Compressed sensing assisted distributed pilot frequency distribution and channel estimation method and device

Abstract

The invention provides a compressed sensing assisted distributed pilot frequency distribution and channel estimation method and device. The method comprises the following steps: automatically distributing an access point for each user by adopting a distributed algorithm; configuring each access point to independently associate with a user on the uppermost layer by using different pilot signals; dividing a to-be-estimated channel in each subcarrier of the current frame into a dense part and a sparse part by utilizing the space public sparsity and the prior support information of the previous frame; carrying out dense part estimation and sparse part estimation in the angle domain; and synthesizing the estimation of the dense part and the sparse part to obtain the estimation of the whole channel in the angle domain. According to the invention, the delay of the distributed antenna system can be reduced, the overall spectrum efficiency is improved, the channel estimation precision is ensured, the consumption of pilot frequency resources is greatly reduced, the system resource allocation is optimized, and the problems of pilot frequency multiplexing and pilot frequency pollution generated by a traditional method are effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a compressed sensing-assisted distributed pilot allocation and channel estimation method and device. Background Art

[0002] In a distributed antenna system, channel resources need to be managed effectively to support the simultaneous access of a large number of users and maintain high-quality services. Multi-antenna technology improves spectrum efficiency through spatial diversity and multiplexing, but at the user equipment (UE) end, due to the limitation of physical space, the closely arranged integrated multi-antenna array will cause spatial correlation fading of user nodes, that is, the signals between antennas interfere with each other. Therefore, each user equipment is required to use more pilot signals to estimate channel state information to overcome the fading effect.

[0003] However, in limited time-frequency resources, increasing the number of pilot signals will reduce the number of users that can be served simultaneously, because each user node requires a dedicated pilot sequence to avoid interference. Therefore, the pilot must be shared between multi-antenna user nodes to ensure that more users are served simultaneously in the same time-frequency resource block, but this also introduces the "pilot pollution" problem. When pilot pollution occurs, the pilot signals of different users cannot be fully distinguished at the receiving end, resulting in a decrease in the accuracy of channel estimation, which in turn affects the downlink and generates coherent interference. Therefore, it is necessary to effectively solve the pilot allocation demand problem caused by the spatial correlation fading of multi-antenna user nodes, as well as the pilot pollution and channel estimation accuracy problems caused by it. Summary of the invention

[0004] The present invention provides a compressed sensing-assisted distributed pilot allocation and channel estimation method and device, which solves the problems of effective utilization of pilot resources and accuracy of channel estimation in wireless communications.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, a compressed sensing-assisted distributed pilot allocation and channel estimation method is provided, comprising the following steps:

[0007] Use a distributed algorithm to automatically assign an access point to each user;

[0008] configuring each of the access points to independently associate with the top-level user using a different pilot signal;

[0009] By using the spatial common sparsity and prior support information of the previous frame, the channel to be estimated in each subcarrier of the current frame is divided into a dense part and a sparse part;

[0010] Dense part estimation and sparse part estimation are performed in the angle domain;

[0011] The estimates of the dense part and the sparse part are synthesized to obtain an estimate of the entire channel in the angle domain.

[0012] In a first possible implementation manner of the first aspect, automatically allocating an access point to each user using a distributed algorithm includes the following steps:

[0013] Calculating the difference between the largest and second largest large-scale fading coefficients of each user, and determining an intermediate threshold; preliminarily selecting a candidate set of access points for each user based on the difference and the intermediate threshold;

[0014] Traversing each of the access points, and updating the access point candidate set according to the difference between the maximum fading coefficient of the user and the fading coefficient of each of the access points and the intermediate threshold;

[0015] Randomly designate an access point in the access point candidate set as an access point for the user;

[0016] Recording the designated access points and their associated users, and checking the load of the access points;

[0017] When the number of users of the access point exceeds the preset capacity, it is marked as an oversaturated state; if it is equal to the preset capacity, it is marked as an inert state;

[0018] If there are access points in the oversaturated state, re-evaluate and preferentially retain users with high fading coefficients associated with the access points in the oversaturated state, and randomly re-assign non-overloaded access points to users that exceed capacity limits;

[0019] After the adjustment of the user access points is completed, the configuration information of each access point and its user is updated.

[0020] Based on the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, the step of automatically allocating an access point to each user using a distributed algorithm is performed in cooperation between the access point end and the user end.

