Method for energy efficiency optimization of relay cooperation network with information and energy carrying

Abstract

The application provides a relay cooperation network energy efficiency optimization method. The method comprises the following steps: obtaining a network system energy efficiency expression according to a network system information transmission rate and total energy consumption of the network system in a preset time slot; constructing a network system energy efficiency optimization model based on the network system energy efficiency expression, in combination with a relay node time splitting factor and a relay node power splitting factor; and obtaining a relay node optimal time splitting factor and a relay node optimal power splitting factor based on the network system energy efficiency model by using an iterative algorithm. The relay cooperation network energy efficiency optimization method provided by the application is based on the network system energy efficiency expression, in combination with the relay node time splitting factor and the relay node power splitting factor to construct the network system energy efficiency optimization model, which is conducive to finding a global optimum. The relay node optimal time splitting factor and the relay node optimal power splitting factor are obtained by using an iterative algorithm with low computational complexity, so that the network system energy efficiency is optimized to the greatest extent.

Description

Technical Field

[0001] This application relates to the field of information-energy-carrying relay cooperative network technology, specifically to an energy efficiency optimization method for information-energy-carrying relay cooperative networks based on a hybrid energy harvesting protocol. Background Technology

[0002] Currently, there are various methods to improve the energy efficiency of cooperative relay networks with unidirectional relay functionality. For example, optimizing power allocation in DF cooperative relay networks using the PSR relay forwarding protocol can improve network system energy efficiency, or optimizing splitting time in AF cooperative relay networks using the TSR relay forwarding protocol can improve network system energy efficiency. While these methods improve the energy efficiency of the network system, they only impose single constraints on the power splitting factor or time splitting factor to obtain the optimal energy efficiency of the network system (while keeping another influencing factor fixed). Therefore, they find a local optimum energy efficiency and fail to guarantee that the energy efficiency of the cooperative relay network system is optimized in the most comprehensive and maximally way. Summary of the Invention

[0003] This application provides an energy efficiency optimization method for a telematics relay cooperative network based on a hybrid energy harvesting protocol, in order to solve the technical problem that the prior art has failed to maximize the energy efficiency of the telematics relay cooperative network system.

[0004] In a first aspect, embodiments of this application provide an energy efficiency optimization method for a cooperative relay network based on a hybrid energy harvesting protocol, comprising:

[0005] Based on the network system's information transmission rate and the total energy consumed by the network system within a preset time slot, the energy efficiency expression of the network system is obtained;

[0006] Based on the network system energy efficiency expression, and combined with the relay node time splitting factor and the relay node power splitting factor, a network system energy efficiency optimization model is constructed.

[0007] Based on the network system energy efficiency model, the optimal time splitting factor and the optimal power splitting factor of the relay node are obtained using an iterative algorithm to optimize the energy efficiency of the network system.

[0008] In one embodiment, the preset time slot includes a first time slot and a second time slot, and the step of obtaining the network system energy efficiency expression based on the network system information transmission rate and the total energy consumed by the network system within the preset time slot includes:

[0009] The network system information transmission rate is obtained based on the first time slot and the approximate signal-to-noise ratio of the destination node;

[0010] Based on the second time slot, the source node's transmit power, the power amplifier efficiency, and the circuit power consumption, the total energy consumed by the network system within the preset time slot is obtained;

[0011] The energy efficiency expression of the network system is obtained based on the ratio of the network system's information transmission rate to its total energy consumption.

[0012] In one embodiment, the step of obtaining the total energy consumption of the network system within a preset time slot based on the second time slot, the source node's transmit power, the power amplifier efficiency, and the circuit power consumption specifically involves:

[0013] Based on the second time slot, the source node's transmit power, the power amplifier efficiency, and the circuit power consumption, the total energy consumed by the network system within the preset time slot can be obtained using the following formula:

[0014] P all =P s αT / ε+P c T,

[0015] Among them, P all The total energy consumed by the network system within a preset time slot is represented by T, where T represents the preset time slot, αT represents the second time slot, and P represents the second time slot. s P represents the source node transmit power, ε represents the power amplifier efficiency, and P represents the source node transmit power. c This indicates the power consumption of the circuit.

[0016] In one embodiment, obtaining the network system information transmission rate based on the first time slot and the approximate signal-to-noise ratio of the destination node includes:

[0017] Based on the signal received by the destination node, the approximate signal-to-noise ratio of the destination node is obtained;

[0018] Based on the approximate signal-to-noise ratio of the first time slot and the destination node, the network system information transmission rate is obtained using the following formula:

[0019] R s = (1-α)Tlog2(1+γ) d ),

[0020] Wherein, the R s The information transmission rate of the network system is represented by (1-α)T, which represents the first time slot, and γ is the first time slot. d This represents the approximate signal-to-noise ratio of the destination node.

[0021] In one embodiment, obtaining the approximate signal-to-noise ratio of the destination node based on the signal received by the destination node includes:

[0022] The signal received by the destination node is obtained based on the energy collected by the relay node, the transmission power of the relay node, and the signal sent by the relay node to the destination node. The signal received by the destination node includes a signal part and a noise part.

[0023] The approximate signal-to-noise ratio of the destination node is obtained based on the signal and noise components of the signal received by the destination node.

[0024] In one embodiment, obtaining the optimal time splitting factor and optimal power splitting factor of the relay node using an iterative algorithm based on the network system energy efficiency model includes:

[0025] Based on the network system energy efficiency model, the first time splitting factor and the first power splitting factor are obtained using an iterative algorithm.

[0026] The information transmission rate of the first network system is obtained based on the first time splitting factor and the first power splitting factor.

[0027] Based on the degree of closeness between the information transmission rate of the first network system and the preset value, the first time splitting factor is determined to be the optimal time splitting factor for the relay node, and the first power splitting factor is determined to be the optimal power splitting factor for the relay node.

