Performance analysis method and device for RSMA cellular-free network under non-ideal transmission condition

Abstract

The invention provides a performance analysis method and device of an RSMA cellular-free network under a non-ideal transmission condition. The method comprises the following steps: constructing a channel model of an RSMA cellular-free network under a non-ideal transmission condition considering channel aging and phase noise; the method comprises the following steps: obtaining a quantized pilot frequency receiving signal described based on a quantized distortion coefficient based on a pilot frequency signal received by an access point in an uplink pilot frequency training stage, and carrying out LMMSE channel estimation on the quantized pilot frequency receiving signal by utilizing a channel model to obtain uplink channel state information; the method comprises the following steps of: splitting, coding and pre-coding a user message by using uplink channel state information according to an RSMA (Received Signal Moving Average) strategy so as to calculate a quantized received signal expression of a user side after ADC (Analog-to-Digital Converter) quantization; calculating an expression of a downlink reachable rate of the RSMA cellular-free network based on the quantitative received signal expression; and evaluating the communication performance of the RSMA cellular-free network under the non-ideal transmission condition based on the expression of the downlink reachable rate.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and specifically to a method and apparatus for performance analysis of RSMA non-cellular networks under non-ideal transmission conditions. Background Technology

[0002] With the widespread adoption of 5G and the evolution towards 6G, mobile data traffic is experiencing explosive growth, placing extremely high demands on network spectral efficiency, coverage consistency, and connection density. Cellular-free network architecture, as a disruptive network paradigm, abandons the traditional cell-centric concept and employs a large number of geographically distributed access points (APs) connected to a central processing unit (CPU) via fronthaul links to collaboratively provide services to users over a wide area. This user-centric transmission mode utilizes macro-diversity gain and high multiplexing gain to eliminate cell boundary interference, providing users with a uniform and high-quality service experience, and is therefore considered a key enabling technology for future wireless communication systems.

[0003] In cellular-free network architectures, multi-user interference becomes a bottleneck restricting system capacity improvement due to the large number of users sharing time-frequency resources. While traditional linear precoding techniques have low complexity, their performance deteriorates sharply when handling strong interference or imperfect Channel State Information (CSI), resulting in ineffective interference suppression. Rate-Spiltting Multiple Access (RSMA), as a general and robust interference management strategy, exhibits superior performance by splitting user messages into public and private messages for transmission. The public message is decoded and discarded by all users, converting some interference into useful signals, while the private message is treated as remaining interference. This flexible interference management mechanism makes RSMA more spectral and energy efficient than Space-Division Multiple Access (SDMA) in scenarios with large CSI errors or high user loads.

[0004] While cellular-free network architectures offer significant advantages, practical deployments require a massive number of access points (APs) to cover wide areas. Equipping each AP with high-precision, high-power RF components would result in prohibitively high hardware costs and energy consumption. Therefore, low-cost APs equipped with low-resolution analog-to-digital converters (ADCs) offer a cost-effective solution. However, low-resolution ADCs exhibit strong nonlinear characteristics, introducing significant quantization noise and distorting the received signal waveform. This not only increases the difficulty of uplink channel estimation, leading to decreased CSI accuracy, but also limits downlink beamforming gain, thus impacting the effective implementation of RSMA strategies.

[0005] Besides the ADC, low-cost access points (APs) typically use local oscillators with poor stability, making them prone to phase noise. During signal transmission, phase noise manifests as random phase rotations that diverge over time. In multi-user multiple-input multiple-output (MIMO) systems, phase noise disrupts the reciprocity and orthogonality of the channel; neglecting this factor in system design will lead to severe performance degradation. Furthermore, in real-world mobile communication scenarios, users are often in a non-stationary state. Due to the Doppler shift caused by user movement, the wireless channel exhibits time-varying characteristics. In time-division duplex mode, there is an inherent processing delay between uplink pilot transmission and downlink data transmission, causing a mismatch between the channel estimate obtained by the AP and the actual channel state at the current moment—a phenomenon known as channel aging. As user movement speed increases or frame length increases, the channel aging effect becomes more pronounced, leading to precoding matrix obsolescence and introducing additional multi-user interference into the downlink, severely limiting system reliability in high-speed mobile scenarios.

