CN116569502A

CN116569502A - Information transmission method and device

Info

Publication number: CN116569502A
Application number: CN202180074712.6A
Authority: CN
Inventors: 高镇; �乔力; 张永平
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2021-01-29
Filing date: 2021-01-29
Publication date: 2023-08-08
Also published as: WO2022160252A1

Abstract

The application provides a method and a device for information transmission, wherein the method comprises the following steps: the terminal equipment sends sequences belonging to a first sequence set to the network equipment, wherein the sequences in the first sequence set are related pairwise; and the network equipment performs sequence detection on the received signals according to the first sequence set to obtain at least one sequence sent by at least one terminal equipment. Meanwhile, the application also provides a sequence generation method for sequence modulation and an example of a first sequence set comprising sequences with different lengths, so that more sequences can be provided under the condition of a certain length, and more users can be supported. In the field of signal processing, the method and the device can realize access and information transmission of a large number of users on the premise of ensuring the detection effect by adopting the non-orthogonal sequence with smaller correlation to simultaneously perform user detection and sequence detection.

Description

Information transmission method and device

Technical Field

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

Background

The internet of things has important roles in various vertical fields due to the advantages of saving cost, increasing income sources, improving efficiency and the like. One typical feature of the internet of things is the ability to support a large number of low power devices. The key of supporting mass connection of the internet of things by the cellular network is how to design an efficient and robust multiple access scheme. In fact, in future mass connectivity scenarios, the access scheduling required by the common grant-based multiple access protocols is generally complex, resulting in intolerable access delays. As a reliable alternative, unlicensed multiple access protocols have recently attracted considerable attention in both academia and industry. Traditional unlicensed protocols require channel estimation first, and then coherent detection of the transmitted data. The accuracy of data detection is easily affected by errors of channel estimation, and meanwhile, the transmission of a small amount of bit data of mass equipment is supported by using the scheme, so that great waste is caused.

At present, an unlicensed access scheme based on incoherent data detection overtakes the uplink pilot frequency and data transmission mode of the traditional unlicensed scheme, overcomes the defects of the traditional unlicensed protocol, and is particularly suitable for uplink transmission of a small amount of data in the scene of the Internet of things. However, this scheme still has difficulty in supporting access and information transfer for a large number of users. Therefore, in the scene of the internet of things, how to realize access and information transmission of massive users is still a problem to be solved.

Disclosure of Invention

The method for transmitting information can realize access and information transmission of a large number of users on the premise of ensuring the detection effect by adopting the non-orthogonal sequence with smaller correlation to simultaneously perform user detection and sequence detection.

In a first aspect, a method for information transmission is provided, including: the method comprises the steps that a terminal device determines a first sequence to be sent, wherein the first sequence belongs to a first sequence set, the first sequence set comprises W sequences with the length of L, L is smaller than W, L and W are positive integers, and the sequences in the first sequence set are related in pairs; the first sequence is sent to a network device.

According to the embodiment, the information transmission is carried out by adopting the non-orthogonal sequence, so that access and information transmission of massive users can be realized.

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

With reference to the first aspect, in some implementations of the first aspect, the first sequence set is a sequence set with a minimum number of times of occurrence of the minimum maximum cross-correlation value in a corresponding normalized correlation matrix, where the sequence set is a normalized matrix of autocorrelation matrices of one sequence set.

According to the embodiment, the information transmission is performed by adopting the non-orthogonal sequence with smaller correlation, so that access and information transmission of massive users can be realized on the premise of ensuring the detection effect.

With reference to the first aspect, in certain implementations of the first aspect, the second sequence set is a set of W sequences of length L in a third sequence set, where the third sequence set includes X sequences of length Y, x≡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, at least one third sequence set, particularly a sequence set including W sequences with length W, is obtained before the second sequence set is obtained, so that the number of extraction results is greatly reduced when the second sequence set and the first sequence set are extracted subsequently and the screening complexity is reduced when the third sequence set is ensured to have lower orthogonality.

