Method and apparatus for information transmission

Abstract

The application provides a method and device for information transmission, the method comprising: a terminal device sending a sequence belonging to a first sequence set to a network device, the sequences in the first sequence set being related to each other; and the network device performing sequence detection on a received signal according to the first sequence set to obtain at least one sequence sent by at least one terminal device. Meanwhile, the application also provides a sequence generation method for sequence modulation and an example of a first sequence set comprising sequences of different lengths, so that more sequences can be provided under the condition of a certain length, thereby supporting more users. The method and device of the application can realize access and information transmission of a large number of users under the premise of ensuring detection effect by simultaneously performing user detection and sequence detection by using non-orthogonal sequences with less correlation in the field of signal processing.

Description

Technical Field

[0001] This application relates to the field of communications, and more specifically, to a method and apparatus for information transmission. Background Technology

[0002] The Internet of Things (IoT) is playing a vital role in various vertical industries due to its advantages such as cost savings, increased revenue streams, and improved efficiency. A key characteristic of IoT is its ability to support massive numbers of low-power devices. The crucial factor for cellular networks to support massive IoT connectivity lies in designing efficient and robust multiple access schemes. In fact, in future scenarios with massive connectivity, commonly used licensed multiple access protocols generally require complex access scheduling, resulting in unacceptable access latency. As a reliable alternative, unlicensed multiple access protocols have recently attracted significant attention from both academia and industry. Traditional unlicensed protocols require channel estimation followed by coherent detection of the transmitted data. However, the accuracy of data detection is easily affected by errors in channel estimation, and using this scheme to support the transmission of small amounts of data from a massive number of devices also results in significant waste.

[0003] Currently, an unlicensed access scheme based on incoherent data detection has revolutionized the traditional unlicensed scheme's uplink transmission of pilot signals and data, overcoming the shortcomings of traditional unlicensed protocols. It is particularly suitable for small-scale data uplink transmission in IoT scenarios. However, this scheme still struggles to support massive user access and information transmission. Therefore, achieving massive user access and information transmission in IoT scenarios remains a problem that needs to be solved. Summary of the Invention

[0004] This application provides a method for information transmission that uses non-orthogonal sequences with low correlation to simultaneously perform user detection and sequence detection, thereby enabling access for a massive number of users and information transmission while ensuring detection effectiveness.

[0005] In a first aspect, a method for transmitting information is provided, comprising: a terminal device determining a first sequence to be sent, the first sequence belonging to a first sequence set, the first sequence set including W sequences of length L, where L < W, and L and W are both positive integers, and the sequences in the first sequence set are pairwise correlated; and sending the first sequence to a network device.

[0006] The above embodiments, by employing non-orthogonal sequences for information transmission, enable the access and information transmission of a massive number of users.

[0007] In conjunction with the first aspect, in some implementations of the first aspect, the first sequence set is the sequence set with the smallest maximum cross-correlation value among at least one second sequence set, the second sequence set comprising W sequences of length L, the maximum cross-correlation value being the maximum value among the correlation values ​​between any two sequences in a sequence set.

[0008] In conjunction with the first aspect, in some implementations of the first aspect, the first sequence set is the sequence set in the at least one second sequence set whose maximum cross-correlation value is the smallest, and the sequence set whose corresponding normalized correlation matrix contains the least occurrence of the smallest maximum cross-correlation value. The normalized correlation matrix is ​​a normalized matrix of the autocorrelation matrix of a sequence set.

[0009] The above embodiments, by using non-orthogonal sequences with low correlation for information transmission, can achieve access and information transmission for a large number of users while ensuring detection effectiveness.

[0010] In conjunction with the first aspect, in some implementations of the first aspect, the second sequence set is a set of W sequences of length L in the third sequence set, the third sequence set includes X sequences of length Y, X≥W, Y≥W, and the range of the maximum cross-correlation value of the third sequence set is determined according to the number of sequences W included in the second sequence set.

[0011] In the above embodiments, at least one third sequence set is obtained before obtaining the second sequence set, particularly a sequence set containing W sequences of length W. By ensuring that the third sequence set has low orthogonality, the number of extraction results when extracting the second and first sequence sets is greatly reduced, thus lowering the screening complexity.

[0012] In conjunction with the first aspect, in some implementations of the first aspect, when L = 6, the first sequence set includes some or all of the following sequence: {1,1,1,0,0,1} T {0,0,1,1,1,1} T {1,0,0,0,1,1} T {1,1,1,0,1,0} T {1,0,0,0,0,1} T {1,1,1,1,1,1} T {0,0,1,1,1,0} T {1,1,1,1,1,0} T {0,1,0,0,1,1} T {0,1,0,1,1,1} T {1,0,0,1,0,1} T {0,0,1,1,0,1}T {1,0,0,1,1,0} T {0,1,0,1,0,1} T {1,0,0,0,0,0} T {1,0,0,0,1,0} T {1,0,0,1,1,1} T {0,0,1,0,0,0} T {0,0,1,0,0,1} T {0,1,0,1,0,0} T {1,1,1,1,0,1} T {0,0,1,0,1,1} T {0,1,0,0,0,1} T {0,1,0,0,1,0} T {0,0,1,0,1,0} T {0,0,1,1,0,0} T {1,1,1,0,1,1} T {1,1,1,1,0,0} T {0,1,0,1,1,0} T {1,1,1,0,0,0} T {1,0,0,1,0,0} T {0,1,0,0,0,0} T ,in,{} T This indicates that a vector has been transposed.

[0013] In conjunction with the first aspect, in some implementations of the first aspect, when L = 12, the first sequence set includes some or all of the following sequence: {1,0,0,1,1,0,1,0,1,1,1,0} T {0,0,0,0,0,1,1,1,1,0,0,1} T {1,0,0,1,0,0,1,1,0,0,0,0} T {1,0,1,0,1,0,0,0,0,0,0,0} T {1,0,0,0,0,0,0,1,0,0,1,1} T {1,1,1,1,1,1,0,1,0,0,1,1} T {0,0,1,0,0,1,1,1,0,1,0,0} T {1,1,0,1,1,1,0,1,1,1,1,0} T {0,1,1,0,1,0,0,1,1,0,1,0}T {0,0,0,1,1,1,0,0,0,1,0,0} T {1,1,1,1,0,1,0,0,1,1,0,1} T {0,0,0,1,0,1,0,1,1,0,1,0} T {1,1,0,0,0,1,1,0,0,0,1,1} T {0,0,0,0,1,1,1,0,0,1,1,1} T {1,0,1,0,0,0,0,1,1,1,1,0} T {1,0,1,1,0,0,1,1,1,1,0,1} T {1,1,1,0,0,1,1,0,1,1,1,0} T {0,1,0,0,0,0,0,0,1,0,0,1} T {0,1,1,0,0,0,0,0,0,1,0,0} T {0,0,1,0,1,1,1,0,1,0,1,0} T {1,1,1,0,1,1,1,1,0,0,0,0} T {0,1,1,1,0,0,1,0,0,1,1,1} T {0,1,1,1,1,0,1,1,1,0,0,1} T {0,1,0,0,1,0,0,1,0,1,1,1} T {0,1,0,1,0,0,1,0,1,0,1,0} T {0,0,1,1,0,1,0,1,0,1,0,1,1,1} T {1,0,0,0,1,0,0,0,1,1,0,1} T {1,1,0,0,1,1,1,1,1,1,0,1} T {0,0,1,1,1,1,0,0,1,0,0,1} T {1,0,1,1,1,0,1,0,0,0,1,1} T {1,1,0,1,0,1,0,0,0,0,0,0} T {0,1,0,1,1,0,1,1,0,1,0,0} T ,in,{} T This indicates that a vector has been transposed.

[0014] In conjunction with the first aspect, in some implementations of the first aspect, when L = 24, the first sequence set includes some or all of the following sequence: {1,0,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,1,1,1,1,0} T ,{0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,0,0,1} T {1,0,0,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1} T {1,0,0,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0} T ,{1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0} T ,{1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,1,1,1} T {0,0,1,1,0,0,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,0} T ,{1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0,1,1,0,1,0} T ,{0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1} T ,{0,0,1,0,1,1,0,1,1,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1} T ,{1,1,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,1,0,0,0} T {0,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,1,1,0,0,1,0} T ,{1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,0} T {0,0,0,0,1,0, 1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1,1,0} T ,{1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,1,1,1,1} T，{1,0,1,1,1,1,1,0,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0,0} T ，{1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1} T ，{0,1,1,0,1,0,1,0,1,1,1,0,0,1,0,1,0,0,1,1,0,1,0,1} T ，{0,1,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0} T ，{0,0,1,1,1,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,0,1,1} T ，{1,1,1,1,1,0,0,1,1,1,1,0,1,1,1,0,0,1,1,0,0,1,0,0} T ，{0,1,1,1,1,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1} T ，{0,1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0} T ，{0,1,1,0,0,0,0,1,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,0} T ，{0,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,0} T ，{0,0,0,1,0,1,1,0,0,1,1,1,1,1,0,1,0,1,1,0,1,1,1,1} T ，{1,0,1,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,1,1,1,0,1} T ，{1,1,0,0,1,0,0,1,0,1,0,1,1,0,1,1,0,1,1,1,1,0,0,1} T ，{0,0,0,1,1,1,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0} T ，{1,0,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,0,0,0,1,1} T ，{1,1,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,1}T {0,1,0,0,0,1,1,1,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1} T ,in,{} T This indicates that a vector has been transposed.

[0015] In a second aspect, a method for information transmission is provided, comprising: a network device receiving a signal; performing sequence detection on the signal according to a first sequence set to obtain at least one sequence, the first sequence set comprising W sequences of length L, where L < W, and L and W are both positive integers, the W sequences including the at least one sequence, and each column in the first sequence set being pairwise correlated.

[0016] The above embodiments, by employing non-orthogonal sequences for information transmission, enable the access and information transmission of a massive number of users.

[0017] In conjunction with the second aspect, in some implementations of the second aspect, the first sequence set is the sequence set with the smallest maximum cross-correlation value in at least one second sequence set, the second sequence set comprising W sequences of length L, wherein the maximum cross-correlation value is the maximum value of the correlation values ​​between any two sequences in a sequence set.

[0018] In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: the first sequence set is the sequence set in which the smallest maximum cross-correlation value appears the fewest times in the normalized correlation matrix of the sequence set with the smallest maximum cross-correlation value in the at least one second sequence set, and the normalized correlation matrix is ​​a normalized matrix of the autocorrelation matrix of a sequence set.

[0019] The above embodiments, by using non-orthogonal sequences with low correlation for information transmission, can achieve access and information transmission for a large number of users while ensuring detection effectiveness.

