A sum rate optimization method for relay cooperative NOMA system under IQI condition

Abstract

The application claims to protect a sum rate optimization method of a relay cooperative NOMA system under an IQI condition, comprising: initializing the maximum transmission power of a base station in a C-NOMA system, the maximum transmission power of a relay, channel gain, Gaussian noise standard deviation, and sum rate initial value; by introducing an IQI model and an IQI coefficient, a signal-to-noise ratio of a relay cooperative NOMA (Cooperative NOMA, C-NOMA) system and a target function model of sum rate are established, and the target function is optimized under the condition of guaranteeing the quality of service (Quality of service, QoS) of each user. According to the characteristics of the target function, the base station sending end and the relay are caused to operate at full power, the derivation is proved, the user with poor channel conditions is caused to just meet the QoS of the system, and the optimization sum rate power distribution of the whole IQI C-NOMA system is determined. The application has the advantages of strong stability and strong practicability.

Description

Technical Field

[0001] This invention belongs to the field of NOMA technology, specifically a sum rate optimization method for relay cooperative NOMA systems under IQI conditions. Background Technology

[0002] With the rapid development of the Internet of Things (IoT), future wireless communication technologies will need to provide a large number of IoT devices while meeting the requirements of high spectral efficiency and low latency. NOMA, as an important multiple access technology, can significantly improve the spectral efficiency of the system and eliminate interference between users using SiC technology. Furthermore, it plays a crucial role in ensuring user fairness.

[0003] Cooperative relay transmission is another promising technology for future wireless communication networks because it improves system reliability, extends network coverage, mitigates channel impairments, and ensures high Quality of Service (QoS). Relay-assisted communication in wireless networks is particularly attractive, especially when there is no direct link between the base station and mobile terminal due to deep fading, severe shadowing, or distances beyond the source. Therefore, research on two-hop relay systems appears to be an attractive area of ​​research.

[0004] With many devices now equipped with wireless capabilities and the significant price pressures on wireless products, the concept of a direct-conversion architecture is highly valuable for amplified repeaters, meeting these requirements. However, the well-known direct-conversion architecture assumes a highly ideal RF front-end. In reality, RF impairments degrade system performance, such as high-power amplifier (HPA) nonlinearity, in-phase and quadrature-phase (I / Q) imbalance (IQI), low-noise amplifier (LNA) nonlinearity, antenna coupling, phase noise (PN), and carrier frequency offset (CFO). In particular, IQI represents the mismatch between analog components in the I and Q branches, caused by the limited precision of analog hardware, and can be either frequency-independent or frequency-dependent. Frequency-independent IQI is primarily caused by non-ideal mixers and phase shifters and remains constant across the entire signal bandwidth, while frequency-dependent IQI is caused by mismatches in the I and Q low-pass filters. For orthogonal frequency division multiplexing (OFDM) systems, IQI severely degrades system performance due to image subcarrier interference. However, there are also many power distribution problems that do not consider IQ imbalance. In practice, IQ imbalance in communication systems cannot be ignored.

[0005] In their paper "Optimal Power Allocation for OFDMA Systems Under I / Q Imbalance," Alexandros-Apostolos and A. Boulogeorgos analyzed the problem of power allocation for I / Q in traditional orthogonal frequency division multiplexing systems, where only the receiver is affected by IQI. This method improves system stability but does not consider the IQI at the transmitter and lacks relays to improve system performance.

[0006] In their paper "I / Q Imbalance in Two-Way AF Relaying" (IEEE Transactions on Communications, vol. 62, no. 7, pp. 2271-2285, July 2014), Jingya Li et al. considered the power allocation problem under IQI conditions in amplified relays. This method improves the system's interrupt performance, but it ignores the IQI present at the base station and receiver and does not apply NOMA to the system.

