CN106911359B - Training Sequence Filling Method for Distributed Compressed Sensing Channel Estimation - Google Patents

Abstract

The present invention provides a training sequence filling method suitable for dynamic compressed sensing channel estimation, which includes the following steps: constructing a sequence T; adopting a sliding window reading method, sequentially reading sequence segments from the sequence T as the training sequence P _j , where j represents the time sequence; the training sequence P _j is inserted between the payload data blocks D _j to form a transmission data frame/stream. The training sequence filled by the training sequence filling method of the present invention can complete channel estimation and multipath interference cancellation, can effectively utilize the advantages of distributed compressed sensing channel estimation, and can resist multipath interference between data blocks at the same time, improving the accuracy of channel estimation At the same time, it can effectively improve the spectrum utilization.

Description

Training Sequence Filling Method for Distributed Compressed Sensing Channel Estimation

technical field

The invention belongs to the technical field of communication, and in particular relates to a training sequence filling method suitable for distributed compressed sensing channel estimation.

Background technique

Channel state detection is one of the key technologies in modern wireless communication systems. Among the existing channel state detection methods, channel estimation based on reference signals is widely used in modern wireless transmission systems because of its significant advantages such as small error and low complexity. The traditional channel estimation method based on reference signals only considers the maximum delay of the channel propagation path, and does not consider the number of channel propagation paths. In the process of broadband data transmission, it is often necessary to add a large number of reference signals for channel estimation, which greatly reduces the utilization of channel spectrum resources. . Multipath propagation in wireless broadband systems usually has sparse characteristics in the time domain. Different from traditional channel estimation methods, compressed sensing channel estimation is a parametric estimation method. Its main idea is to estimate the position, size and phase parameters of each path. , so compared with traditional channel estimation methods, it has the advantage of requiring less reference signals. Constructing an observation matrix with fewer observations and good performance is the key to the realization of compressed sensing channel estimation technology, and the construction of the observation matrix is closely related to the filling method of the reference signal.

Compared with the independent compressed sensing channel estimation method, the distributed compressed sensing channel estimation method makes full use of the characteristics that the channel state does not change much at adjacent times, and combines the observation vectors at each measurement time for multipath delay estimation, which can further reduce pilot overhead and improve Latency estimation accuracy. It is the key to distributed compressed sensing joint channel estimation that the joint measurement moments have different observation matrices.

The block transmission system has the advantage of being easy to eliminate multipath interference, and is widely used in modern wireless communication, underwater acoustic communication and other fields. The reference signals for channel estimation in the block transmission system include pilot symbols and training sequences. The training sequences can be used for multipath interference cancellation and channel estimation at the same time, which can effectively reduce the proportion of redundant signals in the transmission system. The channel estimation method based on the training sequence can be used in single-carrier (such as SC-FDE, single-carrier frequency domain equalization) systems with low peak-to-average ratio, and is suitable for systems that are sensitive to power consumption at the transmitting end.

At present, many literatures have used the training sequence in compressed sensing channel estimation, and proposed a series of training sequence generation methods related to the observation matrix. However, these training sequences are either only suitable for independent CS channel estimation, or do not consider the guard interval function, and cannot meet the requirements of being used as multipath extended guard interval and distributed CS channel estimation at the same time.

Contents of the invention

In order to solve the above-mentioned problems in the prior art, the present invention provides a training sequence filling method suitable for distributed compressed sensing channel estimation. The training sequence obtained by using the filling method can be used as a multipath extended guard interval and satisfy the distribution Compressed sensing channel estimation requirements.

The technical scheme adopted by the present invention is: a training sequence filling method suitable for distributed compressed sensing channel estimation, which is characterized in that it includes the following steps: constructing a sequence T; Take the sequence segment as the training sequence P _j , where j represents the time sequence; insert the training sequence P _j between the payload data blocks D _j to form a transmission data frame/stream.

Further, the sequence T is a pseudo-random sequence with a constant amplitude and a random phase.

Further, the sequence T is obtained by connecting several identical sequences, and the minimum repetition period of the sequence T is not less than the maximum delay length of the channel.

Further, the sequence T is obtained by connecting infinite identical sequences.

Further, in the sliding window, the amount of sliding each time is equal to the observation amount M, the overlapping length of two sliding windows at adjacent moments is equal to the maximum channel length L, and the window length is equal to the length L+M of the training sequence.

Further, the method of using the training sequence to complete the channel estimation is: using the training sequence to obtain measurement matrices that are different among several adjacent data blocks; and using the distributed compressed sensing algorithm to perform channel estimation.

Further, the method of using the training sequence to cancel the multipath interference is: using the training sequence as a multipath extension guard interval; using a frequency domain equalization processing algorithm to eliminate the interference between the channel multipath extension and the data blocks.

