Low-correlation pilot frequency sequence construction method and system of satellite internet

Abstract

The invention discloses a low-correlation pilot frequency sequence construction method, system and device for a satellite internet, and the method comprises the steps: carrying out the pilot frequency detection through employing a zero-correlation domain displacement superposition pilot frequency sequence set, and carrying out the channel estimation; residual k-rb bit information corresponding to no more than T conflict terminals in each frame is decoded and recovered through an SCJD algorithm, and front rb bit information of each UE is recovered through a pilot frequency sequence obtained through pilot frequency detection; and the satellite broadcasts a decoding state feedback message to all activated users. According to the method, the pilot conflict probability can be reduced, so that the lower access failure probability is realized, and in addition, the communication spectrum efficiency is improved by designing a data transmission protocol.

Description

Technical Field

[0001] The present invention relates to the field of satellite Internet large-scale machine communication technology, and in particular to a method, system and device for constructing a low-correlation pilot sequence for satellite Internet mass access services. Background Art

[0002] With the promotion of the fifth generation (5G) communication network and the upcoming development of the sixth generation (6G) communication network, satellite Internet that can support ubiquitous access of massive user equipment (UE) has become one of the major engineering projects of new infrastructure construction with national strategic needs. According to the International Mobile Telecommunications 2030 (IMT-2030) plan, the 6G network requires satellites to support ultra-high density connection mMTC-s business scenarios of 1 million UEs per square kilometer. Therefore, the key challenges that need to be solved in the mMTC-s scenario include: how to effectively resolve pilot conflicts between UEs, reduce AFP, and how to achieve high spectrum efficiency transmission of short packet communications under limited spectrum resources.

[0003] Faced with the challenges of pilot conflicts and communication spectrum efficiency in massive connections, the existing non-orthogonal pilot scheme can improve the pilot conflict situation, but due to the limited expansion of pilot resources or the large average cross-correlation value of pilot sequences, the system reliability is low, and it is still insufficient to cope with the explosive growth of the number of terminals because it cannot recover the terminal data that has conflicted. Therefore, in view of the performance limitations of existing traditional communication schemes for reliability, a system solution that meets large-scale access service scenarios needs to be studied urgently. Summary of the invention

[0004] The main purpose of the present invention is to propose a low-correlation pilot sequence construction method, system and device for satellite Internet, aiming at the massive device access scenario of massive machine-type communications-Satellite (mMTC-s) of satellite Internet, focusing on the core challenges of mMTC-s business, including pilot conflicts, low spectrum efficiency of short packet communications, and high reliability requirements, providing large-scale access services for massive users in mMTC-s scenarios, and reducing the pilot collision probability (Pilot Collision Probability, PCP) by expanding the scale of the pilot sequence set, thereby achieving a lower access failure probability (Access failure Probability, AFP). In addition, the communication spectrum efficiency is improved by designing a data transmission protocol.

[0005] To achieve the above object, the present invention provides a method for constructing a low-correlation pilot sequence of the Internet, the method comprising the following steps:

[0006] Step S100, after the receiving end satellite receives the concatenated information sent by the transmitting end, the zero correlation domain shifted superposition pilot sequence set is used to perform pilot detection and channel estimation, wherein the least squares algorithm is applied to obtain the channel response; wherein the concatenated information is that each activated UE at the transmitting end divides the information of length k bits into r+1 blocks, wherein the length of the first r blocks is b bits, each block is mapped to a zero correlation domain sequence according to b bits, and the r sequences are superimposed to generate a zero correlation domain shifted superposition pilot sequence, and then the remaining k-rb bits are encoded by a T-fold codebook of order T, and concatenated with the zero correlation domain shifted superposition pilot sequence;

[0007] Step S200, recover the remaining k-rb bit information corresponding to no more than T conflicting terminals in each frame through SCJD algorithm decoding, and recover the first rb bit information of each UE through the pilot sequence obtained by pilot detection;

[0008] Step S300: The satellite broadcasts a decoding status feedback message to all activated users.

[0009] A further technical solution of the present invention is that before step S100, the process includes:

[0010] Step S000, constructing a system model of a code domain unlicensed large-scale access protocol based on satellite Internet.

[0011] A further technical solution of the present invention is that the step S000 comprises:

[0012] Step S001, in a communication system covered by a single sub-beam, the satellite Sa provides mMTC-s services to the terminals within the coverage of its beam; it is assumed that these terminals are in a quasi-stationary state and are evenly distributed in the coverage area; it is approximately assumed that all terminals are at the same distance from Sa, so as to achieve almost simultaneous signal arrival time; it is assumed that the duration of each frame matches the duration of a time slot, and the frame length is recorded as n, where the propagation delay and the processing delay of the transceiver are both included in a random access time slot; considering that the maximum two-way propagation delay is about 26 milliseconds, the duration of the random access time slot is set to 50 milliseconds; the mMTC-s scenario allows a potentially unlimited number of terminals to access, but in each random access time slot, only a small number of terminals are activated with a probability of pa = 0.01 and communicate with the satellite, and these activated terminal subsets are recorded as K;

[0013] The shadow Rician fading channel model is used to simulate the actual wireless propagation environment, where the probability density function of the shadow Rician fading channel model is:

[0014] f(h 2 )=(2dn / (2dn+Ω) n (1 / 2d)exp(-h 2 / 2d) 1 F 1 (n,1,Ωh 2 / 2d(2dn+Ω));

[0015] Where d represents the multipath component, Ω represents the average power at line of sight, and n represents the Nakagami-n parameter. 1 F 1 (i, j, k) represents the confluent hypergeometric function. By setting the parameters d = 0.063, n = 1, Ω = 0.000897, the above formula can be simplified to:

[0016] f(r)=we -ηr , r>o;

[0017] In the formula

[0018] Among them, w, η is the coefficient of exponential distribution, r = h 2 As the fading coefficient of SR, P T is the user power, σ 2 is the noise variance.

[0019] A further technical solution of the present invention is that the specific description of the uplink code domain unlicensed large-scale access protocol in step S001 is:

[0020] First, the zero correlation domain shifted superposition pilot set is set to Where L is the pilot length of the zero correlation domain shifted superposition pilot, and S represents the pilot set size of the zero correlation domain shifted superposition pilot. The terminal activated at the UE end selects the pilot using its first r·b bits of information, and then performs multi-stage coding operations on the remaining information. The coded signal is modulated and spread spectrum processed in turn, and then sent to the satellite.

[0021] Let X u Y represents the codeword after UE information is coded and modulated by T-order codebook. p and Y d Respectively represent S a The received pilot and data information are specifically represented as follows:

[0022]

[0023] as well as

[0024]

[0025] Among them, j and u represent different user indexes respectively. is the user power, P Uj is the LPS pilot, Y p represents the received coded signal, S represents the pilot set size, φ j represents a set of terminals whose first r parts are all the same, that is, the first r parts of these terminals select the same zero correlation domain periodic sequence for superposition, and correspond to the same zero correlation domain shift superposition pilot sequence P in the received frame Uj , represents the Kronecker product, is additive Gaussian white noise, h u represents the SR coefficient of UEu;

[0026] Satellite S a Based on the received Y p Data, pilot detection is performed on it, and the first r·b bits of the received frame are decoded, and then the selected P Uj The terminal performs channel estimation;

[0027] The terminal's channel weighted sum estimate is expressed as follows:

[0028]

[0029] Then we can get the selected pilot frequency P Uj The remaining part of the received data Y in the transmission frame d The encoded message in is represented as follows:

[0030]

[0031] Where u represents different users, h u represents the channel fading coefficient of different users, X u Indicates the user-coded data to be transmitted. is the user power, represents the received coded signal, S represents the pilot set size;

[0032] Then, S. a SCJD is used to decode the remaining part of the transmission frame; SCJD first iteratively performs serial elimination decoding, where the residual signal obtained after the i-th step serial elimination decoding is as follows:

[0033]

[0034] Where q represents different users, h q represents the channel fading coefficient of different users, X qIndicates the user-coded data to be transmitted. is the user power, represents the received coded signal, S represents the pilot set size;

[0035] However, if the serial elimination decoding in step i fails, joint decoding is performed, which can recover at most Ti conflicting terminals. The input signal of joint decoding is as follows:

[0036]

[0037] Where δ is the residual interference coefficient after decoding; T is the multi-order coding order, v represents different activated users, and h v represents the channel fading coefficient of different users, is the user power;

[0038] set up Exhaustive residual signal and compare each estimate with the actual residual signal Compare to get the difference g e , which is then compared with a threshold θ as follows:

[0039]

[0040] Where I represents the error between the actual received signal and the estimated signal, and h l represents the channel fading coefficient of different users, represents the estimate of I, V T-i Residual signal Code word X u Set, the threshold is θ=(N c -L)σ 2 , where N c represents the length of the T-order codeword, through continuous iteration until g e If it falls below θ, the estimation is considered successful.