[0028] In one embodiment, obtaining the first time splitting factor and the first power splitting factor based on the network system energy efficiency model using an iterative algorithm includes:

[0029] Preset energy efficiency expectations;

[0030] Based on the network system energy efficiency model, an iterative algorithm is used to converge to the expected energy efficiency value, thereby obtaining the first time splitting factor and the first power splitting factor.

[0031] Secondly, embodiments of this application provide an energy efficiency optimization device for a signal-to-energy relay cooperative network based on a hybrid energy harvesting protocol, comprising:

[0032] The network system energy efficiency expression acquisition module is used to: obtain the network system energy efficiency expression based on the network system information transmission rate and the total energy consumed by the network system within a preset time slot;

[0033] The network system energy efficiency optimization model construction module is used to: construct a network system energy efficiency optimization model based on the network system energy efficiency expression, combined with the relay node time splitting factor and the relay node power splitting factor;

[0034] The network system energy efficiency optimization module is used to: obtain the optimal time splitting factor and the optimal power splitting factor of the relay node based on the network system energy efficiency model using an iterative algorithm, so as to optimize the energy efficiency of the network system.

[0035] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the program to implement the energy efficiency optimization method for a cooperative relay network based on a hybrid energy harvesting protocol as described in the first aspect.

[0036] Fourthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the energy efficiency optimization method for a cooperative relay network based on a hybrid energy harvesting protocol as described in the first aspect.

[0037] The energy efficiency optimization method for a relay cooperative network based on a hybrid energy harvesting protocol provided in this application derives the network system energy efficiency expression based on the network system's information transmission rate and the total energy consumed by the network system within a preset time slot. It then constructs a network system energy efficiency optimization model by combining the relay node time splitting factor and the relay node power splitting factor. This avoids limiting the improvement of network system energy efficiency by imposing single constraints on the power splitting factor or time splitting factor, facilitating the search for the global optimum. Finally, it utilizes a low-computational-complexity iterative algorithm to quickly and accurately obtain the optimal time splitting factor and the optimal power splitting factor for the relay nodes, thereby maximizing the optimization of network system energy efficiency. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart illustrating the energy efficiency optimization method for a signal-to-energy relay cooperative network based on a hybrid energy harvesting protocol provided in this application embodiment.

[0040] Figure 2 The diagram shows the transmission time slot structure of the energy harvesting protocol in the energy-carrying relay cooperative network.

[0041] Figure 3 The algorithm of the energy efficiency optimization method for a cooperative relay network based on a hybrid energy harvesting protocol provided in this application is shown under different channel gains. The horizontal axis represents the number of iterations and the vertical axis represents energy efficiency.

[0042] Figure 4This paper presents a comparison graph showing the relationship between the energy harvesting efficiency and energy efficiency of the energy efficiency optimization method for the energy-carrying relay cooperative network based on the hybrid energy harvesting protocol provided in this application and the existing technology. The horizontal axis represents energy harvesting efficiency and the vertical axis represents energy efficiency.

[0043] Figure 5 The graph shows a comparison of the relationship curves between the energy efficiency optimization method of the information-energy-carrying relay cooperative network based on the hybrid energy harvesting protocol provided in this application and the prior art regarding the path loss factor and energy efficiency. The horizontal axis represents the path loss factor and the vertical axis represents the energy efficiency.

[0044] Figure 6 The graph shows a comparison of the relationship curves between the energy efficiency optimization method of the information-energy-carrying relay cooperative network based on the hybrid energy harvesting protocol provided in the embodiments of this application and the prior art regarding energy harvesting efficiency and average energy efficiency. The horizontal axis represents energy harvesting efficiency and the vertical axis represents average energy efficiency.

[0045] Figure 7 This is a schematic diagram of the structure of the energy efficiency optimization device for the information-energy-carrying relay cooperative network based on the hybrid energy harvesting protocol provided in the embodiments of this application;

[0046] Figure 8 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0048] Simultaneous Wireless Information and Power Transmission (SWIPT) technology, while transmitting information, also transmits radio frequency (RF) signals that can be captured by energy-constrained devices to provide them with a power source, while simultaneously ensuring the transmission of information over the wireless network. This is considered an excellent solution to the energy-constrained problem in wireless networks. However, energy harvesting from the RF signal can corrupt its information content. Therefore, in practical SWIPT systems, it is impossible to simultaneously perform energy harvesting and information forwarding operations on the same received signal. Typically, time switching (TS) or power splitting (PS) protocols are used to divide the RF signal into two separate streams, achieving energy harvesting and information forwarding respectively.

[0049] In addition, wireless relay technology can reduce multipath fading and obstruction in the network, increase the diversity gain of the network, and improve the reliability of information transmission. Combining information-energy-carrying technology with wireless relay technology, and using relay nodes with information-energy-carrying capabilities to complete the energy replenishment and information transmission of the wireless network, has attracted increasing attention from researchers.

[0050] Figure 1 This application provides a flowchart illustrating an energy efficiency optimization method for a cooperative relay network based on a hybrid energy harvesting protocol.

[0051] Reference Figure 1 This application provides an energy efficiency optimization method for a cooperative relay network based on a hybrid energy harvesting protocol, which may include:

[0052] S110. Based on the information transmission rate of the network system and the total energy consumed by the network system within a preset time slot, obtain the energy efficiency expression of the network system.

[0053] S120. Based on the network system energy efficiency expression, and combined with the relay node time splitting factor and the relay node power splitting factor, construct a network system energy efficiency optimization model.

[0054] S130. Based on the network system energy efficiency model, the optimal time splitting factor and the optimal power splitting factor of the relay node are obtained using an iterative algorithm to optimize the energy efficiency of the network system.