Further, the sequence T adopts a sequence whose length is greater than or equal to L+JM, wherein J represents the maximum joint degree of distributed compressed sensing channel estimation.

Further, the transmission data frame/stream adopts the length of J(L+M)+(J-1)N, and the sequence is P ₁ D ₁ P ₂ D ₂ ... P _J-1 D _J-1 P _J A data sequence is transmitted, where N represents the length of the payload data block.

Further, the sequence T is composed of infinite identical sequence connections and has a period of length JM, where J represents the maximum joint degree of distributed compressed sensing channel estimation; the order of the transmission data frame/stream is P ₁ D ₁ P ₂ D ₂ ...P _J D _J P ₁ D _{J+ 1} P ₂ D _J+2 ...P _J D _2J P ₁ D _2J+1 P ₂ D _2J+2 ... the transmitted data sequence.

Due to the adoption of the above technical solution, the beneficial effects of the present invention are: the training sequence filled by the training sequence filling method of the present invention can effectively play the advantages of distributed compressed sensing channel estimation, and can resist multipath interference between data blocks at the same time, While improving the accuracy of channel estimation, the frequency spectrum utilization rate can be effectively improved.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without creative work.

FIG. 1 is a flowchart of a training sequence filling method suitable for distributed compressed sensing channel estimation provided in an embodiment of the present invention;

Fig. 2 is a schematic diagram of training sequence generation and data frame structure of the burst transmission system provided in an embodiment of the present invention;

Fig. 3 is a flow chart of the application of the training sequence filling method based on distributed compressed sensing channel estimation in the burst transmission system provided in another embodiment of the present invention;

Fig. 4 is a schematic structural diagram of a single data block signal processing frame provided in another embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Apparently, the described embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other implementations obtained by persons of ordinary skill in the art without making creative efforts fall within the protection scope of the present invention.

As shown in Figure 1, the present invention provides a training sequence filling method suitable for distributed compressed sensing channel estimation, which includes the following steps:

S1. Construct a sequence T.

S2. Using a sliding window reading method, sequence segments are sequentially read from the sequence T as the training sequence P _j , where j represents a time sequence.

S3. Insert the training sequence P _j between the payload data blocks D _j to form a transmission data frame/stream.

The training sequence filled by the training sequence filling method of the present invention can not only meet the requirements of different observation matrices of distributed compressed sensing, but also meet the requirement of having the same reference signal sequence before and after the load data block. The training sequence filled by the training sequence filling method of the present invention can be used as the multipath extension guard interval, and the frequency domain equalization process can effectively eliminate the interference between the data blocks caused by the multipath extension.

In the above step S1, the sequence T may be a simple pseudo-random sequence with constant amplitude and random phase. Sequence T can be obtained by connecting several identical sequences, as long as the minimum repetition period of sequence T is not less than the maximum delay length of the channel. Sequence T can also be obtained by connecting an infinite number of identical sequences for application in continuous transmission systems.

In the above step S2, as shown in Figure 2, in the sliding window, the amount of sliding each time is equal to the observation amount M, the overlapping length of the two sliding windows at adjacent moments is equal to the maximum channel length L, and the window length is the training sequence length L +M.

The training sequence filled by the training sequence filling method of the present invention can be applied to distributed compressed sensing channel estimation. The length of the training sequence P _j is L+M, the multipath model of compressed sensing channel estimation is described as Q _j = Ψ _j h _j + n _j , Q _j is the observation vector, h _j is the sparse multi-radial vector, Ψ _j is the Toeplitz Structural Observation Array:

When each measurement uses a different training sequence P _j , the corresponding observation matrix Ψ _j also changes with j, which meets the requirements of the distributed compressed sensing algorithm. Using distributed compressed sensing algorithms (such as DCS-SOMP distributed compressed sensing-synchronous orthogonal matching pursuit algorithm) for channel estimation can obtain good channel state estimation performance with a short observation amount M, thereby improving spectrum utilization.

As shown in FIG. 3 , the application of the training sequence filling method applicable to distributed compressed sensing channel estimation in the present invention to a burst transmission system will be described in detail below.

S11. According to the required maximum channel length L, the maximum joint degree J of distributed compressed sensing channel estimation, and the compressed sensing observation amount M, generate a sequence T with a length greater than or equal to L+JM.

S22. Taking the first data as the starting position, select the continuous L+M data of the sequence T as the first training sequence P1; taking the M+ _1th data as the starting position, select the continuous L+M data of the sequence T The M data are used as the second training sequence P ₂ ; similarly, the jM+1th data is used as the starting position, and the L+M data of the sequence T are selected as the j+1th training sequence P _j+1 .

