Method for minimizing energy consumption in HARQ-IR-assisted backscatter short packet communication

Abstract

The invention relates to an energy consumption minimization optimization method in HARQ-IR (Hybrid Automatic Repeat reQuest-Infrared Response) assisted backscattering short packet communication, which comprises the following steps: in a considered network, an Internet of Things node modulates a short data packet to an energy signal sent by an energy source, then backscatters the modulated signal to a receiver, and executes an HARQ mechanism; a problem of minimizing energy consumption at an energy source is formulated by jointly optimizing a power reflection coefficient of an IoT node, a short packet block length and transmission times for short packet communication, and the problem is expressed as a two-dimensional nonlinear integer programming problem. In order to solve the problem, a closed expression of an IoT node power reflection coefficient is deduced by using a convex optimization theory; then, the short block length is relaxed into a continuous variable, and it is proved that the transformed problem is convex under the condition of fixed transmission times. In addition, an iterative algorithm is proposed to obtain an optimal solution. The scheme provided by the invention is superior to a retransmission-free scheme in the aspect of energy consumption.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method for minimizing energy consumption in HARQ-IR assisted backscatter short packet communication. Background Art

[0002] The energy shortage of low-power Internet of Things (IoT) nodes, caused by limited battery capacity, poses a significant challenge to the development of IoT technology. Backscatter communication offers an effective solution to this problem, enabling IoT nodes to not only harvest energy from ambient radio frequency (RF) sources but also modulate and reflect the modulated signal to a receiver. Compared to traditional wireless power supply communications, backscatter communication offers lower power consumption and maintenance costs.

[0003] In IoT networks, backscatter communication has been widely studied. Specifically, reference [2] studied the total rate maximization problem of multi-carrier wireless powered backscatter communication networks. The researchers jointly optimized power allocation, time allocation and reflection coefficient, and proposed an iterative resource allocation algorithm to find the optimal solution. Reference [3] proposed two adaptive power reflection coefficient (PRC) strategies for collaborative backscatter communication to minimize the outage probability of backscatter devices. The study derived closed-form expressions for the optimal power reflection coefficient and outage probability, and analyzed the advantages of these strategies. Reference [4] studied the delay problem of symbiotic radio (SR) backscatter communication in IoT nodes and formulated a non-convex optimization problem to optimize the transmission time of all secondary users. The authors proposed an iterative algorithm based on successive convex approximation (SCA) to solve the problem.

[0004] In the above studies on backscatter communication, the IoT nodes in references [2]–[4] assumed infinite block length when performing long packet communication (LPC). However, most IoT nodes usually perform short packet communication (SPC), in which the transmitted data packets have a finite block length [5]. Unlike long packet communication, the achievable rate of short packet communication in IoT nodes cannot be described by Shannon capacity due to the assumption of finite block length [5]. In addition, short packet communication in IoT nodes is inherently affected by non-zero packet error rate [5]–

[12] . Given these differences, the existing research results on long packet communication are not applicable to IoT scenarios involving short packet communication. Therefore, it is crucial to explore backscatter-assisted short packet communication for IoT applications. In terms of performance analysis, the authors of references [6]–[9] derived closed-form packet error rate expressions for different short packet communication systems, including backscatter communication assisted by multi-antenna drones [6], backscatter communication under mixed short and long packets [7], backscatter communication under relay-assisted cooperation [8], and backscatter communication in vehicle mobile scenarios [9], and analyzed the performance of their proposed methods. In terms of performance optimization, the authors of reference

[10] proposed a reliability-oriented resource allocation scheme to minimize the maximum packet error rate of all IoT nodes in multi-node backscatter-assisted short packet communication. The authors of reference

[11] considered a multi-input single-output symbiotic radio system assisted by a reconfigurable intelligent surface (RIS) and formulated an energy efficiency maximization optimization problem with the base station's transmission beamforming vector and the phase offset matrix at the RIS as variables. The authors of reference

[12] studied two symbiotic radio system models, namely non-cooperative symbiotic radio and cooperative symbiotic radio (classified by backscatter symbol period and transmission rate), and then explored their transmission power minimization and energy efficiency maximization.

[0005] In order to improve the reliability of short packet communication, the authors in references

[13] –

[15] introduced a retransmission mechanism into the short packet communication of IoT nodes and derived the packet error rate. Furthermore, considering the actual retransmission scheme, that is, the scheme combining hybrid automatic repeat request (HARQ) with chase combining (HARQ-CC), the authors of reference

[13] derived a closed-form expression for the packet error rate of IoT nodes and showed that the short packet transmission scheme combined with HARQ-CC significantly improved the performance of IoT nodes. However, the current research on HARQ-assisted short packet backscatter communication is still insufficient. There is only a performance analysis study on HARQ-CC-assisted short packet backscatter communication, and no research on the energy consumption optimization of HARQ-IR-assisted backscatter short packet communication system. Therefore, this application studies the energy consumption optimization strategy of HARQ-IR-assisted short packet backscatter communication. Summary of the Invention

[0006] To solve the above problems, the present invention provides a method for minimizing energy consumption in HARQ-IR assisted backscatter short packet communication.