[0041] Next, joint decoding is performed iteratively, including decoding of the inner and outer codes, to recover all remaining messages for at most Ti terminals; however, if after exhausting all Ti attempts, g e >θ, that is, no valid set V can be found in the set T-i , S a The joint decoding will be declared failed and discarded

[0042] A further technical solution of the present invention is that the method further comprises: designing a zero-correlation domain shifted superposition pilot: introducing a random combination superposition method, that is, randomly selecting r sequences from a zero-correlation domain periodic sequence set, and then superimposing them, the pilot length remains unchanged, and the corresponding elements are summed, thereby obtaining a zero-correlation domain shifted superposition pilot sequence P U :

[0043]

[0044] where p v is the LPS sequence, i, j, ..., v represent different sequence indices, Mτ z represents the size of the zero correlation field sequence set;

[0045] Traverse all the random combinations of r sequences to obtain a zero correlation domain shifted superposition pilot sequence set; according to the construction process of the zero correlation domain shifted superposition pilot sequence set, the size of the zero correlation domain shifted superposition pilot sequence set is obtained as Where Z = M·τ z represents the size of the zero correlation field sequence set, M represents the number of root LPS sequences, τ z Indicates the number of bits by which the zero correlation field sequence can be shifted.

[0046] A further technical solution of the present invention is that the method further comprises: correlation analysis of zero correlation domain shifted superposition pilot:

[0047] set up represents the zero correlation domain sequence pilot and The cross-correlation function between u , τ v Represents different shift numbers corresponding to different sequences, where the shift difference between the two sequences is Δτ u，v =|τ u -τ v | bits, where Δτ u，v Denote the shift difference of two sequences, let represents the zero correlation domain shifted superposition pilot P Uk and P Uq The cross-correlation function between Uk , P Uq Represents different user indexes, and their index sets are and in represents different LPS sequences, τ a , τ b , τ c , τ drepresents different shift numbers, k1, k2, q1, q2 represent different root sequences, where r = 2, so the cross-correlation of any two zero-correlation domain shifted superposition pilots is as follows:

[0048]

[0049] in represents the cross-correlation value of any two ZSPs, Δτ a，c , Δτ a，d , Δτ b，c , Δτ b，d Indicates different shift numbers, represents different LPS sequences, τ a , τ b , τ c , τ d represents different shift numbers, k1, k2, q1, q2 represent different root sequences;

[0050] If the ZSPP Uk and P Uq If the zero correlation domain sequences of the two ZSPs are different, the cross-correlation value between the two ZSPs is zero; probability statistics show that the number of ZSPs composed of completely different sequences is Where Z = M·τ z represents the size of the zero correlation field sequence set, M represents the number of root LPS sequences, τ z represents the number of shifts of the zero correlation domain sequence, and r represents the superposition coefficient; therefore, the other zero correlation domain shifted superposition pilots in the set maintain the same Uk The probability of orthogonality is as follows:

[0051]

[0052] Where Z = M·τ z represents the size of the zero correlation field sequence set, M represents the number of root LPS sequences, τ z represents the number of shifts of the zero correlation domain sequence, and r represents the superposition coefficient;

[0053] For r<<(τ z ·M), P O →1, that is, most of the zero-correlation domain shifted superposition pilots maintain orthogonality, and only a small part shows non-orthogonal characteristics; let represents the zero correlation domain sequence pilot The autocorrelation function of Represents the average autocorrelation as follows:

[0054]

[0055] Where L represents the sequence length, M represents the number of root LPS sequences, and N represents the perfect sequence length;

[0056] Analysis of two zero-correlation domain shifted superposition pilots P Uk and P Uq There are three different cases to consider for the cross-correlation value:

[0057] Case 1: two zero correlation domain shifted superposition pilots have the same zero correlation domain sequence combination;

[0058] Case 2: Two zero-correlation domain shifted superposition pilots share d zero-correlation domain sequences;

[0059] Case 3: The zero correlation domain sequence combinations of the two zero correlation domain shifted superposition pilots do not overlap at all; since the two zero correlation domain shifted superposition pilots are orthogonal to each other in case 3, the first two cases are mainly concerned; therefore, the probability that the two zero correlation domain shifted superposition pilots share d zero correlation domain sequences is given by the following formula:

[0060]

[0061] Where Z represents the size of the zero correlation field sequence set, r represents the superposition coefficient, and d represents that two ZSPs share d zero correlation field sequences;

[0062] Therefore P Uk and P Uq The average cross-correlation value of is derived as follows:

[0063]

[0064] in represents the average cross-correlation value, L represents the sequence length, N represents the perfect sequence length, V′ represents the number of columns reassembled by the Hadamard matrix, and r represents the superposition coefficient;

[0065] Therefore, when r=2, the average cross-correlation value between any two zero-correlation domain shifted and stacked pilots is O(1 / L); in addition, if r increases, the average cross-correlation value will also increase.

[0066] To achieve the above-mentioned purpose, the present invention also proposes a system for constructing a low-correlation pilot sequence of the Internet, the system comprising a memory, a processor, and a low-correlation pilot sequence construction program of the Internet stored on the processor, and the low-correlation pilot sequence construction program of the Internet executes the steps of the method described above when run by the processor.

[0067] To achieve the above object, the present invention also proposes a computer-readable storage device, which stores a low-correlation pilot sequence construction program for the Internet, and when the low-correlation pilot sequence construction program for the Internet is executed by a processor, the steps of the method described above are executed.

[0068] The beneficial effects of the low-correlation pilot sequence construction method, system and device of the satellite Internet of the present invention are:

[0069] 1. Aiming at the problem of massive device access in the mMTC-s communication scenario under satellite Internet, the present invention proposes a ZT-Collision Resolution Grant-Free Random Access (ZT-GFRA; ZT: zero correlation domain low mutual correlation value pilot sequence + T-order high-dimensional general codebook) scheme. The spectrum efficiency of the system is improved by designing an efficient and reliable pilot allocation strategy. This scheme can not only meet the needs of massive device access, but also achieve lower PCP, decoding failure probability (DFP) and AFP in the mMTC-s scenario of short packet communication, providing an important reference for meeting the high-density access challenges of future satellite Internet.

[0070] 2. The present invention designs a ZSP sequence set as the pilot set of the ZT-GFRA scheme. By utilizing the idea of ​​random combination superposition and based on the zero correlation domain sequence, a ZSP sequence with a larger pilot set is generated. The scale of the pilot set far exceeds the pilot length, which can effectively reduce PCP and make it more suitable for future high-density massive device access scenarios.

[0071] 3. The present invention analyzes the average cross-correlation characteristics of the ZSP sequence, obtains the expression of the average cross-correlation value with respect to the superposition coefficient r, and then gives the value of the superposition coefficient r of the ZSP sequence with the best performance. At the same time, it is also deduced that the pilot sequence has an average cross-correlation value close to 0, which can significantly reduce the interference between terminals, and verifies through Monte Carlo simulation that ZSP has better performance in the shadow-Rician fading channel. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0073] Figure 1It is a flow chart of a preferred embodiment of a method for constructing a low-correlation pilot sequence for satellite Internet of the present invention;

[0074] Figure 2 It is a simulation diagram of the AFP performance of the ZT-GFRA scheme based on ZSP with respect to r and SNR, where K = 1000 and T = 2;

[0075] Figure 3 is the simulation of the PCP of ZSP and other pilots with respect to the number of activated UEs K and the pilot length, where r = 2 and the pilot length L = 168 / 167 or L = 336 / 337;

[0076] Figure 4 is the simulation of the DFP of ZSP and other pilots with respect to the number of activated UEs K and the pilot length, where r = 2 and the pilot length L = 168 / 167 or L = 336 / 337;

[0077] Figure 5 It is the simulation of AFP of ZT-GFRA scheme compared with other schemes regarding the number of activated UEs K and pilot length, where r=2, and the pilot length L=168 or L=336;

[0078] Figure 6 It is the simulation of AFP about SNR of ZT-GFRA scheme compared with other schemes, where r=2, pilot length L=168 or L=336;

[0079] Figure 7 It is the simulation of AFP of ZT-GFRA scheme compared with other schemes regarding the number of activated UEs K and pilot length, where r = 2 and pilot length L = 28 / 29;

[0080] Figure 8 It is the simulation of AFP about SNR of ZT-GFRA scheme compared with other schemes, where r=2 and pilot length L=28 / 29;

[0081] Fig. 9 It is the performance simulation analysis of different pilot combined with high-order codebook coding schemes, where r = 2, pilot length L = 168 or L = 336;

[0082] Fig.10 This is a schematic diagram of the satellite Internet communication scenario;

[0083] Fig.11 It is a schematic diagram of a method for constructing a low correlation sequence based on block shift of a zero correlation domain sequence;

[0084] Fig.12 It is a hardware architecture diagram of the low-correlation pilot sequence construction system of the satellite Internet of the present invention.