[0006] In summary, most existing performance analysis studies on RSMA-enhanced non-cellular network architectures are based on assumptions of ideal hardware (infinite resolution ADC, no phase noise) or quasi-static channels (ignoring channel aging). These idealized assumptions often lead to theoretical analysis results that overestimate the actual performance of the system, making it difficult to guide network deployment and parameter optimization in real-world, complex environments. Currently, there is a lack of an analytical method that can simultaneously consider multiple non-ideal transmission characteristics such as channel aging, oscillator phase noise, and low-resolution ADC quantization errors, and derive an accurate closed-form performance expression for RSMA schemes. Summary of the Invention

[0007] In view of this, this application proposes a performance analysis method and apparatus for RSMA non-cellular networks under non-ideal transmission conditions, so as to quantitatively evaluate the communication performance of RSMA non-cellular networks under non-ideal transmission conditions.

[0008] Specifically, this application is implemented through the following technical solution:

[0009] According to a first aspect of the embodiments of this specification, a method for performance analysis of RSMA non-cellular networks under non-ideal transmission conditions is provided, comprising the following steps:

[0010] Step S1: For the RSMA non-cellular network in the target area, construct a channel model of the RSMA non-cellular network under non-ideal transmission conditions considering channel aging and phase noise.

[0011] Step S2: Based on the pilot signal received by the access point during the uplink pilot training phase, the quantized pilot received signal is obtained by quantization using ADC and described by the quantization distortion coefficient. The channel model is then used to perform LMMSE channel estimation on the quantized pilot received signal to obtain uplink channel state information.

[0012] Step S3: Using the uplink channel state information, the user message is split, encoded, and pre-encoded according to the RSMA strategy to calculate the quantized received signal expression of the user terminal after ADC quantization.

[0013] Step S4: Based on the quantized received signal expression, the downlink achievable rate of the RSMA non-cellular network is calculated using the capacity lower bound theory.

[0014] Step S5: Evaluate the communication performance of the RSMA non-cellular network under non-ideal transmission conditions based on the expression for the downlink achievable rate.

[0015] According to a second aspect of the embodiments of this specification, a performance analysis apparatus for RSMA non-cellular networks under non-ideal transmission conditions is provided, the apparatus comprising:

[0016] The channel model construction unit is used to construct a channel model for the RSMA non-cellular network in the target area under non-ideal transmission conditions that consider channel aging and phase noise.

[0017] The channel state estimation unit is used to obtain a quantized pilot received signal based on the quantization distortion coefficient after quantization of the pilot signal received by the access point during the uplink pilot training phase by ADC, and to perform LMMSE channel estimation on the quantized pilot received signal using the channel model to obtain uplink channel state information.

[0018] The signal quantization processing unit is used to use the uplink channel state information to decompose, encode, and precode the user message according to the RSMA strategy, so as to calculate the quantized received signal expression of the user terminal after ADC quantization.

[0019] The evaluation index calculation unit is used to calculate the downlink achievable rate expression of the RSMA non-cellular network based on the quantized received signal expression and using the capacity lower limit theory.

[0020] A communication performance evaluation unit is used to evaluate the communication performance of the RSMA non-cellular network under non-ideal transmission conditions based on the expression of the downlink achievable rate.

[0021] According to a third aspect of the embodiments of this specification, an electronic device is provided, including a processor; and a computer-readable storage medium storing computer program instructions that, when executed by the processor, cause the processor to perform the method described in the first aspect.

[0022] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided having a computer program stored thereon, the computer program being executed by a processor of the method described in the first aspect.