[0007] According to a first aspect of an embodiment of the present invention, a method for minimizing energy consumption in HARQ-IR assisted backscatter short packet communication is provided, comprising the following steps:

[0008] S1: During the mth transmission, the energy source first transmits at power P s Send a signal to the IoT node, the IoT node receives the signal and harvests energy from the energy source, and then transmits the data packet to the IoT node through backscatter communication technology. m modulated and reflected to the receiver;

[0009] S2: The receiver performs joint decoding on the received data packets;

[0010] At this point, the effective block length decoded at the receiver is

[0011] S3: Based on the transmission process of steps S1 and S2, the packet error rate expression after the mth transmission is given:

[0012]

[0013] Where C = log(1+γ) represents the Shannon capacity of the channel, represents the channel dispersion, is the standard Q function;

[0014] S4: Under a given packet error rate ε0 = 1, by jointly optimizing the power reflection coefficient β (0≤β≤1), the short packet block length n, and the number of transmissions M for short packet communication of the IoT node, an optimization problem is constructed to minimize the energy consumption at the energy source:

[0015]

[0016] stC1:n (M) +MD·N,

[0017] C2:ε M (n,β)≤ε th ,

[0018]

[0019] C4:0≤β≤1,

[0020] C5:n∈Z + ,

[0021] C6:0≤M≤M th ,M∈Z + ,

[0022] Among them, P c represents the energy required for each backscatter transmission, ε th Indicates the packet error rate threshold, M th is the upper limit of M;

[0023] Constraint C1 ensures the low latency requirement for short packet transmission;

[0024] Constraint C2 ensures the reliability of the transmission process;

[0025] Constraint C3 imposes energy causality constraints in backscatter communication;

[0026] Constraint C4 gives the range of the power reflection coefficient;

[0027] Constraint C5 ensures that the transmission block length is a positive integer;

[0028] Constraint C6 limits the range of M;

[0029] S5: The optimization problem in step S4 is a mixed-integer nonlinear programming problem and cannot be solved directly. Therefore, we first derive a closed-form expression for the power reflection coefficient of the IoT node by using convex optimization theory. Then, we relax the short packet block length into a continuous variable and prove that under a fixed number of transmissions, the original problem can be transformed into a convex problem, which is then solved using a one-dimensional search algorithm.

[0030] In one embodiment, in the method, in step S1, during the mth transmission, the energy source first transmits at a power of P s Send a signal to the IoT node, the IoT node receives the signal and harvests energy from the energy source, and then transmits the data packet to the IoT node through backscatter communication technology. m Modulated and reflected to the receiver, specifically including:

[0031] During the transmission process, the energy source sends information s1 to the IoT node. After receiving the signal, the IoT node uses Energy is collected and the remaining part is used for modulation and reflection. Its block length is n m The self-information s2 of the IoT node is given by y1, where y1 is the received signal at the IoT node and β (0≤β≤1) describes the power reflection coefficient at the IoT node. Therefore, the energy collected by the IoT node can be expressed as:

[0032] E=η(1-β)P s g1T c , (1)

[0033] Where T c =T s n is the time required for a single transmission.

[0034] In one embodiment, the method, step S1 above, further includes:

[0035] The IoT node sends its own information s2 to the receiver through backscatter communication technology, and the receiver has:

[0036]

[0037] Where y2 is the received signal, n e ~CN(0,σ 2 ) represents the additive white Gaussian noise at the IoT node, so successive interference cancellation is used to cancel the interference from the energy source when all channel CSIs and s1 are known.

[0038] In one embodiment, the method, the signal-to-noise ratio (SNR) γ at the receiver can be described as:

[0039]

[0040] Let Ω m Indicates the event of the mth transmission failure. The packet error rate after the mth transmission is given by Given that, in long packet communication, for m>k, it can be derived that Thus, the packet error rate after a complete transmission round is P(Ω M ).

[0041] In one embodiment, the method, the step S2: the receiver jointly decoding the received data packet, specifically includes:

[0042] If the decoding fails, the receiver will send a negative confirmation to the IoT node; after receiving the negative confirmation, the IoT node will transmit the next data packet n m+1 , the receiver tries to decode again;

[0043] If the decoding is successful, the receiver sends an acknowledgment to the IoT node, and the IoT node completes the current transmission round.

[0044] In one embodiment, the method, step S5: the optimization problem of step S4 is a mixed integer nonlinear programming problem and cannot be solved directly. Therefore, first, a closed-form expression for the power reflection coefficient of the IoT node is derived by using convex optimization theory. Then, the short packet block length is relaxed to a continuous variable, and it is proved that under a fixed number of transmissions, the original problem can be converted into a convex problem, and then solved by a one-dimensional search algorithm, specifically including:

[0045] S51: The closed-form expression of the IoT node power reflection coefficient β is Among them, P c P represents the energy required by the IoT node to perform a backscattering operation. s is the transmission power of the energy source, η represents the energy conversion efficiency, and g1 is the channel gain from the energy source to the IoT node;

[0046] S52: Relax the short packet length to a continuous variable and prove that under a fixed number of transmissions, the original problem can be transformed into a convex problem, specifically:

[0047]

[0048] stC1,C2,C5,C6 and C7.

[0049] It shows that during data packet transmission, the larger β is, the more reliable the transmission is. Therefore, the optimal condition for β can be obtained from C7, which is expressed as:

[0050] The closed-form solution β * Substitute into problem P2 and transform P2 into a simplified problem P3 by combining convex optimization theory:

[0051]

[0052] stC1,C2,C5 and C6.

[0053] Decompose P3 into M th Sub-problems

[0054]

[0055] stC1,C2 and C5.

