Optimal label selection scheme of back scattering auxiliary NOMA system in multi-label environment

Abstract

The invention provides an optimal label selection scheme of a backscattering NOMA communication system in a multi-label environment. The system is composed of a base station BS, two users (UN and UF) and M backscattering labels Ti (i = 1, 2, 3,..., M). The multi-label environment backscattering technology is applied in the NOMA communication system, M labels are placed between the base station BS and the remote user UF, and the label with the optimal performance is selected for transmission by using the selection and combination technology, so that compared with single-label backscattering, the outage probability of the remote user is reduced, the system throughput is improved, and the communication efficiency is improved. And the transmission efficiency of the system is further improved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication, and more specifically to an optimal tag selection scheme for a backscatter-assisted NOMA system in a multi-tag environment. Technical Background

[0002] Ambient Backscatter Communication (AmBC) utilizes ambient radio frequency (RF) signals for communication, offering advantages such as high spectrum efficiency, low cost, and low power consumption. It is one of the potential technologies for building "green communication," providing an effective communication solution for battery-powered IoT devices. For battery-powered IoT devices, frequent battery replacements and supporting the construction of passive IoT systems pose challenges, and backscatter technology offers an important solution. Unlike traditional backscatter technologies, AmBC does not require a specific base station to transmit signals; instead, it utilizes ambient RF signals for communication. An AmBC system typically consists of three nodes: an ambient RF source, a tag, and a reader. The ambient RF source emits RF signals, and the tag transmits these signals to the reader via backscattering. The working principle of AmBC is that the tag uses ambient RF signals as an energy source and converts them into backscattered signals to achieve communication. This technology effectively utilizes ambient RF signals, reduces reliance on batteries, and provides IoT devices with long-term self-contained communication capabilities.

[0003] Non-Orthogonal Multiple Access (NOMA) is a key technology for improving the spectrum efficiency of 6G networks. The application of NOMA technology is significant in 6G networks, as it can improve spectrum efficiency and support more user connections, thereby driving the development of future communication networks. Its main idea is to allow several users to share the same resource block (such as time slots, frequency channels, and code resources) in the power domain, distinguishing each user by differences in power levels. In power-domain NOMA, lower power levels are typically allocated to users with better channel conditions. The receiver uses Successive Interference Cancellation (SIC) technology to first decode and cancel the expected signal with a higher power level, and then recover the expected signal with a lower power level. Users with poorer channel conditions can directly decode the expected signal containing their own information. This approach improves system performance. Compared to orthogonal multiple access, NOMA can achieve approximately 30% system-level performance improvement.

[0004] Currently, while there are numerous articles on single-tag NOMA backscattering systems, multi-tag backscattering applications in orthogonal multiple access (OMA), and conventional NOMA technology applications, relatively few delve into the impact of multi-tag backscattering on the NOMA system itself. This provides a valuable direction for future research and practical applications. In practical scenarios, due to the variability of tag positions, multi-tag backscattering systems can optimize transmission efficiency by placing tags between distant users and the base station and selecting the tag with the best performance for transmission. Simultaneously, ideal and non-ideal successive interference cancellation (SIC) techniques are employed near the user end, decoding is performed sequentially according to the user's channel conditions. Although further research is needed on the impact of multi-tag backscattering on NOMA systems, in practical applications, by flexibly placing multiple tags and combining ideal and non-ideal SIC techniques, the system's transmission efficiency can be further improved. Summary of the Invention

[0005] The purpose of this invention is to provide an optimal tag selection scheme for a multi-tag environment backscatter-assisted NOMA system. This invention applies multi-tag environment backscatter technology in NOMA communication systems, through the base station (BS) and the remote user (U). F By placing M tags between them and selecting the tag with the best performance for transmission, compared with single-tag backscattering, the probability of interruption for distant users is reduced, the system throughput is increased, and the system transmission efficiency is further improved.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] An optimal tag selection scheme for a backscatter-assisted NOMA system in a multi-tag environment, where two user receiving base stations (BS) equipped with single antennas transmit superimposed signals at a constant transmit power (P). Near user U N There is a direct link between the remote user U and the base station (BS). F The signal from the base station (BS) is received using backscattering from M tags. This is done between the base station (BS) and the remote user (U). F During communication, by selecting the optimal transmission tag, the goal of optimizing interrupt performance and improving system throughput is achieved. The method is characterized by the following steps:

[0008] Step 1: The superimposed signal transmitted by a base station BS with a transmission power of P at time t is described as follows:

[0009]

[0010] In the formula: x N (t) and x F (t) is the unit power transferred to U Nand U F The signal, α1 and α2 are NOMA power allocation coefficients, α1 + α2 = 1 and α1 < α2. Since U F The channel quality is poor, so U F Allocate more power, i.e., α2 > 0.5;

[0011] Step 2: Due to the influence of channel transmission performance and channel noise during signal transmission, the signal near user U... N The received signal is:

[0012]

[0013] Where: h BN It is from the base station (BS) to the user (U). N The channel coefficients, n N (t) represents the nearest user U N The additive white Gaussian noise at the location follows a Gaussian distribution with mean 0 and variance δ0;

[0014] Step 3: Since the tag is a passive device, it is difficult to process complex signals. Therefore, the thermal noise of the tag itself can usually be ignored. Therefore, when near the user U... N Similarly, the signal received at the tag is:

[0015]

[0016] After receiving the signal, the tag performs backscattering. Based on the tag's backscattering performance, the backscattered signal at the tag is:

[0017]

[0018] In the formula: η represents the backscattering coefficient, η∈(0,1). B m (t) represents the binary symbol at the label, which can be considered a constant over continuous time t, denoted by B. m To indicate;

[0019] Step 4, Connect with nearby user U N The received signal is similar, for the remote user U F The received signal is:

[0020]

[0021] In the formula: It is the label T i To remote user U F The channel coefficients, n F (t) represents the remote user U F The additive white Gaussian noise at the location follows a Gaussian distribution with mean 0 and variance δ0;

[0022] Step 5: The base station (BS) sends a message to the remote user (U). F When sending overlay messages, according to the formula Select the best transmission label T from M labels. j Besides the connection from base station (BS) to tag (T) j Channel gain In addition, other data sources from base stations (BS) to tags (T) i Channel gain All than The value is small, thus the optimal transmission label is T. j ,in This indicates the distance from base station (BS) to tag (T). i Channel gain, This indicates the distance from the base station (BS) to the best label (T). j Channel gain.

[0023] Preferred, near user U N Received signal Near user U N First, for x F The (t) signal is decoded, and then the ideal continuous interference cancellation technique is used to process x. N (t) signal is decoded; when decoding x F When the signal x is (t), the signal is given. F The power of (t) is obtained by multiplying the channel gain, power allocation coefficient, and total signal power. The noise power is obtained by adding the unwanted signal power to the channel noise power. (The last part, "near user U," appears to be a typo and can be omitted.) N The signal-to-noise ratio at this point is:

[0024]

[0025] In the case of ideal SIC, signal x F (t) is perfectly decoded without residual interference, so when x is decoded... N When the signal is (t), the noise power is the channel additive noise, U N The signal-to-noise ratio at this point is:

[0026]

[0027] Preferably, considering the actual channel conditions, it is assumed that interference cancellation is not perfect; therefore, further analysis is conducted under non-ideal SIC conditions, U N For signal x F When decoding (t), the signal-to-noise ratio of the decoded signal remains unchanged, still being (6); when decoding x N When the signal is (t), the signal power is x. N The signal power (t) is such that the interference noise power at this time is the signal x. FThe residual power of (t) plus the channel noise power, at which point U N The signal-to-noise ratio at this point is:

[0028]

[0029] In the formula: This indicates residual interference caused by the elimination of non-ideal continuous interference. This is the SIC coefficient.

[0030] Preferred, remote user U F Decode x F The signal-to-noise ratio for signal (t) is:

[0031]

[0032] In the formula: the signal power is x F (t) The power of the signal, the interference power is determined by the channel noise power and x. N (t) The sum of signal power.

