FDD cell-free large-scale MIMO downlink channel estimation method based on compressed sensing technology

Abstract

The invention discloses an FDD cell-free large-scale MIMO downlink channel estimation method based on a compressed sensing technology, and relates to the technical field of signal transmission. Comprising the following steps: each AP periodically broadcasts downlink pilot signals to K UE terminals; receiving the downlink pilot signal through the K UE terminals, and executing a channel estimation algorithm based on a compressed sensing OMP algorithm to obtain a channel estimation result of each UE terminal; and quantizing the channel estimation result, and feeding back the quantized channel estimation result to the AP side. The problems of overlarge pilot frequency overhead and overlarge calculation overhead in traditional downlink channel acquisition are solved.

Description

Technical Field

[0001] This specification relates to the field of signal transmission technology, and in particular to a downlink channel estimation method for FDD cell-free massive MIMO based on compressed sensing technology. Background Technology

[0002] With the development of massive MIMO technology, FDD cell-free massive MIMO technology breaks through the cell boundary constraints of traditional cellular networks and ensures that users enjoy a unified high-quality service by deploying a large number of access points (APs) in a collaborative manner.

[0003] In traditional TDD massive MIMO systems, orthogonal pilots are used to acquire uplink and downlink channel state information. However, the orthogonal pilot-based channel estimation method suffers from excessive pilot overhead and computational overhead in downlink channel acquisition in FDD cell-free massive MIMO systems. Summary of the Invention

[0004] The purpose of this invention is to provide a downlink channel estimation method for FDD cell-free massive MIMO based on compressed sensing technology, in order to solve the problems of excessive pilot overhead and excessive computational overhead in traditional downlink channel acquisition.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] On the one hand, this specification provides a downlink channel estimation method for FDD cell-free massive MIMO based on compressed sensing technology, including:

[0007] Each AP periodically broadcasts downlink pilot signals to K UE terminals;

[0008] By receiving the downlink pilot signal through the K UE terminals, a channel estimation algorithm based on the compressed sensing OMP algorithm is executed to obtain the channel estimation result for each UE terminal;

[0009] The channel estimation results are quantized, and the quantized channel estimation results are fed back to the AP side.

[0010] On the other hand, this specification also provides an FDD cell-free massive MIMO downlink channel estimation device based on compressed sensing technology, comprising:

[0011] The downlink broadcast module is used by each AP to periodically broadcast downlink pilot signals to K UE terminals;

[0012] The channel estimation module is used to receive the downlink pilot signal through the K UE terminals, execute the channel estimation algorithm based on the compressed sensing OMP algorithm, and obtain the channel estimation result for each UE terminal;

[0013] The quantization feedback module is used to quantize the channel estimation result and feed the quantized channel estimation result back to the AP side.

[0014] On the other hand, this specification also provides an electronic device,

[0015] Processor; and

[0016] A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the method as described in any of the preceding claims.

[0017] Based on the above technical solution, this specification can achieve the following technical effects:

[0018] This invention utilizes the OMP algorithm based on compressed sensing to estimate the downlink channel state information of an FDD cell-free massive MIMO system using non-orthogonal pilot signals. This solves the problem of high pilot overhead in orthogonal pilot-based channel estimation methods, thus improving communication spectrum efficiency. Simultaneously, it addresses the issues of high computational cost and high feedback overhead in orthogonal pilot estimation for FDD cell-free massive MIMO downlink channel. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating an FDD cell-free massive MIMO downlink channel estimation method based on compressed sensing technology, provided in Embodiment 1 of this specification.

[0020] Figure 2 This is a schematic diagram of the pilot arrangement of the downlink provided in Embodiment 1 of this specification;

[0021] Figure 3 This is a flowchart of the downlink channel estimation based on the OMP algorithm provided in Example 1 of this specification;

[0022] Figure 4 This is a schematic diagram of an FDD cell-free massive MIMO downlink channel estimation device based on compressed sensing technology, provided in Embodiment 2 of this specification. Detailed Implementation

[0023] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer from the following description and claims. It should be noted that the drawings are all in a very simplified form and are not to a precise scale, and are only used to facilitate and clarify the illustration of the embodiments of the present invention.