[0085] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0086] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0087] Pilot conflict is an important problem that needs to be solved in the Grant-Free Random Access (GFRA) scheme. Due to the huge path loss in the satellite-to-ground link, it is difficult for the satellite to effectively distinguish terminals that have selected the same pilot through power control. Therefore, an intuitive solution is to increase pilot resources and expand the pilot set to minimize the probability of pilot conflicts in the terminal, or even avoid conflicts completely. In addition, in the mMTC-s scenario, the length of terminal messages is usually short. As the terminal density continues to grow, if the pilot set is expanded only by increasing the pilot length, the spectrum efficiency will be greatly reduced. In this context, it is necessary to expand the size of the pilot set without significantly increasing the pilot length.

[0088] Traditional pilot sequences are usually designed based on the principle of mutual orthogonality, such as Kasami codes and Zadoff-Chu sequences (ZCS). However, the scale of these orthogonal pilot sets is limited by the pilot length, resulting in serious pilot conflicts in large-scale access scenarios. To solve this problem, researchers began to explore the design of non-orthogonal pilot sequences to expand the number of pilot resources and meet the challenges in large-scale access scenarios. Related literature proposes a non-orthogonal sequence design that can significantly reduce the probability of pilot conflicts. This scheme fully exploits the potential of ZCS, using all available root ZCS to generate pilot sequences and integrate them into a pilot set. Since a ZCS of length N can have at most N-1 root sequences, the scale of the multi-root ZCS (mZCS) pilot set constructed by this method can reach N(N-1), which is N-1 times larger than the orthogonal pilot set, while retaining the excellent characteristics of ZCS. On this basis, some literature further introduces the structure of m sequence into ZCS, and by increasing the maximum cross-correlation value of the sequence set, more non-orthogonal sequences are added, so that the number of pilots is expanded to N(N+1). However, since the cross-correlation value within the same root sequence in the mZCS pilot set is always zero, while the cross-correlation value between different root sequences is high, this design makes interference between most terminals inevitable, and orthogonality is maintained only in very rare cases, which makes mZCS more vulnerable in the channel noise environment of the GFRA system, and ultimately leads to a high AFP of the system.

[0089] In order to solve the user interference problem caused by non-orthogonal pilots, some literatures in recent years have proposed to use the average cross-correlation value instead of the maximum cross-correlation value as an important indicator to measure the correlation of non-orthogonal pilot sets in large-scale access, and designed a low-correlation zone periodic sequence (LPS) different from the traditional Gaussian sequence, Gold sequence and mZCS. LPS can maintain low cross-correlation in the case of random non-orthogonal shifts, which helps to reduce terminal conflicts. However, compared with orthogonal pilots of the same length, LPS can only expand the pilot set by about M times (M represents the order of the Hadamard matrix used to construct LPS), which is still not enough to cope with the explosive growth in the number of terminals. Therefore, it is necessary to design a pilot sequence that is more suitable for massive communication service scenarios, which can further expand the available pilot set while maintaining the low average cross-correlation characteristics, thereby reducing PCP in mMTC-s.

[0090] On the other hand, in the satellite Internet scenario, code domain GFRA is becoming an important research direction to solve the access needs of massive user devices. Due to the wide-area coverage and unique physical characteristics of satellite systems, random access of mMTC-s in satellite Internet faces severe challenges, including serious pilot conflicts, low efficiency of spectrum resource utilization, and high AFP. Traditional ALOHA-type protocols based on time slots and frequency domains are difficult to apply directly in satellite scenarios because these protocols are prone to serious resource conflicts and system performance degradation when faced with extremely high user density. Code domain GFRA achieves efficient resource allocation and conflict management during the access process by utilizing codeword resources shared by multiple users, and has become one of the current research hotspots. For code domain GFRA, Polyanskiy et al. proposed a T-fold random access scheme based on a multi-order codebook in the literature. This scheme solves the shortcomings of ALOHA-type protocols in collision management by optimizing codeword design. However, this method still has bottlenecks in large-scale access systems. In order to be more suitable for mMTC-s services, a literature proposes a pilot-based architecture called the LT-order Collision Resolution Code Domain GFRA (LT-GFRA) scheme. This scheme combines a ZCS sequence set of length L with a T-order codebook, which can theoretically resolve collisions of up to LT terminals. However, the LT-GFRA scheme is limited by the proportional relationship between the pilot sequence length and the number of terminals, and the codebook complexity of the T-fold related scheme will increase significantly with the increase of T, which shows that the above two schemes have inherent limitations in practical applications.

[0091] Therefore, the present invention designs a data transmission protocol based on non-orthogonal pilot sequences and uplink code domain framework for the mMTC-s service scenario under wide-area coverage of satellite Internet, which meets the requirements of high reliability and high data transmission efficiency under the access of massive devices.

[0092] Specifically, the present invention proposes a method for constructing a low-correlation pilot sequence for the Internet. The solution of the present invention is that each activated UE divides information of length k bits into r+1 blocks, wherein the length of the first r blocks is b bits, and each block is mapped to a zero-correlation domain sequence according to b bits, and the r sequences are superimposed to generate a zero-correlation domain shift superposition pilot (zero-correlation-zone shift-and-superposition pilot, ZSP) sequence. Subsequently, the remaining k-rb bits are encoded through a T-fold codebook of order T, and sent to the satellite after being cascaded with the ZSP.

[0093] At the receiving end, the satellite uses the ZSP set to perform pilot detection and channel estimation, in which the least squares (LS) algorithm is applied to obtain the channel response. The SCJD algorithm is used to decode and recover the remaining k-rb bits corresponding to no more than T conflicting terminals in each frame. And the pilot sequence obtained by pilot detection is used to recover the first rb bits of each UE. Finally, the satellite broadcasts a decoding status feedback message to all activated users.

[0094] Please refer to Figure 1 The preferred embodiment of the method for constructing a low-correlation pilot sequence of the Internet of the present invention comprises the following steps:

[0095] Step S100, after the receiving end satellite receives the cascade information sent by the transmitting end, the zero correlation domain shifted superposition pilot sequence set is used to perform pilot detection and channel estimation, wherein the least squares algorithm is applied to obtain the channel response; wherein the cascade information is that each activated UE at the transmitting end divides the information of length k bits into r+1 blocks, wherein the length of the first r blocks is b bits, each block is mapped to a zero correlation domain sequence according to b bits, and the r sequences are superimposed to generate a zero correlation domain shifted superposition pilot sequence, and then the remaining k-rb bits are encoded through a T-fold codebook of order T, and are cascaded with the zero correlation domain shifted superposition pilot sequence.

[0096] Step S200, recover the remaining k-rb bit information corresponding to no more than T conflicting terminals in each frame through SCJD algorithm decoding, and recover the first rb bit information of each UE through the pilot sequence obtained by pilot detection.

[0097] Step S300: The satellite broadcasts a decoding status feedback message to all activated users.

[0098] The ZSP sequence set designed by the present invention can significantly expand the size of the available pilot set without increasing the pilot length, effectively reducing PCP, and ZSP has excellent low cross-correlation characteristics, so that it has better anti-noise performance in actual noisy channel transmission compared with existing non-orthogonal pilots. See the specific flow chart Fig.11 At the same time, the code domain is used to resolve UE conflicts of no higher than T order, greatly increasing the number of UEs that the system can accommodate. In addition, through the multifunctional use of the pilot sequence, it has the functions of estimating channel state information and carrying user data, further improving spectrum efficiency and meeting the needs of short packet communication services.

