Large-scale machine type communication system and method

Abstract

The invention relates to a large-scale machine type communication system and method. The system comprises a base station which is used for sampling a received pre-code signal through a preset sampling rate in a random access period of a terminal to form a measurement vector, designing a measurement matrix for a large-scale machine communication scene according to system characteristics, and determining a random access parameter of the terminal based on a compressed sensing algorithm and the measurement matrix; performing reconstruction processing on the measurement vector to obtain a terminal activation vector, determining terminal information of a successfully accessed first terminal based on the terminal activation vector, and adjusting a retransmission parameter based on terminal feedback information of a second terminal to obtain a target retransmission parameter, so that the second terminal re-initiates random access based on the target retransmission parameter; the second terminal comprises a terminal which has failed or aliasing terminal access signals and is not successfully accessed. Different terminal signals can be effectively distinguished in a sparse scene, and even if part of signals are superposed, the signals can be identified through a reconstruction algorithm, so that the risk of random access collision is reduced.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a large-scale machine-type communication system and method. Background Technology

[0002] With the rapid arrival of the Internet of Things (IoT) and the era of ubiquitous connectivity, the number of terminal devices is exploding. Traditional mobile communication systems are mainly geared towards voice and high-data-volume human-machine communication, and their network architecture and resource allocation mechanisms are not specifically designed for the characteristics of massive numbers of low-power devices. In this technological context, existing wireless access networks face access conflicts when dealing with Massive Machine Type Communication (mMTC) scenarios. Summary of the Invention

[0003] Therefore, it is necessary to provide a large-scale machine-type communication system and method that can reduce terminal access conflicts in response to the above-mentioned technical problems.

[0004] Firstly, this application provides a large-scale machine-type communication system. The system includes:

[0005] A base station is used to sample the received precoded signal at a preset sampling rate during random access by a terminal to form a measurement vector;

[0006] Base stations are used to design measurement matrices for large-scale machine-type communication scenarios based on system characteristics;

[0007] The base station is used to reconstruct the measurement vector based on the compressed sensing algorithm and the measurement matrix to obtain the terminal activation vector;

[0008] The base station is used to determine the terminal information of the first terminal that has successfully accessed the network based on the terminal activation vector, and to adjust the retransmission parameters based on the terminal feedback information of the second terminal to obtain the target retransmission parameters. The target retransmission parameters are used by the second terminal to re-initiate random access based on the target retransmission parameters. The second terminal includes terminals that have failed to access the network due to terminal access signal failure or aliasing.

[0009] In one embodiment, the base station is configured to determine the target retransmission parameters using an adjustment function based on the current network load, the current channel state, and historical collision information within the most recent preset time period, and send the target retransmission parameters to the second terminal; the retransmission parameters are used by the second terminal to execute the retransmission process based on the retransmission parameters, and the retransmission parameters include retransmission probability and / or retransmission interval.

[0010] In one embodiment, the base station is also used to control the first terminal to transmit data simultaneously at different power levels in the same time slot and the same frequency band.

[0011] In one embodiment, the base station is further configured to sample, amplify, and perform preliminary filtering on the mixed signals received from multiple terminals to obtain a target signal; the target signal includes power distribution information;

[0012] The base station is also used to perform an interference cancellation process; the interference cancellation process includes sorting the target signals according to the power distribution information to obtain a first sorting result, determining the target signals with power greater than a preset power in the first sorting result, and decoding the target signals starting from the signal with the highest power in the target signals to obtain decoding information;

[0013] The base station is also used to subtract the contribution of the decoded signal corresponding to the decoded information from the mixed signal to obtain the first residual signal;

[0014] The base station is also used to perform joint multi-user detection on the first residual signal and sequentially perform interference cancellation process on signals in the first residual signal whose power is less than a preset power.

[0015] In one embodiment, the system also includes edge computing nodes deployed within the coverage area of ​​each base station;

[0016] The base station is also used to divide radio resources into continuous time slots in the time domain and multiple subcarriers in the frequency domain based on OFDM technology, forming a two-dimensional time-frequency resource grid, and then dividing the two-dimensional time-frequency resource grid into multiple resource blocks.

[0017] Edge computing nodes are used to allocate resource blocks to terminals that request access based on historical data, local resource usage, and terminal needs, while reserving redundant resources.

[0018] Edge computing nodes are used to dynamically update resource usage based on local terminal traffic, channel status changes, and interference levels through a distributed scheduling algorithm, and to reschedule resource blocks within the coverage area to which the edge computing node belongs.

[0019] Edge computing nodes are used to migrate or reallocate resource blocks in collaboration with each other in the event of sudden traffic or channel degradation in various coverage areas.

[0020] Edge computing nodes are used to evaluate each resource scheduling session to obtain resource scheduling evaluation results; the resource scheduling evaluation results include success rate, latency, and bit error rate indicators;

[0021] Edge computing nodes are used to optimize distributed scheduling algorithms based on resource scheduling evaluation results.

[0022] In one embodiment, the system further includes a terminal;

[0023] The terminal is used to perform pilot measurements using the received reference signal and pilot signal before transmission to obtain channel parameters, including the current channel gain and the level of surrounding interference.

[0024] The terminal is used to receive interference information and current noise levels of the local network sent by the base station or edge node, so that the terminal can obtain local environmental information.

[0025] The terminal is used to calculate a new transmit power value based on channel parameters and local environment information, and compare the new transmit power value with preset upper and lower limits.

[0026] The terminal is configured to adjust the new transmission power value to the preset upper limit if the new transmission power value exceeds the preset upper limit; adjust the new transmission power value to the preset lower limit if the new transmission power value is lower than the preset lower limit; and maintain the new transmission power value if the new transmission power value is within the range of the preset upper and lower limits.

[0027] In one embodiment, the base station is further configured to acquire multiple received signals through multiple antennas, acquire channel state information of each terminal using pilot signals and channel estimation techniques, and establish a matrix containing channel information of each terminal in the cell and neighboring cells as input for joint detection.

[0028] The base station is also used to sort the received signals by power to obtain a second sorting result, and to start decoding from the signal with the highest power in the second sorting result;

[0029] The base station is also used to subtract the contribution of the successfully decoded received signal from multiple received signals to obtain a second residual signal, and to use a joint detection algorithm to decode the second residual signal sequentially until all terminal information is recovered.

[0030] Base stations are also used in multi-base station deployment scenarios to share some channel state information and interference information through high-speed networks and to jointly perform signal processing using a collaborative scheduling mechanism;

[0031] The base station is also used to monitor the bit error rate and signal-to-noise ratio during the decoding process. If the bit error rate is greater than the preset bit error rate and / or the signal-to-noise ratio is less than the preset signal-to-noise ratio, the parameters of the filters in the base station are adjusted or the joint detection algorithm is updated. The adjustment process is carried out in coordination within a single base station and across base stations to optimize the system interference management strategy.

[0032] In one embodiment, the system also includes a data analytics platform;

[0033] The data analysis platform is used to process and analyze monitoring data in real time. When abnormal monitoring data is detected, it sends feedback data to the scheduling and control module in the base station.

[0034] The scheduling and control module is used to receive feedback data and dynamically adjust resource allocation, power control, and access scheduling strategies based on the current network status and prediction models.

