be maintained during changes to the communication environment. A set contains at least one pre-parameter.

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

[0052] A first possible type of preparameter is a formation parameter that describes the geometric arrangement of formation 10. In this disclosure, any formation parameter suitable for describing the geometric arrangement of formation 10 can be used. For example, the formation parameter of formation 10 may include at least one of the following characteristics of the geometric arrangement of formation 10. • The distance between communication nodes 11 in formation 10 (vehicle-to-vehicle distance, i.e., IVD), • The geometric shape of Formation 10 (e.g., linear, rectangular, etc.) • The speed of the communication node 11 in 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 formation 10. When the pre-parameters include formation parameters, this means that the geometric arrangement of formation 10 should remain unchanged during changes in the communication environment, where possible.

[0054] A second possible type of preparameter is a network parameter that describes the communication resources allocated to the formation for exchanging data 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 parameter can use any format suitable for describing the communication resources allocated to the formation. For example, the allocated communication resources defined in the network parameter may be any of the following: • At least one frequency bandwidth (e.g., a frequency channel or one or more subchannels, one or more subcarriers in an orthogonal frequency division multiple access (OFDMA) system, etc.) • At least one spreading code or scrambled code (i.e., a code in a code division multiple access (CDMA) system), • At least one temporal pattern (e.g., one or more slots).

[0055] For example, it is possible to predefine a pool of communication resources available for allocation to formation 10 (also known as a resource pool in the case of a 5G cellular network), and the network parameters of a formation define a subset of the pool to which that formation is initially allocated before a change in the communication environment occurs. It is assumed that at least a portion of the allocated communication resources are common to two or more formations 10, i.e., allocated to two or more formations 10 simultaneously. In this 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 are 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 (which may hereafter be referred to as "global") 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 remain unchanged during changes to the communication environment, where possible.

[0056] In the following, a single (global) network parameter is used, and therefore, we consider it unrestricted that at least one prior parameter contains at most one (global) network parameter.

[0057] It should be noted that in this disclosure, the term “parameter” can refer to either a single fundamental (scalar) parameter or a vector or matrix parameter that includes multiple fundamental parameters. In other words, a parameter includes one or more fundamental parameters, which may be of different types. For example, a formation parameter may include both IVD and velocity. For example, a network parameter may include both one or more frequency bandwidths and one or more temporal patterns.

[0058] The values ​​of the other parameters in the set (i.e., parameters other than the pre-parameters) are first reconfigured when adapting to changes in the communication environment. These other parameters correspond to parameters that affect the convergence speed of the control algorithm used by Formation 10, and include the following for each Formation 10: • Control parameters of the control algorithm used by formation 10 to control the geometric arrangement, • Communication parameters for configuring a wireless link between communication nodes 11 of formation 10, used for exchanging data related to the control algorithm.

[0059] For example, in a control algorithm such as a consensus algorithm, each communication node 11 uses control parameters corresponding to one or more weight coefficients used to weight the data received from neighboring communication nodes 11. Such weight coefficients affect the convergence speed of the control algorithm and can be adjusted to adapt to changes in the communication environment. For example, a continuous-time consensus algorithm can be summarized as follows:

number

[0060] Similarly, the configuration of wireless links is related to their communication performance, i.e., the set of parameters (e.g., PDR) on these wireless links. i This affects the characteristics of (t) and thus the convergence speed of the control algorithm. Therefore, by changing the values ​​of the communication parameters, the configuration of the wireless link can be adjusted to adapt to changes in the communication environment. Non-limiting examples of basic communication parameters include the following: • Modulation scheme (e.g., BPSK, QPSK, 16QAM, etc.) • Channel coding scheme (e.g., channel coder type, channel coder rate, etc.; modulation scheme and channel coding scheme are usually collectively referred to as MCS), • Multi-antenna system (e.g., number of layers, number of antennas, etc.) • Transmission power, • Coverage distance, • Packet rate (e.g., number of packets per second), • Maximum number of retransmissions, etc.

