A smart interference suppression method, device and electronic equipment

Abstract

The application discloses a smart interference suppression method and device and electronic equipment, and the method comprises the following steps: acquiring an observation signal, and constructing an observation matrix based on the observation signal; identifying a DCI scheduling subset based on the observation matrix; the DCI scheduling subset comprises key configuration parameters for guiding the UE to perform data transmission; determining the RBG granularity of the resource allocation list indicated by the CCE index according to the identification result of the DCI scheduling subset; and combining the accelerated proximal gradient algorithm and the RBG granularity to realize DCI smart interference suppression. Through extracting the RBG granularity of the resource allocation list indicated by the CCE index from the identification result of the DCI scheduling subset, the detection features can be effectively extracted, and the 5G communication protocol does not need to be modified. Through combining the accelerated proximal gradient algorithm and the RBG granularity obtained from the identification of the DCI scheduling subset, the received PUSCH data can be reconstructed with high precision to suppress the influence of the DCI smart interference.

Description

Technical Field

[0001] This invention belongs to the field of communication technology, and specifically relates to a clever interference suppression method, device and electronic equipment. Background Technology

[0002] Because the Downlink Control Information (DCI) transmitted in plaintext over the 5G New Radio (NR) air interface lacks integrity and security protection, intelligent attackers can launch targeted attacks, posing a significant security challenge to 5G NR air interface security. For example, attackers can use real-time 5G control channel sniffing tools to access system information, interaction configuration details, and search space indication parameters. They can then use the DCI details carried within to determine the specific resources and configurations for uplink data transmission, launching targeted, covert, and highly efficient interference attacks. Such DCI-smart interference prevents legitimate data packets from successfully reaching the base station, leading to network resource strain. Users may experience service interruptions, reduced data rates, or even temporary disconnections, severely impacting the reliability and quality of 5G services. The precise attack patterns of DCI-smart interference are more covert, posing a significant challenge to traditional security and anti-interference measures. 5G NR urgently needs to develop and implement effective countermeasures to combat and suppress DCI-smart interference.

[0003] Currently, countermeasures against smart jamming are mainly divided into two categories: smart jamming detection and smart jamming suppression. However, DCI smart jamming combines passive sniffing and active jamming, and can accurately attack PUSCH (Physical Uplink Shared Channel) data transmission. There is an urgent need for new defense mechanisms.

[0004] First, regarding smart interference detection techniques, A. Martinen et al. proposed a smart interference detection method based on packet transmission rate, where nodes detect attacks by comparing the percentage of transmission collisions monitored and tracked at a specific threshold. P. Zhou et al. proposed an energy-based detection method that identifies attacks by monitoring sudden changes in the energy of a specific physical layer channel. Building on this, M. Lichtman et al. tracked continuous decoding errors in the physical uplink control channel and monitored sudden changes in signal energy characteristics for attack detection. D. Ciuonzo et al. further extended the detection features to the second-order statistics of the received signal vector in sensor networks, assuming statistical channel information for non-line-of-sight fading components, and designed a suboptimal fusion rule to address the exponential complexity of the likelihood ratio test for interference detection in unknown distribution scenarios. P. Schniter et al. proposed an active attack detection method based on subspace dimension, finding the correct user signal subspace by extracting the subspace dimension of the signal covariance matrix, where the eigenvalue decomposition of the received covariance matrix contains eigenvectors corresponding to legitimate user eigenvalues ​​that can be used for dimension measurement.

[0005] Secondly, regarding smart interference suppression techniques, in scenarios where attackers launch active attacks by transmitting jamming or spoofing signals, adaptive receiver beamforming can significantly suppress the impact of jamming signals on the normal communication signal reception process and improve communication reliability. Physical layer authentication technology can use the physical characteristics of wireless channels and terminal devices to identify and authenticate device identities, thereby suppressing the adverse effects of spoofing signals on wireless communication systems. Secure waveform technology uses appropriate signal transformations to make it difficult for attackers to accurately extract signal features, effectively suppressing tampering attacks launched by attackers by injecting malicious information to replace or eliminate key information. Zeng H. et al. proposed a blind source separation interference suppression scheme for multiple-input multiple-output systems, using known pilot signals from both the transmitter and receiver to estimate the statistical characteristics of the received signal under interference attacks, thereby designing an optimal receiver to resist interference. Do TT et al. proposed a large-scale multiple-input multiple-output anti-jamming receiver, using unused pilots to estimate the jammer's channel, thereby combating malicious interference in cellular network uplink transmission.

[0006] However, existing smart interference detection technologies lack effective detection features for DCI smart interference. For example, detection techniques based on energy statistical features and higher-order signal statistics require significant storage and computational resources, and those based on higher-layer traffic features compromise real-time responsiveness. Given that DCI technical specifications are standardized in 5G NR, secure DCI transmission and detection will require modifications to the 5G communication protocol, which is difficult to implement in practice. Secondly, regarding smart interference suppression techniques, the enhanced ability of DCI smart interference attackers to sniff and obtain key configuration information makes it difficult to obtain inherent discriminative features that can effectively distinguish DCI smart interference to isolate its attack effects. This severely limits the mitigation effectiveness of existing physical layer authentication technologies and the feature hiding performance of secure waveform technologies. Furthermore, the high complexity of channel information estimation and PUSCH data target attacks by DCI smart interference attackers significantly reduces the separation and suppression accuracy of existing adaptive receiver beamforming and blind source separation technologies.