[0056] Relax the constraint C5 to obtain the modified problem P5; in P5, replace the integer constraint C5:n∈Z in P1 + Relaxation While keeping the other constraints unchanged, as shown below:

[0057]

[0058] where β * is the optimal solution of the power reflection coefficient, P s is the emission power of the energy source, T s is the transmission time of a single symbol, n is the transmission block length of a single data packet, M is the number of transmissions, ε m-1 (n,β * ) is the packet error rate at the m-1th data packet transmission;

[0059] S53: Solve P5 using a one-dimensional search algorithm. Next, use the one-dimensional search algorithm to solve each sub-problem and take the minimum value to obtain the minimum transmission energy consumption of P1.

[0060] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0061] This application combines Hybrid Automatic Repeat Request with Incremental Redundancy (HARQ-IR) with backscatter short packet communication to improve transmission reliability at the expense of some time delay. Based on a system model, an approximate expression for the packet error rate (PER) is introduced. An energy consumption minimization problem is formulated with the power reflection coefficient, number of transmissions, and block length as variables. This is essentially a nonlinear integer programming problem. To solve this problem, this application derives the monotonicity between the PER and the PRC. Combining this with convex optimization theory, the optimal PRC can be determined. Next, based on constraints, the problem is decomposed into subproblems with different transmission times, resulting in a single-variable problem with block length. The integer subproblems are then converted into a continuous problem by relaxing the integer constraints. This application then proves that the problem is convex. Since it is a one-dimensional convex problem, a one-dimensional search algorithm (such as the golden section method) is used to find the optimal solution. Finally, simulation results demonstrate that the proposed HARQ scheme with retransmission significantly reduces energy consumption compared to a transmission scheme without retransmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 Schematic diagram of the HARQ-IR assisted backscatter short packet communication system proposed in this application;

[0064] Figure 2 A relationship curve diagram of the relationship between the transmission energy consumption and the transmission power at the energy source proposed in this application;

[0065] Figure 3 A graph showing the relationship between transmission energy consumption and the maximum number of channels used N proposed in this application;

[0066] Figure 4 The transmission energy consumption along with the packet error rate threshold ε proposed in this application is th A graph showing the relationship between the changing trends;

[0067] Figure 5 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0068] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0069] The present invention combines Hybrid Automatic Repeat Request with Incremental Redundancy (HARQ-IR) with backscatter short packet communication to improve transmission reliability at the expense of some time delay. Based on the system model, this application introduces the approximate expression of packet error rate from references

[14]

[15] and formulates an energy consumption minimization problem with power reflection coefficient, number of transmissions and block length as variables. This is actually a nonlinear integer programming problem. In order to solve this problem, this application derives the monotonicity between packet error rate and power reflection coefficient. Combining convex optimization theory, the optimal power reflection coefficient can be first determined. Then, according to the constraints, the problem is decomposed into sub-problems with different number of transmissions, resulting in a single variable problem about block length, and the integer sub-problem is converted into a continuous problem by relaxing the integer constraint.

[0070] The problem is then proven to be a convex problem. Since it is a one-dimensional convex problem, a one-dimensional search algorithm (such as the golden section method) is used to solve it and obtain the optimal solution. Finally, simulation results show that the proposed HARQ scheme with retransmission can significantly reduce energy consumption compared to the transmission scheme without retransmission.

[0071] Figure 1 An embodiment of the HARQ-IR-assisted backscatter short packet communication system of the present invention is shown. The HARQ-IR-assisted backscatter short packet communication system includes a single-antenna energy source for providing RF energy or an initial signal to power an IoT node or activate communication; a single-antenna single IoT node for modulating a received signal (e.g., reflecting a signal from the energy source) using backscattering technology to append its own data to a carrier; and a single-antenna receiver for demodulating the signal reflected by the IoT node and decoding the data.

[0072] In HARQ-IR, B bits of information are encoded into M sub-codewords, denoted as

[0073]

[0074] Among them, n m ,m∈[1,M] represents the block length of the mth subcodeword, M represents the number of transmissions, M th is the maximum number of transmissions allowed. Assume that all data packets in this model are transmitted within a coherence time T = NT s Completed within, where N is the maximum number of channel usage, T s Indicates the single symbol transmission time.

[0075] During the mth transmission, the energy source first transmits at power P s Send a signal to the IoT node. After the IoT node receives the signal and harvests energy from the energy source, it transmits the data packet to the IoT node through backscatter communication technology. m Modulated and reflected to the receiver. The receiver then performs joint decoding on the received data packets. At this point, the effective block length decoded at the receiver is If the decoding fails, the receiver will send a negative acknowledgment (NACK) to the IoT node. After receiving the NACK, the IoT node will transmit the next data packet n m+1The receiver attempts decoding again. If decoding is successful, the receiver sends an acknowledgment (ACK) to the IoT node, completing the current transmission round. It's worth noting that both NACK and ACK feedback introduce an unavoidable time delay, D. In the model, it's assumed that the IoT node can always correctly receive NACK / ACK signals. Furthermore, if decoding still fails after the Mth transmission, the current information transmission is considered a failure and terminated.

[0076] Consider all channels as quasi-static channels, affected by Rayleigh fading and path loss. Therefore, the channel gain is g i =|h i | 2 d i -α ,i∈{0,1,2}, where h i and d i They correspond to the channel coefficients and distances from the energy source to the receiver, the energy source to the IoT node, and the IoT node to the receiver, respectively. α represents the path loss factor. It is assumed that the channel state information (CSI) of all transmissions in this model can be obtained by existing channel estimation methods. The present invention considers a simplified HARQ-IR transmission process and assumes that the block length of each transmission is n, n∈Z + , at this time n (M) =Mn.

