Downlink multi-base-station cooperative transmission method for 6G full-decoupling network without physical layer feedback

Abstract

The invention discloses a downlink multi-base-station cooperative transmission method for a 6G full decoupling network without physical layer feedback, which is characterized in that under a transmission mechanism without physical layer feedback, a plurality of base stations use fixed physical layer parameters to carry out downlink cooperative transmission on a mobile terminal on the same time-frequency resource block. The method comprises the following steps: establishing a multi-base-station cooperative transmission closed expression under a multipath channel model; calculating a precoding indication (PMI), a rank indication (RI) and a channel quality indication (CQI) of multi-base station cooperative transmission based on the historical channel data; and counting mode values of the PMI, the RI and the CQI in the historical channel data, and applying the mode values as fixed physical layer parameters of multi-base-station cooperative transmission to downlink transmission of multiple subframes. Compared with a link adaptive mode for calculating transmission parameters based on real-time channel state information (CSI) in traditional LTE / 5G, the non-physical-layer feedback mechanism provided by the invention has the advantages that the calculation and communication overhead of multi-base-station cooperation is effectively reduced, and a feasible design scheme is provided for multi-base-station cooperation transmission.

Description

Technical Field

[0001] The invention belongs to the field of wireless communications and relates to a physical layer parameter design method based on a downlink multi-base station cooperative transmission technology in a wireless access network. Background Art

[0002] To meet the demand for higher user density, the current 5G network has shortened the communication distance between base stations and users, improved network capacity and expanded coverage by densely deploying base stations. However, dense base station deployment has also caused serious interference between cells, which has become the main bottleneck limiting the performance of large-scale MIMO systems in 5G networks, and has seriously affected the service quality of users at the edge of the cell. In order to effectively manage interference and further improve the overall network capacity, the decellularized multi-base station collaboration mode has received extensive attention and research from academia and industry. Compared with the current point-to-point cellular transmission system of 5G networks, multi-base station collaborative transmission converts interference signals from different cells into useful transmission signals, showing great potential in improving spectrum utilization and increasing network capacity. Therefore, multi-base station collaborative transmission technology is expected to evolve into an innovative paradigm for the next generation of wireless networks for user services.

[0003] At present, the point-to-point cellular system of 5G network usually performs downlink adaptation within every 1 millisecond subframe time, which mainly includes the following process: first, the base station sends a channel state information reference signal (CSI-RS) to the user to measure the channel; the user estimates the channel based on the measurement signal and calculates the physical layer transmission parameters, including the precoding indicator PMI, MIMO rank indicator RI and channel quality indicator CQI for OFDM symbol transmission; the user feeds back PMI, RI and CQI to the base station, and the base station determines the transmission bit based on the physical layer parameters and performs downlink data transmission. In the process of calculating the precoding scheme, the 5G network uses the closed-loop spatial multiplexing (CLSM) mechanism to determine the optimal precoding matrix by traversing the predefined codebook set. Based on the codebook calculation method of the 5G network, the complexity of multi-base station joint precoding will increase exponentially with the number of cooperative base stations, resulting in huge computational overhead, which is theoretically difficult to achieve within the 1 millisecond subframe time. In addition, based on the real-time link adaptation mechanism of the current 5G network, the user needs to measure the channel information between all base stations and users in each subframe time to collaboratively calculate the physical layer parameters, and feed back the calculation results to the cooperative base station. As the number of cooperative base stations increases, multi-base station cooperation will bring significant communication overhead.

[0004] After searching the existing literature, it was found that A. Abdallah et al. published an article entitled "Multi-Agent DRL for Distributed Codebook Design in RIS-aided Cell-Free Massive MIMO Networks" in IEEE Transactions on Communications in 2024. Based on the method of multi-agent deep reinforcement learning, the article trains the neural network of each base station to select its own codebook, thereby avoiding the exponential complexity of the joint selection of codebooks by multiple base stations. Although the multi-agent reinforcement learning method can output the joint precoding scheme of multi-base station cooperation within the subframe time of 1 millisecond, for the real-time dynamically changing wireless channel scenario, multi-agent reinforcement learning is difficult to ensure that its training results can be applied to all different wireless network environments. After searching the existing literature, it was found that Shanghai Bell Co., Ltd. disclosed the national invention patent of "Multi-base station joint precoding method based on codebook, mobile terminal and base station", which measures the channel information of all D cooperative base stations by users [H 1,k ,…,H D,k ], based on the channel information H of the first base station 1,k Calculate the precoding matrix W 1,k , then based on H 1,k , W 1,k and H 2,k Calculate the precoding matrix W of the second base station 2,k , until the precoding matrices of all D cooperative base stations are determined in turn, and the equivalent channel H of multi-base station cooperation is obtained k =[H 1,k W 1,k ,…,H 1,k W D,k ]. This invention patent uses linear complexity to select the precoding of each base station, avoiding the exponential complexity of multiple base stations jointly selecting precoding from the codebook. However, this invention still needs to update the precoding scheme within every 1 millisecond subframe time and feed it back to all cooperative base stations, which has high computing and communication overhead.

