SWIPT multi-hop relay power minimization system and method based on throughput constraint

Abstract

A SWIPT multi-hop relay power minimization method and system based on throughput constraint, and the method comprises the following steps: under the constraint that the throughput of a multi-hop relay wireless communication system is not lower than a required minimum reachable rate threshold, minimizing the transmitting power of a source node by jointly optimizing the PS ratio of each relay and the transmitting power of the source node. The invention aims to solve the technical problems that in the existing SWIPT multi-hop relay technology based on the PS protocol, the optimization target is single, energy efficiency and performance are difficult to consider, optimization variables are isolated, cooperation of power and PS ratio is lacked, the method complexity is high, and the real-time requirement is difficult to meet, so that the overall performance of the network and the energy efficiency of the system are influenced, and the system performance is influenced. The SWIPT multi-hop relay power minimization technology based on the throughput constraint is provided.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to SWIPT multi-hop relay technology based on the PS protocol, specifically to a SWIPT multi-hop relay power minimization system and method based on throughput constraints. Background Technology

[0002] Wireless multi-hop relay networks, through the coordinated operation of multiple relay nodes, forward data hop-by-hop from the source node to the destination node, effectively extending communication coverage and improving signal transmission reliability in complex environments. This network structure has significant application value in scenarios such as wireless sensor networks (WSN), the Internet of Things (IoT), and emergency communications. However, relay nodes typically rely on batteries with limited capacity, and the cost of energy replenishment and replacement maintenance in remote or harsh environments is high, severely restricting the long-term stable operation of the network.

[0003] To address the aforementioned energy constraints, wireless energy harvesting (EH) technology has emerged, allowing nodes to collect energy from ambient radio frequency signals, achieving a degree of self-sufficiency. Building upon this, simultaneous wireless information and power transfer (SWIPT) technology further enables simultaneous information transmission and energy harvesting using the same radio frequency signal, providing a promising solution for energy-constrained multi-hop relay networks. In the practical implementation of SWIPT technology, the power-splitting (PS) protocol is a core mechanism. It uses a power divider to split the received signal into two parts according to a certain ratio: one part for energy harvesting and the other for information decoding. Compared to time-switching protocols, the PS protocol allows devices to perform energy harvesting and information decoding simultaneously, resulting in different trade-offs in system design.

[0004] In a SWIPT multi-hop relay system based on the PS protocol, the power allocation ratio (PS ratio) and the source node transmit power are key parameters affecting system performance. The PS ratio determines the proportion of signal power used by relay nodes for energy harvesting and information decoding, while the source node transmit power directly affects the system's energy consumption and network lifetime. Especially in multi-hop scenarios, the PS ratio and source node power settings at each relay node are coupled: changes in the PS ratio of a single node affect its energy harvesting efficiency, thereby changing the available signal power of downstream nodes; while adjustments to the source node power affect the energy distribution and throughput of the entire network through a hop-by-hop transmission chain. This makes the joint optimization of the PS ratio and source node power a complex nonlinear constraint problem.

[0005] Existing research largely focuses on maximizing system throughput or transmission rate by optimizing the power-to-spindle (PS) ratio, such as using convex optimization techniques or heuristic algorithms to search for the optimal PS ratio combination. However, these methods have significant limitations: on the one hand, existing technologies like "Throughput Optimization Method and Device for Multihop Amplify-and-Forward Relay Systems with Simultaneous Wireless Information and Power Transfer" have a single optimization objective and fail to fully consider the importance of minimizing source node power for extending network lifetime; on the other hand, existing technologies like "Transmit Power Optimization in Multihop Amplify-and-Forward Relay Systems with Simultaneous Wireless Information and Power Transfer" rely on highly complex numerical iterations or exhaustive searches, making them difficult to implement in real-time on resource-constrained relay nodes. Furthermore, how to jointly and dynamically adjust the PS ratio and source node power to achieve energy-efficient operation while meeting specific throughput constraints remains a key problem that existing technologies have not fully solved.