[0077] During the transmission process, the energy source sends information s1 to the IoT node. After receiving the signal, the IoT node uses Energy is collected and the remaining part is used for modulation and reflection. Its block length is n m The self-information s2, where y1 is the received signal at the IoT node, and β (0≤β≤1) describes the power reflection coefficient at the IoT node. Therefore, the energy collected by the IoT node can be expressed as:

[0078] E=η(1-β)P s g1T c , (1)

[0079] Where η represents the energy conversion efficiency, T c =T s n is the time required for a single transmission, P s represents the transmission power at the energy source, and g1 is the channel gain from the energy source to the IoT node. Subsequently, the IoT node sends its own information s2 to the receiver through backscatter communication technology. Therefore, the received signal s2 at the receiver is:

[0080]

[0081] where n e~CN(0,σ 2 ) represents the additive white Gaussian noise (AWGN) at the IoT node, g0 is the channel gain from the energy source to the receiver, g2 represents the channel gain from the IoT node to the receiver, and s1 is the information sent by the energy source to the IoT node. Therefore, when all channel CSIs and s1 are known, successive interference cancellation (SIC) can be used to cancel the interference from the energy source. Then, the signal-to-noise ratio (SNR) γ at the receiver can be described as:

[0082]

[0083] According to the method in

[10] and

[14] , let Ω m Indicates the event of the mth transmission failure. The packet error rate after the mth transmission is given by Given. In long packet communication, for m>k, it can be derived that Thus, the packet error rate after a complete transmission round is P(Ω M ).

[0084] However, it is difficult to accurately describe the packet error rate in short packet communication. Therefore, the packet error rate approximation method proposed in

[14] and

[15] is used for subsequent analysis, and the following is obtained:

[0085]

[0086] where n (m) is the effective block length of the decoded data packet at the receiver at this time, C = log (1 + γ) represents the Shannon capacity of the channel, represents the channel dispersion, B represents the number of information bits transmitted this time, is the standard Q function.

[0087] An embodiment of the method for minimizing energy consumption in HARQ-IR assisted backscatter short packet communication of the present invention.

[0088] In this embodiment, the method disclosed in the present invention comprises the following steps:

[0089] S1: During the mth transmission, the energy source first transmits at power P s Send a signal to the IoT node, the IoT node receives the signal and harvests energy from the energy source, and then transmits the data packet to the IoT node through backscatter communication technology. m modulated and reflected to the receiver;

[0090] S2: The receiver performs joint decoding on the received data packets; at this point, the effective block length decoded at the receiver is

[0091] If the decoding fails, the receiver will send a negative acknowledgement (NACK) to the IoT node; after receiving the NACK, the IoT node will transmit the next data packet n m+1 , the receiver tries to decode again;

[0092] If the decoding is successful, the receiver sends an acknowledgment (ACK) to the IoT node, and the IoT node completes the current transmission round;

[0093] S3: Based on the transmission process of steps S1 and S2, the packet error rate expression after the mth transmission is given:

[0094]

[0095] Where C = log(1+γ) represents the Shannon capacity of the channel, represents the channel dispersion,

[0096] is the standard Q function;

[0097] S4: Under a given packet error rate ε0 = 1, the power reflection coefficient (PRC), short packet length n, and the number of transmissions M for short packet communication of the IoT node are jointly optimized to construct an optimization problem to minimize the energy consumption at the energy source:

[0098]

[0099] stC1:n (M) +MD·N,

[0100]

[0101] C4:0≤β≤1,

[0102] C5:n∈Z + ,

[0103] C6:0≤M≤M th ,M∈Z + .

[0104] Among them, P c represents the energy required for each backscatter transmission, ε th Indicates the packet error rate threshold, M th is the upper limit of M;

[0105] Constraint C1 ensures the low latency requirement for short packet transmission;

[0106] Constraint C2 ensures the reliability of the transmission process;

[0107] Constraint C3 imposes energy causality constraints in backscatter communication;

[0108] Constraint C4 gives the range of the power reflection coefficient;

[0109] Constraint C5 ensures that the transmission block length is a positive integer;

[0110] Constraint C6 limits the range of M because energy consumption cannot always be reduced as M continues to increase;

[0111] S5: The optimization problem in step S4 is a mixed integer nonlinear programming (MINLP) problem and cannot be solved directly. Therefore, we first derive a closed-form expression for the power reflection coefficient of the IoT node by using convex optimization theory. Then, we relax the short packet block length into a continuous variable and prove that under a fixed number of transmissions, the original problem can be transformed into a convex problem. The problem is then solved using a one-dimensional search algorithm (such as the golden section algorithm).

[0112] As a specific embodiment, step S1: during the mth transmission, the energy source first transmits at a power of P s Send a signal to the IoT node, the IoT node receives the signal and harvests energy from the energy source, and then transmits the data packet to the IoT node through backscatter communication technology. m Modulated and reflected to the receiver, specifically including:

[0113] During the transmission process, the energy source sends information s1 to the IoT node. After receiving the signal, the IoT node uses Energy is collected and the remaining part is used for modulation and reflection. Its block length is n m The self-information s2, where y1 is the received signal at the IoT node, and β (0≤β≤1) describes the power reflection coefficient at the IoT node. Therefore, the energy collected by the IoT node can be expressed as:

[0114] E=η(1-β)P s g1T c , (1)

[0115] Where T c =T s n is the time required for a single transmission.