[0033] Preferably, in communication systems, to quantify system reliability, it is necessary to perform outage performance analysis of the transmission network. Outage probability refers to the probability that a momentary outage will occur when the end-to-end signal-to-noise ratio falls below a preset outage threshold in the network; it is one of the important indicators for measuring system performance. Now we begin to derive the precise outage probability formula, assuming the decoding x... F (t) and x N The threshold signal-to-noise ratios of the (t) signals are respectively Where R N To achieve the target rate for the user, R F For the target rate of the remote user. Under the condition of ideal continuous interference cancellation, when Established, considered close to user U N Successfully decoded signal x F (t), when Established, considered close to user U N Successfully decoded signal x N (t), therefore, the near-user U can be derived. N The interruption probability is:

[0034]

[0035] In the formula: P1 represents U N Successfully decoded x F (t) and x N The probability of (t). For ease of derivation, assume X and Y represent the variables g and y, respectively. BN and The probability density functions of X and Y can be written as follows:

[0036] Next, P1 can be calculated by combining (6) and (7):

[0037]

[0038] In the formula:

[0039] Therefore U N The interruption probability can be further written as:

[0040]

[0041] Preferably, considering the elimination of non-ideal continuous interference, U can be derived similarly. N The interruption probability is:

[0042]

[0043] In the formula: when Upon establishment, it is considered to be close to user U. N Successfully decoded signal x F (t);

[0044] Substituting (3) and (5) into (13), we can derive:

[0045]

[0046] In the formula:

[0047] Therefore, U N The interruption probability under non-ideal continuous disturbance cancellation can ultimately be written as:

[0048]

[0049] Preferred, further analysis of remote user U F The interruption probability when U F Unable to decode x F When (t), U F The interruption probability can be expressed as:

[0050]

[0051] In the formula: This indicates that, based on the SC strategy, the label with the highest SINR with the destination node is selected as the best label T from among M labels. j ;

[0052] Next, we will further derive A1:

[0053]

[0054] Select the best label T j The remote user still cannot decode the signal x F The probability of (t) is given by A2:

[0055]

[0056] Substituting (17) and (18) into (16), we can obtain U through further derivation. F The final closed-form expression for the interruption probability is:

[0057]

[0058] Integrating formulas (17) and (18), further utilizing [16,3.324.1] yields the closed-form result (19), where K1(·) represents the second-order modified Bessel function of the first kind.

[0059] Preferably, the asymptotic interruption probability, U, is now derived. N The asymptotic interruption probability can be written as:

[0060]

[0061] When U N When using ideal SIC, Δ=Δ1, U N When using a non-ideal SiC, Δ = Δ2 in the formula;

[0062] For U F The asymptotic interruption probability is obtained by using equivalent infinitesimals. As x→∞, the asymptotic interruption probability can be written as:

[0063]

[0064] Since near users transmit information only through direct links and are not affected by multi-tag backscattering links, let γ0 = P / δ0. Therefore, the diversity order of near users can be derived as:

[0065]

[0066] Furthermore, the diversity order of the multi-label selection scheme proposed at the remote user end is:

[0067]

[0068] Preferably, throughput refers to the maximum data rate at which messages can be successfully received and transmitted through a wireless network per unit time. It is another important indicator for measuring system performance. Now, we will begin throughput analysis, under ideal SIC conditions, for a user U... N The throughput expression is:

[0069]

[0070] Near-user U under non-ideal SIC N The throughput is:

[0071]

[0072] Finally, substituting into the interruption probability expression, the throughput of the near-user can be further written as:

[0073]

[0074] When Δ = Δ1, When Δ = Δ2,

[0075] Preferred, remote user U F The throughput expression is:

[0076]

[0077] Substituting formula (19) into (25), we obtain the final formula for remote user throughput:

[0078]

[0079] The present invention has the following beneficial effects:

[0080] 1. Unlike most existing backscattering models and NOMA models, this invention adopts a NOMA communication system model assisted by backscattering in a multi-label environment, which can reduce the probability of interruption, increase system throughput, and improve system transmission efficiency.

