A ts ratio control system and method for swipt multi-hop relay throughput optimization

Abstract

A TS ratio control system and method for optimizing the throughput of a SWIPT multi-hop relay, comprising a source node (S), the source node (S) being connected to the input end of a decode-and-forward relay group, and the output end of the decode-and-forward relay group outputting an output signal to a destination node (D); wherein the decode-and-forward relay group comprises a plurality of decode-and-forward relays connected in series, and can be represented as DFR1, DFR2, …, DFRn. The purpose of the present application is to solve the technical problem that the existing decode-and-forward (DF) multi-hop relay network based on SWIPT generally adopts a fixed TS ratio allocation scheme, which is easy to cause the system throughput to fail to reach the optimal state; and the optimal TS ratio allocation scheme based on exhaustive search has a very high computational complexity, and is difficult to implement in a real-time communication system; and the present application proposes a time slot switching (TS ratio) control system and method for optimizing the throughput of a wireless communication energy-carrying multi-hop relay network. The performance of the present scheme can reach the optimal state, and the computational complexity is relatively low and the execution is fast.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, specifically to a time-slot switching control system and method for optimizing throughput in a simultaneous wireless information and power transfer (SWIPT) multi-hop relay network. Background Technology

[0002] A wireless multi-hop relay network is a communication system that uses multiple relay nodes to coordinate and forward data from a source node to a destination node. Its core technology replaces the traditional single-hop link with flexible, multi-segment short-hop links, thereby extending the coverage of wireless communication, effectively addressing the challenges of signal attenuation and interference, and ultimately improving system capacity and energy efficiency. Due to its superior network deployment flexibility, this system is widely used in wireless sensor networks (WSNs), the Internet of Things (IoT), and emergency communication networks, and has become one of the fundamental solutions for these network architectures. For example, when obstacles prevent line-of-sight (LoS) transmission between the source and destination nodes, relay nodes in a multi-hop relay network can establish independent line-of-sight links between adjacent hop segments. Although this method requires additional communication time slots, it significantly improves the signal-to-noise ratio (SNR) of the destination node.

[0003] In wireless multi-hop relay networks, powering relay nodes with built-in batteries incurs significant maintenance costs due to frequent battery replacements—a problem particularly pronounced in remote areas, industrial monitoring systems, or large-scale IoT deployments. Furthermore, battery life is affected by factors such as ambient temperature and depth of discharge, further increasing the complexity of network maintenance. Wireless energy harvesting (EH) technology has emerged as a strong candidate solution: this technology utilizes radio frequency signals (a renewable energy source) in the environment surrounding the relay node, enabling self-sustaining operation and eliminating reliance on human intervention. Traditional EH technology focuses solely on harvesting energy from the environment, while simultaneous wireless information and power transfer (SWIPT) is a subset of radio frequency domain EH technology. Its core innovation lies in the ability to simultaneously transmit information and energy using the same radio frequency signal. By implementing SWIPT technology in multi-hop relay networks, relay nodes can assist source nodes in delivering information to destination nodes hop-by-hop without relying on batteries or external power sources.

[0004] Within the SWIPT technology framework, time-switching (TS) protocol is a mainstream implementation scheme. For example, in the paper "Robust Transceiver Design for SWIPT DF MIMO Relay Systems With Time-Switching Protocol," this protocol uses a switching circuit to periodically switch the receiver between energy harvesting and information decoding (ID) states, thus dividing each transmission time slot into two sub-slots: one dedicated to energy harvesting (EH) and the other to ID. The time allocation ratio (TS ratio) is a key parameter determining the system performance trade-off: extending the EH period increases total harvested energy but compresses information transmission time, leading to a decrease in communication rate. Therefore, optimizing the TS ratio under the TS protocol to balance EH efficiency and communication quality constitutes the core challenge in the design of SWIPT system resource allocation algorithms. Especially in multi-hop relay networks with multiple relay nodes, the joint optimization of radio resources such as transmit power and multiple TS ratios is a complex problem that urgently needs to be solved.