[0035] In one embodiment, a data analysis platform is used to store historical data, which is used to optimize the prediction model. The historical data includes real-time monitoring data, alarm records, scheduling policy adjustment records, resource allocation results, power control parameters, access scheduling logs, and corresponding network performance index data generated during network operation.

[0036] Secondly, this application also provides a large-scale machine-type communication method, the method comprising:

[0037] During random access to the terminal, the received precoded signal is sampled at a preset sampling rate to form a measurement vector;

[0038] Design a measurement matrix for large-scale machine-type communication scenarios based on system characteristics;

[0039] Based on the compressed sensing algorithm and the measurement matrix, the measurement vector is reconstructed to obtain the terminal activation vector;

[0040] The terminal information of the first terminal that successfully accessed the network is determined based on the terminal activation vector, and the retransmission parameters are adjusted based on the terminal feedback information of the second terminal to obtain the target retransmission parameters. Random access is then re-initiated based on the target retransmission parameters. The second terminal includes terminals that failed to access the network due to terminal access signal failure or aliasing.

[0041] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0042] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0043] Figure 1 This is a schematic diagram of a communication system provided in an embodiment of this application;

[0044] Figure 2 This is a flowchart illustrating a large-scale machine-type communication method provided in an embodiment of this application. Detailed Implementation

[0045] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0047] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0048] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0049] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0050] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0051] In the description of the embodiments of this application, the technical terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0052] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0053] The large-scale machine-type communication system provided in the embodiments of this application, such as Figure 1 As shown, Figure 1 This is a schematic diagram of a communication system provided in an embodiment of this application. The communication system includes a base station and a terminal, wherein:

[0054] A base station is used to sample the received precoded signal at a preset sampling rate during random access by a terminal to form a measurement vector;

[0055] Base stations are used to design measurement matrices for large-scale machine-type communication scenarios based on system characteristics;

[0056] The base station is used to reconstruct the measurement vector based on the compressed sensing algorithm and the measurement matrix to obtain the terminal activation vector;

[0057] The base station is used to determine the terminal information of the first terminal that has successfully accessed the network based on the terminal activation vector, and to adjust the retransmission parameters based on the terminal feedback information of the second terminal to obtain the target retransmission parameters. The target retransmission parameters are used by the second terminal to re-initiate random access based on the target retransmission parameters. The second terminal includes terminals that have failed to access the network due to terminal access signal failure or aliasing.

[0058] The aforementioned technical principles include the following processes:

[0059] 1) Sparse model construction: Assuming there are a total of N terminals, but only K terminals transmit data in a certain time slot (K≪N), construct an N-dimensional signal vector. As the active state of the terminal, non-zero terms correspond to active terminals. That is, vector x is the precoding signal mentioned above.

[0060] 2) Measurement process modeling: The signal received by the base station can be described by the following formula:

[0061]

[0062] in, For the received low-dimensional measurement vector, For a well-designed measurement matrix, the number of rows in the measurement matrix is ​​much smaller than N, which reflects a low sampling rate. This indicates noise interference. Compressed sensing theory shows that in Under the premise of sparseness, through appropriate design And the corresponding reconstruction algorithm, which can be derived from Accurately recover .

[0063] in, Therefore, the model is: .

[0064] It is the mixed sampling vector received by the base station. Indicates the first Whether a precoded signal is "activated" and each column This refers to the specific representation of the precoded signal. Here... It is the number of sampling points, each These are all time-domain sampling sequences of the precodes performed by the base station on the terminal during a single access time slot. All of them... The possible precoded signals of the path are assembled into a measurement matrix.

[0065] 3) Reconstruction Algorithm Selection: Reconstruction algorithms such as Orthogonal Matching Pursuit (OMP), Basis Pursuit (BP), or LASSO can be used for sparse vectors. The system performs a recovery and identifies which devices are active.

[0066] The specific steps are as follows:

[0067] 1) Data Acquisition: During random access, the base station samples the received precoded signals at a low sampling rate to form a measurement vector. .

[0068] 2) Measurement Matrix Design: Design a measurement matrix suitable for mMTC scenarios based on system characteristics. This ensures that the sparsity and low cross-correlation requirements of compressed sensing are met. Random matrices or matrices with specific structures are often used to ensure reconstruction accuracy.

[0069] 3) Sparse signal recovery: The selected compressed sensing algorithm (such as OMP) is used to process the signal and reconstruct the terminal activation vector, from which the terminal with valid signal is identified.

[0070] 4) Based on the recovered The base station uses zero-component information from China and Africa to determine which terminals have successfully accessed the network. For those identified as faulty or aliased, a feedback mechanism initiates a retransmission process, further reducing the probability of collisions during random access.

[0071] If the non-zero components in the terminal activation vector meet expectations and do not overlap, it can be determined as "successful access"; if the non-zero components are abnormal, it can be determined as "failure"; if multiple terminal signals overlap (non-zero components interfere with each other and cannot be distinguished), it is determined as "aliasing" (i.e., collision). "Failure" means that the signal sent by the terminal is not effectively received by the base station due to channel interference, excessive distance, etc. (e.g., signal energy is below the detection threshold, non-zero components are missing), resulting in an invalid access request. "Aliasing" means that multiple terminals send signals at the same time, causing the signals to overlap and become indistinguishable at the base station (non-zero components overlap and become chaotic), i.e., a collision has occurred. "Feedback mechanism": The base station sends an "access failure" notification to the terminal determined to be failed or aliased through the downlink channel (e.g., a dedicated feedback message), informing it that it needs to re-initiate access.

[0072] "Retransmission Process": Terminals receiving feedback will not wait indefinitely, but will attempt to access the network again according to preset rules (such as randomly delaying for a period of time and then reselecting an access sequence). By staggering the time or sequence, the possibility of another collision is reduced. Alternatively, the retransmission parameters can be adjusted to obtain target retransmission parameters, which are then sent to the terminals that failed to access the network, enabling the terminals to re-initiate random access based on these target retransmission parameters.

[0073] In this embodiment, compressed sensing achieves efficient detection at low sampling rates, significantly reducing hardware and computational burden. It can effectively distinguish different terminal signals in sparse scenarios, and even if some signals overlap, it can be identified through reconstruction algorithms, thereby reducing the risk of random access collisions. Furthermore, through a fast recovery algorithm, the overall access detection process latency is reduced, facilitating real-time scheduling and feedback control.

[0074] In one embodiment, the base station is configured to determine the target retransmission parameters using an adjustment function based on the current network load, the current channel state, and historical collision information within the most recent preset time period, and send the target retransmission parameters to the second terminal; the retransmission parameters are used by the second terminal to execute the retransmission process based on the retransmission parameters, and the retransmission parameters include retransmission probability and / or retransmission interval.

