Low-complexity NOMA signal detector and detection method

Abstract

The low-complexity NOMA signal detector comprises a base station, the base station is used for receiving and sending NOMA signals, and the base station is composed of a signal receiving antenna array, an analog-to-digital converter and a digital signal processor; the signal receiving antenna array is used for receiving superposed signals from a plurality of users; the analog-to-digital converter converts analog signals received by the signal receiving antenna array into digital signals; the digital signal processor executes JOML detection, IOML detection and SIC detection; according to the low-complexity NOMA signal detector and the detection method, through optimizing the signal processing flow, the detection efficiency is improved, the algorithm complexity is reduced, and the low-complexity NOMA signal detector and the detection method are suitable for large-scale antenna and multi-user scenes. The method has the advantages that based on an uplink, effective signal separation, the SIC technology, sequential detection and single-user matched filter detection, multi-user signals are effectively separated and recovered, and interference is reduced.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a low-complexity NOMA signal detector and a detection method. Background Art

[0002] As the fifth generation of mobile communication technology (5G) gradually becomes the backbone of mobile communication services, the data traffic of mobile Internet has experienced explosive growth. How to further improve spectrum efficiency has become one of the key challenges in coping with this explosive data traffic. And due to the popularity of smart terminals and the rapid development of new mobile communication services such as the Internet of Things, 5G increasingly needs to support large-scale connections of users or devices to meet the demand for low-latency, low-cost devices and various types of services.

[0003] Non-orthogonal multiple access (NOMA) technology is different from traditional orthogonal multiple access technology. It allows multiple users to share the same resource unit, so that frequency domain and time domain resources can be more fully utilized. Indicators such as spectrum efficiency and system capacity can be improved exponentially, meeting the access needs of massive users in low-power and large-connection scenarios under future 5G systems.

[0004] The power domain non-orthogonal multiple access (PD-NOMA) technology based on power allocation is very intuitive and can distinguish user signals based on power alone. Without using channel coding, the receiver does not require a very complex design to restore the signal.

[0005] However, the current PD-NOMA system still has deficiencies in multi-user detection and error rate performance, mainly manifested in the following aspects: the complexity of multi-user detection is significantly higher than that of single-user detection, and as the number of users increases, the factors affecting system performance increase, such as the power, phase and channel status information of each user's signal. To obtain the performance of multi-user detection, it is necessary to coordinate the influence of these factors, and the complexity of the analysis will continue to increase with the increase in the number of users; at the same time, the detection of useful users will be affected by more interference signals, so the PD-NOMA system also needs to improve detection efficiency and accuracy. Summary of the invention

[0006] The purpose of the present invention is to provide a low-complexity NOMA signal detector and detection method to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a low-complexity NOMA signal detector, characterized in that it includes: Base station, used to receive and send NOMA signals. The base station consists of a signal receiving antenna array, an analog-to-digital converter, and a digital signal processor; The signal receiving antenna array is used to receive superimposed signals from several users; The analog-to-digital converter converts the analog signal received by the signal receiving antenna array into a digital signal; The digital signal processor performs JOML detection, IOML detection, and SIC detection; The base station also includes a power amplifier, which is used to adjust the transmission power of each user to achieve non-orthogonal multiplexing in the power domain; The base station also includes a feedback control unit, which is used to monitor the channel status and user needs in real time and adjust the power allocation and detection method according to the feedback results.

[0008] A low-complexity NOMA signal detection method is applied to a low-complexity NOMA signal detector.

[0009] Use SIC detection: sequential detection, the superimposed composite user signals are detected in order from the highest to the lowest power of each user; Each round of detection is single-user detection, which can be achieved through single-user matched filter detection; Uplink signal processing and detection; Link signal processing and detection.

[0010] Further, the uplink signal processing and detection process is as follows: ‌Signal Reception‌: The signal receiving antenna array receives the superimposed signals of several users; ‌Analog-to-digital conversion‌: The analog-to-digital converter‌ converts the analog signal received by the signal receiving antenna array into a digital signal; ‌User sorting‌: Sort users by their power and test them in order from large to small; ‌SIC processing‌: Perform single-user matched filter detection on the user signal with the highest power to recover the user signal; subtract the detected user signal from the superimposed signal to update the remaining signal.

[0011] ‌Output results‌: Output the detection signals of all users.

[0012] Further, downlink signal processing and detection process: ‌Signal transmission‌: The base station transmits the superimposed composite user signal according to the power allocation; ‌Signal Reception‌: The user-side antenna receives the signal from the base station; ‌Analog-to-digital conversion‌: The analog-to-digital converter converts the received analog signal into a digital signal; ‌User-specific processing‌: Each user performs single-user matched filter detection based on its own power level and the reference signal sent by the base station.