[0005] Based on the above considerations, the applicant proposes a time-slot switching control system and method for optimizing throughput in a wireless communication-enabled multi-hop relay network. Summary of the Invention

[0006] The purpose of this invention is to address the technical problems of existing SWIPT-based decode-and-forward (DF) multi-hop relay networks, which generally employ a fixed time slot switching (TS) ratio allocation scheme, easily leading to suboptimal system throughput; and the high computational complexity of exhaustive search-based optimal TS ratio allocation schemes, making them difficult to implement in real-time communication systems. The invention proposes a time slot switching (TS ratio) control system and method for optimizing throughput in wireless communication-enabled multi-hop relay networks. This scheme achieves optimal performance with relatively low computational complexity and fast execution.

[0007] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0008] A TS ratio control system for optimizing throughput in a SWIPT multi-hop relay includes a source node. (Also uniformly represented as) ), source node ( The output of the decode-forward relay group is connected to the input terminal of the decode-forward relay group, and the output terminal of the decode-forward relay group outputs the output signal to the destination node. (Also uniformly represented as) );

[0009] Among them, the decode-forward relay group includes A series of sequentially connected decode-forward relays can be represented as follows: .

[0010] At the source node When there is a battery or external power supply, the relay node , It relies entirely on energy harvesting based on radio frequency signals;

[0011] relay node EH and ID cycles It is divided into two consecutive phases, specifically:

[0012] (1) During the EH phase, continue Duration For relay nodes TS ratio;

[0013] (2) During the ID phase, continue Duration;

[0014] Specifically, at the destination node ( At point ), there is no EH phase, and the duration of its ID phase is a complete cycle. .

[0015] set up Represents a node , The information symbols, each satisfying the unit average power, i.e. ;but The received signal at that location can be represented as:

[0016] (1);

[0017] in for The transmission power; express and Channel coefficients between; express The additive white Gaussian noise (AWGN) at a given location is modeled with a power spectral density of zero mean and variance. Independent and identically distributed complex Gaussian random variables, i.e. .

[0018] During the EH phase, relay nodes , ,exist Within a certain period of time, from radio frequency signals Collect energy;

[0019] The rectification efficiency, which is the ratio of the energy harvester's output power to its input power, is denoted as . , ;

[0020] therefore The energy collected is:

[0021] (2);

[0022] in express and Channel gain between; express The transmission power;

[0023] Assuming relay node Fully utilize the collected energy during the ID phase. Its transmission power is:

[0024] (3);

[0025] Recursive derivation from equation (3) We can obtain:

[0026] (4);

[0027] in For the source node ( The transmission power of ) express The transmission power, and Channel gain between It refers to The rectification efficiency It refers to and Channel gain between It refers to The rectification efficiency Refers to relay node The TS ratio, of which .

[0028] For the ID process, receive signals Includes signal power With noise power , For transmission bandwidth, The received SNR at this location is:

[0029] (5);

[0030] in ; It refers to and Channel gain between;

[0031] Received signal , Includes signal power With noise power , The received SNR is:

[0032] (6);

[0033] in ;

[0034] Therefore, the first Jump, The achievable data rate is:

[0035] (7);

[0036] In particular, such as Figure 1 As shown, the destination node has no EH phase, and its first... The hop reachable data rate is:

[0037] (8);

[0038] in, It refers to Received SNR at the location;

[0039] Therefore, in a multi-hop DF relay network, from the source node... ( ) to the destination node ( The end-to-end achievable data rate, or system throughput, is:

[0040] (9);

[0041] Under the TS protocol framework, source node ( ) transmit power and the TS ratio of each relay , These are configurable parameters.

[0042] The system is modeled as follows:

[0043] In fixed Optimize under the premise To maximize end-to-end achievable data rate ;make The throughput maximization problem in the system is modeled as follows:

[0044] (10a);

[0045] st , (10b).

