A power allocation method for superimposed pilot for short packet communication

Abstract

The application discloses a power allocation method for short packet communication superimposed pilot, which is performed according to the following steps: 1) performing LMMSE channel estimation on a superimposed pilot (SP) signal; 2) eliminating pilot interference in the SP signal by using the estimated channel and performing MRC data detection; 3) obtaining a lower bound of an URLLC reachable rate expression of the SP signal by using statistical channel state information; 4) taking the maximum weighted sum rate as an optimization target to determine optimal power allocation of the pilot and the data; and 5) solving the optimization problem by using an iterative algorithm. The power allocation method provided by the application enables short packet transmission based on the superimposed pilot to have a higher transmission rate than short packet transmission based on a traditional pilot, and can more accurately depict transmission delay.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of communication resource allocation, and particularly relates to a power allocation method for short packet communication superimposed pilot BACKGROUND

[0002] With the explosive growth of Internet of Things (IoT) devices with stringent requirements on latency and reliability, Ultra-Reliable and Low-Latency Communication (URLLC) has become an important part of the 5th Generation Mobile Communication (5G) and 6G. URLLC is one of the most innovative technical solutions in 5G mobile networks. In practical applications, factory automation and remote surgery require 1-10 -9 -10 -5 ) reliability and 1ms latency. The Third Generation Partnership Project (3GPP) proposes that the general URLLC requirement is to transmit a data packet of 32 bytes with 99.999% (Block Error Rate (BLER) of 10 -10 -5 ) reliability and 1ms latency. URLLC relies on short packet transmission or limited block length transmission, in which the coding length of data or the number of channel uses is very limited, so the transmission is no longer error-free, and the Shannon formula is no longer applicable. In addition, because the current conventional pilot (RP) based frame structure transmits pilots and data separately, when the data packet is a short packet, the overhead caused by the pilot is not negligible, which will significantly reduce the transmission efficiency of the system. Although some studies have improved the transmission rate in limited block length by resource allocation, the performance improvement is very limited.

[0003] As an alternative to RP, Superimposed Pilot (SP) has attracted extensive attention in recent years. In SP, pilots and data are superimposed and transmitted simultaneously and co-frequency. Compared with RP, SP does not need to reserve additional block length for pilots, so the pilots can have the same sequence length as the data, which not only improves the quality of channel estimation, but also improves the transmission efficiency. In addition, using SP can also increase the number of orthogonal pilots to reduce pilot contamination. However, due to the introduction of data interference, SP will inevitably reduce the quality of channel estimation, which limits the performance of the system. The usual solution is to find a fixed pilot-data power allocation scaling factor for users to suppress interference, so that the total throughput is maximized, but this scaling factor is not necessarily optimal for each user. Similarly, when performing data detection, the pilot symbols are also treated as interference. Since the pilot symbols are known at the base station end, they can be subtracted from the received signal, but since the channel estimation is not perfect, the interference of the pilot cannot be completely eliminated. SUMMARY

[0004] In order to solve the technical problems mentioned in the above background, the present application provides a power allocation method for superimposed pilot in short packet communication, which provides optimal SP power allocation for all URLLC users in a single cell, thereby improving channel estimation quality and obtaining better performance than traditional RP transmission. In order to achieve the above technical purpose, the technical scheme of the present application is:

[0005] A power allocation method for superimposed pilot in short packet communication, comprising the following steps:

[0006] Step one: LMMSE channel estimation is performed on the superimposed pilot SP signal Y to obtain the channel between the kth user and the base station from K users

[0007] Step two: the pilot interference in the SP signal is eliminated by using the estimated channel, and MRC data detection is performed to obtain the estimation of the data sent by the kth user

[0008] Step three: statistical channel state information is obtained by measuring the propagation environment, and the lower bound of the URLLC achievable rate expression of the SP signal is obtained by using the statistical channel state information

[0009] Step four: a weighted sum rate maximization problem is constructed with the optimization goal of maximizing the weighted sum of URLLC achievable rates

[0010] Step five: the weighted sum rate maximization problem is converted into a geometric programming problem by an iterative algorithm, and the optimal power allocation of the pilot and data of all users is obtained.

