WO2022160252A1 - Information transmission method and apparatus - Google Patents

Abstract

The present application provides an information transmission method and apparatus. The method comprises: a terminal device sends to a network device a sequence that belongs to a first sequence set, sequences in the first sequence set being related in pairs; and the network device performs sequence detection on a received signal according to the first sequence set, and obtains at least one sequence sent by at least one terminal device. Moreover, the present application further provides a sequence generation method for sequence modulation and an example of a first sequence set comprising sequences of different lengths, such that more sequences can be provided in the case of a set length, thereby supporting more users. According to the method and apparatus in the present application, in the field of signal processing, user detection and sequence detection are simultaneously performed by using non-orthogonal sequences having small correlation, so that the access of mass users and information transmission can be achieved while ensuring a detection effect.

Description

Method and device for information transmission technical field

The present application relates to the field of communications, and more particularly, to a method and apparatus for information transmission.

Background technique

IoT is already playing an important role in various verticals due to its advantages such as cost savings, increased revenue streams, and increased efficiency. A typical feature of the Internet of Things is the ability to support a large number of low-power devices. The key to cellular networks supporting massive IoT connections lies in how to design an efficient and robust multiple access scheme. In fact, in the scenario of massive connections in the future, the access scheduling required by the commonly used authorization-based multiple access protocols is generally very complicated, which will cause unbearable access delays. As a reliable alternative, license-free multiple access protocols have recently attracted considerable attention in both academia and industry. Traditional license-free protocols require channel estimation first, followed by coherent detection of the transmitted data. The accuracy of data detection is easily affected by the error of channel estimation, and at the same time, using this scheme to support the transmission of a small number of bits of data for a large number of devices will also cause great waste.

At present, a license-free access scheme based on non-coherent data detection subverts the way of upstream transmission of pilots and data in the traditional license-free scheme, overcomes the shortcomings of the traditional license-free protocol, and is especially suitable for a small number of Internet of Things scenarios. Data upstream transmission. However, this solution is still difficult to support the access and information transmission of a large number of users. Therefore, in the IoT scenario, how to realize the access and information transmission of a large number of users is still a problem that needs to be solved.

SUMMARY OF THE INVENTION

The present application provides a method for information transmission. By using non-orthogonal sequences with less correlation to perform user detection and sequence detection simultaneously, the access and information transmission of a large number of users can be realized on the premise of ensuring the detection effect.

A first aspect provides an information transmission method, 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, L <W, L and W are all positive integers, the sequences in the first sequence set are correlated in pairs; the first sequence is sent to the network device.

In the above embodiments, by using non-orthogonal sequences for information transmission, access and information transmission of a large number of users can be realized.

With reference to the first aspect, in some implementations of the first aspect, the first sequence set is a sequence set with the smallest maximum cross-correlation value in at least one second sequence set, and the second sequence set includes W of length L sequence, the maximum cross-correlation value is the maximum value among the correlation values between every two sequences in a sequence set.

With reference to the first aspect, in some implementations of the first aspect, the first sequence set is a sequence set with the smallest maximum cross-correlation value in the at least one second sequence set, and the corresponding normalized correlation matrix appears in the sequence set. The sequence set with the smallest maximum cross-correlation value and the least number of times, the normalized correlation matrix is the normalized matrix of the autocorrelation matrix of a sequence set.

In the above embodiments, by using non-orthogonal sequences with less correlation for information transmission, it is possible to realize access and information transmission of a large number of users on the premise of ensuring the detection effect.

With reference to 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, and the third sequence set includes X sequences of length Y , X≥W, 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.

In the above embodiment, before obtaining the second sequence set, at least one third sequence set, especially the sequence set including W sequences of length W, is obtained, so that the third sequence set has low orthogonality, which will make the When the second sequence set and the first sequence set are subsequently extracted, the number of extraction results is greatly reduced, and the screening complexity is reduced.

In conjunction with the first aspect, in some implementations of the first aspect, when L=6, the first sequence set includes part 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 , where {} ^T indicates that the vector is transposed.

In conjunction with the first aspect, in some implementations of the first aspect, when L=12, the first sequence set includes part 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 , where {} ^T indicates that the vector is transposed.

In conjunction with the first aspect, in some implementations of the first aspect, when L=24, the first sequence set includes part 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 , where {} ^T indicates that the vector is transposed.

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, where the first sequence set includes W sequences of length L , L<W, 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.

In the above embodiments, by using non-orthogonal sequences for information transmission, access and information transmission of a large number of users can be realized.

With reference to the second aspect, in some implementations of the second aspect, the first sequence set is a sequence set with the smallest corresponding maximum cross-correlation value in at least one second sequence set, and the second sequence set includes a length L W sequences, the maximum cross-correlation value is the maximum value among the correlation values between every two sequences in a sequence set.

With reference to the second aspect, in some implementations of the second aspect, the method further includes: the first sequence set is a sequence set with the smallest maximum cross-correlation value in the at least one second sequence set, and the corresponding normalized The sequence set in which the smallest maximum cross-correlation value appears in the least number of times in the correlation matrix, and the normalized correlation matrix is a normalized matrix of the autocorrelation matrix of a sequence set.

In the above embodiments, by using non-orthogonal sequences with less correlation for information transmission, it is possible to realize access and information transmission of a large number of users on the premise of ensuring the detection effect.