[0046] A method for optimizing and solving the above-mentioned system model includes the following steps:

[0047] Step 1: Transform the problem into an equivalent form using logarithmic transformation;

[0048] Step 2: Construct the obstacle problem;

[0049] Step 3: Initialize the logarithmic barrier method;

[0050] Step 4: Use the logarithmic barrier method to output the optimal solution and obtain the optimal TS ratio.

[0051] In step 1, when the problem is equivalently transformed through number transformation, the specific steps are as follows:

[0052] Through transformation and Problem (10) is equivalent to:

[0053] (11a);

[0054] st (11b);

[0055] , (11c);

[0056] (11d);

[0057] , (11e);

[0058] Auxiliary variables , ; It refers to , It refers to Unified in "among which auxiliary variables" , "middle;

[0059] In step 2, the specific steps for constructing the obstacle problem are as follows:

[0060] Using functions , and , By incorporating constraints (11b)-(11e) into the objective function as logarithmic barrier terms, the constructed joint logarithmic barrier function is as follows:

[0061] (12);

[0062] Introducing barrier parameters Define the obstacle objective function:

[0063] (13);

[0064] The obstacle problem restated as:

[0065] (14);

[0066] in yes and The optimal solution. Represents a vector.

[0067] In step 3, when initializing the logarithmic barrier method, specifically:

[0068] The logarithmic barrier method consists of an outer loop and an inner loop;

[0069] The outer loop is used to update the barrier parameters. The inner loop is solved by using Newton's method to fix the fixed... Unconstrained optimization problem (14);

[0070] This process must ensure that the iteration point is always strictly feasible, i.e., satisfying (11b)-(11e);

[0071] Before initiating the logarithmic barrier iteration, perform initialization:

[0072] 1) Select an initial point that satisfies the strict feasibility conditions (11b)-(11e). ; where superscript Representing the The outer loop iteration.

[0073] 2) Setting parameters: Initial obstacle parameters External circulation obstacle parameter update factor External circulation stop tolerance ; Inner circulation Newton's method stopping tolerance ;

[0074] 3) Calculate the initial dual gap of the outer circulation stopping criterion. .

[0075] Step 4 includes the following sub-steps:

[0076] Step 4-1: Execute the outer loop; while executing the outer loop, check the current... The outer loop iteration , The variable is a non-negative integer. If the dual gap is less than the outer loop tolerance, i.e. If the outer loop terminates, the inner loop will begin; otherwise, the outer loop will begin.

[0077] Step 4-2: Execute the inner loop; specifically, fix... , with the current As the initial value, solve the subproblem (14) using Newton's method; repeat the following sub-steps until the inner loop converges;

[0078] 4.2.1: Perform gradient calculation;

[0079] gradient The elements are calculated as follows:

[0080] right ,when Time: here It means In

[0081] (15);

[0082] in Calculated according to the circumstances:

[0083] Scenario 1, when hour:

[0084] (16);

[0085] Scenario 2, when hour:

[0086] (17);

[0087] Scenario 3, when hour:

[0088] (18);

[0089] right :

[0090] (19);

[0091] 4.2.2: Perform Hessian matrix calculation;

[0092] Hessian matrix The dimension is ,

[0093] (20);

[0094] Its elements are calculated as follows:

[0095] diagonal elements :

[0096] (twenty one);

[0097] in The calculation is as follows, depending on the circumstances:

[0098] Scenario 1, when hour:

[0099] (twenty two);

[0100] Scenario 2, when :

[0101] (twenty three);

[0102] Scenario 3, when hour:

[0103] (twenty four);

[0104] diagonal elements :

[0105] (25);

[0106] off-diagonal elements ,in , , :

[0107] (26);

[0108] in Calculate according to the following conditions:

[0109] Scenario 1, when hour:

[0110] (27);

[0111] Scenario 2, when hour:

[0112] (28);

[0113] Scenario 3, when hour:

[0114] (29);

[0115] in Calculate according to the following conditions:

[0116] Scenario 1, when hour:

[0117] (30);

[0118] Scenario 2, when hour:

[0119] (31);

[0120] Scenario 3, when hour:

[0121] (32);

[0122] off-diagonal elements :

[0123] (33);

[0124] 4.2.3: Perform Newtonian direction calculations;

[0125] Solving linear systems:

[0126] (34);

[0127] in For variables Step size, for Step size;

[0128] 4.2.4: Perform Newton reduction calculations;

[0129] (35);

[0130] 4.2.5: Perform a backtracking linear search;

[0131] Set as step size factor If any of the following conditions are not met, then update. ,in , is the step size reduction factor, used to control the step size factor. The reduction rate is usually taken as 0.5:

[0132] 1. Strict feasibility conditions:

[0133] (36);

[0134] , (37);

[0135] (38);

[0136] , (39);

[0137] 2.Armijo conditions:

[0138] (40);

[0139] in , is the Armijo condition coefficient, used to control the sufficiency threshold of function value descent, ensuring that the objective function (i.e., the value function) is adequately priced. The decrease must reach at least the linear predicted value. Times, often taken ;

[0140] 4.2.6: Update the original variables;

[0141] That is to say and ;

[0142] 4.2.7: Check for termination of internal circulation;

[0143] Calculate the Newtonian decrease using equation (35) ;like Terminate the inner loop and output the updated variable. ;

[0144] Step 4-3: Update obstacle parameters; specifically:

[0145] Update obstacle parameters Return to sub-step 4.1 and iterate until the optimal solution is output. ;

[0146] Through transformation ,in To obtain the optimal TS ratio.

[0147] Compared with the prior art, the present invention has the following technical effects:

[0148] 1) This patent addresses a DF multi-hop relay network based on SWIPT, where the source node transmits data to the destination node with the assistance of multiple passive relays. We employ the TS protocol, enabling relays to harvest energy from the received signal of the previous hop to support data forwarding; each relay requires no battery or external power supply. The optimal TS ratio corresponding to maximizing system throughput is determined through logarithmic transformation and the logarithmic barrier method in the interior-point method. The proposed method offers superior performance compared to existing technologies, with faster execution speed and relatively lower computational complexity, making it suitable for real-time communication systems.

[0149] 2) This invention constructs a TS ratio allocation framework for maximizing end-to-end throughput in multi-hop DF relay networks employing the TS-SWIPT protocol that require only source node power supply. This allocation framework is based on the logarithmic barrier method—possessing optimal performance, fast convergence (guaranteeing superlinear convergence rate), low computational complexity, and strong robustness. It offers significant performance advantages over fixed TS ratio schemes and is faster than exhaustive search-based optimal TS ratio allocation schemes. It is suitable for real-time communication systems. TS-SWIPT multi-hop DF relay networks using this scheme can operate with high performance in real time, achieving self-sustaining battery-free operation of the relays, fundamentally eliminating battery replacement costs in remote areas, industrial IoT, and emergency communication scenarios. Attached Figure Description

[0150] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0151] Figure 1 This is a schematic diagram of the multi-hop wireless relay network system structure in this invention;

[0152] Figure 2 This is a comparison chart of system throughput and source node transmit power in this invention;

[0153] Figure 3 This is a comparison chart of system throughput and number of relays in this invention;

[0154] Figure 4 This is a comparison chart of calculation time and number of relays in this invention. Detailed Implementation

[0155] A TS ratio control system for optimizing throughput in SWIPT multi-hop relays, such as Figure 1 As shown, a multi-hop wireless relay network system was constructed, and its structure is as follows:

[0156] Including source node (Also uniformly represented as) ), source node ( The output of the decode-forward relay group is connected to the input terminal of the decode-forward relay group, and the output terminal of the decode-forward relay group outputs the output signal to the destination node. (Also uniformly represented as) );

[0157] Among them, the decode-forward relay group includes A series of sequentially connected decode-forward relays can be represented as follows: ;