[0007] Therefore, how to provide a DCI smart interference suppression method with high accuracy and low implementation difficulty has become an important issue. Summary of the Invention

[0008] To address the aforementioned problems in the prior art, the present invention provides a clever interference suppression method, apparatus, and electronic device.

[0009] The technical problem to be solved by this invention is achieved through the following technical solution:

[0010] In a first aspect, the present invention provides a smart interference suppression method, the smart interference suppression method comprising:

[0011] Acquire the observation signals and construct an observation matrix based on the observation signals;

[0012] A DCI scheduling subset is identified based on the observation matrix; the DCI scheduling subset includes key configuration parameters used to guide the UE in data transmission.

[0013] The RBG granularity of the resource allocation list indicated by the CCE index is determined based on the DCI scheduling subset identification results.

[0014] The DCI smart interference suppression is achieved by combining the accelerated proximal gradient algorithm and the RBG granularity.

[0015] Optionally, identifying a subset of DCI scheduling based on the observation matrix includes:

[0016] Based on the observation matrix, and combined with the dimensional information of the user search space, a preliminary DCI scheduling subset is identified;

[0017] The problem of determining the PUSCH resource allocation subset under the DCI scheduling instruction is solved by using the CCE index and the preliminary identification results of the DCI scheduling subset.

[0018] The DCI scheduling subset identification result is obtained by solving the PUSCH resource allocation subset problem using a combinatorial optimization algorithm.

[0019] Optionally, combining the accelerated proximal gradient algorithm and the RBG granularity to achieve DCI-smart interference suppression includes:

[0020] The low-rank matrix and sparse matrix of the observation matrix are iteratively updated using the accelerated proximal gradient algorithm until the accelerated proximal gradient algorithm converges. Then, the sparse matrix indicated by the RBG granularity is used to achieve DCI smart interference suppression.

[0021] Optionally, DCI-smart interference suppression is achieved using the sparse matrix indicated by the RBG granularity, including:

[0022] Subtract the sparse matrix indicated by the RBG granularity from the observation matrix to achieve DCI smart interference suppression.

[0023] Optionally, the observed signal is:

[0024]

[0025] in, This represents the observed signal of the q-th user received by the i-th receiving antenna of the base station at the m-th OFDM symbol; diag(.) represents the operation of constructing a diagonal matrix based on a given vector; This represents the PUSCH data of the q-th user on the m-th OFDM symbol and the n-th subcarrier. For OFDM sets, Let T be the set of subcarriers, and the superscript T indicates the matrix transpose operation; This represents the PUSCH allocation list for the q-th user; F represents the unit discrete Fourier transform matrix, N f Indicates the total number of subcarriers; Let θ represent the channel impulse response from the q-th user to the ith receiving antenna of the base station; 10log10(θ) represents the interference-to-signal ratio. This represents the interference signal transmitted by the DCI smart jammer on the m-th OFDM symbol and the n-th subcarrier; This represents the PUSCH allocation list of the q-th user obtained by the DCI smart interference attacker during the sniffing phase; This represents the channel impulse response from the DCI-smart jamming attacker to the i-th receiving antenna of the base station; zi,m This represents additive white Gaussian noise with zero mean and variance corresponding to the signal-to-noise ratio.

[0026] In a second aspect, the present invention provides a smart interference suppression device, the smart interference suppression device comprising:

[0027] A construction module is used to acquire observation signals and construct an observation matrix based on the observation signals;

[0028] The identification module is used to identify a DCI scheduling subset based on the observation matrix; the DCI scheduling subset includes key configuration parameters used to guide the UE in data transmission;

[0029] The determination module is used to determine the RBG granularity of the resource allocation list indicated by the CCE index based on the DCI scheduling subset identification results;

[0030] The implementation module is used to combine the accelerated proximal gradient algorithm and the RBG granularity to achieve DCI smart interference suppression.

[0031] Thirdly, the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0032] Memory, used to store computer programs;

[0033] When a processor executes a computer program stored in memory, it implements the steps described in any of the above-described smart interference suppression methods.

[0034] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described smart interference suppression methods.

[0035] This invention provides a smart interference suppression method that effectively extracts detection features by extracting the RBG granularity of the resource allocation list indicated by the CCE index from the DCI scheduling subset identification results, without requiring modifications to the 5G communication protocol. Furthermore, compared to existing smart interference suppression techniques, once DCI smart interference occurs in the current observation time slot, the proposed method, by combining an accelerated near-end gradient algorithm and the RBG granularity obtained from the DCI scheduling subset identification, can reconstruct the received PUSCH data with high accuracy to suppress the impact of DCI smart interference.