[0023] This application significantly improves the realism and accuracy of performance evaluation for RSMA non-cellular networks. In the system modeling stage, this application innovatively considers non-ideal transmission conditions such as channel aging effects caused by high-speed user movement and phase noise introduced by low-cost oscillators. Furthermore, it introduces quantization accuracy parameters during channel state estimation to accurately characterize the channel estimation errors and signal distortion caused by these non-ideal factors. This reduces the overestimation bias of network system communication performance, providing more valuable evaluation results for network performance in complex real-world scenarios. It also provides solid theoretical support for the system design, parameter optimization, and practical deployment of future communication networks in low-cost hardware and highly dynamic channel environments. Attached Figure Description

[0024] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Some specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings in an exemplary and non-limiting manner. The same reference numerals in the drawings indicate the same or similar parts or components. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0025] Figure 1 This is a flowchart illustrating an exemplary embodiment of the present application of a performance analysis method for an RSMA non-cellular network under non-ideal transmission conditions;

[0026] Figure 2This is an exemplary embodiment of the present application illustrating a comparison of the cumulative distribution function of downlink user spectral efficiency between RSMA noncellular networks and traditional SDMA noncellular networks under different access point antenna numbers;

[0027] Figure 3 This is a schematic diagram illustrating the independent and combined effects of various non-ideal factors on the performance of RSMA non-cellular networks, as shown in an exemplary embodiment of this application.

[0028] Figure 4 This is a block diagram illustrating an electronic device according to an exemplary embodiment of this application;

[0029] Figure 5 This is a block diagram illustrating a performance analysis apparatus for an RSMA non-cellular network under non-ideal transmission conditions, as shown in an exemplary embodiment of this application. Detailed Implementation

[0030] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0031] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0032] The embodiments described in this specification will now be described in detail.

[0033] This application provides a method for performance analysis of RSMA non-cellular networks under non-ideal transmission conditions. Figure 1 This is a flowchart illustrating an exemplary embodiment of the present application of a performance analysis method for an RSMA non-cellular network under non-ideal transmission conditions, as shown below. Figure 1 As shown, the performance analysis method includes at least the following steps:

[0034] Step S1: For the RSMA non-cellular network in the target area, construct a channel model of the RSMA non-cellular network under non-ideal transmission conditions considering channel aging and phase noise.

[0035] Step S2: Based on the pilot signal received by the access point during the uplink pilot training phase, the quantized pilot received signal is obtained by quantization using ADC and described by the quantization distortion coefficient. The channel model is then used to perform LMMSE channel estimation on the quantized pilot received signal to obtain uplink channel state information.

[0036] Step S3: Using the uplink channel state information, the user message is split, encoded, and pre-encoded according to the RSMA strategy to calculate the quantized received signal expression of the user terminal after ADC quantization.

[0037] Step S4: Based on the quantized received signal expression, the downlink achievable rate of the RSMA non-cellular network is calculated using the capacity lower bound theory.

[0038] Step S5: Evaluate the communication performance of the RSMA non-cellular network under non-ideal transmission conditions based on the expression for the downlink achievable rate.

[0039] In some embodiments, the channel model in step S1 is a Ricean channel model, where the channel between each receiver and user in the RSMA non-cellular network is at time... The Ricean channel model is as follows:

[0040] (1)

[0041] in, As a time index, a coherent block of the RSMA non-cellular network operating under the time-division duplex protocol. Composed of individual samples, For receiving point With users At any moment Rice channel model, To conform to the phase noise of the discrete-time Wiener process, and Receiving points With users At any moment The oscillation phase, For aging channel model, For a moment With time The time correlation coefficient below, , As an independent innovation component, This represents the initial state of the channel.

[0042] In some embodiments, step S2 includes: obtaining the quantization gain corresponding to the receiving point. And quantization noise, among which , The distortion coefficient is related to the ADC quantization bit depth; based on the quantization gain. The quantization pilot received signal is quantized by quantization noise to obtain the quantization pilot received signal.

[0043] In some embodiments, step S4 includes: obtaining a lower bound expression for a first downlink achievable rate by decoding the common stream portion of a user message based on the capacity lower bound theory; obtaining a lower bound expression for a second downlink achievable rate by decoding the private stream portion of a user message based on the capacity lower bound theory; and obtaining a final expression for the downlink achievable rate based on the lower bound expressions for the first and second downlink achievable rates.

[0044] In some embodiments, the expression for the downlink achievable rate in step S4 is as follows:

[0045] (2)

[0046] in, For users The corresponding downlink achievable rate, Let K be the set of users, and K be the total number of users. For the first downlink achievable rate, This is the achievable rate for the second downlink.