With reference to the second aspect, in some implementations of the second aspect, the second sequence set is W sequences of length L in the third sequence set, and 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.

In the above embodiment, before obtaining the second sequence set, at least one third sequence set, especially the sequence set including W sequences of length W, is obtained, so that the third sequence set has low orthogonality, which will make the When the second sequence set and the first sequence set are subsequently extracted, the number of extraction results is greatly reduced, and the screening complexity is reduced.

In conjunction with the second aspect, in some implementations of the second aspect, when L=6, the first set of sequences includes part 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 , where {} ^T indicates that the vector is transposed.

In conjunction with the second aspect, in some implementations of the second aspect, when L=12, the first sequence set includes part 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 , where {} ^T indicates that the vector is transposed.

In conjunction with the second aspect, in some implementations of the second aspect, when L=24, the first sequence set includes part 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 , where {} ^T indicates that the vector is transposed.

In a third aspect, an apparatus for information transmission is provided, including: modules for executing the method in the first aspect or any optional implementation manners thereof, for example, a processing module and a transceiver module. The transceiver module can include a sending module and a receiving module, and the sending module and the receiving module can be different functional modules, or can be the same functional module but can implement different functions. The processing module may be implemented by a processor. The transceiver module can be implemented by a transceiver, correspondingly, the sending module can be implemented by a transmitter, and the receiving module can be implemented by a receiver. If the apparatus is a terminal device, the transceiver may be a radio frequency transceiver component in the terminal device. If the device is a chip set in the terminal device, the transceiver may be a communication interface in the chip, and the communication interface is connected to the radio frequency transceiver component in the terminal device to realize information transmission and reception through the radio frequency transceiver component.

In a fourth aspect, an apparatus for information transmission is provided, comprising: modules for executing the method in the second aspect or any optional implementation manner thereof, for example, a processing module and a transceiver module. The transceiver module can include a sending module and a receiving module, and the sending module and the receiving module can be different functional modules, or can be the same functional module but can implement different functions. The processing module may be implemented by a processor. The transceiver module can be implemented by a transceiver, correspondingly, the sending module can be implemented by a transmitter, and the receiving module can be implemented by a receiver. If the apparatus is a network device, the transceiver may be a radio frequency transceiver component in the network device. If the device is a chip set in a network device, the transceiver may be a communication interface in the chip, and the communication interface is connected to a radio frequency transceiver component in the network device, so as to realize information transmission and reception through the radio frequency transceiver component.

In a fifth aspect, a communication apparatus 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, so that the apparatus executes the first aspect or the method in any optional implementation manner thereof, or perform the method in the second aspect or any optional implementation manner thereof.

A sixth aspect provides a computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program runs on a computer, the computer is made to execute the first aspect or any of the above. method in an optional implementation, or perform a method in the second aspect or any optional implementation thereof.

In a seventh aspect, a chip system is provided, which is characterized by comprising: a processor for calling and running a computer program from a memory, so that a communication device installed with the chip system executes the first aspect or any optional optional thereof. method in an implementation, or perform a method in the second aspect or any optional implementation thereof.

Description of drawings

FIG. 1 is a schematic interaction diagram of a method 100 for information transmission according to an embodiment of the present application.

FIG. 2 is a schematic block diagram of a method 200 for sequence generation for sequence modulation according to an embodiment of the present application.

FIG. 3 is a schematic interaction diagram of a method 300 for information transmission according to an embodiment of the present application.

FIG. 4 is a schematic block diagram of an example of a terminal device of the present application.

FIG. 5 is a schematic block diagram of an example of a network device of the present application.

FIG. 6 is a schematic block diagram of an example of the communication device of the present application.

FIG. 7 is a schematic block diagram of still another example of the communication device of the present application.

Detailed ways

The technical solutions in the present application will be described below with reference to the accompanying drawings.

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

The terminal device in this embodiment of the present application may refer to a user equipment (user equipment, UE), an access terminal, a subscriber unit, a subscriber station, a mobile station, a mobile station, a remote station, a remote terminal, a mobile device, a user terminal, a terminal, a wireless Communication equipment, user agent or user equipment. The terminal device may also be a cellular phone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), a wireless communication Functional handheld devices, computing devices or other processing devices connected to wireless modems, in-vehicle devices, wearable devices, terminal devices in 5G networks or terminals in the future evolution of the public land mobile network (PLMN) equipment, etc., which are not limited in this embodiment of the present application.

As an example and not a limitation, in this embodiment of the present application, the terminal device may also be a wearable device. Wearable devices can also be called wearable smart devices, which are the general term for the intelligent design of daily wear and the development of wearable devices using wearable technology, such as glasses, gloves, watches, clothing and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothing or accessories. Wearable device is not only a hardware device, but also realizes powerful functions through software support, data interaction, and cloud interaction. In a broad sense, wearable smart devices include full-featured, large-scale, complete or partial functions without relying on smart phones, such as smart watches or smart glasses, and only focus on a certain type of application function, which needs to cooperate with other devices such as smart phones. Use, such as all kinds of smart bracelets, smart jewelry, etc. for physical sign monitoring.

In addition, in the embodiments of the present application, the terminal device may also be a terminal device in an Internet of Things (IoT) system. IoT is an important part of the future development of information technology, and its main technical feature is that items pass through communication technology Connect with the network, so as to realize the intelligent network of human-machine interconnection and interconnection of things.