[0036] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0037] Figure 1This is a flowchart illustrating a clever interference suppression method provided in an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of a PUSCH data transmission scenario;

[0039] Figure 3 This is a schematic diagram illustrating the changes in throughput before and after DCI smart interference suppression under different numbers of PDCCH candidates;

[0040] Figure 4 This is a schematic diagram showing the changes in throughput before and after DCI smart interference suppression under different control resource set sizes;

[0041] Figure 5 This is a schematic diagram of the structure of a smart interference suppression device provided in an embodiment of the present invention;

[0042] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0043] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0044] To address the technical problems of low accuracy and high implementation difficulty in existing smart interference suppression methods, this invention provides a smart interference suppression method, see [link to relevant documentation]. Figure 1 , Figure 1 This is a flowchart illustrating a clever interference suppression method provided in an embodiment of the present invention, which specifically includes the following steps:

[0045] Step S101: Obtain the observation signal and construct the observation matrix based on the observation signal.

[0046] Before implementing smart interference suppression, we first analyze the attacker's smart interference against DCI sniffing:

[0047] 5G NR uses DCI to send physical layer control messages from the base station to each user; however, due to the lack of encryption and integrity protection in the DCI transmission process, such as... Figure 2 As shown, Figure 2 This is a schematic diagram of a PUSCH data transmission scenario. A DCI-smart jamming attacker can identify resource block information allocated to users by sniffing out the active bandwidth portion. This information varies depending on the physical configuration of the control resource set through DCI scheduling. Information sniffing is a prerequisite for initiating DCI-smart jamming and mainly includes the following three steps:

[0048] First, attackers can leverage the initial bandwidth portion and monitor the Physical Downlink Control Channel (PDCCH) to access system information carried in the master information block transmitted by the base station, and combine this with the synchronization signal blocks periodically broadcast by the base station, enabling them to achieve frame synchronization with the cell. Second, after achieving frame synchronization with the cell, attackers continue to monitor the PDCCH candidate set in the active bandwidth portion to obtain the time / frequency domain resource information allocated to the control resource set, i.e., the resource block information allocated to users by the base station via DCI. The PDCCH candidate set refers to a set of possible PDCCH locations or configurations that a UE (User Equipment) needs to monitor within its search space in a 5G NR system to receive DCI. Each candidate in the PDCCH candidate set represents a possible resource allocation situation carrying DCI. To launch targeted interference against scheduled users precisely and covertly, DCI-smart interference attackers need to calculate and index the number of available CCEs (Control Channel Elements) for DCI in the common search space and the user-specific search space. Through the aggregation level and the mapping configuration between CCE and RBG, an attacker can use a Gold generation sequence initialized with a scrambling identifier to decipher the physical configuration of the control resource set. Finally, for common search spaces such as paging, system information, and access responses, an attacker can obtain control resource set physical configuration indication information from devices already connected to the network. Because the system information block defines the control resource set configuration process and provides detailed information about the initial bandwidth portion and control resource set settings, an attacker can obtain the necessary indication information from the system information block. For user-specific search spaces, such as additionally configured control resource set information and network temporary identifiers, an attacker can obtain them by performing an initial attach procedure to the base station and monitoring the radio resource control setting messages sent by the base station. Here, CCE is the basic resource unit for PDCCH transmission. A PDCCH can contain one or more CCEs, the specific number depending on the aggregation level of the PDCCH. The aggregation level determines the number of consecutive CCEs used to transmit a specific PDCCH, which is typically selected based on channel conditions and the required transmission reliability. The aggregation level determines how many CCEs a PDCCH consists of, with each CCE containing 6 RBGs. For example, aggregation level 1 uses 1 CCE (6 RBGs), while aggregation level 4 uses 4 CCEs (24 RBGs), and so on. Higher aggregation levels provide higher transmission reliability. In short, the aggregation level defines the resource allocation of the PDCCH. To speed up the identification of network temporary identifiers (NTIs), attackers can create a priority list of NTIs to test, dynamically adjusted based on recently successfully decoded NTIs and their frequency.Combining the above processes, attackers can effectively decode DCI to obtain users' resource block allocation and scheduling information through information sniffing.

[0049] Based on the sniffing process described above, DCI smart jamming attackers can launch targeted interference against subsequent PUSCH data transmissions.

[0050] This invention embodiment considers a PUSCH data transmission scenario, in which P single-antenna users simultaneously transmit data to a single antenna with N antennas. B The base station transmits signals via an antenna, while simultaneously being attacked by a single-antenna DCI (Digital Cipher Interface) smart jammer, such as... Figure 2 As shown. According to the DCI scheduling control instructions, the user set includes Q active users and PQ silent users. Regarding PUSCH data transmission, data transmission based on the RBG index occupies N... S One OFDM (Orthogonal Frequency Division Multiplexing) time slot and N f There are subcarriers, and these time slots belong to set S, satisfying... Subcarriers belong to set F, satisfying Here, data based on the RBG index refers to information used to identify and allocate downlink or uplink resources to user equipment using the RBG index. Due to the existence of command information estimation errors during the sniffing phase, DCI smart interference may miss RBG index target points for legitimate resource allocations, i.e., resources before the attack. The RBG index target point is an index value used in network resource allocation to identify the location of resource block groups used for data transmission by a specific user equipment. It clarifies which RBGs the UE specifically uses for data transmission or reception in the downlink or uplink. Therefore, data is distributed using... Represents the set of target OFDM time slots for DCI-smart interference (satisfying) )and Represents the target subcarrier set (satisfying) ).make and This represents the PUSCH data of the q-th user (q = 1, 2, ..., Q) and the jamming signal sent by the DCI clever jammer, where the subscript {} is used. m,n This represents the m-th OFDM symbol and the n-th subcarrier. and Symbols were randomly generated from a 16-QAM symbol set and from independent, identically distributed Gaussian random variables with mean 0 and variance 1, respectively. This was achieved through cross-subcarrier and OFDM symbol insertion. and During the process, the basic data multiplexing and layer mapping principles of the PUSCH transmission process are followed.