With reference to the first aspect, in certain implementations of the first aspect, when l=6, the first set of sequences includes some or all of the following sequences: {1,1,1,0,0,1} ^T ，{0,0,1,1,1,1} ^T ，{1,0,0,0,1,1} ^T ，{1,1,1,0,1,0} ^T ，{1,0,0,0,0,1} ^T ，{1,1,1,1,1,1} ^T ，{0,0,1,1,1,0} ^T ，{1,1,1,1,1,0} ^T ，{0,1,0,0,1,1} ^T ，{0,1,0,1,1,1} ^T ，{1,0,0,1,0,1} ^T ，{0,0,1,1,0,1} ^T ，{1,0,0,1,1,0} ^T ，{0,1,0,1,0,1} ^T ，{1,0,0,0,0,0} ^T ，{1,0,0,0,1,0} ^T ，{1,0,0,1,1,1} ^T ，{0,0,1,0,0,0} ^T ，{0,0,1,0,0,1} ^T ，{0,1,0,1,0,0} ^T ，{1,1,1,1,0,1} ^T ，{0,0,1,0,1,1} ^T ，{0,1,0,0,0,1} ^T ，{0,1,0,0,1,0} ^T ，{0,0,1,0,1,0} ^T ，{0,0,1,1,0,0} ^T ，{1,1,1,0,1,1} ^T ，{1,1,1,1,0,0} ^T ，{0,1,0,1,1,0} ^T ，{1,1,1,0,0,0} ^T ，{1,0,0,1,0,0} ^T ，{0,1,0,0,0,0} ^T Wherein { } ^T The representation vector is transposed.

With reference to the first aspect, in certain implementations of the first aspect, when l=12, the first set of sequences includes some or all of the following sequences: {1,0,0,1,1,0,1,0,1,1,1,0} ^T ，{0,0,0,0,0,1,1,1,1,0,0,1} ^T ，{1,0,0,1,0,0,1,1,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,0,0} ^T ，{1,0,0,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,1,0,1,0,0,1,1} ^T ，{0,0,1,0,0,1,1,1,0,1,0,0} ^T ，{1,1,0,1,1,1,0,1,1,1,1,0} ^T ，{0,1,1,0,1,0,0,1,1,0,1,0} ^T ，{0,0,0,1,1,1,0,0,0,1,0,0} ^T ，{1,1,1,1,0,1,0,0,1,1,0,1} ^T ，{0,0,0,1,0,1,0,1,1,0,1,0} ^T ，{1,1,0,0,0,1,1,0,0,0,1,1} ^T ，{0,0,0,0,1,1,1,0,0,1,1,1} ^T ，{1,0,1,0,0,0,0,1,1,1,1,0} ^T ，{1,0,1,1,0,0,1,1,1,1,0,1} ^T ，{1,1,1,0,0,1,1,0,1,1,1,0} ^T ，{0,1,0,0,0,0,0,0,1,0,0,1} ^T ，{0,1,1,0,0,0,0,0,0,1,0,0} ^T ，{0,0,1,0,1,1,1,0,1,0,1,0} ^T ，{1,1,1,0,1,1,1,1,0,0,0,0} ^T ，{0,1,1,1,0,0,1,0,0,1,1,1} ^T ，{0,1,1,1,1,0,1,1,1,0,0,1} ^T ，{0,1,0,0,1,0,0,1,0,1,1,1} ^T ，{0,1,0,1,0,0,1,0,1,0,1,0} ^T ，{0,0,1,1,0,1,0,1,0,1,1,1} ^T ，{1,0,0,0,1,0,0,0,1,1,0,1} ^T ，{1,1,0,0,1,1,1,1,1,1,0,1} ^T ，{0,0,1,1,1,1,0,0,1,0,0,1} ^T ，{1,0,1,1,1,0,1,0,0,0,1,1} ^T ，{1,1,0,1,0,1,0,0,0,0,0,0} ^T ，{0,1,0,1,1,0,1,1,0,1,0,0} ^T Wherein { } ^T The representation vector is transposed.

With reference to the first aspect, in certain implementations of the first aspect, when l=24, the first set of sequences includes some or all of the following sequences: {1,0,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,1,1,1,1,0} ^T ，{0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,0,0,1} ^T ，{1,0,0,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1} ^T ，{1,0,0,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0} ^T ，{1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,1,1,1} ^T ，{0,0,1,1,0,0,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,0} ^T ，{1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0,1,1,0,1,0} ^T ，{0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1} ^T ，{0,0,1,0,1,1,0,1,1,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1} ^T ，{1,1,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,1,0,0,0} ^T ，{0,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,1,1,0,0,1,0} ^T ，{1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,0} ^T ，{0,0,0,0,1,0, 1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1,1,0} ^T ，{1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,1,1,1,1} ^T ，{1,0,1,1,1,1,1,0,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0,0} ^T ，{1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1} ^T ，{0,1,1,0,1,0,1,0,1,1,1,0,0,1,0,1,0,0,1,1,0,1,0,1} ^T ，{0,1,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0} ^T ，{0,0,1,1,1,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,0,0,1,1,1,1,0,1,1,1,0,0,1,1,0,0,1,0,0} ^T ，{0,1,1,1,1,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1} ^T ，{0,1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0} ^T ，{0,1,1,0,0,0,0,1,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,0} ^T ，{0,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,0} ^T ，{0,0,0,1,0,1,1,0,0,1,1,1,1,1,0,1,0,1,1,0,1,1,1,1} ^T ，{1,0,1,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,1,1,1,0,1} ^T ，{1,1,0,0,1,0,0,1,0,1,0,1,1,0,1,1,0,1,1,1,1,0,0,1} ^T ，{0,0,0,1,1,1,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0} ^T ，{1,0,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,0,0,0,1,1} ^T ，{1,1,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,1} ^T ，{0,1,0,0,0,1,1,1,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1} ^T Wherein { } ^T The representation vector is transposed.