[0081] 2. Among all the reflection tags, the best tag is selected based on the SC strategy. The impact of the number of tags and the distance between the tag and the remote user on the performance of the communication system is accurately analyzed based on the physical distance and number of tags between the tag and the remote user. Attached Figure Description

[0082] Figure 1 This is a model diagram of the multi-label environmental backscattering NOMA system disclosed in this invention;

[0083] Figure 2 This is the near-user U disclosed in this invention. N A comparison chart of simulation and theoretical interruption probabilities;

[0084] Figure 3 This invention discloses a remote user U F A comparison chart of simulation and theoretical interruption probabilities;

[0085] Figure 4This is a graph showing the throughput variation with different rates as disclosed in this invention. Detailed Implementation

[0086] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. All embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0087] The multi-label environmental backscattering NOMA system model disclosed in this invention is shown in [link to invention]. Figure 1 Near user U N Comparison of simulation and theoretical results for interruption probability can be found in [link to relevant documentation]. Figure 2 Remote User U F Comparison of simulation and theoretical results for interruption probability can be found in [link to relevant documentation]. Figure 3 The throughput variation curves with different rates are shown below. Figure 4 .

[0088] In this embodiment, the specific parameter settings are as follows:

[0089] The multi-tag environment backscatter NOMA communication system of this invention consists of one base station (BS) and two users (U). N and U F ) and M backscattering tags T i Composed of (i = 1, 2, 3, ..., M). A base station BS with a single antenna transmits superimposed signals to two single-antenna users. Nearby user U N Direct communication with base station (BS), remote user (U) F There is no direct link between the base station (BS) and the receiver; instead, the signal is received from the BS via backscattering from M tags. Assume all channels follow Rayleigh fading, and let h... BN Indicates from base station BS to U N The channel coefficient, Indicates the transition from BS to label T i The channel coefficient, Indicates from T i To U F The channel coefficients. Therefore... They respectively follow the mean λ BN , The exponential distribution.

[0090] In the simulation analysis, assuming γ = 1 / δ0, the basic parameter values ​​are set as follows: P = 10dB, α1 = 0.4, λ BN =10dB, for near user U N Interruption probability simulation and theoretical comparison with remote user U FComparison of interruption probability simulation and theory, R N =1bps, R F =1bps. In analyzing near-user U N When calculating the interruption probability, ideal values ​​are used respectively. Non-ideal SIC technology or In analyzing remote user U F When considering the interruption probability, since the model in question sets M tags at the base station and U... F In typical real-world scenarios, the distance between a tag and a user is very small compared to the distance from the base station to the user and the distance from the base station to the tag. To analyze the impact of tag placement on interruption performance, we will use λ TF Set to: 1dB and 10dB.

[0091] Step 1: The superimposed signal transmitted by a base station BS with a transmission power of P at time t is described as follows:

[0092]

[0093] In the formula: x N (t) and x F (t) is the unit power transferred to U N and U F The signal, α1 and α2 are NOMA power allocation coefficients, α1 + α2 = 1 and α1 < α2. Since U F The channel quality is poor, so U F Allocate more power, i.e., α2 > 0.5;

[0094] Step 2: Due to the influence of channel transmission performance and channel noise during signal transmission, the signal near user U... N The received signal is:

[0095]

[0096] Where: h BN It is from the base station (BS) to the user (U). N The channel coefficients, n N (t) represents the nearest user U N The additive white Gaussian noise at the location follows a Gaussian distribution with mean 0 and variance δ0;

[0097] Step 3: Since the tag is a passive device, it is difficult to process complex signals. Therefore, the thermal noise of the tag itself can usually be ignored. Therefore, when near the user U... N Similarly, the signal received at the tag is:

[0098]

[0099] After receiving the signal, the tag performs backscattering. Based on the tag's backscattering performance, the backscattered signal at the tag is:

[0100]

[0101] In the formula: η represents the backscattering coefficient, η∈(0,1). B m (t) represents the binary symbol at the label, which can be considered a constant over continuous time t, denoted by B. m To indicate;

[0102] Step 4, Connect with nearby user U N The received signal is similar, for the remote user U F The received signal is:

[0103]

[0104] In the formula: It is the label T i To remote user U F The channel coefficients, n F (t) represents the remote user U F The additive white Gaussian noise at the location follows a Gaussian distribution with mean 0 and variance δ0;

[0105] Step 5: The base station (BS) sends a message to the remote user (U). F When sending overlay messages, according to the formula Select the best transmission label T from M labels. j Besides the connection from base station (BS) to tag (T) j Channel gain In addition, other data sources from base stations (BS) to tags (T) i Channel gain All than The value is small, thus the optimal transmission label is T. j ,in This indicates the distance from base station (BS) to tag (T). i Channel gain, This indicates the distance from the base station (BS) to the best label (T). j Channel gain.