[0006] Therefore, for SWIPT multi-hop relay networks, developing an efficient control system and method that can minimize total system power consumption and extend network lifetime by jointly optimizing the PS ratio and source node transmit power while ensuring throughput requirements has significant theoretical and practical value. This patent aims to explore this direction in depth.

[0007] Therefore, the existing technology has the following specific defects and shortcomings: 1. The optimization objective is singular, making it difficult to balance energy efficiency and performance; Most existing technologies treat optimization objectives in isolation, lacking a systematic approach. Many studies focus primarily on maximizing system throughput or transmission rate by optimizing the power-to-spindle (PS) ratio, failing to consider minimizing source node transmit power to extend network lifetime as a key optimization objective. This single-objective optimization approach prevents the system from minimizing total energy consumption while maintaining a certain quality of communication service (such as minimum throughput requirements), making it difficult to meet the stringent energy efficiency requirements of IoT or wireless sensor network applications that require long-term unattended operation. Although some studies have addressed power minimization, they often fail to deeply integrate it with the PS ratio in multi-hop relay networks, thus failing to fully leverage the coupling relationships between parameters to maximize system energy efficiency.

[0008] 2. The optimization variables are isolated, lacking coordination between power and PS ratio; In terms of resource allocation strategies, existing schemes often optimize parameters such as PS ratio or power in isolation, failing to fully consider the profound coupling relationship between PS ratio and source node transmit power. In multi-hop SWIPT relay networks, the transmit power of the source node and the PS ratio of each relay node influence each other, jointly determining the end-to-end throughput and the total energy consumption of the system. For example, simply reducing the power of the source node may force downstream relay nodes to adjust their PS ratios to maintain necessary energy harvesting, thus affecting overall performance. Some studies have attempted joint optimization, but this may be difficult to apply quickly in practical systems due to high model complexity or inefficient solution methods. This lack of coordination in optimization methods fails to achieve optimal allocation of system resources.

[0009] 3. The algorithm has high complexity, making it difficult to meet real-time requirements; To achieve the aforementioned optimizations, existing algorithms are typically computationally complex and lack practical feasibility. Some studies employ highly complex iterative algorithms (such as those based on convex optimization techniques, gradient descent, or intelligent optimization algorithms) to solve joint optimization problems. However, in multi-hop networks, the dimensionality of the optimization variables increases with the number of relay nodes, leading to a sharp rise in computational overhead. This high computational complexity makes it difficult for the algorithms to run in real-time on practical relay nodes where computational power, energy, and latency are limited, thus rendering them unsuitable for application in real-world communication systems requiring rapid response to channel changes. Summary of the Invention

[0010] The purpose of this invention is to address the technical problems in existing SWIPT multi-hop relay technology based on the PS protocol, such as the single optimization objective, difficulty in balancing energy efficiency and performance, isolated optimization variables, lack of coordination between power and PS ratio, high method complexity, and difficulty in meeting real-time requirements, which affect the overall network performance and system energy efficiency. Therefore, this invention proposes a SWIPT multi-hop relay power minimization technology based on throughput constraints.

[0011] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A throughput-constrained SWIPT multi-hop relay power minimization method is proposed. Under the constraint that the throughput of the multi-hop relay wireless communication system is not lower than the required minimum achievable rate threshold, the method jointly optimizes the PS ratio of each relay. Source node transmit power To minimize the source node transmit power .

[0012] The minimum achievable rate threshold is represented by the symbol In a multi-hop relay wireless communication system, the minimum quality of service (measured by reachable rate) requirement that the end-to-end throughput (i.e., reachable rate) from the source node to the destination node must meet is indicated. It is a preset system performance constraint used to ensure the minimum reliability or efficiency of data transmission.

[0013] When performing joint optimization, the source node transmit power is used as the basis. The objective function is the power allocation ratio. Source node transmit power To optimize the variables, we construct the following constrained optimization problem: (13a); st , (13b); (13c); (13d); Where (13a) is the objective function, and (13b), (13c), and (13d) are constraints. and Representing the source node respectively The minimum and maximum transmit power, This represents the minimum achievable rate threshold required by the system.