[0075] Currently, when a large number of terminals simultaneously initiate random access requests, signal collisions and continuous retransmissions are likely to occur, leading to network congestion. Traditional fixed retransmission strategies cannot adapt to dynamically changing network loads, making the retransmission probability and time interval ineffective in responding to sudden traffic fluctuations. This embodiment designs an adaptive retransmission mechanism that dynamically adjusts the retransmission window and retransmission probability based on real-time network load, channel status, and historical collision data through feedback control and statistical analysis, thereby smoothing out sudden access traffic and reducing the risk of system congestion. The specific technical principle is as follows:

[0076] 1) State modeling: The access process of each terminal is regarded as a random process. A model similar to Markov chain is used to describe the state transition of the terminal from initial access to successful access. Each state corresponds to a different number of retransmission attempts and waiting delay.

[0077] 2) Retransmission probability function: Design an adaptive function. ,in:

[0078] This indicates the current network load (e.g., the number of access requests within a unit of time). Indicates the current channel state metrics (e.g., signal-to-noise ratio). This represents historical collision statistics (e.g., the collision rate of recent retransmissions). The function outputs a dynamically adjusted retransmission probability. or retransmission interval The parameters allow for an appropriate extension of the waiting time under high load and high collision conditions, thereby effectively avoiding continuous collisions.

[0079] 3) Feedback control principle: The base station counts the collision events and resource utilization of the entire network in real time, and informs each terminal of the current recommended retransmission parameters through feedback broadcast or signaling. The terminal adjusts the retransmission delay according to the latest parameters.

[0080] The specific steps and processes are as follows: 1) Initial access: When the terminal starts access, it sends a random access request using predetermined initial retransmission parameters (such as minimum retransmission interval and basic retransmission probability).

[0081] 2) Collision Detection and Feedback: The base station detects the received random access signals. If a collision is detected (through signal superposition, missing confirmation, etc.), the collision information is immediately recorded, and the current time slot network load and collision rate are calculated. If the collision rate exceeds a preset threshold, the base station broadcasts a target retransmission parameter to all terminals via feedback signaling, such as increasing the retransmission waiting interval.

[0082] 3) Adaptive parameter calculation: Each terminal calculates parameters based on the received feedback and locally stored historical collision information using an adaptive function. Calculate the new retransmission parameters:

[0083] Retransmission interval calculation:

[0084] in, Based on the retransmission interval, For adjustment coefficients, This represents the recent number of collisions or the probability of collisions, i.e., historical collision information.

[0085] Retransmission probability adjustment: Adjust the retransmission probability based on the current network load. (For example, reducing the probability of active retransmission under network overload conditions to randomize the terminal retransmission time), the adjustment process is as follows:

[0086] (1) Basic probability and upper and lower limits: First, define a basic retransmission probability interval. [ p min , p max ] ,ensure It should not be too large (causing congestion) nor too small (preventing timely retransmission).

[0087] (2) Probability adjustment function: It adopts a function form that combines "negative feedback + positive feedback":

[0088]

[0089] in: Initial retransmission probability (system preset) This is the adjusted retransmission probability. Network load suppression factor; the higher the load, the lower the network load. The smaller, the lower . Collision suppression coefficient; the more collisions, the lower the coefficient. The smaller, the lower. . Channel gain encouragement factor, the better the channel ( (the larger), multiplied by promote This allows for earlier retransmission of channels with better quality. This indicates that the result will be truncated to... Within the range.

[0090] (3) Take typical values: If the network is very congested (λ is large) or there are multiple consecutive collisions (C increases), then both exponents tend to 0, making Extend the retransmission wait time. If the channel quality is very good (h is large), then... This has a positive boosting effect, allowing high-quality links to complete retransmissions faster. Under normal and moderate load conditions, Then in The transitions between them are smooth.

[0091] 4) After determining the target retransmission parameters using the adjustment function, the terminal re-initiates the access request. This process will be delayed incrementally based on the number of retransmissions for each failure, until successful access is achieved or the maximum number of retransmissions is reached.

[0092] 5) Continuous adjustment and closed-loop feedback: The base station continuously monitors the retransmission success rate and network load of the entire system, and updates the new statistical data to each terminal in a timely manner through the feedback mechanism, so that the adaptive mechanism can form a closed-loop control and dynamically adjust and optimize the overall access performance.

[0093] In this embodiment, adaptively adjusting the retransmission interval or retransmission probability effectively reduces network congestion caused by simultaneous retransmissions from a large number of terminals, smoothing out sudden traffic spikes. Parameters are dynamically adjusted based on channel conditions and historical collision data, enabling the system to maintain efficient access capabilities under varying loads. Because a closed loop is formed between the base station and terminals through a feedback mechanism, the overall network responds in real-time to environmental changes, continuously optimizing the retransmission strategy.

[0094] In one embodiment, the base station is also used to control the first terminal to transmit data simultaneously at different power levels in the same time slot and the same frequency band.

[0095] The core of the power-domain non-orthogonal multiple access (NOMA) strategy lies in utilizing the differences in transmit power among different terminals to achieve overlapping transmission of multiple user signals within the same time slot and frequency band through precise power control. The specific approach is as follows:

[0096] Power tiering design: During the access phase, power is allocated to each terminal based on factors such as channel conditions and distance between the terminal and the base station. Typically, terminals with poor channel conditions or longer distances are allocated higher transmit power, while terminals with better channel conditions or shorter distances use lower transmit power.

[0097] Overlapping signal transmission: By utilizing the aforementioned power differences, even if different terminals transmit data simultaneously at the same time and in the same frequency band, the received signals will have significant differences in power, thus providing a basis for subsequent signal separation.

[0098] Interference Management: Unlike traditional orthogonal multiple access technology, it allows overlapping signal transmission through power domain NOMA, while using advanced detection and decoding algorithms at the base station to separate high-power and low-power signals, effectively suppressing mutual interference and improving spectrum utilization.

[0099] The technical implementation steps are as follows:

[0100] 1) Terminal power allocation: Based on real-time channel estimation results (e.g., channel gain, noise level, etc.), the base station issues corresponding power control commands to each terminal. The terminal adjusts its transmit power according to the signaling to ensure that the signals transmitted by each terminal within the same resource unit have a clear power hierarchy, that is, data is transmitted simultaneously at different power levels.

[0101] 2) Signal superposition transmission: Orthogonal frequency division multiple access (OFDM) and other modulation schemes are adopted, and each terminal transmits signals simultaneously on shared time and frequency resources.

[0102] Multiple terminal signals are naturally superimposed during transmission, and the base station receives the mixed signal, in which each signal exhibits different intensities depending on its transmission power.

[0103] 3) Initial processing at the receiving end: The base station uses technologies such as multiple antennas and massive MIMO to initially capture and amplify overlapping signals, maintain signal quality, and provide sufficient signal-to-noise ratio advantage for subsequent signal decoupling.

[0104] In this embodiment, the power domain NOMA strategy enables multiple users to coexist in the same time slot, significantly improving the efficiency of spectrum resource utilization.

[0105] In one embodiment, the base station is further configured to sample, amplify, and perform preliminary filtering on the mixed signals received from multiple terminals to obtain a target signal; the target signal includes power distribution information.

[0106] The base station is also used to perform an interference cancellation process; the interference cancellation process includes sorting the target signals according to the power distribution information to obtain a first sorting result, determining the target signals with power greater than a preset power in the first sorting result, and decoding the target signals starting from the signal with the highest power in the target signals to obtain decoding information;

[0107] The base station is also used to subtract the contribution of the decoded signal corresponding to the decoded information from the mixed signal to obtain the first residual signal;

[0108] The base station is also used to perform joint multi-user detection on the first residual signal and sequentially perform interference cancellation process on signals in the first residual signal whose power is less than a preset power.