[0106] Preferably, the base station (BS) directs traffic to the nearby user (U). N Send superimposed signal Near user U N Received signal Near user U N First, for x F The (t) signal is decoded, and then the ideal continuous interference cancellation technique is used to process x. N (t) signal is decoded. When decoding xF When the signal x is (t), the signal is given. F The power of (t) is obtained by multiplying the channel gain, power allocation coefficient, and total signal power. The noise power is obtained by adding the unwanted signal power to the channel noise power. (The last part, "near user U," appears to be a typo and can be omitted.) N The signal-to-noise ratio at this point is:

[0107]

[0108] In the case of ideal SIC, signal x F (t) is perfectly decoded without residual interference, so when x is decoded... N When the signal is (t), the noise power is the channel additive noise, U N The signal-to-noise ratio at this point is:

[0109]

[0110] Considering the actual channel conditions, and assuming that interference cancellation is not perfect, further analysis is conducted under non-ideal SIC conditions. N For signal x F When decoding (t), the signal-to-noise ratio of the decoded signal remains unchanged, still being (6). When decoding x N When the signal is (t), the signal power is x. N The signal power (t) is such that the interference noise power at this time is the signal x. F The residual power of (t) plus the channel noise power, at which point U N The signal-to-noise ratio at this point is:

[0111]

[0112] In the formula: This indicates residual interference caused by the elimination of non-ideal continuous interference. This is the SIC coefficient.

[0113] Remote User U F Decode x F The signal-to-noise ratio for signal (t) is:

[0114]

[0115] In the formula: the signal power is x F (t) The power of the signal, the interference power is determined by the channel noise power and x. N (t) The sum of signal power.

[0116] Preferably, in communication systems, interruption performance analysis of the transmission network is required to quantify system reliability. Interruption probability refers to the probability of a momentary system interruption when the end-to-end signal-to-noise ratio falls below a preset interruption threshold; it is a crucial indicator of system performance. Now we begin to derive the precise interruption probability formula. Assume decoding x... F (t) and x N The threshold signal-to-noise ratios of the (t) signals are respectively Where R N To achieve the target rate for the user, R F For the target rate of the remote user. Under the condition of ideal continuous interference cancellation, when Established, considered close to user U N Successfully decoded signal x F (t), when Established, considered close to user U N Successfully decoded signal x N (t), therefore, the near-user U can be derived. N The interruption probability is:

[0117]

[0118] In the formula: P1 represents U N Successfully decoded x F (t) and x N The probability of (t). For ease of derivation, assume X and Y represent the variables g and y, respectively. BN and The probability density functions of X and Y can be written as follows:

[0119] Next, P1 can be calculated by combining (6) and (7):

[0120]

[0121] In the formula:

[0122] Therefore U N The interruption probability can be further written as:

[0123]

[0124] Considering the elimination of non-ideal continuous disturbances, U can be derived similarly. N The interruption probability is:

[0125]

[0126] In the formula: when Upon establishment, it is considered to be close to user U. NSuccessfully decoded signal x F (t).

[0127] Substituting (3) and (5) into (13), we can derive:

[0128]

[0129] In the formula:

[0130] Therefore, U N The interruption probability under non-ideal continuous disturbance cancellation can ultimately be written as:

[0131]

[0132] At this point, we will analyze the remote user U again. F The probability of interruption. When U F Unable to decode x F When (t), U F The interruption probability can be expressed as:

[0133]

[0134] In the formula: This indicates that, based on the SC strategy, the label with the highest SINR with the destination node is selected as the best label T from among M labels. j .