[0024] It should be noted that, in order to clearly illustrate the content of this invention, several embodiments are provided to further explain different implementations of the invention. These embodiments are enumerated rather than exhaustive. Furthermore, for the sake of brevity, content mentioned in the preceding embodiments is often omitted in the following embodiments. Therefore, content not mentioned in the later embodiments can be referred to in the preceding embodiments.

[0025] Example 1

[0026] Please refer to Figure 1 , Figure 1 The diagram shown is a flowchart illustrating a downlink channel estimation method for FDD cell-free massive MIMO based on compressed sensing technology provided in this embodiment. The method specifically includes the following steps:

[0027] Step 102: Each AP periodically broadcasts downlink pilot signals to K UE terminals.

[0028] It should be noted that one possible implementation of step 102 is as follows:

[0029] In an FDD cell-free massive MIMO system, the downlink channel is divided into N subcarriers using OFDM, with each 12 subcarriers forming a resource block. Downlink transmission scheduling is based on resource blocks, and the downlink pilot has a total frequency of τ. p The pilot symbol will occupy [number] symbols. OFDM subcarriers, of which This indicates rounding up. For channel estimation, please refer to... Figure 2 At least one set of pilot signals needs to be inserted within each coherent bandwidth. After channel estimation is completed, the channels corresponding to the remaining resource blocks will be obtained through interpolation.

[0030] Next, each AP in the system periodically broadcasts a downlink pilot signal x to all UEs. The pilot signal broadcast by each AP consists of a unique pilot sequence; the pilot signal of the m-th AP is... in This is the pilot sequence code corresponding to the m-th AP. The pilot signal power is constrained by the AP's transmit power. Where P ap It is the transmission power of the pilot signal, τ p is the pilot length.

[0031] Based on this, the present invention uses non-orthogonal pilot signals for downlink channel estimation in FDD cell-free massive MIMO systems. Compared with orthogonal pilot signals, non-orthogonal pilot signals reduce pilot signal overhead.

[0032] Step 104: Receive the downlink pilot signal through the K UE terminals, execute the channel estimation algorithm based on the compressed sensing OMP algorithm, and obtain the channel estimation result for each UE terminal.

[0033] It should be noted that one implementation of step 104 can be:

[0034] Each AP's pilot signal undergoes varying degrees of fading during transmission before being received by the UE. The downlink pilot signal received by the K UEs in the system is composed of the superposition of downlink pilot signals simultaneously transmitted by all APs. The signal received by the k-th UE is represented as...

[0035]

[0036] Among them, g mk Let x be the channel coefficient between the m-th AP and the k-th UE terminal. mk w is the pilot signal sent by the m-th AP and transmitted to the k-th UE terminal. k Let be the additive Gaussian noise of the k-th UE terminal.

[0037] During the channel estimation phase, after the k-th UE terminal receives the non-orthogonal pilot signals transmitted by M APs, it executes a channel estimation algorithm based on compressed sensing OMP locally to complete downlink channel estimation and obtain downlink channel state information from the M APs to the k-th UE terminal in the FDD cell-free massive MIMO system. Please refer to [link to relevant documentation]. Figure 3 The channel estimation process based on the compressed sensing OMP algorithm is described as follows:

[0038] S1. Initialize variable r i =y k ,

[0039] S2, Calculation Where <·,·> denote the inner product of two vectors, and the calculated result is λ. i It receives the downlink pilot signal r i-1 With pilot sequence code The index value j that has the largest inner product;

[0040] S3, λ i Add to support In this context, the support is a set containing the index values ​​of the top P pilot sequences that are most correlated with the received downlink pilot signal, where P is determined by the algorithm parameter threshold.

