System and method for optimizing cable system in telecommunications network

Abstract

A system and method for optimizing a cable system in a telecommunications network is disclosed. The method comprises the steps of receiving a location of each of a plurality of terminal nodes and a minimum bandwidth requirement associated with operation of a telecommunications network comprising the plurality of terminal nodes and a plurality of branch nodes, the telecommunication network is used for facilitating communication between the plurality of terminal nodes through at least one of the plurality of branch nodes; determining the distance between each pair of adjacent nodes in the adjacent node pairs in the terminal node or the branch node and the bandwidth capacity of the cable edge; and providing an optimized geographic location of the branch nodes and an optimized bandwidth capacity for each cable edge, where the telecommunications network deployed with the branch nodes and the cable edges meets quality of service requirements associated with minimum bandwidth requirements.

Description

Technical Field

[0001] The present disclosure relates to a system and method for optimizing a cable system in a telecommunications network, and in particular, but not exclusively, to a system and method for optimizing a submarine cable arrangement in a telecommunications network. Background Art

[0002] While satellite links can provide intercontinental communications, submarine fiber-optic cables on the seabed are preferred for some applications. Submarine Internet cables play a vital role in global communications, transmitting more than 99% of the world's Internet data. Specialized ships deploy submarine cables on the seafloor, connecting terminal stations located on the coast. The cables house multiple optical fibers, each with high bandwidth capacity, allowing for faster and more efficient data transmission than satellites. In part, this is because submarine cables are more cost-effective for long-term, high-capacity communications, although the cost of these cables generally increases with the number of optical fibers. The cost of laying submarine cables can also be affected by the seabed topography, marine environment, and communication needs.

[0003] Typically, newer cables can carry more data than cables laid 15 years ago: the capacity increases from hundreds of megabits per second to several terabits per second (Tb / s). For example, the MAREA cable has a capacity of 224 Tb / s. In a transatlantic system, a capacity of 250 Tb / s across 12 fiber pairs and a span of 4.1 thousand miles (6.6 km) connecting Virginia Beach (USA) and Saint-Hilaire (France) is in operation. Summary of the invention

[0004] According to a first aspect of the present disclosure, a system for optimizing a cable system in a telecommunications network is provided. The system includes: a processing module for determining a distance between each pair of adjacent node pairs in a plurality of terminal nodes or a plurality of branch nodes provided in the telecommunications network based on a location of each terminal node in the plurality of terminal nodes and a bandwidth capacity of a cable edge, wherein the telecommunications network is used to facilitate communication between the plurality of terminal nodes through at least one of the plurality of branch nodes; and an optimization module for providing an optimized geographical location of the branch node and an optimized bandwidth capacity of each cable edge, wherein the telecommunications network deployed with the branch node and the cable edge satisfies a Quality of Service (QoS) requirement associated with a minimum bandwidth requirement associated with operation of the telecommunications network.

[0005] According to a first aspect, the telecommunication network comprises a submarine cable network comprising a plurality of submarine cables having different cable specifications.

[0006] According to a first aspect, the optimized geographic locations of the branch nodes are locations of Steiner nodes determined by the optimization module using a weighted edge Steiner minimum tree process.

[0007] According to a first aspect, the optimization module is used to determine optimized geographical locations of the plurality of branch nodes and cable paths connecting the branch nodes in a subnet connecting the plurality of terminal nodes.

[0008] According to a first aspect, the processing module is used to form a subtree of Steiner nodes connected by connecting edges, wherein the subtree is used to connect with the plurality of terminal nodes in the skeleton tree.

[0009] According to the first aspect, the processing module is further configured to solve a dynamic programming problem associated with an optimization problem of a total cost of a physical cable network formed by the cable edges in the telecommunications network to determine a minimum cumulative cost of the cable edges and the branch nodes.

[0010] According to the first aspect, the optimization module is further configured to determine optimized geographical locations of the plurality of branch nodes and cable paths based on additional attributes associated with deployment of the telecommunication network and / or maintenance of the telecommunication network after deployment.

[0011] According to the first aspect, the additional attribute includes at least one of a laying cost of a submarine cable, a repair rate of a branch node or a submarine cable, and a cable path based on a geodesic line.

[0012] According to the first aspect, the optimization module is further configured to shorten the cable edge with the highest capacity relative to other cable edges within the telecommunication network, thereby optimizing the utilization efficiency of cable resources during deployment of the telecommunication network.

[0013] According to a first aspect, the repair rate of the branch node or the submarine cable is associated with the risk of cable damage associated with earthquakes and the risk associated with human activities at a preset geographical location.

[0014] According to a second aspect of the present disclosure, a method for optimizing a cable system in a telecommunications network is provided. The method comprises the following steps: receiving a location of each of a plurality of terminal nodes and a minimum bandwidth requirement associated with the operation of a telecommunications network including the plurality of terminal nodes and a plurality of branch nodes, wherein the telecommunications network is used to facilitate communication between the plurality of terminal nodes through at least one of the plurality of branch nodes; determining a distance between each pair of adjacent nodes in a pair of adjacent nodes in a terminal node or a branch node and a bandwidth capacity of a cable edge; and providing an optimized geographical location of the branch node and an optimized bandwidth capacity of each cable edge, wherein the telecommunications network deployed with the branch node and the cable edge satisfies a Quality of Service (QoS) requirement associated with the minimum bandwidth requirement.