[0011] Preferably, the step one is specifically: K single-antenna users are randomly and uniformly distributed in a single cell, share the same system bandwidth B, and send superimposed pilot signals to a multi-antenna base station in the center of the cell in TDD mode, which is expressed by the following formula:

[0012]

[0013] Where q i represents the pilot power allocated to the ith user, p i represents the data power allocated to the ith user, h i is the channel between the ith user and the base station, N is a superimposed Gaussian white noise matrix, and s i respectively represent the orthogonal pilot sequence and data sequence of the ith user, both have the same length and are superimposed and transmitted at the same frequency, and H represents conjugate transpose.

[0014] The base station obtains a despread signal y after despreading the received signalk as shown in the following formula:

[0015]

[0016] where τ c represents the length of the transmission block, q k represents the pilot power allocated to the kth user, h k represents the channel between the kth user and the base station, represents the orthogonal pilot sequence of the kth user; and y k is subjected to LMMSE channel estimation, as shown in the following formula:

[0017]

[0018] where β k and β i represent the large-scale fading coefficients of the kth and ith users, respectively.

[0019] Preferably, in the step one, the superimposed pilot signal Y is subjected to limited block length transmission in the form of short data packets, and the achievable rate R k of the kth user in the limited block length transmission is expressed as:

[0020]

[0021] where V k = 1 - (1 + γ k ) -2 is the channel dispersion, ε is the decoding error probability, γ k is the instantaneous signal-to-noise ratio of the kth user, and Q -1 (·) represents the inverse function of the Gaussian function.

[0022] Preferably, in the step two, the data estimation of the kth user is obtained through MRC detection, and is expressed as:

[0023]

[0024]

[0025] where h represents the estimated channel of the kth user, q i and p i represent the pilot power and data power allocated to the ith user, respectively, q k and p k represent the pilot power and data power allocated to the kth user, respectively, represents the orthogonal pilot sequence of the ith user, h k and h iLet s represent the channels between the base station and the k-th user and the i-th user, respectively. k and s i Let N represent the data sequences of the k-th user and the ith user, respectively. Let N represent the superimposed Gaussian white noise matrix, ||·|| represent the Euclidean norm, H represent the conjugate transpose, E{·} represent the expected value, and τ represent the expected value. c σ represents the length of the transport block. k The expression is:

[0026]

[0027] β k β i Let represent the large-scale fading coefficients of the k-th and i-th users, respectively.

[0028] Preferably, in step three, the lower bound of the URLLC reachability expression is as follows:

[0029]

[0030] Where E{·} represents the expected value, γ k Let f be the instantaneous signal-to-interference-plus-noise ratio for the k-th user. k (·) is a monotonically decreasing convex function, and its expression is:

[0031]

[0032] Where x is the independent variable of the function, ε is the decoding error probability, and τ c Q represents the length of the transport block. -1 (·) denotes the inverse function of the Gaussian function. The effective signal-to-interference-plus-noise ratio (SIR) for the k-th user when pilot interference cannot be completely eliminated is expressed as:

[0033]

[0034] in, M represents the number of base station antennas, β k β i Let p represent the large-scale fading coefficients of the k-th and i-th users, respectively. k p i Let q represent the data power of the k-th and i-th users, respectively. k q i These represent the pilot power of the k-th and i-th users, respectively.

[0035] Preferably, in step four, while maximizing the weighted sum of URLLC reachable rates, all users must meet the minimum rate requirement R. req and maximum power constraint P maxThe weighted sum rate maximization problem is expressed as shown in the following formula:

[0036]

[0037]

[0038]

[0039] wherein, represents a lower bound of a URLLC achievable rate expression, w k represents a weight of the kth user, p k and q k respectively represent data power and pilot power allocated to the kth user.