In a second aspect, there is provided a method of information transmission, comprising: the network equipment receives the signal; and carrying out sequence detection on the signal according to a first sequence set to obtain at least one sequence, wherein the first sequence set comprises W sequences with the length of L, L is less than W, L and W are positive integers, the W sequences comprise the at least one sequence, and each row in the first sequence set is related in pairs.

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

With reference to the second aspect, in certain implementations of the second aspect, the method further includes: the first sequence set is the sequence set with the smallest frequency of the smallest maximum cross correlation value in the corresponding normalized correlation matrix, and the normalized correlation matrix is the normalized matrix of the autocorrelation matrix of one sequence set.

With reference to the second aspect, in certain implementations of the second aspect, the second set of sequences is W sequences of length L in a third set of sequences, the third set of sequences including X sequences of length Y, x≡w, and the range of the maximum cross-correlation value of the third set of sequences is determined according to the number W of sequences included in the second set of sequences.

With reference to the second aspect, in certain implementations of the second aspect, when l=6, the first set of sequences includes some or all of the following sequences: {1,1,1,0,0,1} ^T ，{0,0,1,1,1,1} ^T ，{1,0,0,0,1,1} ^T ，{1,1,1,0,1,0} ^T ，{1,0,0,0,0,1} ^T ，{1,1,1,1,1,1} ^T ，{0,0,1,1,1,0} ^T ，{1,1,1,1,1,0} ^T ，{0,1,0,0,1,1} ^T ，{0,1,0,1,1,1} ^T ，{1,0,0,1,0,1} ^T ，{0,0,1,1,0,1} ^T ，{1,0,0,1,1,0} ^T ，{0,1,0,1,0,1} ^T ，{1,0,0,0,0,0} ^T ，{1,0,0,0,1,0} ^T ，{1,0,0,1,1,1} ^T ，{0,0,1,0,0,0} ^T ，{0,0,1,0,0, 1} ^T ，{0,1,0,1,0,0} ^T ，{1,1,1,1,0,1} ^T ，{0,0,1,0,1,1} ^T ，{0,1,0,0,0,1} ^T ，{0,1,0,0,1,0} ^T ，{0,0,1,0,1,0} ^T ，{0,0,1,1,0,0} ^T ，{1,1,1,0,1,1} ^T ，{1,1,1,1,0,0} ^T ，{0,1,0,1,1,0} ^T ，{1,1,1,0,0,0} ^T ，{1,0,0,1,0,0} ^T ，{0,1,0,0,0,0} ^T Wherein { } ^T The representation vector is transposed.

With reference to the second aspect, in certain implementations of the second aspect, when l=12, the first set of sequences includes some or all of the following sequences: {1,0,0,1,1,0,1,0,1,1,1,0} ^T ，{0,0,0,0,0,1,1,1,1,0,0,1} ^T ，{1,0,0,1,0,0,1,1,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,0,0} ^T ，{1,0,0,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,1,0,1,0,0,1,1} ^T ，{0,0,1,0,0,1,1,1,0,1,0,0} ^T ，{1,1,0,1,1,1,0,1,1,1,1,0} ^T ，{0,1,1,0,1,0,0,1,1,0,1,0} ^T ，{0,0,0,1,1,1,0,0,0,1,0,0} ^T ，{1,1,1,1,0,1,0,0,1,1,0,1} ^T ，{0,0,0,1,0,1,0,1,1,0,1,0} ^T ，{1,1,0,0,0,1,1,0,0,0,1,1} ^T ，{0,0,0,0,1,1,1,0,0,1,1,1} ^T ，{1,0,1,0,0,0,0,1,1,1,1,0} ^T ，{1,0,1,1,0,0,1,1,1,1,0,1} ^T ，{1,1,1,0,0,1,1,0,1,1,1,0} ^T ，{0,1,0,0,0,0,0,0,1,0,0,1} ^T ，{0,1,1,0,0,0,0,0,0,1,0,0} ^T ，{0,0,1,0,1,1,1,0,1,0,1,0} ^T ，{1,1,1,0,1,1,1,1,0,0,0,0} ^T ，{0,1,1,1,0,0,1,0,0,1,1,1} ^T ，{0,1,1,1,1,0,1,1,1,0,0,1} ^T ，{0,1,0,0,1,0,0,1,0,1,1,1} ^T ，{0,1,0,1,0,0,1,0,1,0,1,0} ^T ，{0,0,1,1,0,1,0,1,0,1,1,1} ^T ，{1,0,0,0,1,0,0,0,1,1,0,1} ^T ，{1,1,0,0,1,1,1,1,1,1,0,1} ^T ，{0,0,1,1,1,1,0,0,1,0,0,1} ^T ，{1,0,1,1,1,0,1,0,0,0,1,1} ^T ，{1,1,0,1,0,1,0,0,0,0,0,0} ^T ，{0,1,0,1,1,0,1,1,0,1,0,0} ^T Wherein { } ^T The representation vector is transposed.