[0013] ‌SIC processing: If there is a large power difference between users, SIC is implemented at the user end; ‌Output result‌: Each user outputs its own detection signal.

[0014] Further, JOML detection steps; Step 1, receiving signals from all signal receiving antenna arrays; Step 2, jointly processing the received signals; Step 3: Make a joint decision based on the maximum likelihood criterion; Step 4: Output the detection results, including the signal estimation value of each user.

[0015] Furthermore, the working principle of the decision type in IOML detection includes the following steps: received signal modeling, likelihood function calculation, maximization of likelihood function, nonlinear processing, and decision output.

[0016] Further, received signal modeling: modeling the signal received by the signal receiving antenna array; Likelihood function calculation: Calculate the likelihood function, send a signal given the received signal, and construct a decision formula based on the likelihood function; Maximize the likelihood function: adjust the estimate of the transmitted signal to maximize the likelihood function based on gradient ascent or Newton's method; Non-linear processing: The decision type adopts a non-linear processing structure; Decision output: Make a decision output based on the estimated value of the transmitted signal obtained by maximizing the likelihood function.

[0017] Compared with the prior art, the present invention has the following beneficial effects: (1) Low-complexity NOMA signal detector and detection method improves detection efficiency and reduces algorithm complexity by optimizing the signal processing process, and is suitable for large-scale antenna and multi-user scenarios. The specific beneficial effects are as follows: Based on uplink, effective signal separation, SIC technology, sequential detection and single-user matched filter detection, multi-user signals are effectively separated and restored, and interference is reduced.

[0018] ‌ (2) The feedback control unit monitors the channel status and user needs in real time, adjusts the power allocation and detection strategy, and improves the system adaptability.

[0019] ‌ (3) Utilize downlink, power domain multiplexing, and NOMA technology to achieve non-orthogonal multiplexing in the power domain and improve spectrum efficiency.

[0020] ‌ (4) The user performs detection based on its own power level and the reference signal sent by the base station, reducing the processing burden of the base station.

[0021] ‌ (5) JOML and IOML-based detection: Complexity reduction. The computational complexity is significantly reduced through algorithm optimization, such as diagonal matrix decomposition and Neumann series approximation.

[0022] ‌ (6) Under the premise of ensuring similar bit error rates, low-complexity detection is achieved to improve the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a detection flow chart of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] Example:

[0026] See also Figure 1 , the present invention provides a technical solution: a low-complexity NOMA signal detector and detection method.

[0027] 1. The base station of the NOMA system includes a signal receiving antenna array, an analog-to-digital converter (ADC), a digital signal processor (DSP), a power amplifier, and a feedback control unit.

[0028] Signal receiving antenna array: used to receive superimposed signals from multiple users and is a key part of signal input.

[0029] Analog-to-digital converter (ADC): Converts the analog signal received by the signal receiving antenna array into a digital signal for digital signal processing.

[0030] Digital Signal Processor (DSP): Executes low-complexity NOMA detection algorithms (JOML detection, IOML detection, SIC detection).

[0031] Power amplifier: used to adjust the transmission power of each user and realize non-orthogonal multiplexing in the power domain.

[0032] Feedback control unit: used to monitor channel status and user needs in real time, and adjust power allocation and detection strategies based on feedback results.

[0033] 2. SIC detection.

[0034] 2.1 SIC has the following two significant features: First, sequential detection, that is, superimposed composite user signals are detected in order from the highest to the lowest power of each user; Second, each round of detection is single-user detection, which can be achieved through single-user matched filter detection. Since the signal models of uplink and downlink are different, the following is divided into uplink and downlink to explain the signal processing and detection process; 2.1.1 The uplink signal processing and detection process is as follows: ‌Signal Reception‌: The antenna array receives superimposed signals from multiple users.

[0035] ‌Analog-to-Digital Conversion‌: ADC converts the received analog signal into a digital signal.

[0036] ‌User sorting‌: Sort users by their power and test them from large to small.

[0037] ‌SIC processing‌: Perform single-user matched filter detection on the user signal with the highest power to recover the user signal; subtract the detected user signal from the superimposed signal to update the remaining signal; repeat the above steps and detect the remaining user signals in turn until all user signals are detected.

[0038] ‌Output results‌: Output the detection signals of all users.

[0039] An example of uplink signal processing and detection. In the NOMA system, multiple users send signals to the same base station with different powers. The signal complex envelope obtained at the base station is the superposition of these user signals. The SIC detector is used at the base station to gradually detect and recover user signals. The detection order is based on the received power of the user signal. During the detection process, the detected user signal will be subtracted from the superimposed signal to reduce interference with subsequent signal detection.