[0061] Figure 2 schematically illustrates the main steps of configuration method 20, which configures a set of formation parameters in response to expected changes in the communication environment.

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

[0063] Figure 3 schematically shows one exemplary embodiment of a configuration server 30 suitable for executing configuration method 20.

[0064] As shown in Figure 3, the configuration server 30 comprises one or more processors 31 and one or more memories 32. The one or more processors 31 may 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), etc. The one or more memories 32 may include any type of computer-readable volatile memory and computer-readable non-volatile memory (magnetic hard disk, solid disk, optical disk, electronic memory, etc.). The one or more memories 32 may store a computer program product in the form of a set of program code instructions executed by one or more processors 31 to carry out all or part of the steps of the configuration method 20.

[0065] As shown in Figure 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, if data is exchanged directly with the formation 10, the communication module 33 implements at least one wireless communication protocol. If data is exchanged indirectly with the formation 10, i.e., through 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 devices. It should be noted that the configuration server 30 can be contained in a single hardware device or, in a distributed computing architecture, in multiple separate hardware devices.

[0067] In some cases, the configuration server 30 can, as an alternative, 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 control algorithms. If the communication module 33 implements the same wireless communication protocol used by the communication nodes 11, the communication module 33 can also be used by the communication nodes 11 to exchange data related to control algorithms with other communication nodes 11 (i.e., the communication device 11 may consist of a single communication module). The configuration server 30 is also 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 necessary for the execution of the configuration method 20. For example, the received data may include the current values ​​of pre-parameters (before any changes in the communication environment occur), the position and speed of the formation 10 (for example, to determine when changes in the communication environment 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 exchange data with any other formation, directly or indirectly, temporarily or permanently. Depending on the nature of the inter-formation communication, the formation 10 can more or less anticipate future changes in the communication environment in advance. The data sent from the configuration server 30 to the formation includes new values ​​for pre-parameters if the pre-parameters cannot be left unchanged. The data sent from the configuration server 30 to the formation may also include new values ​​for control parameters and / or new values ​​for communication parameters.

[0069] As shown in Figure 2, the configuration method 20 includes step S20 for each pre-parameter, which determines the range of acceptable performance values ​​based on the corresponding pre-parameter value.

[0070] Essentially, the acceptable performance value is a performance indicator value that allows the pre-configured parameter values ​​set before the communication environment change to be maintained during the communication environment change.

[0071] For example, the performance indicator of the formation parameters of formation 10 can be any control performance indicator that represents the performance of the control algorithm used by formation 10. In a preferred embodiment, the control performance indicator represents the convergence speed of the 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 formation 10 is 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 represented 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 each of those 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 each of those control performance indicators u ictrl,t It can be defined by the function θ. To find the range of allowable control performance values, i form The following can be predetermined:

number

[0075] function θ i form We assume that it changes monotonically.

[0076] For example, the function θ i form This can provide the slowest convergence speed that theoretically guarantees the geometric arrangement will be controlled fast enough to prevent collisions between adjacent vehicles in the formation. For example, the convergence speed is such that, given the vehicle velocities, the probability of IVD becoming zero is a predetermined threshold (e.g., 10). -6 It can be made to be lower than the following:

[0077] Furthermore, the value u related to control performance i ctrl,a Given, the formation parameter p i form pcp i form,a A function to find

number

number

[0078] function

number

[0079] If the pre-parameters include network parameters, the network parameter p before the change in the communication environment will be used. net The value of p net,t This is how it is written.

[0080] Regarding (global) network parameters, the range of acceptable resource usage performance values ​​is the resource usage cost c com (For example, upper limit c regarding CBR) com,t It can be defined by the function θ. To find the range of allowable control performance values, net This can be determined in advance as follows.

number

[0081] Furthermore, there is a value c related to resource usage costs. com,a Given the network parameter p net pcp net,a A function to find

number

number

[0082] Therefore, step S20, which obtains the range of acceptable performance values ​​for each pre-parameter, • Function θ i form Use each formation parameter p i form Control performance u i ctrl Lower limit u i ctrl,t The formation parameter p i form Prior value p i form,t To seek based on; and / or • Function θ netUse the network parameter p net resource usage cost c com Upper limit c com,t The network parameter p net Prior value p net,t To seek based on, It can be done this way.