[0116] As a specific implementation scheme, the above step S1 further includes:

[0117] The IoT node sends its own information s2 to the receiver through backscatter communication technology, and the receiver has:

[0118]

[0119] Where y2 is the received signal, n e ~CN(0,σ 2 ) represents the additive white Gaussian noise (AWGN) at the IoT node. Therefore, successive interference cancellation (SIC) is used to cancel the interference from the energy source when all channel CSIs and s1 are known.

[0120] As a specific implementation, the signal-to-noise ratio (SNR) γ at the receiver can be described as:

[0121]

[0122] Let Ω m Indicates the event of the mth transmission failure. The packet error rate after the mth transmission is given by Given that, in long packet communication, for m>k, it can be derived that Thus, the packet error rate after a complete transmission round is P(Ω M ).

[0123] However, it is difficult to accurately describe the packet error rate in short packet communication. As a specific implementation plan, we adopt the approximate scheme in references

[14] and

[15] . At this time, the packet error rate in short packet communication is:

[0124]

[0125] Where C = log(1+γ) represents the Shannon capacity of the channel, represents the channel dispersion, is the standard Q function.

[0126] As a specific implementation plan, step S5: The optimization problem in step S4 is a mixed integer nonlinear programming (MINLP) problem and cannot be solved directly. Therefore, we first derive a closed-form expression for the IoT node power reflection coefficient by using convex optimization theory. Next, we relax the short packet block length into a continuous variable and prove that the original problem can be transformed into a convex problem under a fixed number of transmissions. We then solve it using a one-dimensional search algorithm (such as the golden section algorithm), specifically including:

[0127] In C3, the inequality is only related to β. By adjusting the inequality, the upper limit of β can be obtained, that is,

[0128] C3′ and C4 can be combined into

[0129] Therefore, a new question P2 can be derived:

[0130]

[0131] stC1,C2,C5,C6 and C7.

[0132] The closed-form expression for β is

[0133] It shows that during data packet transmission, the larger β is, the more reliable the transmission is. Therefore, the optimal condition for β can be obtained from C7, which is expressed as:

[0134] The closed-form solution β * Substitute into problem P2 and transform P2 into a simplified problem P3 by combining convex optimization theory:

[0135]

[0136] stC1,C2,C5 and C6.

[0137] At this time, P3 only involves variables M and n; in this way, solving P2 is simplified to solving P3; obviously, it is difficult to obtain closed-form expressions for M and n at the same time; since M is a variable with an upper limit M th A positive integer, P3 can be decomposed into M th The sub-problems are as follows:

[0138]

[0139] stC1,C2 and C5.

[0140] Obviously, by comparing M th The optimal solutions of the subproblems (i.e., P4 under different M) are obtained and their minimum is taken to solve P3; in order to solve this problem, since P4 is a nonlinear integer optimization problem with a unique variable n, the constraint C5 is relaxed to obtain the modified problem P5; in P5, the integer constraint C5:n∈Z in P1 is replaced by + Relaxation While keeping the other constraints unchanged, as shown below:

[0141]

[0142] where β * is the optimal solution of the power reflection coefficient, P s is the emission power of the energy source, T s is the transmission time of a single symbol, n is the transmission block length of a single data packet, M is the number of transmissions, ε m-1 (n,β * ) is the packet error rate during the m-1th data packet transmission.

[0143] Based on the above embodiments and preferred implementations, a specific embodiment is provided to study the optimal energy consumption of a backscatter short packet communication system using the HARQ-IR mechanism during the entire transmission process, as follows:

[0144] Given a packet error rate ε0 = 1, the following optimization problem is proposed:

[0145]

[0146] stC1:n (M) +MD≤N,

[0147] C2:ε M (n,β)≤ε th ,

[0148] C3:E≥P c T c ,

[0149] C4:0≤β≤1,

[0150] C5:n∈Z + ,

[0151] C6:0≤M≤M th ,M∈Z + .

[0152] Among them, P c P represents the energy required by the IoT node to perform a backscattering operation. s is the emission power of the energy source, T s is the transmission time of a single symbol, ε th Indicates the packet error rate threshold, M th is the upper limit of the number of transmissions M, E is the energy absorbed by each IoT node during transmission, D is the delay caused by the receiver when feeding back NACK / ACK, ε m-1 (n,β) is the packet error rate at the m-1th data packet transmission, n (M) = Mn is the effective block length of the data packet at the receiver when retransmitted M times, where n is the block length of a single data packet. In Problem P1, constraint C1 ensures low latency for short packet transmission, C2 ensures reliability during transmission, C3 imposes energy causality constraints in backscatter communication, C4 specifies the range of the power reflection coefficient, C5 ensures that the transmission block length n is a positive integer, and C6 restricts the range of M, because energy consumption does not always decrease as M continues to increase.

[0153] Obviously, P1 is a mixed integer nonlinear programming (MINLP) problem, which is difficult to solve directly. Therefore, the problem needs to be simplified. In C3, the inequality is only related to the power reflection coefficient β. By adjusting the inequality, the upper limit of the power reflection coefficient β can be obtained, that is, Obviously, C3′ and C4 can be combined into Therefore, a new question P2 can be derived:

[0154]

[0155] stC1,C2,C5,C6 and C7.