[0015] According to a second aspect, the telecommunication network comprises a submarine cable network comprising a plurality of submarine cables having different cable specifications.

[0016] According to a second aspect, the optimized geographical locations of the branch nodes are locations of Steiner nodes determined using a weighted edge Steiner minimum tree process.

[0017] According to a second aspect, the weighted edge Steiner minimum tree process includes the step of determining optimized geographical locations of the plurality of branch nodes and cable paths connecting the branch nodes in a subnet connecting the plurality of terminal nodes.

[0018] According to a second aspect, the step of determining optimized geographical locations of a plurality of branch nodes comprises the step of composing a subtree of Steiner nodes connected by connecting edges, wherein the subtree is used to connect with a plurality of terminal nodes in a skeleton tree.

[0019] According to a second aspect, the method further comprises the step of solving a dynamic programming problem associated with the optimization problem of the total cost of a physical cable network formed by the cable edges in the telecommunications network to determine the minimum cumulative cost of the cable edges and the branch nodes.

[0020] According to a second aspect, the method further comprises the step of determining optimized geographical locations of the plurality of branch nodes and cable paths based on additional attributes associated with deployment of the telecommunication network and / or maintenance of the telecommunication network after deployment.

[0021] According to a second aspect, the additional attribute includes at least one of a laying cost of a submarine cable, a repair rate of a branch node or a submarine cable, and a geodesic-based cable path.

[0022] According to a second aspect, the method further comprises the step of shortening the cable edge with the highest capacity relative to other cable edges within the telecommunication network, thereby optimizing the utilization efficiency of cable resources during deployment of the telecommunication network.

[0023] According to a second aspect, the repair rate of the branch node or the submarine cable is associated with the risk of cable damage associated with earthquakes and the risk associated with human activities at a preset geographical location. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Embodiments of the present disclosure will be described below by way of example with reference to the accompanying drawings, in which:

[0025] Figure 1 is a schematic diagram of a computer server, wherein the computer server is used to implement a system for optimizing a cable system in a telecommunication network according to an embodiment of the present disclosure;

[0026] Figure 2 A system block diagram for optimizing a cable system in a telecommunication network according to an embodiment of the present disclosure;

[0027] Figure 3A is a diagram of an example telecommunications network connecting four terminals;

[0028] Figure 3B for Figure 3A An illustration of an example network topology;

[0029] Figure 4 is a diagram of an example Steiner tree and subtrees constructed according to an embodiment of the present disclosure;

[0030] Figure 5A is a diagram of the skeleton tree, where S2 is Figure 4 the roots of the Steiner tree;

[0031] Figure 5B For the corresponding Figure 4 Illustration of the Steiner topology and DAG of the Steiner tree;

[0032] Fig. 6A is a diagram showing locations of five terminal nodes of a telecommunications network optimized by a system for optimizing a cable system in a telecommunications network according to an embodiment of the present disclosure;

[0033] Figure 6B for Fig. 6A The corresponding network topology diagram of the optimized network;

[0034] Fig. 7A To show the connection Fig. 6A a diagram of the paths of the cable edges in an unoptimized network of five terminal nodes; and

[0035] Figure 7B To show the connection Fig. 6A Illustration of the paths of the cable edges in an optimized network of five terminal nodes. DETAILED DESCRIPTION

[0036] According to the inventor's design, submarine cables can be constructed from 4 to 8 fiber pairs, or in some more advanced networks, Internet cables can be constructed from 12, 16, 24, and 36 fiber pairs. In order to optimize the cable network, it is necessary to minimize the cost of all components while maintaining the Quality of Service (QoS) requirements. This includes optimizing the location of branching units (BU) and cable landing stations (CLS), calculating cable paths based on geodesics, and considering the bandwidth requirements of the cable.

[0037] In a preferred embodiment, the submarine cable network optimization problem can be modeled as a weighted edge Steiner minimum tree problem, taking into account the bandwidth capacity of each cable edge on the network. This is an important consideration for optimizing submarine cable networks. Preferably, a weighted edge Steiner minimum tree (WE-SMT) algorithm can be used to effectively optimize the location of Steiner nodes, the bandwidth capacity of each cable edge, and the cable path given the terminal node location and bandwidth capacity requirements. Advantageously, the WE-SMT algorithm can be applied to real-world scenarios, making it a practical solution for optimizing submarine cable networks.

[0038] like Figure 1, a schematic diagram of a computer system or computer server 100 is shown. The computer system or computer server 100 is used to implement an exemplary embodiment of a system for optimizing a cable system in a telecommunications network. In this embodiment, the system includes the server 100. The server 100 includes appropriate components required to receive, store, and execute appropriate computer instructions. These components may include: a processing unit 102, which includes a central processing unit (CPU), a math co-processing unit (Math Processor), a graphics processing unit (GPU), or a tensor processing unit (TPU) for tensor or multi-dimensional array calculation or manipulation operations; a read-only memory (ROM) 104; a random-access memory (RAM) 106; an input / output (I / O) device (e.g., a disk drive 108); an input device 110 (e.g., an Ethernet port, a USB port, etc.); a display 112, such as a liquid crystal display, a light-emitting display, or any other suitable display; and a communication link 114. The computing device 100 may include instructions that may be stored in the ROM 104, the RAM 106, or the disk drive 108 and executed by the processing unit 102. A plurality of communication links 114 may be provided that may be variously connected to one or more computing devices, such as servers, personal computers, terminals, wireless or handheld computing devices, IoT devices, smart devices, and edge computing devices. At least one of the plurality of communication links may be connected to an external computing network via a telephone line or other type of communication link.