[0083] As shown in Figure 2, the configuration method 20 includes step S21 of searching for control parameter and communication parameter values ​​for the formation 10 that enable the achievement of acceptable performance values ​​in each range when considering changes in the communication environment.

[0084] As mentioned 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 a matrix whose elements are the PDR values ​​of various links between agents and at least one control parameter [Pereira2011] (see also European Patent Application No. 21305452.1).

[0085] For each formation 10, a communication parameter x can be configured to adjust the communication performance on the wireless link. i com (MCS, transmit power, packet rate, etc.) will be used to indicate this. All communication nodes 11 in the same formation 10 have the communication parameter x i com We make the non-restrictive assumption that the same value is used. For each formation 10, the control parameter x can be configured to adjust the control performance of the control algorithm. i ctrl I will demonstrate it using this method.

[0086] Therefore, for each formation 10, x i com and / or x i ctrl The value of is the prior value of each prior parameter (i.e., the formation parameter p i form pcpi form,t and / or network parameter p net pcp net,t ) can be optimized to maintain without changing. The prior values ​​of each prior parameter can be kept constant if it is possible to achieve the performance value within the respective range of the allowable performance values, so 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 true: · u i ctrl ≧u i ctrl,t In other words, the values ​​of the control parameters and communication parameters are such that the control performance values ​​can be achieved within an acceptable range. · c com ≤c com,t (i.e. -c com ≥ -c com,t , here, -c com (This corresponds to resource utilization performance), that is, the values ​​of the control parameters and communication parameters enable the achievement of resource utilization performance values ​​within an acceptable range.

[0087] As shown in Figure 2, it is possible to achieve the acceptable performance values ​​for each range without changing the prior values ​​of each prior parameter x i com value

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

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[0089] Control parameter x i ctrl and communication parameter x i com If such a value is found, the configuration method 20 includes step S23, which updates the range of the acceptable performance values ​​to the values ​​of each pre-parameter that was changed during step S22. In this disclosure, all ranges obtained after step S22 are referred to as the “updated range,” but it should be noted that the updated range is not necessarily changed in its entirety relative to the original range, and in some cases it may be sufficient to change only one range of the acceptable performance values ​​or one type of range of the acceptable performance values ​​(for example, only the range corresponding to the control performance value or only the range corresponding to the resource usage performance value).

[0090] In step S23, the values ​​of the control parameters that enabled the achievement of the allowable performance values ​​for each updated range are determined.

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[0091] In Figure 2, the configuration method 20 includes step S24, in which the configuration server 30 configures the formation 10 using the obtained values ​​by distributing the obtained values ​​for the set of parameters to the formation 10. This configuration step S24 is performed before the change in the communication environment begins, or at least before this change reaches its maximum.

[0092] Next, we will show detailed examples of the different steps of configuration method 20. While configuration method 20 is preferably used to configure a set of parameters for two or more formations 10 moving in close proximity, we will first provide an explanation assuming a single formation 10, which is more convenient for introducing some concepts and notation.

[0093] {In the case of a single formation} Control parameter x i ctrl and communication parameter x i com Searching for the value of the control performance u is done using the following formula. i ctrl To maximize resource usage cost c com Minimize resource usage performance -c com (To maximize) the control parameter x i ctrl and communication parameter x i com This can be seen as optimizing something.

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[0094] However, two performance indicators u i ctrl (x i ctrl , x i com ) and c com (x i com These factors are interdependent (for example, allocating more communication resources almost always improves control performance), and as mentioned above, there is no single optimal solution to this problem.