[0156] Lemma 1: The closed-form expression for the optimal solution of the power reflection coefficient β is Among them, P c P represents the energy required by the IoT node to perform a backscattering operation. s is the transmission power of the energy source, η represents the energy conversion efficiency, and g1 is the channel gain from the energy source to the IoT node.

[0157] The specific proof is as follows:

[0158] To prove Lemma 1, we first need to prove And since the signal-to-noise ratio γ increases with the power reflection coefficient β, it can be proved that make Obviously, as x increases, the signal-to-noise ratio γ decreases. Now we only need to prove that At the same time, There is ε m =Q(w m ), thus we can get:

[0159]

[0160] At this time, due to The task is reduced to proving the following inequality:

[0161]

[0162] Now, we only need to prove Taking the first-order derivative of h(x) yields: Based on the reliability constraints, we can determine w m >0, so It is always true, so we can get h'(x)>0. Furthermore, since h(1)=-Bln2<0, it can be seen that h(x)<0 is always true, so we can get Right now

[0163] It shows that during data packet transmission, the larger the power reflection coefficient β is, the more reliable the transmission is. Therefore, the optimal condition for the power reflection coefficient β can be obtained from C7, which is expressed as: It can be seen that Lemma 1 always holds true in the system model under study.

[0164] Therefore, the closed-form solution β can be * Substitute into problem P2 and transform P2 into a simplified problem P3 by combining convex optimization theory:

[0165]

[0166] stC2′=ε M (n,β * )≤ε th ,

[0167] C1, C5 and C6,

[0168] At this time, P3 only involves the variables transmission number M and transmission block length n. In this way, solving P2 is simplified to solving P3. Obviously, it is difficult to obtain closed-form expressions for transmission number M and transmission block length n at the same time. Since transmission number M is a variable with an upper limit M th A positive integer, P3 can be decomposed into M th The sub-problems are as follows:

[0169]

[0170] stC1,C2'and C5.

[0171] Obviously, by comparing M th The optimal solution of the subproblems (i.e., P4 under different transmission times M) is obtained and their minimum is taken to solve P3. To solve this problem, since P4 is a nonlinear integer optimization problem with a unique variable block length n, the constraint C5 is relaxed to obtain the modified problem P5. In P5, the integer constraint C5:n∈Z in P1 is replaced by + Relaxation While keeping the other constraints unchanged, as shown below:

[0172]

[0173] stC1,C2',and C5′.

[0174] Where β * is the optimal solution of the power reflection coefficient, P s is the emission power of the energy source, T s is the transmission time of a single symbol, n is the transmission block length of a single data packet, M is the number of transmissions, εm-1 (n,β * ) is the packet error rate during the m-1th data packet transmission.

[0175] Lemma 2: P5 is a convex problem that can be solved by many mature solution methods.

[0176] The specific proof is as follows:

[0177] To prove that problem P5 is convex, first prove that Due to ε m =Q(w m ), we can get:

[0178]

[0179]

[0180] It can be observed Always holds true, so now we only need to prove Given n (m) =mn and A=Bln2, then we have Then we can get:

[0181]

[0182] It can be clearly seen It is always established, so there is At the same time, it can obviously be proved that C2' is a convex set.

[0183] Next, to prove It is a convex function, and we need to prove that P s T s nε m-1 (n,β) is convex, which proves that f(n)=nε m-1 (n) is convex, so:

[0184]

[0185] where f′(n) and f″(n) represent the first-order derivative and second-order derivative of f(n), respectively. From formula (9), we can deduce the following:

[0186]

[0187] because So we just need to prove

[0188]

[0189] The above formula shows that we need to prove R m-1 =C 2 n2 (m-1) -4Vn (m-1) -A 2 > 0. R m-1 It is a quadratic equation, and its positive roots can be obtained by solving this quadratic equation Under the reliability constraint and the approximate conditions of packet error rate (n>100), we have From this we can deduce the following:

[0190]

[0191] this means Established, so R m-1 >0 is always true, then P s T s nε m-1 (n,β) is convex. Since the objective function of problem P4 is In form, it is P s T s nε m-1 (n,β * ) is the non-negative weighted sum under different conditions of m, so from the convex optimization theory we can get is a convex function. In summary, the objective function of problem P5 is a convex function and the constraint set is a convex set, so P5 is a convex set.

[0192] From Appendix B, we know that C2' is a convex set, and through inequality transformation, C2' can be transformed into Combining C1, C2' and C5', we can get the constraint condition At this point, the P5 problem is transformed into finding the minimum value of the function t(n,M) under the constraint condition C8, where is the objective function of P5. Therefore, P5 can be solved by a one-dimensional search algorithm (such as the golden section algorithm). Since n0 obtained by P5 is not necessarily an integer solution, we need to round it up and get and round down to get Compare this time and The smaller value among them is taken as the minimum energy consumption t(n * ,M), where. Use the same method to solve each sub-problem under different transmission times M, and take the minimum value among them to obtain the minimum transmission energy consumption of P3. Algorithm 1 shows this process.

[0193]

[0194] Based on the above embodiments and preferred implementations, a specific embodiment is provided as follows:

[0195] Simulation analysis was performed, and the simulation results show the advantages of the HARQ-IR scheme proposed in this invention in terms of energy consumption. Unless otherwise specified, the basic parameters are set as follows: the distance from the energy source to the IoT node d1 = 10m, the distance from the IoT node to the receiver d2 = 30m, the path loss factor α = -2.7, the number of transmitted information bits B = 240 bits, the number of channels N = 2000, and the single symbol transmission time T s =0.025ms, total transmission time T=T s N = 5ms, the power required for a single backscattering is P c =10 -5 mW, maximum error tolerance δ=10 -2 , D = 20, frequency bandwidth is f = 1MHz, noise power density is P n =-120dBm / Hz, η = 0.7. In addition, when M = 1, it indicates a transmission scheme without HARQ.