[0020] In conjunction with the second aspect, in some implementations of the second aspect, the second sequence set is a set of W sequences of length L in the third sequence set, the third sequence set includes X sequences of length Y, X≥W, Y≥W, and the range of the maximum cross-correlation value of the third sequence set is determined according to the number of sequences W included in the second sequence set.

[0021] In the above embodiments, at least one third sequence set is obtained before obtaining the second sequence set, particularly a sequence set containing W sequences of length W. By ensuring that the third sequence set has low orthogonality, the number of extraction results when extracting the second and first sequence sets is greatly reduced, thus lowering the screening complexity.

[0022] In conjunction with the second aspect, in some implementations of the second aspect, when L = 6, the first sequence set includes some or all of the following sequence: {1,1,1,0,0,1} T {0,0,1,1,1,1} T {1,0,0,0,1,1} T {1,1,1,0,1,0} T {1,0,0,0,0,1} T {1,1,1,1,1,1} T {0,0,1,1,1,0} T {1,1,1,1,1,0} T {0,1,0,0,1,1} T {0,1,0,1,1,1} T {1,0,0,1,0,1} T {0,0,1,1,0,1} T {1,0,0,1,1,0} T {0,1,0,1,0,1} T {1,0,0,0,0,0} T {1,0,0,0,1,0} T {1,0,0,1,1,1} T {0,0,1,0,0,0} T {0,0,1,0,0, 1} T {0,1,0,1,0,0} T {1,1,1,1,0,1} T {0,0,1,0,1,1} T {0,1,0,0,0,1} T {0,1,0,0,1,0} T {0,0,1,0,1,0} T {0,0,1,1,0,0} T {1,1,1,0,1,1} T {1,1,1,1,0,0} T {0,1,0,1,1,0} T {1,1,1,0,0,0} T {1,0,0,1,0,0} T {0,1,0,0,0,0} T ,in,{} T This indicates that a vector has been transposed.

[0023] In conjunction with the second aspect, in some implementations of the second aspect, when L = 12, the first sequence set includes some or all of the following sequence: {1,0,0,1,1,0,1,0,1,1,1,0} T {0,0,0,0,0,1,1,1,1,0,0,1} T {1,0,0,1,0,0,1,1,0,0,0,0} T {1,0,1,0,1,0,0,0,0,0,0,0} T {1,0,0,0,0,0,0,1,0,0,1,1} T {1,1,1,1,1,1,0,1,0,0,1,1} T {0,0,1,0,0,1,1,1,0,1,0,0} T {1,1,0,1,1,1,0,1,1,1,1,0} T {0,1,1,0,1,0,0,1,1,0,1,0} T {0,0,0,1,1,1,0,0,0,1,0,0} T {1,1,1,1,0,1,0,0,1,1,0,1} T {0,0,0,1,0,1,0,1,1,0,1,0} T {1,1,0,0,0,1,1,0,0,0,1,1} T {0,0,0,0,1,1,1,0,0,1,1,1} T {1,0,1,0,0,0,0,1,1,1,1,0} T {1,0,1,1,0,0,1,1,1,1,0,1} T {1,1,1,0,0,1,1,0,1,1,1,0} T {0,1,0,0,0,0,0,0,1,0,0,1} T {0,1,1,0,0,0,0,0,0,1,0,0} T {0,0,1,0,1,1,1,0,1,0,1,0} T {1,1,1,0,1,1,1,1,0,0,0,0} T {0,1,1,1,0,0,1,0,0,1,1,1} T {0,1,1,1,1,0,1,1,1,0,0,1} T {0,1,0,0,1,0,0,1,0,1,1,1} T{0,1,0,1,0,0,1,0,1,0,1,0} T {0,0,1,1,0,1,0,1,0,1,0,1,1,1} T {1,0,0,0,1,0,0,0,1,1,0,1} T {1,1,0,0,1,1,1,1,1,1,0,1} T {0,0,1,1,1,1,0,0,1,0,0,1} T {1,0,1,1,1,0,1,0,0,0,1,1} T {1,1,0,1,0,1,0,0,0,0,0,0} T {0,1,0,1,1,0,1,1,0,1,0,0} T ,in,{} T This indicates that a vector has been transposed.

[0024] In conjunction with the second aspect, in some implementations of the second aspect, when L = 24, the first sequence set includes some or all of the following sequence: {1,0,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,1,1,1,1,0} T ,{0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,0,0,1} T {1,0,0,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1} T {1,0,0,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0} T ,{1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0} T ,{1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,1,1,1} T {0,0,1,1,0,0,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,0} T ,{1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0,1,1,0,1,0} T ,{0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1} T，{0,0,1,0,1,1,0,1,1,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1} T ，{1,1,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,1,0,0,0} T ，{0,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,1,1,0,0,1,0} T ，{1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,0} T ，{0,0,0,0,1,0,1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1,1,0} T ，{1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,1,1,1,1} T ，{1,0,1,1,1,1,1,0,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0,0} T ，{1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1} T ，{0,1,1,0,1,0,1,0,1,1,1,0,0,1,0,1,0,0,1,1,0,1,0,1} T ，{0,1,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0} T ，{0,0,1,1,1,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,0,1,1} T ，{1,1,1,1,1,0,0,1,1,1,1,0,1,1,1,0,0,1,1,0,0,1,0,0} T ，{0,1,1,1,1,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1} T ，{0,1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0} T ，{0,1,1,0,0,0,0,1,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,0} T ，{0,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,0}T ,{0,0,0,1,0,1,1,0,0,1,1,1,1,0,1,0,1,1,1,0,1,0,1,1,1,1} T {1,0,1,0,0,0,1,1,0,1,1,0,0,0,0,1,0,0,1,1,1,0,1} T ,{1,1,0,0,1,0,0,1,0,1,0,1,1,0,1,1,1,0,0,1} T {0,0,0,1,1,1,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0} T ,{1,0,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,0,0,0,1,1} T ,{1,1,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,0,1} T {0,1,0,0,0,1,1,1,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1} T ,in,{} T This indicates that a vector has been transposed.

[0025] Thirdly, an information transmission apparatus is provided, comprising: a module for performing the method described in the first aspect or any optional implementation thereof, such as a processing module and a transceiver module. The transceiver module may include a sending module and a receiving module, which may be different functional modules or the same functional module capable of performing different functions. The processing module may be implemented using a processor. The transceiver module may be implemented using a transceiver; correspondingly, the sending module may be implemented using a transmitter, and the receiving module may be implemented using a receiver. If the apparatus is a terminal device, the transceiver may be a radio frequency transceiver component in the terminal device. If the apparatus is a chip disposed in the terminal device, the transceiver may be a communication interface in the chip, which is connected to the radio frequency transceiver component in the terminal device to achieve information transmission and reception via the radio frequency transceiver component.

[0026] Fourthly, an information transmission apparatus is provided, comprising: a module for performing the method described in the second aspect or any optional implementation thereof, such as a processing module and a transceiver module. The transceiver module may include a sending module and a receiving module, which may be different functional modules or the same functional module capable of performing different functions. The processing module may be implemented using a processor. The transceiver module may be implemented using a transceiver; correspondingly, the sending module may be implemented using a transmitter, and the receiving module may be implemented using a receiver. If the apparatus is a network device, the transceiver may be a radio frequency transceiver component in the network device. If the apparatus is a chip disposed in a network device, the transceiver may be a communication interface in the chip, which is connected to the radio frequency transceiver component in the network device to achieve information transmission and reception via the radio frequency transceiver component.

[0027] Fifthly, a communication device is provided, comprising: a processor and a memory; the memory for storing a computer program; the processor for executing the computer program stored in the memory, such that the device performs a method of the first aspect or any optional implementation thereof, or performs a method of the second aspect or any optional implementation thereof.

[0028] In a sixth aspect, a computer-readable storage medium is provided, characterized in that a computer program is stored on the computer program, which, when run on a computer, causes the computer to perform the method of the first aspect or any optional implementation thereof, or to perform the method of the second aspect or any optional implementation thereof.

[0029] A seventh aspect provides a chip system, characterized in that it includes: a processor for calling and running a computer program from a memory, causing a communication device on which the chip system is installed to execute the method of the first aspect or any optional implementation thereof, or to execute the method of the second aspect or any optional implementation thereof. Attached Figure Description

[0030] Figure 1 This is a schematic interactive diagram of an information transmission method 100 according to an embodiment of this application.

[0031] Figure 2 This is a schematic block diagram of a sequence generation method 200 for sequence modulation according to an embodiment of this application.

[0032] Figure 3 This is a schematic interactive diagram of an information transmission method 300 according to an embodiment of this application.

[0033] Figure 4 This is a schematic block diagram of an example of the terminal device of this application.

[0034] Figure 5 This is a schematic block diagram of an example of a network device of this application.

[0035] Figure 6 This is a schematic block diagram of an example of the communication device of this application.

[0036] Figure 7 This is a schematic block diagram of another example of the communication device of this application. Detailed Implementation

[0037] The technical solutions in this application will now be described with reference to the accompanying drawings.

[0038] The technical solutions of this application embodiment can be applied to various communication systems, such as: wireless local area network (WLAN) communication systems, global system of mobile communication (GSM) systems, code division multiple access (CDMA) systems, wideband code division multiple access (WCDMA) systems, general packet radio service (GPRS), long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, universal mobile telecommunication system (UMTS), worldwide interoperability for microwave access (WiMAX) communication systems, 5th generation (5G) systems or new radio (NR) systems, and future beyond-5th-generation (B5G) or 6th generation (6G) systems, etc.

[0039] In this application, the terminal device can refer to user equipment (UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent, or user device. The terminal device can also be a cellular phone, cordless phone, session initiation protocol (SIP) phone, wireless local loop (WLL) station, personal digital assistant (PDA), handheld device with wireless communication capabilities, computing device, or other processing device connected to a wireless modem, vehicle-mounted device, wearable device, terminal device in a 5G network, or terminal device in a future public land mobile network (PLMN), etc. This application does not limit the scope of the terminal device to these specific types.

[0040] By way of example and not limitation, in this embodiment, the terminal device can also be a wearable device. Wearable devices, also known as wearable smart devices, are a general term for devices that utilize wearable technology to intelligently design and develop everyday wearables, such as glasses, gloves, watches, clothing, and shoes. Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not merely hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are feature-rich, large in size, and can achieve complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those that focus on a specific type of application function and require the use of other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0041] Furthermore, in this embodiment of the application, the terminal device can also be a terminal device in an Internet of Things (IoT) system. IoT is an important component of the future development of information technology. Its main technical feature is to connect objects to the network through communication technology, thereby realizing an intelligent network of human-machine interconnection and object-to-object interconnection.

