A V2X Network Resource Allocation and Relay Selection Method Based on AP Relay

Abstract

This invention belongs to the field of vehicle-to-everything (V2X) communication technology, specifically relating to a method for resource allocation and relay selection in V2X networks based on wireless access point (AP) relays. The method includes: first, constructing a V2X two-hop transmission system model based on AP relays, using Frequency Division Multiple Access (FDMA) technology to divide communication into two time-orthogonal stages; second, establishing a hybrid integer nonlinear programming model for joint AP relay selection and two-stage communication resource allocation, with the goal of minimizing end-to-end network latency; then, using a two-layer joint optimization algorithm to solve the model, with the inner layer decoupled into two convex optimization subproblems for resource allocation, and the outer layer using a genetic algorithm to globally search for the optimal relay strategy; finally, outputting the optimal relay selection scheme and the corresponding resource allocation scheme. This patent effectively reduces end-to-end transmission latency in V2X networks, achieves load balancing among APs, and ensures communication fairness among different vehicle users.

Description

Technical Field

[0001] This invention belongs to the field of vehicle-to-everything (V2X) communication technology, specifically relating to a method for allocating network resources and selecting relays based on wireless access point (AP) relays in vehicle-to-everything (V2X) networks. Background Technology

[0002] With the deep integration of smart cities and V2X technology, vehicles increasingly demand real-time information from Roadside Units (RSUs), such as safety warnings and traffic incidents. Low-latency, high-reliability information transmission has become a core prerequisite for ensuring the stable operation of autonomous driving and intelligent transportation systems. In complex urban wireless environments, obstacles such as dense building clusters and overpasses can easily obstruct wireless signals, leading to deterioration or even interruption of the direct communication link between the RSU and the target vehicle, creating communication blind spots and severely affecting the real-time transmission of safety-critical information. To address this issue, utilizing fixed access points (APs) deployed on the roadside as relay nodes to construct a two-hop transmission link between the RSU, AP, and target vehicle has become an effective technical approach to fill communication blind spots and improve transmission reliability. This technology has become one of the research hotspots in the field of V2X communication.

[0003] Currently, in V2X two-hop transmission technology based on AP relays, Frequency Division Multiple Access (FDMA) is widely used for phase division in two-hop communication because it can effectively avoid co-channel interference. The communication process is typically divided into two time-orthogonal phases: the first phase involves the RSU transmitting data to the AP, and the second phase involves the AP forwarding data to the target vehicle. Meanwhile, relay selection and communication resource allocation are two core technologies that determine the transmission performance of this type of network, and related research has gradually evolved from single-technology optimization to joint optimization. Existing research attempts to select relay nodes by combining factors such as AP channel conditions and geographical location, and then optimize communication resources such as bandwidth and power under a fixed relay strategy; other studies first fix the resource allocation scheme, then select the optimal AP relay node, and often use convex optimization and heuristic algorithms to solve the resource allocation problem to improve single-link transmission efficiency.

[0004] Although existing technologies have explored relay selection and resource allocation in AP-based V2X networks, the following specific and pressing challenges remain for scenarios involving multiple RSUs, multiple APs, and multiple target vehicles: First, the deep coupling between relay selection and resource allocation is ignored. Existing solutions often decouple these two aspects, fixing one before optimizing the other, resulting in only locally optimal optimizations that fail to fully exploit the overall system performance potential. Second, latency optimization lacks a global perspective. Most solutions focus only on the single-hop link latency between RSU and AP or AP and target vehicle, failing to optimize the total latency from an end-to-end global perspective, making it difficult to meet the low-latency requirements of V2X safety-critical services. Third, load balancing among APs is not considered. A large volume of communication flows tends to flow to the few APs with optimal channel conditions, causing these APs to overload while other APs' communication resources remain idle. This reduces overall network efficiency and undermines communication fairness among users of different target vehicles. Fourth, for two-stage resource allocation under FDMA technology, existing solution methods fail to adequately adapt to the complexity of mixed-integer nonlinear programming problems, resulting in low computational efficiency and insufficient optimization accuracy.