[0047] Next, the performance analysis process of the embodiments of this application will be described in detail.

[0048] In this embodiment, the RSMA non-cellular network system in the target area includes M APs and K single-antenna users, where each AP is equipped with N antennas. The AP set is represented as... The user combination is represented as The system operates under a time-division duplex protocol, and one coherent block consists of... It consists of 10 samples, of which uplink pilot training occupies 100% of the total. One sample.

[0049] For the RSMA non-cellular network system in the target area mentioned above, step S1 is first performed to construct the Rice channel model of the system.

[0050] Taking channel aging into account, the first AP and user The aging channel between time The model is:

[0051] (3)

[0052] in, The initial channel state, in the uplink, can be understood as the channel state corresponding to the signal transmission time at the user end, while in the downlink, the initial channel state can be understood as... The channel state corresponding to the given time. ; For a moment With time The time correlation coefficient below, , , Denotes the zeroth-order Bessel function of the first kind. Indicates the sampling interval. Let be a scalar, and represent the Doppler frequency, where , and Representing users respectively The speed of light, carrier frequency, and speed of carrier waves; As an independent innovation component, And with Unrelated for Innovative parameters at any moment Indicates a complex Gaussian distribution. Represents the spatial correlation matrix. Represents the set of complex numbers. Represents the dimension of the matrix. The diagonal phase offset matrix representing the Loss component, whose diagonal elements consist of random phase terms, can be represented as follows: , This indicates a phase shift.

[0053] Due to oscillator phase noise and channel aging, the channel changes at each time point. AP and user Between moments The Ricean channel model is modeled as shown in Equation (1), which incorporates channel aging and phase noise. In Equation (1) above, and The first AP and user At any moment The oscillation phase, for example, and It can be modeled as:

[0054] (4)

[0055] in, Indicates the first Each AP at time phase increment, Indicates user At any moment phase increment, Represents the variance of the phase increment. It is a constant determined by the oscillator, and .

[0056] Then, step S2 is performed to estimate the uplink channel state information.

[0057] Assuming the pilot transmission time is , In each coherent block, assuming the system uses The system uses time-division multiplexing of pilot sequences, which are mutually orthogonal, meaning the user... The transmitted pilot sequence corresponds only to time [time]. Send pilot signal.

[0058] Assuming the number of users is large enough, that is With users The set of users using the same pilot signal is denoted as . No. Each AP is assigned to a user pilot time The received signal is:

[0059] (5)

[0060] in, For users pilot transmit power, For the first The additive white Gaussian noise matrix at each AP.

[0061] For example, a non-uniform quantizer is used to quantize the received pilot signal to obtain a quantized pilot received signal. :

[0062] (6)

[0063] in, Indicates the first Quantization gain at each AP This represents the quantization distortion coefficient related to the number of bits in the ADC quantization. It is additive quantization noise and satisfies the condition. , , .

[0064] In this embodiment, It can also be expressed as follows:

[0065] (7)

[0066] In formula (7), , .

[0067] The following formula can be used to... LMMSE estimation is performed to obtain uplink channel state information:

[0068] (8)

[0069] In formula (8), .

[0070] make , Then we can get:

[0071] (9)

[0072] In addition, it can also be defined For the LMMSE estimation error, we can obtain the following:

[0073] (10)

[0074] In formula (10), Users sharing pilot signals With users In the The estimated covariance matrix at each AP is:

[0075] (11)

[0076] Next, step S3 is executed to construct the quantized received signal expression for the user end.

[0077] In the downlink, each AP sends the same signals to each user as all other APs, and each AP communicates with each user... Message splitting to generate common sub-messages and private messages All users' public sub-messages are combined into a unified public message. It is then encoded into a common stream. Private messages Encoded independently into different private streams .

[0078] The downlink in the system uses maximum ratio transmission signals. In time... within, no. The downlink signal sent by an AP can be represented as:

[0079] (12)

[0080] In formula (12), This indicates the maximum transmit power of each AP. Let represent the power control coefficients of the common flow and private flow that satisfy the power constraints of each AP, respectively, and let represent the constraints:

[0081] (13)

[0082] Correspondingly, users The received signal can be represented as:

[0083]

[0084] =

[0085]

[0086] (14)

[0087] In formula (14), Indicates user Received noise.

