A partial superposition pilot optimization method for short packet communication

Abstract

The present invention discloses a partial superposition pilot optimization method for short packet communication, which performs LMMSE channel estimation on the partial superposition pilot signal to obtain an estimated channel; the estimated channel is used to eliminate the pilot interference in the partial superposition pilot signal and perform MRC data detection to obtain the effective signal to noise ratio of the user, and the expression of the lower bound of the URLLC traversal reachable rate is determined according to the effective signal to noise ratio; based on the expression of the lower bound of the URLLC traversal reachable rate, a weighted sum rate maximization problem is constructed with maximizing the weighted sum of the URLLC reachable rate as the optimization target; the weighted sum rate maximization problem is transformed into a geometric programming problem and solved to obtain the optimal power allocation of all user pilots and data. Advantages: The present invention solves the problem of significant pilot overhead in short packet transmission, so that the short packet transmission based on partial superposition pilot has a higher transmission rate than that based on traditional superposition pilot and conventional pilot.

Description

Technical Field

[0001] The invention relates to a partial superposition pilot optimization method for short packet communication, belonging to the technical field of communication performance optimization. Background Art

[0002] With the explosive growth of IoT devices that have stringent requirements on latency and reliability, ultra-reliable low-latency communication (URLLC) has become one of the three major application scenarios of 5G and B5G. In practical applications, factory automation and remote surgery have reliability requirements of 1-10. -9 , and the end-to-end delay is less than 1ms. Other services, such as smart grids, intelligent transportation systems, and process automation, have more relaxed requirements for reliability. The Third Generation Partnership Project (3GPP) proposes the general URLLC requirement of transmitting a 32-byte data packet with 99.999% (block error rate (BLER) of 10 -5 ) reliability and 1ms latency. URLLC relies on short packet transmission or limited block length transmission, in which the data coding length or the number of times the channel is used is very limited. Therefore, the transmission is no longer error-free and the Shannon formula is no longer applicable. In addition, because the current short packet communication based on conventional pilot (RP) transmits the pilot 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 lengths through resource allocation, the performance improvement is very limited. Therefore, it is necessary to further study whether RP is suitable for URLLC transmission.

[0003] As an alternative to RP, fully superimposed pilot (CSP) has also attracted widespread attention in recent years. In CSP, pilots and data are superimposed together and transmitted at the same frequency. Compared with RP, CSP does not need to reserve additional transmission time for pilots, so pilots and data have the same sequence length, which not only improves the quality of channel estimation, but also improves transmission efficiency. In addition, the use of CSP can also increase the number of orthogonal pilots to reduce pilot pollution and support more user access. However, due to the introduction of data interference, CSP will inevitably reduce the quality of channel estimation, thereby affecting the data detection process. Even so, a large number of studies have shown that CSP can improve spectral efficiency compared with traditional RP in large-scale multiple-input multiple-output (MIMO) systems. In order to improve the performance of CSP, many studies are also devoted to solving the problem of mutual interference between pilot data. However, none of the above studies considers the optimization of superposition length. Therefore, in CSP, whether the system performance is optimal when the length of the pilot sequence is the same as that of the data sequence is still a question to be studied. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a partial superposition pilot optimization method and system for short packet communication, which provides an optimal pilot length and power allocation scheme for the uplink single-cell multi-user large-scale MIMO system, thereby achieving better system performance than traditional CSP and RP transmission.

[0005] In order to solve the above technical problems, the present invention provides a partial superposition pilot optimization method for short packet communication, characterized in that it is applied to an uplink single-cell multi-user large-scale MIMO system, and the method comprises:

[0006] Perform LMMSE channel estimation on the partially superimposed pilot signal to obtain an estimated channel; the partially superimposed pilot signal represents a signal in which the pilot and data are partially overlapped and transmitted simultaneously at the same frequency;

[0007] The estimated channel is used to eliminate pilot interference in some superimposed pilot signals and perform MRC data detection to obtain the user's effective signal to interference and noise ratio. The expression of the lower bound of the URLLC traversal achievable rate is determined based on the effective signal to interference and noise ratio.

[0008] Based on the expression of the lower bound of URLLC traversal achievable rate, a weighted sum rate maximization problem is constructed with maximizing the weighted sum of URLLC achievable rate as the optimization objective;

[0009] The weighted sum rate maximization problem is transformed into a geometric programming problem and solved to obtain the optimal power allocation of all user pilots and data.

[0010] Furthermore, the uplink single-cell multi-user massive MIMO system includes:

[0011] There is a base station with M antennas located at the center of a cell and K randomly evenly distributed single-antenna users. All users communicate with the base station using short data packets and share the same system bandwidth B.