[0041] S4. Solving the least squares problem The solution is Where ||·||2 represents the 2-norm of the vector, This indicates a pseudo-inverse operation. express The index is The non-zero values ​​of are determined by the least squares solution;

[0042] S5. Calculate the residuals vector Assign the residual value to r for the next iteration. i And return to step S2 until the residual r i Output the result after the value is less than the threshold or the maximum number of iterations is reached.

[0043] Based on this, the present invention uses the OMP algorithm of compressed sensing to complete the downlink channel estimation, which can reduce the amount of computation and feedback overhead compared with the estimation algorithm based on orthogonal pilots.

[0044] Step 106: Quantize the channel estimation result and feed the quantized channel estimation result back to the AP side.

[0045] It should be noted that one implementation of step 106 can be:

[0046] For the estimation results Quantification It can be represented by a quantization function The estimation results are fed back to the AP / CPU side via the uplink.

[0047] Based on this, the present invention quantizes the channel estimation results and then feeds them back, thereby reducing the overhead of channel estimation feedback.

[0048] This completes the acquisition of downlink channel information for the FDD cell-free massive MIMO system. A comparison of the quantization feedback overhead of different channel estimation schemes is shown in Table 1 below.

[0049] Table 1 Comparison of quantization feedback overhead for different channel estimation schemes

[0050]

[0051]

[0052] In summary, this invention utilizes a non-orthogonal pilot signal to estimate the downlink channel state information of an FDD cell-free massive MIMO system using an OMP algorithm based on compressed sensing. This addresses the problem of high pilot overhead in orthogonal pilot-based channel estimation methods, thereby improving communication spectrum efficiency. Simultaneously, it solves the problems of high computational cost and high feedback overhead in orthogonal pilot estimation for FDD cell-free massive MIMO downlink channel estimation.

[0053] Example 2

[0054] Please refer to Figure 4 , Figure 4 The diagram shown is a schematic of an FDD cell-free massive MIMO downlink channel estimation device based on compressed sensing technology provided in this embodiment. The device includes:

[0055] Downlink broadcast module 202 is used for each AP to periodically broadcast downlink pilot signals to K UE terminals;

[0056] The channel estimation module 204 is used to receive the downlink pilot signal through the K UE terminals, execute the channel estimation algorithm based on the compressed sensing OMP algorithm, and obtain the channel estimation result for each UE terminal;

[0057] The quantization feedback module 206 is used to quantize the channel estimation result and feed the quantized channel estimation result back to the AP side.

[0058] In summary, this invention utilizes a non-orthogonal pilot signal to estimate the downlink channel state information of an FDD cell-free massive MIMO system using an OMP algorithm based on compressed sensing. This addresses the problem of high pilot overhead in orthogonal pilot-based channel estimation methods, thereby improving communication spectrum efficiency. Simultaneously, it solves the problems of high computational cost and high feedback overhead in orthogonal pilot estimation for FDD cell-free massive MIMO downlink channel estimation.

[0059] Example 3

[0060] In another feasible embodiment, this embodiment provides a device for downlink channel estimation of FDD cell-free massive MIMO based on compressed sensing technology, the device specifically including:

[0061] Processor; and

[0062] A memory is configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps as described in any of the above method embodiments.

[0063] Example 4

[0064] In another feasible embodiment, this embodiment provides a storage medium for downlink channel estimation of FDD cell-free massive MIMO based on compressed sensing technology, wherein the storage medium may specifically include:

[0065] The storage medium stores a processing program for downlink channel estimation of FDD cell-free massive MIMO based on compressed sensing technology. When the processing program for downlink channel estimation of FDD cell-free massive MIMO based on compressed sensing technology is executed by the processor, it implements the steps as described in any of the above method embodiments.

[0066] The above description is merely an embodiment of this specification and is not intended to limit this specification. Various modifications and variations can be made to this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of the claims of this specification.