[0099] In this embodiment, the step before step S100 includes:

[0100] Step S000, constructing a system model of a code domain unlicensed large-scale access protocol based on satellite Internet.

[0101] The step S000 specifically includes:

[0102] Step S001, in a communication system covered by a single sub-beam, the satellite Sa provides mMTC-s services to the terminals within the coverage of its beam; it is assumed that these terminals are in a quasi-stationary state and are evenly distributed in the coverage area; it is approximately assumed that all terminals are at the same distance from Sa, so as to achieve almost simultaneous signal arrival time; it is assumed that the duration of each frame matches the duration of a time slot, and the frame length is recorded as n, where the propagation delay and the processing delay of the transceiver are both included in a random access time slot; considering that the maximum two-way propagation delay is about 26 milliseconds, the duration of the random access time slot is set to 50 milliseconds; the mMTC-s scenario allows a potentially unlimited number of terminals to access, but in each random access time slot, only a small number of terminals are activated with a probability of pa = 0.01 and communicate with the satellite, and these activated terminal subsets are recorded as K;

[0103] The shadow Rician fading channel model is used to simulate the actual wireless propagation environment, where the probability density function of the shadow Rician fading channel model is:

[0104] f(h 2 )=(2dn / (2dn+Ω) n (1 / 2d)exp(-h 2 / 2d) 1 F 1 (n,1,Ωh 2 / 2d(2dn+Ω));

[0105] Where d represents the multipath component, Ω represents the average power at line of sight, and n represents the Nakagami-n parameter. 1 F 1 (i, j, k) represents the confluent hypergeometric function. By setting the parameters d = 0.063, n = 1, Ω = 0.000897, the above formula can be simplified to:

[0106] f(r)=we -ηr , r>o;

[0107] In the formula

[0108] Among them, w, η is the coefficient of exponential distribution, r = h 2 As the fading coefficient of SR, P T is the user power, σ 2 is the noise variance.

[0109] In this embodiment, the specific description of the uplink code domain unlicensed large-scale access protocol in step S001 is:

[0110] First, the zero correlation domain shifted superposition pilot set is set to Where L is the pilot length of the zero correlation domain shifted superposition pilot, and S represents the pilot set size of the zero correlation domain shifted superposition pilot. The terminal activated at the UE end selects the pilot using its first r·b bits of information, and then performs multi-stage coding operations on the remaining information. The coded signal is modulated and spread spectrum processed in turn, and then sent to the satellite.

[0111] Let X u Y represents the codeword after UE information is coded and modulated by T-order codebook. p and Y d Respectively represent S a The received pilot and data information are specifically represented as follows:

[0112]

[0113] as well as

[0114]

[0115] Among them, j and u represent different user indexes respectively. is the user power, P Uj is the LPS pilot, Y p represents the received coded signal, S represents the pilot set size, φ j represents a set of terminals whose first r parts are all the same, that is, the first r parts of these terminals select the same zero correlation domain periodic sequence for superposition, and correspond to the same zero correlation domain shift superposition pilot sequence P in the received frame Uj , represents the Kronecker product, is additive Gaussian white noise, h u represents the SR coefficient of UEu;

[0116] Satellite S a Based on the received Y p Data, pilot detection is performed on it, and the first r·b bits of the received frame are decoded, and then the selected P Uj The terminal performs channel estimation;

[0117] The terminal's channel weighted sum estimate is expressed as follows:

[0118]

[0119] Then we can get the selected pilot frequency P Uj The remaining part of the received data Y in the transmission frame d The encoded message in is represented as follows:

[0120]

[0121] Where u represents different users, h u represents the channel fading coefficient of different users, X u Indicates the user-coded data to be transmitted. is the user power, represents the received coded signal, S represents the pilot set size;

[0122] Then, S. a SCJD is used to decode the remaining part of the transmission frame; SCJD first iteratively performs serial elimination decoding, where the residual signal obtained after the i-th step serial elimination decoding is as follows:

[0123]

[0124] Where q represents different users, h q represents the channel fading coefficient of different users, X q Indicates the user-coded data to be transmitted. is the user power, represents the received coded signal, S represents the pilot set size;

[0125] However, if the serial elimination decoding in step i fails, joint decoding is performed, which can recover at most Ti conflicting terminals. The input signal of joint decoding is as follows:

[0126]

[0127] Where δ is the residual interference coefficient after decoding; T is the multi-order coding order, v represents different activated users, and h v represents the channel fading coefficient of different users, is the user power;

[0128] set up Exhaustive residual signal and compare each estimate with the actual residual signal Compare to get the difference g e , which is then compared with a threshold θ as follows:

[0129]

[0130] Where I represents the error between the actual received signal and the estimated signal, h l represents the channel fading coefficient of different users, represents the estimate of I, V T-i Residual signal Code word X u Set, the threshold is θ=(N c-L)σ 2 , where N c represents the length of the T-order codeword, through continuous iteration until g e If it falls below θ, the estimation is considered successful.

[0131] Next, joint decoding is performed iteratively, including decoding of the inner and outer codes, to recover all remaining messages for at most Ti terminals; however, if after exhausting all Ti attempts, g e >θ, that is, no valid set V can be found in the set T-i , S a The joint decoding will be declared failed and discarded

[0132] In this embodiment, the method further includes: designing a zero-correlation domain shifted superposition pilot: introducing a random combination superposition method, that is, randomly selecting r sequences from a zero-correlation domain periodic sequence set, and then superimposing them, the pilot length remains unchanged, and the corresponding elements are summed, so as to obtain a zero-correlation domain shifted superposition pilot sequence P U :

[0133]

[0134] where p v is the LPS sequence, i, j, ..., v represent different sequence indices, Mτ z represents the size of the zero correlation field sequence set;

[0135] Traverse all the random combinations of r sequences to obtain a zero correlation domain shifted superposition pilot sequence set; according to the construction process of the zero correlation domain shifted superposition pilot sequence set, the size of the zero correlation domain shifted superposition pilot sequence set is obtained as Where Z = M·τ z represents the size of the zero correlation field sequence set, M represents the number of root LPS sequences, τ z Indicates the number of bits by which the zero correlation field sequence can be shifted.

[0136] In this embodiment, the method further includes: correlation analysis of zero correlation domain shifted superposition pilots:

[0137] set up represents the zero correlation domain sequence pilot and The cross-correlation function between u , τ z Represents different shift numbers corresponding to different sequences, where the shift difference between the two sequences is Δτ u，v =|τ u -τ v | bits, where Δτ u，v Denote the shift difference of two sequences, let represents the zero correlation domain shifted superposition pilot P Uk and P Uq The cross-correlation function between Uk , P Uq Represents different user indexes, and their index sets are and in represents different LPS sequences, τ a , τ b , τ c , τ d represents different shift numbers, k1, k2, q1, q2 represent different root sequences, where r = 2, so the cross-correlation of any two zero-correlation domain shifted superposition pilots is as follows:

[0138]

[0139] in represents the cross-correlation value of any two ZSPs, Δτ a，c , Δτ a，d , Δτ b，c , Δτ b，d Indicates different shift numbers, represents different LPS sequences, τ a , τ b , τ c , τ d represents different shift numbers, k1, k2, q1, q2 represent different root sequences;

[0140] If the ZSPP Uk and P Uq If the zero correlation domain sequences of the two ZSPs are different, the cross-correlation value between the two ZSPs is zero; probability statistics show that the number of ZSPs composed of completely different sequences is Where Z = M·τ z represents the size of the zero correlation field sequence set, M represents the number of root LPS sequences, τ z represents the number of shifts of the zero correlation domain sequence, and r represents the superposition coefficient; therefore, the other zero correlation domain shifted superposition pilots in the set maintain the same Uk The probability of orthogonality is as follows:

[0141]

[0142] Where Z = M·τ z represents the size of the zero correlation field sequence set, M represents the number of root LPS sequences, τ z represents the number of shifts of the zero correlation domain sequence, and r represents the superposition coefficient;

[0143] For r<<(τ z ·M), PO →1, that is, most of the zero-correlation domain shifted superposition pilots maintain orthogonality, and only a small part shows non-orthogonal characteristics; let represents the zero correlation domain sequence pilot The autocorrelation function of Represents the average autocorrelation as follows:

[0144]

[0145] Where L represents the sequence length, M represents the number of root LPS sequences, and N represents the perfect sequence length;

[0146] Analysis of two zero-correlation domain shifted superposition pilots P Uk and P Uq There are three different cases to consider for the cross-correlation value:

[0147] Case 1: two zero correlation domain shifted superposition pilots have the same zero correlation domain sequence combination;

[0148] Case 2: Two zero-correlation domain shifted superposition pilots share dd zero-correlation domain sequences;

[0149] Case 3: The zero correlation domain sequence combinations of the two zero correlation domain shifted superposition pilots do not overlap at all; since the two zero correlation domain shifted superposition pilots are orthogonal to each other in case 3, the first two cases are mainly concerned; therefore, the probability that the two zero correlation domain shifted superposition pilots share d zero correlation domain sequences is given by the following formula:

[0150]

[0151] Where Z represents the size of the zero correlation field sequence set, r represents the superposition coefficient, and d represents that two ZSPs share d zero correlation field sequences;

[0152] Therefore P Uk and P Uq The average cross-correlation value of is derived as follows:

[0153]

[0154] in represents the average cross-correlation value, L represents the sequence length, N represents the perfect sequence length, V′ represents the number of columns reassembled by the Hadamard matrix, and r represents the superposition coefficient;

[0155] Therefore, when r=2, the average cross-correlation value between any two zero-correlation domain shifted and stacked pilots is O(1 / L); in addition, if r increases, the average cross-correlation value will also increase.

[0156] The following combination Figures 1 to 11The simulation results of the method for constructing low-correlation pilot sequences for the Internet of the present invention and the adopted technical solution are further elaborated in detail.

[0157] 1. The simulation results of the low-correlation pilot sequence construction method of the Internet of the present invention are:

[0158] Figure 2 The AFP performance of the ZSP-based ZT-GFRA scheme under different superposition coefficients r and signal-to-noise ratios is shown. We observe that when r=2, the system AFP performance is optimal, and the pilot length is set to L=168, and the ZSP pilot set size is 11476. When r=1, the available pilot set is small (672), resulting in a high PCP, which affects the AFP performance. When r>2, even if the pilot set is further expanded to 573800, the average cross-correlation value of ZSP increases with the increase of r. Therefore, when the pilot resources are absolutely sufficient relative to the number of UEs, the system performance in the case of r>2 will deteriorate instead. It can be obtained that when r=2, the performance of ZSP is optimal.

[0159] The PCP performance is mainly related to the size of the pilot set and the number of activated terminals K. Figure 3 The relationship between the PCP of different pilots and the number of activated terminals under different pilot lengths is shown, and the PCP performance of the GFRA scheme under three different pilots (ZSP, LPS, and mZCS) is compared. It can be observed that since mZCS is obtained by shifting N-1 root ZCSs, the number of pilots can reach N(N-1), so it exhibits the lowest PCP under the same pilot length. ZSP has The PCP performance of ZSP is closely followed by LPS, which has the smallest pilot set and has the highest PCP. In addition, it can be observed that under the same parameters (pilot length is set to 168 / 336), the PCP of ZSP is about 10 times lower than that of LPS.

[0160] Figure 4 The DFP performance comparison of GFRA schemes based on different pilots (ZSP, LPS, mZCS) is shown. DFP is not only affected by SNR and the number of activated terminals, but also depends on factors such as pilot length, coding scheme, decoding algorithm, and average cross-correlation of pilot sequence sets. Here we control the other variables to be consistent under different pilots to compare the impact of pilots on the DFP performance of the system. Among them, the average cross-correlation values ​​of ZSP and LPS are both O(1 / L) (the superposition coefficient of ZSP is set to r=2), which is higher than that of mZCS. It can be observed that as the number of UEs increases, the DFP of ZSP significantly outperforms LPS and mZCS. In addition, when K is small, the DFP of ZSP with different pilot lengths shows only minimal differences, which is due to the large pilot set of ZSP (much larger than the number of terminals) and the low cross-correlation value. In this case, the DFP performance is mainly limited by the decoder.

[0161] Figure 5 The simulation of AFP of ZT-GFRA scheme versus LSP-GFRA scheme with respect to the number of activated UEs K and pilot length is shown. Figure 6 The simulation of AFP on SNR is shown. It can be observed that when the SNR is lower than 5dB, the performance of the ZT-GFRA scheme is worse than that of the LPS-GFRA scheme. This is due to the degradation of the decoding performance of the SCJD decoder at low SNR. As the SNR increases, it can be seen that the ZT-GFRA scheme gradually outperforms the LPS-GFRA scheme. This is because the ZSP sequence based on the ZT-GFRA scheme has a large enough pilot set, which has a stronger ability to resolve pilot conflicts. For conflicting users, some UE codewords can also be successfully recovered.

[0162] Figure 7 The simulation of AFP of ZT-GFRA scheme versus LT-GFRA scheme with respect to the number of activated UEs K and pilot length is shown. Figure 8 The simulation of AFP on SNR is shown. It can be observed that when the number of users is small, the difference between the two is not big, because the number of activated UEs is far less than the pilot set size, and the UE conflict is not obvious. As the number of users increases, the disadvantages of the LT-GFRA scheme gradually emerge. Due to the limited orthogonal pilot resources, as K increases, the conflicts gradually increase, and the system access performance gradually deteriorates. In summary, compared with the two large-scale access schemes, the ZT-GFRA scheme shows obvious performance advantages. It not only reduces the probability of conflict through more sufficient pilot resources, but also adopts a multi-order coding scheme to further solve some conflicting terminals, making it more suitable for massive device access scenarios.

[0163] Fig. 9 By combining ZSP, LPS pilot and T-fold coding schemes for simulation comparison, it can be observed that the code domain GFRA system performance under ZSP is significantly better than the latter. This is because ZSP can greatly increase the number of pilots while ensuring the low correlation characteristics, allowing the system to accommodate more terminals, so the system access performance is better.

[0164] 2. The system model of the code domain unlicensed large-scale access protocol based on satellite Internet in the low-correlation pilot sequence construction method of the Internet of the present invention is:

[0165] In actual scenarios, it is often a multi-beam system, but we study it separately. Fig.10 Figure 2 shows a communication system covered by one of the single sub-beams shown in Figure 2. In this scenario, the satellite Sa provides mMTC-s services to the terminals within the coverage area of ​​its beam. The terminals are assumed to be quasi-stationary and evenly distributed in the coverage area. Given that the link length between the satellite and the ground can reach hundreds of kilometers, we approximate that all terminals are equidistant from Sa, thereby achieving almost simultaneous signal arrival times. We assume that the duration of each frame matches the duration of a time slot, denoted by n, where the propagation delay and the processing delay of the transceiver are both included in a random access slot. Considering that the maximum two-way propagation delay is about 26 milliseconds, we set the duration of the random access slot to 50 milliseconds. The mMTC-s scenario allows a potentially unlimited number of terminals to access, but in each random access slot, only a small number of terminals are activated with probability pa = 0.01 and communicate with the satellite. The subset of these activated terminals is denoted by K.

[0166] In wireless communication systems, due to the multipath effect and shadow fading in the environment, the channel characteristics of the satellite-to-ground link will be affected by many factors, such as the presence of obstacles such as buildings, vegetation, and terrain. These factors will cause the signal propagation path to become more complicated, resulting in unstable signal strength. In order to accurately simulate this situation, the Shadowed Rician Fading Channel (SR) model is usually used. This model can fully consider the shadow effect and fading effect, thereby more realistically reflecting the actual wireless propagation environment. The probability density function (PDF) of SR is as follows:

[0167] f(h 2 )=(2dn / (2dn+Ω) n (1 / 2d)exp(-h 2 / 2d) 1 F 1 (n,1,Ωh 2 / 2d(2dn+Ω));

[0168] Where d represents the multipath component, Ω represents the average power of Line of Sight (LoS), and n represents the Nakagami-n parameter. 1 F 1 (i, j, k) represents the confluence hypergeometric function. By setting the parameters d = 0.063, n = 1, Ω = 0.000897, the above formula can be simplified to:

[0169] f(r)=we -ηr , r>o;

[0170] In the formula Where w, η is the coefficient of the exponential distribution, r = h 2 As the fading coefficient of SR, P T is the user power, σ 2 is the noise variance.

[0171] 3. The specific description of the uplink code domain unlicensed large-scale access protocol in the low-correlation pilot sequence construction method of the satellite Internet of the present invention is as follows:

[0172] First, set the ZSP pilot set to Where L is the pilot length of ZSP, and S represents the pilot set size of ZSP. The terminal activated at the UE side selects the pilot using its first r·b bits of information, and then performs multi-stage coding operations on the remaining information. The coded signal is modulated and spread spectrum processed successively, and then sent to the satellite.