[0012] Further, the partially superimposed pilot signal includes a channel training phase and a data transmission phase;

[0013] During the channel training phase, the base station receives the signal Y p for:

[0014]

[0015] Among them, h i represents the channel vector between the i-th user and the base station, represents a column vector of dimension M, represents the orthogonal pilot sequence of the i-th user, Represents the dimension as τ p Column vector ofp Indicates the pilot length, K≤τ p ≤τ c , τ c Indicates the length of the coding block, s i,p represents the data sequence sent during the channel training phase, N p represents the additive white Gaussian noise matrix in the channel training phase, Represents the dimension as M×τ p The matrix, q i and p i denote the pilot power and data power of the i-th user, respectively, and the superscript H denotes the conjugate transpose of the matrix;

[0016] During the data transmission phase, the base station receives the signal Y d for:

[0017]

[0018] Among them, ρ i represents the data power of the ith user during the data transmission phase, s i,d Represents the data sequence sent during the data transmission phase, Represents the dimension as τ d Column vector of d Indicates the length of the data sequence, represents the additive white Gaussian noise matrix during the data transmission phase, Represents the dimension as M×τ d Matrix of

[0019] Y p After despreading, the despread signal y is obtained k , as shown below:

[0020]

[0021] Among them, q k represents the pilot power of the kth user, represents the orthogonal pilot sequence of the kth user, h k represents the channel vector between the kth user and the base station,

[0022] For k The signal is estimated by LMMSE channel as shown below:

[0023]

[0024] Among them, β k , β irepresent the large-scale fading coefficients of the k-th user and the i-th user respectively.

[0025] Furthermore, the expression of the lower bound of the URLLC traversal achievable rate is:

[0026]

[0027] in, represents the lower bound of the URLLC traversal rate achievable by the k-th user, and They represent the lower bounds of the URLLC traversal rate of the kth user in the channel training phase and the data transmission phase when there is no pilot overhead, and represents the effective signal to interference and noise ratio of the kth user in the channel training phase and the data transmission phase;

[0028]

[0029]

[0030]

[0031] Among them, p k and ρ k represents the data power of the kth user in the channel training phase and the data transmission phase, respectively, and b i Indicates an intermediate quantity.

[0032] Furthermore, the weighted sum rate maximization problem is expressed as:

[0033]

[0034]

[0035]

[0036] K≤τ p ≤τ c ,τ p ∈N + .

[0037] Among them, w k represents the weight of the kth user, p and q represent the data power set and pilot power set of all users in the channel training phase, ρ represents the data power set of all users in the data transmission phase, E represents the maximum energy of the user, N + Represents the set of positive integers.

[0038] Furthermore, the weighted sum rate maximization problem is transformed into a geometric programming problem and solved to obtain the optimal power allocation of all user pilots and data, including:

[0039] Effective signal to interference and noise ratio and Through analysis, it is deduced that the optimal pilot length of the partially superimposed pilot is equal to the number of users. This conclusion is used to transform the weighted sum rate maximization problem into problem P2, which is expressed as:

[0040]

[0041]

[0042]

[0043]

[0044] Among them, a, G(x) is an intermediate quantity. x=x k,p or x k,d ;

[0045] χ p and χ d is the auxiliary variable introduced, χ k,p and χ k,d Respectively represent the set χ p and the set χ d Elements in

[0046] Problem P2 is transformed into a geometric programming problem and solved using an iterative algorithm to obtain the optimal power allocation for all user pilots and data.

[0047] Furthermore, the iterative algorithm is:

[0048] 6) Construct a maximization minimum rate problem, transform the problem into a geometric programming problem and solve it, obtain a set of initial solutions of the algorithm, and set n = 1;

[0049] 7) Use the initial solution to initialize the iteration parameters and calculate the objective function of the original problem, denoted as Obj (0) ;

[0050] 8) Using the parameters obtained in the n-1th iteration, a new geometric programming problem is constructed and solved using the CVX toolbox of MATLAB to obtain the optimal solution of the nth iteration;

[0051] 9) Update the iteration parameters using the optimal solution obtained in the nth iteration;

[0052] 10) Calculate the new objective function, denoted as Obj (n) , and let n=n+1;

[0053] 6) Repeat steps 3) to 5) until the value of the objective function converges.