[0021] In a third possible implementation manner of the first aspect, configuring each access point to independently associate with a top-level user using a different pilot signal includes the following steps:

[0022] Traversing all access points and corresponding users, and constructing a pollution matrix for recording whether any two of the users are assigned the same pilot;

[0023] Allocate a pilot frequency that is not occupied by other users to a user that has not yet been assigned a pilot frequency and has an available pilot frequency set;

[0024] Notify other related access points about pilot allocation information of a specific user;

[0025] Traverse each access point to check whether the same pilot is allocated to two or more users. If so, keep the pilot with a smaller cardinality value and remove the other pilot allocation;

[0026] The set of available pilots for a particular user is updated by removing pilots for other users.

[0027] Based on the third possible implementation manner of the first aspect, in a fourth possible implementation manner of the first aspect, the step of configuring each access point to independently associate with a top-level user using a different pilot signal is performed in parallel at the access point end.

[0028] Based on the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner of the first aspect, the estimating the dense part and the estimating the sparse part in the angle domain includes the following steps:

[0029] Estimating a dense portion of the angle domain channel: estimating channel coefficients of the dense portion using a least squares method;

[0030] Sparse part estimation of the angle domain channel: using compressed sensing to assist in estimating the channel coefficients of the sparse part.

[0031] Based on the fifth possible implementation manner of the first aspect, in a sixth possible implementation manner of the first aspect,

[0032] Length L p The pilot signal is used to detect the channel at time t, and the signal received at the kth subcarrier is:

[0033]

[0034] in, is additive Gaussian white noise, ρ is the signal-to-noise ratio;

[0035] The pilot structure is:

[0036]

[0037] in, and Used to detect angle domain coefficients and

[0038] Using the prior support information in the previous channel block, the training signal received by the user side in the frequency domain k subcarrier includes two parts, namely:

[0039]

[0040] Dense part angle domain coefficient pass Make an estimate:

[0041]

[0042] Angle domain coefficient is a sparse vector, which is expressed as a sparse recovery problem using compressed sensing. Further using the common sparsity shared by subchannels associated with different subcarriers, the channel information collection of the sparse parts of different subcarriers is expressed as the following optimization:

[0043]

[0044] Ω u,1 =Ω u,2 =…=Ω u,K

[0045] in, For the measurement matrix that satisfies the finite isometry property (RIP), Ω u,k is the channel vector support set of user u in subcarrier k;

[0046] The optimization problem is solved by using distributed compressed sensing, which specifically includes the following steps:

[0047] S501, initialize the residual vector Support set Γ 0 =φ, channel coefficient α in the angle domain k =0, iteration index i=1;

[0048] S502, calculating the distributed correlation of different subcarriers k:

[0049] S503, maximum correlation index between subcarriers:

[0050] S504, storing the maximum correlation index in the support set: Γ i =Γ i-1 ∪γ;

[0051] S505, using the least square method to calculate the channel coefficient of the corresponding support set at subcarrier k:

[0052]

[0053] S506, update the residual vector in a distributed manner and input it to the next iteration:

[0054]

[0055] S507, repeat S502-S506 until i>S n , the iteration ends, and the channel coefficient of each subcarrier is obtained:

[0056] For all subcarriers, the formula The channel vectors are combined to obtain an estimate of the entire channel in the angular region.

[0057] In a second aspect, a compressed sensing-assisted distributed pilot allocation and channel estimation device is provided, the device comprising:

[0058] An access point selection module, for automatically assigning an access point to each user using a distributed algorithm;

[0059] A distributed pilot allocation module, used to configure each of the access points to independently associate with the top-level user using a different pilot signal;

[0060] A distributed antenna system downlink channel division module is used to divide the channel to be estimated in each subcarrier of the current frame into a dense part and a sparse part by using the spatial common sparsity and prior support information of the previous frame;

[0061] An angle domain channel estimation module, used for performing dense part estimation and sparse part estimation in the angle domain;

[0062] The channel synthesis module is used to synthesize the estimation of the dense part and the sparse part to obtain an estimation of the entire channel in the angle domain.

[0063] According to a third aspect, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the compressed sensing-assisted distributed pilot allocation and channel estimation method as described in the first aspect.

[0064] In a fourth aspect, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the compressed sensing-assisted distributed pilot allocation and channel estimation method as described in the first aspect are implemented.