[0039] The server 100 may include storage devices such as a disk drive 108, which may include a solid-state drive, a hard disk drive, an optical drive, a tape drive, or a remote or cloud-based storage device. The server 100 may use a single disk drive or multiple disk drives, or a remote storage service 120. The server 100 may also have a suitable operating system 116 resident on the disk drive or in ROM of the server 100.

[0040] The computer or computing device may also provide the necessary computing power to operate or interface with a machine learning network such as a neural network to provide various functions and outputs. The neural network may be implemented locally, or may be accessed or partially accessed through a server or cloud-based service. The machine learning network may also be untrained, partially trained, or fully trained, and / or may also be retrained, modified, or updated over time.

[0041] refer to Figure 2 , an embodiment of a system 200 for optimizing a cable system in a telecommunication network is shown. In this embodiment, a server 100 is used as a part of the system 200. The system 200 is used to receive the locations of multiple terminal nodes in the telecommunication network, such as the coordinates of ports in different towns / cities. The server 200 can then analyze the optimal cable layout of the network so that the telecommunication network can facilitate communication between all terminal nodes through at least one branch node that needs to be deployed, and finally provide an optimized location for each branch node based on the bandwidth of each cable connecting adjacent branch nodes or terminal nodes and the quality of service requirements.

[0042] In this embodiment, the system 200 includes a processing module 202. The processing module 202 is used to receive input coordinates 206 of terminal nodes, such as coordinates of network facilities deployed at multiple locations or countries on the ocean. The processing module 202 can determine the distances between different terminal nodes to determine possible implementations of branch nodes, where the branch nodes can be deployed to facilitate network deployment, also based on broadband capacity 208 or quality of service requirements or expectations.

[0043] For example, a telecommunications network implemented by a plurality of submarine network cables is to be deployed to facilitate communications between three geographic locations A, B, and C (i.e., terminal nodes 206), and the processing module may determine the distance between each pair of adjacent nodes A and B, B and C, and A and C. Instead of deploying three submarine cables nodes A to B, A to C, and B to C, it is more preferred to deploy branch nodes accessible to the midpoints of nodes A, B, and C, and assuming the bandwidth requirements of the network between each pair of adjacent node pairs as input, submarine cables are connected from nodes A, B, and C to the branch nodes, respectively.

[0044] The bandwidth between each pair of terminal nodes may be different depending on the specification, such as the maximum bandwidth supported by the cable, and preferably, the bandwidth of the cable to be deployed and the location of the branching nodes are determined more accurately. In a preferred embodiment, the system can also be used to optimize the telecommunications network based on one or more cable deployment attributes 210 provided to the optimization module 204 of the system 200 to facilitate communication between multiple terminal nodes through at least one of the multiple branching nodes.

[0045] For example, if the quality of service requirements or bandwidth capacity of the cable link connecting node A is higher than the cable link connecting nodes B or C, such as if A is a city with a larger population or a major data center is located near node A, it may be preferable to move the branch node to a location closer to terminal node A than nodes B and C. In this example, the optimization module 204 can consider different cable deployment attributes 210, such as the laying cost and maintenance cost of the cable, and the risks associated with the operation of the network including cable edges of different cable specifications, and determine an optimized network topology 212 including these optimization parameters for actual implementation.

[0046] Preferably, reference Figure 2 A system 200 for optimizing a cable system in a telecommunications network includes: a processing module 202 for determining the distance between pairs of terminal nodes, between pairs of branch nodes, or between terminal nodes and branch nodes based on the locations of the terminal nodes and the bandwidth capacity of the cable edge between each pair of nodes in the entire network; and an optimization module 204 for optimizing the distance between each pair of nodes based on multiple parameters, such as the bandwidth capacity of each cable edge, the service quality requirements of the network, specifically the minimum bandwidth capacity of each pair of nodes, the difficulty of deploying each cable, and the cost of each cable.

[0047] The processing module 202 and the optimization module 204 may also consider other attributes or parameters that may affect the operations and / or operating costs involved in the network. For example, it is generally desirable to avoid laying cables along nodes or locations that are vulnerable to damage, or locations that are difficult to access for maintenance. Thus, high maintenance costs that may be incurred by operating the network are avoided.

[0048] For example, if the cable has to be laid along a path that includes one or more "danger points", the cable can be additionally protected. In this way, the laying cost may increase. But even so, it is more economical than routing a long cable between two nodes, and can be used as an alternative to avoid the high maintenance costs that may occur. When optimizing the path or distance between each adjacent pair to obtain the optimized network topology 212, the optimization module 204 takes these additional cable deployment attributes into account.