[0005] In addition, the existing literature assumes that in the time division duplex (TDD) scenario, the user uplink channel is measured by the base station, and the downlink channel is equivalently obtained by using the uplink and downlink reciprocity, and it is sent to the edge cloud server to collaboratively calculate the physical layer parameters of multi-base station transmission. However, as the number of base stations increases, all base stations need to share channel information to collaboratively calculate feedback parameters, which will lead to significant communication overhead. After searching, it was found that S. Chen et al. and M. Silva et al. published articles entitled "Energy-efficient cell-free massive MIMO through sparse large-scale fading processing" and "CSI compression for distributed-MIMO with centralized precoding and power allocation" in the 2023 "IEEE Transactions on Wireless Communications" and 2023 "IEEE Communications Letters", respectively. These articles are based on the method of sparse and compressed channel matrix, which can reduce the communication overhead of base stations transmitting channel data to edge servers, and effectively achieve the collaborative gain of multiple base stations in the downlink. However, due to the large differences in the delay of each base station in measuring channel information, some of the feedback channel information may be outdated during the backhaul process, which affects the effect of multi-base station collaboration and causes system performance degradation. Moreover, in the actual transmission process, due to factors such as the different antenna polarizations of the base station and the user, the uplink channel is not completely equivalent to the downlink channel, and it is difficult to achieve the full performance gain of downlink multi-base station collaboration by using uplink and downlink reciprocity. In addition, uplink and downlink reciprocity only exists in FDD scenarios, and is not applicable in frequency division duplex (FDD) scenarios, so it does not have the universality of multi-base station collaboration.

[0006] In summary, the problems existing in the prior art are: (1) The downlink multi-base station collaborative joint precoding selection has extremely high computational complexity and needs to be updated in real time within a 1 millisecond subframe time, resulting in significant computational overhead. (2) Downlink multi-base station collaboration requires joint measurement of collaborative base station channel information and feedback of transmission parameters to all collaborative base stations within a 1 millisecond subframe time, resulting in large communication overhead. (3) The use of uplink and downlink mutual differences to collect collaborative base station channel information on the edge cloud server is easily affected by the feedback delay of some base stations and is only applicable to the TDD transmission mode, which is not universal. The significance of solving the above technical problems is that more feasible and efficient multi-base station collaborative transmission technology can help improve spectrum efficiency and network capacity, support the development of next-generation wireless communication technology, and support complex network application scenarios such as future immersive communications, large-scale connections, and AI-enabled communications. Summary of the invention

[0007] In view of the significant computing and communication overhead problems faced by the current 5G network point-to-point cellular communication technology in the multi-base station cooperation mechanism, the purpose of the present invention is to provide a 6G fully decoupled network downlink multi-base station cooperation transmission method without physical layer feedback, which provides a feasible design scheme for the multi-base station cooperation of the next generation mobile communication network.

[0008] To achieve the above-mentioned purpose of the invention, the present invention provides a 6G fully decoupled network downlink multi-base station cooperative transmission method without physical layer feedback, comprising the following steps:

[0009] Step 1: Based on the multipath channel model of clustered delay line (CDL), a closed-form expression for multi-base station cooperative transmission is established;

[0010] Step 2: Calculate the precoding indicator (PMI), rank indicator (RI) and channel quality indicator (CQI) of multi-base station cooperative transmission based on historical channel data;

[0011] Step 3: Count the mode values ​​of PMI, RI and CQI in the historical channel data, use them as fixed physical layer parameters for multi-base station cooperative transmission, and apply them to downlink transmission of multiple subframes.