[0158] Specifically, source node ( )pass A decoding forwarding relay ,through Jump to transmit data to the destination node ( In the process of wireless radio frequency signal transmission, SWIPT technology is only applied between adjacent nodes. Jump to adjacent nodes and The channel coefficient between them is denoted as , Its energy harvesting (EH) and information decoding (ID) cycles The internal frequency remains constant. Furthermore, due to factors such as obstruction or deep fading, it is assumed that the channel between non-adjacent nodes is negligible—an assumption borrowed from mature routing scenarios employing path selection algorithms in wireless sensor networks. All relay nodes are equipped with a single antenna and operate in half-duplex mode, meaning they only forward packets after they have been fully received.

[0159] Only source node Relay nodes are powered by batteries or external power sources. , It relies entirely on energy harvesting based on radio frequency signals. Relay nodes EH and ID cycles It is divided into two consecutive phases: (1) EH phase, which lasts... Duration, of which For relay nodes The TS ratio, (2) ID phase, continued Duration. Specifically, at the destination node. ( At point ), there is no EH phase, and the duration of its ID phase is a complete cycle. .

[0160] set up Represents a node , The information symbols, each satisfying the unit average power, i.e. . The received signal at that location can be represented as:

[0161] (1);

[0162] in for The transmission power; express and Channel coefficients between; express The power spectral density of the additive white Gaussian noise (AWGN) at a given location is modeled as zero mean and zero variance. Independent and identically distributed complex Gaussian random variables, i.e. .

[0163] exist Figure 1 The relay node is shown in the EH phase. , ,exist Within a certain period of time, from radio frequency signals Collect energy. The rectification efficiency (the ratio of energy harvester output power to input power) is denoted as... ( ).therefore The energy collected is:

[0164] (2);

[0165] in express and Channel gain between; express The transmission power.

[0166] Assuming relay node Fully utilize the collected energy during the ID phase. Its transmission power is:

[0167] (3);

[0168] Recursive derivation from equation (3) We can obtain:

[0169] (4);

[0170] in For the source node ( ) transmission power.

[0171] For the ID process, receive signals Includes signal power With noise power ( (Transmission bandwidth). The SNR at the connection point is:

[0172] (5);

[0173] in .

[0174] Received signal , Includes signal power With noise power . The received SNR is:

[0175] (6);

[0176] in .

[0177] Therefore, the first Jump, The achievable data rate is:

[0178] (7);

[0179] In particular, such as Figure 1 As shown, the destination node has no EH phase, and its first... The hop reachable data rate is:

[0180] (8);

[0181] Therefore, in a multi-hop DF relay network, from the source node... ( ) to the destination node ( The end-to-end achievable data rate (i.e., system throughput) is:

[0182] (9);

[0183] Under the TS protocol framework, source node ( ) transmit power and the ratio of each relay TS , , is a configurable parameter.

[0184] The present invention is fixed Optimize under the premise To maximize end-to-end achievable data rate .make The throughput maximization problem in the system is modeled as follows:

[0185] (10a);

[0186] st , (10b);

[0187] The proposed methods include:

[0188] Step 1: Transform the problem into an equivalent form using logarithmic transformation;

[0189] First, through transformation and Problem (10) is equivalent to being transformed

[0190] (11a);

[0191] st (11b);

[0192] , (11c);

[0193] (11d);

[0194] , (11e);

[0195] in ( ); It refers to , It refers to Unified in "among which auxiliary variables" , "middle.

[0196] Step 2: Construct the obstacle problem;

[0197] First, using functions , ,and , Constraints (11b)-(11e) are incorporated into the objective function as logarithmic barrier terms. The constructed joint logarithmic barrier function is:

[0198] (12);

[0199] Introducing barrier parameters Define the obstacle objective function:

[0200] (13);

[0201] The obstacle problem restated as:

[0202] (14);

[0203] Step 3: Initialize the logarithmic barrier method;

[0204] The logarithmic barrier method consists of an outer loop and an inner loop. The outer loop updates the barrier parameters. The inner loop is solved using Newton's method to determine the fixed value. The unconstrained optimization problem (14) requires that the iteration point always be strictly feasible (i.e., satisfy (11b)-(11e)).