[0049] As mentioned earlier, submarine cables can be provided in the form of different numbers of fiber pairs to provide different bandwidth capacities. It may not be desirable to deploy cables with the same specifications to connect nodes that require different QoS. For example, considering that the laying cost and maintenance cost of 100Tbps fiber optic cables are significantly higher than the laying cost and maintenance cost of 50Tbps cables, it is not an effective method to use cables that can support a maximum bandwidth of 100Tbps to connect terminal nodes and branch nodes that only require a low bandwidth capacity of 50Tbps. On the other hand, because the quality of service requirements must be met, it is not desirable to use a 50Tbps cable to connect two nodes that require a quality of service requirement of 100Tbps.

[0050] Without wishing to be bound by theory, submarine cable path optimization can be defined in several sub-areas. For example, for point-to-point cable path planning, a grating-based path planning method can be used to determine the cable path at minimum cost by minimizing the weighted sum of seismic risk and cable length. In this example, the technique utilizes the Dijkstra algorithm. However, one disadvantage of this method is that the path must cross diagonals or lateral edges between adjacent cells. Furthermore, this method can only be applied to point-to-point cable path planning.

[0051] In an alternative example, a method based on the Fast Marching Method (FMM) can be used for cable path planning on the surface of the earth. FMM is a path planning method that avoids the shortcomings of the Dijkstra algorithm by solving the Eikonal equation when the grid size of the triangular manifold is close to zero. Alternatively, multiple design levels of cable shielding can be considered and FMM can be applied to design the optimal cable path. Considering the risk of cable breakage, cable laying cost and multiple design levels of cable shielding, the cable path planning problem can be solved as a multi-objective optimization problem. However, bandwidth optimization is not considered.

[0052] For the cable network design problem, another method based on FMM and integer linear programming can be used to optimize the tree topology network design of the submarine cable system connecting multiple stations. In addition, a lightweight heuristic algorithm can also be used to solve the existing problem. The optimization problem can be formulated as a minimum spanning tree (MST) problem, where the goal is to construct a tree topology cable network with minimum cost without additional Steiner nodes.

[0053] In yet another optional example, a trunk and branch tree network topology for a submarine cable system connecting multiple landing stations can be implemented using an FMM-based approach. In this example, the optimization of the trunk and branch tree network problem can be performed as a Steiner Minimum Tree (SMT) problem, and the minimum tree on an irregular two-dimensional manifold in a three-dimensional Euclidean space of a given topology can be obtained in polynomial time based on dynamic programming. Although these methods take into account cable construction costs, they do not take into account the costs associated with bandwidth requirements and BU installation.

[0054] According to the inventor's design, to optimize the cable network, it is necessary to minimize the cost of all components while maintaining the quality of service requirements. Preferably, it involves optimizing the locations of BUs and CLSs, calculating the cable paths based on geodesics, and considering the bandwidth requirements of the cables.

[0055] Preferably, the optimization module 204 is used to provide the optimized geographical location of the branch node and the optimized bandwidth capacity of each cable edge. For example, the optimized geographical location of the branch node in the optimized network topology 212 is the location of the Steiner node determined by the optimization module 204 using the weighted edge Steiner minimum tree process. The purpose is to build a trunk branch tree topology cable network including Steiner nodes and meet the capacity requirements between the specified nodes with minimum cost. The cost will be discussed below.

[0056] In this example, the edges of the SMT are computed as minimum cost geodesics in M, rather than straight lines or great circles. The surrounding space in this example is a triangular manifold M representing the surface of the earth. In the manifold M, the cable paths between terminal pairs or Steiner nodes are geodesics with respect to a cost function, which can be computed using the FMM. Based on the localized cost of a particular path, the optimal connection between nodes is formed.

[0057] Preferably, for greater precision, M can be defined as the set of grid points in the compact triangular manifold M, N represents the number of terminal nodes, H represents the set of capacity requirements between pairs of terminal nodes, and N b represents the number of Steiner nodes, S = {s1,s2,…,s Nb}, Γ represents the geodesic set of the Steiner tree, c b represents the cost of BU (all BUs have the same cost in the model), l i represents the link in the Steiner tree, b i Indicates the corresponding link l i f(s) represents the cost of the cable system, which depends on the location of the BU and the cost of the BU and the cable link. The cost of the cable link depends on the capacity of the cable and the length of the cable.

[0058]

[0059] The cost C(γ) of the cable along the geodesic line γ depends on several factors, but approaches optimal cost control as the geodesic length of the cable increases:

[0060]

[0061] Location s The local cost at location c(γ(s), b) is a function of the cost of laying at location γ(s) and the risk associated with that location and the bandwidth required at the edge of the cable. The relationship between bandwidth and cost per kilometer of cable is assumed to be c(x) = B x + A, where B represents the bandwidth capacity of the cable, measured in the number of optical fibers. The fixed cost of cable construction, represented by A, is determined by industry experts or can be determined based on other references or data tables.

[0062] Preferably, the optimization module 204 is used to determine the optimized geographical locations of multiple branch nodes and cable paths connecting the branch nodes in the subnet connecting the multiple terminal nodes. Figure 3A , shows an example cable system 300 including four terminals 302 and two BUs 304, Figure 3B The corresponding topology is shown, which includes four terminals, namely x1, x2, x3 and x4, connected to two Steiner nodes (representing BUs) represented by s1 and s2, and five cable links represented by edges e1, e2, e3, e4 and e5.