[0014] The following steps are used for optimization: Step 1: Calculate the baseline power value for each jump. ; Step 2: Compare the power reference with the transmit power range, and obtain the optimal source node transmit power based on the comparison results.

[0015] In step 1, based on system parameters and the minimum achievable rate threshold Calculate the power baseline value for each hop: , (14); in, These are auxiliary variables related to channel gain, noise power, and rectification efficiency. This reflects the need to meet system throughput requirements in the first... The minimum power contribution required for a jump; In step 2, the sum of the power reference values ​​for each jump is calculated. and the allowable range of the source node's transmit power. Compare the results and perform the following operations based on the comparison results: Case 1) If Then the optimal source node transmit power takes the minimum value: (15); Case 2) If The optimal source node transmit power is taken as the sum of the power reference: , (18); Case 3) If If the system throughput constraint is not met, then there is no feasible solution.

[0016] In step 2, the optimal source node transmit power is obtained. To obtain the optimized PS ratio for each relay, the specific steps are as follows: For situation 1), the following steps are adopted: Step 1-1) First calculate the last relay Optimal power allocation ratio at: (16); Computational relay The optimal PS ratio at this location; Steps 1-2) use an iterative approach to calculate the optimal power allocation ratio for the remaining relays sequentially from front to back: , (17); In an iterative manner, it is known Calculate the optimal PS ratio .

[0017] For situation 2), the following steps are adopted: Step 2-1): Calculate the last relay The optimal power allocation ratio at the location is the same as in formula (16), that is: ; Computational relay The optimal PS ratio at this location; Step 2-2): Iteratively calculate the optimal power allocation ratio for the remaining relays, using the same formula as (17), i.e.: , ; In an iterative manner, it is known Calculate the optimal PS ratio .

[0018] The system (see) Figure 1 Specifically: Including source node Source node via relay set hop-by-hop forwarding of information to the destination node Source node Powered by internal battery or external power source; relay To relay All operate in a passive state; relay nodes ,in Using SWIPT technology from relay nodes The previous hop neighbor node Energy is collected from the radio frequency signal, and the power allocation (PS) protocol is used to simultaneously decode data packets and forward them to the next hop neighboring node; the power allocation ratio, also known as the PS ratio, is set as... Then the relay node The radio frequency signal at that location is split into two paths: Part of it is used for energy harvesting, the remainder Part of it is used for information decoding.

[0019] Assume all nodes are equipped with a single antenna and operate in half-duplex mode, meaning relay nodes only forward data after fully receiving the information; and The channel coefficient between them is denoted as ,in Furthermore, the channel coefficient remains constant during the time it takes for data to be transmitted from one node to the next adjacent node, i.e., within a transmission frame.

[0020] relay node The energy harvesting circuit receives the radio frequency signal. Part of the energy is harvested and stored in a supercapacitor; all the harvested energy is used for information decoding and data retransmission. Set source node It possesses channel state information for all nodes, and each relay node... and destination node Knowing only the channel state information of its corresponding communication link and ; Assume that there are direct links only between adjacent nodes; relay nodes From the previous hop adjacent node The received radio frequency signal can be represented as: (1); in, Indicates that it comes from the previous hop neighbor node. The information symbols, each with a unit average power, i.e. ; express The power spectral density of the emitted power.

[0021] like Figure 2 (a), Represents a node The power spectral density of the additive white Gaussian noise introduced at the antenna follows a distribution. , express The variance; such as Figure 1 and Figure 2 As shown in (a), relay node The received radio frequency signal is then distributed according to the power allocation ratio. It was divided into two paths, one for energy harvesting and the other for information decoding.