[0040] The above technical scheme brings the beneficial effects:

[0041] (1) The method can guarantee that the SP power allocation of all users is optimal, thereby maximally suppressing the interference caused by inaccurate channel estimation, and achieving higher transmission rate;

[0042] (2) The method is suitable for a more practical scenario, i.e., a limited block length transmission scenario, and can accurately depict the delay and reliability in URLLC;

[0043] (3) The method is based on user large-scale fading coefficients for power allocation, and the large-scale fading changes very slowly, so the calculation delay can be greatly reduced. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 is a system model diagram for a single-cell URLLC uplink scenario;

[0045] Figure 2 is a flowchart of a power allocation method for short packet communication superimposed pilot;

[0046] Figure 3 is a structure diagram of a traditional pilot (RP) and superimposed pilot (SP);

[0047] Figure 4 is a system and rate versus the number of transmit antennas relationship diagram provided by the embodiment of the application;

[0048] Figure 5 is a system and rate versus the number of system block length relationship diagram provided by the embodiment of the application;

[0049] Figure 6 is a system and rate versus the number of users relationship diagram provided by the embodiment of the application. DETAILED DESCRIPTION

[0050] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0051] This invention provides a power allocation method for short packet communication with superimposed pilots. This method provides optimal SP power allocation for all URLLC users in a single cell, thereby improving channel estimation quality and achieving better performance than traditional RP transmission.

[0052] The system model in this example is as follows: Figure 1 As shown, assume that in a single cell, there is a base station equipped with M antennas located in the center of the cell, and K single-antenna users are randomly distributed in the cell. The channel between the users and the base station is block fading and follows CN(0,β) . k I M The complex Gaussian distribution of β i Let represent the large-scale fading coefficient of the i-th user. All users send short packet data to the base station (finite block length transmission). The achievable rate expression for the k-th user in finite block length transmission is:

[0053]

[0054] Where τ c =BT represents the length of the transport block, ε is the decoding error probability, and V k =1-(1+γ) k ) -2 The data packet transmission time T is less than the channel coherence time.

[0055] Based on the above systems and combined with Figure 2 This invention provides a detailed description of a power allocation method for superimposed pilot signals in short packet communication, which includes the following steps:

[0056] Step 201: As Figure 3 As shown in the diagram, this embodiment of the invention provides a frame structure diagram of conventional pilot (RP) and superimposed pilot (SP). In RP, pilot and data are transmitted separately; in SP, pilot and data are superimposed and transmitted simultaneously at the same frequency, and have the same length. All users in this cell share the same system bandwidth B, and in TDD mode, they send superimposed pilot signals to the multi-antenna base station in the cell center:

[0057]

[0058] Where, q i p represents the pilot power allocated to the i-th user. i h represents the data power allocated to the i-th user. i It is the channel between the i-th user and the base station, and N is a superimposed white Gaussian noise matrix. and s iLet H represent the orthogonal pilot sequence and data sequence of the i-th user, respectively. Both sequences have the same length, are superimposed, and are transmitted simultaneously at the same frequency. H represents the conjugate transpose. The base station obtains the following after despreading the received signal:

[0059]

[0060] Then, LMMSE channel estimation is performed on the signal to obtain the channel between the k-th user and the base station.

[0061]

[0062] At this stage, the data is considered to affect the quality of channel estimation due to interference.

[0063] Step 202: Use the estimated channel to eliminate pilot interference in the SP signal and perform MRC data detection to obtain an estimate of the data transmitted by the k-th user.

[0064]

[0065] in This represents the estimated channel for the k-th user. Since channel estimation is imperfect, pilot interference cannot be completely eliminated, and residual pilot interference will affect the estimation quality of the data.

[0066] Step 203: Obtain the lower bound of the URLLC reachable rate expression for the SP signal using statistical channel state information. The following will provide a detailed explanation of the specific process for step 203:

[0067] (1) First, calculate the effective signal-to-interference-plus-noise ratio (SIR) for the user. Statistical channel state information refers to the large-scale fading information of the channel, including path loss and shadowing fading effects. Therefore, the effective SIR for user k when pilot interference cannot be completely eliminated is:

[0068]

[0069] in

[0070] (2) The lower bound of the URLLC reachability rate expression is independent of small-scale fading of the channel, and its lower bound is:

[0071]

[0072] Where the function f k The expression for (·) is:

[0073]

[0074] It is a monotonically decreasing convex function.