The network device in this embodiment of the present application may be a device for communicating with terminal devices, and the network device may be a base station (base transceiver station, BTS) in the GSM system or code division multiple access (CDMA), or a broadband code division multiple access (CDMA) base station. A base station (nodeB, NB) in a wideband code division multiple access (WCDMA) system, an evolved base station (evolutional nodeB, eNB or eNodeB) in an LTE system, or a cloud radio access network (cloud radio access network (CRAN) scenario, or the network device may be a relay station, an access point, a vehicle-mounted device, a network device in a future 5G network or a network device in a future evolved PLMN network, etc. This application implements Examples are not limited.

In the following, as an exemplary illustration, only the Internet of Things is taken as an example to describe the application scenarios of the embodiments of the present application and the methods of the embodiments of the present application.

IoT is already playing an important role in various verticals due to its advantages such as cost savings, increased revenue streams, and improved efficiency. At present, about 87% of global enterprises that have deployed IoT applications hope to continue to increase IoT applications; at the same time, by 2030, by designing various IoT applications and solutions for individuals and enterprises, global communication services will provide The compound annual growth rate of IoT revenue will reach 24.9%, and the need for next-generation mobile communication networks (B5G or 6G) to support IoT applications has become an industry consensus. A typical feature of the Internet of Things is the connection of a large number of low-power devices. Relevant reports pointed out that by 2030, the number of IoT devices connected to cellular networks will reach 50 billion, which is 59 times the total population at that time, which requires future base stations to achieve massive connections with tens of billions of devices. Although massive machine-type communications (mMTC) has been included in one of the three major application scenarios of 5G, how to support massive device access while ensuring low latency and high reliability is very important to the current network. remains a very challenging problem.

The key to cellular networks supporting massive IoT connections lies in how to design an efficient and robust multiple access scheme. The traditional authorization-based random multiple access protocol requires control signaling interaction and uplink access request scheduling to achieve resource allocation. Its typical representative is the physical random access channel (PRACH) adopted by 4G LTE and 5G NR. ). However, in the scenarios that need to support massive connections in the future, authorization-based multiple access protocols generally require complex access scheduling, resulting in unbearable access delays for users.

The license-free multiple access protocol alleviates the above problems to a certain extent, and has attracted great attention in both academia and industry. In the traditional license-free multiple access protocol, users who need to access the network do not need the authorization of the base station, and can directly send pilot frequencies and data to the base station; the base station performs user identification and data detection according to the received signal. Since complex access scheduling is avoided, the protocol can significantly reduce access delay. The base station first decouples different user signals (ie, user identification) according to the received pilot signal, and then detects the transmitted data. In short, the above-mentioned traditional license-free protocol requires channel estimation first, followed by coherent detection of the transmitted data. Therefore, the error of the channel estimation in the above scheme is likely to affect the data detection accuracy, and at the same time, this scheme is economically uneconomical for the transmission of a small amount of bit data that widely exists in IoT devices.

At present, a license-free access scheme based on non-coherent data detection subverts the way of upstream transmission of pilots and data in the traditional license-free scheme, overcomes the shortcomings of the traditional license-free protocol, and is especially suitable for a small number of Internet of Things scenarios. Data upstream transmission. However, in the Internet of Things scenario, how to realize the access and information transmission of a large number of users is still a problem that needs to be solved.

In the above-mentioned authorization-free access scheme based on non-coherent data detection, the sequence sets pre-allocated by different users are directly related to the performance of data detection at the receiving end. Specifically, when there is a high correlation between different sequences in the set, the data detection performance of the receiving end will deteriorate, and when the correlation between different sequences in the set is low, then the receiving end will obtain a relatively low correlation. Good data detection performance. Therefore, how to generate a sequence set required for sequence modulation is a problem that needs to be solved.

In order to facilitate the understanding of the embodiments of the present application, several terms or terms involved in the present application are briefly introduced below.

1. Sequence modulation

The existing "sequence modulation" refers to the direct sequence modulation (spread spectrum) technique in spread spectrum communication. The difference between "sequence modulation" and direct sequence spread spectrum in this application can be summarized as follows:

(1) The information modulation methods are different: in the "sequence modulation" in this application, the valid information is encoded in the sequence number selection; direct sequence spread spectrum is to multiply the low-rate symbol carrying the valid information by the high-rate pseudo-random code to realize spread spectrum.

(2) The sequence expansion method is different: the sequence of "sequence modulation" in this application can be expanded in time, that is, multiple consecutive time symbols, or it can be expanded on adjacent multiple subcarriers, or it can be expanded in phase On the time-frequency resource block composed of multiple adjacent time slots and subcarriers; direct sequence spreading is just frequency spreading.

(3) For the receiver, "sequence modulation" does not need to accurately estimate the channel, because there is no subsequent coherent data detection step; direct sequence spread spectrum also requires two steps of channel estimation and data detection.

2. Maximum cross-correlation value: the maximum value of correlation values between every two sequences in a sequence set.

3. Normalized correlation matrix: The normalized matrix of the autocorrelation matrix of a sequence set.

4. {} ^T : Indicates that the vector is transposed, that is, {A} ^T represents the transposition of vector A.

The technical solutions provided by the present application will be described in detail below with reference to the accompanying drawings.

FIG. 1 is a schematic interaction diagram of a method 100 for information transmission provided by an embodiment of the present application. The method 100 shown in FIG. 1 may include the following steps.