[0005] In summary, for the problem of communication blind spots between RSUs and target vehicles in urban environments, V2X two-hop transmission systems based on AP relays have become an effective solution. However, existing technologies still have significant shortcomings in joint optimization of relay selection and resource allocation, global optimization of end-to-end latency, and load balancing among APs. They cannot meet the requirements of low latency, high reliability, and high fairness in V2X communication. Therefore, there is an urgent need for a joint optimization method that can specifically solve the above problems and improve the transmission performance of V2X networks based on AP relays. Summary of the Invention

[0006] To address the aforementioned technical issues, a V2X network resource allocation and relay selection method based on AP relays is proposed, comprising the following steps:

[0007] S1: Construct a V2X dual-hop transmission system model based on AP relay;

[0008] S2: To minimize the end-to-end network latency, a mixed-integer nonlinear programming model is established for joint AP relay selection and two-stage communication resource allocation.

[0009] S3: The model is solved using a two-layer joint optimization algorithm. Under the given AP relay selection strategy, the inner layer decouples the resource allocation problem into two independent convex optimization subproblems: the first-stage bandwidth allocation and the second-stage joint bandwidth and power allocation.

[0010] S4: The outer layer uses a genetic algorithm, combined with the end-to-end delay calculated by the inner layer algorithm, to perform a global search in all possible AP relay selection strategy space. The reciprocal of the end-to-end delay calculated by the inner layer is used as the fitness function to obtain the optimal relay selection strategy.

[0011] The beneficial effects of this invention: Addressing the problem of communication link quality degradation or interruption between RSUs and target vehicles in urban environments due to obstruction, this invention proposes a V2X network resource allocation and relay selection method based on AP relays. This solves the pain points of existing technologies, such as decoupling optimization between the two, lack of global optimization of end-to-end latency, unbalanced AP load, and low solution efficiency. This invention constructs a two-hop transmission system model based on FDMA, and, combined with large-scale path loss channel characteristics, establishes a hybrid integer nonlinear programming model for joint relay selection and two-stage resource allocation. A two-layer joint optimization algorithm decouples the complex problem, reducing computational complexity, improving solution accuracy, and minimizing end-to-end latency. Simultaneously, the outer layer genetic algorithm achieves optimal AP relay selection, while the inner layer convex optimization dynamically allocates bandwidth and power, avoiding AP overload, achieving load balancing, ensuring fairness in user communication, improving network transmission real-time performance and reliability, and adapting to the needs of intelligent transportation and autonomous driving scenarios, demonstrating strong practicality. Attached Figure Description

[0012] Figure 1 This is a diagram illustrating a V2X relay network scenario using APs as relays according to the present invention.

[0013] Figure 2 This is a flowchart of a V2X network resource allocation and relay selection method based on AP relay according to the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] In the embodiments provided by this invention, communication between the RSU and the vehicle is sometimes interrupted, preventing direct communication. To address this, this invention constructs a two-hop relay transmission model based on FDMA, such as... Figure 1 As shown, where:

[0016] RSU: Roadside Unit, is a wireless infrastructure deployed on both sides of the road. In this embodiment of the invention, it serves as an information source and is mainly responsible for collecting real-time information such as surrounding traffic events, safety warnings, and high-precision maps, and sending the data required by the target vehicle to the selected AP through the transmission link.

[0017] AP: or Fixed Access Point, is a relay node deployed on the roadside. In this embodiment of the invention, it is mainly responsible for receiving data transmitted by the RSU and forwarding the data to the corresponding target vehicle through the forwarding link. A single AP can provide relay forwarding services to multiple target vehicles at the same time.

[0018] like Figure 2 As shown, this invention provides a V2X network resource allocation and relay selection method based on AP relay. This method constructs a two-hop relay transmission model based on FDMA, involving roadside units (RSUs), fixed wireless access points (APs), and target vehicles. A two-layer joint optimization algorithm is proposed: the inner layer, given a relay strategy, decouples the resource allocation problem into two convex optimization subproblems with minimax optimization forms. The Lambert W function combined with a bisection method is used to solve the first-stage bandwidth allocation, and the Lagrange duality and ellipsoidal method are used to solve the second-stage bandwidth and power joint allocation. The outer layer uses a genetic algorithm with the reciprocal of end-to-end delay as the fitness function to globally search for the optimal relay selection strategy. The algorithm is executed iteratively based on the two-layer iterative mechanism until convergence, outputting the optimal relay selection and resource allocation scheme, achieving coordinated optimization of end-to-end delay minimization, AP load balancing, and user communication fairness.