[0063] Preferably, in a tree topology network, only one path connects each pair of nodes; p(xi,xj) represents the path between nodes xi and xj. Each path has a data bandwidth requirement represented by h(xi,xj). This means that edge e1 must meet the bandwidth requirements of paths p(x1,x2), p(x1,x3) and p(x1,x4), so that B(e1) ≥ h(x1,x2) + h(x1,x3) + h(x1,x4). Similarly, edges e2 and e3 need to meet the bandwidth requirements of paths p(x1,x2), p(x2,x3), p(x2,x4) and paths p(x1,x3), p(x1,x4), p(x2,x3) and p(x2,x4), respectively.

[0064] Preferably, the cable edge cost can be determined by the number of optical fibers therein and the capacity of the cable. In a preferred example, in order to minimize the total cost, the higher bandwidth edge can be shortened. Minimization of the total cost of the cable system requires the optimal arrangement of two BUs, s1 and s2, which involves five cable edge cost factors, each with different bandwidth requirements and costs, and the cost of the BU.

[0065] For these four end-node examples, considering the cable system optimization problem, the cable bandwidth capacity can be expressed as:

[0066]

[0067] stB(e1)≥(x1,x2)+(x1,x3)+(x1,x4),

[0068] B(e2)≥(x1,x2)+(x2,x3)+(x2,x4),

[0069] B(e3)≥(x1,x3)+(x1,x4)+(x2,x3)+(x2,x4),

[0070] B(e4)≥(x1,x4)+(x2,x4)+(x3,x4),

[0071] B(e5)≥(x1,x3)+(x2,x3)+(x3,x4).

[0072] For the general problem, given the locations of the terminal nodes, the bandwidth capacity requirements between these nodes can also be provided to the processing module 202. It is necessary to optimize the location of the Steiner nodes, the bandwidth capacity of each cable edge, and the cable path.

[0073] The cable network optimization problem is a SMT problem with weighted edges and a tree topology T. BUs are at Steiner nodes and CLSs are at terminal nodes. Connections between nodes (Steiner and terminal) are edges in the tree. If the degree of each Steiner node (terminal node) is equal to 3 (≤3 respectively), the topology T is called a Steiner topology.

[0074] For N terminals x1, x2, ..., xN∈M, with a given topology T, let N = {1, 2, ..., N} be the index set of the terminals, S = {N+1, N+2, ..., N+M} be the index set of the Steiner nodes, (M ≤ N-2); V = N∪S. Let E = E1∪E2 be the set of all edges, that is, T = (V, E) where E1 = {(i, j) | i∈N, j∈S} and E2 = {(i, j) | i∈N, j∈S}. It is best to find the coordinates of the Steiner node: x N+1 ,x N+2 ,…,x N+M ∈M, the path Γ={γ=(e)|e∈E} (i.e., the geodesic corresponding to the edge in E) and the specific cable bandwidth level required by the edge, so that the cable network has the minimum cost to meet the bandwidth requirement of each pair of terminal nodes.

[0075] Preferably, the high terminal and all potential Steiner nodes can be constrained to be the grid nodes xi of M, xi∈M (in practice, only the coordinates of the grid nodes of M are given). In order to adapt and balance the laying cost and the risk of the cable, at each grid point x in D, let:

[0076]

[0077] where h(x,u) and g(x,u) represent the laying cost and repair rate at a grid point x with a cable bandwidth level, respectively, and the cable bandwidth is u. Applying the FMM to this cost yields an optimal path between any pair of grid nodes in M.

[0078] First, based on the bandwidth requirements of each path between a specified pair of nodes, the minimum bandwidth requirement of each cable edge B(e1),…,B(e5) can be calculated. The cable cost per kilometer of the edge can be determined based on the expertise of the cable expert, i.e., β(e1),…,β(e5). It should be noted that the cable bandwidth within each link is fixed. For example, cables commonly used in the industry may contain 4, 8, or 16 optical fibers, with corresponding costs of C4, C8, and C16, respectively, due to the capacities B4, B8, and B16. The size of this set of available bandwidth can vary depending on the available industry capacity. The goal of the problem is to find the optimal location of the BU, the optimal cable path connecting two Steiner nodes and the Steiner terminal node, and assign the appropriate cable bandwidth level B4, B8, or B16 to each cable edge.

[0079] The optimization problem of the total cost Ψ(T) of the physical cable network T is expressed as formula (3).

[0080]

[0081] x={x N+1 ,x N+2 ,…,x N+M},x N+j ∈M, is the coordinate of the Steiner node.

[0082] is the sum of the minimum weighted costs from each terminal that is a neighbor of xj to itself, which can be calculated using FMM with the terminal as the source node while considering the cable bandwidth β()∈{C4,C8,C16}. Let If there is no terminal adjacent to Steiner node xj, then M and C BU They are the number and cost of BUs respectively.