With reference to the second aspect, in certain implementations of the second aspect, when l=24, the first set of sequences includes some or all of the following sequences: {1,0,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,1,1,1,1,0} ^T ，{0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,0,0,1} ^T ，{1,0,0,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1} ^T ，{1,0,0,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0} ^T ，{1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,1,1,1} ^T ，{0,0,1,1,0,0,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,0} ^T ，{1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0,1,1,0,1,0} ^T ，{0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1} ^T ，{0,0,1,0,1,1,0,1,1,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1} ^T ，{1,1,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,1,0,0,0} ^T ，{0,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,1,1,0,0,1,0} ^T ，{1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,0} ^T ，{0,0,0,0,1,0,1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1,1,0} ^T ，{1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,1,1,1,1} ^T ，{1,0,1,1,1,1,1,0,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0,0} ^T ，{1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1} ^T ，{0,1,1,0,1,0,1,0,1,1,1,0,0,1,0,1,0,0,1,1,0,1,0,1} ^T ，{0,1,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0} ^T ，{0,0,1,1,1,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,0,0,1,1,1,1,0,1,1,1,0,0,1,1,0,0,1,0,0} ^T ，{0,1,1,1,1,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1} ^T ，{0,1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0} ^T ，{0,1,1,0,0,0,0,1,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,0} ^T ，{0,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,0} ^T ，{0,0,0,1,0,1,1,0,0,1,1,1,1,1,0,1,0,1,1,0,1,1,1,1} ^T ，{1,0,1,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,1,1,1,0,1} ^T ，{1,1,0,0,1,0,0,1,0,1,0,1,1,0,1,1,0,1,1,1,1,0,0,1} ^T ，{0,0,0,1,1,1,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0} ^T ，{1,0,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,0,0,0,1,1} ^T ，{1,1,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,1} ^T ，{0,1,0,0,0,1,1,1,1,0,1, 0,0,0,1,0,0,1,0,0,1,0,0,1} ^T Wherein { } ^T The representation vector is transposed.

In a third aspect, an apparatus for information transmission is provided, including: means, such as a processing module and a transceiver module, for performing the method of the first aspect described above or any alternative implementation manner thereof. The transceiver module may include a transmitting module and a receiving module, and the transmitting module and the receiving module may be different functional modules, or may be the same functional module but may implement different functions. The processing module may be implemented by a processor. The transceiver module may be implemented by a transceiver, and correspondingly, the transmitting module may be implemented by a transmitter, and the receiving module may 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 arranged in the terminal equipment, the transceiver can be a communication interface in the chip, and the communication interface is connected with a radio frequency transceiver component in the terminal equipment so as to realize the information receiving and transmitting through the radio frequency transceiver component.

In a fourth aspect, there is provided an apparatus for information transmission, comprising: means, e.g. a processing module and a transceiver module, for performing the method of the second aspect described above or any alternative implementation thereof. The transceiver module may include a transmitting module and a receiving module, and the transmitting module and the receiving module may be different functional modules, or may be the same functional module but may implement different functions. The processing module may be implemented by a processor. The transceiver module may be implemented by a transceiver, and correspondingly, the transmitting module may be implemented by a transmitter, and the receiving module may 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 arranged in the network equipment, the transceiver can be a communication interface in the chip, and the communication interface is connected with a radio frequency transceiver component in the network equipment so as to realize the receiving and transmitting of information through the radio frequency transceiver component.

In a fifth aspect, there is provided a communication apparatus comprising: a processor and a memory; the memory is used for storing a computer program; the processor is configured to execute a computer program stored in the memory to cause the apparatus to perform the method of the first aspect or any alternative implementation thereof, or to perform the method of the second aspect or any alternative implementation thereof.

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

In a seventh aspect, a chip system is provided, which includes: a processor for invoking and running a computer program from memory to cause a communication device on which the chip system is installed to perform the method of the first aspect or any alternative implementation thereof, or to perform the method of the second aspect or any alternative implementation thereof.

Drawings

Fig. 1 is a schematic interaction diagram of a method 100 of 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 in an embodiment of the present application.