[0095] Therefore, we are dealing with multi-objective optimization here. In the case of multi-objective optimization, one performance indicator (here u) can be improved by changing one variable. i ctrl or -c com Improving ) inevitably degrades all other performance indicators (u i ctrl -c com It is common to look for the Pareto frontier (or Pareto front) that corresponds to ) (see Figure 4).

[0096] Communication parameter x i com This may exist on support that is limited (e.g., maximum transmit power) and / or discrete (e.g., MCS, packet rate). The same applies to control parameter x. i ctrl This also applies to optimization, which is a so-called decision (variable) space.

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[0097] Therefore, when dealing with multi-objective optimization, if there is a single "optimal" point on the Pareto frontier, it is necessary to define the criteria for selecting this optimal point. Below, we will refer to the criteria for selecting a point on the Pareto frontier as the "selection policy."

[0098] For example, the selection policy is as follows: resource usage cost c com This can be described as searching for a solution that minimizes [the specified value].

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[0099] Control performance u i ctrl Resource usage cost c under the equality constraint on com It is important to note that minimizing does not necessarily guarantee a solution on the Pareto frontier. Therefore, it is preferable to adhere to the inequality. In the above equation, optimization aims to minimize resource usage costs while ensuring acceptable control performance. In other words, this selection policy prioritizes minimizing resource usage costs (hereinafter referred to as the "communication priority selection policy"). This method is shown in Figure 5A.

[0100] Another approach is to prioritize maximizing control performance while ensuring an acceptable resource usage cost (hereinafter referred to as the "control priority selection policy").

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

[0102] First, we 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:

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

[0104] For example, as shown in Figure 6, a change in the communication environment (causing a transmission failure) imposes constraints. i ctrl ≧u i ctrl,t There are cases where this cannot be achieved (reference numeral S211 in Figure 2). In such cases, one thing that can be done is to allow the prior values ​​of the formation parameters to be changed. This is done as follows, under the resource usage cost constraint c com,tThis can be achieved by maximizing control performance without changing anything.

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[0105] This essentially corresponds to updating the range of allowable control performance values ​​by enabling any control performance value given the communication cost constraint. Subsequently, the values ​​of the formation parameters are set within the updated range c. com ≤c com,t (not changed during step S22) and u i ctrl In the case of >0 (changed during step S22), the following values ​​made it possible to achieve the acceptable performance value.

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[0106] Figure 7 shows the resource usage cost constraint c. com,t This shows another example where no solution exists because the constraints on control performance u i ctrl,t Simply easing the constraints on resource usage costs is not enough. The only solution is to reduce the constraints on resource usage costs. com,t This also involves mitigating the constraints. When dealing with communication priority selection policies, the solution is to then reduce the resource usage cost constraints as well, so that the first non-zero control performance value is reached, as follows:

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

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[0108] More generally, when it is not possible to find control and communication parameter values ​​that enable the achievement of performance values ​​within the original range of allowable performance values, the following four main situations are possible: • Control performance constraints u i ctrl,t This cannot be achieved, but resource usage cost constraint c com,t It can be achieved. • Resource usage cost constraint c com,t This cannot be achieved, but control performance constraints u i ctrl,t It can be achieved. • Resource usage cost constraint c com,t and control performance constraints u i ctrl,t None of these can be achieved. • Resource usage cost constraint c com,t and control performance constraints ui ctrl,t It is possible to achieve both, but not simultaneously.

[0109] Therefore, during step S22, control performance constraints u are imposed according to predetermined criteria. i ctrl,t Reduction or resource usage cost constraints c com,t An increase in either or both of these is required. Below, the criteria applied to selectively reduce the range of acceptable performance values ​​are referred to as “priority policies.” For example, in the case of a communication-priority policy, the range of acceptable control performance values ​​is changed (reduced) first. In the case of a control-priority policy, the range of acceptable resource utilization performance values ​​is changed (reduced) first.