[0088] If the user terminal is also equipped with a low-resolution ADC, the received signal is quantized to obtain the quantized received signal:

[0089] (15)

[0090] In formula (15), , indicating user Quantization noise at the user end .

[0091] because Since all terms of the expression have zero mean, its second moment equals its variance, which can be expressed as follows: , Indicates the common flow portion, Let these represent the private flow components, which are independent of each other and noise. Therefore, the following equation holds:

[0092] (16)

[0093] For formula (16) :

[0094]

[0095] in,

[0096]

[0097]

[0098]

[0099]

[0100] .

[0101] For formula (16) :

[0102]

[0103] in,

[0104]

[0105] .

[0106] Therefore, expanding the quantized received signal yields:

[0107] (17)

[0108] Then, step S4 is executed to construct an expression for the downlink reachable rate.

[0109] By using the capacity lower bound theory, first decode Get users The corresponding lower bound expression for the first downlink achievable rate:

[0110] (18)

[0111] in, , . Indicates common current power. For target users Common flow expected signal power, The variance term caused by the common flow channel estimation error. This refers to the additional interference power caused by channel aging in the common flow. Indicates private flow power. , , , , .

[0112] In the RSMA SIC (Successive Interference Cancellation) process, the user The received signal at the location is:

[0113] (19)

[0114] Based on the capacity lower bound theory, decoding This yields the lower bound expression for the achievable rate of the second downlink:

[0115] (20)

[0116] in, , , Indicates target user Private stream expected signal power, This represents the variance term caused by the private flow channel estimation error. This indicates that the target user is affected by channel aging. Private flow interference power, Interference power caused by other users' private stream signals. , , , .

[0117] To ensure all users can successfully decode The corresponding transmission rate must be limited by the user with the worst channel conditions in each decoding set. Therefore, The achievable rate is Then there are users The corresponding expression for the final downlink reachable rate is shown in Equation (2).

[0118] Based on the derived expressions for downlink achievable rates under various non-ideal transmission conditions, simulation experiments were conducted to accurately evaluate the system's communication performance.

[0119] The simulation parameters are set as follows: The simulation scenario is set to one. A square region is used, with folded edges to simulate an infinitely extending network environment. Randomly distributed elements are used within the region. AP and Each single-antenna user, each AP equipped with The antenna is configured as follows: A model combining large-scale fading (including path loss and shadowing) and small-scale Rayleigh fading is adopted. The large-scale fading coefficient is generated based on a commonly used three-slope model. The carrier frequency is... System bandwidth Normalized Doppler frequency shift is set to coherent block length Symbol, pilot length The phase noise variance of both the AP and the user terminal is set to... . Figure 2 The ADC quantization bit depth is Bit, Figure 3 The ADC quantization bit depth is Bit, corresponding quantization parameter Based on the additive quantization noise model, the total downlink transmit power is normalized to 1.

[0120] Figure 2 This demonstrates how the RSMA scheme proposed in this application compares with the traditional SDMA scheme under multiple non-ideal transmission characteristics, depending on the number of antennas at different access points (e.g., ...). and The cumulative distribution function of downlink user spectral efficiency is compared under different conditions, and Monte Carlo simulation results are provided. In the figure, solid and dashed lines represent theoretical results calculated based on the closed-form expression derived in step S4 of the embodiments of this application, while circles indicate results obtained after... Numerical simulations of Monte Carlo obtained independently. Figure 2 It can be seen that the theoretical curve and the Monte Carlo simulation mark highly overlap, which fully verifies the accuracy of the downlink achievable rate expression constructed in the embodiments of this application, proving that the analysis framework can accurately characterize the impact of non-ideal hardware and time-varying channels on system performance. Furthermore, under the same non-ideal conditions and antenna configuration, the CDF curve of RSMA is always to the right of SDMA, indicating that the performance of the RSMA scheme is significantly better than that of the SDMA scheme. This is because SDMA, relying solely on linear precoding, is unable to eliminate residual interference introduced by phase noise and quantization error, while RSMA utilizes the common data stream to absorb some strong interference, thus exhibiting stronger robustness. Moreover, with the increase in the number of antennas... Increasing the array gain from 10 to 20 improves the performance of both schemes by achieving higher array gain.