[0196] Figure 2 Shows the transmission energy consumption and the transmission power P at the energy source s The relationship between the transmission power P s As the transmission power increases, the transmission energy consumption also increases. This is because despite the higher transmission power P s Allows the system to meet the packet error rate threshold ε with a smaller transmission block length th , but the exponential growth of transmission power leads to increased energy consumption.

[0197] Figure 3 The relationship between transmission energy consumption and the maximum number of channels used N is shown; as the number of channels used N increases, energy consumption also increases. A larger number of channels used N allows the transmission to use more block lengths n to meet the threshold ε th At the same time, if the channel conditions are poor, the transmission cannot work properly, which may cause the IoT node to use all the allowed time for transmission, resulting in increased energy consumption.

[0198] Figure 4 The transmission energy consumption varies with the packet error rate threshold ε th The trend of change; with the packet error rate threshold ε th As the feedback delay D increases, energy consumption increases due to longer transmission delays.

[0199] exist Figure 2-Figure 4The proposed scheme is compared with a scheme without HARQ. The results show that the HARQ-IR method of the present invention effectively reduces transmission energy consumption. This is because the scheme without HARQ sends complete data packets, while the HARQ-IR scheme of the present invention divides the data into multiple sub-codewords for transmission, thereby completing data transmission with less energy consumption while meeting the packet error rate threshold.

[0200] The existing references involved in the patent are as follows:

[0201] [1] T.Jiang, Y.Zhang, W.Ma, M.Peng, Y.Peng, M.Feng, and G.Liu, "BackscatterCommunication Meets Practical Battery-Free Internet of Things: A Survey andOutlook," IEEE Communications Surveys&Tutorials, vol.25, no.3, pp.2021–2051, 2023.

[0202] [2] Y.Xu and G.Gui, "Optimal Resource Allocation for Wireless PoweredMulti-Carrier Backscatter Communication Networks," IEEE WirelessCommunications Letters, vol.9, no.8, pp.1191–1195, 2020.

[0203] [3]Y.Ye, R.Xu, G.Chen, D.Benevides da Costa, and G.Lu, "QoS-GuaranteedAdaptive Power Reflection Coefficient for Self-Powered Cooperative AmbientBackscatter Communication," IEEE Wireless Com-

[0204] munications Letters,vol.13,no.10,pp.2812–2816,2024.

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[0206] [5]G.Durisi,T.Koch,and P.Popovski,“Toward Massive,Ultrareliable,andLow-Latency Wireless Communication With Short Packets,”Pro-ceedings oftheIEEE,vol.104,no.9,pp.1711–1726,2016.

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[10] X.Gao,L.Shi,Y.Ye,G.Zheng,and G.Lu,“Reliability-Oriented Re-sourceAllocation in Short-Packet Backscatter Communications,”IEEE Transactions onVehicular Technology,vol.74,no.2,pp.3468–3473,2025.

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[11] K.Cao and Q.Tang,“Energy Efficiency Maximization for RIS-AssistedMISO Symbiotic Radio Systems Based on Deep Reinforcement Learn-ing,”IEEE Communications Letters,vol.28,no.1,pp.88–92,2024.

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[12] Z.Chu,W.Hao,P.Xiao,M.Khalily,and R.Tafazolli,“ResourceAllocations for Symbiotic Radio With Finite Blocklength Backscatter Link,”IEEE Internet of Things Journal,vol.7,no.9,pp.8192–8207,2020.

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[13] L.Shi,J.Shi,Y.Ye,G.Zheng,and G.Lu,“Ambient BackscatterCommunication With HARQ AssistedHybrid Long-Short Packets,”IEEECommunications Letters,vol.28,no.10,pp.2258–2262,2024.

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[0218] In summary, the present invention studies the HARQ-IR assisted backscatter short packet communication system. Based on this system, an optimization problem of minimizing energy consumption is constructed with the short packet block length, the number of transmissions and the power reflection coefficient (PRC) of the IoT node as variables. To solve this problem, the monotonicity of the packet error rate and the power reflection coefficient is first proved, and the optimal power reflection coefficient is derived by using the convex optimization theory. Then, it is proved that after relaxing the short packet block length to a continuous variable and fixing the number of transmissions, the problem is a convex optimization problem. Therefore, an iterative algorithm is proposed to obtain the minimum energy consumption. Finally, the simulation results show that the HARQ-IR method proposed in the present invention has better performance than the transmission without HARQ.

[0219] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment are implemented.

[0220] Those skilled in the art will understand that Figure 5 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0221] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiment when executing the computer program.