S101. A terminal device determines a first sequence to be sent.

Specifically, the first sequence belongs to a first sequence set, and the first sequence set includes W sequences of length L, where L<W, L and W are both positive integers, and the sequences in the first sequence set are related pairwise.

S102. The terminal device sends the first sequence to the network device.

It should be understood that, correspondingly, the network device receives a signal sent by at least one terminal device.

S103. The network device performs sequence detection according to the first sequence set to obtain at least one sequence.

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

In the embodiments of the present application, by using non-orthogonal sequences for information transmission, access and information transmission of a large number of users can be realized.

Specifically, the first sequence set is a 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 in a sequence set is The largest of the correlation values between each two series.

Further, the first sequence set is the sequence set with the smallest maximum cross-correlation value in the at least one second sequence set, and the sequence set with the least number of times the smallest maximum cross-correlation value occurs in the corresponding normalized correlation matrix, The normalized correlation matrix is a normalized matrix of autocorrelation matrices of a sequence set.

In the embodiments of the present application, by using non-orthogonal sequences with less correlation for information transmission, access and information transmission of a large number of users can be realized on the premise of ensuring the detection effect.

Optionally, 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, the 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.

In the embodiments of the present 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 is obtained, so as to ensure that the third sequence set has a lower orthogonality This will greatly reduce the number of extraction results in the subsequent extraction of the second sequence set and the first sequence set, and reduce the complexity of screening.

The following three examples of L=6, 12, and 24 are used as examples for illustrative description.

Example 1: L=6, the first sequence set includes part 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 , where {} ^T indicates that the vector is transposed.

It should be understood that the above sequence set is given by taking W=32 as an example here.

Example 2, when L=12, the first sequence set includes part 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 , where {}T indicates that the vector is transposed.

Example 3, when L=24, the first sequence set includes part 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 , Among them, {} ^T indicates that the vector is transposed.

FIG. 2 is a schematic block diagram of a method 200 for sequence generation for sequence modulation provided by an embodiment of the present application. The method 200 shown in FIG. 2 may include the following steps.

Taking generating a set of W sequences of length L as an example, the method 200 is introduced.

S201. Find out W sequences of length W from the mother sequence to form an approximately orthogonal square matrix.

It should be understood that the mother sequence may be a Gold sequence, a discrete Fourier transform (discrete fourier transform, DFT) sequence, or a zedoff-chu sequence (ZC sequence).

It should be understood that the "approximately orthogonal" is to keep the correlation between the above W sequences as low as possible.

As an example, a Gold sequence with a period of 31 is selected as the mother sequence, and after determining the sequence length L and the number of users to be supported W, using the generation method given in this embodiment, the optimal length that meets the requirements is obtained as L When there are W sequences of , the correlation between the W sequences of length W in the approximately orthogonal square matrix is the above-mentioned "lower correlation".

S202. Extract L rows from a square matrix with W rows and W columns to obtain W sequences with a length of L. As an extraction result, calculate the maximum cross-correlation value of all extraction results and its maximum cross-correlation value in the normalized correlation matrix occurrences in .

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

As an example, the decimation matrix is represented as

where p={1,2,...,P}, that is, there are P kinds of extraction results; the normalized autocorrelation matrix of the pth extraction matrix is expressed as:

express

The conjugate transpose of .

S203. Search for a sequence set with the smallest maximum cross-correlation value in the extraction result.

S204 , in the extraction result with the smallest maximum cross-correlation value, search for a sequence set with the smallest maximum cross-correlation value appearing in the normalized correlation matrix.

It should be understood that there may be many extraction results satisfying step S203, and the extraction results here include many sequence sets with a large number of maximum cross-correlation values in the corresponding normalized correlation matrix. Such sequence sets may affect the detection. Effect. By performing step S204, a sequence set with lower correlation can be further screened to ensure the detection effect.

Further, a matrix with L rows and W columns that satisfies both steps S203 and S204 is screened out by the method 200, which is the final generated sequence set. In this embodiment, three examples of L=6, 12, and 24 are used as examples for exemplary description. For specific description, see method 100, and details are not repeated here.

In the embodiment of the present application, the sequence generation method for sequence modulation is used to obtain sequences with low correlation, and under the condition that the sequence length is guaranteed to be constant, more sequences can be provided, so that on the premise of ensuring the detection effect, Realize the access and information transmission of a large number of users.

FIG. 3 is a schematic interaction diagram of a method 300 for information transmission provided by an embodiment of the present application. The method 300 shown in FIG. 3 may include the following steps.

S301, the terminal device determines the sequence to be sent according to the information to be sent and the first mapping relationship.

As an example, the terminal device determines to send sequence 4 to the network device in the next step according to the information bit to be sent is '11' and the first mapping relationship.

Table 1 shows the first mapping relationship between the terminal equipment pre-configured information bits and the sequence.

Table 1

information bits	‘00’	‘01’	‘10’	‘11’
sequence	sequence 1	sequence 2	sequence 3	sequence 4

It should be understood that the sequences 1 to 4 in Table 1 may be any one of the “first sequence set” mentioned in the method 100 or the “finally generated sequence set” mentioned in the method 200 .

In this example, the terminal device can send information of four states 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 maintained a low correlation. At the same time sequences 1 to 4 belong exclusively to the terminal device.