[0055] It should be noted that the hybrid energy harvesting protocol involved in the information-energy-carrying relay cooperative network refers to comprehensively considering the impact of relay node time splitting factor and relay node power splitting factor on the energy efficiency of the information-energy-carrying relay cooperative network, transforming the local optimization brought about by a single factor into global optimization, so as to maximize the energy efficiency of the information-energy-carrying relay cooperative network.

[0056] The energy efficiency optimization method for a cooperative relay network based on a hybrid energy harvesting protocol provided in this application proposes an optimization problem to maximize the energy efficiency of the network system. This problem can be solved using the computationally inefficient Dinkelbach iterative algorithm to obtain the optimal energy efficiency for different channel gains. Compared to network systems that only use power splitting factors or time splitting factors to limit energy efficiency optimization (using PSR or TSR protocols), this application, in an AF (Amplify-and-Forward) cooperative relay network using a hybrid energy transmission protocol, comprehensively considers both time and power splitting factors at the relay nodes, performing global optimization and significantly improving the network system's energy efficiency.

[0057] It should be noted that the execution subject of the energy efficiency optimization method for information-energy-carrying relay cooperative network based on hybrid energy harvesting protocol provided by the present invention can be any network-side device, such as an information-energy-carrying relay cooperative network system.

[0058] It should be noted that the energy-carrying relay cooperative network involved in this invention includes a source node, a unidirectional relay node with energy-carrying capability, and a destination node. Each of the three nodes is equipped with a single antenna. Assuming that the source node and destination node cannot directly establish a communication link due to poor channel conditions, the relay node is required for signal forwarding. The source node and destination node do not have energy constraints, while the relay node is energy-constrained, powered by an energy harvesting module. The harvested energy is used for signal transmission by the relay node. Energy loss in the relay node's signal processing circuitry is not considered. The relay node adopts a half-duplex mode. Considering the delay-constrained transmission mode, energy harvesting and information forwarding in the network are completed based on a hybrid energy harvesting protocol. This energy-carrying relay cooperative network is applicable to the mathematical model used in the subsequent algorithm of this invention.

[0059] In step S110, the network-side device will obtain the network system energy efficiency expression based on the network system information transmission rate and the total energy consumed by the network system within a preset time slot.

[0060] Specifically, step S110 may include:

[0061] The network system information transmission rate is obtained based on the first time slot and the approximate signal-to-noise ratio of the destination node;

[0062] Based on the second time slot, the source node's transmit power, the power amplifier efficiency, and the circuit power consumption, the total energy consumed by the network system within the preset time slot is obtained;

[0063] The energy efficiency expression of the network system is obtained based on the ratio of the network system's information transmission rate to its total energy consumption.

[0064] It should be noted that the preset time slot includes a first time slot and a second time slot. The preset time slot can be a unit time slot, a single time slot, etc., set according to the actual situation.

[0065] See Figure 2This diagram illustrates the transmission time slot structure of the energy harvesting protocol in a relay cooperative network. T represents a preset time slot, α represents a time splitting factor, (1-α)T represents the first time slot, and αT represents the second time slot. Each relay node has both energy harvesting and signal processing circuits. In the second time slot αT, the relay node splits the received signal into two parts based on power. The ρP part enters the energy harvesting circuit for energy harvesting, while the (1-ρ)P part enters the signal processing circuit for information transmission. In the first time slot (1-α)T, the relay node amplifies the received signal and sends it to the destination node. Let the channel gains from the source node to the relay node and from the relay node to the destination node be h1 and h2, respectively, and the distances from the source node to the relay node and from the relay node to the destination node be d1 and d2, respectively. The channel gain is quasi-static Rayleigh block fading; within each time block, the channel gain is constant, and the channel gain between different transmission blocks follows a Rayleigh distribution.

[0066] Specifically, the signal received by relay node R from the source node within the second time slot αT can be represented as:

[0067]

[0068] Among them, y r (t) indicates that relay node R receives the signal sent by the source node within the second time slot αT, d1 represents the distance from the source node to the relay node, and P s Let h1 represent the source node's transmit power, h1 represent the channel gain from the source node to the relay node, and s(t) represent the source node's transmitted signal, which satisfies E{s(t)}. 2}=1, n a (t) represents the signal received by the antenna of the relay node, with a mean of 0 and a variance of . The additive white Gaussian noise, where m represents the path loss factor and ρ represents the power splitting factor.

[0069] Using equation (1) above, the signal used for energy harvesting can be expressed as:

[0070]

[0071] The signal used for information transmission can be represented as:

[0072]

[0073] Among them, y r1 (t) represents the signal used for energy harvesting, y r2 (t) represents the signal used for information transmission, s(k) represents the signal transmitted by the source node, ρ represents the power splitting factor, and n r (k) represents the relay node noise. nc This represents the conversion noise generated when a radio frequency signal is converted to a baseband signal. Additive white Gaussian noise, assuming so

[0074] Therefore, the energy collected by the relay node can be expressed as:

[0075]

[0076] Among them, E h P represents the energy collected by the relay node, η represents the energy collection efficiency of the relay node, which is determined by the energy collection circuit. s h1 represents the source node transmit power, h1 represents the channel gain from the source node to the relay node, d1 represents the distance from the source node to the relay node, m represents the path loss factor, T represents the preset time slot, α represents the time splitting factor, and ρ represents the power splitting factor.

[0077] According to equation (4), the relay node transmit power can be expressed as:

[0078]

[0079] In equation (5), P r Let P represent the relay node transmit power, α represent the time splitting factor, ρ represent the power splitting factor, and P represent the power splitting factor. s E represents the source node's transmit power. h denoted by , h1 represents the channel gain from the source node to the relay node, d1 represents the distance from the source node to the relay node, m represents the path loss factor, α represents the time splitting factor, and ρ represents the power splitting factor.