[0121] Figure 3 The independent and combined effects of various non-ideal transmission conditions on the performance of RSMA non-cellular networks are illustrated. The simulation scenario is set to the number of access point antennas. The study compared five scenarios: ideal conditions, only channel aging, only quantization noise, only phase oscillation, and full non-ideal transmission conditions. Figure 3The simulation results clearly demonstrate the destructive power of various quantization factors: First, compared to the ideal situation represented by the black solid line, the introduction of any single non-ideal transmission condition leads to a decrease in spectral efficiency (curve shifts to the left). Second, under the set simulation parameters, the quantization noise, shown by the green dashed line, has the most significant impact on performance, while the channel aging effect, shown by the blue dashed line, and the phase oscillation, shown by the pink dashed line, have relatively smaller impacts. Finally, the red solid line shows the system performance under the combined effect of the three non-ideal factors. It can be seen that the performance loss after the superposition of multiple factors is not a simple linear sum; the system is in its worst operating state. This result proves that in practical engineering deployments, it is insufficient to analyze a single type of non-ideal characteristic in isolation; a comprehensive analysis of multiple types of non-ideal transmission characteristics is necessary to accurately assess the system's performance limits, thereby guiding actual parameter design and interference management.

[0122] Based on the above embodiments of this application, this application constructs a cellular-free massive MIMO system model based on time-division duplex mode. This model comprehensively considers the channel aging effect caused by high-speed user movement in actual communication scenarios, the phase noise introduced by low-cost oscillators, and the quantization error generated by low-resolution ADCs at the transceiver end. In this model, the channel is modeled as a Ricean fading channel affected by both time-varying phase noise and Doppler frequency shift. The phase noise follows a discrete-time Wiener process, while the quantization process is linearized using an additive quantization noise model.

[0123] During the uplink transmission phase of the system, this application performs a pilot training process. If the user sends an orthogonal pilot sequence, the AP uses a low-resolution ADC to quantize the received signal. To address the resulting quantization noise and phase distortion, embodiments of this application estimate the uplink channel state information based on LMMSE, thereby obtaining highly accurate channel parameters under non-ideal hardware conditions.

[0124] During the downlink transmission phase, the system employs an RSMA strategy for signal processing. The AP splits user messages into common and private messages and uses estimated uplink channel state information for precoding and power allocation to construct a hybrid transmit signal containing both common and private streams. Upon receiving the signal, the user terminal also performs low-resolution ADC quantization. This embodiment, based on UatF technology, simulates the process of the user terminal using a SIC receiver to first decode the common stream and then the private stream, deriving mathematical expressions for the SINR and downlink achievable rate of both the common and private streams. These expressions explicitly include channel aging coefficients, phase noise variance, and ADC quantization accuracy parameters, enabling precise quantification of the combined impact of various non-ideal factors on the overall communication performance of the system.

[0125] In summary, the non-cellular network performance analysis method proposed in this application, applicable to various non-ideal transmission characteristics and RSMA, can derive a closed-form expression for downlink achievable rate by constructing a comprehensive system model that includes channel aging, phase noise, and low-resolution ADC quantization error. This method accurately quantifies the combined impact of multiple non-ideal factors on system performance and demonstrates that the RSMA strategy has stronger interference management capabilities and robustness compared to traditional SDMA under harsh communication conditions. It shows promising application prospects and can provide solid theoretical support for the system design, parameter optimization, and practical deployment of future communication networks (such as 6G networks) in low-cost hardware and high-dynamic channel environments.