[0054] The beneficial effects achieved by the present invention are:

[0055] This method can solve the problem of excessive pilot overhead in short packet transmission; this method can reduce pilot interference caused by superimposed transmission during data detection; this method can obtain a higher weighted sum rate than that based on traditional CSP and RP. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a model diagram of an uplink single-cell multi-user massive MIMO system;

[0057] Figure 2 A flowchart of a partial superposition pilot optimization method for short packet communication;

[0058] Figure 3 It is a frame structure diagram of a partially superimposed pilot (PSP);

[0059] Figure 4 Figure 1 is a frame structure diagram of a completely superimposed pilot (CSP);

[0060] Figure 5 It is a frame structure diagram of a regular pilot (RP);

[0061] Figure 6 A diagram showing the relationship between the system weighted sum rate and the number of transmitting antennas provided in an embodiment of the present invention;

[0062] Figure 7 A diagram showing the relationship between the system weighted sum rate and the system block length provided in an embodiment of the present invention;

[0063] Figure 8 A diagram showing the relationship between the system weighted sum rate and the number of users provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0065] The embodiment of the present invention provides a partial superposition pilot optimization method for short packet communication, which provides an optimal pilot length and power allocation scheme for an uplink single-cell multi-user large-scale MIMO system, thereby achieving better system performance than traditional CSP and RP transmission.

[0066] The system model in this example is Figure 1 As shown in Figure 2, it is assumed that in a single cell, there is a base station equipped with M antennas located in the center of the cell, K single-antenna users are randomly distributed in the cell, the channel between the user and the base station is block fading, and β k Denotes the large-scale fading coefficient of the kth user. All users communicate with the base station using short data packets and share the same system bandwidth B.

[0067] Based on the above system and combined Figure 2 , a partial superposition pilot optimization method for short packet communication provided by an embodiment of the present invention is described in detail, the method comprising the following steps:

[0068] Step 201: Figure 3 As shown, an embodiment of the present invention provides a schematic diagram of the frame structure of a partially superimposed pilot (PSP), a completely superimposed pilot (CSP) and a regular pilot (RP). In RP, the pilot and data are transmitted separately; in CSP, the pilot and data are completely superimposed together and transmitted at the same frequency at the same time; in PSP, the pilot and data are partially overlapped and transmitted at the same frequency at the same time. All users in the cell send PSP signals to the base station. The signal received by the base station during the channel training phase is

[0069]

[0070] in, represents the channel vector between the i-th user and the base station, represents the orthogonal pilot sequence of the i-th user, τ p Indicates the pilot length, K≤τ p ≤τ c , represents the data sequence sent during the channel training phase, represents the additive white Gaussian noise matrix in the channel training phase, q i and p i denote the pilot power and data power of the i-th user, respectively, and A H Represents the conjugate transpose of the matrix A.

[0071] The signal received by the base station during the data transmission phase is:

[0072]

[0073] Among them, ρ i represents the data power of the ith user during the data transmission phase, represents the data sequence sent during the data transmission phase, τ d Indicates the length of the data sequence. Represents the additive white Gaussian noise matrix during the data transmission phase.

[0074] The base station is in the Y p After despreading, the despread signal y is obtained k , as shown below:

[0075]

[0076] For k The signal is estimated by LMMSE channel estimation to obtain the channel between the kth user and the base station

[0077]

[0078] Step 202: The specific process of step 202 is described in detail below:

[0079] 1) Use the estimated channel to eliminate Y p The pilot interference in the signal is processed to obtain the signal: Where IPPR (imperfect pilot removal) means that the pilot interference is imperfectly eliminated;

[0080] 2) Using MRC receiver to d and Y IPPR Perform data detection to obtain the data sent by the kth user during the channel training phase and the data transmission phase:

[0081]

[0082]

[0083] Where K represents the number of users, represents the channel estimation error of the i-th user;

[0084] 3) The use-and-then-forget (UatF) method is used to represent the effective signal-to-interference-noise ratio of the user in the channel training phase and the data transmission phase, as shown in the following formula:

[0085]

[0086]

[0087]

[0088]

[0089] in, represents the expected operation, || || represents the norm operation, and It is further expressed as the following formula:

[0090]

[0091]

[0092]

[0093] Among them, p k and ρ k They represent the data power of the kth user in the channel training phase and the data transmission phase, M represents the number of base station antennas, τ d =τ c -τ p , τ c Indicates the maximum block length available in short packet transmission;

[0094] 4) Obtained from 3) and The lower bound expression of URLLC traversal achievable rate of the kth user is:

[0095]

[0096] in, is the lower bound of the URLLC traversal rate achievable by the kth user when there is no pilot overhead, and its expression is: represents the effective signal to interference and noise ratio of the kth user. The function f(x) is a monotonically decreasing convex function, and its expression is:

[0097]

[0098] Among them, x is the function variable, ε is the decoding error probability, Q -1 (·) represents the inverse function of the Gaussian function.