[0022] The signal used for energy harvesting can be represented as: (2); relay node The signal used for information decoding can be represented as: (3); in It is a relay node The power spectral density of the additive white Gaussian noise introduced by the information decoding circuit follows a distribution. , express The variance; assume all collected energy is used for information decoding and data retransmission; therefore, according to formula (2), the node The power spectral density of the emitted power can be derived as follows: (4); in Represents relay node The rectification efficiency factor; due to Then we further obtain: (5); in Represents a node and Channel gain between; Indicates the source node The power spectral density of the emitted power; according to formula (3) and relay node The received signal-to-noise ratio at that point can be expressed as: (6); in, This refers to the power spectral density of additive noise. The variance of This refers to the power spectral density of additive noise. variance For relay The rectification efficiency For relay PS ratio, for and Channel gain between.

[0023] In power-sharing-based SWIPT systems, the additive noise at the antenna is typically much smaller than the noise introduced by the information decoding circuitry; therefore, in Under these conditions, we further obtain: (7); in ; This refers to a node corresponding to a relay node. Auxiliary variables, Refers to the source node With relay node Channel gain between; Similarly, based on formulas (3), (5) and relay node The received signal-to-noise ratio at that point can be expressed as: (8); in It is a node corresponding to a relay node. Auxiliary variables.

[0024] like Figure 1 and Figure 2 As shown in (b), due to the destination node If only information decoding is performed without energy harvesting, the signal used for information decoding at this node can be represented as: (9); in, For relay nodes With the target node Channel coefficients between; It is a relay The power spectral density of the emitted power; It comes from The information symbol has a unit average power, that is... ; Indicates the destination node The power spectral density of additive white Gaussian noise introduced at the antenna. ; Indicates the destination node The power spectral density of additive white Gaussian noise introduced by the information decoding circuit. .

[0025] node The received signal-to-noise ratio at this location is: (10); in , is a corresponding Auxiliary variables. It is a relay node With the target node Channel gain between.

[0026] In obtaining signal-to-noise ratio After that, the The reachability of a jump can be expressed as: (11); in Indicates the transmission bandwidth; information needs to pass through [various stages] to travel from the source node to the destination node. Jump Therefore, the end-to-end throughput of the multi-hop decode-forward relay network can be expressed as: (The number of transmission frames is not specified in the original text.) (12).

[0027] Compared with the prior art, the present invention has the following technical effects: This invention significantly reduces the source node's transmit power by jointly optimizing the power allocation ratio and source node transmit power, while ensuring that the system throughput does not fall below the minimum quality of service requirement, thereby effectively extending the overall network lifetime. The core advantages of this method lie in its superior energy efficiency, low computational complexity, and excellent engineering feasibility. Compared to existing strategies that only optimize throughput or employ fixed power allocation, this invention achieves the optimal balance between system energy consumption and communication performance through coordinated optimization of the power allocation ratio (PS ratio) and source node power. The proposed optimization algorithm can be solved quickly with a light computational burden that does not increase dramatically with the number of relay nodes, making it highly suitable for real-time operation on real-world relay nodes with limited computational resources, ensuring the applicability of the solution in practical communication systems. In typical multi-hop scenarios, this solution can significantly reduce source node power consumption and increase the network lifetime to more than 10 times that of fixed PS ratio solutions, providing an efficient and practical solution for the long-term stable operation of energy-constrained multi-hop relay networks. Attached Figure Description

[0028] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of a multi-hop DF relay communication network using PS-SWIPT in this invention; Figure 2 This is a schematic diagram of the signal processing flow between the relay node and the destination node in this invention; wherein, (a) corresponds to the relay node. ,in (b) Corresponding to the destination node ; Figure 3 This is a comparison chart of system throughput and minimum quality of service requirements in the embodiment. ); Figure 4 This is a comparison chart of network lifetime and minimum service quality requirements in the embodiment. ); Figure 5 This is a flowchart of the method of the present invention. Detailed Implementation

[0029] like Figure 5 As shown, a throughput-constrained SWIPT multi-hop relay power minimization method is proposed. Under the constraint that the throughput of the multi-hop relay wireless communication system is not lower than the required minimum achievable rate threshold, the method jointly optimizes the PS ratio of each relay. Source node transmit power To minimize the source node transmit power .