[0135] Next, we will further derive A1:

[0136]

[0137] Select the best label T j The remote user still cannot decode the signal x F The probability of (t) is given by A2:

[0138]

[0139] Substituting (17) and (18) into (16), we can obtain U through further derivation. F The final closed-form expression for the interruption probability is:

[0140]

[0141] Integrating formulas (17) and (18), further utilizing [16,3.324.1] yields the closed-form result (19), where K1(·) represents the second-order modified Bessel function of the first kind.

[0142] Now we derive the asymptotic interruption probability. U N The asymptotic interruption probability can be written as:

[0143]

[0144] When U N When using ideal SIC, Δ=Δ1, U N When using a non-ideal SiC, Δ = Δ2 in the formula.

[0145] For U F The asymptotic interruption probability is obtained by using equivalent infinitesimals. As x→∞, the asymptotic interruption probability can be written as:

[0146]

[0147] Since near users transmit information only through direct links and are not affected by multi-tag backscattering links, let γ0 = P / δ0. Therefore, the diversity order of near users can be derived as:

[0148]

[0149] Furthermore, the diversity order of the multi-label selection scheme proposed at the remote user end is:

[0150]

[0151] Throughput refers to the maximum data rate at which messages can be successfully received and transmitted through a wireless network per unit of time, and it is another important metric for measuring system performance. We will now begin throughput analysis, considering the near-user U under ideal SIC conditions. N The throughput expression is:

[0152]

[0153] Near-user U under non-ideal SIC N The throughput is:

[0154]

[0155] Finally, substituting into the interruption probability expression, the throughput of the near-user can be further written as:

[0156]

[0157] When Δ = Δ1, When Δ = Δ2,

[0158] Remote User U F The throughput expression is:

[0159]

[0160] Substituting formula (19) into (25), we obtain the final formula for remote user throughput:

[0161]

[0162] Figure 2 The final simulation results are presented, and compared with those using ideal... Non-ideal SIC technology or U at that time N Interruption probability. It can be observed that as the parameter γ increases, U... N The interruption probability gradually decreases. This is because a larger γ leads to a smaller δ0 value, thereby increasing U. N The transmission signal-to-noise ratio. Additionally, ideal SiC and non-ideal SiC in U... N Significant differences were observed in the interruption probability. When the SIC coefficient... When the value is smaller, the probability of interruption decreases significantly. This is because when the value is smaller... The value reduces residual interference in the near-user decoded signal. Therefore, the experimental results fully verify the correctness of the analysis of formulas (12), (15) and (20).

[0163] When the path loss exponent and antenna gain reach a certain order of magnitude, the distance and λ... TF The relationship is negatively correlated. (By...) Figure 3 It can be seen that the purpose of simulation analysis is to clarify the effects of different numbers of labels and labels in different positions on U. F The extent of the impact on performance, among which The change in the value represents the change in tag position. The results show that as the number of tags increases, the slope of the curve increases, and the interruption probability decreases. This demonstrates that the diversity order derived earlier is correct, and proves the theoretical analysis that more tags result in better performance. Furthermore, the results show that as the distance between the tag and the user decreases, the interruption performance also improves to some extent. In addition, it can be observed that the interruption performance of the multi-tag AmBC-NOMA system is better than that of conventional NOMA. This is because the introduction of backscattering in NOMA increases the received signal-to-noise ratio at distant users, thus reducing the interruption probability. The theoretical analysis curves in the figure agree well with the simulation curves with different numbers of tags.

[0164] Figure 4 The image shows U N with U F Throughput simulation analysis is performed, examining how throughput changes under different target rates. As the target rate increases, the throughput also increases, due to the improved interrupt performance resulting from the increased target rate. While maintaining R... F With R remaining unchanged, NThe increase of U N The throughput increased significantly as a result; with the increase in the number of tags, U F The throughput increases accordingly. Therefore, increasing the target rate and increasing the number of tags can improve U... F The throughput reached U N The transmission level was assessed. The results showed that, besides adjusting the location and channel quality of each NOMA user to achieve the goal of improving user performance, the same goal could also be achieved by changing the number of tags. Simulations also revealed that as the rate increases to a certain order of magnitude, the throughput approaches 1, which, according to analysis, is due to the outage probability P. out The value is below 10 -2 At that time, for 1-P out The effect is almost negligible, so T = R × (1 - P) out The value of ) approaches 1.