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

Fig. 4 is a schematic block diagram of an example of the 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 a communication device of the present application.

Fig. 7 is a schematic block diagram of still another example of the communication apparatus of the present application.

Detailed Description

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

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

The terminal device in the embodiments of the present application may refer to a User Equipment (UE), an access terminal, a subscriber unit, a subscriber station, a mobile station, a remote terminal, a mobile device, a user terminal, a wireless communication device, a user agent, or a user equipment. The terminal device may also be a cellular telephone, a cordless telephone, a session initiation protocol (session initiation protocol, SIP) phone, a wireless local loop (wireless local loop, WLL) station, a personal digital assistant (personal digital assistant, PDA), a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a wearable device, a terminal device in a 5G network or a terminal device in a future evolved public land mobile network (public land mobile network, PLMN), etc., as the embodiments of the application are not limited in this regard.

By way of example, and not limitation, in embodiments of the present application, the terminal device may also be a wearable device. The wearable device can also be called as a wearable intelligent device, and is a generic name for intelligently designing daily wear by applying wearable technology and developing wearable devices, such as glasses, gloves, watches, clothes, shoes and the like. The wearable device is a portable device that is worn directly on the body or integrated into the clothing or accessories of the user. The wearable device is not only a hardware device, but also can realize a powerful function through software support, data interaction and cloud interaction. The generalized wearable intelligent device includes full functionality, large size, and may not rely on the smart phone to implement complete or partial functionality, such as: smart watches or smart glasses, etc., and focus on only certain types of application functions, and need to be used in combination with other devices, such as smart phones, for example, various smart bracelets, smart jewelry, etc. for physical sign monitoring.

In addition, in the embodiment of the application, the terminal device may also be a terminal device in an internet of things (internet of things, ioT) system, and the IoT is an important component of future information technology development, and the main technical characteristic of the terminal device is that the article is connected with a network through a communication technology, so that an intelligent network for man-machine interconnection and internet of things interconnection is realized.

The network device in the embodiments of the present application may be a device for communicating with a terminal device, where the network device may be a base station (base transceiver station, BTS) in a GSM system or a code division multiple access CDMA system, a base station (nodeB, NB) in a wideband code division multiple access (wideband code division multiple access, WCDMA) system, an evolved base station (evolutional nodeB, eNB or eNodeB) in an LTE system, a wireless controller in a cloud wireless access network (cloud radio access network, CRAN) scenario, or the network device may be a relay station, an access point, a vehicle device, a network device in a future 5G network, or a network device in a future evolved PLMN network, or the like, which is not limited in the embodiments of the present application.

The application scenario and the method according to the embodiments of the present application will be described below by taking the internet of things as an example.

The Internet of things has important roles in various vertical fields due to the advantages of saving cost, increasing income sources, improving efficiency and the like. Currently, about 87% of global enterprises which have deployed the internet of things application hope to continue to increase the internet of things application; meanwhile, by designing various internet of things applications and solutions for individuals and enterprises, by 2030, the composite annual average growth rate of internet of things revenue for global communication service providers will reach 24.9%, and the next generation mobile communication network (B5G or 6G) needs to support the internet of things applications has become an industry consensus. One typical feature of the internet of things is the connection of massive low power consumption devices. The relevant report indicates that by 2030, the number of internet of things devices accessing the cellular network will be up to 500 billion, 59 times the population count at that time, which requires that future base stations be able to make mass connections with hundreds of billions of devices. Although large-scale machine-type communication (mctc) has been listed in one of three application scenarios of 5G, supporting mass device access while guaranteeing low latency and high reliability is still a challenging problem for current networks.

The key of supporting mass connection of the internet of things by the cellular network is how to design an efficient and robust multiple access scheme. Conventional grant-based random multiple access protocols require control signaling interactions and uplink access request scheduling to achieve resource allocation, which is typically represented by the physical random access channel (physical random access channel, PRACH) employed by 4G LTE and 5G NR. However, in future scenarios where massive connections need to be supported, grant-based multiple access protocols generally require complex access scheduling, resulting in access delays that are intolerable to users.

Unlicensed multiple access protocols alleviate the above problems to some extent, and have attracted considerable attention in both academia and industry. In the traditional unlicensed multiple access protocol, users needing to access the network can directly send pilot frequency and data to the base station in an uplink manner without the authorization of the base station; and the base station performs user identification and data detection according to the received signals. The protocol can significantly reduce access latency since complex access scheduling is avoided. The base station firstly decouples different user signals (namely user identification) according to the received pilot signals, and then detects the transmitted data. In short, the conventional unlicensed protocol requires that channel estimation be performed first and then coherent detection of the transmitted data be performed. Therefore, the error of the channel estimation in the above scheme easily affects the data detection accuracy, and at the same time, the scheme is economically not efficient for transmission of a small amount of bit data widely existing in the internet of things equipment.