[0040] 2.1.2 Downlink signal processing and detection process: ‌Signal transmission‌: The base station sends the superimposed composite user signal according to the power allocation strategy.

[0041] ‌Signal Reception‌: The user-side antenna receives the signal from the base station.

[0042] ‌Analog-to-Digital Conversion‌: ADC converts the received analog signal into a digital signal.

[0043] ‌User-specific processing‌: Each user performs single-user matched filter detection based on its own power level and the reference signal sent by the base station.

[0044] ‌SIC processing: If there is a large power difference between users, SIC can be considered to be implemented at the user end to further improve the detection performance. This usually involves sorting the user signals and iterative detection, similar to the uplink.

[0045] ‌Output result‌: Each user outputs its own detection signal.

[0046] An example of downlink signal processing and detection. In the downlink, the base station is responsible for pre-modulating the signal planned to be transmitted to each user, that is, allocating different powers to each user signal as needed, and superimposing them to form a composite signal to be sent. In this process, the total power transmitted is fixed, and the power portion allocated to each user determines the strength of the user's signal.

[0047] When the signal is sent to the user end, the signal received by the user will be affected by the channel fading coefficient between the base station and the user end, and will be superimposed with the noise at the terminal receiver. The user end needs to demodulate and detect the received signal to recover the signal sent by the base station. The demodulation and detection process is basically the same as that in the uplink, involving steps such as signal matching filtering, interference elimination, and symbol decision.

[0048] In addition, in the downlink, the user end only needs to pay attention to and recover the signal transmitted to it by the base station, so the detection process is completed after detecting the useful signal.

[0049] In the uplink, SIC processing is mainly performed at the base station end to gradually separate the signals of each user from the superimposed signals. In the downlink, SIC processing can be performed at the user end to improve the detection performance of the user's own signal. Whether it is uplink or downlink, the core of SIC technology is sequential detection and single-user matched filter detection to achieve effective separation and recovery of multi-user signals.

[0050] 3. JOML and IOML detection 3.1 The core purpose of JOML detection is to minimize the detection error rate of the joint signal of two users. According to the ML criterion, this is equivalent to maximizing the likelihood function under specific conditions. Through a series of mathematical derivations, the expression of the joint optimal decision is obtained, which only depends on the observation statistics and is not affected by the noise power. However, the JOML detector is not scale invariant because only knowing the ratio of the user received power cannot determine the sign of the signal.

[0051] When calculating the error rate of a user, it is important to note that the decision boundary is divided separately for each signal point, but in fact, when calculating the error rate of a certain user, such trivial division is not necessary. For example, when considering whether user 1 has a detection error, what signal user 2 sent and the detection status of user 2's signal can be ignored.

[0052] This embodiment is based on the user's BER (Bit Error Rate), is applicable to any user receiving power, carrier phase difference and noise power value, and is commonly used for error rate calculation of any user among two users.

[0053] 3.1.1 The specific steps of JOML detection usually include signal collection, joint processing, optimal decision and output results; First, the signal is received from all antennas to ensure signal integrity; Secondly, the received signals are processed jointly, considering the correlation of all user signals to improve the accuracy of detection; Then, based on the maximum likelihood criterion, a joint optimal decision is made to minimize the joint signal detection error rate of all users; Finally, the detection results are output, including the signal estimation value of each user, for subsequent analysis and application. JOML detection achieves global optimal detection performance by comprehensively considering all user signals, and is suitable for multi-user, multi-antenna and interference-limited environments.

[0054] 3.2 In IOML detection, the main goal is to make the best decision for a specific user to minimize the detection error rate of that user. This involves complex hypothesis testing problems, and the resulting decision may be different from the JOML decision. The decision expression of the IOML detector is obtained by maximizing a specific probability function, which is related to the received power of the user signal, the carrier phase difference, and the noise power.

[0055] 3.2.1 The working principle of the judgment method in IOML detection includes the following steps ‌Received Signal Modeling‌: First, the signal received by the signal receiving antenna array is modeled, taking into account the interference of noise and other user signals. This model describes the relationship between the received signal and the transmitted signal.

[0056] ‌Likelihood function calculation‌: Next, the likelihood function is calculated, which represents the transmitted signal given the received signal. The likelihood function is the basis of the decision formula, which is a criterion or rule built based on the likelihood function.

[0057] ‌Maximize the likelihood function‌: Then, the likelihood function is maximized by adjusting the estimate of the transmitted signal. This process usually involves mathematical optimization techniques such as gradient ascent or Newton's method.