[0165] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the content and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An optimal tag selection scheme for a multi-tag environment backscatter-assisted NOMA system, wherein, Two users equipped with single antennas receive superimposed signals transmitted by base station BS at a constant transmit power P, near user U. N There is a direct link between the remote user U and the base station (BS). F The signal from the base station (BS) is received by backscattering from M tags; the signal between the base station (BS) and the remote user (U) is received by backscattering from the base station (BS). F During communication, by selecting the optimal transmission tag, the goal of optimizing interrupt performance and improving system throughput is achieved. This is characterized by the following steps: Step 1: The superimposed signal transmitted by a base station BS with a transmission power of P at time t is described as follows: In the formula: x N (t) and x F (t) is the unit power transferred to U N and U F The signal, α1 and α2 are NOMA power allocation coefficients, α1 + α2 = 1 and α1 < α2. Since U F The channel quality is poor, so U F Allocate more power, i.e., α2 > 0.5; Step 2: Due to the influence of channel transmission performance and channel noise during signal transmission, the signal near user U... N The received signal is: Where: h BN It is from the base station (BS) to the user (U). N The channel coefficients, n N (t) represents the nearest user U N The additive white Gaussian noise at the location follows a Gaussian distribution with a mean of 0 and a variance of δ0. Step 3: Since the tag is a passive device, it is difficult to process complex signals. Therefore, the thermal noise of the tag itself can usually be ignored. Therefore, when near the user U... N Similarly, the signal received at the tag is: After receiving the signal, the tag performs backscattering. Based on the tag's backscattering performance, the backscattered signal at the tag is: In the formula: η represents the backscattering coefficient, η∈(0,1). B m (t) represents the binary symbol at the label, which can be considered a constant over continuous time t, denoted by B. m To indicate; Step 4, Connect with nearby user U N The received signal is similar, for the remote user U F The received signal is: Where: h TiF It is the label T i To remote user U F The channel coefficients, n F (t) represents the remote user U F The additive white Gaussian noise at the location follows a Gaussian distribution with a mean of 0 and a variance of δ0. Step 5: The base station (BS) sends a message to the remote user (U). F When sending overlay messages, according to the formula Select the best transmission label T from M labels. j Besides the connection from base station (BS) to tag (T) j Channel gain In addition, other data sources from base stations (BS) to tags (T) i Channel gain All than The value is small, thus the optimal transmission label is T. j ,in This indicates the distance from base station (BS) to tag (T). i Channel gain, This indicates the distance from the base station (BS) to the best label (T). j Channel gain.

2. The optimal tag selection scheme for a multi-tag environment backscatter-assisted NOMA system according to claim 1, characterized in that: Near user U N Received signal Near user U N First, for x F The (t) signal is decoded, and then the ideal continuous interference cancellation technique is used to process x. N (t) signal is decoded; when decoded x F When the signal x is (t), the signal is given. F The power of (t) is obtained by multiplying the channel gain, power allocation coefficient, and total signal power. The noise power is obtained by adding the unwanted signal power to the channel noise power. (The last part, "near user U," appears to be a typo and can be omitted.) N The signal-to-noise ratio at this point is: In the case of ideal SIC, signal x F (t) is perfectly decoded without residual interference, so when x is decoded... N When the signal is (t), the noise power is the channel additive noise, U N The signal-to-noise ratio at this point is:

3. The optimal tag selection scheme for a multi-tag environment backscatter-assisted NOMA system according to claim 2, characterized in that: Considering the actual channel conditions, and assuming that interference cancellation is not perfect, further analysis is conducted under non-ideal SIC conditions. N For signal x F When decoding (t), the signal-to-noise ratio of the decoded signal remains unchanged, still being (6); when decoding x N When the signal is (t), the signal power is x. N The signal power (t) is such that the interference noise power at this time is the signal x. F The residual power of (t) plus the channel noise power, at which point U N The signal-to-noise ratio at this point is: In the formula: This indicates residual interference caused by the elimination of non-ideal continuous interference. This is the SIC coefficient.