[0205] Before initiating the logarithmic barrier iteration, perform initialization:

[0206] 1. Select an initial point that satisfies the strict feasibility conditions (11b)-(11e). ;

[0207] 2. Setting parameters: Initial obstacle parameters External circulation obstacle parameter update factor External circulation stop tolerance ; Inner circulation Newton's method stopping tolerance ;

[0208] 3. Calculate the initial dual gap of the outer circulation stopping criterion. .

[0209] Step 4: Use the logarithmic barrier method to output the optimal solution and obtain the optimal TS ratio;

[0210] Step 4 includes the following sub-steps:

[0211] Step 4-1: Execute the outer loop; while executing the outer loop, check the current... The outer loop iteration , The variable is a non-negative integer. If the dual gap is less than the outer loop tolerance, i.e. If the outer loop terminates, the inner loop will begin; otherwise, the outer loop will begin.

[0212] Step 4-2: Execute the inner loop; specifically, fix... , with the current As the initial value, solve the subproblem (14) using Newton's method; repeat the following sub-steps until the inner loop converges;

[0213] 4.2.1: Perform gradient calculation;

[0214] gradient The elements are calculated as follows:

[0215] right ,when Time: here It means In

[0216] (15);

[0217] in Calculated according to the circumstances:

[0218] Scenario 1, when hour:

[0219] (16);

[0220] Scenario 2, when hour:

[0221] (17);

[0222] Scenario 3, when hour:

[0223] (18);

[0224] right :

[0225] (19);

[0226] 4.2.2: Perform Hessian matrix calculation;

[0227] Hessian matrix The dimension is ,

[0228] (20);

[0229] Its elements are calculated as follows:

[0230] diagonal elements :

[0231] (twenty one);

[0232] in The calculation is as follows, depending on the circumstances:

[0233] Scenario 1, when hour:

[0234] (twenty two);

[0235] Scenario 2, when :

[0236] (twenty three);

[0237] Scenario 3, when hour:

[0238] (twenty four);

[0239] diagonal elements :

[0240] (25);

[0241] off-diagonal elements ,in , , :

[0242] (26);

[0243] in Calculate according to the following conditions:

[0244] Scenario 1, when hour:

[0245] (27);

[0246] Scenario 2, when hour:

[0247] (28);

[0248] Scenario 3, when hour:

[0249] (29);

[0250] in Calculate according to the following conditions:

[0251] Scenario 1, when hour:

[0252] (30);

[0253] Scenario 2, when hour:

[0254] (31);

[0255] Scenario 3, when hour:

[0256] (32);

[0257] off-diagonal elements :

[0258] (33);

[0259] 4.2.3: Perform Newtonian direction calculations;

[0260] Solving linear systems:

[0261] (34);

[0262] in For variables Step size, for step size

[0263] 4.2.4: Perform Newton reduction calculations;

[0264] (35);

[0265] 4.2.5: Perform a backtracking linear search;

[0266] Set as step size factor If any of the following conditions are not met, then update. ,in , is the step size reduction factor, used to control the step size factor. The reduction rate is usually taken as 0.5:

[0267] 1. Strict feasibility conditions:

[0268] (36);

[0269] , (37);

[0270] (38);

[0271] , (39);

[0272] 2.Armijo conditions:

[0273] (40);

[0274] in , is the Armijo condition coefficient, used to control the sufficiency threshold of function value descent, ensuring that the objective function (i.e., the value function) is adequately priced. The decrease must reach at least the linear predicted value. Times, often taken ;

[0275] 4.2.6: Update the original variables;

[0276] That is to say and ;

[0277] 4.2.7: Check for termination of internal circulation;

[0278] Calculate the Newtonian decrease using equation (35) ;like Terminate the inner loop and output the updated variable. ;

[0279] Step 4-3: Update obstacle parameters; specifically:

[0280] Update obstacle parameters Return to sub-step 4.1 and iterate until the optimal solution is output. ;

[0281] Through transformation ,in To obtain the optimal TS ratio.