[0099] Step 203: By taking the pilot length and the pilot power and data power of each user as optimization variables, the weighted sum rate of all users is maximized. The weighted sum rate maximization problem is expressed as follows:

[0100]

[0101]

[0102]

[0103] K≤τ p ≤τ c ,τ p ∈N + .

[0104] Among them, w k represents the weight of the kth user, p and q represent the data power set and pilot power set of all users respectively, ρ represents the data power set of all users in the data transmission phase, τ p represents the pilot length, q k and p k They represent the pilot power and data power of k users respectively, E represents the maximum energy of the user, N + Represents the set of positive integers.

[0105] Step 204: Analyze the effective signal to noise ratio in the channel training phase and the data transmission phase, and deduce that the optimal pilot length of PSP is equal to the number of users. This conclusion is valid in the scenario of single-cell massive MIMO when MRC is used as the receiver. Applying the above conclusion, the weighted sum rate maximization problem is transformed into the following optimization problem P1:

[0106]

[0107]

[0108]

[0109] Step 205: Introduce auxiliary variables in P1 and because About As an increasing function, the optimization problem can be transformed into P2:

[0110]

[0111]

[0112]

[0113]

[0114] in,

[0115] Step 206: P2 is transformed into a geometric programming (GP) problem and an iterative algorithm is designed to solve it. The specific process of step 206 is described in detail below:

[0116] 1) Use the log function to approximate the objective function of P2 and obtain its lower bound:

[0117]

[0118] in and is the parameter used to approximate the objective function in the n+1th iteration, by and Substitute them into the following expressions to obtain:

[0119]

[0120]

[0121] The lower bound is used to replace the objective function of P2 for optimization, and the constant term is omitted to obtain:

[0122]

[0123] in,

[0124] 2) The continuous convex approximation method is used to deal with the constraints in P2 that do not conform to the standard form of GP. The main idea is to approximate the positive term by constructing a series of monomial functions. is the optimal power allocation set for all users in the nth iteration, and the effective signal-to-interference-noise ratio Medium i The denominator of is a positive term, and its expression is:

[0125]

[0126] In the n+1th iteration, this positive term can be approximated by the following monomial:

[0127]

[0128] in, and The expression is

[0129]

[0130] use Replace g i (P) is optimized so that P2 can be transformed into the standard form of the GP problem.

[0131] 3) Design an iterative algorithm. The specific steps of the algorithm are:

[0132] Step 1: Initialize the number of iterations n = 1, and the error threshold ξ is 10 -5 , solve the following maximization minimum rate problem to obtain a set of initial solutions

[0133]

[0134]

[0135]

[0136] Step 2: If and Initialization parameters And calculate the original objective function of P2, recorded as Obj (0) ; Otherwise, let Obj (0) =0, return to the first step.

[0137] Step 3: Given Solve GP problems using MATLAB's CVX toolbox to obtain the optimal solution

[0138] Step 4: Update the iterative parameters using the optimal solution obtained in step 3

[0139] Step 5: Calculate the original objective function of P2, denoted as Obj (n) , and let n=n+1.

[0140] Step 6: Repeat the above process until |Obj (n) -Obj (n-1) | / Obj (n) <ξ.

[0141] The technical solution provided by the present invention is further described below through specific embodiments: The initialization of wireless communication network parameters is as follows:

[0142] Path loss model (dB): 35.3+37.6log10(d), d(km); number of devices K = 20; number of base station antennas M = 100; transmission block length τ c =50; decoding error probability ε = 10 -9 ; Equipment energy 13dB.

[0143] This example is a special case of the embodiment of the present invention and can be extended to other similar cases.

[0144] Figure 4 , Figure 5 and Figure 6The relationship between the rate and the number of base station antennas, block lengths, and number of devices for PSP, CSP, and RP under long packet transmission (Shannon rate) and short packet transmission (URLLC rate) is given respectively. It can be seen that under the optimal power allocation, even when the pilot interference is imperfectly eliminated, the performance of PSP is still better than that of CSP and RP. The perfect elimination of pilot interference is the ideal case of SP and can be regarded as its upper bound. In addition, the performance under Shannon rate is better than that of URLLC rate, which means that in short packet transmission, we should use URLLC achievable rate formula instead of Shannon formula for transmission design, otherwise the transmission delay and reliability are underestimated.