4. The optimal tag selection scheme for a multi-tag environmental backscatter-assisted NOMA system according to claim 3, characterized in that: Remote User U F Decode x F The signal-to-noise ratio for signal (t) is: In the formula: the signal power is x F (t) The power of the signal, the interference power is determined by the channel noise power and x. N (t) The sum of signal power.

5. The optimal tag selection scheme for a multi-tag environmental backscatter-assisted NOMA system according to claim 4, characterized in that: In communication systems, to quantify system reliability, it is necessary to perform outage performance analysis of the transmission network. Outage probability refers to the probability of a momentary system interruption when the end-to-end signal-to-noise ratio falls below a preset outage threshold; it is one of the important indicators for measuring system performance. Now we will begin to derive the precise outage probability formula, assuming decoding x... F (t) and x N The threshold signal-to-noise ratios of the (t) signals are respectively Where R N To achieve the target rate for the user, R F For the target rate of the remote user. Under the condition of ideal continuous interference cancellation, when Established, considered close to user U N Successfully decoded signal x F (t), when Established, considered close to user U N Successfully decoded signal x N (t), therefore, the near-user U can be derived. N The interruption probability is: In the formula: P1 represents U N Successfully decoded x F (t) and x N The probability of (t). For ease of derivation, assume X and Y represent the variables g and y, respectively. BN and The probability density functions of X and Y can be written as follows: Next, P1 can be calculated by combining (6) and (7): In the formula: Therefore U N The interruption probability can be further written as:

6. The optimal tag selection scheme for a multi-tag environmental backscatter-assisted NOMA system according to claim 5, characterized in that: Considering the elimination of non-ideal continuous disturbances, U can be derived similarly. N The interruption probability is: In the formula: when Upon establishment, it is considered to be close to user U. N Successfully decoded signal x F (t); Substituting (3) and (5) into (13), we can derive: In the formula: Therefore, U N The interruption probability under non-ideal continuous disturbance cancellation can ultimately be written as:

7. The optimal tag selection scheme for a multi-tag environment backscatter-assisted NOMA system according to claim 6, characterized in that: Further analysis of remote user U F The interruption probability when U F Unable to decode x F When (t), U F The interruption probability can be expressed as: In the formula: This indicates that, based on the SC strategy, the label with the highest SINR with the destination node is selected as the best label T from among M labels. j ; Next, we will further derive A1: Select the best label T j The remote user still cannot decode the signal x F The probability of (t) is given by A2: Substituting (17) and (18) into (16), we can obtain U through further derivation. F The final closed-form expression for the interruption probability is: Integrating formulas (17) and (18), further utilizing [16,3.324.1] yields the closed-form result (19), where K1(·) represents the second-order modified Bessel function of the first kind.

8. The optimal tag selection scheme for a multi-tag environmental backscatter-assisted NOMA system according to claim 7, characterized in that: Now let's derive the asymptotic interruption probability, U. N The asymptotic interruption probability can be written as: When U N When using ideal SIC, Δ=Δ1, U N When using a non-ideal SiC, Δ = Δ2 in the formula; For U F The asymptotic interruption probability is obtained by using equivalent infinitesimals. As x→∞, the asymptotic interruption probability can be written as: Since near users transmit information only through direct links and are not affected by multi-tag backscattering links, let γ0 = P / δ0. Therefore, the diversity order of near users can be derived as: Furthermore, the diversity order of the multi-label selection scheme proposed at the remote user end is:

9. The optimal tag selection scheme for a multi-tag environment backscatter-assisted NOMA system according to claim 8, characterized in that: Throughput refers to the maximum data rate at which messages can be successfully received and transmitted through a wireless network per unit of time. It is another important metric for measuring system performance. We will now begin throughput analysis, under ideal SIC conditions, for a user U... N The throughput expression is: Near-user U under non-ideal SIC N The throughput is: Finally, substituting into the interruption probability expression, the throughput of the near-user can be further written as: When Δ = Δ1, When Δ = Δ2, Preferred, remote user U F The throughput expression is: Substituting formula (19) into (25), we obtain the final formula for remote user throughput:

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