[0083] Preferably, reference Figure 4, the processing module 202 is used to form a subtree 402 of Steiner nodes 408 connected by connection edges 404, wherein the subtree 402 is used to connect to multiple terminal nodes 406 in the skeleton tree 400. In this exemplary embodiment, it is more preferred to determine the location of the branch nodes and the cable edges connecting the branch nodes. This is because the cable edges between branch nodes (i.e., connection edges 404) generally have higher bandwidth capacity than the cable edges connecting the terminal nodes to the branch unit - the reason is that the connection edges 404 in the branch unit must generally support higher network traffic.

[0084] refer to Figure 4 , the skeleton tree 400 of the topology T is a subtree T=(S, E2) 402 consisting only of Steiner nodes 408 with connecting edges 404 .

[0085] Continue to refer Figure 5A , for a tree 500 with a Steiner topology T, an arbitrary Steiner node S can be selected as the root of the skeleton tree T, S∈T, and directions toward the root can be assigned to the edges of T. T then becomes a directed rooted tree (i.e., an anti-tree). The nodes 502 of the skeleton tree are sorted so that the children of a given node s appear earlier in the list than the parent node (i.e., as shown by the arrow from the node to s). This order is called the topological order. Without loss of generality, the Steiner nodes are relabeled 1, 2,…, M, where M corresponds to the root of T.

[0086] Define xi = x N+i , i = 1, 2, ..., M, change the Steiner node x in formula (3) N+i ∈M does not change the optimization problem, then

[0087]

[0088] For any leaf definition but:

[0089]

[0090] Preferably, the processing module 202 is further configured to solve a dynamic programming problem associated with the optimization problem of the total cost of the physical cable network formed by the cable edges in the telecommunication network to determine the minimum cumulative cost of the cable edges and branch nodes. For example, by combining the definition with equations (3) and (4), the problem can be transformed into a dynamic programming (DP) problem:

[0091]

[0092] refer to Figure 5BTo solve this problem, a new directed acyclic graph (DAG) G = (V′, E′) based on T can be constructed. Each Steiner node i∈T is associated with a subset Ai of V′, where Ai represents the grid nodes of M and the weight on each node is It follows that V′=∪ i∈S Ai; that is, V′ consists of m copies of the grid nodes of M. For arc e = (i, j) ∈ E2, where Sj is the parent node of Si, a complete connection from Ai to Aj of G can be constructed; that is, an arc ε = (p, q) from each xp ∈ Ai to each xq ∈ Aj in G. The cost of arc ε is defined as the minimum cost from node xp to node xq, calculated by FMM, that is, W(ε) = W(p, q) = mincβ(xp, xq). For node x∈A, the minimum cumulative cost for:

[0093]

[0094] With reference to the DAG minimum cost tree algorithm, the minimum cost tree on the DAG can be found, and the coordinates of the Steiner nodes can also be obtained. According to a preferred embodiment, a weighted edge SMT algorithm (WSTM algorithm) is provided as follows, which considers the cable capacity, the cable length and the number of Steiner nodes to find the minimum total cost tree on the DAG and return the coordinates of the Steiner nodes.

[0095] WE-STM algorithm

[0096] Input: Graph G = (V′, E′) connecting the BU cost (bi) of each BU specifying three edges, and the cost of each cable edge per kilometer β(e1),…,β(e5).

[0097] Output: coordinates Si of the Steiner nodes, i=1,…,M, total cost Ψ(T) of the cable network.

[0098]

[0099] In the WE-STM algorithm, Line 1-13 calculates the cable cost of the path connecting the leaves of the Steiner node tree with the corresponding terminal. The implementation of formula (6) is located in lines 14-28. If a leaf node in T has the same coordinates as a terminal node, the BU cost b at that location is zero. Once the iteration reaches the root, in line 32, a grid node can be selected in A (have ).node is the physical Steiner node corresponding to the root. To obtain the coordinates of the remaining Steiner nodes, Start tracing back to G. In this way, a path through the tree with a specific cable bandwidth and total cost and total length can be found.

[0100] refer to Fig. 6A , shows an example network topology 600 in which the method described in an embodiment of the present method is applied to a deployment in an area from the northwest corner (45.000°N, 0.000°E) to the southeast corner (36.000°N, 11.000°E). In this example, the cable system connects the following six locations in the area: Genoa (44.407°N, 8.963°E), Alghero (40.580°N, 8.324°E), Tunis (37.341°N, 9.078°E), Algiers (36.832°N, 3.052°E) and Barcelona (41.399°N, 2.085°E), which can be optimized, respectively, by A, B, C, D, E.

[0101] Optionally and additionally, the optimization module 204 is also used to determine the optimized geographical locations of multiple branch nodes and cable paths based on additional attributes associated with the deployment of the telecommunications network and / or the maintenance of the telecommunications network after deployment. For example, additional attributes such as the cost of laying submarine cables, the repair rate of branch nodes or submarine cables, and the cable paths based on geodesics can be considered. Therefore, although it is generally preferred to shorten the submarine cables used, the submarine cables and / or branch nodes can be deployed in locations that are not close to "risky" geographical locations, i.e., locations where the probability of cables being damaged is higher. This may result in the need to use longer cables than deploying a cable network with the shortest possible path.