[0065] The present application aims to solve the problem of estimation errors and system performance degradation caused by pilot multiplexing in wireless communication systems. The present application adopts a distributed method to jointly perform pilot allocation and access point-user association, which can ensure that each access point is independently associated with the top-level user using different pilots, reduce the delay of the distributed antenna system, improve the overall spectrum efficiency, effectively reduce the pilot multiplexing and pilot pollution problems, and has great advantages in scalability and overall system throughput.

[0066] At the same time, the channel structure with spatial co-sparseness and partial co-sparseness of the channel vector in several consecutive frames is fully utilized, and the least squares and compressed sensing methods are combined to estimate the two parts of the channel vector in the angular domain of the current frame. While ensuring the accuracy of channel estimation, the consumption of pilot resources is greatly reduced and the system resource allocation is optimized. It not only effectively solves the problems of effective utilization of pilot resources and channel estimation accuracy in wireless communications, but also improves the overall performance of wireless communication systems, providing key technical support for the efficient and stable operation of modern wireless networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic diagram of pilot pollution provided in an embodiment of the present application;

[0068] Figure 2 A schematic flow chart of a compressed sensing-assisted distributed pilot allocation and channel estimation method according to an embodiment of the present application;

[0069] Figure 3 A schematic flow chart of another compressed sensing-assisted distributed pilot allocation and channel estimation method according to an embodiment of the present application;

[0070] Figure 4 A schematic flow chart of an access point selection algorithm according to an embodiment of the present application;

[0071] Figure 5 A schematic flow chart of distributed pilot allocation according to an embodiment of the present application;

[0072] Figure 6 A schematic flow chart of a distributed antenna system channel estimation algorithm according to an embodiment of the present application;

[0073] Figure 7 A structural block diagram of a compressed sensing-assisted distributed pilot allocation and channel estimation device according to an embodiment of the present application;

[0074] Figure 8 A schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0075] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose, the technical solutions in the embodiments of the present application are clearly described. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present application.

[0076] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0077] The description of the method flow in the specification of the present application and the steps of the flowchart in the drawings of the present specification do not have to be strictly executed according to the step numbers, and the method steps can be executed in a different order. Moreover, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0078] The following is a detailed description of the compressed sensing-assisted distributed pilot allocation and channel estimation method and device provided in the embodiment of the present application in conjunction with the accompanying drawings and preferred embodiments.

[0079] First, an application scenario of a compressed sensing-assisted distributed pilot allocation and channel estimation method and device in an embodiment of the present application is described in detail.

[0080] In a distributed antenna system, antennas are completely distributed over a larger geographical area or can be clustered on access points (APs) that are deployed anywhere in the coverage area of ​​a cell. These distributed APs are connected to a central processor via high-capacity backhaul links.

[0081] Traditional multi-antenna user nodes can improve spectral efficiency through the additional diversity / multiplexing gain provided by the multi-antenna array. However, at the user (UE) end, user nodes are usually spatially constrained, so multiple antennas must be closely arranged. On the one hand, these compact antenna arrays will lead to spatially correlated fading of user nodes, and on the other hand, additional antennas translate into more uplink pilots. However, in the case of orthogonal pilot allocation, the number of user nodes served simultaneously must be sacrificed, so pilots must be shared between multi-antenna user nodes to ensure that more users are served simultaneously in the same time-frequency resource block.

[0082] This pilot sharing will cause pilot pollution during the uplink channel estimation of each AP, see Figure 1, when multiple devices (such as smartphones) are connected to multiple access points (such as base stations or antenna towers), each device requires a unique pilot signal for channel estimation and data transmission. If two or more devices use the same pilot signal (such as p1), their channels will be cross-contaminated, that is, the channel estimation between devices will be affected by other devices. This situation is called pilot contamination. This contamination will reduce the accuracy of channel estimation, which in turn affects the quality and rate of data transmission, resulting in coherent interference in downlink transmission. Therefore, innovations in pilot allocation and channel estimation must be made to reduce pilot contamination and reduce pilot overhead.