[0282] Example:

[0283] The performance of the proposed method in a SWIPT multi-hop DF relay network was evaluated through simulation. Unless otherwise specified, all simulations used the following parameter settings: noise power spectral density. dBm / Hz; Rectification efficiency of each relay node , System bandwidth MHz; ; ; ; ; Initial value is 1; step size factor Tolerance Set the source node to maximum power. Transmit. It is also assumed that the distance between each hop in the multi-hop relay network is equal, and the total transmission distance is 10 m.

[0284] Regarding the channel model: 1. For large-scale fading, a logarithmic distance path loss model (path loss exponent 3.8) is adopted, with a carrier frequency of 2.4 GHz and a reference distance of 1 meter; 2. For small-scale fading, since line-of-sight links are established between each hop transceiver node through relay deployment, the channel coefficient per hop is... It follows a Ricean fading distribution with a Ricean factor of 7. All simulation results are generated based on the average value of 1000 random channel implementations.

[0285] The proposed method is compared with the following benchmark schemes: 1. The best-performing exhaustive search scheme (TS ratio search step size 0.01); 2. The scheme in which all relay nodes use the same fixed TS ratio (specific values ​​are 0.25, 0.5 and 0.75).

[0286] Figure 2 (Or Table 1) shows a comparison of system throughput and source node transmit power. ), first in Figure 2 In the figure, we plotted the end-to-end throughput and maximum transmit power of the source node in a multi-hop DF relay network supporting SWIPT. The relationship curve. The variation range is 20 dBm to 40 dBm, and the network hop count is set to... Therefore, the single jump distance is Meters. As shown in the figure, the throughput of all schemes increases with the increase of the maximum transmit power of the source node. For example, the throughput of the proposed scheme increases from... The value increased from 11.29 bps at dBm to The throughput is 1122.91 bps at dBm. Notably, the curves of the proposed method based on the logarithmic barrier method completely overlap with those of the exhaustive search scheme, indicating that they have the same throughput performance—a phenomenon that persists in other configuration tests, fully confirming the optimality of the proposed scheme. Furthermore, as expected, the proposed scheme significantly outperforms the fixed TS ratio scheme in throughput performance (supplementary simulations show that this advantage still holds true for other fixed ratio values).

[0287] Table 1. System throughput and maximum transmit power of the source node for five schemes Relationship

[0288]

[0289] Figure 3 (Or Table 2) shows a comparison between system throughput and the number of relays. (dBm) Next, we will explore the relationship between system throughput and the number of relays. We will set the maximum transmit power of the source node. dBm, the number of relays in a multi-hop relay network. Increasing from 1 to 5 corresponds to an increase in the number of hops between the source and destination nodes from 2 to 6. Under the condition that the total distance is 10 meters and the distance of each hop is equal, the single hop distances are 5 meters, 3.33 meters, 2.5 meters, 2 meters and 1.67 meters respectively. Figure 3 This demonstrates how system throughput changes with the number of relays. The changing trend can be derived from this. Figure 2 Similarly, the proposed method exhibits optimal performance, with its throughput significantly outperforming the fixed TS ratio scheme. For example, when... At that time, both the proposed scheme and the exhaustive scheme achieved a throughput of 1437.3 bps, while the scheme with a fixed TS ratio of 0.75 only achieved 379.68 bps. On the other hand, it can be observed that the throughput of all schemes decreases sharply with the increase of the number of relays. It is noteworthy that the throughput decay rate of the proposed scheme is significantly lower than that of the scheme with a fixed TS ratio (especially in high hop count scenarios).