[0012] Furthermore, in step 1, CDL is used to model the channel impulse response (CIR) of multiple base stations. Consider that each user equipment UE is equipped with N r Each downlink base station (DBS) is equipped with N receiving antennas. t The CIRh between the rth receiving antenna and the tth transmitting antenna r,t (τ), by combining the line-of-sight (LoS) channel and non-line-of-sight (NLoS) channels h r,t,c,p (τ,Δτ c,p ) components are added together to describe the mathematical expression as follows:

[0013]

[0014] Among them, τ represents the instantaneous time, Δτ LoS and Δτ c,p are the time delays of the LoS and NLoS paths, c∈[1,C] represents the modeled channel cluster, and p∈[1,N pc ] is the subpath of cluster c. LoS and G c,p The Ricean factor K is the channel gain of the LoS path and the channel gain of the cluster subpath, respectively. R The power scaling relationship between LoS paths and NLoS paths is described. and are the initial phase of the channel path, f c is the carrier signal frequency, δ(·) is the Dirac delta function used to simulate the unit channel pulse including the time delay effect.

[0015] Furthermore, all downlink base stations DBS in cooperative transmission will send the same OFDM symbol to obtain diversity gain. Assuming that all downlink base stations DBS All transmission path delays are less than the duration of the cyclic prefix (CP), and the discrete received signal at UE position i on subcarrier k and receiving antenna r is The closed form expression for is calculated as follows:

[0016]

[0017] in, is the downlink base station set, D is the number of downlink base stations, N t For each downlink base station transmit antenna number, is the frequency domain discrete channel between DBSd and UE position i, s i [k] and n i [k] are the discrete transmitted signal and received noise in the frequency domain, respectively.

[0018] Further, in step 2, the PMI of the downlink multi-base station cooperation is obtained by finding the optimal precoding matrix that maximizes the mutual information entropy over the entire system bandwidth and subframe duration in the predefined codebook set w. OK, the calculation expression is as follows:

[0019]

[0020] Where K is the number of subcarriers, B is the number of OFDM symbols, D is the number of downlink base stations, W d,kis the precoding matrix of the downlink base station DBSd on subcarrier k, represents the mutual information entropy of the kth subcarrier on the bth OFDM symbol, L i is the number of MIMO transmission streams, Represents the signal-to-interference-noise ratio of the signal of the lth stream after equalization.

[0021] Furthermore, the precoding rank is equal to the number of MIMO transmission streams L i , the optimal precoding matrix At the same time, the optimal number of MIMO streams is determined It is expressed as follows:

[0022]

[0023] in, is the downlink base station set, is a subcarrier set.

[0024] Further, solve the optimal precoding matrix When using DN t Precoding matrix for transmit antenna Approximate the joint precoding of D base stations and obtain the optimal precoding matrix by maximizing the following mutual information entropy

[0025]

[0026] in, Defined as having DN t The precoding set used by the transmitting antennas; by traversing the set Calculated Number of data streams transmitted by multiple base stations in collaboration

[0027] Further, solve the optimal precoding matrix When , by traversing the codebook from the first base station, based on the channel H of base station 1 1,k Get the optimal precoding matrix of base station 1 Then based on the joint channel [H 1,k ,H 2,k ], and the optimal precoding matrix of base station 1 Get the optimal precoding matrix of base station 2 Until the Dth base station.

[0028] Furthermore, multiple base stations cooperate in transmitting the same OFDM symbol in the downlink to obtain diversity gain. They should have the same number of transmit streams, that is, multiple base stations use the same RI, as shown below:

[0029]

[0030] Furthermore, in step 2, the CQI selection for downlink multi-base station cooperative transmission follows a single base station-user equipment connection strategy. This strategy processes the equalized SINR on the used frequency-time resources and maps the equalized SINR value to an equivalent signal-to-noise ratio SNR on the additive white Gaussian noise AWGN channel using the exponential equivalent SINR mapping (EESM) method. eff Value; SNR eff The AWGN channel has a block error rate similar to that of the original OFDM system; assuming there are E equalized SINR values, the EESM is expressed as:

[0031]

[0032] Among them, β eff is a set of predefined values ​​for different modulation and coding schemes (MCS), which are obtained by different physical layer simulation software through a large number of transmission tests; different β eff Will result in different SNR eff The optimal CQI value for downlink multi-base station cooperation is the maximum SNR that satisfies the average transmission block error rate (BLER) less than the preset empirical threshold ∈ eff Determine and pass SNR eff The CQI mapping table is obtained as follows:

[0033]

[0034] Furthermore, the fixed physical layer parameters of multi-base station cooperative transmission are obtained by offline calculation based on the historical channel information at different locations. The process first calculates the PMI, RI and CQI of multi-base station cooperative transmission based on the historical channel data of each subframe; then the mode values ​​of PMI, RI and CQI used by all historical channel data are counted and used as the fixed physical layer parameters of multi-base station cooperative transmission; finally, the fixed physical layer parameters are applied to the downlink transmission of multiple subframes.