[0083] In response to the above-mentioned pilot multiplexing and pilot pollution problems, this application adopts a distributed algorithm to select a controller-access point for each user (UE), and then allocates pilots through a distributed method of AP-UE association to achieve the purpose of reducing pilot pollution. Then the channel to be estimated is divided into two parts: a dense part and a sparse part. The least squares and compressed sensing methods are used to estimate the channel coefficients of the two parts respectively. Finally, the two parts are combined to obtain an estimate of the entire channel in the angle domain, which can effectively reduce the pilot overhead.

[0084] The present application embodiment provides a compressed sensing assisted distributed pilot allocation and channel estimation method, see Figure 2-3 As shown in the figure, the pilot allocation and channel estimation method of the embodiment of the present application includes the following steps:

[0085] Step 100: Use a distributed algorithm to automatically assign an access point to each user.

[0086] Access Point Allocation,In a distributed antenna system, each user follows the standard random access,procedure, and a specific access point is assigned to each UE as,the controller-access point through a distributed algorithm, thus ensuring that each UE is,served by at least one AP and manages the pilot allocation.

[0087] Step 200, configure each access point to independently associate with the top-level user using different pilot signals.

[0088] Distributed pilot allocation: Each access point uses different pilot signals to independently associate with the top-level user, that is, each AP independently allocates different pilot signals to the top-level user it is directly associated with, which can reduce the delay of the distributed antenna system and improve the overall spectrum efficiency. The distributed method of AP-UE association is used to assist in eliminating pilot pollution.

[0089] Step 300, using the spatial common sparsity and prior support information of the previous frame, the channel to be estimated in each subcarrier of the current frame is divided into a dense part and a sparse part.

[0090] The downlink channel of the distributed antenna system is divided based on the spatiotemporal characteristics of the channel. Using the results of the channel estimation of the previous frame, the current channel to be estimated is decomposed into two parts: the "dense" (strong signal, high correlation) and "sparse" (weak signal, compressible) parts in the frequency domain (subcarrier), in preparation for the subsequent differentiated processing.

[0091] Step 400, estimating the dense part of the angle domain channel: using the least square method to estimate the channel coefficients of the dense part.

[0092] The channel coefficients in the angle domain are processed to accurately estimate the strong signal part of the channel. The channel coefficients of the dense part are estimated using the traditional least squares (LS) method. The LS algorithm processes the dense part with strong signals and high correlation, and can effectively extract this part of the channel information.

[0093] Step 500, estimating the sparse part of the angle domain channel: using compressed sensing to assist in estimating the channel coefficients of the sparse part.

[0094] For the sparse part, the compressed sensing technology (DCS) is used for estimation. The DCS algorithm processes the sparse part where the signal is weak and sparse. It can use the sparsity of the signal to recover the signal with only a small number of measurements, significantly reducing the demand for pilots.

[0095] Step 600, combining the estimation results of the dense part and the sparse part to obtain an estimation of the entire channel in the angle domain.

[0096] The channel estimation results of the dense part and the sparse part obtained in step 400 and step 500 are combined in the angle domain, and the channel estimation results are integrated to finally obtain a comprehensive estimation of the entire channel and obtain complete channel information.

[0097] See also Figure 4 Further, the above step 100 uses a distributed algorithm to automatically allocate an access point to each user, including the following steps:

[0098] Step 110, calculating the difference between the largest and second largest large-scale fading coefficients of each user, and determining an intermediate threshold; based on the difference and the intermediate threshold, preliminarily selecting a candidate set of access points for each user;

[0099] Step 120, traverse each access point, and update the access point candidate set according to the difference between the maximum fading coefficient of the user and the fading coefficient of each access point and the intermediate threshold;

[0100] Step 130, randomly designate an access point in the access point candidate set as the user's access point;

[0101] Step 140, recording the designated access points and their associated users, and checking the load of the access points;

[0102] Step 150, when the number of users of the access point exceeds the preset capacity, it is marked as an oversaturated state; if it is equal to the preset capacity, it is marked as an inert state;

[0103] Step 160: if there are oversaturated access points, re-evaluate and preferentially retain high fading coefficient users associated with these oversaturated access points, and randomly re-assign non-overloaded access points to users that exceed capacity limits;

[0104] Step 170: After the adjustment of the user access points is completed, the configuration information of each access point and its user is updated.