[0109] The decoding information may include the user data content carried by the strongest signal, such as the service data and control commands transmitted by the user, as well as auxiliary information related to the signal, such as modulation and demodulation parameters and encoding verification results, which are used to characterize the effectiveness of the signal decoding.

[0110] Joint detection and multi-user decoding are key technologies for fine processing of overlapping signals at the base station. Their basic principle is to utilize the power differences and channel information between different terminals in the received signal, employing joint detection and advanced interference cancellation algorithms to achieve parallel processing and decoding of multiple user signals. This mainly includes the following:

[0111] Joint multi-user detection: The base station uses joint detection algorithms (such as maximum likelihood detection, sequential interference cancellation (SIC)) to treat all received signals as a whole and process them jointly, thereby accurately separating the signal of each user by utilizing the power and channel differences between the signals.

[0112] Interference cancellation and reconstruction: During the detection process, high-power signals are first decoded to eliminate their interference with subsequent signals, and then low-power signals are detected and decoded sequentially. In this way, by gradually eliminating interference, an effective multi-user decoding chain is formed, thereby reducing the bit error rate and improving the overall system performance.

[0113] The technical implementation steps are as follows:

[0114] 1) Signal preprocessing: The base station samples, amplifies, and performs preliminary filtering on the received mixed signal to ensure the preservation and differentiation of information from each terminal within the signal. Multi-antenna technology is used to acquire channel state information for each user, providing a foundational input for subsequent joint detection.

[0115] 2) Power sorting and preliminary decoding: Based on the power distribution information obtained in the preprocessing stage, the received signals are sorted to determine the signals with higher power. The strongest signal is decoded first to obtain preliminary data decision results.

[0116] 3) Interference cancellation and signal reconstruction: The contribution of the decoded signal is subtracted from the mixed signal to obtain the residual signal. Joint multi-user detection is then performed on the residual signal, and the same decoding and interference cancellation process is applied to subsequent signals with lower power.

[0117] 4) Joint Multi-User Detection Algorithm Optimization: Algorithm optimization techniques, such as iterative detection and multi-layer decision merging, are introduced to further reduce errors in the signal separation process and improve decoding accuracy. The joint detection strategy is dynamically adjusted based on specific network load and terminal conditions to achieve optimal multi-user decoding performance.

[0118] Model and simulation verification: The received mixed signal is modeled as a multi-user superposition model. By setting the power hierarchy and channel matrix, the joint multi-user detection is theoretically analyzed.

[0119]

[0120] in, Indicates the first Channel gain of each terminal, Its transmission power, For transmitting signals, This is the noise term.

[0121] In this embodiment, the combined multi-user detection and multi-user decoding technologies effectively distinguish overlapping signals while achieving low bit error rate and high-quality data recovery through gradual interference cancellation. Combined with dynamic power control and joint detection optimization, stable and efficient signal separation can be achieved even in complex channel environments, providing a reliable guarantee for large-scale mMTC access.

[0122] In one embodiment, the system further includes edge computing nodes deployed within the coverage area of ​​each base station;

[0123] The base station is also used to divide radio resources into continuous time slots in the time domain and multiple subcarriers in the frequency domain based on OFDM technology, forming a two-dimensional time-frequency resource grid, and then dividing the two-dimensional time-frequency resource grid into multiple resource blocks; wherein each resource block serves as the smallest unit for scheduling.

[0124] Edge computing nodes are used to allocate resource blocks to terminals that request access based on historical data, local resource usage, and terminal needs, while reserving redundant resources.

[0125] Edge computing nodes are used to dynamically update resource usage based on local terminal traffic, channel status changes, and interference levels through a distributed scheduling algorithm, and to reschedule resource blocks within the coverage area to which the edge computing node belongs.

[0126] Edge computing nodes are used to migrate or reallocate resource blocks in collaboration with each other in the event of sudden traffic or channel degradation in various coverage areas.

[0127] Edge computing nodes are used to evaluate each resource scheduling session to obtain resource scheduling evaluation results; the resource scheduling evaluation results include success rate, latency, and bit error rate indicators;

[0128] Edge computing nodes are used to optimize distributed scheduling algorithms based on resource scheduling evaluation results.

[0129] In massive machine-type communication (mMTC) scenarios, due to the large number of terminals and the dispersed communication needs, traditional centralized scheduling often suffers from problems such as high latency, low scheduling efficiency, and the formation of single-point bottlenecks. To address this, this solution uses OFDM technology to divide the overall time-frequency resources into multiple fine-grained resource blocks. Through the collaborative work of distributed scheduling algorithms and edge computing nodes, it achieves precise and dynamic management of resources.

[0130] The overall design concept of this embodiment includes:

[0131] Time-frequency resource block subdivision: With the help of OFDM technology, radio resources are subdivided in two dimensions, time and frequency, to form a two-dimensional resource grid, with each resource block serving as the smallest unit for scheduling.

[0132] Distributed scheduling architecture: Scheduling modules are deployed on each base station and its edge computing nodes, each responsible for real-time monitoring and allocation of local area resources, and dynamically adjusted through regional coordination mechanisms to avoid single-point delays and bottlenecks caused by centralized scheduling.

[0133] Real-time feedback and dynamic adjustment: The entire system achieves low-latency feedback through edge computing nodes, monitors network load, channel status and terminal access status in real time, dynamically optimizes resource allocation strategies and adjusts scheduling parameters to ensure that the entire network operates in a highly efficient and balanced state at all times.

[0134] The time-frequency resource block subdivision technology includes the following:

[0135] 1) Basic principle of OFDM: OFDM (Orthogonal Frequency Division Multiplexing) decomposes a broadband signal into multiple orthogonal subcarriers, each carrying a portion of data. Because the subcarriers are orthogonal, they can be transmitted in parallel at the same time without interference, thereby improving spectrum utilization.

[0136] Time-frequency domain resource partitioning: Utilizing the OFDM frame structure, the time domain is divided into continuous time slots, and the frequency domain is divided into multiple subcarriers. These are combined to form a two-dimensional time-frequency resource grid, with each small unit called a resource block. For example, a resource block may consist of several consecutive subcarriers within a specific time slot, serving as the basic unit of scheduling.

[0137] Flexible scheduling strategy: Different terminals can request different amounts or locations of resources in resource blocks due to different service requirements and channel conditions. Fine-grained resource block partitioning enables base stations to perform precise scheduling for different terminals, which can meet the access needs of high-density terminals while reducing the impact on channel interference.

[0138] 2) Modeling: Assume there are a total of M time slots and N subcarriers in the OFDM system, forming M×N basic resource units. Each resource block can be represented by a two-dimensional matrix, where each element... Indicates the first Time slot number Available resources on subcarriers. The resource scheduling problem can be transformed into a dynamic allocation problem of resource elements in this time-frequency matrix, so that each terminal obtains the optimal allocation under its transmission requirements, channel state, and interference constraints.