[0035] The present invention also provides a computer system, including a memory, a processor, and a computer program / instruction stored in the memory and executable on the processor. When the computer program / instruction is executed by the processor, the steps of the 6G fully decoupled network downlink multi-base station collaborative transmission method without physical layer feedback are implemented.

[0036] The throughput of the UE in downlink multi-base station cooperative transmission depends on the physical layer transmission parameters including PMI, RI and CQI, which are used to determine the precoding matrix, number of data streams and modulation coding scheme. Based on the link adaptation mechanism of LTE / 5G network that uses real-time channel feedback to calculate transmission parameters in 1 millisecond transmission subframes, multi-base station cooperation needs to frequently share channel information and calculate multi-base station joint precoding in real time to obtain diversity gain. The 6G fully decoupled network without physical layer feedback uses fixed PMI, RI and CQI for downlink multi-base station cooperative transmission, thereby avoiding the significant computational and communication overhead of multi-base station cooperation using real-time channel feedback.

[0037] Compared with the prior art, the advantages and positive effects of the present invention are as follows: First, the downlink multi-base station cooperative transmission method without physical layer feedback can effectively avoid the exponential complexity of multi-base station joint precoding selection in each subframe time, which is one of the outstanding contributions of the present invention; second, the downlink multi-base station cooperative transmission method without physical layer feedback does not need to measure the channel state information of all cooperative base stations in each subframe and perform frequent feedback, which can greatly reduce the communication overhead of multi-base station cooperation; finally, based on the complete physical layer software simulation results, it is shown that the transmission mechanism without physical layer feedback can effectively obtain downlink multi-base station cooperation gain, providing a feasible reference solution for the multi-base station cooperation design of the next generation wireless access network. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a diagram of a downlink multi-base station collaborative transmission scenario adopted in an embodiment of the present invention.

[0039] Figure 2 It is a schematic diagram of fixed transmission parameters based on no physical layer feedback in an embodiment of the present invention.

[0040] Figure 3 This is a diagram of an outdoor simulation environment of multi-base station collaboration based on DeepMIMO O1 outdoor scene in an embodiment of the present invention.

[0041] Figure 4 It is a coordinate diagram of 1138 user locations and 2 downlink base stations used in the multi-base station collaboration simulation according to an embodiment of the present invention.

[0042] Figure 5 It is a schematic diagram of multi-base station cooperative throughput without physical layer feedback of user 1 simulated in an embodiment of the present invention.

[0043] Figure 6 This is a comparison diagram of average throughput without physical layer feedback and real-time channel state information feedback simulated in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the embodiments of the present invention are described in detail below in conjunction with the accompanying drawings: This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given. It should be understood that the specific examples described here are only used to explain the present invention, but the protection scope of the present invention is not limited to the following embodiments.

[0045] by Figure 1 Taking the downlink multi-base station cooperation scenario shown in the figure as an example, a 6G fully decoupled network downlink multi-base station cooperation transmission method without physical layer feedback disclosed in an embodiment of the present invention mainly includes the following steps:

[0046] Step 1: Based on the multipath channel model of clustered delay line (CDL), a closed-form expression for multi-base station cooperative transmission is established.

[0047] In this embodiment, the clustered delay line (CDL) defined in 3GPP38.901 is used to model the channel impulse response (CIR) of multiple base stations. Consider that each user equipment UE is equipped with N r Each downlink base station (DBS) is equipped with N receiving antennas. t The CIRh between the rth receiving antenna and the tth transmitting antenna r,t (τ), by combining the line-of-sight (LoS) channel and non-line-of-sight (NLoS) channels h r,t,c,p (τ,Δτ c,p ) components are added together to describe the mathematical expression as follows:

[0048]

[0049] Among them, τ represents the instantaneous time, Δτ LoS and Δτ c,p are the time delays of LoS and NLoS paths respectively, c∈[1,C] represents the modeled channel cluster, is the subpath of cluster c. LoS and G c,p The Ricean factor K is the channel gain of the LoS path and the channel gain of the cluster subpath, respectively. R The power scaling relationship between LoS paths and NLoS paths is described. and are the initial phase of the channel path, f c is the carrier signal frequency, δ(·) is the Dirac delta function used to simulate the unit channel pulse including the time delay effect. In order to obtain the diversity gain of downlink multi-base station cooperation, all downlink base stations DBS of cooperative transmission will send the same OFDM symbol. Assume that all downlink base stations DBS All transmission path delays are less than the duration of the cyclic prefix (CP), and the discrete received signal at UE position i on subcarrier k and receiving antenna r is The closed form expression for is calculated as follows:

[0050]

[0051] in, is the frequency domain discrete channel between DBSd and UE position i, s i [k] and n i [k] are the discrete transmitted signal and received noise in the frequency domain, respectively.