[0030] The minimum achievable rate threshold is represented by the symbol In a multi-hop relay wireless communication system, the minimum quality of service (measured by reachable rate) requirement that the end-to-end throughput (i.e., reachable rate) from the source node to the destination node must meet is indicated. It is a preset system performance constraint used to ensure minimum reliability or efficiency of data transmission. The minimum reachable rate threshold is described in the embodiments of this specification. It varies between 1 bps and 10 bps (as shown in Table 1).

[0031] When performing joint optimization, the source node transmit power is used as the basis. The objective function is the power allocation ratio. Source node transmit power To optimize the variables, we construct the following constrained optimization problem: (13a); st , (13b); (13c); (13d); Where (13a) is the objective function, and (13b), (13c), and (13d) are constraints. and Representing the source node respectively The minimum and maximum transmit power, This represents the minimum achievable rate threshold required by the system.

[0032] The following steps are used for optimization: Step 1: Calculate the baseline power value for each jump. ; Step 2: Compare the power reference with the transmit power range, and obtain the optimal source node transmit power based on the comparison results.

[0033] In step 1, based on system parameters and the minimum achievable rate threshold Calculate the power baseline value for each hop: , (14); in, These are auxiliary variables related to channel gain, noise power, and rectification efficiency. This reflects the need to meet system throughput requirements in the first... The minimum power contribution required for a jump; In step 2, the sum of the power reference values ​​for each jump is calculated. and the allowable range of the source node's transmit power. Compare the results and perform the following operations based on the comparison results: Case 1) If Then the optimal source node transmit power takes the minimum value: (15); Case 2) If The optimal source node transmit power is taken as the sum of the power reference: , (18); Case 3) If If the system throughput constraint is not met, then there is no feasible solution.

[0034] In step 2, the optimal source node transmit power is obtained. To obtain the optimized PS ratio for each relay, the specific steps are as follows: For situation 1), the following steps are adopted: Step 1-1) First calculate the last relay Optimal power allocation ratio at: (16); Computational relay The optimal PS ratio at this location; Steps 1-2) use an iterative approach to calculate the optimal power allocation ratio for the remaining relays sequentially from front to back: , (17); In an iterative manner, it is known Calculate the optimal PS ratio .

[0035] For situation 2), the following steps are adopted: Step 2-1): Calculate the last relay The optimal power allocation ratio at the location is the same as in formula (16), that is: ; Computational relay The optimal PS ratio at this location; Step 2-2): Iteratively calculate the optimal power allocation ratio for the remaining relays, using the same formula as (17), i.e.: , ; In an iterative manner, it is known Calculate the optimal PS ratio .

[0036] The system (see) Figure 1 Specifically: Including source node Source node via relay set hop-by-hop forwarding of information to the destination node Source node Powered by internal battery or external power source; relay To relay All operate in a passive state; relay nodes ,in Using SWIPT technology from relay nodes The previous hop neighbor node Energy is collected from the radio frequency signal, and the power allocation (PS) protocol is used to simultaneously decode data packets and forward them to the next hop neighboring node; the power allocation ratio, also known as the PS ratio, is set as... Then the relay node The radio frequency signal at that location is split into two paths: Part of it is used for energy harvesting, the remainder Part of it is used for information decoding.

[0037] Assume all nodes are equipped with a single antenna and operate in half-duplex mode, meaning relay nodes only forward data after fully receiving the information; and The channel coefficient between them is denoted as ,in Furthermore, the channel coefficient remains constant during the time it takes for data to be transmitted from one node to the next adjacent node, i.e., within a transmission frame.