[0007] Based on the above analysis, this invention considers a relay cooperative NOMA communication system where both the receiver and transmitter have IQI. Assuming the system has perfect CSI, and designing the receiving user as a distant user and a near user communicating with the same base station through a relay, this invention proposes a new resource allocation method based on IQI for the entire C-NOMA system, with maximizing the sum and rate as the criteria. Summary of the Invention

[0008] This invention aims to solve the problems of the prior art. It proposes a sum rate optimization method for relay cooperative NOMA systems under IQI conditions. The technical solution of this invention is as follows:

[0009] A method for optimizing the sum rate of a relay cooperative non-orthogonal multiple access (NOMA) system under IQI conditions, comprising the following steps:

[0010] Step 1): Initialize the base station maximum transmit power, relay maximum transmit power, channel gain, Gaussian noise standard deviation, and initial rate value in the C-NOMA system;

[0011] Step 2): By introducing the IQI model, an objective function model for the signal-to-noise ratio and rate of the C-NOMA system is established based on the IQI factors of the base station and users. The objective function is then optimized while ensuring the QoS of each user.

[0012] Step 3): Maximize the system's sum rate to ensure both base stations and relays operate at full power. At this point, the only coefficient to optimize is the user's power allocation coefficient. Based on the non-convex nature of the objective function, we first take the first derivative of the sum of the power allocation coefficients a1 of the user and the remote user, and then determine the target power allocation coefficient of the remote user while ensuring QoS.

[0013] Step 4): For the determined This determines the target power allocation coefficient for the user. This allows us to determine the corresponding sum and rate.

[0014] Furthermore, step 1): initializing the base station maximum transmit power, relay maximum transmit power, channel gain, Gaussian noise standard deviation, and initial rate value in the C-NOMA system specifically includes:

[0015] Indicates the maximum transmit power of the base station, Given the maximum transmit power of the relay, the channel gain from the base station to the relay is h1 = g. SR PL -1 The channel gain from relay to a remote user is h2 = g RF PL -1 The channel gain from relay to the nearest user is h3 = g RN PL -1 g SR ,g RF ,g RN These represent the small-scale Ruili fading channel gain from base station to relay, relay to distant user, and relay to near user, respectively. PL -1 σ represents the path loss at different distances. 2 This represents the standard deviation of Gaussian noise.

[0016] Furthermore, step 2) introduces the IQI coefficient u from the base station transmitter. t and v t and the IQI coefficient u at the receiving end r v r ,in

[0017] u t g T This indicates an imbalance in amplitude at the transmitting end, φ T This indicates a phase imbalance at the transmitting end, g RThis indicates amplitude imbalance at the receiving end, φ R This indicates a phase imbalance at the receiving end. It affects the signal transmitted by the base station. g t This indicates a normally transmitted baseband signal. These represent the complex conjugates of the corresponding coefficients. The signal y = u received at the receiver... r x+v r x*, where x represents the perfect signal at the receiving end. Assuming perfect Channel State Information (CSI) and Successive Interference Cancellation (SIC), and considering an ideal scenario where the far user employs direct decoding, the signal-to-noise ratio is...

[0018] h1 and h2 represent the channel gain from the base station to the relay and the channel gain from the relay to the remote user, respectively. Indicates its complex conjugate, P r For relay transmission power, P s N represents the base station's transmit power, and N0 represents the background noise at the receiver. a1 represents the background noise from the base station to the user, and a1 represents the power allocation coefficient for the distant user.

[0019] Near-end users utilize perfect SiC to remove interference from distant users, achieving a signal-to-noise ratio of [value missing].

[0020]

[0021] a2 represents the power allocation factor for the near-user, and h3 represents the channel gain from the relay to the far-user. It indicates its complex conjugate.

[0022] Establishing the system and rate problem 1 / 2 represents the system being divided into two time slots, with system QoS introduced simultaneously, and the system operating at full power.

[0023] Furthermore, step 2) simplifies the expression by variable substitution.