[0042] The network device in this application embodiment can be a device for communicating with terminal devices. The network device can be a base station (BTS) in a GSM system or CDMA system, a base station (nodeB, NB) in a wideband code division multiple access (WCDMA) system, an evolved NodeB (eNB or eNodeB) in an LTE system, a radio controller in a cloud radio access network (CRAN) scenario, or a relay station, access point, vehicle-mounted equipment, and network equipment in a future 5G network or a future evolved PLMN network, etc. The embodiments of this application are not limited.

[0043] The following is an illustrative example, using the Internet of Things as an example, to describe the application scenarios and methods of the embodiments of this application.

[0044] Due to its advantages such as cost savings, increased revenue streams, and improved efficiency, the Internet of Things (IoT) is playing a vital role in various vertical industries. Currently, among global enterprises that have already deployed IoT applications, approximately 87% expect to continue expanding their IoT footprint. Furthermore, by designing various IoT applications and solutions for individuals and businesses, the compound annual growth rate of IoT revenue for global communication service providers is projected to reach 24.9% by 2030. It is widely agreed in the industry that next-generation mobile communication networks (B5G or 6G) need to support IoT applications. A key characteristic of IoT is connecting a massive number of low-power devices. Reports indicate that by 2030, the number of IoT devices connected to cellular networks will reach 50 billion, 59 times the total population at that time. This necessitates future base stations capable of handling massive connections between tens of billions of devices. Although massive machine-type communications (mMTC) has been listed as one of the three major application scenarios of 5G, ensuring low latency and high reliability while supporting massive device access remains a significant challenge for current networks.

[0045] The key to supporting massive IoT connectivity in cellular networks lies in designing efficient and robust multiple access schemes. Traditional licensed random multiple access protocols require control signaling interactions and uplink access request scheduling to achieve resource allocation; a typical example is the physical random access channel (PRACH) used in 4G LTE and 5G NR. However, in future scenarios requiring massive connectivity, licensed multiple access protocols generally require complex access scheduling, resulting in access latency that is intolerable to users.

[0046] Unlicensed multiple access protocols alleviate the aforementioned problems to some extent and have attracted significant attention from both academia and industry. In traditional unlicensed multiple access protocols, users needing network access do not require authorization from the base station and can directly send pilot signals and data uplink to the base station; the base station performs user identification and data detection based on the received signals. By avoiding complex access scheduling, this protocol can significantly reduce access latency. The base station first decouples different user signals (i.e., user identification) based on the received pilot signals, and then detects the transmitted data. In short, the aforementioned traditional unlicensed protocols require channel estimation first, followed by coherent detection of the transmitted data. Therefore, errors in channel estimation in the above scheme can easily affect the accuracy of data detection. Furthermore, this scheme is economically unfeasible for transmitting small amounts of data, which are prevalent in IoT devices.

[0047] Currently, an unlicensed access scheme based on incoherent data detection has revolutionized the traditional unlicensed scheme's uplink transmission of pilot signals and data, overcoming the shortcomings of traditional unlicensed protocols. It is particularly suitable for small-scale data uplink transmission in IoT scenarios. However, in IoT scenarios, how to achieve access and information transmission for massive numbers of users remains a problem to be solved.

[0048] In the aforementioned unlicensed access scheme based on incoherent data detection, the pre-assigned sequence sets for different users are directly related to the performance of data detection at the receiver. Specifically, high correlation between different sequences in the set degrades the receiver's data detection performance, while low correlation results in better data detection performance. Therefore, generating the sequence set required for sequence modulation is a problem that needs to be solved.

[0049] To facilitate understanding of the embodiments of this application, a brief introduction to several terms or nouns involved in this application will be given below.

[0050] 1. Sequence Modulation

[0051] The existing "sequence modulation" refers to direct sequence modulation (spread spectrum) technology in spread spectrum communication. The differences between "sequence modulation" in this application and direct sequence spread spectrum can be summarized as follows:

[0052] (1) Different information modulation methods: In the "sequence modulation" of this application, the effective information is encoded in the selection of sequence number; direct sequence spread spectrum is achieved by multiplying a low-rate symbol carrying effective information by a high-rate pseudo-random code.

[0053] (2) Different sequence spreading methods: The sequence of "sequence modulation" in this application can be spread in time, that is, multiple consecutive time symbols, or it can be spread on multiple adjacent subcarriers, or it can be spread on a time-frequency resource block composed of multiple adjacent time slots and subcarriers; direct sequence spread spectrum is only frequency spread.

[0054] (3) For the receiver, "sequence modulation" does not require accurate channel estimation because there is no subsequent coherent data detection step; direct sequence spread spectrum requires both channel estimation and data detection steps.

[0055] 2. Maximum cross-correlation value: The maximum value of the correlation between any two sequences in a set of sequences.

[0056] 3. Normalized correlation matrix: The normalized matrix of the autocorrelation matrix of a set of sequences.

[0057] 4. {} T : indicates that the vector is transposed, i.e., {A} T This represents the transpose of vector A.

[0058] The technical solution provided in this application will be described in detail below with reference to the accompanying drawings.

[0059] Figure 1 This is a schematic interactive diagram of the information transmission method 100 provided in the embodiments of this application. Figure 1 The method 100 shown may include the following steps.

[0060] S101, The terminal device determines the first sequence to be sent.

[0061] Specifically, the first sequence belongs to the first sequence set, which includes W sequences of length L, where L < W, and L and W are both positive integers. The sequences in the first sequence set are pairwise correlated.

[0062] S102, The terminal device sends the first sequence to the network device.

[0063] It should be understood that, accordingly, the network device receives a signal sent by at least one terminal device.

[0064] S103. The network device performs sequence detection based on the first sequence set to obtain at least one sequence.

[0065] It should be understood that the network device performs sequence detection on the received signal according to the first sequence set to obtain at least one sequence. The first sequence set includes W sequences of length L, where L < W, and L and W are both positive integers. The W sequences include the at least one sequence, and each column in the first sequence set is pairwise correlated.

[0066] In the embodiments of this application, by using non-orthogonal sequences for information transmission, it is possible to achieve access and information transmission for a massive number of users.

[0067] Specifically, the first sequence set mentioned above is the sequence set with the smallest maximum cross-correlation value among at least one second sequence set. The second sequence set includes W sequences of length L, and the maximum cross-correlation value is the maximum value of the correlation between any two sequences in a sequence set.

[0068] Furthermore, the first sequence set is the sequence set in which the smallest maximum cross-correlation value appears the fewest times in the corresponding normalized correlation matrix among the sequence sets with the smallest maximum cross-correlation value in the at least one second sequence set, and the normalized correlation matrix is ​​the normalized matrix of the autocorrelation matrix of a sequence set.

[0069] In the embodiments of this application, by using non-orthogonal sequences with low correlation for information transmission, it is possible to achieve access and information transmission for a large number of users while ensuring detection effectiveness.

[0070] Optionally, the second sequence set is a set of W sequences of length L in the third sequence set, the third sequence set including X sequences of length Y, where X≥W and Y≥W, and the range of the maximum cross-correlation value of the third sequence set is determined according to the number of sequences W included in the second sequence set.

[0071] In the embodiments of this application, at least one third sequence set is obtained before obtaining the second sequence set, in particular a sequence set including W sequences of length W. By ensuring that the third sequence set has low orthogonality, the number of extraction results when extracting the second sequence set and the first sequence set is greatly reduced, thereby reducing the screening complexity.

[0072] The following text uses three examples, L=6, 12, and 24, as examples to illustrate the point.

[0073] Example 1: L = 6, the first set of sequences includes some or all of the following sequences: {1,1,1,0,0,1} T {0,0,1,1,1,1} T{1,0,0,0,1,1} T {1,1,1,0,1,0} T {1,0,0,0,0,1} T {1,1,1,1,1,1} T {0,0,1,1,1,0} T {1,1,1,1,1,0} T {0,1,0,0,1,1} T {0,1,0,1,1,1} T {1,0,0,1,0,1} T {0,0,1,1,0,1} T {1,0,0,1,1,0} T {0,1,0,1,0,1} T {1,0,0,0,0,0} T {1,0,0,0,1,0} T {1, 0,0,1,1,1} T {0,0,1,0,0,0} T {0,0,1,0,0,1} T {0,1,0,1,0,0} T {1,1,1,1,0,1} T {0,0,1,0,1,1} T {0,1,0,0,0,1} T {0,1,0,0,1,0} T {0,0,1,0,1,0} T {0,0,1,1,0,0} T {1,1,1,0,1,1} T {1,1,1,1,0,0} T {0,1,0,1,1,0} T {1,1,1,0,0,0} T {1,0,0,1,0,0} T {0,1,0,0,0,0} T ,in,{} T This indicates that a vector has been transposed.

[0074] It should be understood that the above set of sequences is given here as an example, with W=32.

[0075] Example 2: When L = 12, the first sequence set includes some or all of the following sequence: {1,0,0,1,1,0,1,0,1,1,1,0} T，{0,0,0,0,0,1,1,1,1,0,0,1} T ，{1,0,0,1,0,0,1,1,0,0,0,0} T ，{1,0,1,0,1,0,0,0,0,0,0,0} T ，{1,0,0,0,0,0,0,1,0,0,1,1} T ，{1,1,1,1,1,1,0,1,0,0,1,1} T ，{0,0,1,0,0,1,1,1,0,1,0,0} T ，{1,1,0,1,1,1,0,1,1,1,1,0} T ，{0,1,1,0,1,0,0,1,1,0,1,0} T ，{0,0,0,1,1,1,0,0,0,1,0,0} T ，{1,1,1,1,0,1,0,0,1,1,0,1} T ，{0,0,0,1,0,1,0,1,1,0,1,0} T ，{1,1,0,0,0,1,1,0,0,0,1,1} T ，{0,0,0,0,1,1,1,0,0,1,1,1} T ，{1,0,1,0,0,0,0,1,1,1,1,0} T ，{1,0,1,1,0,0,1,1,1,1,0,1} T ，{1,1,1,0,0,1,1,0,1,1,1,0} T ，{0,1,0,0,0,0,0,0,1,0,0,1} T ，{0,1,1,0,0,0,0,0,0,1,0,0} T ，{0,0,1,0,1,1,1,0,1,0,1,0} T ，{1,1,1,0,1,1,1,1,0,0,0,0} T ，{0,1,1,1,0,0,1,0,0,1,1,1} T ，{0,1,1,1,1,0,1,1,1,0,0,1} T ，{0,1,0,0,1,0,0,1,0,1,1,1} T ，{0,1,0,1,0,0,1,0,1,0,1,0} T ，{0,0,1,1,0,1,0,1,0,1,1,1} T ，{1,0,0,0,1,0,0,0,1,1,0,1}T {1,1,0,0,1,1,1,1,1,1,0,1} T {0,0,1,1,1,1,0,0,1,0,0,1} T {1,0,1,1,1,0,1,0,0,0,1,1} T {1,1,0,1,0,1,0,0,0,0,0,0} T {0,1,0,1,1,0,1,1,0,1,0,0} T , where {}T represents the transpose of a vector.