[0019] like Figure 2 As shown, this embodiment of the invention provides a method for V2X network resource allocation and relay selection based on AP relays, specifically including the following steps:

[0020] S1: Construct a V2X dual-hop transmission system model based on AP relay;

[0021] S2: To minimize the end-to-end network latency, a mixed-integer nonlinear programming model is established for joint AP relay selection and two-stage communication resource allocation.

[0022] S3: The model is solved using a two-layer joint optimization algorithm. Under the given AP relay selection strategy, the inner layer decouples the resource allocation problem into two independent convex optimization subproblems: the first-stage bandwidth allocation and the second-stage joint bandwidth and power allocation.

[0023] S4: The outer layer uses a genetic algorithm, combined with the end-to-end delay calculated by the inner layer algorithm, to perform a global search in all possible AP relay selection strategy space. The reciprocal of the end-to-end delay calculated by the inner layer is used as the fitness function to obtain the optimal relay selection strategy.

[0024] In this embodiment of the invention, step S1, which involves constructing a V2X two-hop transmission system model based on AP relay, specifically includes:

[0025] S111: The system includes Target vehicle nodes A fixed AP that can be used as a relay Each vehicle needs to acquire the perception information from an RSU equipped with sensing data. Due to poor quality of the direct link between the target vehicle and the corresponding RSU caused by obstruction, an access point (AP) is required to assist in forwarding the data to each target vehicle. Each RSU forwards the data required by the target vehicle through the AP.

[0026] S112: Target vehicle assembly This indicates that the RSU set is used AP relay set used It means that, among them .

[0027] S113: Assume that RSU-vehicle pairs are prior to each other, and that multiple communication pairs may exist based on different specific applications. Adjacent RSUs can be determined prior to each vehicle's navigation. Let the... The target vehicle needs to obtain the first... The data for each RSU, and the mapping relationship between them are as follows: Therefore, there is .

[0028] In this embodiment of the invention, after constructing the V2X dual-hop transmission system based on AP relay, the communication resource allocation is also described. Frequency division multiple access (FDMA) technology is used to divide the entire communication process into two time-orthogonal and non-overlapping stages, as specifically implemented as follows:

[0029] S121: In the first phase, all RSUs transmit data to the selected AP simultaneously. The total system bandwidth is divided into multiple orthogonal sub-bands and allocated to parallel communication links. Each RSU-AP link occupies a different sub-band. Therefore, even if multiple RSUs transmit to the same AP or multiple RSUs transmit to different APs, there will be no co-channel interference.

[0030] S122: In the second phase, all APs forward data to the target vehicle simultaneously, and interference between APs and between APs and different vehicles is avoided through orthogonal frequency band allocation.

[0031] In this embodiment of the invention, in step S13, it is assumed that the channel remains unchanged within a resource scheduling period, and the large-scale path loss determined by distance is mainly considered. Therefore, the first... The AP and the first The channel gain between the target vehicles is modeled as follows: ,in, No. The AP and the first The distance between the target vehicles For reference distance, Indicates the reference distance Path loss at the location, For path loss exponent, specifically, the l-th RSU and the l-th RSU The link channel gain of each AP is used It means that the first AP to the first The link channel gain of each target vehicle is used express.

[0032] In this embodiment of the invention, the joint AP relay selection and two-stage communication resource allocation stage in step S2 also needs to consider the dual objective constraints of minimizing the total end-to-end network latency and ensuring load balancing among APs. Therefore, taking the establishment process of a mixed-integer nonlinear programming problem as an example, the joint AP relay selection and two-stage communication resource allocation process may include:

[0033] S21: Define the relay selection strategy matrix The matrix is A two-dimensional matrix, where each element As the relay selection factor, representing the service relationship between the AP and the target vehicle, it is defined as follows: Indicates the first The AP serves the first One target vehicle.

[0034] S22: Set the optimization objective, which is to minimize the end-to-end network latency. The mathematical expression is: ,in, Allocate vectors for the first-phase link transmission bandwidth. Allocate vectors for the second-stage link transmission bandwidth. Assign vectors to the transmit power of the second-stage AP. For time delay vector, This refers to the transmission delay in the first and second stages.