[0080] Therefore, the signal sent from the relay node to the destination node can be represented as:

[0081]

[0082] Where, x r (k) represents the signal sent from the relay node to the destination node, P r y represents the relay node's transmit power. r2 (k) represents the signal used for information transmission, d1 represents the distance from the source node to the relay node, and P s Let h1 represent the source node's transmit power, h1 represent the channel gain from the source node to the relay node, ρ represent the power splitting factor, and m represent the path loss factor.

[0083] The signal received by the destination node can be represented as:

[0084]

[0085] Among them, y d (k) represents the signal received by the destination node, x r (k) represents the signal sent from the relay node to the destination node, h2 represents the channel gain from the relay node to the destination node, d2 represents the distance from the relay node to the destination node, and n a (k) represents the signal received by the antenna of the relay node, which has a mean of 0 and a variance of . Additive white Gaussian noise, n c (k) represents the conversion noise generated when the radio frequency signal is converted to a baseband signal. Additive white Gaussian noise, assuming so

[0086] Combining equations (1) to (7), the signal received by the destination node can also be expressed as:

[0087]

[0088] Among them, y d (k) represents the signal received by the destination node, including the signal part and the noise part, n d (k) represents the destination node noise, n d (k)=n a (k)+n c (k), h1 and h2 represent the channel gain from the source node to the relay node and from the relay node to the destination node, respectively, and d1 and d2 represent the distances from the source node to the relay node and from the relay node to the destination node, respectively. P s Let s(k) represent the source node's transmit power, s(k) represent the source node's transmit signal, and ρ represent the power splitting factor. so n r (k) represents the relay node noise. m represents the path loss factor.

[0089] According to equation (8), the approximate signal-to-noise ratio of the destination node can be obtained, and the obtained approximate signal-to-noise ratio of the destination node can be expressed as:

[0090]

[0091] Where h1 and h2 represent the channel gain from the source node to the relay node and from the relay node to the destination node, respectively, and d1 and d2 represent the distances from the source node to the relay node and from the relay node to the destination node, respectively, P s Let m represent the source node's transmit power, ρ represent the path loss factor, and ρ represent the power splitting factor. so

[0092] Specifically, network-side devices can obtain the approximate signal-to-noise ratio (SNR) of the destination node based on the signal received by the destination node. Specifically, they can first obtain the signal received by the destination node based on the energy collected by the relay node, the transmission power of the relay node, and the signal sent by the relay node to the destination node. The signal received by the destination node includes a signal component and a noise component. Then, based on the signal component and the noise component of the signal received by the destination node, the approximate SNR of the destination node can be obtained.

[0093] Furthermore, the network-side equipment can obtain the network system information transmission rate based on the first time slot and the approximate signal-to-noise ratio of the destination node. The network system information transmission rate can be expressed as:

[0094] R s = (1-α)Tlog2(1+γ) d (10),

[0095] Among them, R s The information transmission rate of the network system is represented by (1-α)T, which represents the first time slot, and γ is the first time slot. d α represents the approximate signal-to-noise ratio of the destination node, and α represents the time splitting factor.

[0096] Specifically, when calculating the information transmission rate of the network system, the network-side equipment can first obtain the approximate signal-to-noise ratio of the destination node based on the signal received by the destination node, and then obtain the information transmission rate of the network system based on the first time slot and the approximate signal-to-noise ratio of the destination node.

[0097] Furthermore, the network-side equipment can obtain the total energy consumed by the network system within the preset time slot based on the second time slot, the source node's transmit power, the power amplifier efficiency, and the circuit power consumption. The total energy consumed by the network system within the preset time slot can be expressed as:

[0098] P all =P s αT / ε+P c T (11),

[0099] Among them, P all The total energy consumed by the network system within a preset time slot is represented by T, where T represents the preset time slot, αT represents the second time slot, and P represents the second time slot. s P represents the source node transmit power, ε represents the power amplifier efficiency, and P represents the source node transmit power. c Indicates circuit power consumption, ε and P c The values ​​of are all constants, and α represents the time splitting factor.

[0100] The energy efficiency of a network system is defined as the number of information bits transmitted per unit of energy consumed within a transmission time block (the number of information bits transmitted divided by the total energy consumed). Network-side devices can derive the network system energy efficiency expression based on the ratio of the network system's information transmission rate to its total energy consumption within a preset time slot. The network system energy efficiency can be expressed as:

[0101]

[0102] Where, η EE R represents the energy efficiency of a network system. s P represents the information transmission rate of a network system. all P represents the total energy consumed by the network system within a preset time slot, h1 and h2 represent the channel gain from the source node to the relay node and from the relay node to the destination node, respectively, d1 and d2 represent the distance from the source node to the relay node and from the relay node to the destination node, respectively. s Let s(k) represent the source node's transmit power and s(k) represent the source node's transmit signal. so n r (k) represents the relay node noise. m represents the path loss factor, ε represents the power amplifier efficiency, and P c The circuit power consumption is represented by T, the preset time slot is represented by α, the time splitting factor is represented by ρ, and the power splitting factor is represented by ρ.

[0103] In step S120, the network-side device will construct a network system energy efficiency optimization model based on the network system energy efficiency expression, combined with the relay node time splitting factor and the relay node power splitting factor.

[0104] It should be noted that network-side devices can jointly optimize network system energy efficiency by combining the relay node time splitting factor α and the relay node power splitting factor ρ, thereby maximizing network system energy efficiency. This optimization problem can be modeled as follows:

[0105]

[0106] Where, max EE denoted by , where α represents the maximum energy efficiency of the network system, ρ represents the time splitting factor of the relay node, and ρ represents the power splitting factor of the relay node.

[0107] In step S130, the network-side device will use an iterative algorithm based on the network system energy efficiency model to obtain the optimal time splitting factor and the optimal power splitting factor of the relay node, so as to optimize the energy efficiency of the network system.