[0038] relay node The energy harvesting circuit receives the radio frequency signal. Part of the energy is harvested and stored in a supercapacitor; all the harvested energy is used for information decoding and data retransmission. Set source node It possesses channel state information for all nodes, and each relay node... and destination node Knowing only the channel state information of its corresponding communication link and ; Assume that there are direct links only between adjacent nodes; relay nodes From the previous hop adjacent node The received radio frequency signal can be represented as: (1); in, Indicates that it comes from the previous hop neighbor node. The information symbols, each with a unit average power, i.e. ; express The power spectral density of the emitted power.

[0039] like Figure 2 (a), Represents a node The power spectral density of the additive white Gaussian noise introduced at the antenna follows a distribution. , express The variance; such as Figure 1 and Figure 2 As shown in (a), relay node The received radio frequency signal is then distributed according to the power allocation ratio. It was divided into two paths, one for energy harvesting and the other for information decoding.

[0040] The signal used for energy harvesting can be represented as: (2); relay node The signal used for information decoding can be represented as: (3); in It is a relay node The power spectral density of the additive white Gaussian noise introduced by the information decoding circuit follows a distribution. , express The variance; assume all collected energy is used for information decoding and data retransmission; therefore, according to formula (2), the node The power spectral density of the emitted power can be derived as follows: (4); in Represents relay node The rectification efficiency factor; due to Then we further obtain: (5); in Represents a node and Channel gain between; Indicates the source node The power spectral density of the emitted power; according to formula (3) and relay node The received signal-to-noise ratio at that point can be expressed as: (6) in, This refers to the power spectral density of additive noise. The variance of This refers to the power spectral density of additive noise. variance For relay The rectification efficiency For relay PS ratio, for and Channel gain between.

[0041] In power-sharing-based SWIPT systems, the additive noise at the antenna is typically much smaller than the noise introduced by the information decoding circuitry; therefore, in Under these conditions, we further obtain: (7); in ; This refers to a node corresponding to a relay node. Auxiliary variables, Refers to the source node With relay node Channel gain between; Similarly, based on formulas (3), (5) and relay node The received signal-to-noise ratio at that point can be expressed as: (8); in It is a node corresponding to a relay node. Auxiliary variables.

[0042] like Figure 1 and Figure 2 As shown in (b), due to the destination node If only information decoding is performed without energy harvesting, the signal used for information decoding at this node can be represented as: (9); in, For relay nodes With the target node Channel coefficients between; It is a relay The power spectral density of the emitted power; It comes from The information symbol has a unit average power, that is... ; Indicates the destination node The power spectral density of additive white Gaussian noise introduced at the antenna. ; Indicates the destination node The power spectral density of additive white Gaussian noise introduced by the information decoding circuit. .

[0043] node The received signal-to-noise ratio at this location is: (10); in , is a corresponding Auxiliary variables. It is a relay node With the target node Channel gain between.

[0044] In obtaining signal-to-noise ratio After that, the The reachability of a jump can be expressed as: (11); in Indicates the transmission bandwidth; information needs to pass through [various stages] to travel from the source node to the destination node. Jump Therefore, the end-to-end throughput of the multi-hop decode-forward relay network can be expressed as: (The number of transmission frames is not specified in the original text.) (12).

[0045] Example: The performance of the proposed optimal solution based on closed-form was evaluated through numerical simulation. This solution was applied to a multi-hop DF relay system supporting SWIPT. The numerical simulation was performed on a laptop equipped with an AMD Ryzen R9 7945HX processor and 64GB of memory. The algorithm implementation and data analysis were performed using MATLAB R2022b as the software platform.

[0046] Unless otherwise specified, the simulation in this section uses the following parameter settings: nodes ( The power spectral density of the additive white Gaussian noise introduced by the information decoding circuit is: dBm / Hz; transmission bandwidth MHz; the rectification efficiency of each relay node is uniformly set to ( ); source node The minimum transmit power is dBm, maximum transmit power is dBm.

[0047] Furthermore, it is assumed that in the multi-hop relay network system under consideration, the distance between any two nodes in each hop is equal, and the total distance is 5 meters. Specifically, if the model contains... A relay network contains a multi-hop relay network. Jump, with a spacing of 1 / 20 between each jump. rice.