[0024]

[0025] Since the objective function is to maximize the sum and rate, let The objective function can be further simplified by the following variable substitutions:

[0026] Furthermore, step 3) applies to the sum-rate objective function. The variables to be optimized at this point are the power allocation coefficients a1 and a2. Given the non-convex nature of the objective function, we differentiate the objective function with respect to a1. That is, the rate objective function decreases as a1 increases, and due to the introduction of QoS, the rate of the distant user receiving a1 exactly meets the minimum rate requirement. Where Y = 2 2Rmin -1. Rmin represents the minimum rate requirement of the system, i.e., QoS.

[0027] Furthermore, step 4) is based on the power allocation coefficient for distant users obtained in step 3). Calculate the power allocation factor near the user by reverse hand. This allows us to maximize the target and rate functions under QoS conditions;

[0028] The sum-rate objective function is:

[0029]

[0030] in The maximum power transmitted by the base station. For the maximum power transmitted by the relay, A1,A 2, A3, B1, B2, C1, C2 are constants related to the given IQI, and N0 is the background noise.

[0031] The advantages and beneficial effects of this invention are as follows:

[0032] This invention provides an optimization method for improving system performance and data rate based on IQI (In-Quality Information). This invention establishes an optimization model to maximize system performance and data rate while satisfying user service quality, QoS (Quality of Service), and power allocation constraints at the base station and relay transmitters. Based on the properties of the objective function, to maximize the power operation of the base station transmitter and relays, the formula is simplified to a function that optimizes the power allocation coefficients of the NOMA system. By taking the first derivative of the objective function with respect to the power allocation coefficients, the power allocation coefficients for distant users are obtained. The power allocation coefficients for the sum-rate maximizing system are derived and proved. Simulation results show that, while ensuring the QoS of each user, the proposed method improves the sum-rate of the system and enhances the overall system interrupt performance.

[0033] This invention considers the simultaneous presence of IQI at both the receiver and transmitter, and uses NOMA power allocation to improve the system's spectrum. Compared with the traditional (classical) model that does not consider IQI factors, it not only meets the user's quality of service, but also improves the system's speed and stability. Attached Figure Description

[0034] Figure 1 This is a flowchart of a preferred embodiment of the present invention, which describes a rate optimization method for a relay cooperative NOMA system under IQI conditions;

[0035] Figure 2 The system model diagram is given;

[0036] Figure 3 The sum rate plots obtained under different IQI conditions using the method in this example are presented;

[0037] Figure 4 The graphs show the sum and rate of the optimized algorithm under certain IQI conditions and the traditional algorithm without considering IQI.

[0038] Figure 5 The maximum number of users allowed by the optimized algorithm and the traditional algorithm system under certain QoS conditions in IQI is given. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0040] The technical solution of the present invention to solve the above-mentioned technical problems is:

[0041] This embodiment presents a resource allocation optimization scheme for maximizing sum and rate in a C-NOMA system under IQI conditions. The system model involves a base station simultaneously communicating with a distant user and a nearby user via an AF-amplified relay. IQ imbalance between the base station and the receiver is considered, while the relay, due to its often high precision, is considered an ideal scenario. Both the base station and the relay use single-antenna reception, and the channel employs a Rayleigh fading model. The channel gain from the base station to the relay is h1 = g. SR PL -1 The gain from relay to the remote user is h2 = g RF PL -1 The gain from relaying to the nearest user is h3 = g RN PL -1 g SR ,g RF ,g RN These represent the small-scale Ruili fading channel gains from base station to relay, relay to distant user, and relay to near user, respectively, where PL -1 The path loss is d represents the distance in km, meaning the channel gain of a distant user is less than the channel gain of a nearby user, h2 < h3.

[0042] The IQI model will be briefly introduced below. First, the IQI coefficients at the base station transmitter will be introduced. and receiving end g T / R and φ T / R These are the amplitude and phase imbalance coefficients at the transmitting and receiving ends, respectively. Let the signal transmitted by the base station... The signal y=u received at the receiving end r x+v r x*, x and g t These are the signals under ideal IQ imbalance, i.e., g T =g R =1, At this time u t =u r =1,v t =v r =0.