S302. The terminal device sends the sequence through time domain and/or frequency domain resources.

Specifically, the user equipment maps the to-be-sent sequence to time-frequency and/or frequency-domain resources.

It should be understood that the mapping can be performed only in the time domain, that is, the sequence is mapped on the same subcarrier of different symbols; the mapping can also be performed only in the frequency domain, that is, the sequence is mapped on different subcarriers of the same symbol; the mapping can also be performed in It is performed in two dimensions of time and frequency, that is, the sequence is mapped on different subcarriers of different symbols.

It should be understood that, correspondingly, the signal received by the network device includes at least one sequence sent by at least one terminal device.

S303, the network device performs sequence detection according to the observation matrix, obtains the sequence, and determines information corresponding to the sequence according to the first mapping relationship.

It should be understood that the “observation matrix” is the “first sequence set” described in the method 100 or the “finally generated sequence set” described in the method 200 . The sequences assigned to all terminal devices communicating with the network device constitute the observation matrix of the network device.

It should be understood that, by performing sequence detection on the received signal, the network device can obtain at least one sequence, and the at least one sequence includes the sequence sent by the terminal device.

As an example, corresponding to step S301, the network device performs sequence detection according to the received signal, and at least one of the obtained sequences includes sequence 4, and the network device determines, according to the first mapping relationship and sequence 4, that the information bit sent by the terminal device is ' 11'.

Specifically, for the convenience of description, a single-antenna user is taken as an example below to describe the receiving process.

Assuming that the terminal equipment is a single-antenna user, the number of base station antennas is M, and the system model is expressed as: Y=ΦD+N.

Among them, the pre-allocated sequences of all terminal equipment constitute an observation matrix, Φ∈C ^L×KN ; the equivalent channel matrix of all sequences in the observation matrix is represented as D∈C ^KN×M ; the received signal at the base station is represented as Y∈C ^{L× M} ; additive white Gaussian noise is represented as N∈C ^L×M , assuming that the noise variance is σ ² .

It is worth noting 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-assignment sequence. Specifically, the rows [(k-1)N+1] to kN of the equivalent channel matrix D correspond to the first to Nth pre-allocation sequences of the k-th device, respectively, k∈{ 1,2,...,K}. Assuming that the kth device actually sends its Nth pre-allocated sequence, then the kNth row of the equivalent channel matrix D corresponds to the channel complex gain between the kth device and M antennas, and the equivalent channel matrix D Lines [(k-1)N+1] to [kN-1] are all equivalent to zero values. Similarly, if the kth device actually sends 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 value of D corresponding to other sequences is equivalent to zero. In addition, if the kth device does not send a sequence, then the row values of D corresponding to this device are all equivalent to zero values.

For the detection process, the problem can be summarized as: Knowing the received signal Y and the observation matrix Φ, recover the sequence number of the non-zero row 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 nature of IoT data, the number of active devices at the same time is often much smaller than the total number of devices, that is, Ka ₌ K. Due to the _sparseness of active devices, the equivalent channel matrix D is sparse in rows, that is, only when the row of Ka is non-zero, can ensure that the network device can detect the sequence number from the received signal. A typical method is to use a 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.

Table 2

Table 2 shows the SOMP algorithm for sequential 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

The third step uses the least squares method to restore the channel elements at the corresponding positions of the support set, and the fourth step updates the residuals according to the restored channel elements. If the normalized energy of the residuals is less than the noise variance, terminate the iterative process, otherwise return to the first step Find new support sets. After the iteration is terminated, the output set Ω ^t+1 represents the number of the non-zero row of the equivalent channel matrix D, and the number of the non-zero row corresponds to the sequence number of the corresponding device, and the information bits transmitted by the sequence modulation can be obtained; the output set Γ=[Ω ^t+1 /N], indicating the number of the active device, that is, the device corresponding to the non-zero row of the equivalent channel matrix D.

In the embodiments of the present application, by performing user detection and sequence detection based on non-orthogonal sequences, the access and information transmission of a large number of users are realized on the premise of ensuring the detection effect.

In the above, the method for providing information transmission according to the embodiment of the present application has been described in detail with reference to FIG. 1 to FIG. 3 . Hereinafter, the apparatus for information transmission provided by the embodiments of the present application will be described in detail with reference to FIG. 4 to FIG. 7 .

FIG. 4 is a schematic block diagram of an apparatus for information transmission provided by an embodiment of the present application. As shown in FIG. 4 , the apparatus 10 may include a transceiver module 11 and a processing module 12 .

In a possible design, the apparatus 10 may correspond to the terminal device in the above method embodiment. For example, it may be user equipment, or a chip configured in the user equipment.

Specifically, the communication apparatus 10 may correspond to the terminal device in the method 100 and the method 300 according to the embodiments of the present application, and the communication apparatus 10 may include a method for performing the method 100 in FIG. 1 or the method 300 in FIG. 3 . A module of a method executed by an end device. In addition, each unit in the communication device 10 and the other operations and/or functions mentioned above are respectively to implement the corresponding flow of the method 100 in FIG. 1 or the method 300 in FIG. 3 .

Wherein, when the communication device 10 is used to execute the method 100 in FIG. 1 , the transceiver module 11 can be used to execute the step S102 of the method 100 , and the processing module 12 can be used to execute the step S102 of the method 100 .