[0139] Therefore, the following optimization objective can be established:

[0140]

[0141] in, Obtain resource blocks for terminal k The resulting utility function must also satisfy constraints, such as interference constraints, power limits, and time delay requirements.

[0142] Resource block partitioning: Each resource block typically contains several time slots and subcarriers, serving as a scheduling unit for terminal allocation. Fine-grained resource partitioning allows for higher scheduling precision, better matching terminal requirements.

[0143] The distributed scheduling architecture and collaborative scheduling technology are as follows:

[0144] 1) Distributed scheduling architecture design, which includes the following:

[0145] Edge computing node deployment: Edge computing nodes are deployed in the coverage area of ​​each base station. Each node collects local terminal access information, channel status, traffic load and other data in real time, and has independent data processing and local scheduling capabilities.

[0146] Local scheduling and global collaboration: Each edge node is responsible for the real-time allocation of resource blocks within its jurisdiction and responds quickly to sudden traffic surges. Simultaneously, nodes share resource usage and scheduling information through high-speed links and collaborative mechanisms, achieving balanced global resource scheduling and avoiding resource waste or interference caused by excessive load on a single node.

[0147] Avoiding single-point bottlenecks: The distributed scheduling structure disperses centralized scheduling to various edge nodes, effectively reducing the latency and failure risks caused by centralized management at a single point, and using regional coordination mechanisms to ensure the continuity and stability of the overall network scheduling.

[0148] 2) The implementation steps of the distributed scheduling algorithm are as follows:

[0149] Resource partitioning and initial allocation: Based on historical data and prediction algorithms, the time-frequency resource blocks within each base station area are initially partitioned, and a resource allocation plan is formulated. Each edge node initially allocates resource blocks to terminals requesting access based on local resource usage and terminal needs, while reserving a certain amount of redundant resources to cope with sudden requests.

[0150] Real-time dynamic monitoring and feedback mechanism: Each edge computing node monitors local terminal traffic, channel status changes, and interference levels in real time, and uploads the monitoring data to the regional scheduling coordinator. Based on the feedback information, the distributed scheduling algorithm within the node dynamically updates resource usage and performs rescheduling within a local area.

[0151] Regional coordination and cross-node scheduling: Neighboring base stations or edge nodes exchange load and resource usage information through a coordinated scheduling protocol, and adjust resource allocation in real time. When a sudden traffic surge or channel degradation occurs in a certain area, neighboring nodes can coordinate to migrate or reallocate resource blocks to ensure balanced resource utilization across regions and reduce interference risks.

[0152] Closed-loop feedback and scheduling optimization: After each scheduling decision, each node evaluates the resource scheduling effect and records key performance indicators such as success rate, latency, and bit error rate. This data is used to continuously train and optimize the distributed scheduling algorithm, forming an online feedback closed loop to continuously improve the adaptive scheduling strategy.

[0153] In this embodiment, the fine-grained partitioning of time-frequency resource blocks allows for precise allocation based on the actual needs of the terminals, improving spectrum utilization and overall system throughput. The distributed scheduling architecture, leveraging edge computing nodes, significantly reduces local resource scheduling response time, enabling rapid resource allocation adjustments even under sudden traffic surges to ensure service quality. The distributed and collaborative mechanisms distribute the scheduling load, avoiding single-point-of-failure risks and achieving global resource balancing through cross-base station cooperation, enhancing overall system stability. The closed-loop feedback mechanism allows the system to perceive changes in network load and channel conditions in real time, dynamically adjusting scheduling strategies to achieve proactive and intelligent resource management, further reducing energy consumption and latency.

[0154] In one embodiment, the system further includes a terminal;

[0155] The terminal is used to perform pilot measurements using the received reference signal and pilot signal before transmission to obtain channel parameters, including the current channel gain and the level of surrounding interference.

[0156] The terminal is used to receive interference information and current noise levels of the local network sent by the base station or edge node, so that the terminal can obtain local environmental information.

[0157] The terminal is used to calculate a new transmit power value based on channel parameters and local environment information, and compare the new transmit power value with preset upper and lower limits.

[0158] The terminal is configured to adjust the new transmission power value to the preset upper limit if the new transmission power value exceeds the preset upper limit; adjust the new transmission power value to the preset lower limit if the new transmission power value is lower than the preset lower limit; and maintain the new transmission power value if the new transmission power value is within the range of the preset upper and lower limits.

[0159] To reduce interference from neighboring cells and co-channel transmissions by a large number of terminals on the same frequency band, this solution proposes a distributed power control mechanism based on local feedback information. This algorithm allows each terminal to automatically adjust its transmit power before transmitting data, based on its real-time channel environment, received interference level, and target signal-to-noise ratio (SINR) requirements. This ensures transmission quality while effectively reducing interference to other terminals and neighboring cells.

[0160] The technical principle of this embodiment is as follows:

[0161] Basic model: Assuming the system has a total of Each terminal has a channel gain with the base station. ,terminal The transmission power is At the receiving end, the signal is affected by interference from other terminals and background noise. The impact. Terminal The received signal-to-noise ratio (SINR) can be described as:

[0162]

[0163] Objectives and constraints: Each terminal needs to meet the preset minimum SINR requirement. At the same time, it's undesirable for excessively high transmit power to increase interference within the system. The problem can be modeled as a distributed optimization problem, with the goal of adjusting the transmit power of all terminals. To satisfy:

[0164]

[0165] And while satisfying the overall system interference constraints, the power of each terminal is made possible. Keep them as low as possible (or balance them within acceptable limits) to avoid wasting resources and reduce mutual interference.

[0166] Iterative update formula: Similar to the Foschini-Miljanic power control algorithm, each terminal can use the following iterative update formula:

[0167]

[0168] in, and These represent the minimum and maximum allowable transmit powers for the terminal, respectively. This formula dynamically adjusts the power of each terminal using local feedback (such as currently received interference information and its own channel conditions) to achieve a stable state.

[0169] The implementation steps are as follows:

[0170] 1) Channel state measurement and feedback

[0171] Measurement: Before transmission, the terminal measures the current channel gain by receiving reference signals, pilot signals, etc. And the level of surrounding interference.

[0172] Feedback: Base stations or edge nodes feed back local network interference information and current noise levels to the terminals, enabling each terminal to obtain accurate local environmental information.

[0173] 2) Power update calculation

[0174] Based on the received local channel parameters and feedback information, the terminal calculates a new transmit power value using the iterative formula described above. The terminal compares the newly calculated power value with preset upper and lower limits to ensure that it does not exceed the allowable range.

[0175] 3) Iterative adjustment and adaptive optimization

[0176] The terminals continuously repeat the above measurement and update process, gradually approaching a stable equilibrium point under dynamic system changes. This point ensures that each terminal's transmit power meets its own communication quality requirements while avoiding excessive interference with other terminals. Through closed-loop control, the entire network can adaptively adjust power allocation under different load and channel variations.

[0177] In this embodiment, each terminal calculates independently based on local information. Distributed decision-making reduces the central processing load and communication latency. By dynamically adjusting the transmission power, overall network interference is reduced, improving overall system performance and signal quality. This distributed implementation is suitable for large-scale terminal scenarios, easily integrated with other intelligent scheduling algorithms, and readily scalable.