[0048] The channel model used in the simulation considers both large-scale and small-scale fading. Large-scale fading employs a logarithmic distance path loss model with a path loss exponent of 3.8, a carrier frequency of 2.4 GHz, and a reference distance of 1 meter. The small-scale fading model assumes a line-of-sight link between each hop's transmitting and receiving nodes, and the channel gain... ( The distribution follows a Rice distribution with a K-factor of 7. All simulation results were obtained by averaging the results after 1000 random channel simulations.

[0049] Our comparison algorithm includes an optimal solution obtained through exhaustive search, and a suboptimal solution using a fixed PS ratio and a fixed source node transmit power. The configuration of the optimal solution based on exhaustive search is as follows: in addition to setting the PS ratio search step size to 0.02, we also set the source node transmit power search step size to 1 dB. Furthermore, for the three suboptimal solutions, the PS ratios are fixed at 0.25, 0.5, and 0.75, respectively, and the source node transmit power is fixed at 30 dBm.

[0050] first, Figure 3 Table 1 shows the system throughput and minimum quality of service requirements (i.e., the minimum achievable rate threshold of the system). The relationship between ) . The number of relays is set to The corresponding hop distance is 2.5 meters. The minimum service quality requirement varies between 1 bps and 10 bps. Results show that the proposed scheme achieves performance identical to the optimal scheme based on exhaustive search, confirming the former's optimality. Furthermore, it can be observed that the throughput of the proposed scheme exactly meets and equals the set minimum service quality requirement. Conversely, the throughput of the three schemes using a fixed PS ratio and fixed source node transmit power remains unchanged, unaffected by changes in the minimum service quality requirement. Although when At lower values, the suboptimal solutions with fixed PS ratios of 0.5 and 0.75 have higher throughput than the proposed solution, but they obviously cannot guarantee the minimum quality of service requirements of the system.

[0051] Table 1. Relationship between system throughput and minimum service quality requirements for five schemes

[0052] Next, we evaluate the proposed scheme's ability to extend network lifetime. Network lifetime is defined as the time period from the initial state of the source node until its energy is depleted. The number of relays is set to... The jump distance is 2.5 meters. The initial energy of the source node is set to 1 joule. We assume the frame duration for data transmission is 10 milliseconds. In the theoretical analysis, the execution time of all schemes is assumed to be negligible. Figure 4 As shown in Table 2, the proposed scheme achieves a longer network lifetime compared to the suboptimal scheme using a fixed PS ratio.

[0053] Table 2. Relationship between network lifespan and minimum service quality requirements for five schemes

[0054] As can be seen from this table, for example, when At bps, the network lifetime is seconds; when At bps, for The proposed scheme achieves network lifetimes of 10 times and 1.42 times that of the three fixed PS ratio schemes, respectively. Although the proposed scheme's network lifetime shortens as the minimum quality of service (QoS) requirement increases, it simultaneously guarantees the QoS requirement, which is something the three fixed PS ratio schemes cannot achieve.

Claims

1. A method for minimizing the power of a SWIPT multi-hop relay based on throughput constraints, characterized in that, Under the constraint that the throughput of a multi-hop relay wireless communication system is not less than the required minimum achievable rate threshold, the PS ratio of each relay is jointly optimized. Source node transmit power To minimize the source node transmit power .

2. The method according to claim 1, characterized in that, The minimum achievable rate threshold is represented by the symbol In a multi-hop relay wireless communication system, the minimum quality of service (QoS) required to achieve the end-to-end throughput (AQ) from the source node to the destination node must be met, as measured by the AQ. It is a preset system performance constraint used to ensure minimum reliability or efficiency in data transmission.

3. The method according to claim 1, characterized in that, When performing joint optimization, the source node transmit power is used as the basis. The objective function is the power allocation ratio. Source node transmit power To optimize the variables, we construct the following constrained optimization problem: (13a)； s.t. , (13b); (13c)； (13d)； Where (13a) is the objective function, and (13b), (13c), and (13d) are constraints. and Representing the source node respectively The minimum and maximum transmit power, This represents the minimum achievable rate threshold required by the system.