[0105] See also Figure 5 Further, the above step 200 configures each access point to independently associate with the top-level user using different pilot signals, including the following steps:

[0106] Step 210, traverse all access points and corresponding users, and construct a pollution matrix to record whether any two users are assigned the same pilot;

[0107] Step 220, allocating a pilot frequency that is not occupied by other users to a user that has not yet been allocated a pilot frequency and has an available pilot frequency set;

[0108] Step 230, notifying other related access points of pilot allocation information about a specific user;

[0109] Step 240, traverse each access point to check whether the same pilot is allocated to two or more users. If so, retain the pilot with a smaller cardinality value and remove the other pilot allocation;

[0110] Step 250, update the available pilot set of a particular user by removing pilots of other users. For those users whose allocations need to be removed due to conflict resolution, update their available pilot sets to remove pilots that have been allocated to other users.

[0111] The above steps 100-600 are exemplarily described below through a specific implementation process.

[0112] In a distributed antenna system, assume that the distributed antenna system consists of U UEs and M APs. The set of UEs is denoted as U, and the set of APs is denoted as M. p is the number of mutually orthogonal pilots, L for each resource block p The length is used for pilot training and the rest is used for information transmission. is the set of UEs with APm as AP. is the available pilot set of UEu, initially including all L p Pilot. UE receives signals from a collection of APs Assign an AP as its AP.

[0113] Step 100 is used to implement access point allocation, and specifically includes the following steps:

[0114] Step 101, calculate the difference between the largest and second largest large-scale fading coefficient (LSFC value) of each UEu, denoted as calculate Calculate the value of each UEu As its m max , and update the collection for

[0115] Step 102, traverse APm and determine If the value is less than or equal to γ th , then the new set for

[0116] Step 103, An AP is randomly assigned as the AP of user u.

[0117] Step 104: If APm is the AP of UEu, update the set for

[0118] Step 105, determine if Update the oversaturated AP set M D M D ∪m, if Then update the lazy AP set M I M I ∪m.

[0119] Step 106, when M D When not empty, all the D APm in the set is the user of controller-AP according to Arrange in ascending order, giving priority to all UEs with larger LSFC values ​​connected to APm, forming a new set U new ;make And contains U new The front L p UE; at the same time, APm cancels the The AP of the UE in If UEu in , update for Then from In , m is randomly selected as the controller-AP of UEu.

[0120] Among them, the initial settings M D and M I and are all empty sets. The above steps 101 - 103 can ensure that if the UE selects an AP with a higher LSFC value as its controller - AP, thus ensuring system performance.

[0121] The above step 105 classifies APs into two categories: one is the oversaturated APs: those that identify services for more than L p UEs and require some relevant UEs to request new controller - AP allocations, and M D represents the set of oversaturated APs; the other is the lazy APs: APs that only serve L p UEs and cannot accept controller - AP requests from other users, and M I represents the set of lazy APs. Steps 105 - 106 solve the limitation of the number of mutually orthogonal pilots (L p < U), reducing pilot contamination. And a key prerequisite for a distributed antenna system is that each UE is served by at least one AP. In addition, each AP must allocate a given pilot to at most one terminal to minimize pilot contamination. Therefore, in order to ensure that each UE is served by at least one AP and manage pilot allocation, a specific AP is designated as the controller - AP for each UE.

[0122] In some possible embodiments, the above step 100 adopts a distributed algorithm to automatically allocate an access point for each user, and the algorithm process is carried out cooperatively on the AP and the UE. Specifically, the process of the controller - access point selection algorithm is not unilaterally completed independently by the AP side or the UE side, but certain interactions and collaborations are required between the two.

[0123] In the specific implementation process, the UE can report its basic information and status to the surrounding APs, and the APs can also broadcast their own status information. Based on the information exchanged between the two sides, the central controllers of the UE and the AP jointly participate in the decision - making process. The UE can initially screen out potential candidate APs based on the received AP information, and the AP can also evaluate whether it can meet the service requirements of the UE based on the UE's needs and its own conditions. Through this cooperation mechanism, after multiple rounds of request - response message exchanges, the system can more flexibly and efficiently allocate a suitable controller - access point for each UE, and can also perform dynamic adjustments when necessary, achieving flexibility, efficiency, and self - adaptability in resource allocation and management in the wireless network.