[0178] In one embodiment, the base station is also used to acquire multiple received signals through multiple antennas, acquire channel state information of each terminal using pilot signals and channel estimation techniques, and establish a matrix containing channel information of each terminal in the cell and neighboring cells as input for joint detection.

[0179] The base station is also used to sort the received signals by power to obtain a second sorting result, and to start decoding from the signal with the highest power in the second sorting result;

[0180] The base station is also used to subtract the contribution of the successfully decoded received signal from multiple received signals to obtain a second residual signal, and to use a joint detection algorithm to decode the second residual signal sequentially until all terminal information is recovered.

[0181] Base stations are also used in multi-base station deployment scenarios to share some channel state information and interference information through high-speed networks and to jointly perform signal processing using a collaborative scheduling mechanism;

[0182] The base station is also used to monitor the bit error rate and signal-to-noise ratio during the decoding process. If the bit error rate is greater than the preset bit error rate and / or the signal-to-noise ratio is less than the preset signal-to-noise ratio, the parameters of the filters in the base station are adjusted or the joint detection algorithm is updated. The adjustment process is carried out in coordination within a single base station and across base stations to optimize the system interference management strategy.

[0183] The basic principle of this embodiment is as follows:

[0184] In densely deployed environments, simultaneous access by multiple terminals can lead to base stations receiving multiple overlapping signals, causing interference. To address this, this solution incorporates a cooperative interference cancellation technique at the base station side. This technique fully leverages the collaborative processing capabilities of multiple antennas and the base station to jointly detect and eliminate interfering signals. The solution employs a joint multi-user detection and interference suppression algorithm, utilizing spatial and channel dimensions to differentiate signals from different terminals.

[0185] The technical principles and model are as follows:

[0186] 1) Joint signal model: The composite signal received by the base station can be represented as:

[0187]

[0188] in, For the first Channel vectors of each terminal, Its transmission power, For transmitting symbols, This is the noise vector.

[0189] 2) Cooperative Interference Cancellation: Through multi-user joint detection, base stations can employ step-by-step interference cancellation algorithms, such as Successive Interference Cancellation (SIC) or Joint Detection (JD) techniques. The basic idea is:

[0190] Sorting: First, sort the received signals according to their received power, and prioritize decoding signals with higher power.

[0191] Signal decoding and subtraction: For signals that are ranked first, they are first decoded, and then the contribution of the decoded signals is subtracted from the total received signals, thereby reducing their interference with other signals.

[0192] Iterative detection: Repeat the above process for the remaining signals until all signals are decoded.

[0193] Linear filters and minimum mean square error (MMSE): In addition to nonlinear SIC, MMSE filters can also be used to construct the filter matrix during joint interference cancellation. This ensures that the filtered output satisfies:

[0194]

[0195] Wherein, the filter vector This is achieved by minimizing interference and noise power, thereby realizing optimal signal separation.

[0196] The specific implementation steps and process are as follows:

[0197] 1) Real-time channel monitoring: The base station receives signals through multiple antennas and uses pilot signals and channel estimation techniques (such as least squares method or maximum likelihood estimation) to obtain the channel state information (CSI) of each terminal. For the access signals of the cell and neighboring cells, the base station establishes a matrix containing the channel information of each terminal, which is used as the input for joint detection.

[0198] 2) Signal sorting and preliminary decoding: The received signals are sorted according to their power levels to determine the decoding order, usually starting with the strongest signal. For higher power signals, traditional modulation and decoding methods are used directly to complete the preliminary decoding.

[0199] 3) Interference cancellation and joint detection: For the decoded signal, its contribution is subtracted from the total received signal to form the residual signal.

[0200]

[0201] in, This is the set of terminals that have been successfully decoded. The decoding result is then obtained. On the remaining signal, a joint detection algorithm (such as MMSE, SIC, or joint maximum likelihood detection) is used to decode the remaining signal sequentially until all terminal information is recovered.

[0202] 4) Collaborative Processing and Information Sharing: In multi-base station deployments, base stations share some CSI and interference information through high-speed networks to jointly optimize joint detection results. The collaborative scheduling mechanism allows multiple base stations to jointly process signals, improving overall interference suppression capabilities in densely populated areas.

[0203] 5) Dynamic Adjustment and Feedback: Key indicators such as bit error rate and signal-to-noise ratio are monitored during the decoding process. If interference cancellation is found to be ineffective, filter parameters are adjusted or the joint detection algorithm is updated. Feedback adjustments are not only performed within a single base station but also through cross-base station collaboration to further optimize the interference management strategy of the entire system.

[0204] In this embodiment, decoding accuracy is improved: joint detection and interference cancellation techniques can effectively distinguish overlapping signals, gradually reduce multi-user interference, and thus significantly reduce the bit error rate. Stability is enhanced in densely deployed environments: through real-time monitoring, CSI sharing, and collaborative processing, base stations can more effectively cope with complex and changing interference environments, achieving overall network stability. It flexibly adapts to different scenarios: whether in indoor cells or densely covered areas by macro base stations, this technology can dynamically adjust detection and cancellation parameters according to specific channel conditions to achieve optimal interference management.

[0205] By employing distributed power control algorithms and cooperative interference cancellation techniques, this solution achieves the following objectives: Each terminal adaptively adjusts its transmit power based on the local environment, satisfying its own communication needs while minimizing interference to other users. At the base station level, joint detection and multi-base station collaboration effectively separate overlapping signals through iterative interference cancellation, significantly improving decoding success rate and data transmission quality in densely deployed terminal scenarios. Through closed-loop feedback and dynamic adaptive adjustment, the system maintains consistently high-efficiency and stable communication performance in the face of changes in network load and channel conditions, providing strong technical support for large-scale machine-type communication environments. These complementary technologies together constitute a comprehensive solution for dynamic power control and interference coordination, providing practical interference management and channel optimization strategies for large-scale, densely deployed wireless networks.

[0206] In one embodiment, the system also includes a data analysis platform;

[0207] The data analysis platform is used to process and analyze monitoring data in real time. When abnormal monitoring data is detected, it sends feedback data to the scheduling and control module in the base station.

[0208] The scheduling and control module is used to receive feedback data and dynamically adjust resource allocation, power control, and access scheduling strategies based on the current network status and prediction models.

[0209] The design goals and basic ideas of this embodiment are as follows:

[0210] To achieve dynamic scheduling and intelligent optimization, the system needs to be able to monitor network operation status in real time and collect and analyze key indicators (such as terminal access status, channel interference, and node load) in real time. Based on end-to-end monitoring data, a closed-loop feedback mechanism is established to enable dynamic adjustment of scheduling strategies, ensuring the system is always in optimal operating condition. Design objectives include:

[0211] Real-time performance: Comprehensive monitoring of the status of all layers of the network to ensure that faults and performance bottlenecks can be detected as soon as possible.

[0212] Closed-loop feedback: Establish an automatic feedback system to transmit monitoring data to the scheduling and control module in real time, enabling dynamic policy updates.

[0213] Adaptive adjustment: Through data analysis and predictive models, the parameters of resource allocation, power control, and scheduling algorithms are adjusted to adapt to changes in the external environment.