Claims

1. A downlink channel estimation method for FDD cell-free massive MIMO based on compressed sensing technology, characterized in that, include: Each AP periodically broadcasts downlink pilot signals to K UE terminals; By receiving the downlink pilot signal through the K UE terminals, a channel estimation algorithm based on the compressed sensing OMP algorithm is executed to obtain the channel estimation result for each UE terminal; The channel estimation results are quantized, and the quantized channel estimation results are fed back to the AP side.

2. The method according to claim 1, characterized in that, The downlink pilot signal is a non-orthogonal downlink pilot signal.

3. The method according to claim 2, characterized in that, Before each AP periodically broadcasts the downlink pilot signal to K UE terminals, the following is also included: The downlink channel of FDD cell-free massive MIMO is divided into N subcarriers using OFDM, and each 12 subcarriers form a resource block. Downlink transmission scheduling is based on resource blocks. At least one set of the downlink pilot signals is inserted within each coherent bandwidth, and the pilot length of the downlink pilot signals is τ. p .

4. The method according to claim 3, characterized in that, The downlink pilot signal broadcast by each AP consists of a unique pilot sequence.

5. The method according to claim 4, characterized in that, The power of the downlink pilot signal needs to meet the following conditions: Among them, P ap x represents the AP's transmit power; x represents the downlink pilot signal. Let x be the downlink pilot signal of the m-th AP. m The conjugate transpose of .

6. The method according to claim 5, characterized in that, The step of receiving the downlink pilot signal through the K UE terminals includes: The downlink pilot signal received by each UE terminal is composed of the superposition of downlink pilot signals transmitted simultaneously by all APs, and is represented as: Among them, g mk x is the channel coefficient between the m-th AP and the k-th UE terminal; mk w is the pilot signal sent by the m-th AP and transmitted to the k-th UE terminal. k The additive Gaussian noise of the k-th UE terminal; Let M be the pilot sequence code corresponding to the m-th AP; M is the total number of APs.

7. The method according to claim 6, characterized in that, The process of receiving the downlink pilot signal through the K UE terminals and executing a channel estimation algorithm based on the compressed sensing OMP algorithm to obtain the channel estimation result for each UE terminal includes: S1. Initialize variable r i =y k , S2, Calculation Where <·,·> denote the inner product of two vectors, and the calculated result is λ. i It receives the downlink pilot signal r i-1 With pilot sequence code The index value j that has the largest inner product; S3, λ i Add to support In this context, the support is a set containing the index values ​​of the top P pilot sequences that are most correlated with the received downlink pilot signal, where P is determined by the algorithm parameter threshold. S4. Solving the least squares problem The solution is Where ||·||2 represents the 2-norm of the vector, This indicates a pseudo-inverse operation. express The index is The non-zero values ​​of are determined by the least squares solution; S5. Calculate the residuals vector Assign the residual value to r for the next iteration. i And return to step S2 until the residual r i Output the result after the value is less than the threshold or the maximum number of iterations is reached.

8. The method according to claim 7, characterized in that, The step of quantizing the channel estimation result and feeding the quantized channel estimation result back to the AP side includes: The channel estimation result is quantized using a quantization function, which is expressed as follows: in, This is the quantized channel estimation result.

9. A downlink channel estimation device for FDD cell-free massive MIMO based on compressed sensing technology, characterized in that, include: The downlink broadcast module is used by each AP to periodically broadcast downlink pilot signals to K UE terminals; The channel estimation module is used to receive the downlink pilot signal through the K UE terminals, execute the channel estimation algorithm based on the compressed sensing OMP algorithm, and obtain the channel estimation result for each UE terminal; The quantization feedback module is used to quantize the channel estimation result and feed the quantized channel estimation result back to the AP side.

10. An electronic device, characterized in that, include: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the method as described in any one of claims 1 to 8.

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