[0075] Step 204: Determine the optimal power allocation for each user's pilot and data by maximizing the weighted sum rate as the optimization objective. Where rate refers to the achievable rate of URLLC, p i q represents the data power allocated to the i-th user. i This represents the pilot power allocated to the i-th user, while all users must meet the minimum rate requirement R. req and maximum power constraint P max The weighted rate optimization problem P1 is expressed as:

[0076] P1:

[0077]

[0078]

[0079] Introducing the auxiliary variable χ k ,because It is about Given an increasing function, the optimization problem can be transformed into P2:

[0080] P2:

[0081]

[0082]

[0083]

[0084] in,

[0085] Step 205: Transform the optimization problem into a geometric programming (GP) problem using an iterative algorithm. The specific process of step 205 will be explained in detail below:

[0086] (1) Use the log function to approximate the objective function and obtain its lower bound:

[0087]

[0088] in These are the parameters used to approximate the objective function in the (n+1)th iteration. and for The function is expressed as:

[0089]

[0090]

[0091] Replace the objective function of P2 with this lower bound, and omit the constant term to obtain a new objective function:

[0092]

[0093] Further simplification yields

[0094]

[0095] (2) The method of continuous convex approximation is used to handle constraints that do not conform to the GP standard form. The main idea is to approximate positive terms by constructing a series of monomial functions. Assume It is the set of optimal power allocations for all users in the nth iteration, and the effective signal-to-interference-plus-noise ratio. c i The denominator of is a positive term, and its expression is:

[0096]

[0097] In the (n+1)th iteration, this positive term can approximate the following monomial:

[0098]

[0099] in, and The expression is

[0100]

[0101] Therefore, P2 can be transformed into the standard form of the GP problem:

[0102] P3:

[0103]

[0104]

[0105]

[0106] in, The specific process for solving this problem is as follows: P3 is solved using CVX's Mosek solver to obtain the optimal power allocation for the k-th user in the (n+1)th iteration. Update parameters using this power allocation And calculate the objective function value Obj of P2. (n+1) Meanwhile, increment the iteration count by 1 and repeat the above process until Obj...(n+1) convergence.

[0107] The technical solution provided by the present invention will be further illustrated below through specific embodiments:

[0108] Assume the number of iterations n = 1 and the error threshold ξ is 10. -5 The initial values ​​for optimal power allocation are obtained by solving the following optimization problem. and

[0109] P4:

[0110]

[0111]

[0112] The optimization problem described above can be transformed into a general problem for solution using the same method as in P1. When θ≥1, utilize... and calculate The initial value of the objective function of P2, Obj (0) Otherwise, let Obj (0) =0, and repeat the above process. The initialization of wireless communication network parameters is as follows:

[0113]

[0114] This example is a special case of an embodiment of the present invention, but it can be extended to other similar situations.

[0115] Figure 4 , Figure 5 and Figure 6 The relationships between RP and SP rates and the number of central controller antennas, block length, and actuators are presented for long packet transmission (Shannon rate) and short packet transmission (URLLC rate), respectively. It is evident that under optimal power allocation, even with imperfect cancellation of pilot interference, SP still outperforms RP. Perfect cancellation of pilot interference represents the ideal situation for SP and can be considered its upper bound. Furthermore,

[0116] The performance at the Shannon rate is better than that at the URLLC rate. This means that in short packet transmission, we should use the URLLC reachable rate formula instead of the Shannon formula for transmission design. Otherwise, the transmission delay and reliability will be underestimated.