[0076] Example 3: When L = 24, the first sequence set includes some or all of the following sequence: {1,0,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,1,1,1,1,0} T ,{0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,0,0,1} T {1,0,0,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1} T {1,0,0,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0} T ,{1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0} T ,{1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,1,1,1} T {0,0,1,1,0,0,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,0} T ,{1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0,1,1,0,1,0} T ,{0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1} T ,{0,0,1,0,1,1,0,1,1,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1} T ,{1,1,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,1,0,0,0} T，{0,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,1,1,0,0,1,0} T ，{1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,0} T ，{0,0,0,0,1,0,1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1,1,0} T ，{1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,1,1,1,1} T ，{1,0,1,1,1,1,1,0,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0,0} T ，{1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1} T ，{0,1,1,0,1,0,1,0,1,1,1,0,0,1,0,1,0,0,1,1,0,1,0,1} T ，{0,1,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0} T ，{0,0,1,1,1,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,0,1,1} T ，{1,1,1,1,1,0,0,1,1,1,1,0,1,1,1,0,0,1,1,0,0,1,0,0} T ，{0,1,1,1,1,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1} T ，{0,1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0} T ，{0,1,1,0,0,0,0,1,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,0} T ，{0,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,0} T ，{0,0,0,1,0,1,1,0,0,1,1,1,1,1,0,1,0,1,1,0,1,1,1,1} T ，{1,0,1,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,1,1,1,0,1}T ,{1,1,0,0,1,0,0,1,0,1,0,1,1,0,1,1,1,0,0,1} T {0,0,0,1,1,1,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0} T ,{1,0,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,0,0,0,1,1} T ,{1,1,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,0,1} T {0,1,0,0,0,1,1,1,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1} T ,in,{} T This indicates that a vector has been transposed.

[0077] Figure 2 This is a schematic block diagram of a sequence generation method 200 for sequence modulation provided in an embodiment of this application. Figure 2 The method 200 shown may include the following steps.

[0078] Method 200 is introduced by taking the generation of a set of W sequences of length L as an example.

[0079] S201. Find W sequences of length W from the parent sequence to form an approximately orthogonal square matrix.

[0080] It should be understood that the parent sequence can be a Gold sequence, a Discrete Fourier Transform (DFT) sequence, or a Zedoff-Chu sequence (ZC sequence).

[0081] It should be understood that this "approximate orthogonality" means maintaining the lowest possible correlation between any two of the above W sequences.

[0082] As an example, after selecting a Gold sequence with a period of 31 as the parent sequence, determining the sequence length L and the number of users W to be supported, and using the generation method given in this embodiment to obtain the optimal W sequences of length L that meet the requirements, the correlation between the W sequences of length W in the approximately orthogonal matrix is ​​the aforementioned "lower correlation".

[0083] S202. Extract L rows from a W-row, W-column square matrix to obtain W sequences of length L as one extraction result. Calculate the maximum cross-correlation value of all extraction results and the number of times the maximum cross-correlation value appears in the normalized correlation matrix.

[0084] It should be understood that each extraction result, that is, each set of sequences including W sequences of length L, corresponds to a normalized correlation matrix, and each normalized correlation matrix has a maximum correlation value.

[0085] As an example, the extraction matrix is ​​represented as Where p = {1, 2, ..., P}, that is, there are P kinds of extraction results; the normalized autocorrelation matrix of the p-th extraction matrix is ​​expressed as: express The conjugate transpose of .

[0086] S203. Find the set of sequences with the smallest maximum cross-correlation value in the extraction results.

[0087] S204. Among the extraction results with the smallest maximum cross-correlation value, find the set of sequences whose minimum maximum cross-correlation value appears the least in the normalized correlation matrix.

[0088] It should be understood that there may be many extraction results that satisfy step S203, and these extraction results include many sets of sequences whose corresponding normalized correlation matrices show a very high frequency of maximum cross-correlation values. Such sets of sequences may affect the detection performance. By performing step S204, sequences with lower correlation can be further filtered out to ensure the detection performance.

[0089] Furthermore, the L-row, W-column matrix that simultaneously satisfies steps S203 and S204, selected by method 200, is the final generated sequence set. This embodiment uses three examples of L=6, 12, and 24 for illustrative purposes; for detailed explanation, please refer to method 100, which will not be elaborated upon here.

[0090] In the embodiments of this application, sequences with low correlation are obtained by a sequence generation method for sequence modulation. While ensuring a certain sequence length, more sequences can be provided, thereby enabling access and information transmission for a massive number of users while ensuring detection performance.

[0091] Figure 3 This is a schematic interactive diagram of the information transmission method 300 provided in the embodiments of this application. Figure 3 The method 300 shown may include the following steps.

[0092] S301, the terminal device determines the sequence to be sent based on the information to be sent and the first mapping relationship.

[0093] As an example, the terminal device determines the next step to send sequence 4 to the network device based on the information bit to be sent being '11' and the first mapping relationship.

[0094] Table 1 shows the first mapping relationship between the pre-configured information bits and sequences of the terminal device.

[0095] Table 1

[0096] Information bits ‘00’ ‘01’ ‘10’ ‘11’ sequence Sequence 1 Sequence 2 Sequence 3 Sequence 4

[0097] It should be understood that sequences 1 to 4 in Table 1 can be any of the sequences in the “first sequence set” mentioned in method 100 or the “final generated sequence set” mentioned in method 200.

[0098] In this example, the terminal device can send four states of information to the network device through sequences 1 to 4. The sequences that can be scheduled by the same terminal device maintain a low correlation, and the sequences that can be scheduled by different terminal devices also maintain a low correlation. At the same time, sequences 1 to 4 uniquely belong to this terminal device.

[0099] S302. The terminal device transmits the sequence through time domain and / or frequency domain resources.

[0100] Specifically, the user equipment maps the sequence to be transmitted to time-frequency and / or frequency-domain resources.

[0101] It should be understood that mapping can be performed only in the time domain, i.e., the sequence is mapped to the same subcarrier of different symbols; mapping can also be performed only in the frequency domain, i.e., the sequence is mapped to different subcarriers of the same symbol; mapping can also be performed in both time and frequency dimensions, i.e., the sequence is mapped to different subcarriers of different symbols.

[0102] It should be understood that, accordingly, the signals received by the network device include at least one sequence sent by at least one terminal device.

[0103] S303, the network device performs sequence detection based on the observation matrix, obtains the sequence, and determines the information corresponding to the sequence based on the first mapping relationship.

[0104] It should be understood that the "observation matrix" is the "first sequence set" described in method 100 or the "finally generated sequence set" described in method 200. The sequences assigned to all terminal devices communicating with the network device constitute the network device's observation matrix.

[0105] It should be understood that when a network device performs sequence detection on a received signal, it can obtain at least one sequence, which includes the sequence sent by the terminal device.

[0106] As an example, corresponding to step S301, the network device performs sequence detection based on the received signal, and obtains at least one sequence including sequence 4. Based on the first mapping relationship and sequence 4, the network device determines that the information bit sent by the terminal device is '11'.

[0107] Specifically, for the sake of convenience of explanation, we take a single-antenna user as an example below to illustrate the receiving process.

[0108] Suppose the terminal device is a single-antenna user and the number of base station antennas is M. The system model is expressed as: Y = ΦD + N.

[0109] Among them, the pre-allocated sequences of all terminal devices constitute the observation matrix, Φ ∈ C L×KN ; the equivalent channel matrix of all sequences in the observation matrix is expressed as D ∈ C KN×M ; the received signal at the base station side is expressed as Y ∈ C L×M ; the additive white Gaussian noise is expressed as N ∈ C L×M , assuming the noise variance is σ 2 .

[0110] It should be noted that the column dimension of the equivalent channel matrix D corresponds to the antenna dimension M, and the row dimension corresponds to the dimension of the device pre-allocated sequence. Specifically, the [(k - 1)N + 1]-th row to the kN-th row of the equivalent channel matrix D respectively correspond to the 1st pre-allocated sequence to the N-th pre-allocated sequence of the k-th device, k ∈ {1, 2,..., K}. Suppose the k-th device actually transmits its N-th pre-allocated sequence, then the kN-th row of the equivalent channel matrix D corresponds to the channel complex gain between the k-th device and M antennas. At the same time, the [(k - 1)N + 1]-th row to the [kN - 1]-th row of the equivalent channel matrix D are all equivalent to zero values. Similarly, if the k-th device actually transmits a pre-allocated sequence with any number, the row value of D corresponding to this sequence is the real channel complex gain, and the row values corresponding to other sequences are equivalent to zero values. In addition, if the k-th device does not transmit a sequence, then the row values of D corresponding to this device are all equivalent to zero values.

[0111] For the detection process, the problem can be summarized as: given the received signal Y and the observation matrix Φ, recover the sequence numbers of the non-zero rows of the equivalent channel matrix D. Although the number of rows of the observation matrix Φ is less than the number of columns (L < KN), that is, the above formula representing the system model is an underdetermined equation and a unique solution cannot be obtained. However, due to the intermittent characteristics of Internet of Things data, the number of active devices at the same moment is often much smaller than the total number of devices, that is: K a = K. Due to the sparsity of active devices, the equivalent channel matrix D is row-sparse, that is: only when K a rows are non-zero values, can it be ensured that the network device detects the sequence number from the received signal. A typical method is to use the simultaneous orthogonal matching pursuit (SOMP) algorithm for detection. Specifically, the SOMP algorithm is a greedy algorithm that searches for the row with the largest correlation value in each iteration until the residual is less than the noise power or the specified number of iterations is reached and then terminates.

[0112] Table 2

[0113]

[0114] Table 2 shows the SOMP algorithm used for sequence modulation information extraction. As shown in Table 2, the first step selects the row number of the equivalent channel matrix D with the largest correlation value. Step 2: Update the support set Step 3 uses the least squares method to recover the channel elements at the corresponding positions in the support set. Step 4 updates the residuals based on the recovered channel elements. If the normalized energy of the residuals is less than the noise variance, the iteration process terminates; otherwise, it returns to step 1 to find a new support set. After the iteration terminates, the output set Ω is obtained. t+1 , representing the non-zero row number of the equivalent channel matrix D, mapping the non-zero row number to the sequence number of the corresponding device yields the information bits transmitted by sequence modulation; the output set Γ = [Ω t+1 [ / N] represents the number of the active device, that is, the device corresponding to the non-zero row of the equivalent channel matrix D.

[0115] The embodiments of this application achieve massive user access and information transmission by performing user detection and sequence detection based on non-orthogonal sequences, while ensuring detection effectiveness.