[0051] Based on the above PUSCH data transmission process, PUSCH data and interference signals are in N f Stacked on each subcarrier as follows and Among them, if and but use and These represent the distance from the q-th user and the DCI smart jamming attacker to the i-th receiving antenna of the base station (i = 1, 2, ..., N), respectively. B The channel impulse response of q has L channel taps. For the q-th user, the signal received at the i-th receiving antenna and the m-th OFDM symbol of the base station, i.e., the observed signal, can be expressed as:

[0052]

[0053] in, This represents the observed signal of the q-th user received by the i-th receiving antenna of the base station at the m-th OFDM symbol; F is the unit discrete Fourier transform matrix. diag(.) represents the operation of constructing a diagonal matrix from a given vector. 10log10(θ) is equivalent to the ratio of the received interference signal to the target signal, i.e., the interference-to-signal ratio. It is the PUSCH allocation list for the q-th user, consisting of 0s and 1s; This is the PUSCH allocation list of the q-th user obtained by the DCI clever interference attacker during the sniffing phase. i,m It is additive white Gaussian noise with zero mean and variance corresponding to the signal-to-noise ratio.

[0054] This completes the construction of the DCI smart interference model.

[0055] Based on the DCI smart interference model, the core principle of the smart interference suppression method provided in this invention is to fully utilize the spatial feature differences between legitimate users and DCI smart interference attackers within the scheduling time slot. Since sniffing configuration information takes time, the spatial feature differences can be further amplified by extracting the underlying RBG granularity subspace, indicated by the CCE index, from historical data prior to the attack. Legitimate users within the scheduling time slot refer to active users among the legitimate users, while legitimate users outside the scheduling time slot are silent users. When DCI smart interference occurs within the current observation time slot, the received PUSCH data can be reconstructed with high precision by combining robust principal component analysis and RBG prior information obtained from DCI scheduling subset identification to suppress the impact of interference. The RBG prior information, i.e., the RBG granularity, clarifies the location and allocation of resource block groups used by the UE for data transmission in a specific subframe.

[0056] First, a set of OFDM symbol samples is collected, each set containing symbols from all N. B One antenna in N f Data transmitted on each subcarrier. For each time slot, all antennas and N... s Data vector of OFDM symbols Stacked into a matrix This represents the data stitching result of all antennas and subcarriers within a single time slot. Then, the matrix of η time slots is... Vertically stacked to form a large sample data matrix The data is centered by subtracting the column mean from each column of data.

[0057]

[0058] in, yes The mean vector of each column, It is of length ηN B A column vector of all 1s. To simplify the symbolic representation of the method proposed in this embodiment, the problem will be restated in the real number field. By... real part and the virtual part By splicing them side by side, it can Transforming it into a real matrix, its size is twice that of the original complex matrix, i.e., (2ηN) B )×(2N f N s ), structure is Given the dimension information k of the corresponding user search space, and based on the principle of k-truncation singular value decomposition, the observation matrix can be represented as follows: U i and V i It is a singular vector, γ i It is the i-th singular value of Ξ.

[0059] Step S102: Identify a DCI scheduling subset based on the observation matrix; the DCI scheduling subset includes key configuration parameters used to guide the UE in data transmission.

[0060] In this embodiment of the invention, the DCI scheduling subset refers to a specific set determined during the blind detection PDCCH process, including key configuration parameters used to guide the UE in data transmission, specifically including key parameters such as modulation and coding scheme, HARQ (Hybrid Automatic Repeat Request) process information, and transmission format.

[0061] In this embodiment of the invention, the process of identifying a DCI scheduling subset based on the observation matrix is ​​as follows:

[0062] Based on the observation matrix and combined with the dimensional information of the user search space, a preliminary DCI scheduling subset is identified;

[0063] The problem of determining the PUSCH resource allocation subset under the DCI scheduling instruction is solved by using the CCE index and the preliminary identification results of the DCI scheduling subset.

[0064] The DCI scheduling subset identification result is obtained by solving the PUSCH resource allocation subset problem using a combinatorial optimization algorithm.

[0065] In this embodiment of the invention, based on the observation matrix Ξ, the rank of the observation matrix Ξ is... Combining the dimensional information k of the user search space, under the condition that k ≤ r, the preliminary identification result of the PUSCH data matrix obtained from the preliminary identification of the DCI scheduling subset can be expressed as:

[0066]

[0067] Among them, ||.|| k The partial trace norm of dimension k is represented by Tr(.); Tr(.) represents the trace of the matrix; I k This represents an identity matrix of size k×k.