[0108] Specifically, step S130 may include:

[0109] Based on the network system energy efficiency model, the first time splitting factor and the first power splitting factor are obtained using an iterative algorithm.

[0110] The information transmission rate of the first network system is obtained based on the first time splitting factor and the first power splitting factor.

[0111] Based on the degree of closeness between the information transmission rate of the first network system and the preset value, the first time splitting factor is determined to be the optimal time splitting factor for the relay node, and the first power splitting factor is determined to be the optimal power splitting factor for the relay node.

[0112] It should be noted that the process of obtaining the first time splitting factor and the first power splitting factor based on the network system energy efficiency model using an iterative algorithm includes:

[0113] Preset energy efficiency expectations;

[0114] Based on the network system energy efficiency model, an iterative algorithm is used to converge to the expected energy efficiency value, thereby obtaining the first time splitting factor and the first power splitting factor.

[0115] Specifically, the optimization problem P1 is a non-convex finite optimization problem, which is difficult for network-side devices to solve directly. Therefore, e can be predefined. * To determine the optimal energy efficiency (expected energy efficiency), which is the maximum energy efficiency of the network system, we have:

[0116]

[0117] Among them, e * α represents the energy efficiency of the optimal network system. * ρ represents the optimal time splitting factor for relay nodes. * R represents the optimal power splitting factor for relay nodes. s P represents the information transmission rate of a network system. all This represents the total energy consumed by the network system within a preset time slot.

[0118] Based on the network system energy efficiency model, the network-side equipment uses the computationally inefficient Dinkelbach iterative algorithm to solve the optimization problem P1, obtaining the optimal network system energy efficiency value for different channel gains. Specifically, this can be solved using the following Lemma 1:

[0119] For R s (α,ρ)>0, P all The optimal network system energy efficiency e can be obtained if and only if α and ρ satisfy the following premise. * :

[0120]

[0121] Among them, R s P represents the information transmission rate of a network system. all e represents the total energy consumed by the network system within a preset time slot. * Let α represent the energy efficiency of the optimal network system, α represent the time splitting factor of the relay node, and ρ represent the power splitting factor of the relay node. * ρ represents the optimal time splitting factor for relay nodes. * This represents the optimal power splitting factor for the relay node.

[0122] According to Lemma 1, the network-side device can transform the optimization problem P1 into an optimization problem P2 in the form of subtraction of two functions. Then, based on the Dinkelbach iterative algorithm, the optimal solution to the optimization problem P1 is obtained, and the iterative algorithm converges to the expected energy efficiency value e. * That is, finding the optimal solution to problem P1, according to the Dinkelbach iterative algorithm, requires solving problem P2 to obtain the value of parameter e in each iteration, given the parameter e.

[0123] P2:max F(α,ρ)=R s (α,ρ)-eP all (α,ρ)

[0124] st

[0125] C1: 0 < α < 1

[0126] C2:0<ρ<1 (16),

[0127] Where maxF(α, ρ) represents the phase difference, R s P represents the information transmission rate of a network system. all α represents the total energy consumed by the network system within a preset time slot, ρ represents the relay node time splitting factor, e represents the relay node power splitting factor, and e represents the parameter that needs to be given in the iterative algorithm.

[0128] Let α' and ρ' be the optimal solutions to problem P2, and ζ be the allowable error (an arbitrarily small positive number). If α' and ρ' satisfy R s (α,ρ)-eP all If (α,ρ)<ζ, then α' and ρ' are the optimal solutions to optimization problem P1, and solving problem P1 is transformed into solving problem P2.

[0129] The network-side equipment will obtain the first time splitting factor and the first power splitting factor based on the network system energy efficiency model using an iterative algorithm. Then, based on the first time splitting factor and the first power splitting factor, the first network system information transmission rate will be obtained. Then, based on the degree of closeness of the first network system information transmission rate to a preset value (the product of a given parameter e and the total energy consumed by the network system in a preset time slot) (if it is less than the allowable error ζ, it means that it is close enough), the first time splitting factor will be determined as the optimal time splitting factor for the relay node, and the first power splitting factor will be determined as the optimal power splitting factor for the relay node.

[0130] The energy efficiency optimization method for a signal-to-energy relay cooperative network based on a hybrid energy harvesting protocol provided in this application obtains the energy efficiency expression of the AF relay cooperative network using the hybrid energy harvesting protocol during the energy efficiency optimization process. It establishes an optimization problem for the network's energy efficiency, transforming the non-convex optimization problem into a convex one. Using the low-computational-complexity Dinkelbach iterative algorithm, it obtains the optimal time splitting factor and optimal power splitting factor of the relay nodes under different conditions (different signal gains) to optimize the network system's energy efficiency and obtain the optimal energy efficiency value of the network system under different signal gains.

[0131] The energy efficiency optimization method for a relay cooperative network based on a hybrid energy harvesting protocol provided in this application derives the network system's energy efficiency expression based on the network system's information transmission rate and total energy consumption within a preset time slot. Combining the relay node's time splitting factor and power splitting factor, a network system energy efficiency optimization model is constructed. This avoids limiting the network system's energy efficiency improvement by imposing single constraints on either the power splitting factor or the time splitting factor, facilitating the search for the global optimum. Then, a low-computational-complexity iterative algorithm is used to quickly and accurately obtain the optimal time splitting factor and optimal power splitting factor for each relay node, thereby maximizing the optimization of the network system's energy efficiency. The energy efficiency optimization method for a relay cooperative network based on a hybrid energy harvesting protocol provided in this application reduces computational complexity while overcoming the limitation of a fixed power splitting factor on network energy efficiency improvement, facilitating the finding of the network system's optimal energy efficiency.