4. The method according to claim 3, characterized in that, The following steps are used for optimization: Step 1: Calculate the baseline power value for each jump. ; Step 2: Compare the power reference with the transmit power range, and obtain the optimal source node transmit power based on the comparison results.

5. The method according to claim 4, characterized in that, In step 1, based on system parameters and the minimum achievable rate threshold Calculate the power baseline value for each hop: , (14)； in, These are auxiliary variables related to channel gain, noise power, and rectification efficiency. This reflects the need to meet system throughput requirements in the first... The minimum power contribution required for a jump; In step 2, the sum of the power reference values ​​for each jump is calculated. and the allowable range of the source node's transmit power. Compare the results and perform the following operations based on the comparison results: Case 1) If Then the optimal source node transmit power takes the minimum value: (15)； Case 2) If The optimal source node transmit power is taken as the sum of the power reference: , (18)； Case 3) If If the system throughput constraint is not met, then there is no feasible solution.

6. The method according to claim 5, characterized in that, In step 2, the optimal source node transmit power is obtained. To obtain the optimized PS ratio for each relay, the specific steps are as follows: For situation 1), the following steps are adopted: Step 1-1) First calculate the last relay Optimal power allocation ratio at: (16)； Computational relay The optimal PS ratio at this location; Steps 1-2) use an iterative approach to calculate the optimal power allocation ratio for the remaining relays sequentially from front to back: , ； (17)； In an iterative manner, it is known Calculate the optimal PS ratio ; For situation 2), the following steps are adopted: Step 2-1): Calculate the last relay The optimal power allocation ratio at the location is the same as in formula (16), that is: ； Computational relay The optimal PS ratio at this location; Step 2-2): Iteratively calculate the optimal power allocation ratio for the remaining relays, using the same formula as (17), i.e.: , ； In an iterative manner, it is known Calculate the optimal PS ratio .

7. The method according to any one of claims 1 to 6, characterized in that, The system is specifically as follows: Including source node Source node via relay set hop-by-hop forwarding of information to the destination node Source node Powered by internal battery or external power source; relay To relay All operate in a passive state; relay nodes ,in Using SWIPT technology from relay nodes The previous hop neighbor node Energy is collected from the radio frequency signal, and the power allocation (PS) protocol is used to simultaneously decode data packets and forward them to the next hop neighboring node; the power allocation ratio, also known as the PS ratio, is set as... Then the relay node The radio frequency signal at that location is split into two paths: Part of it is used for energy harvesting, the remainder Part of it is used for information decoding.

8. The method according to claim 7, characterized in that, wherein... All nodes are equipped with a single antenna and operate in half-duplex mode, meaning that relay nodes only forward data after fully receiving the information; and The channel coefficient between them is denoted as ,in Furthermore, the channel coefficient remains constant during the time it takes for data to be transmitted from one node to the next adjacent node, i.e., within a transmission frame.

9. The method according to claim 8, the relay node The energy harvesting circuit receives the radio frequency signal. Part of the energy is harvested and stored in a supercapacitor; all the harvested energy is used for information decoding and data retransmission. Set source node It possesses channel state information for all nodes, and each relay node... and destination node Knowing only the channel state information of its corresponding communication link and ; Assume that there are direct links only between adjacent nodes; relay nodes From the previous hop adjacent node The received radio frequency signal can be represented as: （1）； in, Indicates that it comes from the previous hop neighbor node. The information symbols, each with a unit average power, i.e. ; express The power spectral density of the emitted power.

10. The method according to claim 8 or 9, characterized in that, Represents a node The power spectral density of the additive white Gaussian noise introduced at the antenna follows a distribution. , express variance; relay node The received radio frequency signal is then distributed according to the power allocation ratio. It was divided into two paths, one for energy harvesting and the other for information decoding.

Images