[0110] This can be generalized as follows:

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[0111] {General multi-purpose problem} A general multi-objective problem can be expressed by the following equation:

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[0112] Since F(x) is a vector of 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, we must define what is meant by max(F(x)), and we will examine the Pareto meaning of this maximum value. If we consider a certain special vector as the maximum value, the maximum value of such a vector function is one for which it is not possible to increase one of the components without decreasing another component. In this sense, generally, there are many solutions. That is, the set of such solutions

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

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

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[0115] ​​​​​​​​​​​​​​​​​​​​​​​​​​We will consider the following general method. This method encompasses all optimization methods based on the following max-min optimization method.

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[0117] The function min is, here, a vector function of dimension m' ≤ m (where m ≥ N and N is the number of formations).

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[0118] {In the case of multiple formations} Even with multiple formations 10, we face a multi-objective optimization problem. When a communication priority selection policy is considered, a typical method is to calculate the resource usage cost c of all formations 10 under the control performance inequality constraint of each formation. com The goal is to minimize it.

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[0119] The optimization described above focuses on minimizing resource usage costs. As with a single formation, it may be more appropriate to focus on maximizing control performance instead, for example, as follows:

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

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

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

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[0123] Therefore, the acceptable range of resource usage costs is c com ≤c com,t The value itself remains unchanged, but the tolerance range for control performance values ​​is the same for any control performance value u i ctrl This is changed by allowing ≥0. Since the tolerance range for the control performance value is changed (reduced) first, the communication priority policy is adopted here. Once the tolerance range for the control performance value is changed, the control priority selection policy is used under the fair (proportional) control performance degradation constraint.

[0124] Optimization is

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[0125] Instead of using a fair (proportional) control performance degradation constraint, it is possible to use a fair degradation of the formation parameter by using a fair constraint such as the following:

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

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[0127] Some of the equations may not be attainable on the Pareto frontier. From this perspective, it is preferable to rely on the following inequality constraints.

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

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[0129] This method can be further extended when the range of acceptable resource usage cost values ​​does not overlap with the Pareto frontier. In that case, the original control performance constraint u i ctrl,t Not only is it required to operate at a level of control performance below the original communication cost constraint c com,t It is also necessary to operate at communication costs exceeding [a certain threshold]. The general problem can be solved as follows:

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[0130] A possible solution would be to request a proportional reduction in all performance metrics (control and resource usage). That is,

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

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

[0133] During step S21, the control parameter x i ctrl and communication parameter x i com The system is optimized to satisfy control performance constraints and resource usage cost constraints according to a given selection policy (e.g., communication priority, control priority, fairness, i.e., proportional reduction).

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

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[0135] Once a solution is found, the values ​​of the control and communication parameters that enabled 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 To provide.

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

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

[0138] {Achieving a fair solution} From an application perspective, a solution that guarantees the formation parameters decrease proportionally to their prior values ​​seems very suitable, although it is not limited to this, as it provides a certain degree of fairness among all formations 10. This optimization problem can sometimes be solved using well-known max-min optimization techniques, as follows:

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[0139] {Separation of formation parameters into two groups} In some cases, the formation parameter is a parameter that is not updated for several reasons. i form,2 And p is a function of effective control performance that can be updated. i form,1 It can be separated into two groups, namely, p i form =[p i form,1 ,p i form,2 ] is p i form,2 The resulting p i form,1 It should be noted that this may depend on [something].

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[0140] function

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[0141] {Generalization of the proposed optimization algorithm} Next, we provide some detailed examples of steps S21 and S22 within the framework of the above section {General Multi-Objective Problems}. We present a first example of a function that can be used to transform a multi-objective optimization problem into a single-objective optimization problem. The acceptable performance ranges are defined by each performance target value, with a maximum of N+1 ranges, i.e., • Control performance constraints:

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[0142] Next, the function h can be used between steps S21 and S22. i Possible examples of i=1...N and matrix A (see formula (1) above) are shown.

[0143] For example, constraints (range of acceptable performance values) can be expressed as follows:

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[0144] For example, h i Regarding this, we can consider the following function. Case 1: Relative objective function (formation and network) relative to the basic target value:

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[0145] For example, we can consider the following definition for matrix A. Case 1 (α) i (This can be considered equal to 1 for any i = 1...N+1).