At present, an unlicensed access scheme based on incoherent data detection overtakes the uplink pilot frequency and data transmission mode of the traditional unlicensed scheme, overcomes the defects of the traditional unlicensed protocol, and is particularly suitable for uplink transmission of a small amount of data in the scene of the Internet of things. However, in the internet of things scenario, how to realize access and information transmission of a large number of users is still a problem to be solved.

In the unlicensed access scheme based on incoherent data detection, the sequence sets pre-allocated by different users are directly related to the performance quality of the data detection of the receiving end. Specifically, when there is a higher correlation between different sequences in the set, the data detection performance of the receiving end is degraded, and when the correlation between different sequences in the set is lower, the receiving end obtains a better data detection performance. Thus, how to generate the sequence set required for sequence modulation is a problem to be solved.

In order to facilitate an understanding of the embodiments of the present application, a brief description of several terms or expressions referred to herein follows.

1. Sequence modulation

The existing "sequence modulation" refers to: direct sequence modulation (spread spectrum) techniques in spread spectrum communications. The distinction between "sequence modulation" and direct sequence spread spectrum "in this application can be mainly generalized to the following points:

(1) The information modulation modes are different: the effective information in the sequence modulation is encoded on the serial number selection of the sequence; direct sequence spreading is the multiplication of low rate symbols carrying significant information by a high rate pseudo-random code to achieve spreading.

(2) The sequence expansion modes are different: the sequence of the 'sequence modulation' in the application can be expanded in time, namely a plurality of continuous time symbols, can be expanded on a plurality of adjacent subcarriers, and can also be expanded on a time-frequency resource block formed by a plurality of adjacent time slots and subcarriers; direct sequence spread spectrum is simply a frequency spread.

(3) For a receiver, "sequence modulation" does not require accurate channel estimation, since there is no subsequent step of coherent data detection; direct sequence spread spectrum also requires two steps of channel estimation and data detection.

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

3. Normalizing the correlation matrix: normalized matrix of autocorrelation matrices for a set of sequences.

4、{} ^T : the representation vector being transposed, i.e. { A } ^T Representing the transpose of vector a.

The technical scheme provided by the 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 in an embodiment of the present application. The method 100 shown in fig. 1 may include the following steps.

S101, the terminal equipment determines a first sequence to be sent.

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

S102, the terminal equipment sends the first sequence to the network equipment.

It will be appreciated that the network device accordingly receives the signal transmitted by the at least one terminal device.

And S103, the network equipment 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 a first sequence set, to obtain at least one sequence, where the first sequence set includes W sequences with a length of L, where L < W, where L and W are positive integers, and where the W sequences include the at least one sequence, and each column in the first sequence set is related two by two.

In the embodiment of the application, the access and information transmission of massive users can be realized by adopting the non-orthogonal sequence for information transmission.

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 with a length of L, and the maximum cross-correlation value is the maximum value in correlation values between every two sequences in one sequence set.

Further, the first sequence set is a sequence set with the smallest frequency of occurrence of the smallest maximum cross correlation value in the corresponding normalized correlation matrix, and the normalized correlation matrix is a normalized matrix of autocorrelation matrices of one sequence set.

In the embodiment of the application, the information transmission is performed by adopting the non-orthogonal sequence with smaller correlation, so that the access and the information transmission of massive users can be realized on the premise of ensuring the detection effect.

Optionally, the second sequence set is a set of W sequences with a length L in a third sequence set, the third sequence set includes X sequences with a length Y, X is greater than or equal to W, Y is greater than or equal to 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 embodiment of the present application, at least one third sequence set is obtained before the second sequence set is obtained, especially a sequence set including W sequences with length W, so that the number of extraction results is greatly reduced when the second sequence set and the first sequence set are subsequently extracted while ensuring that the third sequence set has lower orthogonality, thereby reducing screening complexity.

The following is an exemplary description taking three examples of l= 6,12,24.

Example one: l=6, the first set of sequences comprising 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 Wherein { } ^T The representation vector is transposed.

It should be understood that the above set of sequences is given here by way of example given w=32.