[0124] Step 200 is used for distributed pilot allocation. An Ad contamination matrix (binary indication matrix) is introduced to indicate whether any two UEs can share a pilot. Each AP allocates different pilots to its served terminals, and If any two users have the same pilot and the pollution matrix Ad indicates that the two users cannot share the pilot, then the AP allows a UE to retain the pilot, and the available pilot set of this user is The cardinality of UE is smaller than that of other UEs. The specific steps include:

[0125] Step 201, traverse APm, and find m UEu, judge if UEu belongs to Update Collection for in Indicates the AP of UEu, update set for At the same time, traverse the set U m For UEu and UEu' within, as long as u≠u', update the pollution matrix Ad{u,u'}=1.

[0126] Step 202: When any UEu is not assigned a pilot and its available pilot set When not empty, APm is UEu (UEu belongs to the set of UEs with APm as AP ) from its available pilot set In , different but never used pilots are allocated.

[0127] Step 203, each APm notifies the Other Aps in the, about the pilot allocation information of UE with APm as AP.

[0128] Step 204, traverse APm, and find m UEu and UEu' are allocated the same pilot, and if they are the same, the pilot with the smaller cardinality of the available pilot set is retained. Then step 203 is repeated.

[0129] Step 205: traverse APm, and for UEu with APm as AP, update the available pilot set of UEu by removing the pilot of UEu'

[0130] Among them, U m is a subset of UEs, which includes at most the first L of the mth AP p UEs, according to the parameter β u,m (large-scale fading coefficient) in descending order.

[0131] Furthermore, the above distributed pilot allocation algorithm can be executed in parallel on the AP, which can further shorten the execution time.

[0132] See also Figure 6When implementing step 300 of dividing the downlink channels of the distributed antenna system, during the information transmission process, the entire process includes channel training and data transmission. The fewer training cycles used, the more channels can be used for downlink signal transmission. The length is L p The pilot signal is used to detect the channel at time t, and the signal received at the kth subcarrier is:

[0133]

[0134] in is additive white Gaussian noise, ρ is the signal-to-noise ratio, and since the angle domain channel vector is sparse, so the required pilot period can be reduced. To take advantage of the slow change of the support set in consecutive frames, the pilot is decomposed into two parts to adaptively detect the unknown CSI (channel state information) of a given subcarrier. The pilot is constructed as:

[0135]

[0136] in, and Used to detect angle domain coefficients and

[0137] Using the prior support information in the previous channel block, the training signal received by the user side in the frequency domain k subcarrier includes two parts, namely:

[0138]

[0139] Next, when the dense portion of the angle domain channel is estimated in step 400, the dense portion angle domain coefficients is through Make an estimate:

[0140]

[0141] When the sparse part of the angle domain channel is estimated in step 500, the angle domain coefficient is a sparse vector, which is expressed as a sparse recovery problem using compressed sensing, and further exploits the common sparsity shared by subchannels associated with different subcarriers, thereby reducing the pilot overhead of the estimated channel. The CSI acquisition of the sparse parts of different subcarriers is expressed as the following optimization:

[0142]

[0143] Ω u,1 =Ω u,2 =…=Ω u,K

[0144] in, For the measurement matrix that satisfies the Restricted Isometric Property (RIP), the successful recovery of the sparse vector can be guaranteed. u,k is the channel vector support set of user u in subcarrier k. The above optimization problem is solved by distributed compressed sensing, which specifically includes the following steps:

[0145] Step 501, initializing the residual vector Support set Γ 0 =φ, channel coefficient α in the angle domain k =0, iteration index i=1.

[0146] Step 502, calculating the distributed correlation of different subcarriers k:

[0147] Step 503: Maximum correlation index between subcarriers:

[0148] Step 504, store the calculated maximum correlation index into the support set: Γ i =Γ i-1 ∪γ.

[0149] Step 505, using the least square method to calculate the channel coefficient of the corresponding support set at subcarrier k:

[0150]

[0151] Step 506, update the residual vector in a distributed manner and input it to the next iteration:

[0152]

[0153] Step 507, repeat steps 502-506 until i>S n , the iteration ends. Thus, the channel coefficient of each subcarrier is obtained:

[0154] When combining the estimates of the dense part and the sparse part in step 600, for all subcarriers, the equation By combining the channel vectors, we can obtain an estimate of the entire channel in the angular region.