[0116] The above, combined with Figures 1 to 3 The method for information transmission provided in the embodiments of this application is described in detail below. Figures 4 to 7 This application provides a detailed description of the information transmission apparatus provided in the embodiments.

[0117] Figure 4 This is a schematic block diagram of an information transmission apparatus provided in an embodiment of this application. Figure 4 As shown, the device 10 may include a transceiver module 11 and a processing module 12.

[0118] In one possible design, the device 10 may correspond to the terminal device in the above method embodiments. For example, it may be a user equipment, or a chip configured in a user equipment.

[0119] Specifically, the communication device 10 may correspond to the terminal device in method 100 and method 300 according to embodiments of this application, and the communication device 10 may include tools for performing... Figure 1 Method 100 or Figure 3 The module executing the method in method 300 of the terminal device. Furthermore, each unit in the communication device 10 and the aforementioned other operations and / or functions are respectively for implementing... Figure 1 Method 100 or Figure 3 The corresponding process of method 300 in the middle.

[0120] Wherein, when the communication device 10 is used to perform Figure 1 When using method 100, the transceiver module 11 can be used to execute step S102 in method 100, and the processing module 12 can be used to execute step S102 in method 100.

[0121] When the communication device 10 is used to perform Figure 3 When performing method 300, the transceiver module 11 can be used to execute step S302 in method 300, and the processing module 12 can be used to execute step S301 in method 300.

[0122] Specifically, the processing module 12 is used to determine a first sequence to be sent, the first sequence belonging to a first sequence set, the first sequence set including W sequences of length L, where L < W, and L and W are both positive integers, and the sequences in the first sequence set are pairwise correlated; the transceiver module 11 is used to send the first sequence to the network device.

[0123] Wherein, the first sequence set is the sequence set with the smallest maximum cross-correlation value among at least one second sequence set, and the second sequence set includes W sequences of length L, wherein the maximum cross-correlation value is the maximum value of the correlation values ​​between any two sequences in a sequence set.

[0124] Wherein, the first sequence set is the sequence set with the smallest maximum cross-correlation value among the at least one second sequence set, and the sequence set with the fewest occurrences of the smallest maximum cross-correlation value in the corresponding normalized correlation matrix, wherein the normalized correlation matrix is ​​the normalized matrix of the autocorrelation matrix of a sequence set.

[0125] Wherein, the second sequence set is a set of W sequences of length L in the third sequence set, the third sequence set includes X sequences of length Y, X≥W, Y≥W, and the range of the maximum cross-correlation value of the third sequence set is determined according to the number of sequences W included in the second sequence set.

[0126] As an example, when L=6, the first set of sequences includes some or all of the following sequences: {1,1,1,0,0,1} T {0,0,1,1,1,1} T {1,0,0,0,1,1} T {1,1,1,0,1,0} T {1,0,0,0,0,1} T {1,1,1,1,1,1} T {0,0,1,1,1,0} T {1,1,1,1,1,0} T {0,1,0,0,1,1}T {0,1,0,1,1,1} T {1,0,0,1,0,1} T {0,0,1,1,0,1} T {1,0,0,1,1,0} T {0,1,0,1,0,1} T {1,0,0,0,0,0} T {1,0,0,0,1,0} T {1,0,0,1,1,1} T {0,0,1,0,0,0} T {0,0,1,0,0,1} T {0,1,0,1,0,0} T {1,1,1,1,0,1} T {0,0,1,0,1,1} T {0,1,0,0,0,1} T {0,1,0,0,1,0} T {0,0,1,0,1,0} T {0,0,1,1,0,0} T {1,1,1,0,1,1} T {1,1,1,1,0,0} T {0,1,0,1,1,0} T {1,1,1,0,0,0} T {1,0,0,1,0,0} T {0,1,0,0,0,0} T ,in,{} T This indicates that a vector has been transposed.

[0127] As an example, when L = 12, the first set of sequences includes some or all of the following sequences: {1,0,0,1,1,0,1,0,1,1,1,0} T {0,0,0,0,0,1,1,1,1,0,0,1} T {1,0,0,1,0,0,1,1,0,0,0,0} T {1,0,1,0,1,0,0,0,0,0,0,0} T {1,0,0,0,0,0,0,1,0,0,1,1} T {1,1,1,1,1,1,0,1,0,0,1,1} T {0,0,1,0,0,1,1,1,0,1,0,0} T{1,1,0,1,1,1,0,1,1,1,1,0} T {0,1,1,0,1,0,0,1,1,0,1,0} T {0,0,0,1,1,1,0,0,0,1,0,0} T {1,1,1,1,0,1,0,0,1,1,0,1} T {0,0,0,1,0,1,0,1,1,0,1,0} T {1,1,0,0,0,1,1,0,0,0,1,1} T {0,0,0,0,1,1,1,0,0,1,1,1} T {1,0,1,0,0,0,0,1,1,1,1,0} T {1,0,1,1,0,0,1,1,1,1,0,1} T {1,1,1,0,0,1,1,0,1,1,1,0} T {0,1,0,0,0,0,0,0,1,0,0,1} T {0,1,1,0,0,0,0,0,0,1,0,0} T {0,0,1,0,1,1,1,0,1,0,1,0} T {1,1,1,0,1,1,1,1,0,0,0,0} T {0,1,1,1,0,0,1,0,0,1,1,1} T {0,1,1,1,1,0,1,1,1,0,0,1} T {0,1,0,0,1,0,0,1,0,1,1,1} T {0,1,0,1,0,0,1,0,1,0,1,0} T {0,0,1,1,0,1,0,1,0,1,0,1,1,1} T {1,0,0,0,1,0,0,0,1,1,0,1} T {1,1,0,0,1,1,1,1,1,1,0,1} T {0,0,1,1,1,1,0,0,1,0,0,1} T {1,0,1,1,1,0,1,0,0,0,1,1} T {1,1,0,1,0,1,0,0,0,0,0,0} T {0,1,0,1,1,0,1,1,0,1,0,0} T ,in,{} TThis indicates that a vector has been transposed.

[0128] As an example, when L = 24, the first set of sequences includes some or all of the following sequences: {1,0,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,1,1,1,1,0} T ,{0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,0,0,1} T {1,0,0,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1} T {1,0,0,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0} T ,{1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0} T ,{1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,1,1,1} T {0,0,1,1,0,0,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,0} T ,{1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0,1,1,0,1,0} T ,{0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1} T ,{0,0,1,0,1,1,0,1,1,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1} T ,{1,1,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,1,0,0,0} T {0,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,1,1,0,0,1,0} T ,{1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,0} T {0,0,0,0,1,0,1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1,1,0} T，{1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,1,1,1,1} T ，{1,0,1,1,1,1,1,0,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0,0} T ，{1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1} T ，{0,1,1,0,1,0,1,0,1,1,1,0,0,1,0,1,0,0,1,1,0,1,0,1} T ，{0,1,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0} T ，{0,0,1,1,1,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,0,1,1} T ，{1,1,1,1,1,0,0,1,1,1,1,0,1,1,1,0,0,1,1,0,0,1,0,0} T ，{0,1,1,1,1,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1} T ，{0,1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0} T ，{0,1,1,0,0,0,0,1,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,0} T ，{0,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,0} T ，{0,0,0,1,0,1,1,0,0,1,1,1,1,1,0,1,0,1,1,0,1,1,1,1} T ，{1,0,1,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,1,1,1,0,1} T ，{1,1,0,0,1,0,0,1,0,1,0,1,1,0,1,1,0,1,1,1,1,0,0,1} T ，{0,0,0,1,1,1,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0} T ，{1,0,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,0,0,0,1,1}T ,{1,1,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,0,1} T {0,1,0,0,0,1,1,1,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1} T ,in,{} T This indicates that a vector has been transposed.

[0129] Figure 5 This is a schematic block diagram of an information transmission apparatus provided in an embodiment of this application. As shown in the figure, the communication apparatus 20 may include a transceiver module 21 and a processing module 22.

[0130] In one possible design, the communication device 20 may correspond to the network device in the method embodiments described above. For example, it may be a base station, or a chip configured in a base station.

[0131] Specifically, the communication device 20 may correspond to the network device in method 100 and method 300 according to embodiments of this application, and the communication device 20 may include functions for performing... Figure 1 Method 100 or Figure 3 The network device in method 300 is a module that executes the method. Furthermore, each unit in the communication device 20 and the other operations and / or functions described above are respectively for implementing... Figure 1 Method 100 or Figure 3 The corresponding process of method 300 in the middle.

[0132] When the communication device 20 is used to perform Figure 1 When performing method 100, the transceiver module 21 can be used to execute step S102 in method 100, and the processing module 22 can be used to execute step S103 in method 100.

[0133] Wherein, when the communication device 20 is used to perform Figure 3 When performing method 300, the transceiver module 21 can be used to execute step S302 in method 300, and the processing module 22 can be used to execute step S303 in method 300.

[0134] Specifically, the transceiver module 21 is used to receive signals; the processing module 22 is used to perform sequence detection on the signals according to the first sequence set to obtain at least one sequence, the first sequence set includes W sequences of length L, where L < W, and L and W are both positive integers, the W sequences include the at least one sequence, and each column in the first sequence set is pairwise correlated.

[0135] Wherein, the first sequence set is the sequence set with the smallest maximum cross-correlation value in at least one second sequence set, the second sequence set includes W sequences of length L, and the maximum cross-correlation value is the maximum value of the correlation values ​​between any two sequences in a sequence set.

[0136] Wherein, the first sequence set is the sequence set with the smallest maximum cross-correlation value among the at least one second sequence set, and the sequence set with the fewest occurrences of the smallest maximum cross-correlation value in the corresponding normalized correlation matrix, wherein the normalized correlation matrix is ​​the normalized matrix of the autocorrelation matrix of a sequence set.

[0137] Wherein, the second sequence set is W sequences of length L in the third sequence set, the third sequence set includes X sequences of length Y, X≥W, Y≥W, and the range of the maximum cross-correlation value of the third sequence set is determined according to the number W of sequences included in the second sequence set.

[0138] As an example, when L=6, the first set of sequences includes some or all of the following sequences: {1,1,1,0,0,1} T {0,0,1,1,1,1} T {1,0,0,0,1,1} T {1,1,1,0,1,0} T {1,0,0,0,0,1} T {1,1,1,1,1,1} T {0,0,1,1,1,0} T {1,1,1,1,1,0} T {0,1,0,0,1,1} T {0,1,0,1,1,1} T {1,0,0,1,0,1} T {0,0,1,1,0,1} T {1,0,0,1,1,0} T {0,1,0,1,0,1} T {1,0,0,0,0,0} T {1,0,0,0,1,0} T {1,0,0,1,1,1} T {0,0,1,0,0,0} T {0,0,1,0,0,1} T {0,1,0,1,0,0} T {1,1,1,1,0,1} T {0,0,1,0,1,1}T {0,1,0,0,0,1} T {0,1,0,0,1,0} T {0,0,1,0,1,0} T {0,0,1,1,0,0} T {1,1,1,0,1,1} T {1,1,1,1,0,0} T {0,1,0,1,1,0} T {1,1,1,0,0,0} T {1,0,0,1,0,0} T {0,1,0,0,0,0} T ,in,{} T This indicates that a vector has been transposed.