[0214] The key implementation steps are as follows:

[0215] 1) Data Analysis and Alarm Mechanism: Utilize a data analysis platform (e.g., based on big data or machine learning platforms) to perform real-time processing and trend analysis on monitoring data. Set key indicator thresholds; when abnormal fluctuations or exceeding preset thresholds are detected, the system automatically triggers an alarm and notifies the relevant scheduling module to intervene.

[0216] 2) Closed-loop feedback and scheduling strategy adjustment: The scheduling control module receives real-time feedback data and dynamically adjusts resource allocation, power control, and access scheduling strategies based on the current network status and prediction models. A control feedback loop is established to achieve adaptive adjustment of system parameters. For example, when congestion or a sharp increase in interference is detected in a certain area, the resource quota for that area is automatically increased or the power control parameters are adjusted.

[0217] 3) Continuous monitoring and optimization: The monitoring platform is not only used for real-time scheduling, but also for storing historical data for offline analysis and model training. As data accumulates, the system gradually optimizes the prediction model and feedback algorithm, achieving more precise control over the long-term network operation status.

[0218] The model and algorithm in this embodiment are as follows:

[0219] Real-time monitoring model: A monitoring network is formed by distributed monitoring nodes. Each node is responsible for collecting local data and uploading the data to the central data platform through a dedicated protocol.

[0220] Closed-loop feedback control algorithm: Establish a state feedback control model, and adjust scheduling parameters based on monitoring data using PID control, fuzzy control, or machine learning-based adaptive algorithms to achieve optimal control output.

[0221] .

[0222] in, This indicates the error between the monitored indicator and the expected target. It is a dynamically adjusted control output.

[0223] In this embodiment, the real-time monitoring platform ensures that faults and performance degradation are quickly detected, and the system can automatically trigger scheduling adjustments to reduce the risk of fault propagation and respond to anomalies promptly. The closed-loop feedback mechanism enables the system to continuously and adaptively adjust various parameters based on the latest data, achieving efficient dynamic scheduling and resource optimization. Abundant monitoring data not only improves the accuracy of real-time decision-making but also provides a solid data foundation for subsequent optimization, prediction, and model training.

[0224] In one embodiment, multiple edge nodes are deployed at the edge computing layer to form a local cluster for mutual backup, and redundant scheduling servers are configured at the core scheduling layer. The nodes are interconnected through a high-speed local area network or dedicated channel and a data synchronization and state sharing mechanism is established. The distributed collaborative working mode and consensus algorithms such as Paxos or Raft are used to ensure data synchronization and state consistency between backup nodes, so that when a node fails, other nodes can quickly take over the work tasks.

[0225] In communication networks, multiple backup links are set up for critical data paths. Redundant transmission protocols are used to monitor the health status of the links. A link quality assessment function based on latency, packet loss rate, and bandwidth indicators is introduced. When an increase in the latency of the main link or abnormal packet loss is detected, the system automatically switches to the backup link for transmission based on real-time link quality indicators through dynamic assessment algorithms, threshold control, and dynamic switching mechanisms, thereby achieving automatic routing adjustment.

[0226] Redundancy mechanisms are introduced at the terminal layer, edge computing layer, and core scheduling layer, respectively. Stress tests and fault injection simulations are conducted on the fault tolerance capabilities of nodes at each level to evaluate the system's recovery latency and data consistency when nodes fail or links switch. Based on the test results, the redundancy design and automatic switching algorithm are optimized, and emergency response plans are formulated.

[0227] This embodiment introduces a multi-layered fault tolerance mechanism. The design concept of the multi-layered fault tolerance mechanism is as follows:

[0228] In large-scale machine-type communication networks, individual communication nodes (such as terminals, edge computing nodes, and core scheduling nodes) may experience failures or delays due to hardware malfunctions, environmental interference, or network congestion. To ensure high stability and continuous operation of the entire system, this solution introduces a multi-layered redundancy design in the system architecture, configuring backup nodes and redundant links at different levels. Key design concepts include:

[0229] Distributed backup: Multiple backup nodes are set up at the edge computing layer and the core scheduling layer respectively. The nodes work in a distributed manner, so that even if one node fails, the other nodes can quickly take over the work tasks.

[0230] Redundant link design: Redundant communication links are constructed through multiple data transmission paths. When the main link is abnormal or congested, the backup link can automatically switch to ensure the real-time transmission of data and control signals.

[0231] Hierarchical fault tolerance: Redundancy mechanisms are introduced at different levels such as terminals, edges, and cores, enabling the entire system to have fault tolerance capabilities from local to global, ensuring the overall stability of the system.

[0232] The key implementation steps are as follows:

[0233] Backup node deployment: Multiple edge nodes are deployed at the edge computing layer, providing mutual backup through local clustering; redundant scheduling servers are configured at the core scheduling layer to ensure that scheduling tasks are not interrupted due to single node failure. Nodes are interconnected via high-speed local area networks or dedicated channels, establishing a data synchronization and state sharing mechanism between nodes.

[0234] Redundant Link Construction and Automatic Switching: In the communication network, multiple backup links are set up for critical data paths, employing redundant transmission protocols to monitor link health. When an increase in primary link latency or abnormal packet loss is detected, automatic switchover to the backup link is initiated. The link switching strategy uses a dynamic evaluation algorithm to determine the optimal transmission path based on real-time link quality metrics (such as latency, bandwidth, and bit error rate) to ensure communication quality.

[0235] Fault tolerance mechanism verification and contingency plan: Stress tests and fault injection simulations are conducted on the fault tolerance capabilities of nodes at all levels to evaluate the system's recovery latency and data consistency during node failures and link switching. Based on the test results, redundancy design and automatic switching algorithms are continuously optimized, and contingency response plans are developed to ensure rapid system recovery under large-scale failure scenarios.

[0236] The model and principle are as follows:

[0237] Multi-node backup model: Utilizing distributed system theory, this model treats each node as a whole and uses consensus algorithms (such as Paxos or Raft) to ensure data synchronization and state consistency among backup nodes. When the primary node fails, other nodes can quickly become the new master unit.

[0238] Link redundancy and handover model: Introducing a link quality assessment function (For example, based on metrics such as latency, packet loss rate, and bandwidth), automatic route adjustment is achieved by comparing the quality values ​​of the primary link and the backup link, using threshold control and dynamic switching mechanisms.

[0239] .

[0240] In this embodiment, when a node failure or link problem occurs, backup nodes and redundant links can quickly take over the task, avoiding system interruption caused by a single point of failure and improving system reliability. Multi-level redundancy and distributed backup mechanisms enable the system to maintain the continuity of data and control signals, enhancing overall service quality. The modular redundancy architecture facilitates system expansion; as the network scales, new backup nodes and links can be added as needed to maintain fault tolerance.

[0241] In summary, through multi-layered fault tolerance mechanisms and real-time monitoring and feedback adjustment mechanisms, this solution achieves end-to-end redundancy and fault tolerance design from edge to core, and from data acquisition to intelligent scheduling. It effectively ensures the stability and continuous operation of the system in the event of node failures, link anomalies, and environmental changes, providing a practical and feasible anti-interference and security technology for large-scale machine-type communication networks.