[0132] Furthermore, when the network-side device executes the energy efficiency optimization method for a cooperative relay network based on a hybrid energy harvesting protocol provided in this application embodiment, it can pre-set the parameters of the cooperative relay network, the maximum number of iterations of the iterative algorithm, and the value of the allowable error ζ; then initialize the channel state information, giving the channel gain h1 from the source node to the relay node and the channel gain h2 from the relay node to the destination node; then initialize i=1, Flag=0, and set the initial value of network system energy efficiency e(1)=0; then, given the value of e(i), solve the optimization problem P2, and obtain the optimal values ​​of the time splitting factor and the power splitting factor corresponding to problem P2, as the first time splitting factor and the first power splitting factor; then, obtain the first network system information transmission based on the first time splitting factor and the first power splitting factor. The rate is compared with the preset value (the product of the given parameter e and the total energy consumed by the network system in the preset time slot). When the difference between the information transmission rate of the first network system and the preset value is less than the allowable error ζ, it means that the information transmission rate of the first network system is close enough to the preset value. The first time splitting factor and the first power splitting factor are determined as the optimal time splitting factor and the optimal power splitting factor of the relay node. The optimal time splitting factor and the optimal power splitting factor of the relay node can be substituted into equation (14) to obtain the optimal network system energy efficiency value, and the Flag is marked as 1. However, when the difference between the information transmission rate of the first network system and the preset value is not less than the allowable error ζ, the network-side device increments the iteration number by 1 until the Flag is marked as 1 or the entire iteration number is terminated.

[0133] The following simulation demonstrates the effectiveness of the energy efficiency optimization method for the energy-carrying relay cooperative network based on the hybrid energy harvesting protocol provided in this application.

[0134] The simulation parameters were set as follows: energy harvesting efficiency η = 0.8, circuit loss P c =0.1w, source node transmit power P s =1w, the distance from the source node to the relay node and the distance from the relay node to the destination node are d1 = d2 = 1.2, and the noise power of the relay node and the noise power of the destination node are... The path loss factor is m = 3, and the power amplifier efficiency is ε = 0.38.

[0135] See Figures 3-6 , Figure 3The convergence values ​​of the energy efficiency optimization method for information-energy-carrying relay cooperative networks based on hybrid energy harvesting protocols provided in this application embodiment were compared under different channel gains. Simulation results show that the better the channel conditions, the higher the energy efficiency convergence value. The algorithm used in the energy efficiency optimization method for information-energy-carrying relay cooperative networks based on hybrid energy harvesting protocols provided in this application embodiment has an extremely fast computational convergence speed, and can achieve convergence within 4 iterations.

[0136] Figure 4 The diagram shows a comparison of the energy efficiency optimization method for energy-carrying relay cooperative networks based on a hybrid energy harvesting protocol provided in this application with existing technologies (where the time splitting factor is a fixed value of 0.1 and the power splitting factor is a fixed value of 0.8) regarding the relationship between energy harvesting efficiency and energy efficiency. Figure 5 The diagram shows a comparison of the path loss factor and energy efficiency curves between the energy efficiency optimization method for the information-energy-carrying relay cooperative network based on the hybrid energy harvesting protocol provided in this application and existing technologies (with a fixed time splitting factor of 0.1 and a fixed power splitting factor of 0.8). Figure 4 and Figure 5 The curves in the image, from top to bottom, represent the changing pattern as channel conditions h1 and h2 gradually worsen. Figure 4 It can be seen that the energy efficiency of the network system increases with the increase of the energy harvesting efficiency η. Under the same channel conditions, the energy efficiency optimization method for the energy-carrying relay cooperative network based on the hybrid energy harvesting protocol provided in this application embodiment is more energy efficient than the prior art (the time splitting factor is a fixed value of 0.1, and the power splitting factor is a fixed value of 0.8). Figure 5 It can be seen that the energy efficiency of the network system decreases as the path loss factor increases. Under the same channel conditions, the energy efficiency optimization method of the information-energy-carrying relay cooperative network based on the hybrid energy harvesting protocol provided in this application embodiment is more energy efficient than the prior art (the time splitting factor is a fixed value of 0.1 and the power splitting factor is a fixed value of 0.8).

[0137] Figure 6 This paper presents a comparison of the energy efficiency optimization method for energy-carrying relay cooperative networks based on a hybrid energy harvesting protocol provided in this application and existing technologies (with a fixed time splitting factor of 0.1 and a fixed power splitting factor of 0.8) with respect to energy harvesting efficiency and average energy efficiency. 105 Rayleigh fading channels were randomly generated, and the average energy efficiency of the network system was calculated using both the energy efficiency optimization method for energy-carrying relay cooperative networks based on a hybrid energy harvesting protocol provided in this application and existing technologies. Figure 6As can be seen, when the energy harvesting efficiency η = 0.8, the average energy efficiency of the network system implemented by the energy efficiency optimization method of the information-energy-carrying relay cooperative network based on the hybrid energy harvesting protocol provided in this application reaches 3 bps / w, which is 15.4% higher than the average energy efficiency of the prior art (2.6 bps / w).

[0138] The energy efficiency optimization method for a signal-to-energy relay cooperative network based on a hybrid energy harvesting protocol provided in this application, in a unidirectional AF relay cooperative network employing a hybrid energy harvesting protocol, utilizes the network system's information transmission rate and total energy consumption within a preset time slot, combined with source node transmit power, time splitting factor, and power splitting factor to establish an optimization problem. Based on the low-complexity Dinkelbach iterative algorithm, it significantly improves the network system's energy efficiency. The energy efficiency optimization method for a signal-to-energy relay cooperative network based on a hybrid energy harvesting protocol provided in this application combines time splitting factor and power splitting factor to comprehensively optimize the network system's performance, exhibiting low computational complexity and high optimization efficiency.

[0139] The energy efficiency optimization device for a telematics relay cooperative network based on a hybrid energy harvesting protocol, provided in the embodiments of this application, is described below. The energy efficiency optimization device for a telematics relay cooperative network based on a hybrid energy harvesting protocol described below can be referred to in correspondence with the energy efficiency optimization method for a telematics relay cooperative network based on a hybrid energy harvesting protocol described above.