[0139] As an example, when L = 12, the first set of sequences includes some or all of the following sequences: {1,0,0,1,1,0,1,0,1,1,1,0} T {0,0,0,0,0,1,1,1,1,0,0,1} T {1,0,0,1,0,0,1,1,0,0,0,0} T {1,0,1,0,1,0,0,0,0,0,0,0} T {1,0,0,0,0,0,0,1,0,0,1,1} T {1,1,1,1,1,1,0,1,0,0,1,1} T {0,0,1,0,0,1,1,1,0,1,0,0} T {1,1,0,1,1,1,0,1,1,1,1,0} T {0,1,1,0,1,0,0,1,1,0,1,0} T {0,0,0,1,1,1,0,0,0,1,0,0} T {1,1,1,1,0,1,0,0,1,1,0,1} T {0,0,0,1,0,1,0,1,1,0,1,0} T {1,1,0,0,0,1,1,0,0,0,1,1} T {0,0,0,0,1,1,1,0,0,1,1,1} T {1,0,1,0,0,0,0,1,1,1,1,0} T {1,0,1,1,0,0,1,1,1,1,0,1} T{1,1,1,0,0,1,1,0,1,1,1,0} T {0,1,0,0,0,0,0,0,1,0,0,1} T {0,1,1,0,0,0,0,0,0,1,0,0} T {0,0,1,0,1,1,1,0,1,0,1,0} T {1,1,1,0,1,1,1,1,0,0,0,0} T {0,1,1,1,0,0,1,0,0,1,1,1} T {0,1,1,1,1,0,1,1,1,0,0,1} T {0,1,0,0,1,0,0,1,0,1,1,1} T {0,1,0,1,0,0,1,0,1,0,1,0} T {0,0,1,1,0,1,0,1,0,1,0,1,1,1} T {1,0,0,0,1,0,0,0,1,1,0,1} T {1,1,0,0,1,1,1,1,1,1,0,1} T {0,0,1,1,1,1,0,0,1,0,0,1} T {1,0,1,1,1,0,1,0,0,0,1,1} T {1,1,0,1,0,1,0,0,0,0,0,0} T {0,1,0,1,1,0,1,1,0,1,0,0} T ,in,{} T This indicates that a vector has been transposed.

[0140] As an example, when L = 24, the first set of sequences includes some or all of the following sequences: {1,0,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,1,1,1,1,0} T ,{0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,0,0,1} T {1,0,0,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1} T {1,0,0,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0} T，{1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0} T ，{1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,1,1,1} T ，{0,0,1,1, 0,0,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,0} T ，{1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0,1,1,0,1,0} T ，{0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1} T ，{0,0,1,0,1,1,0,1,1,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1} T ，{1,1,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,1,0,0,0} T ，{0,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,1,1,0,0,1,0} T ，{1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,0} T ，{0,0,0,0,1,0,1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1,1,0} T ，{1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,1,1,1,1} T ，{1,0,1,1,1,1,1,0,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0,0} T ，{1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1} T ，{0,1,1,0,1,0,1,0,1,1,1,0,0,1,0,1,0,0,1,1,0,1,0,1} T ，{0,1,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0} T ，{0,0,1,1,1,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,0,1,1}T {1,1,1,1,1,0,0,1,1,1,1,0,1,1,1,0,0,1,1,0,0,1,0,0,1,0,0} T {0,1,1,1,1,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1} T {0,1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0} T {0,1,1,0,0,0,0,1,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,0} T {0,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,0} T ,{0,0,0,1,0,1,1,0,0,1,1,1,1,0,1,0,1,1,1,0,1,0,1,1,1,1} T {1,0,1,0,0,0,1,1,0,1,1,0,0,0,0,1,0,0,1,1,1,0,1} T ,{1,1,0,0,1,0,0,1,0,1,0,1,1,0,1,1,1,0,0,1} T {0,0,0,1,1,1,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0} T ,{1,0,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,0,0,0,1,1} T ,{1,1,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,0,1} T {0,1,0,0,0,1,1,1,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1} T ,in,{} T This indicates that a vector has been transposed.

[0141] Figure 6 A schematic diagram of the information transmission apparatus 30 provided in the embodiments of this application is shown below. Figure 6 As shown, the device 30 can be a terminal device, including various handheld devices, vehicle-mounted devices, wearable devices, computing devices or other processing devices connected to a wireless modem with wireless communication functions, as well as various forms of terminals, mobile stations, terminals, user equipment, soft terminals, etc., and can also be a chip or chip system located on the terminal device.

[0142] The device 30 may include a processor 31 (i.e., an example of a processing module) and a memory 32. The memory 32 is used to store instructions, and the processor 31 is used to execute the instructions stored in the memory 32 to cause the device 30 to perform, as... Figure 1 or Figure 3 The steps executed by the terminal device in the corresponding method.

[0143] Furthermore, the device 30 may also include an input port 33 (i.e., an example of a transceiver module) and an output port 34 (i.e., another example of a transceiver module). Furthermore, the processor 31, memory 32, input port 33, and output port 34 can communicate with each other through internal connection paths to transmit control and / or data signals. The memory 32 is used to store computer programs, and the processor 31 can be used to call and run the computer program from the memory 32 to control the input port 33 to receive signals and control the output port 34 to send signals, thus completing the steps of the terminal device in the above method. The memory 32 can be integrated into the processor 31 or disposed separately from the processor 31.

[0144] Optionally, if the information transmission device 30 is a communication device, the input port 33 is a receiver, and the output port 34 is a transmitter. The receiver and transmitter can be the same or different physical entities. When they are the same physical entity, they can be collectively referred to as transceivers.

[0145] Optionally, if the device 30 is a chip or circuit, the input port 33 is an input interface and the output port 34 is an output interface.

[0146] As one implementation approach, the functions of input port 33 and output port 34 can be implemented using transceiver circuits or dedicated transceiver chips. Processor 31 can be implemented using dedicated processing chips, processing circuits, processors, or general-purpose chips.

[0147] As another implementation method, the device provided in this application embodiment can be implemented using a general-purpose computer. The program code that implements the functions of processor 31, input port 33 and output port 34 is stored in memory 32, and the general-purpose processor implements the functions of processor 31, input port 33 and output port 34 by executing the code in memory 32.

[0148] Each module or unit in device 30 can be used to perform the actions or processes performed by the device (e.g., terminal device) that performs random access in the above method. Here, to avoid redundancy, its detailed description is omitted.

[0149] For the concepts, explanations, detailed descriptions, and other steps related to the technical solutions provided in the embodiments of this application involved in the device 10, please refer to the descriptions of these contents in the foregoing methods or other embodiments, which will not be repeated here.

[0150] Figure 7 A schematic diagram of the apparatus 40 for information transmission provided in the embodiments of this application, as shown below. Figure 7 As shown, the device 40 can be a network device, including network elements with information transmission functions, such as base stations.

[0151] The device 40 may include a processor 41 (i.e., an example of a processing module) and a memory 42. The memory 42 is used to store instructions, and the processor 41 is used to execute the instructions stored in the memory 42 to cause the device 40 to perform, for example... Figure 1 or Figure 3 The steps performed by the network device in the corresponding method.

[0152] Furthermore, the device 40 may also include an input port 43 (i.e., an example of a transceiver module) and an output port 44 (i.e., another example of a transceiver module). Furthermore, the processor 41, memory 42, input port 43, and output port 44 can communicate with each other through internal connection paths to transmit control and / or data signals. The memory 42 is used to store computer programs, and the processor 41 can be used to call and run the computer program from the memory 42 to control the input port 43 to receive signals and control the output port 44 to send signals, thus completing the steps of the terminal device in the above method. The memory 42 may be integrated into the processor 41 or may be disposed separately from the processor 41.

[0153] Optionally, if the device 40 is a communication device, the input port 43 is a receiver, and the output port 44 is a transmitter. The receiver and transmitter can be the same or different physical entities. When they are the same physical entity, they can be collectively referred to as transceivers.

[0154] Optionally, if the device 40 is a chip or circuit, the input port 43 is an input interface and the output port 44 is an output interface.

[0155] As one implementation method, the functions of input port 43 and output port 44 can be implemented using transceiver circuits or dedicated transceiver chips. Processor 41 can be implemented using dedicated processing chips, processing circuits, processors, or general-purpose chips.

[0156] As another implementation method, the device provided in this application embodiment can be implemented using a general-purpose computer. The program code that implements the functions of processor 41, input port 43, and output port 44 is stored in memory 42, and the general-purpose processor implements the functions of processor 41, input port 43, and output port 44 by executing the code in memory 42.

[0157] Each module or unit in device 40 can be used to execute the actions or processes performed by the device (i.e., the access node) that accepts random access in the above method. Here, to avoid redundancy, its detailed description is omitted.

[0158] For the concepts, explanations, detailed descriptions, and other steps related to the technical solutions provided in the embodiments of this application involved in the device 40, please refer to the descriptions of these contents in the foregoing methods or other embodiments, which will not be repeated here.

[0159] It should be understood that in the embodiments of this application, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0160] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0161] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0162] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0163] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0164] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0165] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, the functional units in the various embodiments of this application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. If the function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0166] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for transmitting information, executed by a terminal device or a chip in a terminal device, characterized in that, include: Determine a first sequence to be sent. The first sequence belongs to a first sequence set. The first sequence set includes W sequences of length L, where L < W, and L and W are both positive integers. The sequences in the first sequence set are pairwise related. Send the first sequence to the network device.

2. The method according to claim 1, characterized in that, The first sequence set is the sequence set with the smallest maximum cross-correlation value among at least one second sequence set, the second sequence set comprising W sequences of length L, wherein the maximum cross-correlation value is the maximum value of the correlation between any two sequences in a sequence set.

3. The method according to claim 2, characterized in that, The first sequence set is the sequence set with the smallest maximum cross-correlation value among the at least one second sequence set, and the sequence set with the fewest occurrences of the smallest maximum cross-correlation value in the corresponding normalized correlation matrix, wherein the normalized correlation matrix is ​​the normalized matrix of the autocorrelation matrix of a sequence set.

4. The method according to claim 2 or 3, characterized in that, The second sequence set is a set of W sequences of length L in the third sequence set. The third sequence set includes X sequences of length Y, where X ≥ W and Y ≥ W. The range of the maximum cross-correlation value of the third sequence set is determined according to the number W of sequences included in the second sequence set.