Example two, when l=12, the first set of sequences includes some or all of the following sequences: {1,0,0,1,1,0,1,0,1,1,1,0} ^T ，{0,0,0,0,0,1,1,1,1,0,0,1} ^T ，{1,0,0,1,0,0,1,1,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,0,0} ^T ，{1,0,0,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,1,0,1,0,0,1,1} ^T ，{0,0,1,0,0,1,1,1,0,1,0,0} ^T ，{1,1,0,1,1,1,0,1,1,1,1,0} ^T ，{0,1,1,0,1,0,0,1,1,0,1,0} ^T ，{0,0,0,1,1,1,0,0,0,1,0,0} ^T ，{1,1,1,1,0,1,0,0,1,1,0,1} ^T ，{0,0,0,1,0,1,0,1,1,0,1,0} ^T ，{1,1,0,0,0,1,1,0,0,0,1,1} ^T ，{0,0,0,0,1,1,1,0,0,1,1,1} ^T ，{1,0,1,0,0,0,0,1,1,1,1,0} ^T ，{1,0,1,1,0,0,1,1,1,1,0,1} ^T ，{1,1,1,0,0,1,1,0,1,1,1,0} ^T ，{0,1,0,0,0,0,0,0,1,0,0,1} ^T ，{0,1,1,0,0,0,0,0,0,1,0,0} ^T ，{0,0,1,0,1,1,1,0,1,0,1,0} ^T ，{1,1,1,0,1,1,1,1,0,0,0,0} ^T ，{0,1,1,1,0,0,1,0,0,1,1,1} ^T ，{0,1,1,1,1,0,1,1,1,0,0,1} ^T ，{0,1,0,0,1,0,0,1,0,1,1,1} ^T ，{0,1,0,1,0,0,1,0,1,0,1,0} ^T ，{0,0,1,1,0,1,0,1,0,1,1,1} ^T ，{1,0,0,0,1,0,0,0,1,1,0,1} ^T ，{1,1,0,0,1,1,1,1,1,1,0,1} ^T ，{0,0,1,1,1,1,0,0,1,0,0,1} ^T ，{1,0,1,1,1,0,1,0,0,0,1,1} ^T ，{1,1,0,1,0,1,0,0,0,0,0,0} ^T ，{0,1,0,1,1,0,1,1,0,1,0,0} ^T Where { T represents the vector transposed.

Example three, when l=24, the first set of sequences includes some or all of the following sequences: {1,0,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,1,1,1,1,0} ^T ，{0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,0,0,1} ^T ，{1,0,0,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1} ^T ，{1,0,0,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0} ^T ，{1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,1,1,1} ^T ，{0,0,1,1,0,0,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,0} ^T ，{1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0,1,1,0,1,0} ^T ，{0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1} ^T ，{0,0,1,0,1,1,0,1,1,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1} ^T ，{1,1,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,1,0,0,0} ^T ，{0,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,1,1,0,0,1,0} ^T ，{1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,0} ^T ，{0,0,0,0,1,0,1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1,1,0} ^T ，{1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,1,1,1,1} ^T ，{1,0,1,1,1,1,1,0,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0,0} ^T ，{1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1} ^T ，{0,1,1,0,1,0,1,0,1,1,1,0,0,1,0,1,0,0,1,1,0,1,0,1} ^T ，{0,1,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0} ^T ，{0,0,1,1,1,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,0,0,1,1,1,1,0,1,1,1,0,0,1,1,0,0,1,0,0} ^T ，{0,1,1,1,1,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1} ^T ，{0,1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0} ^T ，{0,1,1,0,0,0,0,1,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,0} ^T ，{0,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,0} ^T ，{0,0,0,1,0,1,1,0,0,1,1,1,1,1,0,1,0,1,1,0,1,1,1,1} ^T ，{1,0,1,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,1,1,1,0,1} ^T ，{1,1,0,0,1,0,0,1,0,1,0,1,1,0,1,1,0,1,1,1,1,0,0,1} ^T ，{0,0,0,1,1,1,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0} ^T ，{1,0,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,0,0,0,1,1} ^T ，{1,1,1,0,0,1,0,0,0,0,0, 1,1,1,0,0,0,0,0,0,0,1,0,1} ^T ，{0,1,0,0,0,1,1,1,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1} ^T Wherein { } ^T The representation 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 as an example the generation of a set of W sequences of length L, the method 200 is described.

S201, finding out W sequences with the length of W from the parent sequences to form an approximately orthogonal square matrix.

It is understood that the parent sequence may be a Gold sequence, a discrete fourier transform (discrete fourier transform, DFT) sequence, a zedoff-chu sequence (ZC sequence).

It should be appreciated that the "near orthogonality" is such that the correlation between the W sequences is kept as low as possible.

As an example, after the Gold sequence with the period of 31 is selected as the parent sequence and the sequence length L and the number of users W to be supported are determined, when the W sequences with the length L that are optimal and meet the requirement are obtained by using the generating method given in this embodiment, the correlation between the W sequences with the length W in the approximately orthogonal square matrix is the "lower correlation".

S202, extracting L rows from a square matrix of W rows and W columns to obtain W sequences with the length of L, and calculating the maximum cross correlation value of all the extraction results and the occurrence times of the maximum cross correlation value in the normalized correlation matrix as one extraction result.