[0140] Figure 7 This application provides a schematic diagram of the structure of an energy efficiency optimization device for a telemetry relay cooperative network based on a hybrid energy harvesting protocol.

[0141] Reference Figure 7 This application provides an energy efficiency optimization device for a cooperative relay network based on a hybrid energy harvesting protocol, which may include:

[0142] The network system energy efficiency expression obtaining module 710 is used to: obtain the network system energy efficiency expression based on the network system information transmission rate and the total energy consumed by the network system within a preset time slot;

[0143] The network system energy efficiency optimization model construction module 720 is used to: construct a network system energy efficiency optimization model based on the network system energy efficiency expression and in combination with the relay node time splitting factor and the relay node power splitting factor;

[0144] The network system energy efficiency optimization module 730 is used to: obtain the optimal time splitting factor and the optimal power splitting factor of the relay node based on the network system energy efficiency model using an iterative algorithm, so as to optimize the energy efficiency of the network system.

[0145] It should be noted that the preset time slot includes a first time slot and a second time slot.

[0146] In one embodiment, the network system energy efficiency expression obtaining module 710 may include:

[0147] The first network system information transmission rate acquisition submodule is used to: obtain the network system information transmission rate based on the first time slot and the approximate signal-to-noise ratio of the destination node;

[0148] The total energy consumption submodule is used to: obtain the total energy consumption of the network system within a preset time slot based on the second time slot, the source node transmit power, the power amplifier efficiency, and the circuit power consumption;

[0149] The network system energy efficiency expression submodule is used to: obtain the network system energy efficiency expression based on the ratio of the network system information transmission rate to the total energy consumption.

[0150] It should be noted that the total energy consumption obtaining submodule is specifically used for:

[0151] Based on the second time slot, the source node's transmit power, the power amplifier efficiency, and the circuit power consumption, the total energy consumed by the network system within the preset time slot can be obtained using the following formula:

[0152] P all =P s αT / ε+P c T,

[0153] Among them, P all The total energy consumed by the network system within a preset time slot is represented by T, where T represents the preset time slot, αT represents the second time slot, and P represents the second time slot. s P represents the source node transmit power, ε represents the power amplifier efficiency, and P represents the source node transmit power. c This indicates the power consumption of the circuit.

[0154] In one embodiment, the first network system information transmission rate obtaining submodule may include:

[0155] The first destination node approximate signal-to-noise ratio (SNR) acquisition submodule is used to: obtain the destination node approximate SNR based on the signal received by the destination node;

[0156] The second network system information transmission rate acquisition submodule is used to: obtain the network system information transmission rate based on the first time slot and the approximate signal-to-noise ratio of the destination node using the following formula:

[0157] R s = (1-α)Tlog2(1+γ) d ),

[0158] Wherein, the Rs The information transmission rate of the network system is represented by (1-α)T, which represents the first time slot, and γ is the first time slot. d This represents the approximate signal-to-noise ratio of the destination node.

[0159] In one embodiment, the submodule for obtaining the approximate signal-to-noise ratio of the first destination node may include:

[0160] The signal receiving submodule at the destination node is used to: obtain the signal received by the destination node based on the energy collected by the relay node, the transmission power of the relay node, and the signal sent by the relay node to the destination node. The signal received by the destination node includes a signal part and a noise part.

[0161] The second destination node approximate signal-to-noise ratio submodule is used to: obtain the destination node approximate signal-to-noise ratio based on the signal part and noise part of the signal received by the destination node.

[0162] In one embodiment, the network system energy efficiency optimization module 730 may include:

[0163] The first splitting factor acquisition submodule is used to: obtain the first time splitting factor and the first power splitting factor based on the network system energy efficiency model using an iterative algorithm;

[0164] The first network system information transmission rate obtaining submodule is used to: obtain the first network system information transmission rate based on the first time splitting factor and the first power splitting factor;

[0165] The optimal splitting factor determination submodule is used to: determine, based on the proximity of the information transmission rate of the first network system to a preset value, that the first time splitting factor is the optimal time splitting factor for the relay node, and the first power splitting factor is the optimal power splitting factor for the relay node.

[0166] In one embodiment, the first splitting factor obtaining submodule may include:

[0167] The Energy Efficiency Expectation Preset Submodule is used to: preset energy efficiency expectations;

[0168] The second splitting factor acquisition submodule is used to: based on the network system energy efficiency model, converge to the expected energy efficiency value using an iterative algorithm to obtain the first time splitting factor and the first power splitting factor.

[0169] Figure 8 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 8As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call a computer program in the memory 830 to execute the steps of an energy efficiency optimization method for a cooperative information-energy-carrying relay network based on a hybrid energy harvesting protocol, such as including:

[0170] Based on the network system's information transmission rate and the total energy consumed by the network system within a preset time slot, the energy efficiency expression of the network system is obtained;

[0171] Based on the network system energy efficiency expression, and combined with the relay node time splitting factor and the relay node power splitting factor, a network system energy efficiency optimization model is constructed.

[0172] Based on the network system energy efficiency model, the optimal time splitting factor and the optimal power splitting factor of the relay node are obtained using an iterative algorithm to optimize the energy efficiency of the network system.

[0173] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0174] On the other hand, embodiments of this application also provide a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the steps of the energy efficiency optimization method for the energy-carrying relay cooperative network based on the hybrid energy harvesting protocol provided in the above embodiments, such as including:

[0175] Based on the network system's information transmission rate and the total energy consumed by the network system within a preset time slot, the energy efficiency expression of the network system is obtained;

[0176] Based on the network system energy efficiency expression, and combined with the relay node time splitting factor and the relay node power splitting factor, a network system energy efficiency optimization model is constructed.