5. The method according to any one of claims 1 to 4, characterized in that, When L=6, the first set of sequences includes some or all of the following sequences: {1, 1, 1, 0, 0, 1} T ，{0, 0, 1, 1, 1, 1} T ，{1, 0, 0, 0, 1, 1} T ，{1, 1, 1, 0,1, 0} T ，{1, 0, 0, 0, 0, 1} T ，{1, 1, 1, 1, 1, 1} T ，{0, 0, 1, 1, 1, 0} T ，{1, 1, 1,1, 1, 0} T ，{0, 1, 0, 0, 1, 1} T ，{0, 1, 0, 1, 1, 1} T ，{1, 0, 0, 1, 0, 1} T ，{0, 0,1, 1, 0, 1} T ，{1, 0, 0, 1, 1, 0} T ，{0, 1, 0, 1, 0, 1} T ，{1, 0, 0, 0, 0, 0} T ，{1,0, 0, 0, 1, 0} T ，{1, 0, 0, 1, 1, 1} T ，{0, 0, 1, 0, 0, 0} T ，{0, 0, 1, 0, 0, 1} T ，{0, 1, 0, 1, 0, 0} T ，{1, 1, 1, 1, 0, 1} T ，{0, 0, 1, 0, 1, 1} T ，{0, 1, 0, 0, 0, 1} T ，{0, 1, 0, 0, 1, 0} T ，{0, 0, 1, 0, 1, 0} T ，{0, 0, 1, 1, 0, 0} T ，{1, 1, 1, 0, 1,1} T ，{1, 1, 1, 1, 0, 0} T ，{0, 1, 0, 1, 1, 0} T ，{1, 1, 1, 0, 0, 0} T ，{1, 0, 0, 1,0, 0} T ，{0, 1, 0, 0, 0, 0} T ， in,{} T This indicates that a vector has been transposed.

6. The method according to any one of claims 1 to 4, characterized in that, When L=12, the first set of sequences includes some or all of the following sequences: {1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0} T ，{0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1} T ，{1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0} T ，{1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0} T ，{1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1} T ，{1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1} T ，{0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0} T ，{1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0} T ，{0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0} T ，{0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0} T ，{1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1} T ，{0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0} T ，{1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1} T ，{0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1} T ，{1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0} T ，{1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1} T ，{1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0} T ，{0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1} T ，{0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0} T ，{0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0} T ，{1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0} T ，{0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1} T ，{0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1} T ，{0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1} T ，{0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0} T ，{0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1} T ，{1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1} T ，{1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1} T ，{0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1} T ，{1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1} T ，{1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0} T ，{0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0} T ， in,{} T This indicates that a vector has been transposed.

7. The method according to any one of claims 1 to 4, characterized in that, When L=24, the first set of sequences includes some or all of the following sequences: {1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0} T ，{0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1} T ，{1,0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1} T ，{1, 0,0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0} T ，{1, 0, 1,0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0} T ，{1, 1, 0, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1} T ，{0, 0, 1, 1, 0,0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0} T ，{1, 1, 1, 0, 1, 1,1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0} T ，{0, 1, 0, 1, 0, 0, 0,1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1} T ，{0, 0, 1, 0, 1, 1, 0, 1,1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1} T ，{1, 1, 0, 1, 0, 1, 0, 0, 1,0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0} T ，{0, 0, 1, 0, 0, 1, 1, 0, 1, 1,0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0} T ，{1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0} T ，{0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0,1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0} T ，{1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0,1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1} T ，{1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,1, 0, 1, 1, 1, 1, 1, 1, 0, 0} T ，{1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,1, 1, 0, 1, 1, 1, 0, 1, 1} T ，{0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1,0, 0, 1, 1, 0, 1, 0, 1} T ，{0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,0, 1, 0, 1, 0, 0, 0} T ，{0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,0, 1, 0, 0, 1, 1} T ，{1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1,0, 0, 1, 0, 0} T ，{0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,1, 0, 1, 1} T ，{0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0,1, 0, 0} T ，{0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0,1, 0} T ，{0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1,0} T ，{0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1} T ，{1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1} T ，{1,1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1} T ，{0, 0,0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0} T ，{1, 0, 1,1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1} T ，{1, 1, 1, 0,0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1} T ，{0, 1, 0, 0, 0,1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1} T ， in,{} T This indicates that a vector has been transposed.

8. A method for transmitting information, executed by a network device or a chip in a network device, characterized in that, include: Receive signal; Sequence detection is performed on the signal according to the first sequence set to obtain at least one sequence. The first sequence set includes W sequences of length L, where L < W, and L and W are both positive integers. The W sequences include the at least one sequence, and each column in the first sequence set is pairwise correlated.

9. The method according to claim 8, characterized in that, The first sequence set is the sequence set with the smallest maximum cross-correlation value in at least one second sequence set, and the second sequence set includes W sequences of length L, wherein the maximum cross-correlation value is the maximum value of the correlation values ​​between any two sequences in a sequence set.

10. The method according to claim 9, characterized in that, The first sequence set is the sequence set with the smallest maximum cross-correlation value among the at least one second sequence set, and the sequence set with the fewest occurrences of the smallest maximum cross-correlation value in the corresponding normalized correlation matrix, wherein the normalized correlation matrix is ​​the normalized matrix of the autocorrelation matrix of a sequence set.

11. The method according to claim 9 or 10, characterized in that, The second sequence set consists of W sequences of length L in the third sequence set, which includes X sequences of length Y, where X ≥ W and Y ≥ W. The range of the maximum cross-correlation value of the third sequence set is determined based on the number W of sequences included in the second sequence set.

12. The method according to any one of claims 8 to 11, characterized in that, When L=6, the first set of sequences includes some or all of the following sequences: {1, 1, 1, 0, 0, 1} T ，{0, 0, 1, 1, 1, 1} T ，{1, 0, 0, 0, 1, 1} T ，{1, 1, 1, 0,1, 0} T ，{1, 0, 0, 0, 0, 1} T ，{1, 1, 1, 1, 1, 1} T ，{0, 0, 1, 1, 1, 0} T ，{1, 1, 1,1, 1, 0} T ，{0, 1, 0, 0, 1, 1} T ，{0, 1, 0, 1, 1, 1} T ，{1, 0, 0, 1, 0, 1} T ，{0, 0,1, 1, 0, 1} T ，{1, 0, 0, 1, 1, 0} T ，{0, 1, 0, 1, 0, 1} T ，{1, 0, 0, 0, 0, 0} T ，{1,0, 0, 0, 1, 0} T ，{1, 0, 0, 1, 1, 1} T ，{0, 0, 1, 0, 0, 0} T ，{0, 0, 1, 0, 0, 1} T ，{0, 1, 0, 1, 0, 0} T ，{1, 1, 1, 1, 0, 1} T ，{0, 0, 1, 0, 1, 1} T ，{0, 1, 0, 0, 0, 1} T ，{0, 1, 0, 0, 1, 0} T ，{0, 0, 1, 0, 1, 0} T ，{0, 0, 1, 1, 0, 0} T ，{1, 1, 1, 0, 1,1} T ，{1, 1, 1, 1, 0, 0} T ，{0, 1, 0, 1, 1, 0} T ，{1, 1, 1, 0, 0, 0} T ，{1, 0, 0, 1,0, 0} T ，{0, 1, 0, 0, 0, 0} T ， in,{} T This indicates that a vector has been transposed.

13. The method according to any one of claims 8 to 11, characterized in that, When L=12, the first set of sequences includes some or all of the following sequences: {1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0} T ，{0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1} T ，{1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0} T ，{1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0} T ，{1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1} T ，{1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1} T ，{0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0} T ，{1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0} T ，{0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0} T ，{0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0} T ，{1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1} T ，{0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0} T ，{1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1} T ，{0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1} T ，{1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0} T ，{1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1} T ，{1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0} T ，{0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1} T ，{0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0} T ，{0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0} T ，{1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0} T ，{0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1} T ，{0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1} T ，{0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1} T ，{0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0} T ，{0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1} T ，{1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1} T ，{1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1} T ，{0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1} T ，{1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1} T ，{1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0} T ，{0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0} T ， in,{} T This indicates that a vector has been transposed.

14. The method according to any one of claims 8 to 11, characterized in that, When L=24, the first set of sequences includes some or all of the following sequences: {1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0} T ，{0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1} T ，{1,0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1} T ，{1, 0,0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0} T ，{1, 0, 1,0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0} T ，{1, 1, 0, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1} T ，{0, 0, 1, 1, 0,0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0} T ，{1, 1, 1, 0, 1, 1,1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0} T ，{0, 1, 0, 1, 0, 0, 0,1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1} T ，{0, 0, 1, 0, 1, 1, 0, 1,1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1} T ，{1, 1, 0, 1, 0, 1, 0, 0, 1,0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0} T ，{0, 0, 1, 0, 0, 1, 1, 0, 1, 1,0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0} T ，{1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0} T ，{0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0,1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0} T ，{1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0,1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1} T ，{1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,1, 0, 1, 1, 1, 1, 1, 1, 0, 0} T ，{1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,1, 1, 0, 1, 1, 1, 0, 1, 1} T ，{0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1,0, 0, 1, 1, 0, 1, 0, 1} T ，{0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,0, 1, 0, 1, 0, 0, 0} T ，{0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,0, 1, 0, 0, 1, 1} T ，{1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1,0, 0, 1, 0, 0} T ，{0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,1, 0, 1, 1} T ，{0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0,1, 0, 0} T ，{0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0,1, 0} T ，{0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1,0} T ，{0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1} T ，{1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1} T ，{1,1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1} T ，{0, 0,0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0} T ，{1, 0, 1,1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1} T ，{1, 1, 1, 0,0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1} T ，{0, 1, 0, 0, 0,1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1} T ， in,{} T This indicates that a vector has been transposed.

15. An information transmission device, characterized in that, include: The processing module is used to determine a first sequence to be sent, the first sequence belonging to a first sequence set, the first sequence set including W sequences of length L, where L < W, L and W are both positive integers, and the sequences in the first sequence set are pairwise related. The transceiver module is used to send the first sequence to the network device.

16. The apparatus according to claim 15, characterized in that, The first sequence set is the sequence set with the smallest maximum cross-correlation value among at least one second sequence set, the second sequence set comprising W sequences of length L, wherein the maximum cross-correlation value is the maximum value of the correlation between any two sequences in a sequence set.

17. The apparatus according to claim 16, characterized in that, The first sequence set is the sequence set with the smallest maximum cross-correlation value among the at least one second sequence set, and the sequence set with the fewest occurrences of the smallest maximum cross-correlation value in the corresponding normalized correlation matrix, wherein the normalized correlation matrix is ​​the normalized matrix of the autocorrelation matrix of a sequence set.