[0043] The first step is to initialize the maximum transmit power of the base station, the maximum transmit power of the relay, the channel gain, the standard deviation of Gaussian noise, and the sum rate of the system in the C-NOMA system, and to determine the base station transmit signal.

[0044] The second step involves establishing a C-NOMA system that incorporates IQI factors at both the base station and the user through an IQI model, assuming perfect SIC and CSI, thereby determining the signal-to-noise ratio for distant and near users.

[0045] h1 and h2 represent the channel gain from the base station to the relay and the channel gain from the relay to the remote user, respectively. Indicates its complex conjugate, P r For relay transmission power, P s N represents the base station's transmit power, and N0 represents the background noise at the receiver. a1 represents the background noise from the base station to the user, and a1 represents the power allocation coefficient for the distant user.

[0046] a2 represents the power allocation factor for the near-user, and h3 represents the channel gain from the relay to the far-user. It indicates its complex conjugate.

[0047] Establishing the system and rate problem 1 / 2 represents the system being divided into two time slots, while simultaneously introducing system QoS. The formula is simplified by variable substitution.

[0048]

[0049]

[0050] Since the objective function is to maximize the sum and rate, let The objective function can be further simplified by the following variable substitution:

[0051]

[0052] The third step is to consider the sum-rate objective function. The variables to be optimized at this point are the power allocation coefficients a1 and a2. Given the non-convex nature of the objective function, we differentiate the objective function with respect to a1. That is, the rate objective function decreases as a1 increases, and because we introduce QoS, the rate of the distant user receiving a1 exactly meets the minimum rate requirement. Where Y = 2 2Rmin -1, Rmin is the minimum speed requirement of the system.

[0053] The fourth step is to use the target power allocation coefficient for distant users obtained in the third step. Calculate the target power allocation factor near the user by reverse hand. This allows us to maximize the target and rate functions under QoS conditions.

[0054] The sum-rate objective function is:

[0055]

[0056] in The maximum power transmitted by the base station. For the maximum power transmitted by the relay, A1,A 2, A3, B1, B2, C1, C2 are constants related to the given IQI, and N0 is the background noise.

[0057] In this embodiment, Figure 2 For the system model diagram, Figure 3 The sum rate plots obtained under different IQI conditions using the method in this example are presented; Figure 4 The graphs show the sum and rate of the optimized algorithm under certain IQI conditions and the traditional algorithm without considering IQI. Figure 5 This gives the maximum number of users allowed by the system under certain QoS conditions and IQI requirements. Figure 3 It is evident that IQI does indeed reduce system performance, especially in the high signal-to-noise ratio region. IQI is indeed a factor that cannot be ignored, proving the importance of considering IQI factors.

[0058] Depend on Figure 4 It is evident that, under the same IQI conditions, the proposed method achieves a significantly higher summation rate than the traditional method that does not consider IQI, indicating that the proposed method performs better than the traditional method.

[0059] Depend on Figure 5 The results show a comparison of the proposed method's performance with the traditional method (which does not consider IQI) in terms of the number of users admitted under minimum rate QoS constraints and different maximum transmission powers. Simulation results demonstrate that the proposed method outperforms the traditional method, indicating that the algorithm can better meet the system's quality requirements and reduce the probability of system outages.