S33. The length of the payload data block is N, and the j-th payload data block is recorded as D _j , and the generated length is J according to the order of P ₁ D ₁ P ₂ D ₂ ... P _J-1 D _{J- 1} P _J (L+M)+(J-1)N transmit data sequence X.

S44. After the transmitted data sequence X passes through channel noise and multipath interference, time-frequency synchronization is performed at the receiving end, and the time synchronization is head-path synchronization, so as to obtain a received data sequence R corresponding to the transmitted data sequence X.

S55. Using the (j-1)th (L+M+N)+L+1 data as the starting position, select the continuous M data of the received data sequence R as the jth received training sequence Q _j (j∈[ 1,J]), at most J received training sequences can be used for joint channel estimation using distributed compressed sensing techniques. Wherein, the length of the received training sequence is M.

S66. Using the (j-1)th (L+M+N)+L+M+1 data as the starting position, select the continuous N+L data of the received data sequence R as the jth received data block (j ∈[1,J-1]), the received data block can eliminate multipath interference through frequency domain equalization technology. Wherein, the length of the received data block is N+L.

As shown in FIG. 4 , the received training sequence Q _j described in step S55 corresponds to the observation window shown in FIG. 4 , and the received data block available for frequency domain equalization described in step S66 corresponds to the FFT window shown in FIG. 4 .

In addition, according to the above-mentioned training sequence filling method used in the embodiment of the burst transmission system, the training sequence filling method of the present invention can be used in the continuous transmission system through the following modifications. In step S11, the sequence T is formed by connecting infinite identical sequences and Take the length JM as the cycle; in step S33, the transmitted data sequence X is P ₁ D ₁ P ₂ D ₂ ... P _J D _J P ₁ D _J+1 P ₂ D _J+2 ... P _J D _2J P ₁ D _{2J +1} P ₂ D _2J+2 …. Since the sequence P _j is obtained by sliding M every time the sequence T with period JM, so P _j takes J as the period, that is, P _j =P _J+j .

The above is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Anyone skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present invention. Should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the protection scope of the claims.

Claims (5)

1. it is a kind of suitable for distributed compression channel perception estimation training sequence fill method, which is characterized in that it include with Lower step:

Construct a sequence T, wherein sequence T is for the random pseudo-random sequence of constant amplitude phase or by several identical sequences The sequence that column connection obtains or the sequence obtained by unlimited identical sequence connection；

Using sliding window reading manner, tract is successively read from sequence T as training sequence P_j, wherein j indicates that the time is suitable Sequence；

By training sequence P_jIt is inserted into payload data block D_jBetween, form transmitting data frame/stream；

Wherein, each slippage is equal to observed quantity M in the sliding window, and the overlap length of two sliding window of adjacent moment is equal to maximum Channel length L, window length, that is, training sequence length are L+M；

Further include: channel estimation is completed using the training sequence；

It is described using the training sequence complete channel estimation method include:

The mutually different calculation matrix between several adjacent data blocks is obtained using training sequence；

Channel estimation is carried out using distributed compression perception algorithm；

Further include: multi-path Interference Cancellation is completed using the training sequence；

It is described using training sequence complete multi-path Interference Cancellation method include:

Using the partial sequence of the training sequence as cyclic prefix protection interval；

Channel multi-path extension is eliminated to the interference between data block using frequency domain equalization Processing Algorithm.

2. the training sequence fill method suitable for the estimation of distributed compression channel perception as described in claim 1, feature It is, when the sequence T is the sequence obtained by several identical sequence connections, the minimum repetition period of sequence T is not less than Channel maximum delay length.

3. the training sequence fill method suitable for the estimation of distributed compression channel perception as claimed in claim 1 or 2, special Sign is that the sequence T is greater than or equal to the sequence of L+JM using length, wherein J indicates the estimation of distributed compression channel perception Maximum combined length.

4. the training sequence fill method suitable for the estimation of distributed compression channel perception as claimed in claim 3, feature It is, it is sequentially P that the transmitting data frame/stream, which uses length for J (L+M)+(J-1) N,₁D₁P₂D₂......P_J-1D_J-1P_JHair Penetrate data sequence, wherein the length of N expression payload data block.

5. the training sequence fill method suitable for the estimation of distributed compression channel perception as claimed in claim 1 or 2, special Sign is that the sequence T is connected and composed and by unlimited identical sequence using length JM as the period, wherein J indicates distributed compression Channel perception estimates maximum combined length；Transmitting data frame/the stream uses sequence for P₁D₁P₂D₂......P_JD_JP₁D_{J+ 1}P₂D_J+2…P_JD_2JP₁D_2J+1P₂D_2J+2... transmission of data sequences.