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

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

[0148] Next, we will detail three different examples of step S22 with different priority policies (priority regarding the range of acceptable performance values) and different selection policies (selection of solutions on the Pareto frontier).

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

[0150] During step S21, when considering the communication priority selection policy, it should be noted that λ=1 is usually set (to maximize resource utilization performance). However, in this example, a priority policy is used. This priority policy means that the initial range of allowable resource utilization performance values ​​is not changed, and only all or part of the range of allowable control performance values ​​is changed (reduced). Since the range of allowable resource utilization performance values ​​is not changed, in this example, the control priority selection policy is applied, and control performance is maximized within the changed (reduced) range of allowable control performance values. Therefore, λ=0 is initially set, and the changed range of allowable control performance values ​​can allow any positive value for control performance.

[0151] If a solution cannot be found even by allowing any positive value for the control performance of the control algorithm in formation 10, the range of acceptable resource usage performance values ​​is also changed (reduced). For example, any further arbitrary resource usage performance values ​​can be allowed. Then, by setting λ=1 and applying the communication priority selection policy, a solution on the Pareto frontier that maximizes resource usage performance (i.e., minimizes resource usage cost) can be searched for.

[0152] Therefore, in this example, the range of the allowable control performance value is reduced first, and only if a solution cannot be found by reducing only the range of the allowable control performance value is the range of the allowable resource usage performance value reduced next. Thus, a solution can be found during step S22 by performing at most two optimization phases.

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

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

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

[0156] If a solution cannot be found even by allowing arbitrary values ​​for resource usage performance in formation 10, the range of acceptable control performance values ​​is also changed (reduced). For example, any further arbitrary control performance values ​​can be allowed. Then, λ=0 can be set and the control priority selection policy can be applied to search for a solution on the Pareto frontier that maximizes control performance.

[0157] Therefore, in this example, the range of acceptable resource usage performance values ​​is first reduced, and only if a solution cannot be found by reducing only the range of acceptable resource usage performance values ​​is the range of acceptable control performance values ​​then reduced. Thus, a solution can be found during step S22 by performing at most two optimization phases.

[0158] {Step S22 without priority regarding constraints} function h i However, even if step S21 corresponds to case 3 or 4, in step S22, function h i It is preferable to consider case 1 or 2. During step S22, cases 1, 2, or 3 of matrix A are possible. If it is perfectly fair, i.e., there is no priority regarding constraints, in step S22 it is possible to consider matrix A corresponding to an identity matrix of size N+1. Thus the target is over the entire range of acceptable performance values, i.e., the target value u i ctrl,t i=1...N+1, and c com,tThe objective is to search for solutions that tend to apply the same reduction coefficient to the given matrix A, and the function h i Considering Case 1, max min maximizes the degrading coefficient ρ when any performance value of control performance and resource utilization performance is allowed.

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[0159] Therefore, in this example, the entire range of acceptable performance values ​​is reduced simultaneously. Thus, the solution can be found during step S22 by performing a single optimization phase.

[0160] Next, we will describe some optional, non-limiting embodiments of several aspects of the present disclosure.

[0161] {Optimization using a benchmark performance model} In the optimization stage (steps S21 and S22), it is preferable to rely on an analytical model to evaluate control performance. The problem is that the control algorithms actually used are generally complex, and therefore, in some cases, it is difficult to obtain an analytical representation of their control performance. The solution is to then rely on a control performance model associated with a simpler control algorithm, and to use a different control algorithm for the actual control of formation 10. The goal in that case is, for example, to apply control performance constraints u i ctrl,t The difference in control algorithms can be addressed by applying a margin (for example, one whose effectiveness has been confirmed through simulation).

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

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[0163] In addition to what has already been mentioned, control performance depends on communication performance. Therefore, to perform optimization, the communication performance function is derived from the communication parameters of all communication nodes 11.