18. The apparatus according to claim 16 or 17, characterized in that, The second sequence set is a set of W sequences of length L in the third sequence set. The third sequence set includes X sequences of length Y, where X ≥ W and Y ≥ W. The range of the maximum cross-correlation value of the third sequence set is determined according to the number W of sequences included in the second sequence set.

19. The apparatus according to any one of claims 15 to 18, characterized in that, When L=6, the first set of sequences includes some or all of the following sequences: {1, 1, 1, 0, 0, 1} T ，{0, 0, 1, 1, 1, 1} T ，{1, 0, 0, 0, 1, 1} T ，{1, 1, 1, 0,1, 0} T ，{1, 0, 0, 0, 0, 1} T ，{1, 1, 1, 1, 1, 1} T ，{0, 0, 1, 1, 1, 0} T ，{1, 1, 1,1, 1, 0} T ，{0, 1, 0, 0, 1, 1} T ，{0, 1, 0, 1, 1, 1} T ，{1, 0, 0, 1, 0, 1} T ，{0, 0,1, 1, 0, 1} T ，{1, 0, 0, 1, 1, 0} T ，{0, 1, 0, 1, 0, 1} T ，{1, 0, 0, 0, 0, 0} T ，{1,0, 0, 0, 1, 0} T ，{1, 0, 0, 1, 1, 1} T ，{0, 0, 1, 0, 0, 0} T ，{0, 0, 1, 0, 0, 1} T ，{0, 1, 0, 1, 0, 0} T ，{1, 1, 1, 1, 0, 1} T ，{0, 0, 1, 0, 1, 1} T ，{0, 1, 0, 0, 0, 1} T ，{0, 1, 0, 0, 1, 0} T ，{0, 0, 1, 0, 1, 0} T ，{0, 0, 1, 1, 0, 0} T ，{1, 1, 1, 0, 1,1} T ，{1, 1, 1, 1, 0, 0} T ，{0, 1, 0, 1, 1, 0} T ，{1, 1, 1, 0, 0, 0} T ，{1, 0, 0, 1,0, 0} T ，{0, 1, 0, 0, 0, 0} T ， in,{} T This indicates that a vector has been transposed.

20. The apparatus according to any one of claims 15 to 18, characterized in that, When L=12, the first set of sequences includes some or all of the following sequences: {1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0} T ，{0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1} T ，{1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0} T ，{1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0} T ，{1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1} T ，{1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1} T ，{0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0} T ，{1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0} T ，{0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0} T ，{0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0} T ，{1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1} T ，{0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0} T ，{1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1} T ，{0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1} T ，{1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0} T ，{1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1} T ，{1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0} T ，{0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1} T ，{0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0} T ，{0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0} T ，{1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0} T ，{0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1} T ，{0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1} T ，{0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1} T ，{0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0} T ，{0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1} T ，{1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1} T ，{1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1} T ，{0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1} T ，{1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1} T ，{1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0} T ，{0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0} T ， in,{} T This indicates that a vector has been transposed.

21. The apparatus according to any one of claims 15 to 18, characterized in that, When L=24, the first set of sequences includes some or all of the following sequences: {1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0} T ，{0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1} T ，{1,0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1} T ，{1, 0,0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0} T ，{1, 0, 1,0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0} T ，{1, 1, 0, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1} T ，{0, 0, 1, 1, 0,0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0} T ，{1, 1, 1, 0, 1, 1,1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0} T ，{0, 1, 0, 1, 0, 0, 0,1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1} T ，{0, 0, 1, 0, 1, 1, 0, 1,1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1} T ，{1, 1, 0, 1, 0, 1, 0, 0, 1,0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0} T ，{0, 0, 1, 0, 0, 1, 1, 0, 1, 1,0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0} T ，{1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0} T ，{0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0,1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0} T ，{1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0,1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1} T ，{1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,1, 0, 1, 1, 1, 1, 1, 1, 0, 0} T ，{1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,1, 1, 0, 1, 1, 1, 0, 1, 1} T ，{0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1,0, 0, 1, 1, 0, 1, 0, 1} T ，{0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,0, 1, 0, 1, 0, 0, 0} T ，{0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,0, 1, 0, 0, 1, 1} T ，{1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1,0, 0, 1, 0, 0} T ，{0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,1, 0, 1, 1} T ，{0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0,1, 0, 0} T ，{0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0,1, 0} T ，{0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1,0} T ，{0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1} T ，{1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1} T ，{1,1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1} T ，{0, 0,0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0} T ，{1, 0, 1,1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1} T ，{1, 1, 1, 0,0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1} T ，{0, 1, 0, 0, 0,1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1} T ， in,{} T This indicates that a vector has been transposed.

22. An information transmission device, characterized in that, include: The transceiver module is used to receive signals; The processing module is configured to perform sequence detection on the signal according to a first sequence set to obtain at least one sequence. The first sequence set includes W sequences of length L, where L < W, and L and W are both positive integers. The W sequences include the at least one sequence, and each column in the first sequence set is pairwise correlated.

23. The apparatus according to claim 22, characterized in that, The first sequence set is the sequence set with the smallest maximum cross-correlation value in at least one second sequence set, and the second sequence set includes W sequences of length L, wherein the maximum cross-correlation value is the maximum value of the correlation values ​​between any two sequences in a sequence set.

24. The apparatus according to claim 23, characterized in that, The first sequence set is the sequence set with the smallest maximum cross-correlation value among the at least one second sequence set, and the sequence set with the fewest occurrences of the smallest maximum cross-correlation value in the corresponding normalized correlation matrix, wherein the normalized correlation matrix is ​​the normalized matrix of the autocorrelation matrix of a sequence set.

25. The apparatus according to claim 23 or 24, characterized in that, The second sequence set consists of W sequences of length L in the third sequence set, which includes X sequences of length Y, where X ≥ W and Y ≥ W. The range of the maximum cross-correlation value of the third sequence set is determined based on the number W of sequences included in the second sequence set.

26. The apparatus according to any one of claims 22 to 25, characterized in that, When L=6, the first set of sequences includes some or all of the following sequences: {1, 1, 1, 0, 0, 1} T ，{0, 0, 1, 1, 1, 1} T ，{1, 0, 0, 0, 1, 1} T ，{1, 1, 1, 0,1, 0} T ，{1, 0, 0, 0, 0, 1} T ，{1, 1, 1, 1, 1, 1} T ，{0, 0, 1, 1, 1, 0} T ，{1, 1, 1,1, 1, 0} T ，{0, 1, 0, 0, 1, 1} T ，{0, 1, 0, 1, 1, 1} T ，{1, 0, 0, 1, 0, 1} T ，{0, 0,1, 1, 0, 1} T ，{1, 0, 0, 1, 1, 0} T ，{0, 1, 0, 1, 0, 1} T ，{1, 0, 0, 0, 0, 0} T ，{1,0, 0, 0, 1, 0} T ，{1, 0, 0, 1, 1, 1} T ，{0, 0, 1, 0, 0, 0} T ，{0, 0, 1, 0, 0, 1} T ，{0, 1, 0, 1, 0, 0} T ，{1, 1, 1, 1, 0, 1} T ，{0, 0, 1, 0, 1, 1} T ，{0, 1, 0, 0, 0, 1} T ，{0, 1, 0, 0, 1, 0} T ，{0, 0, 1, 0, 1, 0} T ，{0, 0, 1, 1, 0, 0} T ，{1, 1, 1, 0, 1,1} T ，{1, 1, 1, 1, 0, 0} T ，{0, 1, 0, 1, 1, 0} T ，{1, 1, 1, 0, 0, 0} T ，{1, 0, 0, 1,0, 0} T ，{0, 1, 0, 0, 0, 0} T ， in,{} T This indicates that a vector has been transposed.

27. The apparatus according to any one of claims 22 to 25, characterized in that, When L=12, the first set of sequences includes some or all of the following sequences: {1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0} T ，{0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1} T ，{1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0} T ，{1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0} T ，{1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1} T ，{1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1} T ，{0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0} T ，{1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0} T ，{0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0} T ，{0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0} T ，{1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1} T ，{0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0} T ，{1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1} T ，{0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1} T ，{1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0} T ，{1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1} T ，{1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0} T ，{0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1} T ，{0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0} T ，{0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0} T ，{1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0} T ，{0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1} T ，{0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1} T ，{0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1} T ，{0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0} T ，{0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1} T ，{1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1} T ，{1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1} T ，{0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1} T ，{1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1} T ，{1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0} T ，{0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0} T ， in,{} T This indicates that a vector has been transposed.

28. The apparatus according to any one of claims 22 to 25, characterized in that, When L=24, the first set of sequences includes some or all of the following sequences: {1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0} T ，{0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1} T ，{1,0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1} T ，{1, 0,0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0} T ，{1, 0, 1,0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0} T ，{1, 1, 0, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1} T ，{0, 0, 1, 1, 0,0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0} T ，{1, 1, 1, 0, 1, 1,1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0} T ，{0, 1, 0, 1, 0, 0, 0,1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1} T ，{0, 0, 1, 0, 1, 1, 0, 1,1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1} T ，{1, 1, 0, 1, 0, 1, 0, 0, 1,0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0} T ，{0, 0, 1, 0, 0, 1, 1, 0, 1, 1,0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0} T ，{1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0} T ，{0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0,1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0} T ，{1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0,1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1} T ，{1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,1, 0, 1, 1, 1, 1, 1, 1, 0, 0} T ，{1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,1, 1, 0, 1, 1, 1, 0, 1, 1} T ，{0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1,0, 0, 1, 1, 0, 1, 0, 1} T ，{0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,0, 1, 0, 1, 0, 0, 0} T ，{0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,0, 1, 0, 0, 1, 1} T ，{1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1,0, 0, 1, 0, 0} T ，{0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,1, 0, 1, 1} T ，{0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0,1, 0, 0} T ，{0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0,1, 0} T ，{0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1,0} T ，{0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1} T ，{1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1} T ，{1,1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1} T ，{0, 0,0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0} T ，{1, 0, 1,1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1} T ，{1, 1, 1, 0,0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1} T ，{0, 1, 0, 0, 0,1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1} T ， in,{} T This indicates that a vector has been transposed.

29. A communication device, characterized in that, include: Processor and memory; The memory is used to store computer programs; The processor is configured to execute a computer program stored in the memory, such that the communication device performs the method of any one of claims 1 to 7, or the method of any one of claims 8 to 14.

30. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when run on a computer, causes the computer to perform the method as described in any one of claims 1 to 7, or the method as described in any one of claims 8 to 14.

31. A computer program product, characterized in that, The computer program product includes instructions that, when executed, cause the method of any one of claims 1 to 7 to be implemented, or cause the method of any one of claims 8 to 14 to be implemented.

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