[0052] Step 2: Calculate the precoding indicator (PMI), rank indicator (RI) and channel quality indicator (CQI) of multi-base station cooperative transmission based on historical channel data.

[0053] In this embodiment, MIMO-OFDM symbol transmission with multi-stream data is considered, assuming With L i The emission data of the stream, is the received signal at UE position i at subcarrier k of ODFM symbol b, defined as follows:

[0054]

[0055]

[0056] Based on the closed-form expression of the discrete received signal, the following received signal of the frequency domain multi-base station joint transmission can be obtained:

[0057]

[0058] in, is the precoding matrix used by DBSd on subcarrier k, which is used to convert L i Streaming Data Mapping to N t transmit antenna ports. is the frequency domain channel matrix between DBSd and user i, and its element in the rth row and tth column is The variance is σ 2 In addition, the number of MIMO transmission streams L i It should not exceed the minimum number of base station transmitting antennas and user receiving antennas, expressed as follows:

[0059]

[0060] in, Represents the set of all UE locations.

[0061] When the UE receives a signal from the cooperative DBS After that, the received signal is channel equalized using a zero forcing (ZF) or minimum mean square error (MMSE) equalizer. Define the equivalent channel matrix for multi-base station transmission The product of the channel and precoding between each DBS and the user is calculated as follows:

[0062]

[0063] in, and It is expressed as:

[0064]

[0065]

[0066] The channel equalization process will reverse the channel effect, thus restoring the received signal to the original transmitted signal. Consider using a ZF equalizer, that is, through the effective channel matrix The pseudo-inverse of is calculated:

[0067]

[0068] in, represents the ZF equalizer for the kth subcarrier on OFDM symbol b. (·) H is the conjugate transpose. pass After equalization, the equalized received signal It can be expressed as:

[0069]

[0070] in, Reverse the channel effect and recover the original L from the received signal i According to Shannon channel capacity theory, we can calculate the l∈1,…,L i The signal-to-interference-noise ratio of the equalized signal Its expression is as follows:

[0071]

[0072] in, express The lth row, the lth ′ Column elements, express The channel effect is effectively alleviated by channel equalization, and the SINR after equalization can more accurately reflect the received signal quality of each data stream.

[0073] Downlink multi-base station cooperative transmission requires joint determination of PMI, RI and CQI. In order to measure the DBS joint precoding matrix (W d,k is the precoding matrix of the downlink base station DBSd on subcarrier k) and the number of MIMO streams L i The impact on downlink multi-base station cooperative transmission is calculated by using the concept of mutual information entropy to calculate the total rate of signal-to-interference-plus-noise ratio (SINR) of all streams in MIMO transmission after equalization. Based on Shannon channel capacity theory, the mutual information entropy of the kth subcarrier on the bth OFDM symbol is It is expressed as follows:

[0074]

[0075] Determine whether the PMI for downlink multi-base station coordination is in the predefined codebook set Find the optimal precoding matrix that maximizes the mutual information entropy over the entire system bandwidth and subframe duration That is, the following expression is calculated over the subcarrier (1, ..., K) range and the OFDM symbol (1, ..., B) time range:

[0076]

[0077] The 5G single-base-station single-user transmission mode adopts the closed-loop space division CLSM mechanism, which provides an optimal precoding for each subframe by traversing the precoding of the predefined codebook. When it comes to the downlink joint transmission of D base stations, the computational complexity of CLSM is is the codebook set The number of elements grows exponentially, making it difficult to implement within a 1 millisecond subframe.

[0078] This embodiment proposes a heuristic codebook iteration (HCI) algorithm, which uses linear complexity to calculate the precoding of each base station in multi-base station collaboration. The specific process is as follows:

[0079] The HCI method first uses DN t Precoding matrix for transmit antenna To approximate the joint precoding of D base stations, and based on W′ k Calculate the physical layer transmission parameters, W′ k The mathematical expression is:

[0080]

[0081] Therefore, the received signal of the frequency domain multi-base station joint transmission can be expressed as:

[0082] y k =[H 1,k ,…,H D,k ]W′ k x k +n k

[0083] Next, the optimal precoding matrix is ​​obtained by maximizing the following mutual information entropy

[0084]

[0085] in, Defined as having DN t The precoding set used by the transmitting antenna. Since only one precoding matrix needs to be determined The above formula can be obtained by traversing the set Calculated In addition, the number of data streams transmitted by multiple base stations can be based on The rank of is calculated and expressed as follows:

[0086]