[0173] Let X u Y represents the codeword after UE information is coded and modulated by T-order codebook. p and Y d Respectively represent S a The received pilot and data information are specifically represented as follows:

[0174]

[0175] as well as

[0176]

[0177] Where j and u represent different user indexes respectively. is the user power, P Uj is the LPS pilot, Y p represents the received coded signal, S represents the pilot set size, φ j represents the set of terminals whose first r parts are all the same, that is, the first r parts of these terminals select the same zero correlation field periodic sequence for superposition, and correspond to the same ZSP sequence P in the received frame Uj , represents the Kronecker product, is additive white Gaussian noise (AWGN), which is In addition, h u Represents the SR coefficient of UEu.

[0178] Satellite S a Based on the received Y p Data, pilot detection is performed on it, and the first r·b bits of the received frame are decoded, and then the selected P UjThe terminal performs channel estimation.

[0179] The terminal's channel weighted sum estimate is expressed as follows:

[0180]

[0181] Then we can get the selected pilot frequency P Uj The remaining part of the received data Y in the transmission frame d The encoded message in is represented as follows:

[0182]

[0183] Where u represents different users, h u represents the channel fading coefficient of different users, X u Indicates the user-coded data to be transmitted. is the user power, represents the received coded signal, and S represents the pilot set size. a SCJD is used to decode the remaining part of the transmission frame. SCJD first iteratively performs serial elimination decoding, where the residual signal obtained after the i-th step serial elimination decoding is as follows:

[0184]

[0185] Where q represents different users, h q represents the channel fading coefficient of different users, X q Indicates the user-coded data to be transmitted. is the user power, represents the received coded signal, and S represents the size of the pilot set. However, if the serial elimination decoding in step i fails, joint decoding is performed, and joint decoding can recover at most Ti conflicting terminals. The input signal of joint decoding is as follows:

[0186]

[0187] Where δ is the residual interference coefficient after decoding, T is the multi-order coding order, v represents different activated users, and h v represents the channel fading coefficient of different users, is the user power. We need to exhaustively enumerate the residual signal and compare each estimate with the actual residual signal Compare to obtain the difference g. , which is then compared with the threshold θ as follows:

[0188]

[0189] Where I represents the error between the actual received signal and the estimated signal, h l represents the channel fading coefficient of different users, represents the estimate of I, V T-i Residual signal Code word X u Set, the threshold is θ=(N c -L)σ 2 , where N c represents the length of the T-order codeword. By continuously iterating until g e If it falls below θ, the estimation is considered successful.

[0190] Next, we iteratively perform joint decoding, including decoding of the inner and outer codes, to recover all remaining messages for at most Ti terminals. However, if after exhausting all Ti attempts, g e >θ, that is, no valid set V can be found in the set T-i , S a The joint decoding will be declared failed and discarded

[0191] 4. The design scheme of ZSP in the low-correlation pilot sequence construction method of satellite internet of the present invention is as follows:

[0192] Zero correlation domain sequences have perfect autocorrelation and cross-correlation characteristics, which can satisfy the mutual orthogonality between sequences periodically shifted in the zero correlation domain under the same root sequence, and at the same time, the shifted sequences from different root sequences are also mutually orthogonal. Compared with the traditional orthogonal pilot (ZCS pilot), zero correlation domain sequences also have a larger sequence set. Therefore, we consider making full use of the properties of zero correlation domain sequences to construct new sequences to further expand pilot resources.

[0193] In order to expand the sequence set, we introduce a random combination superposition method, that is, randomly select r sequences from the zero correlation domain periodic sequence set, and then superimpose them. The pilot length remains unchanged, and the corresponding elements are summed to obtain the ZSP sequence P U .

[0194]

[0195] where p v is the LPS sequence, i, j, ..., v represent different sequence indices, Mτ z Denotes the size of the zero correlation domain sequence set. Traverse all random combinations of r sequences to obtain the ZSP sequence set. According to the construction process of the ZSP sequence set, we can obtain the size of the ZSP sequence set as Where Z = M·τ zrepresents the size of the zero correlation field sequence set, M represents the number of root LPS sequences, τ z represents the number of shifts of the zero correlation field sequence. Fig.11 As shown, in order to better understand the ZSP set, we give a specific example as follows:

[0196] First, a zero correlation domain sequence set is generated, where the order of the Hadamard matrix is ​​set to M = 2 (i.e., the number of root sequences), the length of the perfect sequence e = {1, 1, 0, 1, 0, 0, -1} is N = 7, and the number of columns after the reorganized Hadamard matrix is ​​V′ = 2. Therefore, the pilot length of the ZSP is equal to the length of the zero correlation domain sequence, which is L = M·N = 14, and the number of bits that can be shifted in the zero correlation domain is Therefore, we can get the size of the zero correlation region to be Z = M·τ z =10, the zero correlation field sequence is as follows:

[0197] p 1，1 ={+1,+1,0,0,0,+1,-1,0,+1,0,+1,-1,0,+1},

[0198] p 1，2 ={+1,0,0,0,+1,-1,0,+1,0,+1,-1,0,+1,+1},

[0199] p 1，3 ={0, 0, 0, +1, -1, 0, +1, 0, +1, -1, 0, +1, +1, +1},

[0200] p 1，4 ={0, 0, +1, -1, 0, +1, 0, +1, -1, 0, +1, +1, +1, 0},

[0201] p 1，5 ={0,+1,-1,0,+1,0,+1,-1,0,+1,+1,+1,0,0},

[0202] p 2，1 ={-1,+1,0,0,0,+1,+1,0,-1,0,-1,-1,0,+1},

[0203] p 2,2 ={+1,0,0,0,+1,+1,0,-1,0,-1,-1,0,+1,-1},

[0204] p 2,3 ={0,0,0,+1,+1,0,-1,0,-1,-1,0,+1,-1,+1},

[0205] p 2,4={0,0,+1,+1,0,-1,0,-1,-1,0,+1,-1,+1,0},

[0206] p 2,5 ={0,+1,+1,0,-1,0,-1,-1,0,+1,-1,+1,0,0}.

[0207] Assume A q ={a 1 , a 2 , ..., a r} represents the qth selected index set, q = 1, 2, ..., S. Therefore, ZSP randomly selects r = 2 sequences from the zero correlation field periodic sequence for superposition, which can generate a total of ZSP sequence, namely A q ={p i，j , p u，v}, 1≤i, u≤2, 1≤j, v≤5. Then, the qth ZSP can be defined as:

[0208]

[0209] where a l represents the sequence index, P Uq Indicates different ZSP sequences, A q represents the index set of the qth selection, l represents the different sequence indexes, and r represents the superposition coefficient. Obviously, in terms of ZSP, the probability that an activated terminal does not collide with other activated terminals is given by:

[0210]

[0211] Where P cf represents the probability that an activated terminal does not collide with other activated terminals, S represents the size of the ZSP sequence set, and K represents the number of activated users. Therefore, if the superposition coefficient r = 1 (i.e., the traditional method of directly using zero correlation domain pilots), and the number of activated UEs is set to K = 100, the probability that an activated terminal does not collide is where τ m represents the maximum number of shifts in the low correlation domain sequence, M represents the number of root LPS sequences, and K represents the number of activated users. However, when using ZSP, the superposition coefficient r is set to 2, and the probability becomes Obviously, ZSP significantly reduces the probability of terminal conflicts.

[0212] 5. The correlation analysis of ZSP in the low-correlation pilot sequence construction method of satellite Internet of the present invention is as follows: represents the zero correlation domain sequence pilot and The cross-correlation function betweenu , τ v Indicates the different shift numbers corresponding to different sequences. The shift difference between the two sequences is Δτ u,v =|τ u -τ v | bits, where Δτ u，v represents the shift difference of the two sequences. In addition, let ZSPP Uk and P Uq The cross-correlation function between Uk , P Uq Represents different user indexes, and their index sets are and in represents different LPS sequences, τ a , τ b , τ c , τ d represents different shift numbers, k1, k2, q1, q2 represent different root sequences, and r = 2. Therefore, the cross-correlation of any two ZSPs is as follows:

[0213] in represents the cross-correlation value of any two ZSPs, Δτ a，c , Δτ a，d , Δτ b，c , Δτ b，d Indicates different shift numbers, represents different LPS sequences, τ a , τ b , τ c , τ d represents different shift numbers, and k1, k2, q1, q2 represent different root sequences.