[0102] In an example embodiment, bathymetric data from a global multi-resolution topological synthesis can be used. The cost function takes into account the trade-off between the laying cost and the total number of repairs and the appropriate cable protection at each point in the manifold. At a preset geographic location, the total number of repairs (i.e., the repair rate) can be associated with the risk of cable damage related to earthquakes and the risk related to human activities. On the other hand, the cable deployment cost is based on the cable bandwidth level and the seabed depth, and the optimization module 204 can shorten the cable edge with the highest capacity instead of other cable edges within the telecommunications network, thereby optimizing the utilization efficiency of cable resources during the deployment of the telecommunications network.

[0103] For example, suppose the cost of a BU varies between $1 and $3 million. Fig. 6A and 6B In an exemplary embodiment of the present invention, the bandwidth requirements for each edge 602 and an exemplary network to be deployed are shown in the following table.

[0104] Node Pair bandwidth Node Pair bandwidth (A, B) 10Tbps (B, D) 20Tbps (A, C) 20Tbps (B, E) 10Tbps (A, D) 10Tbps (C, D) 30Tbps (A, E) 10Tbps (C, E) 20Tbps (B, C) 30Tbps (D, E) 10Tbps

[0105] Based on these service quality requirements and Figure 6B The network topology 600 shown can obtain the minimum feasible bandwidth and cable cost per kilometer for each edge in the tree network, as shown in the following table.

[0106] edge Bandwidth requirements Cable cost (per km) e1 50Tbps $25,000 e2 70Tbps $28,000 e3 100Tbps $32,000 e4 70Tbps $28,000 e5 50Tbps $25,000 e6 80Tbps $38,000 e7 110Tbps $42,000

[0107] In this example, the basic bandwidth and cost of the cable structure are assumed to be 50Tbps and $25,000 / km respectively. These figures, while not exact, serve as a reasonable proxy for actual costs (including laying costs). As bandwidth increases, the total cost of cable construction also increases.

[0108] Ignoring the cable capacity requirements, allocating the same bandwidth to all cables yields the following: Fig. 7A An unoptimized network deployment topology is shown as 702. With this cable configuration, to meet the bandwidth requirements of all paths, a capacity of 110 Tbps or more is required at a cost of $42,000 per kilometer, and the total cost of the cable network is $13.312 million.

[0109] Alternatively, when capacity diversity is considered in the optimization, each edge in the tree network only needs to satisfy its assigned network bandwidth requirement. Figure 7B , the final optimal network topology 704 can be obtained at a total cost of $9.218 million, which represents a 30.75% reduction in cost. In this optimized network 702B, the total length of cable edges 6 and 7 is shortened by 97.75 km compared to the unoptimized network 702A. Advantageously, the high-capacity cables are shortened to reduce the overall cost of the cable network.

[0110] Those skilled in the art will appreciate that the method may also be used to optimize other types of networks, such as power networks or wireless communication networks, when the quality of service requirements and costs of deploying appropriate links with different capacities between different base stations or branch stations are different.

[0111] The advantages of these embodiments are that a method for optimizing the deployment of network paths in a telecommunications network is provided, which also takes into account the laying costs of cables and the quality of service requirements and the risks of long-term operation of the network. The present disclosure provides a significant advancement in the method for optimizing submarine cable systems, which can effectively optimize the location of Steiner nodes, bandwidth capacity and cable paths, while taking into account the requirements of real-world scenarios.

[0112] Submarine cables are a critical component of global telecommunications infrastructure, responsible for transmitting vast amounts of data across oceans and connecting people and businesses around the world. The higher the number and quality of optical fibers in a cable, the higher the cost.

[0113] Advantageously, network costs can be minimized during the construction of a submarine cable system by optimizing the network topology during the planning phase, which requires considering appropriate bandwidth capacity to meet network requirements.

[0114] Considering the bandwidth capacity of each edge, the optimization problem is provided as a weighted edge Steiner minimum tree problem. In addition, for a given terminal node location and bandwidth capacity requirements, the WE-SMT algorithm can be employed to optimize the network by optimizing the location of the Steiner node, the bandwidth capacity of each cable edge, and the cable path.

[0115] Although not required, the embodiments described with reference to the accompanying drawings may be implemented as an application programming interface (API) or a series of libraries used by developers, or may be included in another software application, such as a terminal or personal computer operating system or a portable computing device operating system. Generally, since program modules include routines, programs, objects, components, and data files that help perform specific functions, those skilled in the art will understand that the functionality of a software application may be distributed among multiple routines, objects, or components to achieve the same functionality required herein.

[0116] It should also be understood that in the case where the methods and systems of the present disclosure are implemented in whole or in part by a computing system, any suitable computing system architecture may be used. Computing systems may include tablet computers, wearable devices, smart phones, IoT devices, edge computing devices, stand-alone computers, network computers, cloud-based computing devices, and dedicated hardware devices. Where the terms "computing system" and "computing device" are used, these terms are intended to cover any suitable configuration of computer hardware capable of implementing the described functionality.

[0117] It will be appreciated by those skilled in the art that many changes and / or modifications may be made to the present disclosure as shown in the specific embodiments without departing from the spirit or scope of the present disclosure as broadly described. Therefore, the present embodiments are to be considered in all aspects as illustrative and not restrictive.

[0118] Unless otherwise indicated, any reference to prior art contained herein should not be taken as an admission that the information is common general knowledge.