[0087] However, multi-base station cooperative transmission still requires selecting a separate precoding matrix for each base station d The HCI algorithm traverses the codebook starting from the first base station and based on the channel H of base station 1 1,k Get the optimal precoding matrix of base station 1 Then based on the joint channel [H 1,k ,H 2,k ], and the optimal precoding matrix of base station 1 Get the optimal precoding matrix of base station 2 Until the Dth base station. The process is as follows:

[0088]

[0089] Among them, operation It is expressed as:

[0090]

[0091] After obtaining the precoding of each base station based on the linear HCI method, since the number of MIMO transmission streams is determined by the precoding matrix The rank of is obtained, that is, All base stations select their own optimal precoding matrix based on the number of streams, and the cooperative base station The same number of transmit streams means that multiple base stations use the same RI, which is expressed as follows:

[0092]

[0093] The above HCI method determines the joint precoding of multiple base stations and the number of MIMO streams, which can meet the requirement of multiple downlink base stations transmitting the same OFDM symbols at the same frequency to obtain diversity gain.

[0094] For the CQI selection of downlink multi-base station cooperative transmission, this embodiment follows the single base station-user equipment connection strategy. This strategy processes the equalized SINR on the used frequency-time resources and maps the equalized SINR value into an equivalent signal-to-noise ratio SNR on the additive white Gaussian noise AWGN channel using the exponential equivalent SINR mapping (EESM) method. eff SNR eff The block error rate in the AWGN channel is similar to that of the original OFDM system. Assuming there are E equalized SINR values, the EESM can be expressed as:

[0095]

[0096] Among them, β eff It is a set of predefined values ​​for different modulation and coding schemes (MCS), which are obtained by different physical layer simulation software through a large number of transmission tests. eff Will result in different SNR eff The optimal CQI value for downlink multi-base station cooperation is the maximum SNR that satisfies the average transmission block error rate (BLER) less than the empirical threshold of 0.1. eff Determine and pass SNR eff The CQI mapping table is obtained as follows:

[0097]

[0098] Step 3: Count the mode values ​​of PMI, RI and CQI in the historical channel data, use them as fixed physical layer parameters for multi-base station cooperative transmission, and apply them to downlink transmission of multiple subframes.

[0099] Based on the above process, the PMI, data stream number RI and modulation coding scheme CQI for downlink multi-base station cooperative transmission can be obtained for determining the precoding matrix. This embodiment uses fixed PMI, RI and CQI for downlink multi-base station non-feedback cooperative transmission based on the 6G fully decoupled network, thereby avoiding the significant calculation and communication overhead of multi-base station cooperation using real-time channel feedback. No physical layer feedback mechanism uses fixed physical layer transmission parameters such as Figure 2As shown. The fixed physical layer parameters of multi-base station collaboration are obtained by offline calculation based on the historical channel information at different locations. The process is as follows: first, the PMI, RI and CQI of multi-base station collaboration transmission are calculated based on the historical channel data of each subframe; then, the mode values ​​of PMI, RI and CQI used by all historical channel data are counted and used as the fixed physical layer parameters of multi-base station collaboration transmission; finally, the fixed physical layer parameters are applied to the downlink transmission of multiple subframes. Assuming that the UE position i has a total of F subframes of historical channel data, the fixed physical layer parameters of multi-base station collaboration transmission are expressed as follows:

[0100]

[0101]

[0102]

[0103] Where mode means taking the majority. The PMI, RI and CQI of multi-base station cooperative transmission are calculated based on the historical channel data of the f-th subframe respectively.

[0104] In order to make this embodiment more intuitive and verify the feasibility of the feedback-free transmission mechanism in realizing downlink multi-base station cooperative transmission, this embodiment uses the outdoor O1 scenario provided by the DeepMIMO channel tracking platform (https: / / www.deepmimo.net / ) to simulate the performance of downlink multi-base station cooperation. The simulation environment is as follows: Figure 3 As shown in the figure, the time-varying channel running at the 3.5 GHz center frequency is simulated based on the 3D ray tracing technology of DeepMIMO and the CDL channel model of MATLAB. Figure 4 Shown in Figure 3 The coordinates of 1138 user locations and 2 downlink base stations in the simulation area, where the 2 downlink base stations are Figure 3 Base station 7 and base station 8 in the figure. Each base station is equipped with 8×1 dual-polarization transmitting antennas, and the number of antennas is N. t =16, UE is equipped with 4×1 single-polarization receiving antennas, the number of antennas is N r=4. A transmission subframe contains 72 subcarriers and 14 OFDM symbols, with a duration of 1ms. 100 subframe times are used to simulate time-varying channels, and the total time period is 100ms, so the channel will undergo changes for a long time. In addition, downlink multi-base station collaboration also uses low-density parity-check code (LDPC) channel coding, and the CQI value range is 1 to 15, covering modulation modes from QPSK (coding efficiency 0.1523) to 64QAM (coding efficiency 5.5547). The downlink multi-base station collaborative transmission of this embodiment is based on the complete physical layer module of the Vienna University 5G link-level simulator for performance testing, and the data throughput of the UE successfully transmitted measured in megabits per second (Mbit / s) can be obtained.