[0214] If the ZSPP Uk and P Uq If the zero correlation domain sequences of the two ZSPs are different, the cross-correlation value between the two ZSPs is zero. Probability statistics show that the number of ZSPs composed of completely different sequences is Where Z = M·τ z represents the size of the zero correlation field sequence set, M represents the number of root LPS sequences, τ z represents the number of shifts in the zero correlation field sequence, and r represents the superposition coefficient. Therefore, the other ZSPs in this set remain the same as P Uk The probability of orthogonality is as follows:

[0215]

[0216] Where Z = M·τ zrepresents the size of the zero correlation field sequence set, M represents the number of root LPS sequences, τ z represents the number of shifts in the zero correlation field sequence, and r represents the superposition coefficient. Please note that for r<<(τ z ·M), P O →1, that is, most ZSPs maintain orthogonality, and only a small part exhibits non-orthogonal characteristics. represents the zero correlation domain sequence pilot The autocorrelation function of Represents the average autocorrelation as follows:

[0217]

[0218] Where L represents the sequence length, M represents the number of root LPS sequences, and N represents the perfect sequence length. Uk and P Uq The cross-correlation value of needs to consider three different cases: Case 1: the two ZSPs have the same zero correlation field sequence combination; Case 2: the two ZSPs share d zero correlation field sequences; Case 3: the zero correlation field sequence combinations of the two ZSPs do not overlap at all. Since the two ZSPs are mutually orthogonal in case 3, we mainly focus on the first two cases. Therefore, the probability that two ZSPs share d zero correlation field sequences is given by the following formula:

[0219]

[0220] Where Z represents the size of the zero correlation field sequence set, r represents the superposition coefficient, and d represents that two ZSPs share d zero correlation field sequences.

[0221] Therefore P Uk and P Uq The average cross-correlation value of is derived as follows:

[0222]

[0223] in represents the average cross-correlation value, L represents the sequence length, N represents the perfect sequence length, V′ represents the number of columns recombined by the Hadamard matrix, and r represents the superposition coefficient. Therefore, when r=2, the average cross-correlation value between any two ZSPs is O(1 / L). In addition, if r increases, the average cross-correlation value will also increase.

[0224] Table 1 Probability distribution of cross-correlation values

[0225]

[0226] In order to clearly and intuitively demonstrate the cross-correlation characteristics of ZSP, the table above summarizes the probability distribution of the cross-correlation values ​​between the pilots selected by UE1 (respectively mZCS, LPS, and ZSP) and the pilots selected by other terminals, where r = 2 and the pilot length is L = 168 / 167. It can be observed that for ZSP and LPS, about 97% of the cross-correlation values ​​between other terminals and UE1 are equal to 0. However, since the mZCS constructed from the shifts of different root ZCS sequences show the same cross-correlation values, this also results in 99% of the pilots having non-zero cross-correlation. This shows that most mZCS are non-orthogonal, resulting in significant mutual interference between terminals, making mZCS vulnerable to the noisy channel of GFRA.

[0227] It should be noted that the present invention can further improve system reliability by improving the decoding method.

[0228] The beneficial effects of the low-correlation pilot sequence construction method of the satellite Internet of the present invention are:

[0229] 1. Aiming at the problem of massive device access in the mMTC-s communication scenario under satellite Internet, the present invention proposes a ZT-Collision Resolution Grant-Free Random Access (ZT-GFRA; ZT: zero correlation domain low mutual correlation value pilot sequence + T-order high-dimensional general codebook) scheme. The spectrum efficiency of the system is improved by designing an efficient and reliable pilot allocation strategy. This scheme can not only meet the needs of massive device access, but also achieve lower PCP, decoding failure probability (DFP) and AFP in the mMTC-s scenario of short packet communication, providing an important reference for meeting the high-density access challenges of future satellite Internet.

[0230] 2. The present invention designs a ZSP sequence set as the pilot set of the ZT-GFRA scheme. By utilizing the idea of ​​random combination superposition and based on the zero correlation domain sequence, a ZSP sequence with a larger pilot set is generated. The scale of the pilot set far exceeds the pilot length, which can effectively reduce PCP and make it more suitable for future high-density massive device access scenarios.

[0231] 3. The present invention analyzes the average cross-correlation characteristics of the ZSP sequence, obtains the expression of the average cross-correlation value with respect to the superposition coefficient r, and then gives the value of the superposition coefficient r of the ZSP sequence with the best performance. At the same time, it is also deduced that the pilot sequence has an average cross-correlation value close to 0, which can significantly reduce the interference between terminals, and verifies through Monte Carlo simulation that ZSP has better performance in the shadow-Rician fading channel.

[0232] To achieve the above object, the present invention also proposes a low-correlation pilot sequence construction system for satellite Internet, such as Fig.12 As shown, the system includes a processor 1001, a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002, and a low-correlation pilot sequence construction program for satellite Internet stored on the processor, wherein the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0233] Those skilled in the art will understand that Fig.12 The system structure shown in the figure does not constitute a limitation of the system, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0234] like Fig.12 As shown, the memory 1005 as a computer storage device may include an operating device, a network communication module, a user interface module, and a low-correlation pilot sequence construction program for satellite Internet.

[0235] exist Fig.12 In the system shown, the network interface 1004 is mainly used to connect to the network server and communicate data with the network server; the user interface 1003 is mainly used to interact with the user terminal and receive instructions input by the user; and the processor 1001 can be used to call the low-correlation pilot sequence construction program of the satellite Internet stored in the memory 1005.

[0236] To achieve the above objectives, the present invention also proposes a computer-readable storage device, which stores a joint order likelihood decoding program for satellite Internet. When the joint order likelihood decoding program for satellite Internet is run by a processor, the steps of the method described above are executed, which will not be repeated here.

[0237] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for constructing a low-correlation pilot sequence for the Internet, characterized in that: The method comprises the following steps: Step S100, after the receiving end satellite receives the concatenated information sent by the transmitting end, the zero correlation domain shifted superposition pilot sequence set is used to perform pilot detection and channel estimation, wherein the least squares algorithm is applied to obtain the channel response; wherein the concatenated information is that each activated UE at the transmitting end divides the information of length k bits into r+1 blocks, wherein the length of the first r blocks is b bits, each block is mapped to a zero correlation domain sequence according to b bits, and the r sequences are superimposed to generate a zero correlation domain shifted superposition pilot sequence, and then the remaining k-rb bits are encoded by a T-fold codebook of order T, and concatenated with the zero correlation domain shifted superposition pilot sequence; Step S200, recover the remaining k-rb bit information corresponding to no more than T conflicting terminals in each frame through SCJD algorithm decoding, and recover the first rb bit information of each UE through the pilot sequence obtained by pilot detection; Step S300: The satellite broadcasts a decoding status feedback message to all activated users.

2. The method for constructing a low-correlation pilot sequence of the Internet according to claim 1, characterized in that: The step S100 includes: Step S000, constructing a system model of a code domain unlicensed large-scale access protocol based on satellite Internet.

3. The method for constructing a low-correlation pilot sequence of the Internet according to claim 2, characterized in that: The step S000 includes: Step S001, in a communication system covered by a single sub-beam, the satellite Sa provides mMTC-s services to the terminals within the coverage of its beam; it is assumed that these terminals are in a quasi-stationary state and are evenly distributed in the coverage area; it is approximately assumed that all terminals are at the same distance from Sa, so as to achieve almost simultaneous signal arrival time; it is assumed that the duration of each frame matches the duration of a time slot, and the frame length is recorded as n, where the propagation delay and the processing delay of the transceiver are both included in a random access time slot; considering that the maximum two-way propagation delay is about 26 milliseconds, the duration of the random access time slot is set to 50 milliseconds; the mMTC-s scenario allows a potentially unlimited number of terminals to access, but in each random access time slot, only a small number of terminals are activated with a probability of pa = 0.01 and communicate with the satellite, and these activated terminal subsets are recorded as K; The shadow Rician fading channel model is used to simulate the actual wireless propagation environment, where the probability density function of the shadow Rician fading channel model is: f(h 2 )=(2dn / (2dn+Ω) n (1 / 2d)exp(-h 2 / 2d)1F1(n,1,Ωh 2 / 2d(2dn+Ω)); Where d represents the multipath component, Ω represents the average power of the line-of-sight, n represents the Nakagami-n parameter, and 1F1(i, j, k) represents the confluence hypergeometric function. By setting the parameters d = 0.063, n = 1, Ω = 0.000897, the above formula can be simplified to: f(r)=we -ηr ,r>o; In the formula Among them, w, η is the coefficient of exponential distribution, r = h 2 As the fading coefficient of SR, P T is the user power, σ 2 is the noise variance.