[0035] S23: Set relay selection constraints: This means that each target vehicle has one and only one AP providing relay services, and a single AP can serve multiple target vehicles simultaneously.

[0036] S24: Set bandwidth resource constraints: ,in, The first phase is allocated to the first Bandwidth of a communication pair The second phase is allocated to the first Bandwidth of a communication pair This represents the total system bandwidth.

[0037] S25: Set power resource constraints: ,in, For the first An AP is forwarding vehicles Data is allocated to vehicles The transmission power, For the first Maximum transmit power of each AP.

[0038] S26: Set the first-stage delay constraint: ,in, For the first The amount of data that each RSU needs to transmit to the corresponding target vehicle. This refers to the transmit power of the RSU. This represents the noise power spectral density.

[0039] S27: Set the second-stage delay constraint: .

[0040] In this embodiment of the invention, the inner resource allocation stage in step S3 also needs to consider the total bandwidth constraint of the first stage and the maximum transmit power constraint of the AP in the second stage, and generate a two-stage optimal resource allocation scheme that satisfies all link delay constraints. Therefore, given a relay selection strategy Taking the convex optimization process of solving subproblems as an example, the joint optimization process of bandwidth in the first stage and bandwidth power in the second stage can be divided into two major sub-steps, as detailed below:

[0041] S31: This sub-step is given any relay selection strategy. Under the given conditions, solving the first-stage bandwidth allocation convex optimization subproblem involves the following three sub-steps:

[0042] S311: Proof of the first-stage bandwidth allocation subproblem P2: The constraint is C2: And C3: This is a convex optimization problem, and the optimal solution satisfies the following constraints: , .

[0043] S312: Using the Lambert W function and the equality condition, the optimal bandwidth allocation is obtained as follows: ,in, , This is the -1 branch of the Lambert W function. This is the optimal first-stage delay.

[0044] S313: Substitute the optimal bandwidth allocation We obtain information about the optimal solution. The equation is The optimal solution is found using the bisection method. The equation is used to obtain the optimal latency and bandwidth allocation.

[0045] S32: This sub-step also uses a given relay selection strategy. Under the given conditions, the second-stage convex optimization subproblem of joint bandwidth and power allocation is solved in the following six steps:

[0046] S321: Prove the second-stage bandwidth and power joint allocation subproblem The constraints are , , This is a convex optimization problem.

[0047] S322: Construct the Lagrangian function for this subproblem, transforming the original optimization problem into a dual problem by introducing non-negative Lagrangian multipliers. , and After simplification, we obtain the expression for the dual problem: The dual function is: .

[0048] S323: Given , and Problem P3.2 can be divided into two subproblems: , .

[0049] S324: For subproblems The optimal solution for end-to-end delay is obtained as follows: .

[0050] S325: Since each subproblem in subproblem P3.4 is convex and satisfies the Slater conditions, there is strong duality between problem P3.2 and its dual problem. Therefore, this problem can be solved by applying the KKT conditions, yielding the optimal solution. and Solve problem P3.1.

[0051] S326: The ellipsoidal method is used to solve problem P3.1. Finally, when the algorithm converges, the optimal solution to the dual problem P3.1 is obtained as follows: , and Therefore, the optimal solution for the time delay of problem P3 is obtained as follows: .

[0052] In this embodiment of the invention, the AP relay selection stage in step S4 also needs to consider the relay selection uniqueness constraint, the AP maximum transmit power constraint, and the multiple constraints of the end-to-end delay minimization objective, and generate the optimal AP relay selection strategy that satisfies all resource constraint boundaries. Therefore, taking the genetic algorithm global search solution of the subproblem process under a given inner-layer optimal resource allocation scheme as an example, the global optimal relay selection process for relay pairing between the target vehicle and the AP can include the following 6 sub-steps:

[0053] S41: Uses integer encoding, where the length of each chromosome equals the number of target vehicles. The chromosome length is equal to the number of target vehicles. , No. Value of each gene locus Indicates the target vehicle The selected AP number.