[0068] Using the CCE index, the problem of PUSCH resource allocation subset under DCI scheduling instructions can be represented as:

[0069]

[0070] Where ||.||0 represents the zero norm. Integer Ψ1≤2ηN B and Ψ2≤2N f N s The data processing dimension constraint must be met. The low rank required for subset identification is determined by k ≤ min{Ψ1,Ψ2}. By combining a low-rank approximation based on the dimension information k with sparsity processing indicated by Ψ1(|Ψ1|≤ψ1) and Ψ2(|Ψ2|≤ψ2), the DCI scheduling subset identification result can be obtained through a combinatorial optimization algorithm.

[0071] Step S103: Determine the RBG granularity of the resource allocation list indicated by the CCE index based on the DCI scheduling subset identification result.

[0072] In this embodiment of the invention, the CCE index can clearly identify which RBGs the UE uses for data transmission or reception in the downlink or uplink, based on the DCI scheduling subset identification result. The RBG granularity [Ψ1; Ψ2] of the resource allocation list indicated by the CCE index can be extracted.

[0073] Step S104: Combine the accelerated proximal gradient algorithm and RBG granularity to achieve DCI smart interference suppression.

[0074] In this embodiment of the invention, the accelerated proximal gradient algorithm can be combined to reconstruct the PUSCH data in the allocation list using prior information at the RBG granularity. Robust principal component analysis based on the accelerated proximal gradient method is an efficient subspace separation algorithm. Corresponding to the example of this invention, it can be used to extract low-rank signal components from the noisy data matrix, i.e., the observation matrix, i.e., the RBG granularity accumulated from historical data, and to eliminate sparse interference components, i.e., sparse errors caused only by DCI smart interference occurring in the current time slot.

[0075] In one implementation, combining accelerated proximal gradient algorithms and RBG granularity to achieve DCI-smart interference suppression includes:

[0076] The low-rank and sparse matrices of the observation matrix are iteratively updated using the accelerated proximal gradient algorithm until the accelerated proximal gradient algorithm converges. Then, the sparse matrix with RBG granularity is used to achieve DCI smart interference suppression.

[0077] First, initialize the low-rank matrix L of the observation matrix. (0) and sparse matrix S (0) It is a zero matrix, and the momentum term is also included. and Initialize to a zero matrix. The regularization parameter Λ and the initial value of the iteration count κ are set to... And 0. The step size parameter α and momentum parameter β are set to 0 respectively. And RBG granularity [Ψ1; Ψ2]. Next, for the observation matrix Ξ of the current time slot, the proposed algorithm enters an iterative update loop, which continues until the maximum number of iterations is reached or the F-norm of the residual ||Ξ is reached. c -L (κ) -S (κ) || F Below the specified tolerance ε (set to 10) -4 Until then. In each iteration, the momentum term is calculated according to... Update. Next, update the low-rank matrix using the proximal operator of the nuclear norm: The proximal operator of the nuclear norm is defined as: prox Λ||.||* (X)=U∑ thresh V T , and ∑ thresh =max(∑-Λ,0). Similarly, the sparse matrix can be updated: The proximal operator of the L1 norm is defined as follows: The sign function `sign(.)` returns the sign of each element in the input; `⊙` represents the Hadamard product, i.e., element-wise multiplication. The iteration count `κ` is incremented by 1, and the loop continues until the algorithm converges. Once converged, legitimate and interfering signals can be identified using the bitmap of the RBG granularity [Ψ1; Ψ2] and the PUSCH allocation list in the DCI message, and DCI-smart interference suppression can be achieved using the sparse matrix indicated by the RBG granularity.

[0078] In one implementation, DCI-smart interference suppression is achieved using a sparse matrix with RBG granularity indicators, including:

[0079] Subtract the sparse matrix indicated by RBG granularity from the observation matrix to achieve DCI smart interference suppression.

[0080] Specifically, this is achieved by subtracting the sparse matrix indicated by RBG granularity from the observation matrix of the current time slot. To eliminate the effects of DCI smart interference, i.e.

[0081] In this embodiment of the invention, the specific process of identifying legitimate and interfering signals using the bitmap of the RBG granularity [Ψ1; Ψ2] and the PUSCH allocation list in the DCI message is as follows:

[0082] 1) Resource Allocation Information Extraction: The DCI message contains PUSCH resource allocation information for a specific UE. This information is usually presented in the form of a bitmap, indicating which RBGs have been allocated to the UE for data transmission. For example, each bit in the bitmap corresponds to one RBG, with a value of 1 indicating that the RBG has been allocated to the UE, and a value of 0 indicating that it has not been allocated.

[0083] 2) Legitimate Signal Identification: The UE parses its own resource allocation bitmap based on the received DCI message to determine which RBGs are legitimately allocated to it. This means that any signal on these RBGs can be considered a legitimate signal, i.e., an uplink transmission from itself or a transmission from other authorized users.

[0084] 3) Interference Signal Detection: For RBGs that have not been assigned to the UE, i.e., RBGs with a bitmap of 0, if a signal is detected on these RBGs, it can be considered an interference signal. This is because these RBGs have not been scheduled for use by the current UE or any other legitimate user, so the signal appearing here is likely unauthorized or caused by other factors.