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[0164] Similarly, the control performance function u can be derived from the communication performance function and control parameters. i ctrlA method for calculating (·) may also be needed. Similar to the case of the communication performance function, several methods such as Monte Carlo simulation can be used for this purpose. It is easier to rely on analytical expressions such as the lower bound of the convergence speed of the first-order static consensus algorithm in [Pereira2011]. In this case, due to 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 optionally a correlation matrix from correlation characteristics), and then the (symmetric) matrix can be constructed to finally calculate its spectral radius.

[0165] Another solution is to perform Monte Carlo simulations of the control algorithms to obtain the relationship between the communication performance profile and the control performance. This makes it possible to consider all control algorithms, scenario types, and control performance indicator types (e.g., the probability of emergency braking), as well as those with analysis versions (e.g., lower bounds on true performance as described above).

[0166] {Using separate transmission models for multiple formations} The proposed technique is applicable to all types of formations 10. However, it is easier to implement in the case of homogeneous formations, i.e., formations organized according to a regular geometric arrangement in which all communication nodes 11 can use the same communication and control parameters (except for the lead vehicle, which may have different parameters from those of the following vehicles). In the homogeneous case, the number of parameters to be optimized is reduced (by approximately N), and communication performance can be evaluated by relying on a simplified but accurate model.

[0167] When dealing with multiple platoons, the interacting formations 10 are generally homogeneous individually but not as a whole. Designing a communication model that fits such a situation is obviously quite complex. Here, we propose a method that allows the use of a simple homogeneous model even in a non-homogeneous situation. We assume that a homogeneous communication model is available as a LUT (which can be obtained from a system-level simulation or a simplified (quasi)analysis model).

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

[0169] Regarding communication parameters, it is considered here that PDR depends at least on coverage and packet rate. Here, coverage relates to transmit power, mean path gain power, MCS frame error rate (FER) vs. signal-to-noise ratio (SNR), and possibly a reference FER value of the detection threshold, and / or half-duplex error, corresponding to coverage without shadowing and packet collisions. If the transmit power is constant (e.g., at maximum possible), coverage is not required. Vehicle spacing or distance to coverage can be normalized.

[0170] For the non-restrictive examples considered here, coverage Cov and packet rate PR are considered as basic communication parameters. That is, x com =(Cov,PR). Furthermore, PDR (PDR profile) is considered as a communication performance factor. That is, the homogeneous model (and therefore the homogeneous model of a formation with homogeneous intra-formation parameters) is the following function:

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[0171] This PDR should represent a PDR without half-duplex errors. To obtain the final PDR, consider PDR(d;Cov,PR)×(1-HDER(PR)), where HDER(PR) is a function of the half-duplex error rate, independent of distance or basic communication parameters.

[0172] To obtain a heterogeneous model from a homogeneous model, the following assumptions can be made. When d is a distance normalized to the coverage distance (i.e., divided by the coverage distance), each formation is the same PDR model PDR(d;Cov Glob PR Glob ) to be considered to have · Cov Glob and PR Glob For all i, x i com =( Cov i PR i The value of ) and, in some cases, the interval Δ of each formation 10 i It is calculated as a given function of (IVD) (and, in some cases, those values ​​averaged to additional non-platoon vehicles).

[0173] Here, x i com =( Cov i PR i ), ∀i=1...N(interval Δ i (This is not considered here) from CovGlob and PR Glob Two possible ways to obtain it are described. · Method 1:

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

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

[0176] On the other hand, Method 2 is consistent for very different communication parameters (as in the case of two platoons of the same size with clearly different control capabilities), but is not accurate for homogeneous, identical formations (i.e., identical 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 being used can be used. In this case, the given control performance indicator can be used for optimization in steps S21 and S22 (due to computational complexity), and another more realistic control performance indicator (for example, this indicator can be calculated by Monte Carlo simulation) can be used with the acquired communication parameters (and optionally control parameters). Subsequently, this control performance indicator is fixed u icom That is, it is possible to optimize the control parameters associated with this control performance indicator by using it in conjunction with fixed communication parameters, i.e., to maximize the control performance indicator separately for each formation over its own control parameters. The function that updates 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 this disclosure is not limited to the exemplary embodiments described above. Variations of the exemplary embodiments described above are also within the scope of the present invention.