Claims

1. A system for optimizing a cable system in a telecommunications network, characterized in that include: a processing module for determining a distance between each pair of adjacent node pairs in the plurality of terminal nodes or a plurality of branch nodes provided in a telecommunications network based on a location of each of the plurality of terminal nodes and a bandwidth capacity of a cable edge; wherein the telecommunications network is configured to facilitate communication between the plurality of terminal nodes through at least one of the plurality of branch nodes; and An optimization module for providing an optimized geographic location of the branch node and an optimized bandwidth capacity of each cable edge; wherein the telecommunications network deployed with the branch node and the cable edge meets quality of service requirements associated with a minimum bandwidth requirement associated with operation of the telecommunications network.

2. The system according to claim 1, characterized in that in, The telecommunication network comprises a submarine cable network including a plurality of submarine cables having different cable specifications.

3. The system according to claim 2, characterized in that in, The optimized geographic locations of the branch nodes are the locations of Steiner nodes determined by the optimization module using a weighted edge Steiner minimum tree process.

4. The system according to claim 3, characterized in that in, The optimization module is used to determine the optimized geographical locations of the plurality of branch nodes and the cable paths connecting the branch nodes in the subnet connecting the plurality of terminal nodes.

5. The system according to claim 4, characterized in that in, The processing module is used to form a subtree of Steiner nodes connected by connecting edges; wherein the subtree is used to connect with the multiple terminal nodes in the skeleton tree.

6. The system according to claim 5, characterized in that in, The processing module is further configured to solve a dynamic programming problem associated with an optimization problem of a total cost of a physical cable network formed by the cable edges in the telecommunications network to determine a minimum cumulative cost of the cable edges and the branch nodes.

7. The system according to claim 4, characterized in that in, The optimization module is further configured to determine optimized geographic locations of the plurality of branch nodes and cable pathways based on additional attributes associated with deployment of the telecommunications network and / or maintenance of the telecommunications network after deployment.

8. The system according to claim 7, characterized in that in, The additional attributes include at least one of a laying cost of a submarine cable, a repair rate of a branch node or a submarine cable, and a cable path based on a geodesic line.

9. The system according to claim 8, characterized in that in, The optimization module is further configured to shorten the cable edge with the highest capacity relative to other cable edges within the telecommunication network, thereby optimizing the utilization efficiency of cable resources during deployment of the telecommunication network.

10. The system according to claim 8, characterized in that in, The repair rate of the branch node or the submarine cable is associated with the risk of cable damage associated with earthquakes and the risk associated with human activities at a preset geographical location.

11. A method for optimizing a cable system in a telecommunication network, characterized in that The following steps are involved: receiving a location of each of a plurality of terminal nodes and a minimum bandwidth requirement associated with operation of a telecommunications network including the plurality of terminal nodes and a plurality of branch nodes; wherein the telecommunications network is configured to facilitate communication between the plurality of terminal nodes through at least one of the plurality of branch nodes; determining the distance between each pair of adjacent nodes in a terminal node or a branch node and the bandwidth capacity at the edge of the cable; and Providing an optimized geographic location of the branch nodes and an optimized bandwidth capacity of each cable edge; wherein the telecommunications network deployed with the branch nodes and the cable edges meets quality of service requirements associated with minimum bandwidth requirements.

12. The method according to claim 11, characterized in that in, The telecommunication network comprises a submarine cable network including a plurality of submarine cables having different cable specifications.

13. The method according to claim 12, characterized in that in, The optimized geographic locations of the branch nodes are the locations of the Steiner nodes determined using a weighted edge Steiner minimum tree process.

14. The method according to claim 13, characterized in that in, The weighted edge Steiner minimum tree process includes the step of determining optimized geographical locations of the plurality of branch nodes and cable paths connecting the branch nodes in a subnet connecting the plurality of terminal nodes.

15. The method according to claim 14, characterized in that in, The step of determining the optimized geographical locations of the plurality of branch nodes includes the step of composing a subtree of Steiner nodes connected by connecting edges, wherein the subtree is used to connect with the plurality of terminal nodes in the skeleton tree.

16. The method according to claim 15, characterized in that Further including: A step of solving a dynamic programming problem associated with an optimization problem of the total cost of a physical cable network formed by the cable edges in the telecommunications network to determine a minimum cumulative cost of the cable edges and the branch nodes.

17. The method according to claim 14, characterized in that Further including: The step of determining optimized geographic locations of the plurality of branch nodes and cable pathways based on additional attributes associated with deployment of the telecommunications network and / or maintenance of the telecommunications network after deployment.

18. The method according to claim 17, characterized in that in, The additional attributes include at least one of a laying cost of a submarine cable, a repair rate of a branch node or a submarine cable, and a cable path based on a geodesic line.

19. The method according to claim 18, characterized in that Further including: The step of shortening the cable edge with the highest capacity relative to other cable edges within the telecommunications network, thereby optimizing the efficiency of utilization of cable resources during deployment of the telecommunications network.

20. The method according to claim 18, characterized in that in, The repair rate of the branch node or the submarine cable is associated with the cable damage risk associated with earthquakes and the risk associated with human activities at a preset geographical location.

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