[0085] 4) Comparison and Verification: Further, the system can verify the existence of interference by comparing the actual received signals with the expected resource allocation. If a signal is received on a certain RBG but it should be idle according to DCI allocation, then it can be confirmed that this is an interference signal.

[0086] In this embodiment of the invention, by extracting the RBG granularity of the resource allocation list indicated by the CCE index from the DCI scheduling subset identification results, detection features can be effectively extracted without requiring modifications to the 5G communication protocol. Furthermore, compared to existing smart interference suppression techniques, once DCI smart interference is initiated in the current observation time slot, the method proposed in this embodiment, by combining an accelerated near-end gradient algorithm and the RBG granularity obtained from the DCI scheduling subset identification, can reconstruct the received PUSCH data with high accuracy to suppress the impact of DCI smart interference.

[0087] The simulation experiment of a smart interference suppression method provided by the embodiments of the present invention is as follows:

[0088] This simulation experiment uses standard parameters established for DCI and PUSCH transmission processes under the 5G NR standard. Channel information (relevant parameters for urban microcells) is generated using the 5G NR system-level QuaDRiGa simulation platform, and angle spread is defined individually for each cluster. In a 3.5GHz urban scenario, the typical value for horizontal angle spread is approximately 10° to 20°, while the common value for vertical angle spread is approximately 5° to 10°. The signal-to-interference ratio is 10log. 10 (θ) is set to 0dB, and the signal-to-noise ratio is set to 5dB. To evaluate the performance of the proposed method in suppressing DCI smart interference, this simulation experiment presents the changes in system throughput before and after DCI smart interference suppression.

[0089] 1) For the parameter configuration of the DCI format, 25 resource blocks are allocated in the bandwidth portion of the uplink transmission, containing a 2-bit power offset value to instruct the user to adjust their transmit power relative to a predefined power level or path loss estimate. DCI is mapped to QPSK (Quadrature Phase Shift Keying) symbols, and a unique phase and amplitude are assigned to each symbol according to the QPSK constellation diagram;

[0090] 2) Allocate two consecutive time slots for PUSCH in each subframe, using a demodulation reference signal with a density of 1. Each time slot contains one demodulation reference signal symbol, the position of which is determined according to the configured demodulation reference signal mode (every four symbols). Depending on the allocation, a portion of each time slot is used for control information, and the remainder is used for PUSCH data. For example, if two symbols are used for PDCCH in each time slot, then 12 symbols remain in each time slot for PUSCH.

[0091] See Figure 3 , Figure 3 This diagram illustrates the changes in throughput before and after DCI smart interference suppression under different numbers of PDCCH candidates. This simulation experiment evaluates and verifies the impact of DCI smart interference on throughput and the DCI smart interference suppression effect of the proposed method under different numbers of PDCCH candidates. It can be observed that for different numbers of PDCCH candidates, the proposed method achieves good DCI smart interference suppression performance, closely approximating the network throughput under attack-free conditions. Furthermore, as the number of PDCCH candidates increases, the network throughput initially increases, then decreases. This is because using more candidates to transmit DCI improves transmission reliability and robustness, and reduces the bit error rate and retransmission frequency. However, more candidates also consume more resources, reducing the resources available for other data transmissions, ultimately decreasing the overall network throughput.

[0092] See Figure 4 , Figure 4 This diagram illustrates the changes in throughput before and after DCI-smart interference suppression under different control resource set sizes. First, as the control resource set size increases, the interference suppression performance of the method proposed in this embodiment remains at a high level, especially for smaller control resource set sizes, significantly reducing the network throughput loss rate. Second, increasing the control resource set size allows for more granular resource allocation, enhancing the flexibility of uplink resource scheduling and thus improving network throughput. However, as the control resource set size continues to increase, more control information consumes more resources, causing the throughput growth to gradually slow down.

[0093] This paper proposes a smart interference suppression method based on DCI scheduling subset identification and physical uplink shared channel resource reconstruction, which fully leverages the spatial characteristic differences between legitimate users and DCI smart interference attackers within the scheduling time slot. Based on key information such as DCI bit segment identifier configuration and information bit mapping, the method utilizes CCE and RBG granularity characteristics to suppress DCI smart interference. Specifically, since DCI smart interference attackers require time and configuration to sniff and interpret resource allocation instructions, this invention first uses historical data from attack-free time slots to identify legitimate CCE indices based on DCI scheduling subsets before precise interference. Then, robust principal component analysis is used to extract the underlying RBG granularity subspace indicating the resource allocation list by the legitimate CCE indices. Finally, the proposed method can effectively filter out sparse anomalous components in the current observation time slot, i.e., suppress the impact of additional DCI smart interference, and significantly reduce the blocking probability caused by DCI smart interference compared to existing interference suppression methods.

[0094] In this embodiment of the invention, DCI-smart interference is modeled by combining the transmission process of DCI and full PUSCH in the standard 3G Partner Program. Starting from the sniffing phase, the attacker synchronizes with the cellular network to identify the network temporary identifier of the scheduled user and monitors the physical downlink control channel and its associated DCI time-frequency information allocated to the target user. Subsequently, the attacker significantly impacts network functionality by launching targeted DCI-smart interference against the resource blocks initially allocated to the scheduled user.