[0058] ‌Non-linear processing‌: In some cases, the decision formula may use a non-linear processing structure to further improve the detection performance. Non-linear processing can better adapt to the non-linear characteristics of the signal, such as non-linear changes in the amplitude, phase or frequency of the signal.

[0059] Decision output: Finally, a decision output is made based on the estimated value of the transmitted signal obtained by maximizing the likelihood function. This output is the detection result of a specific user signal.

[0060] The low-complexity NOMA signal detector and detection method aim to improve the efficiency of signal detection in NOMA systems and reduce the algorithm complexity.

[0061] The NOMA signal detection solution provided in this embodiment can meet the requirements of high spectrum efficiency, high transmission rate and large system capacity in low-complexity connection scenarios. For signal detection in the NOMA system, the efficiency of NOMA signal detection is effectively improved. Through the joint cooperation of hardware and software, low-complexity NOMA signal detection is achieved, which improves the spectrum efficiency and user capacity of the system.

[0062] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A low-complexity NOMA signal detector, characterized in that: include: Base station, used to receive and send NOMA signals. The base station consists of a signal receiving antenna array, an analog-to-digital converter, and a digital signal processor; The signal receiving antenna array is used to receive superimposed signals from several users; The analog-to-digital converter converts the analog signal received by the signal receiving antenna array into a digital signal; The digital signal processor performs JOML detection, IOML detection, and SIC detection.

2. A low-complexity NOMA signal detector according to claim 1, characterized in that: The base station also includes a power amplifier, which is used to adjust the transmission power of each user to achieve non-orthogonal multiplexing in the power domain.

3. A low complexity NOMA signal detector according to claim 1, characterized in that: The base station also includes a feedback control unit, which is used to monitor the channel status and user needs in real time and adjust the power allocation and detection method according to the feedback results.

4. A low-complexity NOMA signal detection method, characterized in that: A low-complexity NOMA signal detector as described in any one of claims 1-3.

5. A low-complexity NOMA signal detection method according to claim 4, characterized in that: Use SIC detection; Sequential detection: the superimposed composite user signals are detected in order from the highest to the lowest power of each user; Each round of detection is single-user detection, which is achieved through single-user matched filter detection; Uplink signal processing and detection; Link signal processing and detection.

6. A low-complexity NOMA signal detection method according to claim 5, characterized in that: The uplink signal processing and detection process is as follows: ‌Signal Reception‌: The signal receiving antenna array receives the superimposed signals of several users; ‌Analog-to-digital conversion‌: The analog-to-digital converter‌ converts the analog signal received by the signal receiving antenna array into a digital signal; ‌User sorting‌: Sort users by their power and test them in order from large to small; ‌SIC processing‌: Perform single-user matched filter detection on the user signal with the highest power to recover the user signal; subtract the detected user signal from the superimposed signal to update the remaining signal; ‌Output results‌: Output the detection signals of all users.

7. A low-complexity NOMA signal detection method according to claim 5, characterized in that: Downlink signal processing and detection process: ‌Signal transmission‌: The base station transmits the superimposed composite user signal according to the power allocation; ‌Signal Reception‌: The user-side antenna receives the signal from the base station; ‌Analog-to-digital conversion‌: The analog-to-digital converter converts the received analog signal into a digital signal; ‌User-specific processing‌: Each user performs single-user matched filter detection based on its own power level and the reference signal sent by the base station; ‌SIC processing: When there is a large power difference between users, SIC is implemented at the user end; ‌Output result‌: Each user outputs its own detection signal.

8. A low-complexity NOMA signal detection method according to claim 4, characterized in that: JOML detection steps; Step 1, receiving signals from all signal receiving antenna arrays; Step 2, jointly processing the received signals; Step 3: Make a joint decision based on the maximum likelihood criterion; Step 4: Output the detection results, including the signal estimation value of each user.

9. A low-complexity NOMA signal detection method according to claim 4, characterized in that: The working principle of the decision-making method in IOML detection includes the following steps: received signal modeling, likelihood function calculation, maximization of likelihood function, nonlinear processing, and decision output.

10. A low-complexity NOMA signal detection method according to claim 9, characterized in that: Received signal modeling: Modeling the signal received by the signal receiving antenna array; Likelihood function calculation: Calculate the likelihood function, send a signal given the received signal, and construct a decision formula based on the likelihood function; Maximize the likelihood function: adjust the estimate of the transmitted signal to maximize the likelihood function based on gradient ascent or Newton's method; Non-linear processing: The decision type adopts a non-linear processing structure; Decision output: Make a decision output based on the estimated value of the transmitted signal obtained by maximizing the likelihood function.

Images