[0054] S42: Fitness Function Design: The design of the fitness function is the core of the genetic algorithm's performance. Essentially, it transforms the inner-layer optimization results into a quantitative indicator for evaluating the merits of the relay selection strategy. The specific steps are as follows:

[0055] S421: Decode each chromosome in the population to obtain the corresponding... dimensional relay selection matrix .

[0056] S422: Call the inner algorithm of S3 to calculate the end-to-end delay under this relay selection strategy. .

[0057] S423: The fitness function is the reciprocal of the end-to-end delay, i.e. A higher fitness value indicates better performance of the relay selection strategy.

[0058] S43: A truncation selection scheme is used to preserve high-quality chromosomes and maintain population diversity. Specifically, in each generation of evolution, all chromosomes in the population are first sorted according to their fitness values, and then only the chromosomes with the highest fitness are retained. Each individual serves as a parent chromosome. These selected parent chromosomes are copied into a mating pool for subsequent crossover operations. The parent chromosomes in the mating pool are randomly paired in preparation for the next step of the crossover operation.

[0059] S44: A uniform crossover strategy is used to perform the crossover operation. Two parental chromosomes are randomly selected from the mating pool. and The generated length is cross mask vector Each element Each chromosome independently takes the value 0 or 1 with a probability of 0.5. Then, two offspring chromosomes are generated according to the following calculation rules. and :

[0060] S441: If Then offspring In the Inherited at each gene locus The value of the offspring In the Inherited at each gene locus The value of .

[0061] S442: If Then offspring and In the Inherited from each gene locus and The value of .

[0062] S45: Perform the mutation operation again, maintaining population diversity by introducing random perturbations. For each offspring chromosome, first generate a random number with a preset mutation probability. The value is 1, with probability. If the random value is 0, then two different gene loci are randomly selected from that chromosome. and ,in They exchange gene loci at these two locations.

[0063] S46: Let the number of generations be... The population size is The algorithm iterates until the preset maximum number of iterations is reached. Or the optimal fitness value is in a continuous The relative change within a generation is less than the threshold. After the algorithm terminates, it outputs the chromosome with the highest fitness in the current population. The relay selection strategy matrix corresponding to the decoded chromosome is the approximate global optimal solution.

[0064] The above-described embodiments further illustrate the purpose, technical solution, and advantages of the present invention. It should be understood that the above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made to the present invention within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for resource allocation and relay selection in a vehicle-to-everything (V2X) network based on wireless access point (AP) relay, characterized in that, include: S1: Construct a V2X dual-hop relay transmission system model based on AP relay; S2: To minimize the end-to-end network latency, a mixed-integer nonlinear programming model is established for joint AP relay selection and two-stage communication resource allocation. S3: The model is solved using a two-layer joint optimization algorithm. Under the given AP relay selection strategy, the inner layer decouples the resource allocation problem into two independent convex optimization subproblems: the first-stage bandwidth allocation and the second-stage joint bandwidth and power allocation. S4: The outer layer uses a genetic algorithm, combined with the end-to-end delay calculated by the inner layer algorithm, to perform a global search in all possible AP relay selection strategy space. The reciprocal of the end-to-end delay calculated by the inner layer is used as the fitness function to obtain the optimal relay selection strategy.

2. The V2X network resource allocation and relay selection method based on AP relay according to claim 1, characterized in that, The S1 constructs a V2X dual-hop transmission system model based on AP relay, including: S11: Define the system includes One target vehicle A fixed AP that can act as a relay and There are 10 roadside units (RSUs), among which Each target vehicle corresponds to an RSU for which information needs to be acquired; S12: Frequency Division Multiple Access (FDMA) technology is used to divide the communication process into two time-orthogonal stages. The first stage is that all RSUs transmit data to the selected AP at the same time. The second stage is that all APs forward data to the target vehicles they serve at the same time. The two stages are performed sequentially and do not overlap. S13: Assuming the channel remains constant within a resource scheduling cycle, considering only the large-scale path loss determined by distance, the... The AP and the first The channel gain between the target vehicles is: ,in, No. The AP and the first The distance between the target vehicles For reference distance, Indicates the reference distance Path loss at the location, This is the path loss index.