[0242] In one embodiment, a data analysis platform is used to store historical data, which is used to optimize the prediction model. The historical data includes real-time monitoring data, alarm records, scheduling policy adjustment records, resource allocation results, power control parameters, access scheduling logs, and corresponding network performance index data generated during network operation.

[0243] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0244] Based on the same inventive concept, this application also provides a large-scale machine communication device for implementing the large-scale machine communication method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more embodiments of the large-scale machine communication device provided below can be found in the limitations of the large-scale machine communication method described above, and will not be repeated here.

[0245] Each module in the aforementioned large-scale machine-type communication device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware within or independently of the processor in a computer device, or stored in software within the memory of the computer device, so that the processor can invoke and execute the operations corresponding to each module.

[0246] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0247] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0248] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A large-scale machine-type communication system, characterized in that, The system includes: A base station is used to sample the received precoded signal at a preset sampling rate during random access by a terminal to form a measurement vector; The base station is used to design a measurement matrix for large-scale machine-type communication scenarios based on system characteristics; The base station is used to reconstruct the measurement vector based on the compressed sensing algorithm and the measurement matrix to obtain the terminal activation vector; The base station is used to determine the terminal information of the first terminal that has successfully accessed the network based on the terminal activation vector, and to adjust the retransmission parameters based on the terminal feedback information of the second terminal to obtain the target retransmission parameters. The target retransmission parameters are used for the second terminal to re-initiate random access based on the target retransmission parameters. The second terminal includes terminals that have failed to access the network due to terminal access signal failure or aliasing.

2. The system according to claim 1, characterized in that, The base station is configured to determine the target retransmission parameters based on the current network load, the current channel state, and historical collision information within the most recent preset time period using an adjustment function, and send the target retransmission parameters to the second terminal; the retransmission parameters are used by the second terminal to execute a retransmission process based on the retransmission parameters, and the retransmission parameters include retransmission probability and / or retransmission interval.

3. The system according to claim 1 or 2, characterized in that, The base station is also used to control the first terminal to transmit data simultaneously at different power levels in the same time slot and frequency band.

4. The system according to claim 1 or 2, characterized in that, The base station is also used to sample, amplify, and perform preliminary filtering on the mixed signals received from multiple terminals to obtain a target signal; the target signal includes power distribution information. The base station is also used to perform an interference cancellation process; the interference cancellation process includes sorting the target signals according to the power distribution information to obtain a first sorting result, determining the target signals with power greater than a preset power in the first sorting result, and decoding the target signals starting from the signal with the highest power in the target signals to obtain decoding information; The base station is further configured to subtract the contribution of the decoded signal corresponding to the decoded information from the mixed signal to obtain a first residual signal; The base station is further configured to perform joint multi-user detection on the first residual signal, and sequentially perform the interference cancellation process on signals in the first residual signal whose power is less than the preset power.

5. The system according to claim 1, characterized in that, The system also includes edge computing nodes deployed within the coverage area of ​​each base station; The base station is also used to divide wireless resources into continuous time slots in the time domain and multiple subcarriers in the frequency domain based on OFDM technology to form a two-dimensional time-frequency resource grid, and to divide the two-dimensional time-frequency resource grid into multiple resource blocks. The edge computing node is used to allocate resource blocks to terminals that apply for access based on historical data, local resource usage and terminal needs, and to reserve redundant resources. The edge computing node is used to dynamically update resource usage based on local terminal traffic, channel state changes, and interference levels using a distributed scheduling algorithm, and to reschedule resource blocks within the coverage area to which the edge computing node belongs. The edge computing nodes are used to perform resource block migration or reallocation collaboratively in the event of sudden traffic or channel degradation in each of the coverage areas. The edge computing node is used to evaluate each resource scheduling to obtain a resource scheduling evaluation result; the resource scheduling evaluation result includes indicators such as success rate, latency, and bit error rate. The edge computing node is used to optimize the distributed scheduling algorithm based on the resource scheduling evaluation results.

6. The system according to claim 1 or 2, characterized in that, The system also includes a terminal; The terminal is used to perform pilot measurement using the received reference signal and pilot signal before transmission to obtain channel parameters; the channel parameters include the current channel gain and the surrounding interference level. The terminal is used to receive interference information and current noise level of the local network sent by the base station or edge node, so as to enable the terminal to obtain local environmental information. The terminal is configured to calculate a new transmit power value based on the channel parameters and the local environment information, and compare the new transmit power value with preset upper and lower limits. The terminal is configured to adjust the new transmission power value to the preset upper limit if the new transmission power value exceeds the preset upper limit; adjust the transmission power value to the preset lower limit if the new transmission power value is lower than the preset lower limit; and maintain the new transmission power value if the new transmission power value is within the range of the preset upper and lower limits.

7. The system according to claim 1 or 2, characterized in that, The base station is also used to acquire multiple received signals through multiple antennas, acquire channel state information of each terminal using pilot signals and channel estimation technology, and establish a matrix containing channel information of each terminal in the cell and neighboring cells as input for joint detection. The base station is further configured to sort the received signals according to their power to obtain a second sorting result, and to start decoding from the signal with the highest power in the second sorting result; The base station is further configured to subtract the contribution of the successfully decoded received signals from the plurality of received signals to obtain a second residual signal, and to use a joint detection algorithm to sequentially decode the second residual signal until all terminal information is recovered. The base station is also used in multi-base station deployment scenarios to share some channel state information and interference information through a high-speed network and to jointly perform signal processing using a collaborative scheduling mechanism. The base station is also used to monitor the bit error rate and signal-to-noise ratio during the decoding process. If the bit error rate is greater than the preset bit error rate and / or the signal-to-noise ratio is less than the preset signal-to-noise ratio, the parameters of the filter in the base station are adjusted or the joint detection algorithm is updated. The adjustment process is carried out collaboratively within a single base station and across base stations to optimize the system interference management strategy.

8. The system according to claim 1 or 2, characterized in that, The system also includes a data analysis platform; The data analysis platform is used to process and analyze the monitoring data in real time. When an anomaly is detected in the monitoring data, it sends feedback data to the scheduling and control module in the base station. The scheduling control module is used to receive the feedback data and dynamically adjust resource allocation, power control, and access scheduling strategies based on the current network status and prediction model.

9. The system according to claim 8, characterized in that, The data analysis platform is used to store historical data, which is used to optimize the prediction model. The historical data includes real-time monitoring data, alarm records, scheduling strategy adjustment records, resource allocation results, power control parameters, access scheduling logs, and corresponding network performance index data generated during network operation.

10. A large-scale machine-type communication method, characterized in that, The method includes: During random access to the terminal, the received precoded signal is sampled at a preset sampling rate to form a measurement vector; Design a measurement matrix for large-scale machine-type communication scenarios based on system characteristics; Based on the compressed sensing algorithm and the measurement matrix, the measurement vector is reconstructed to obtain the terminal activation vector; Based on the terminal activation vector, the terminal information of the first terminal that successfully accessed is determined, and the retransmission parameters are adjusted based on the terminal feedback information of the second terminal to obtain the target retransmission parameters. Random access is then re-initiated based on the target retransmission parameters. The second terminal includes terminals that failed to access due to terminal access signal failure or aliasing.

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