[0222] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0223] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0224] Each embodiment in this specification is described in a related manner. The same or similar parts between the embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences from other embodiments. The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A method for minimizing energy consumption in HARQ-IR assisted backscatter short packet communication, characterized in that: The steps include: S1: During the mth transmission, the energy source first transmits at power P s Send a signal to the IoT node, the IoT node receives the signal and harvests energy from the energy source, and then transmits the data packet to the IoT node through backscatter communication technology. m modulated and reflected to the receiver; S2: The receiver performs joint decoding on the received data packets; at this point, the effective block length decoded at the receiver is S3: Based on the transmission process of steps S1 and S2, the packet error rate expression after the mth transmission is given: Where C = log(1+γ) represents the Shannon capacity of the channel, represents the channel dispersion, is the standard Q function; S4: Under a given packet error rate ε0 = 1, by jointly optimizing the power reflection coefficient β (0≤β≤1), the short packet block length n, and the number of transmissions M for short packet communication of the IoT node, an optimization problem is constructed to minimize the energy consumption at the energy source: s.t.C1:n (M) +MD·N, C2:e M (n,β)≤ε th , C4:0≤β≤1, C5:n∈Z + , C6:0≤M≤M th ,M∈Z + , Among them, P c represents the energy required for each backscatter transmission, ε th Indicates the packet error rate threshold, M th is the upper limit of M; Constraint C1 ensures the low latency requirement for short packet transmission; Constraint C2 ensures the reliability of the transmission process; Constraint C3 imposes energy causality constraints in backscatter communication; Constraint C4 gives the range of the power reflection coefficient; Constraint C5 ensures that the transmission block length is a positive integer; Constraint C6 limits the range of M; S5: For the optimization problem in step S4, we first derive a closed-form expression for the IoT node power reflection coefficient by using convex optimization theory. Then, we relax the short packet length into a continuous variable and prove that under a fixed number of transmissions, the original problem can be transformed into a convex problem, which is then solved using a one-dimensional search algorithm.

2. The method for minimizing energy consumption in HARQ-IR assisted backscatter short packet communication according to claim 1, characterized in that: In step S1, during the mth transmission, the energy source first transmits at power P s Send a signal to the IoT node, the IoT node receives the signal and harvests energy from the energy source, and then transmits the data packet to the IoT node through backscatter communication technology. m Modulated and reflected to the receiver, specifically including: During the transmission process, the energy source sends information s1 to the IoT node. After receiving the signal, the IoT node uses Energy is collected and the remaining part is used for modulation and reflection. Its block length is n m The self-information s2 of the IoT node is given by y1, where y1 is the received signal at the IoT node and β (0≤β≤1) describes the power reflection coefficient at the IoT node. Therefore, the energy collected by the IoT node can be expressed as: E=η(1-β)P s g1T c , (1) Where T c =T s n is the time required for a single transmission.

3. The method for minimizing energy consumption in HARQ-IR assisted backscatter short packet communication according to claim 2, characterized in that: The above step S1 further includes: The IoT node sends its own information s2 to the receiver through backscatter communication technology, and the receiver has: Where y2 is the received signal, n e ~CN(0,σ 2 ) represents the additive white Gaussian noise at the IoT node, so successive interference cancellation is used to cancel the interference from the energy source when all channel CSIs and s1 are known.

4. The method for minimizing energy consumption in HARQ-IR assisted backscatter short packet communication according to claim 3, characterized in that: The signal-to-noise ratio γ at the receiver is described as: Let Ω m Indicates the event of the mth transmission failure. The packet error rate after the mth transmission is given by Given; For long packet communication, when m>k, it can be derived Thus, the packet error rate after a complete transmission round is P(Ω M ).

5. The method for minimizing energy consumption in HARQ-IR assisted backscatter short packet communication according to claim 1, characterized in that: The step S2: the receiver jointly decodes the received data packet, specifically comprising: If the decoding fails, the receiver will send a negative confirmation to the IoT node; after receiving the negative confirmation, the IoT node will transmit the next data packet n m+1 , the receiver tries to decode again; If the decoding is successful, the receiver sends an acknowledgment to the IoT node, and the IoT node completes the current transmission round.

6. The method for minimizing energy consumption in HARQ-IR assisted backscatter short packet communication according to claim 1, characterized in that: Step S5: For the optimization problem in step S4, first derive a closed-form expression for the IoT node power reflection coefficient by using convex optimization theory. Next, relax the short packet block length to a continuous variable and prove that the original problem can be transformed into a convex problem under a fixed number of transmissions. The problem is then solved using a one-dimensional search algorithm, specifically: S51: The closed-form expression of the IoT node power reflection coefficient β is: Among them, P c P represents the energy required by the IoT node to perform a backscattering operation. s is the transmission power of the energy source, η represents the energy conversion efficiency, and g1 is the channel gain from the energy source to the IoT node; S52: Relax the short packet length to a continuous variable and prove that under a fixed number of transmissions, the original problem can be transformed into a convex problem, specifically: stC1,C2,C5,C6 and C7. It shows that during data packet transmission, the larger β is, the more reliable the transmission is. Therefore, the optimal condition for β can be obtained from C7, which is expressed as: The closed-form solution β * Substitute into problem P2 and transform P2 into a simplified problem P3 by combining convex optimization theory: stC1,C2,C5 and C6, Decompose P3 into M th The subproblem is P4: stC1,C2 and C5. Relax the constraint C5 to obtain the modified problem P5; in P5, replace the integer constraint C5:n∈Z in P1 + Relaxation While keeping the other constraints unchanged, as shown below: where β * is the optimal solution of the power reflection coefficient, P s is the emission power of the energy source, T s is the transmission time of a single symbol, n is the transmission block length of a single data packet, M is the number of transmissions, ε m-1 (n,β * ) is the packet error rate at the m-1th data packet transmission; S53: Solve P5 using a one-dimensional search algorithm. Next, use the one-dimensional search algorithm to solve each sub-problem and take the minimum value to obtain the minimum transmission energy consumption of P1.

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