[0105] Figure 5 The multi-base station collaboration throughput without physical layer feedback is shown for user 1 in the simulation area. It can be seen that as the number of collaborative base stations increases, the multi-base station collaboration throughput without physical layer feedback increases, effectively achieving the diversity gain of downlink multi-base station collaboration. The simulation results verify the feasibility of the transmission mechanism without physical layer feedback in realizing downlink multi-base station collaboration. Figure 6 The average throughput of all 1138 users in the simulation area under the transmission mechanism with and without feedback is shown. Due to the exponential complexity of the selection of the joint precoding for downlink multi-base station cooperation, the closed-loop space division multiplexing CLSM transmission mechanism with feedback can only achieve the transmission of a single base station and a single user. For the single-base station single-user scenario, based on the historical channel data, the most frequently used PMI, RI and CQI at each location are used as the non-feedback parameters for the single-base station single-user transmission, and the average throughput is reduced by 13.16% compared with the CLSM feedback mechanism. This is because the fixed physical layer parameters without feedback do not adapt to the time-varying nature of the wireless channel in real time, resulting in a decrease in throughput performance. However, when the non-feedback transmission mechanism is used for the cooperative transmission of two downlink base stations, the average throughput is improved by 8.16% compared with CLSM. This result further demonstrates the feasibility and effectiveness of the non-feedback transmission mechanism in downlink multi-base station cooperative transmission. Although the transmission throughput without feedback decreased by 12.67% compared with the two-base station cooperative transmission with linear HCI feedback, and the full performance of multi-base station cooperation was not obtained, the transmission mechanism without feedback does not require frequent calculation and feedback of physical layer parameters of multi-base station cooperation within each 1 millisecond subframe transmission time, which can greatly reduce the calculation and communication overhead of the HCI method, and provide a feasible reference solution for the design of multi-base station cooperative transmission technology.

[0106] An embodiment of the present invention also discloses a computer system, including a memory, a processor, and a computer program / instruction stored in the memory and executable on the processor. When the computer program / instruction is executed by the processor, the steps of the 6G fully decoupled network downlink multi-base station collaborative transmission method without physical layer feedback are implemented.

[0107] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A 6G fully decoupled network downlink multi-base station cooperative transmission method without physical layer feedback, characterized in that: The following steps are involved: Step 1: Based on the multipath channel model of clustered delay line (CDL), a closed-form expression for multi-base station cooperative transmission is established; Step 2: Calculate the precoding indicator (PMI), rank indicator (RI) and channel quality indicator (CQI) of multi-base station cooperative transmission based on historical channel data; Step 3: Count the mode values ​​of PMI, RI and CQI in the historical channel data, use them as fixed physical layer parameters for multi-base station cooperative transmission, and apply them to downlink transmission of multiple subframes.

2. According to a 6G fully decoupled network downlink multi-base station cooperative transmission method without physical layer feedback according to claim 1, it is characterized by: In step 1, CDL is used to model the channel impulse response (CIR), considering that each user equipment (UE) is equipped with N r Each downlink base station (DBS) is equipped with N receiving antennas. t transmit antennas; CIRh between the rth receive antenna and the tth transmit antenna r,t (τ), by combining the line-of-sight (LoS) channel and non-line-of-sight (NLoS) channels h r，t,cp (τ, Δτ c,p ) components are added together to describe the mathematical expression as follows: Among them, τ represents the instantaneous time, Δτ LoS and Δτ c，p are the time delays of LoS and NLoS paths respectively, c∈[1,C] represents the modeled channel cluster, is the subpath of cluster c, G LoS and G c，p are the channel gain of the LoS path and the channel gain of the cluster subpath, respectively, and the Ricean factor K R Describes the power scaling relationship between LoS paths and NLoS paths, and are the initial phase of the channel path, f c is the carrier signal frequency, δ(·) is the Dirac delta function used to simulate the unit channel pulse including the time delay effect.