[0155] Compared with the related technologies known to the inventors, the present application aims at the problem of estimation errors and system performance degradation caused by pilot multiplexing in wireless communication systems. It adopts a distributed method to jointly perform pilot allocation and access point-user association, which can ensure that each access point is independently associated with the top-level user with a different pilot, reduce the delay of the distributed antenna system, and improve the overall spectrum efficiency, effectively reduce the problems of pilot multiplexing and pilot pollution, and has great advantages in scalability and overall system throughput.

[0156] At the same time, the channel structure with spatial co-sparseness and partial co-sparseness of the channel vector in several consecutive frames is fully utilized, and the least squares and compressed sensing methods are combined to estimate the two parts of the channel vector in the angular domain of the current frame. While ensuring the accuracy of channel estimation, the consumption of pilot resources is greatly reduced and the system resource allocation is optimized. It not only effectively solves the problems of effective utilization of pilot resources and channel estimation accuracy in wireless communications, but also improves the overall performance of wireless communication systems, providing key technical support for the efficient and stable operation of modern wireless networks.

[0157] See also Figure 7 , corresponding to the above-mentioned compressed sensing assisted distributed pilot allocation and channel estimation method embodiment, the embodiment of the present application provides a compressed sensing assisted distributed pilot allocation and channel estimation device, the device comprising:

[0158] An access point selection module 1001 is used to automatically allocate an access point to each user using a distributed algorithm;

[0159] A distributed pilot allocation module 1002, configured to configure each of the access points to independently associate with the top-level user using a different pilot signal;

[0160] The distributed antenna system downlink channel division module 1003 is used to divide the channel to be estimated in each subcarrier of the current frame into a dense part and a sparse part by using the spatial common sparsity and prior support information of the previous frame;

[0161] Angle domain channel estimation module 1004, used for performing dense part estimation and sparse part estimation in the angle domain;

[0162] The channel synthesis module 1005 is used to synthesize the estimation of the dense part and the sparse part to obtain an estimation of the entire channel in the angle domain.

[0163] The above-mentioned compressed sensing assisted distributed pilot allocation and channel estimation device implements the steps and various processes of the above-mentioned compressed sensing assisted distributed pilot allocation and channel estimation method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0164] Referring to the figure, corresponding to the above-mentioned compressed sensing assisted distributed pilot allocation and channel estimation method embodiment, an embodiment of the present application provides an electronic device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, the steps and various processes of the above-mentioned compressed sensing assisted distributed pilot allocation and channel estimation method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0165] The memory 1009 can be used to store software programs and various data. The memory 1009 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 1009 may include a volatile memory or a non-volatile memory, or the memory 1009 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 1009 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0166] The processor 1010 may include one or more processing units; optionally, the processor 1010 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 1010.

[0167] Corresponding to the above-mentioned compressed sensing-assisted distributed pilot allocation and channel estimation method embodiment, the embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by the processor, the steps and various processes of the above-mentioned compressed sensing-assisted distributed pilot allocation and channel estimation method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0168] The processor is the processor in the electronic device described in the above embodiment of the present application. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0169] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0170] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0171] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A compressed sensing-assisted distributed pilot allocation and channel estimation method, characterized in that: The steps include: Use a distributed algorithm to automatically assign an access point to each user; configuring each of the access points to independently associate with the top-level user using a different pilot signal; By using the spatial common sparsity and prior support information of the previous frame, the channel to be estimated in each subcarrier of the current frame is divided into a dense part and a sparse part; Dense part estimation and sparse part estimation are performed in the angle domain; The estimates of the dense part and the sparse part are synthesized to obtain an estimate of the entire channel in the angle domain.

2. The compressed sensing-assisted distributed pilot allocation and channel estimation method according to claim 1, characterized in that: The method of automatically allocating an access point to each user using a distributed algorithm comprises the following steps: Calculating the difference between the largest and second largest large-scale fading coefficients of each user, and determining an intermediate threshold; Preliminarily selecting a candidate set of access points for each user based on the difference and the intermediate threshold; Traversing each of the access points, and updating the access point candidate set according to the difference between the maximum fading coefficient of the user and the fading coefficient of each of the access points and the intermediate threshold; Randomly designate an access point in the access point candidate set as an access point for the user; Recording the designated access points and their associated users, and checking the load of the access points; When the number of users of the access point exceeds a preset capacity, marking it as being in an oversaturated state; If it is equal to the preset capacity, it is marked as an inert state; If there are access points in the oversaturated state, re-evaluate and preferentially retain users with high fading coefficients associated with the access points in the oversaturated state, and randomly re-assign non-overloaded access points to users that exceed capacity limits; After the adjustment of the user access points is completed, the configuration information of each access point and its user is updated.