When the communication device 10 is used to execute the method 300 in FIG. 3 , the transceiver module 11 can be used to execute the step S302 of the method 300 , and the processing module 12 can be used to execute the step S301 of the method 300 .

Specifically, the processing module 12 is configured to determine a first sequence to be sent, the first sequence belongs to a first sequence set, and the first sequence set includes W sequences of length L, L<W, both L and W is a positive integer, the sequences in the first sequence set are correlated in pairs; the transceiver module 11 is configured to send the first sequence to the network device.

The first sequence set is a 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 a sequence set The maximum value in the correlation value between each two series in .

The first sequence set is the sequence set with the smallest maximum cross-correlation value in the at least one second sequence set, and the sequence with the least number of times the smallest maximum cross-correlation value appears in the corresponding normalized correlation matrix set, the normalized correlation matrix is a normalized matrix of autocorrelation matrices of a sequence set.

The second sequence set is a set of W sequences of length L in the third sequence set, and the third sequence set includes X sequences of length Y, where X≥W, Y≥W, so 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.

As an example, when L=6, the first sequence set includes part 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 , where {} ^T indicates that the vector is transposed.

As an example, when L=12, the first sequence set includes part 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 , {1,1,0,0,1,1,1,1,1,1,1,0,1} ^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 means that the vector is transposed operation.

As an example, when L=24, the first sequence set includes part 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 , where {} ^T indicates that the vector is transposed.

FIG. 5 is a schematic block diagram of an apparatus for information transmission provided by an embodiment of the present application. As shown in the figure, the communication device 20 may include a transceiver module 21 and a processing module 22 .

In a possible design, the communication apparatus 20 may correspond to the network device in the above method embodiment. For example, it may be a base station, or a chip configured in the base station.

Specifically, the communication apparatus 20 may correspond to the network device in the method 100 and the method 300 according to the embodiment of the present application, and the communication apparatus 20 may include a method for performing the method 100 in FIG. 1 or the method 300 in FIG. 3 . A module of a method performed by a network device. Moreover, each unit in the communication device 20 and the other operations and/or functions mentioned above are respectively for implementing the corresponding flow of the method 100 in FIG. 1 or the method 300 in FIG. 3 .

When the communication device 20 is used to execute the method 100 in FIG. 1 , the transceiver module 21 can be used to execute the step S102 of the method 100 , and the processing module 22 can be used to execute the step S103 of the method 100 .

Wherein, when the communication device 20 is used to execute the method 300 in FIG. 3 , the transceiver module 21 can be used to execute the step S302 in the method 300 , and the processing module 22 can be used to execute the step S303 in the method 300 .

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

The first sequence set is a sequence set with the smallest corresponding 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 one The maximum of the correlation values between every two sequences in the set of sequences.

The first sequence set is the sequence set with the smallest maximum cross-correlation value in the at least one second sequence set, and the sequence with the least number of times the smallest maximum cross-correlation value appears in the corresponding normalized correlation matrix set, the normalized correlation matrix is a normalized matrix of autocorrelation matrices of a sequence set.

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

As an example, when L=6, the first sequence set includes part 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 , where {} ^T indicates that the vector is transposed.

As an example, when L=12, the first sequence set includes part 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 , {1,1,0,0,1,1,1,1,1,1,1,0,1} ^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 means that the vector is transposed operation.

As an example, when L=24, the first sequence set includes part 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 , where {} ^T indicates that the vector is transposed.

FIG. 6 is a schematic diagram of an apparatus 30 for information transmission provided by an embodiment of the present application. As shown in FIG. 6 , the apparatus 30 may be a terminal device, including various handheld devices, vehicle-mounted devices, wearable devices, computing The device or other processing device connected to the wireless modem, and various forms of terminal, mobile station, terminal, user equipment, soft terminal, etc., can also be a chip or a chip system, etc. located on the terminal device.

The apparatus 30 may include a processor 31 (ie, an example of a processing module) and a memory 32 . The memory 32 is used for storing instructions, and the processor 31 is used for executing the instructions stored in the memory 32, so that the apparatus 30 implements the steps performed by the terminal device in the method corresponding to FIG. 1 or FIG. 3 .

Further, the device 30 may further include an input port 33 (ie, an example of a transceiver module) and an output port 34 (ie, another example of a transceiver module). Further, the processor 31, the memory 32, the input port 33 and the output port 34 can communicate with each other through an internal connection path to transmit control and/or data signals. The memory 32 is used to store a computer program, 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, control the output port 34 to send signals, and complete the process of the terminal device in the above method. step. The memory 32 may be integrated in the processor 31 or may be provided separately from the processor 31 .

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 the transmitter may be the same or different physical entities. When they are the same physical entity, they can be collectively referred to as transceivers.

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

As an implementation manner, the functions of the input port 33 and the output port 34 can be considered to be implemented by a transceiver circuit or a dedicated chip for transceiver. The processor 31 can be considered to be implemented by a dedicated processing chip, a processing circuit, a processor or a general-purpose chip.

As another implementation manner, a general-purpose computer may be used to implement the device provided by the embodiments of the present application. The program codes that will implement the functions of the processor 31 , the input port 33 and the output port 34 are stored in the memory 32 , and the general-purpose processor implements the functions of the processor 31 , the input port 33 and the output port 34 by executing the codes in the memory 32 .