[0179] For example, the exemplary embodiment described above is provided by focusing on a single (global) network parameter. However, as mentioned 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 techniques described above for handling multiple formation parameters can be similarly applied to handling 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 a set of parameters for at least two moving formations, each of which comprises a plurality of separate communication nodes, the set of parameters comprising at least one pre-parameter, the pre-parameter corresponding to a formation parameter 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, the pre-parameter having a predetermined value, and a predetermined change in the communication environment of the formation is about to occur, the set of parameters for the formation, Control parameters of the control algorithm used by the formation to maintain the geometric arrangement, The wireless link between the communication nodes of the formation, which is used for exchanging data related to the control algorithm, and which comprises communication parameters constituting the wireless link, It further includes, The aforementioned method, With respect to the aforementioned pre-parameters, the range of acceptable performance values ​​based on the corresponding pre-parameter values ​​is obtained, When considering that a change has occurred in the communication environment of the formation, the values ​​of the control parameters and communication parameters of the formation that enable the achievement of the allowable performance value within the range are searched, If it is not possible to achieve the allowable performance value within the range, the updated range of the allowable performance value is searched for, which allows for the determination of the values ​​of the control parameters and communication parameters that enable the achievement of the allowable performance value when considering the change in the communication environment. Based on the values ​​of the control parameters and communication parameters that enable the achievement of the allowable performance value within the updated range of the allowable performance value, the updated value of at least one pre-parameter is determined. Methods that include...

2. The method according to claim 1, wherein the range of the allowable performance values ​​of the formation parameters corresponds to the range of the allowable control performance values ​​of the control algorithm used by the formation, and / or the range of the allowable performance values ​​of the network parameters corresponds to the range of the allowable resource usage performance values ​​for the use of the allocated communication resources by the formation.

3. The method according to claim 2, wherein the set of parameters has a plurality of pre-parameters, each of which has one formation parameter and at least one network parameter for all of the formations, and if it is not possible to find values ​​for the control parameter and the communication parameter of the formation that enable the achievement of the acceptable performance value within the range, the search of the updated range includes reducing the acceptable control performance value of at least one of the formations.

4. The method according to claim 3, wherein exploring the updated range includes first reducing the allowable control performance value and then reducing the allowable resource utilization performance value as necessary.

5. The method according to claim 2, wherein the set of parameters has a plurality of pre-parameters, each of which has one formation parameter and at least one network parameter for all of the formations, and if it is not possible to find values ​​for the control parameter and the communication parameter of the formation that enable the achievement of the acceptable performance value within the range, the search of the updated range includes reducing the acceptable resource utilization performance value of the formation.

6. The method according to claim 5, wherein exploring the updated range includes first reducing the allowable resource usage performance value, and then reducing the allowable control performance value as necessary.

7. The method according to any one of claims 3, 4, and 6, wherein exploring the updated range by reducing the allowable control performance value of the formation includes using the same reduction coefficient for all or more of the formations.

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

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

10. The formation parameters of the aforementioned formation are: The distance between the communication nodes in the aforementioned formation, The geometric arrangement of the aforementioned formation and The speed of the communication nodes in the aforementioned formation, A method according to any one of claims 1 to 6, which represents at least one of the following.

11. The allocated communication resources included in the aforementioned network parameters are At least one frequency bandwidth, At least one spreading code or scrambled code, At least one temporal pattern, The method according to any one of claims 1 to 6, wherein the method is any one of the following.

12. The method according to any one of claims 1 to 6, which is performed by a configuration server that is included in or separate from the formation, at least one of the communication nodes of the formation.

13. A computer program product comprising instructions that, when executed by at least one processor, configure the at least one processor to perform the method according to any one of claims 1 to 6.

14. 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 the method according to any one of claims 1 to 6.

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

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