3. The compressed sensing-assisted distributed pilot allocation and channel estimation method according to claim 2, characterized in that: The step of automatically allocating an access point to each user using a distributed algorithm is performed in cooperation between the access point end and the user end.

4. The compressed sensing-assisted distributed pilot allocation and channel estimation method according to claim 1, characterized in that: The configuring each of the access points to independently associate with the top-level user using different pilot signals comprises the following steps: Traversing all access points and corresponding users, and constructing a pollution matrix for recording whether any two of the users are assigned the same pilot; Allocate a pilot frequency that is not occupied by other users to a user that has not yet been assigned a pilot frequency and has an available pilot frequency set; Notify other related access points about pilot allocation information of a specific user; Traverse each access point to check whether the same pilot is allocated to two or more users. If so, keep the pilot with a smaller cardinality value; remove the pilot allocation of another; The set of available pilots for a particular user is updated by removing pilots for other users.

5. The compressed sensing-assisted distributed pilot allocation and channel estimation method according to claim 4, characterized in that: The step of configuring each access point to independently associate with the top-level user using different pilot signals is performed in parallel at the access point end.

6. The compressed sensing-assisted distributed pilot allocation and channel estimation method according to claim 1, characterized in that: The dense part estimation and the sparse part estimation in the angle domain include the following steps: Estimating a dense portion of the angle domain channel: estimating channel coefficients of the dense portion using a least squares method; Sparse part estimation of the angle domain channel: using compressed sensing to assist in estimating the channel coefficients of the sparse part.

7. The compressed sensing-assisted distributed pilot allocation and channel estimation method according to claim 6, characterized in that: Length L p The pilot signal is used to detect the channel at time t, and the signal received at the kth subcarrier is: in, is additive Gaussian white noise, ρ is the signal-to-noise ratio; The pilot structure is: in, and Used to detect angle domain coefficients and Using the prior support information in the previous channel block, the training signal received by the user side in the frequency domain k subcarrier includes two parts, namely: Dense part angle domain coefficient pass Make an estimate: Angle domain coefficient is a sparse vector, which is expressed as a sparse recovery problem using compressed sensing. Further using the common sparsity shared by subchannels associated with different subcarriers, the channel information collection of the sparse parts of different subcarriers is expressed as the following optimization: Oh u,1 =Oh u,2 =…=Oh u,K in, For the measurement matrix that satisfies the finite isometry property (RIP), Ω u,k is the channel vector support set of user u in subcarrier k; The optimization problem is solved by using distributed compressed sensing, which specifically includes the following steps: S501, initialize the residual vector Support set Γ 0 =φ, channel coefficient α in the angle domain k =0, iteration index i=1; S502, calculating the distributed correlation of different subcarriers k: S503, maximum correlation index between subcarriers: S504, storing the maximum correlation index in the support set: Γ i =Γ i-1 ∪γ; S505, using the least square method to calculate the channel coefficient of the corresponding support set at subcarrier k: S506, update the residual vector in a distributed manner and input it to the next iteration: S507, repeat S502-S506 until i>S n , the iteration ends, and the channel coefficient of each subcarrier is obtained: For all subcarriers, the formula The channel vectors are combined to obtain an estimate of the entire channel in the angular region.

8. A compressed sensing-assisted distributed pilot allocation and channel estimation device, characterized in that: include: An access point selection module, for automatically assigning an access point to each user using a distributed algorithm; A distributed pilot allocation module, used to configure each of the access points to independently associate with the top-level user using a different pilot signal; A distributed antenna system downlink channel division module is used to divide the channel to be estimated in each subcarrier of the current frame into a dense part and a sparse part by using the spatial common sparsity and prior support information of the previous frame; An angle domain channel estimation module, used for performing dense part estimation and sparse part estimation in the angle domain; The channel synthesis module is used to synthesize the estimation of the dense part and the sparse part to obtain an estimation of the entire channel in the angle domain.

9. An electronic device, characterized in that: The electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the compressed sensing-assisted distributed pilot allocation and channel estimation method as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by a processor, the steps of the compressed sensing-assisted distributed pilot allocation and channel estimation method as described in any one of claims 1 to 7 are implemented.

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