The modules or units in the apparatus 30 may be used to perform actions or processing procedures performed by the random access device (eg, terminal device) in the above method, and detailed descriptions thereof are omitted here to avoid redundant description.

For the concepts related to the technical solutions provided by the embodiments of the present application involved in the apparatus 10, for explanations and detailed descriptions, and other steps, please refer to the descriptions of the foregoing methods or other embodiments, which will not be repeated here.

FIG. 7 is a schematic diagram of an apparatus 40 for information transmission provided by an embodiment of the present application. As shown in FIG. 7 , the apparatus 40 may be a network device, including a network element with an information transmission function, such as a base station.

The apparatus 40 may include a processor 41 (ie, an example of a processing module) and a memory 42 . The memory 42 is used for storing instructions, and the processor 41 is used for executing the instructions stored in the memory 42, so that the apparatus 40 implements the steps performed by the network device in the method corresponding to FIG. 1 or FIG. 3 .

Further, the device 40 may further include an input port 43 (ie, an example of a transceiver module) and an output port 44 (ie, another example of a transceiver module). Further, the processor 41, the memory 42, the input port 43 and the output port 44 can communicate with each other through an internal connection path to transmit control and/or data signals. The memory 42 is used to store a computer program, 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, control the output port 44 to send signals, and complete the process of the terminal device in the above method. step. The memory 42 may be integrated in the processor 41 or may be provided separately from the processor 41 .

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

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

As an implementation manner, the functions of the input port 43 and the output port 44 can be considered to be realized by a transceiver circuit or a dedicated chip for transceiver. The processor 41 can be considered to be implemented by a dedicated processing chip, a processing circuit, a processor or a general-purpose chip.

As another implementation manner, a general-purpose computer may be used to implement the device provided by the embodiments of the present application. The program codes that will implement the functions of the processor 41 , the input port 43 and the output port 44 are stored in the memory 42 , and the general-purpose processor implements the functions of the processor 41 , the input port 43 and the output port 44 by executing the codes in the memory 42 .

Wherein, each module or unit in the apparatus 40 may be used to perform each action or processing process performed by the device (ie, the access node) that accepts random access in the above method.

For the concepts related to the technical solutions provided by the embodiments of the present application involved in the apparatus 40, for explanations and detailed descriptions and other steps, please refer to the descriptions of the foregoing methods or other embodiments, which will not be repeated here.

It should be understood that, in this embodiment of the present application, the processor may be a central processing unit (CPU, central processing unit), and the processor may also be other general-purpose processors, digital signal processors (DSP, digital signal processors), dedicated integrated Circuit (application specific integrated circuit, ASIC), off-the-shelf programmable gate array (field programmable gate array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

It should also be understood that the memory in the embodiments of the present application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically programmable Erase programmable read-only memory (electrically EPROM, EEPROM) or flash memory. Volatile memory may be random access memory (RAM), which acts as an external cache. By way of example and 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 (double data rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (synchlink DRAM, SLDRAM) and direct Memory bus random access memory (direct rambus RAM, DR RAM).

The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general purpose computer, special purpose computer, computer network, or other programmable device. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be downloaded from a website site, computer, server, or data center Transmission to another website site, computer, server or data center by wire (eg, infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, a data center, or the like that contains one or more sets of available media. The usable media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVDs), or semiconductor media. The semiconductor medium may be a solid state drive.

It should be understood that the term "and/or" in this document is only an association relationship to describe associated objects, indicating that there can be three kinds of relationships, for example, A and/or B, which can mean that A exists alone, and A and B exist at the same time , there are three cases of B alone. In addition, the character "/" in this document generally indicates that the related objects are an "or" relationship.

It should be understood that, in various embodiments of the present application, the size of the sequence numbers of the above-mentioned processes does not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not be dealt with in the embodiments of the present application. implementation constitutes any limitation.

Those of ordinary skill in the art can realize that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application. Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above may refer to the corresponding process in the foregoing method embodiments, which will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment. In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The functions, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution, and the computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk and other mediums that can store program codes.

The above are only specific embodiments of the present application, but the protection scope of the present application is not limited to this. should be covered within the scope of protection of this application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims (30)

1. A method for information transmission, comprising:

   The terminal device determines the first sequence to be sent, the first sequence belongs to the first sequence set, the first sequence set includes W sequences of length L, L<W, L and W are both positive integers, the first sequence set Sequences in a sequence set are related pairwise;

   The first sequence is sent to a network device.
2. The method according to claim 1, wherein the first sequence set is a 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 , the maximum cross-correlation value is the maximum value among the correlation values between every two sequences in a sequence set.
3. The method according to claim 2, wherein the first sequence set is a sequence set with the smallest maximum cross-correlation value in the at least one second sequence set, and the corresponding normalized correlation matrix appears in the sequence set. The sequence set with the smallest maximum cross-correlation value and the least number of times, the normalized correlation matrix is a normalized matrix of the autocorrelation matrix of a sequence set.
4. The method according to claim 2 or 3, wherein the second sequence set is a set of W sequences with a length of L in a third sequence set, and the third sequence set includes X with a length of Y of sequences, 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.
5. The method according to any one of claims 1 to 4, characterized in that,

   When L=6, the first sequence set includes part 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 ,

   Among them, {} ^T indicates that the vector is transposed.
6. The method according to any one of claims 1 to 4, characterized in that,

   When L=12, the first sequence set includes part 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 ,