[0117] The embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A power allocation method for superimposed pilot signals in short packet communication, characterized in that, Includes the following steps: Step 1: Perform LMMSE channel estimation on the superimposed pilot signal Y to obtain the channel between the k-th user and the base station from the K users. Step one specifically involves: K single-antenna users are randomly and evenly distributed in a single cell, sharing the same system bandwidth B, and transmitting superimposed pilot signals to the multi-antenna base station in the cell center in TDD mode, as expressed by the following formula: Where, q i p represents the pilot power allocated to the i-th user. i h represents the data power allocated to the i-th user. i It is the channel between the i-th user and the base station, and N is a superimposed white Gaussian noise matrix. and s i Let H represent the orthogonal pilot sequence and data sequence of the i-th user, respectively. Both have the same length, are superimposed, and are transmitted at the same frequency. H represents the conjugate transpose. The base station obtains the despread signal y after despreading the received signal. k As shown in the following formula: Where, τ c q represents the length of the transport block. k h represents the pilot power allocated to the k-th user. k This represents the channel between the k-th user and the base station. This represents the orthogonal pilot sequence of the k-th user; For y k The signal undergoes LMMSE channel estimation as shown in the following equation: Where, β k β i Let represent the large-scale fading coefficients of the k-th and i-th users, respectively; Step 2: Eliminate pilot interference in the SP signal using the estimated channel and perform MRC data detection. Obtain an estimate of the data sent by the kth user. Step 3: Obtain statistical channel state information by measuring the propagation environment, and use the statistical channel state information to obtain the lower bound of the URLLC reachable rate expression for the SP signal. Step 4: Construct a weighted sum rate maximization problem with the optimization objective of maximizing the weighted sum of reachable rates in URLLC; Step 5: Transform the weighted sum rate maximization problem into a geometric programming problem through an iterative algorithm to obtain the optimal power allocation for all user pilots and data.

2. The power allocation method for superimposed pilot signals in short packet communication according to claim 1, characterized in that, In step one, the superimposed pilot signal Y is transmitted in the form of short data packets with a finite block length. The achievable rate R of the k-th user in the finite block length transmission is... k The expression is: Among them, V k =1-(1+γ) k ) -2 Let γ be the channel discreteness, ε be the decoding error probability, and γ be the channel error probability. k Let Q be the instantaneous signal-to-interference-plus-noise ratio for the k-th user. -1 (·) denotes the inverse function of the Gaussian function.

3. The power allocation method for superimposed pilot signals in short packet communication according to claim 1, characterized in that, In step two, the data estimate of the kth user is obtained through MRC detection, and the formula is expressed as follows: in, Let q represent the estimated channel for the k-th user. i p i q represents the pilot power and data power allocated to the i-th user, respectively. k p k These represent the pilot power and data power allocated to the k-th user, respectively. Let h represent the orthogonal pilot sequence of the i-th user. k and h i Let s represent the channels between the base station and the k-th user and the i-th user, respectively. k and s i Let N represent the data sequences of the k-th user and the ith user, respectively. Let N represent the superimposed Gaussian white noise matrix, ||·|| represent the Euclidean norm, and H represent the conjugate transpose. τ represents the expected value. c σ represents the length of the transport block. k The expression is: β k β i Let represent the large-scale fading coefficients of the k-th and i-th users, respectively.

4. The power allocation method for superimposed pilot signals in short packet communication according to claim 3, characterized in that, In step three, the lower bound of the URLLC reachability expression is shown in the following equation: in, Indicates the expected value, γ k Let f be the instantaneous signal-to-interference-plus-noise ratio for the k-th user. k (·) is a monotonically decreasing convex function, and its expression is: Where x is the independent variable of the function, ε is the decoding error probability, and τ c Q represents the length of the transport block. -1 (·) denotes the inverse function of the Gaussian function. The effective signal-to-interference-plus-noise ratio (SIR) for the k-th user when pilot interference cannot be completely eliminated is expressed as: in, M represents the number of base station antennas, β k β i Let p represent the large-scale fading coefficients of the k-th and i-th users, respectively. k p i Let q represent the data power of the k-th and i-th users, respectively. k q i These represent the pilot power of the k-th and i-th users, respectively.

5. The power allocation method for superimposed pilot signals in short packet communication according to claim 1, characterized in that, In step four, while maximizing the weighted sum of URLLC reachable rates, all users must meet the minimum rate requirement R. req and maximum power constraint P max The weighted rate maximization problem is expressed as follows: in, w represents the lower bound of the reachable rate expression for URLLC. k p represents the weight of the k-th user. k and q k These represent the data power and pilot power allocated to the k-th user, respectively.

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