[0290] Table 2. System throughput and number of relays for five schemes Relationship

[0291]

[0292] Figure 4 (Or Table 3) shows a comparison between calculation time and the number of relays. dBm);

[0293] Table 3 shows the calculation time and number of relays for the five schemes. Relationship

[0294]

[0295] Figure 4 Further investigation into the computation time of each scheme was conducted using simulations performed on a laptop platform with an AMD Ryzen R9 7945HX processor and 64GB of RAM. As shown in the figure, the computation time of the exhaustive search scheme increases dramatically with the number of relays. The latency of 1730 seconds is unacceptably high in practical communication systems; while the three fixed TS schemes have extremely short and essentially consistent latency. Only when (seconds). Crucially, the proposed method takes only an order of magnitude longer than the fixed TS solution. At that time (seconds), and its time consumption increases relatively slowly with the increase of relay nodes.

Claims

1. A TS ratio control system for optimizing throughput in a SWIPT multi-hop relay, characterized in that, Including source node ( ), decoding and forwarding relay group, destination node ( ), source node ( The output of the decode-forward relay group is connected to the input terminal of the decode-forward relay group, and the output terminal of the decode-forward relay group outputs the output signal to the destination node. ( ); Among them, the decode-forward relay group includes A series of relay nodes connected sequentially are represented as follows: ; In the source node When there is a battery or external power supply, the relay node , It relies entirely on energy harvesting based on radio frequency signals; relay node Energy harvesting (EH) and information decoding (ID) cycle It is divided into two consecutive phases, specifically: (1) During the energy harvesting (EH) phase, continuous Duration For relay nodes TS ratio; (2) During the information decoding ID stage, continue Duration; At the destination node ( At this location, there is no energy harvesting (EH) phase, and the information decoding (ID) phase lasts for a complete cycle. ; For the information decoding ID process, the received signal Includes signal power With noise power , For transmission bandwidth, The received SNR at this location is: （5）； in ; It refers to and Channel gain between; Refers to the source node ( The transmission power of ) Received signal , Includes signal power With noise power , The received SNR is: （6）； in ; Therefore, the first Jump, The achievable data rate is: （7）； During the EH phase without energy harvesting at the target node, its first... The hop reachable data rate is: （8）； in, It refers to Received SNR at the location; Therefore, in a multi-hop DF relay network, from the source node... ( ) to the destination node ( The end-to-end achievable data rate, or system throughput, is: （9）； Under the TS protocol framework, source node ( ) transmit power and the TS ratio of each relay , These are configurable parameters; In fixed Optimize under the premise To maximize end-to-end achievable data rate ;make The throughput maximization problem in the system is modeled as follows: （10a）； s.t. , (10b); The optimization solution to the throughput maximization problem includes the following steps: Step 1: Transform the problem into an equivalent form using logarithmic transformation; Step 2: Construct the obstacle problem; Step 3: Initialize the logarithmic barrier method; Step 4: Use the logarithmic barrier method to output the optimal solution and obtain the optimal TS ratio.

2. The system according to claim 1, characterized in that, set up Represents a node , The information symbols, each satisfying the unit average power, i.e. ;but The received signal at that location is represented as: （1）； in for The transmission power; express and Channel coefficients between; express The additive white Gaussian noise (AWGN) at a given location is modeled with a power spectral density of zero mean and variance. Independent and identically distributed complex Gaussian random variables, i.e. .

3. The system according to claim 1, characterized in that, During the energy harvesting (EH) phase, relay nodes , ,exist Within a certain period of time, from radio frequency signals Collect energy; The rectification efficiency, which is the ratio of the energy harvester's output power to its input power, is denoted as . , ; therefore The energy collected is: （2）； in express and Channel gain between; express The transmission power; Set up relay nodes Fully utilize the collected energy during the information decoding ID stage. Its transmission power is: （3）； Recursive derivation from equation (3) ,get: （4）； in For the source node ( The transmission power of ) express The transmission power, and Channel gain between It refers to The rectification efficiency It refers to and Channel gain between It refers to The rectification efficiency Refers to relay node The TS ratio, of which .

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