3. The V2X network resource allocation and relay selection method based on AP relay according to claim 1, characterized in that, The S2 establishes a mixed-integer nonlinear programming model for joint AP relay selection and two-stage communication resource allocation, including: S21: Define the relay selection strategy matrix The matrix is A two-dimensional matrix, where each element As a relay selection factor, when When, it indicates the first The AP is the first Provide relay services for each target vehicle; S22: Set the optimization objective to minimize network end-to-end latency: ,in, Allocate vectors for the first-phase link transmission bandwidth. Allocate vectors for the second-stage link transmission bandwidth. Assign vectors to the transmit power of the second-stage AP. For time delay vector, This refers to the transmission delay in the first and second stages; S23: Set relay selection constraints: This means that each target vehicle has one and only one AP providing relay service, and a single AP can serve multiple target vehicles simultaneously. S24: Set bandwidth resource constraints: ,in, The first phase is allocated to the first Bandwidth of a communication pair The second phase is allocated to the first Bandwidth of a communication pair This represents the total system bandwidth. S25: Set power resource constraints: ,in, For the first An AP is forwarding vehicles Data is allocated to vehicles The transmission power, For the first Maximum transmit power of each AP; S26: Set the first-stage delay constraint: ,in, For the first The amount of data that each RSU needs to transmit to the corresponding target vehicle. This refers to the transmit power of the RSU. The noise power spectral density; S27: Set the second-stage delay constraint: .

4. The method for V2X network resource allocation and relay selection based on AP relay according to claim 1, characterized in that, The solution method for the first-stage bandwidth allocation convex optimization subproblem in S3 is as follows: It is proven that this subproblem is a convex optimization problem and that the total bandwidth constraint and all constraint inequalities hold true for the optimal solution. Using the Lambert W function, the optimal bandwidth allocation is obtained as follows: ,in, , This is the -1 branch of the Lambert W function. To achieve the optimal first-stage delay; Find the optimal solution using the bisection method. The equation: This allows for optimal latency and bandwidth allocation.

5. The V2X network resource allocation and relay selection method based on AP relay according to claim 1, characterized in that, The solution method for the second-stage convex optimization subproblem of joint bandwidth and power allocation in S3 is as follows: Prove that the subproblem is a convex optimization problem, construct a Lagrangian function to transform the original problem into a dual problem, and then divide the dual problem into two subproblems; Subproblem 1 can be directly solved by obtaining the optimal end-to-end delay solution as follows: ,in , and The nonnegative Lagrange multipliers introduced for this dual problem; Subproblem 2 can be solved by applying the KKT conditions to obtain the optimal solution. and Solve the dual problem; The transformed dual problem is solved using the ellipsoidal method. Finally, when the algorithm converges, the optimal solution to the dual problem is found. , and This leads to the optimal second-stage delay expression: .

6. The method for V2X network resource allocation and relay selection based on AP relay according to claim 1, characterized in that, The outer layer of S4 employs a genetic algorithm to globally search for the optimal AP relay selection strategy, including: S41: The population is initialized using integer encoding. The chromosome length is equal to the number of target vehicles, and the value of each gene bit represents the AP number selected by the corresponding target vehicle. S42: Decode each chromosome to obtain the corresponding... Dimensional relay selection strategy matrix The inner algorithm is called to calculate the end-to-end latency under this strategy. The fitness function is defined as follows: ; S43: Employ a truncation selection scheme, retaining the candidates with the highest fitness values. Each individual serves as a parent and is replicated into the mating pool, where parental chromosomes are randomly paired. S44: Perform uniform crossover on randomly paired parent chromosomes in the mating pool to generate offspring chromosomes; S45: Perform a crossover mutation operation on the offspring chromosome with a preset mutation probability, randomly exchanging gene loci at two positions; S46: The algorithm iterates until the preset maximum number of iterations is reached, or the optimal fitness value is continuously... The relative change within a generation is less than the threshold. Output the AP relay selection strategy corresponding to the chromosome with the highest fitness value.

7. The method for V2X network resource allocation and relay selection based on AP relay according to any one of claims 1-6, characterized in that, The method is applicable to scenarios in urban environments where the quality of the direct communication link between the RSU and the target vehicle deteriorates or is interrupted due to building obstruction or excessive distance. The relay node uses a decoding and forwarding protocol to forward signals.

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