[0126] Figure 4 This is a schematic diagram of an electronic device illustrated in this specification according to an exemplary embodiment. Please refer to... Figure 4 At the hardware level, the device includes a processor 402, an internal bus 404, a network interface 406, memory 408, a hardware acceleration device 410, and non-volatile memory 412, and may also include other hardware required for its functions. One or more embodiments of this application can be implemented in software, for example, the processor 402 reads the corresponding computer program from the non-volatile memory 412 into memory 408 and then runs it. Of course, in addition to software implementation, one or more embodiments of this application do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the above processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0127] Figure 5 This is a block diagram illustrating an exemplary embodiment of an RSMA non-cellular network under non-ideal transmission conditions, the performance analysis device being applicable to, for example... Figure 4 The electronic device shown implements the technical solution of this application. The performance analysis device includes: a channel model construction unit 510, a channel state estimation unit 520, a signal quantization processing unit 530, an evaluation index calculation unit 540, and a communication performance evaluation unit 550, wherein:

[0128] The channel model construction unit 510 is used to construct a channel model for the RSMA non-cellular network in the target area under non-ideal transmission conditions that consider channel aging and phase noise.

[0129] The channel state estimation unit 520 is used to obtain a quantized pilot received signal based on the quantization distortion coefficient after quantization of the pilot signal received by the access point during the uplink pilot training phase by ADC, and to perform LMMSE channel estimation on the quantized pilot received signal using the channel model to obtain uplink channel state information.

[0130] The signal quantization processing unit 530 is used to use the uplink channel state information to split, encode and precode the user message according to the RSMA strategy, so as to calculate the quantized received signal expression of the user end after ADC quantization.

[0131] The evaluation index calculation unit 540 is used to calculate the downlink achievable rate expression of the RSMA non-cellular network based on the quantized received signal expression and using the capacity lower limit theory.

[0132] The communication performance evaluation unit 550 is used to evaluate the communication performance of the RSMA non-cellular network under non-ideal transmission conditions based on the expression of the downlink achievable rate.

[0133] In some embodiments, the channel model is a Ricean channel model, and the Ricean channel model of the channel between each receiver and user in the RSMA non-cellular network at time n is: .

[0134] In some embodiments, the channel state estimation unit 520 is used to obtain the quantization gain corresponding to the receiving point. And quantization noise, among which , The quantization distortion coefficient is related to the quantization bit depth of the ADC; based on the quantization gain. The quantization pilot received signal is quantized by quantization noise to obtain the quantization pilot received signal.

[0135] In some embodiments, the evaluation index calculation unit 540 is configured to obtain a lower bound expression for a first downlink achievable rate by decoding the public stream portion of a user message based on the capacity lower bound theory; obtain a lower bound expression for a second downlink achievable rate by decoding the private stream portion of a user message based on the capacity lower bound theory; and obtain a final expression for the downlink achievable rate based on the lower bound expressions for the first and second downlink achievable rates.

[0136] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0137] Accordingly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the methods described in any of the above embodiments.

[0138] Accordingly, embodiments of this application also provide a computer program product configured to perform the methods described in any of the above embodiments.

[0139] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can take the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.

[0140] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0141] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0142] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0143] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather are primarily intended to describe features of specific embodiments of a particular invention. Certain features described in the various embodiments herein may also be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment may also be implemented separately in various embodiments or in any suitable sub-combination. Furthermore, while features may function in certain combinations as described above and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and a claimed combination may refer to a sub-combination or a variation thereof.

[0144] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order shown or sequentially, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0145] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings are not necessarily shown in a specific order or sequence to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0146] 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.

[0147] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A performance analysis method for RSMA non-cellular networks under non-ideal transmission conditions, characterized in that, Includes the following steps: Step S1: For the RSMA non-cellular network in the target area, construct a channel model of the RSMA non-cellular network under non-ideal transmission conditions considering channel aging and phase noise. Step S2: Based on the pilot signal received by the access point during the uplink pilot training phase, the quantized pilot received signal is obtained by quantization using ADC and described by the quantization distortion coefficient. The channel model is then used to perform LMMSE channel estimation on the quantized pilot received signal to obtain uplink channel state information. Step S3: Using the uplink channel state information, the user message is split, encoded, and pre-encoded according to the RSMA strategy to calculate the quantized received signal expression of the user terminal after ADC quantization. Step S4: Based on the quantized received signal expression, the downlink achievable rate of the RSMA non-cellular network is calculated using the capacity lower bound theory. Step S5: Evaluate the communication performance of the RSMA non-cellular network under non-ideal transmission conditions based on the expression for the downlink achievable rate.