[0060] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0061] The above embodiments should be understood as illustrative only and not as limiting the scope of protection of the present invention. After reading the description of the present invention, those skilled in the art can make various alterations or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A method for optimizing the sum rate of a relay cooperative NOMA system under IQI conditions, characterized in that, Includes the following steps: Step 1): Initialize the base station maximum transmit power, relay maximum transmit power, channel gain, Gaussian noise standard deviation, and initial rate value in the C-NOMA system; Step 2): By introducing the IQI model, an objective function model for the signal-to-noise ratio and rate of the C-NOMA system is established based on the IQI factors of the base station and users. The objective function is then optimized while ensuring the QoS of each user. Step 3): Maximize the system's sum rate to ensure both base stations and relays operate at full power. At this point, the only coefficient to optimize is the user's power allocation coefficient. Based on the non-convex nature of the objective function, we first take the first derivative of the sum of the power allocation coefficients a1 of the user and the remote user, and then determine the target power allocation coefficient of the remote user while ensuring QoS. Step 4): For the determined This determines the target power allocation coefficient for the user. This allows us to determine the corresponding sum and rate; Step 1): Initialize the maximum transmit power of the base station, the maximum transmit power of the relay, the channel gain, the standard deviation of Gaussian noise, and the initial value of the rate in the C-NOMA system, specifically including: Indicates the maximum transmit power of the base station, The maximum transmit power of the relay is represented by h1 = g, and the channel gain from the base station to the relay is also represented by g. SR PL -1 The channel gain from relay to a remote user is h2 = g RF PL -1 The channel gain from relay to the nearest user is h3 = g RN PL -1 g SR ,g RF ,g RN These represent the small-scale Ruili fading channel gain from base station to relay, relay to distant user, and relay to near user, respectively. PL -1 σ represents the path loss at different distances. 2 Indicates the standard deviation of Gaussian noise; Step 2) introduces the IQI coefficient u from the base station transmitter. t and v t and the IQI coefficient u at the receiving end r v r ,in g T This indicates an imbalance in amplitude at the transmitting end, φ T This indicates a phase imbalance at the transmitting end, g R This indicates amplitude imbalance at the receiving end, φ R This indicates a phase imbalance at the receiving end; it causes the signal transmitted by the base station to be unbalanced. g t This indicates a normally transmitted baseband signal. These represent the complex conjugates of the corresponding coefficients; the signal y = u received at the receiver. r x+v r x*, where x represents the perfect signal at the receiving end, assuming perfect channel state information (Channel State). Information (CSI) and Successive Interference Cancellation Cancellation (SIC), considering the ideal case, where the signal-to-noise ratio for the far user using direct decoding is... h1 and h2 represent the channel gain from the base station to the relay and the channel gain from the relay to the remote user, respectively. Indicates its complex conjugate, P r For relay transmission power, P s N represents the base station's transmit power, and N0 represents the background noise at the receiver. a1 represents the background noise from the base station to the user, and a1 represents the power allocation coefficient for the distant user. Near-end users utilize perfect SiC to remove interference from distant users, achieving a signal-to-noise ratio of [value missing]. a2 represents the power allocation factor for the near-user, and h3 represents the channel gain from the relay to the far-user. Indicates its complex conjugate; Establishing the system and rate problem 1 / 2 represents the system being divided into two time slots, with system QoS introduced simultaneously, and the system operating at full power. Step 2) simplifies the expression by variable substitution. Since the objective function is to maximize the sum and rate, let The objective function can be further simplified by the following variable substitution: Step 3) is for the sum rate objective function The variables to be optimized at this point are the power allocation coefficients a1 and a2. Given the non-convex nature of the objective function, we differentiate the objective function with respect to a1. That is, the rate objective function decreases as a1 increases, and due to the introduction of QoS, the rate of the distant user receiving a1 exactly meets the minimum rate requirement. Where Y = 2 2Rmin -1, Rmin represents the minimum rate required by the coefficient, i.e., QoS.

2. The sum rate optimization method for a relay cooperative NOMA system under IQI conditions according to claim 1, characterized in that, Step 4) is based on the target power allocation coefficient for remote users obtained in step 3). Calculate the target power allocation factor near the user by reverse hand. This allows us to maximize the target and rate functions under QoS conditions; The sum-rate objective function is: in The maximum power transmitted by the base station. For the maximum power transmitted by the relay, A1,A 2, A3, B1, B2, C1, C2 are constants related to a given IQI, and N0 is background noise.

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