[0177] Based on the network system energy efficiency model, the optimal time splitting factor and the optimal power splitting factor of the relay node are obtained using an iterative algorithm to optimize the energy efficiency of the network system.

[0178] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program. The computer program is used to cause a processor to execute the steps of the energy efficiency optimization method for a cooperative relay network based on a hybrid energy harvesting protocol provided in the above embodiments, including, for example:

[0179] Based on the network system's information transmission rate and the total energy consumed by the network system within a preset time slot, the energy efficiency expression of the network system is obtained;

[0180] Based on the network system energy efficiency expression, and combined with the relay node time splitting factor and the relay node power splitting factor, a network system energy efficiency optimization model is constructed.

[0181] Based on the network system energy efficiency model, the optimal time splitting factor and the optimal power splitting factor of the relay node are obtained using an iterative algorithm to optimize the energy efficiency of the network system.

[0182] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0183] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0184] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0185] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing energy efficiency in a cooperative relay network for information and energy transport based on a hybrid energy harvesting protocol, characterized in that, include: Based on the network system's information transmission rate and the total energy consumed by the network system within a preset time slot, the energy efficiency expression of the network system is obtained, including: The preset time slot includes a first time slot and a second time slot; The network system information transmission rate is obtained based on the first time slot and the approximate signal-to-noise ratio of the destination node; Based on the second time slot, the source node's transmit power, the power amplifier's efficiency, and the circuit's power consumption, the total energy consumed by the network system within the preset time slot is obtained. Based on the ratio of the network system's information transmission rate to its total energy consumption, the energy efficiency expression of the network system is obtained. Based on the network system energy efficiency expression, and combined with the relay node time splitting factor and the relay node power splitting factor, a network system energy efficiency optimization model is constructed. Based on the network system energy efficiency optimization model, an iterative algorithm is used to obtain the optimal time splitting factor and the optimal power splitting factor of the relay nodes for optimizing the energy efficiency of the network system, including: Based on the network system energy efficiency optimization model, the first time splitting factor and the first power splitting factor are obtained using an iterative algorithm. The information transmission rate of the first network system is obtained based on the first time splitting factor and the first power splitting factor. Based on the degree of closeness between the information transmission rate of the first network system and the preset value, the first time splitting factor is determined to be the optimal time splitting factor for the relay node, and the first power splitting factor is determined to be the optimal power splitting factor for the relay node.

2. The energy efficiency optimization method for a cooperative relay network based on a hybrid energy harvesting protocol according to claim 1, characterized in that, The total energy consumed by the network system within the preset time slot is obtained based on the second time slot, the source node's transmit power, the power amplifier efficiency, and the circuit power consumption. Specifically: Based on the second time slot, the source node's transmit power, the power amplifier efficiency, and the circuit power consumption, the total energy consumed by the network system within the preset time slot can be obtained using the following formula: ， Among them, P all This represents the total energy consumed by the network system within a preset time slot, where T represents the preset time slot. Indicates the second time slot, P s Indicates the source node's transmit power. P represents the power amplifier efficiency. c This indicates the power consumption of the circuit.

3. The energy efficiency optimization method for a cooperative relay network based on a hybrid energy harvesting protocol according to claim 1, characterized in that, The step of obtaining the network system information transmission rate based on the first time slot and the approximate signal-to-noise ratio of the destination node includes: Based on the signal received by the destination node, the approximate signal-to-noise ratio of the destination node is obtained; Based on the approximate signal-to-noise ratio of the first time slot and the destination node, the network system information transmission rate is obtained using the following formula: ， Wherein, the R s Indicates the information transmission rate of the network system. Indicates the first time slot. This represents the approximate signal-to-noise ratio of the destination node.

4. The energy efficiency optimization method for a cooperative relay network based on a hybrid energy harvesting protocol according to claim 3, characterized in that, The step of obtaining the approximate signal-to-noise ratio of the destination node based on the signal received by the destination node includes: The signal received by the destination node is obtained based on the energy collected by the relay node, the transmission power of the relay node, and the signal sent by the relay node to the destination node. The signal received by the destination node includes a signal part and a noise part. The approximate signal-to-noise ratio of the destination node is obtained based on the signal and noise components of the signal received by the destination node.

5. The energy efficiency optimization method for a cooperative relay network based on a hybrid energy harvesting protocol according to claim 1, characterized in that, The first time splitting factor and the first power splitting factor are obtained using an iterative algorithm based on the network system energy efficiency optimization model, including: Preset energy efficiency expectations; Based on the network system energy efficiency optimization model, an iterative algorithm is used to converge to the expected energy efficiency value, thereby obtaining the first time splitting factor and the first power splitting factor.

6. An energy efficiency optimization device for a signal-to-energy relay cooperative network based on a hybrid energy harvesting protocol, characterized in that, The method for optimizing the energy efficiency of a cooperative relay network based on a hybrid energy harvesting protocol as described in claim 1 includes: The network system energy efficiency expression acquisition module is used to: obtain the network system energy efficiency expression based on the network system information transmission rate and the total energy consumed by the network system within a preset time slot; The network system energy efficiency optimization model construction module is used to: construct a network system energy efficiency optimization model based on the network system energy efficiency expression, combined with the relay node time splitting factor and the relay node power splitting factor; The network system energy efficiency optimization module is used to: obtain the optimal time splitting factor and the optimal power splitting factor of the relay node based on the network system energy efficiency optimization model using an iterative algorithm, so as to optimize the energy efficiency of the network system.

7. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the energy efficiency optimization method for a cooperative relay network based on a hybrid energy harvesting protocol as described in any one of claims 1 to 5.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the energy efficiency optimization method for the information-energy-carrying relay cooperative network based on the hybrid energy harvesting protocol as described in any one of claims 1 to 5.

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