[0095] To address the high processing complexity of preprocessed data related to DCI signaling bit segment identifiers and information bit mappings, a CCE-based index selection swap method is adopted to effectively identify active subsets of the DCI scheduling data matrix. Furthermore, an accelerated proximal gradient algorithm based on RBG granularity is used to reconstruct PUSCH resources for DCI smart interference suppression.

[0096] Based on the same inventive concept, embodiments of the present invention also provide a clever interference suppression device, see [link to relevant documentation]. Figure 5 , Figure 5 This is a schematic diagram of a smart interference suppression device provided in an embodiment of the present invention. The smart interference suppression device includes:

[0097] The construction module 501 is used to acquire the observation signal and construct the observation matrix based on the observation signal;

[0098] The identification module 502 is used to identify a DCI scheduling subset based on the observation matrix; the DCI scheduling subset includes key configuration parameters used to guide the UE in data transmission;

[0099] The determination module 503 is used to determine the RBG granularity of the resource allocation list indicated by the CCE index based on the DCI scheduling subset identification result;

[0100] The implementation module 504 is used to combine the accelerated proximal gradient algorithm and the RBG granularity to achieve DCI smart interference suppression.

[0101] In this embodiment of the invention, by extracting the RBG granularity of the resource allocation list indicated by the CCE index from the DCI scheduling subset identification results, detection features can be effectively extracted without requiring modifications to the 5G communication protocol. Furthermore, compared to existing smart interference suppression techniques, once DCI smart interference is initiated in the current observation time slot, the method proposed in this embodiment, by combining an accelerated near-end gradient algorithm and the RBG granularity obtained from the DCI scheduling subset identification, can reconstruct the received PUSCH data with high accuracy to suppress the impact of DCI smart interference.

[0102] Optionally, the identification module is specifically used for:

[0103] Based on the observation matrix, the DCI scheduling subset is initially identified by combining the dimensional information of the user search space; the PUSCH resource allocation subset problem under the DCI scheduling instruction is determined by using the CCE index and the initial identification result of the DCI scheduling subset; the DCI scheduling subset identification result is obtained by solving the PUSCH resource allocation subset problem through a combinatorial optimization algorithm.

[0104] Optionally, the implementation module is specifically used for:

[0105] The low-rank matrix and sparse matrix of the observation matrix are iteratively updated using the accelerated proximal gradient algorithm until the accelerated proximal gradient algorithm converges. Then, the sparse matrix indicated by the RBG granularity is used to achieve DCI smart interference suppression.

[0106] Optionally, the implementation module utilizes the sparse matrix indicated by the RBG granularity to achieve DCI-smart interference suppression, including:

[0107] Subtract the sparse matrix indicated by the RBG granularity from the observation matrix to achieve DCI smart interference suppression.

[0108] Optionally, the observed signal is:

[0109]

[0110] in, This represents the observed signal of the q-th user received by the i-th receiving antenna of the base station at the m-th OFDM symbol; diag(.) represents the operation of constructing a diagonal matrix based on a given vector; This represents the PUSCH data of the q-th user on the m-th OFDM symbol and the n-th subcarrier, where S is the OFDM set. Let T be the set of subcarriers, and the superscript T indicates the matrix transpose operation; This represents the PUSCH allocation list for the q-th user; F represents the unit discrete Fourier transform matrix, N f Indicates the total number of subcarriers; Let θ represent the channel impulse response from the q-th user to the ith receiving antenna of the base station; 10log10(θ) represents the interference-to-signal ratio; θ represents the signal-to-interference ratio. This represents the interference signal transmitted by the DCI smart jammer on the m-th OFDM symbol and the n-th subcarrier; This represents the PUSCH allocation list of the q-th user obtained by the DCI smart interference attacker during the sniffing phase; This represents the channel impulse response from the DCI-smart jamming attacker to the i-th receiving antenna of the base station; z i,mThis represents additive white Gaussian noise with zero mean and variance corresponding to the signal-to-noise ratio.

[0111] This invention also provides an electronic device, such as... Figure 6 As shown, it includes a processor 601, a communication interface 602, a memory 603, and a communication bus 604, wherein the processor 601, the communication interface 602, and the memory 603 communicate with each other through the communication bus 604.

[0112] Memory 603 is used to store computer programs;

[0113] When the processor 601 executes the program stored in the memory 603, it implements the steps of the method described in any of the above-mentioned smart interference suppression methods.

[0114] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0115] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0116] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0117] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0118] The present invention also provides a computer-readable storage medium. A computer program is stored in the computer-readable storage medium, and when executed by a processor, the computer program implements the steps of any of the above-described smart interference suppression methods.

[0119] Optionally, the computer-readable storage medium may be non-volatile memory (NVM), such as at least one disk storage device.

[0120] Optionally, the aforementioned computer-readable storage medium may also be at least one storage device located remotely from the aforementioned processor.

[0121] In another embodiment of the present invention, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to perform the steps of any of the above-described smart interference suppression methods.

[0122] It should be noted that the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention.