3. According to a 6G fully decoupled network downlink multi-base station cooperative transmission method without physical layer feedback according to claim 1, it is characterized by: All the downlink base stations DBS of the cooperative transmission will send the same OFDM symbol to obtain diversity gain. Assuming that all the downlink base stations All transmission path delays are less than the duration of the cyclic prefix (CP), and the discrete received signal at UE position i on subcarrier k and receiving antenna r is The closed form expression for is calculated as follows: in, is the downlink base station set, D is the number of downlink base stations, N t For each downlink base station transmit antenna number, is the frequency domain discrete channel between DBSd and UE position i, s i [k] and n i [k] are the discrete transmitted signal and received noise in the frequency domain, respectively.

4. According to a 6G fully decoupled network downlink multi-base station cooperative transmission method without physical layer feedback according to claim 1, it is characterized by: In step 2, it is determined that the PMI of the downlink multi-base station cooperation is in the predefined codebook set Find the optimal precoding matrix that maximizes the mutual information entropy over the entire system bandwidth and subframe duration The calculation expression is as follows: Where K is the number of subcarriers, B is the number of OFDM symbols, D is the number of downlink base stations, W d,k is the precoding matrix of the downlink base station DBS d on subcarrier k, represents the mutual information entropy of the kth subcarrier on the bth OFDM symbol, L i is the number of MIMO transmission streams, represents the signal to interference and noise ratio of the signal of the lth stream after equalization; The precoding rank is equal to the number of MIMO transmission streams L i ; Optimal precoding matrix Determine the optimal number of MIMO streams at the same time It is expressed as follows: in, is the downlink base station set, is a subcarrier set.

5. According to a 6G fully decoupled network downlink multi-base station cooperative transmission method without physical layer feedback according to claim 4, it is characterized by: Solving for the optimal precoding matrix When using DN t Precoding matrix for transmit antenna Approximate the joint precoding of D base stations and obtain the optimal precoding matrix by maximizing the following mutual information entropy in, Defined as having DN t The precoding set used by the transmitting antennas; by traversing the set Calculated Number of data streams transmitted by multiple base stations in collaboration 6. According to claim 4, a 6G fully decoupled network downlink multi-base station cooperative transmission method without physical layer feedback is characterized by: Solving for the optimal precoding matrix When , by traversing the codebook from the first base station, based on the channel H of base station 1 1，k Get the optimal precoding matrix of base station 1 Then based on the joint channel [H 1，k , H 2，k ], and the optimal precoding matrix of base station 1 Get the optimal precoding matrix of base station 2 Until the Dth base station.

7. According to claim 4, a 6G fully decoupled network downlink multi-base station cooperative transmission method without physical layer feedback is characterized by: Multiple base stations use the same RI, as shown below:

8. The 6G fully decoupled network downlink multi-base station cooperative transmission method without physical layer feedback according to claim 1, characterized in that: In step 2, the CQI selection of downlink multi-base station cooperative transmission follows the single base station-user equipment connection strategy, processes the equalized SINR on the used frequency-time resources, and uses the exponential equivalent SINR mapping (EESM) method to map the equalized SINR value into an equivalent signal-to-noise ratio SNR on the additive white Gaussian noise AWGN channel. eff Value; SNR eff The AWGN channel has a block error rate similar to that of the original OFDM system; assuming there are E equalized SINR values, the EESM is expressed as: Among them, β eff is a set of predefined values ​​for different modulation and coding schemes (MCS), which are obtained by different physical layer simulation software through a large number of transmission tests; different β eff Will result in different SNR eff The optimal CQI value for downlink multi-base station cooperation is the maximum SNR that satisfies the average transmission block error rate (BLER) less than the preset empirical threshold ∈ eff Determine and pass SNR eff The CQI mapping table is obtained as follows:

9. The 6G fully decoupled network downlink multi-base station cooperative transmission method without physical layer feedback according to claim 1, characterized in that: The fixed physical layer parameters of multi-base station cooperative transmission are obtained through offline calculation based on the historical channel information at different locations. First, the PMI, RI and CQI of multi-base station cooperative transmission are calculated based on the historical channel data of each subframe. Then, the mode values ​​of PMI, RI and CQI used in all historical channel data are counted and used as the fixed physical layer parameters of multi-base station cooperative transmission. Finally, the fixed physical layer parameters are applied to the downlink transmission of multiple subframes.

10. A computer system comprising a memory, a processor, and a computer program / instruction stored in the memory and executable on the processor, characterized in that: When the computer program / instructions are executed by the processor, the steps of a 6G fully decoupled network downlink multi-base station collaborative transmission method without physical layer feedback are implemented according to any one of claims 1-9.

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