   Among them, {} ^T indicates that the vector is transposed.
7. The method according to any one of claims 1 to 4, characterized in that,

   When L=24, the first sequence set includes part 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 ,

   Among them, {} ^T indicates that the vector is transposed.
8. A method for information transmission, comprising:

   The network device receives the signal;

   Sequence detection is performed 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, L and W are both positive integers, and the W sequences include For the at least one sequence, each column in the first set of sequences is correlated in pairs.
9. The method according to claim 8, wherein the first sequence set is a sequence set with the smallest corresponding maximum cross-correlation value in at least one second sequence set, and the second sequence set comprises a length L of W sequences, the maximum cross-correlation value is the maximum value among the correlation values between every two sequences in a sequence set.
10. The method according to claim 9, wherein the first sequence set is a sequence set with the smallest maximum cross-correlation value in the at least one second sequence set, and the corresponding normalized correlation matrix appears in the sequence set. The sequence set with the smallest maximum cross-correlation value and the least number of times, the normalized correlation matrix is a normalized matrix of the autocorrelation matrix of a sequence set.
11. The method according to claim 9 or 10, wherein the second sequence set is W sequences of length L in a third sequence set, and the third sequence set includes X sequences of length Y sequence, 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.
12. The method according to any one of claims 8 to 11, wherein:

    When L=6, the first sequence set includes part 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 ,

    Among them, {} ^T indicates that the vector is transposed.
13. The method according to any one of claims 8 to 11, wherein:

    When L=12, the first sequence set includes part 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 ,

    Among them, {} ^T indicates that the vector is transposed.
14. The method according to any one of claims 8 to 11, wherein:

    When L=24, the first sequence set includes part 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 ,

    Among them, {} ^T indicates that the vector is transposed.
15. A device for information transmission, comprising:

    a processing module, configured to determine a first sequence to be sent, where the first sequence belongs to a first sequence set, and the first sequence set includes W sequences of length L, where L<W, L and W are both positive integers, The sequences in the first sequence set are related in pairs;

    A transceiver module, configured to send the first sequence to a network device.
16. The apparatus according to claim 15, wherein the first sequence set is a 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 , the maximum cross-correlation value is the maximum value among the correlation values between every two sequences in a sequence set.
17. The apparatus according to claim 16, wherein the first sequence set is a sequence set with the smallest maximum cross-correlation value in the at least one second sequence set, and the corresponding normalized correlation matrix appears in the sequence set. The sequence set with the smallest maximum cross-correlation value and the least number of times, the normalized correlation matrix is a normalized matrix of the autocorrelation matrix of a sequence set.
18. The apparatus according to claim 16 or 17, wherein the second sequence set is a set of W sequences with a length of L in a third sequence set, and the third sequence set includes X with a length of Y of sequences, 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.
19. The device according to any one of claims 15 to 18, characterized in that:

    When L=6, the first sequence set includes part 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 ,

    Among them, {} ^T indicates that the vector is transposed.
20. The device according to any one of claims 15 to 18, characterized in that:

    When L=12, the first sequence set includes part 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 ,

    Among them, {} ^T indicates that the vector is transposed.
21. The device according to any one of claims 15 to 18, characterized in that:

    When L=24, the first sequence set includes part 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 ,

    Among them, {} ^T indicates that the vector is transposed.
22. A device for information transmission, comprising:

    Transceiver module for receiving signals;

    a processing module, 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, L<W, L and W are both positive integers, so The W sequences include the at least one sequence, and each column in the first set of sequences is correlated pairwise.
23. The apparatus according to claim 22, wherein the first sequence set is a sequence set with a minimum corresponding maximum cross-correlation value in at least one second sequence set, and the second sequence set includes a length L of W sequences, the maximum cross-correlation value is the maximum value among the correlation values between every two sequences in a sequence set.
24. The apparatus according to claim 23, wherein the first sequence set is a sequence set with the smallest maximum cross-correlation value in the at least one second sequence set, and the corresponding normalized correlation matrix appears in the sequence set. The sequence set with the smallest maximum cross-correlation value and the least number of times, the normalized correlation matrix is a normalized matrix of the autocorrelation matrix of a sequence set.
25. The apparatus according to claim 23 or 24, wherein the second sequence set is W sequences of length L in a third sequence set, and the third sequence set includes X sequences of length Y sequence, 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.
26. The device according to any one of claims 22 to 25, characterized in that,

    When L=6, the first sequence set includes part 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 ,

    Among them, {} ^T indicates that the vector is transposed.
27. The device according to any one of claims 22 to 25, characterized in that,

    When L=12, the first sequence set includes part 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 ,

    Among them, {} ^T indicates that the vector is transposed.
28. The device according to any one of claims 22 to 25, characterized in that,

    When L=24, the first sequence set includes part 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 ,

    Among them, {} ^T indicates that the vector is transposed.
29. A communication device, comprising:

    processor and memory;

    the memory for storing computer programs;

    The processor is configured to execute the computer program stored in the memory, so that the communication device executes the method described in any one of claims 1 to 7, or executes the method described in any one of claims 8 to 14. method described.
30. A computer-readable storage medium, characterized in that, a computer program is stored on the computer-readable storage medium, and when the computer program runs on a computer, the computer is made to execute any one of claims 1 to 7. claim 8 to 14, or perform a method as claimed in any one of claims 8 to 14.

Images