[0123] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0124] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings and the disclosure in carrying out the claimed invention. In the description of the invention, the word "comprising" does not exclude other components or steps, "a" or "an" does not exclude a plurality, and "a plurality" means two or more, unless otherwise explicitly specified. Furthermore, while different embodiments may describe certain measures, this does not mean that these measures cannot be combined to produce good results.

[0125] The method provided in this invention can be applied to electronic devices. Specifically, the electronic device can be a desktop computer, a portable computer, a smart mobile terminal, a server, etc. No limitation is made herein; any electronic device that can implement this invention falls within the protection scope of this invention.

[0126] For the embodiments of the device / electronic device / storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to in the description of the method embodiments.

[0127] It should be noted that the device, electronic device and storage medium in the embodiments of the present invention are respectively devices, electronic devices and storage media that apply the above-mentioned smart interference suppression method. Therefore, all embodiments of the above-mentioned smart interference suppression method are applicable to the device, electronic device and storage medium, and can achieve the same or similar beneficial effects.

[0128] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A clever interference suppression method, characterized in that, The clever interference suppression method includes: Acquire the observation signals and construct an observation matrix based on the observation signals; A DCI scheduling subset is identified based on the observation matrix; the DCI scheduling subset includes key configuration parameters used to guide the UE in data transmission. The RBG granularity of the resource allocation list indicated by the CCE index is determined based on the DCI scheduling subset identification results. Combine the accelerated proximal gradient algorithm with the RBG granularity to achieve DCI smart interference suppression; The identification of the DCI scheduling subset based on the observation matrix includes: Based on the observation matrix, and combined with the dimensional information of the user search space, a preliminary DCI scheduling subset is identified; The problem of determining the PUSCH resource allocation subset under the DCI scheduling instruction is solved by using the CCE index and the preliminary identification results of the DCI scheduling subset. The DCI scheduling subset identification result is obtained by solving the PUSCH resource allocation subset problem using a combinatorial optimization algorithm. The method of combining the accelerated proximal gradient algorithm and the RBG granularity to achieve DCI smart interference suppression includes: The low-rank matrix and sparse matrix of the observation matrix are iteratively updated using the accelerated proximal gradient algorithm until the accelerated proximal gradient algorithm converges. Then, the sparse matrix with RBG granularity is used to achieve DCI smart interference suppression. The method of using the sparse matrix indicated by the RBG granularity to achieve DCI smart interference suppression includes: Subtract the sparse matrix indicated by the RBG granularity from the observation matrix to achieve DCI smart interference suppression.

2. The clever interference suppression method according to claim 1, characterized in that, The observed signal is: ； in, Indicates the base station number The receiving antenna is at the first The first OFDM symbol received at the [number]th OFDM symbol Observed signals from individual users; This represents the operation of constructing a diagonal matrix from a given vector; , Indicates the first The user in the first The OFDM symbol, the first PUSCH data for each subcarrier, For OFDM sets, For the set of subcarriers, superscript This represents the matrix transpose operation; Indicates the first A list of PUSCH allocations for each user; , Represents the unit discrete Fourier transform matrix. Indicates the total number of subcarriers; Indicates from the first The user to the base station Channel impulse response of each receiving antenna; Indicates the dry-to-sound ratio; , This indicates that the DCI cleverly interferes with the attacker in the first... The OFDM symbol, the first Interference signals transmitted by each subcarrier; This indicates that the DCI cleverly interferes with the attacker's acquisition of the first [unit / item] during the sniffing phase. A list of PUSCH allocations for each user; This indicates the distance from the DCI smart jamming attacker to the base station. Channel impulse response of each receiving antenna; This represents additive white Gaussian noise with zero mean and variance corresponding to the signal-to-noise ratio.

3. A clever interference suppression device, characterized in that, The smart interference suppression device includes: A construction module is used to acquire observation signals and construct an observation matrix based on the observation signals; The identification module is used to identify a DCI scheduling subset based on the observation matrix; the DCI scheduling subset includes key configuration parameters used to guide the UE in data transmission; The determination module is used to determine the RBG granularity of the resource allocation list indicated by the CCE index based on the DCI scheduling subset identification results; The implementation module is used to combine the accelerated proximal gradient algorithm and the RBG granularity to achieve DCI smart interference suppression; The identification module is specifically used for: Based on the observation matrix, and combined with the dimensional information of the user search space, a DCI scheduling subset is initially identified; the CCE index and the results of the initial identification of the DCI scheduling subset are used to determine the PUSCH resource allocation subset problem under the DCI scheduling instruction; the PUSCH resource allocation subset problem is solved by a combinatorial optimization algorithm to obtain the DCI scheduling subset identification result. The implementation module is specifically used for: The low-rank matrix and sparse matrix of the observation matrix are iteratively updated using the accelerated proximal gradient algorithm until the accelerated proximal gradient algorithm converges. Then, the sparse matrix with RBG granularity is used to achieve DCI smart interference suppression. The implementation module utilizes the sparse matrix indicated by the RBG granularity to achieve DCI-smart interference suppression, including: Subtract the sparse matrix indicated by the RBG granularity from the observation matrix to achieve DCI smart interference suppression.

4. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a computer program stored in memory, implements the smart interference suppression method according to any one of claims 1 and 2.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the smart interference suppression method according to any one of claims 1 and 2.

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