4. The method for constructing a low-correlation pilot sequence of the Internet according to claim 3, characterized in that: The specific description of the uplink code domain unlicensed large-scale access protocol in step S001 is: First, the zero correlation domain shifted superposition pilot set is set to Where L is the pilot length of the zero correlation domain shifted superposition pilot, and S represents the pilot set size of the zero correlation domain shifted superposition pilot. The terminal activated at the UE end selects the pilot using its first r·b bits of information, and then performs multi-stage coding operations on the remaining information. The coded signal is modulated and spread spectrum processed in turn, and then sent to the satellite. Let X u Y represents the codeword after UE information is coded and modulated by T-order codebook. p and Y d Respectively represent S a The received pilot and data information are specifically represented as follows: as well as Among them, j and u represent different user indexes respectively. is the user power, P Uj is the LPS pilot, Y p represents the received coded signal, S represents the pilot set size, φ j represents a set of terminals whose first r parts are all the same, that is, the first r parts of these terminals select the same zero correlation domain periodic sequence for superposition, and correspond to the same zero correlation domain shift superposition pilot sequence P in the received frame Uj , represents the Kronecker product, is additive Gaussian white noise, h u represents the SR coefficient of UE u; Satellite S a Based on the received Y p Data, pilot detection is performed on it, and the first r·b bits of the received frame are decoded, and then the selected P Uj The terminal performs channel estimation; The terminal's channel weighted sum estimate is expressed as follows: Then we can get the selected pilot frequency P Uj The remaining part of the received data Y in the transmission frame d The encoded message in is represented as follows: Where u represents different users, h u represents the channel fading coefficient of different users, X u Indicates the user-coded data to be transmitted. is the user power, represents the received coded signal, S represents the pilot set size; Then, S. a SCJD is used to decode the remaining part of the transmission frame; SCJD first iteratively performs serial elimination decoding, where the residual signal obtained after the i-th step serial elimination decoding is as follows: Where q represents different users, h q represents the channel fading coefficient of different users, X q Indicates the user-coded data to be transmitted. is the user power, represents the received coded signal, s represents the pilot set size; However, if the serial elimination decoding in step i fails, joint decoding is performed, which can recover at most Ti conflicting terminals. The input signal of joint decoding is as follows: Where δ is the residual interference coefficient after decoding; T is the multi-order coding order, v represents different activated users, and h v represents the channel fading coefficient of different users, is the user power; set up Exhaustive residual signal and compare each estimate with the actual residual signal Compare to get the difference g e , which is then compared with a threshold θ as follows: Where I represents the error between the actual received signal and the estimated signal, and h l represents the channel fading coefficient of different users, represents the estimate of I, V T-i Residual signal Code word X u Set, the threshold is θ=(N c -L)σ 2 , where N c represents the length of the T-order codeword, through continuous iteration until g e If it falls below θ, the estimation is considered successful. Next, joint decoding is performed iteratively, including decoding of the inner and outer codes, to recover all remaining messages for at most Ti terminals; however, if after exhausting all Ti attempts, g e >θ, that is, no valid set V can be found in the set T-i , S a The joint decoding will be declared failed and discarded 5. The method for constructing a low-correlation pilot sequence of the Internet according to claim 4, characterized in that: The method further includes: designing a zero-correlation domain shifted superposition pilot: introducing a random combination superposition method, that is, randomly selecting r sequences from a zero-correlation domain periodic sequence set, and then superimposing them, the pilot length remains unchanged, and the corresponding elements are summed, thereby obtaining a zero-correlation domain shifted superposition pilot sequence P U : where p v is the LPS sequence, i, j, ..., v represent different sequence indices, Mτ z represents the size of the zero correlation field sequence set; Traverse all the random combinations of r sequences to obtain a zero correlation domain shifted superposition pilot sequence set; according to the construction process of the zero correlation domain shifted superposition pilot sequence set, the size of the zero correlation domain shifted superposition pilot sequence set is obtained as Where Z = M·τ z represents the size of the zero correlation field sequence set, M represents the number of root LPS sequences, τ z Indicates the number of bits by which the zero correlation field sequence can be shifted.

6. The method for constructing a low-correlation pilot sequence of the Internet according to claim 5, characterized in that: The method further comprises: correlation analysis of zero correlation domain shifted superposition pilots: set up represents the zero correlation domain sequence pilot and The cross-correlation function between u , τ v Represents different shift numbers corresponding to different sequences, where the shift difference between the two sequences is Δτ u，v =|τ u -τ v | bits, where Δ τ u，v Denote the shift difference of two sequences, let represents zero correlation domain shifted superposition pilot P Uk and P Uq The cross-correlation function between Uk , P Uq Represents different user indexes, and their index sets are and in represents different LPS sequences, τ a , τ b , τ c , τ d represents different shift numbers, k1, k2, q1, q2 represent different root sequences, where r = 2, so the cross-correlation of any two zero-correlation domain shifted superposition pilots is as follows: in represents the cross-correlation value of any two ZSPs, Δτ a，c , Δτ a，d , Δτ b，c , Δτ b，d Indicates different shift numbers, represents different LPS sequences, τ a , τ b , τ c , τ d represents different shift numbers, k1, k2, q1, q2 represent different root sequences; If ZSP P Uk and P Uq If the zero correlation domain sequences of the two ZSPs are different, the cross-correlation value between the two ZSPs is zero; probability statistics show that the number of ZSPs composed of completely different sequences is Where Z = M·τ z represents the size of the zero correlation field sequence set, M represents the number of root LPS sequences, τ z represents the number of shifts of the zero correlation domain sequence, and r represents the superposition coefficient; therefore, the other zero correlation domain shifted superposition pilots in the set maintain the same Uk The probability of orthogonality is as follows: Where Z = M·τ z represents the size of the zero correlation field sequence set, M represents the number of root LPS sequences, τ z represents the number of shifts of the zero correlation domain sequence, and r represents the superposition coefficient; For r<<(τ z ·M), P O →1, that is, most of the zero-correlation domain shifted superposition pilots maintain orthogonality, and only a small part shows non-orthogonal characteristics; let represents the zero correlation domain sequence pilot The autocorrelation function of Represents the average autocorrelation as follows: Where L represents the sequence length, M represents the number of root LPS sequences, and N represents the perfect sequence length; Analysis of two zero-correlation domain shifted superposition pilots P Uk and P Uq There are three different cases to consider for the cross-correlation value: Case 1: two zero correlation domain shifted superposition pilots have the same zero correlation domain sequence combination; Case 2: Two zero-correlation domain shifted superposition pilots share d zero-correlation domain sequences; Case 3: The zero correlation domain sequence combinations of the two zero correlation domain shifted superposition pilots do not overlap at all; since the two zero correlation domain shifted superposition pilots are orthogonal to each other in case 3, the first two cases are mainly concerned; therefore, the probability that the two zero correlation domain shifted superposition pilots share d zero correlation domain sequences is given by the following formula: Where Z represents the size of the zero correlation field sequence set, r represents the superposition coefficient, and d represents that two ZSPs share d zero correlation field sequences; Therefore P Uk and P Uq The average cross-correlation value of is derived as follows: in represents the average cross-correlation value, L represents the sequence length, N represents the perfect sequence length, V′ represents the number of columns reassembled by the Hadamard matrix, and r represents the superposition coefficient; Therefore, when r=2, the average cross-correlation value between any two zero-correlation domain shifted and stacked pilots is O(1 / L); in addition, if r increases, the average cross-correlation value will also increase.

7. A low-correlation pilot sequence construction system for the Internet, characterized in that: The system includes a memory, a processor, and a low correlation pilot sequence construction program for the Internet stored on the processor. When the low correlation pilot sequence construction program for the Internet is executed by the processor, the steps of the method described in any one of claims 1 to 6 are performed.

8. A computer-readable storage device, characterized in that: The computer-readable storage device stores a low-correlation pilot sequence construction program for the Internet, and the low-correlation pilot sequence construction program for the Internet executes the steps of the method according to any one of claims 1 to 6 when executed by the processor.

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