2. The method according to claim 1, characterized in that, The channel model in step S1 is the Ricean channel model. The Ricean channel model of the channel between each receiving point and the user in the RSMA non-cellular network at time n is: in, As a time index, a coherent block of the RSMA non-cellular network operating under the time-division duplex protocol. Composed of individual samples, For receiving point With users At any moment Rice channel model, To conform to the phase noise of the discrete-time Wiener process, and Receiving points With users At any moment The oscillation phase, For aging channel model, For a moment With time The time correlation coefficient below, , As an independent innovation component, This represents the initial state of the channel.

3. The method according to claim 1, characterized in that, Step S2 includes: Obtain the quantization gain corresponding to the receiving point And quantization noise, among which , The quantization distortion coefficient is related to the number of quantization bits of the ADC. Based on the quantization gain The quantization pilot received signal is quantized by quantization noise to obtain the quantization pilot received signal.

4. The method according to claim 1, characterized in that, Step S4 includes: Based on the capacity lower bound theory, by decoding the common stream part of user messages, the lower bound expression of the first downlink achievable rate is obtained; Based on the capacity lower bound theory, by decoding the private stream part of the user message, the lower bound expression of the second downlink achievable rate is obtained; Based on the lower bound expression of the first downlink achievable rate and the lower bound expression of the second downlink achievable rate, the final expression for the downlink achievable rate is obtained.

5. The method according to claim 1, characterized in that, The expression for the downlink reachable rate in step S4 is as follows: in, For users The corresponding downlink achievable rate, Let K be the set of users, and K be the total number of users. For the first downlink achievable rate, This is the achievable rate for the second downlink.

6. A performance analysis device for RSMA non-cellular networks under non-ideal transmission conditions, characterized in that, The device includes: The channel model construction unit is used to construct a channel model for the RSMA non-cellular network in the target area under non-ideal transmission conditions that consider channel aging and phase noise. The channel state estimation unit is used to obtain a quantized pilot received signal based on the quantization distortion coefficient after quantization of the pilot signal received by the access point during the uplink pilot training phase by ADC, and to perform LMMSE channel estimation on the quantized pilot received signal using the channel model to obtain uplink channel state information. The signal quantization processing unit is used to use the uplink channel state information to decompose, encode, and precode the user message according to the RSMA strategy, so as to calculate the quantized received signal expression of the user terminal after ADC quantization. The evaluation index calculation unit is used to calculate the downlink achievable rate expression of the RSMA non-cellular network based on the quantized received signal expression and using the capacity lower limit theory. A communication performance evaluation unit is used to evaluate the communication performance of the RSMA non-cellular network under non-ideal transmission conditions based on the expression of the downlink achievable rate.

7. The apparatus according to claim 6, characterized in that, The channel model is the Ricean channel model, and the Ricean channel model for the channel between each receiver and user in the RSMA non-cellular network at time n is: in, As a time index, a coherent block of the RSMA non-cellular network operating under the time-division duplex protocol. Composed of individual samples, For receiving point With users At any moment Rice channel model, To conform to the phase noise of the discrete-time Wiener process, and Receiving points With users At any moment The oscillation phase, For aging channel model, For a moment With time The time correlation coefficient below, , As an independent innovation component, This represents the initial state of the channel.

8. The apparatus according to claim 6, characterized in that, The channel state estimation unit is used to obtain a lower bound expression for the first downlink achievable rate by decoding the common flow portion of the user message based on the capacity lower bound theory; to obtain a lower bound expression for the second downlink achievable rate by decoding the private flow portion of the user message based on the capacity lower bound theory; and to obtain a final expression for the downlink achievable rate based on the lower bound expressions for the first and second downlink achievable rates.

9. An electronic device, characterized in that, include: processor; A computer-readable storage medium storing computer program instructions that, when executed by the processor, cause the processor to perform the method as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which is executed by a processor according to any one of claims 1 to 5.

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