[0145] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A partial superposition pilot optimization method for short packet communication, characterized in that: Applied in an uplink single-cell multi-user massive MIMO system, the method comprises: Perform LMMSE channel estimation on the partially superimposed pilot signal to obtain an estimated channel; the partially superimposed pilot signal represents a signal in which the pilot and data are partially overlapped and transmitted simultaneously at the same frequency; The estimated channel is used to eliminate pilot interference in some superimposed pilot signals and perform MRC data detection to obtain the user's effective signal to interference and noise ratio. The expression of the lower bound of the URLLC traversal achievable rate is determined based on the effective signal to interference and noise ratio. Based on the expression of the lower bound of URLLC traversal achievable rate, a weighted sum rate maximization problem is constructed with maximizing the weighted sum of URLLC achievable rate as the optimization objective; The weighted sum rate maximization problem is transformed into a geometric programming problem and solved to obtain the optimal power allocation of all user pilots and data; The uplink single-cell multi-user massive MIMO system comprises: A building located in the center of the community M Base stations with antennas and K There are randomly and uniformly distributed single-antenna users. All users communicate with the base station using short data packets and share the same system bandwidth B. The partially superimposed pilot signal includes a channel training phase and a data transmission phase; The signal received by the base station during the channel training phase for: ； in, represents the channel vector between the i-th user and the base station, , represents a column vector of dimension M, represents the orthogonal pilot sequence of the i-th user, , The dimension is , represents the pilot length, , Indicates the length of the coding block, represents the data sequence sent during the channel training phase, , represents the additive white Gaussian noise matrix in the channel training phase, , The dimension is The matrix of and denote the pilot power and data power of the ith user, respectively. H represents the conjugate transpose of a matrix; The signal received by the base station during the data transmission phase for: ； in, represents the data power of the ith user during the data transmission phase, Represents the data sequence sent during the data transmission phase, , The dimension is , Indicates the length of the data sequence, represents the additive white Gaussian noise matrix during the data transmission phase, , The dimension is Matrix of right After despreading, the despread signal is obtained , as shown below: ； in, represents the pilot power of the kth user, represents the orthogonal pilot sequence of the kth user, , represents the channel vector between the kth user and the base station, ; right The signal is estimated by LMMSE channel as shown below: ； in, , denote the large-scale fading coefficients of the k-th user and the i-th user respectively; The expression of the lower bound of the URLLC traversal achievable rate is: ； in, represents the lower bound of the URLLC traversal rate achievable by the k-th user, and They represent the lower bounds of the URLLC traversal rate of the kth user in the channel training phase and the data transmission phase when there is no pilot overhead, and represents the effective signal to interference and noise ratio of the kth user in the channel training phase and the data transmission phase; ； in, and They represent the data power of the kth user in the channel training phase and the data transmission phase, respectively. Indicates an intermediate quantity.

2. The method for optimizing partial superposition pilots for short packet communication according to claim 1, characterized in that: The weighted sum rate maximization problem is expressed as: ； in, represents the weight of the kth user, and They represent the data power set and pilot power set of all users in the channel training phase, represents the data power set of all users in the data transmission phase, Indicates the maximum energy of the user, Represents the set of positive integers.

3. The method for optimizing partial superposition pilots for short packet communication according to claim 2, characterized in that: The weighted sum rate maximization problem is transformed into a geometric programming problem and solved to obtain the optimal power allocation of all user pilots and data, including: Effective signal-to-interference-noise ratio and Through analysis, it is deduced that the optimal pilot length of the partially superimposed pilot is equal to the number of users. This conclusion is used to transform the weighted sum rate maximization problem into problem P2, which is expressed as: ； in, All are intermediate quantities. , , , , ; and is the auxiliary variable introduced, , , and Respectively and Elements in Problem P2 is transformed into a geometric programming problem and solved using an iterative algorithm to obtain the optimal power allocation for all user pilots and data.

4. The method for optimizing partial superposition pilots for short packet communication according to claim 3, characterized in that: The iterative algorithm is: Construct a maximization minimum rate problem, transform it into a geometric programming problem and solve it, obtain a set of initial solutions of the algorithm, and set n=1; Use the initial solution to initialize the iterative parameters and calculate the objective function of the original problem, recorded as ; A new geometric programming problem is constructed using the parameters obtained in the n-1th iteration, and the problem is solved using the CVX toolbox of MATLAB to obtain the optimal solution of the nth iteration; Update the iteration parameters using the optimal solution obtained in the nth iteration; Calculate the new objective function, denoted as , and let n=n+1; 6) Repeat steps 3) to 5) until the value of the objective function converges.

Images