It should be appreciated that each extraction result, i.e. each set of sequences comprising W sequences of length L, corresponds to a normalized correlation matrix, each normalized correlation matrix having a maximum correlation value.

As one example, the extraction matrix is represented asWhere p= {1,2,..p }, i.e. there are P extraction results in total; the normalized autocorrelation matrix of the p-th decimation matrix is denoted as:representation ofIs a conjugate transpose of (a).

S203, searching a sequence set with the smallest maximum cross correlation value in the extraction result.

S204, searching a sequence set with the minimum frequency of occurrence of the minimum maximum cross-correlation value in the normalized correlation matrix in the extraction result with the minimum maximum cross-correlation value.

It should be understood that there may be a plurality of extraction results satisfying step S203, and the extraction results herein include a plurality of sequence sets with a plurality of corresponding normalized correlation matrices, where the number of times of occurrence of the maximum cross correlation value is very large, and such sequence sets may affect the detection effect. By performing step S204, a sequence set with lower correlation can be further screened out, so as to ensure the detection effect.

Further, the matrix of L rows and W columns satisfying both step S203 and step S204 is screened out by the method 200 to be the finally generated sequence set. In this embodiment, three examples of l= 6,12,24 are taken as examples, and specific description is given in the method 100, which is not repeated here.

In the embodiment of the application, the sequence with lower correlation is obtained by the sequence generation method for sequence modulation, and more sequences can be provided under the condition of ensuring a certain sequence length, so that access and information transmission of a large number of users can be realized on the premise of ensuring a detection effect.

Fig. 3 is a schematic interaction diagram of a method 300 of 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 equipment determines a sequence to be sent according to the information to be sent and the first mapping relation.

As an example, the terminal device determines that the sequence 4 is to be transmitted to the network device next according to the information bit to be transmitted as '11' and the first mapping relation.

Table 1 shows a first mapping relationship of terminal device preconfigured information bits and sequences.

TABLE 1

Information bits	‘00’	‘01’	‘10’	‘11’
Sequence(s)	Sequence 1	Sequence 2	Sequence 3	Sequence 4

It should be appreciated that sequences 1 through 4 in table 1 may be any one of the "first set of sequences" mentioned in method 100 or the "final set of sequences" mentioned in method 200.

In this example, the terminal device may send 4 status information to the network device through sequences 1 to 4, where the same terminal device can schedule sequences with a low correlation, and different terminal devices can schedule sequences with a low correlation. While sequences 1 to 4 belong exclusively to the terminal device.

S302, the terminal equipment transmits the sequence through time domain and/or frequency domain resources.

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

It should be understood that the mapping may be performed only in the time domain, i.e. the sequences are mapped on the same subcarrier of different symbols; the mapping can also be performed only in the frequency domain, i.e. the sequences are mapped on different subcarriers of the same symbol; the mapping may also be performed in time-frequency two dimensions, i.e. the sequences are mapped on different subcarriers of different symbols.

It will be appreciated that the signal received by the network device accordingly comprises at least one sequence transmitted by at least one terminal device.

S303, the network equipment performs sequence detection according to the observation matrix to obtain a sequence, and determines information corresponding to the sequence according to the first mapping relation.

It should be understood that the "observation matrix" is the "first set of sequences" described in method 100 or the "final set of sequences" described in method 200. The sequence to which all terminal devices communicating with the network device are assigned constitutes the observation matrix of the network device.

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

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

Specifically, for convenience of explanation, the following describes a receiving process by taking a single antenna user as an example.

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.

Wherein the preallocated sequences of all terminal devices form an observation matrix, phi epsilon C ^L×KN The method comprises the steps of carrying out a first treatment on the surface of the The equivalent channel matrix of all sequences in the observation matrix is expressed as D E C ^KN×M The method comprises the steps of carrying out a first treatment on the surface of the The received signal at the base station is expressed as Y E C ^L×M The method comprises the steps of carrying out a first treatment on the surface of the Additive white gaussian noiseAcoustic representation as N E C ^L×M Let the noise variance be sigma ² 。

It is noted that the column dimension of the equivalent channel matrix D corresponds to the antenna dimension M and the row dimension corresponds to the dimension of the device pre-allocation sequence. Specifically, the [ (K-1) n+1] th to kN-th rows of the equivalent channel matrix D correspond to the 1 st to nth pre-allocation sequences of the kth device, K e {1, 2..and K } respectively. Assuming that the kth device actually transmits its nth pre-allocation sequence, then the kth row of the equivalent channel matrix D corresponds to the channel complex gain between the kth device and the M antennas, and all of the [ (k-1) n+1] th row to the [ kN-1] th row of the equivalent channel matrix D are equivalent to zero values. Similarly, if the kth device actually sends a pre-allocation sequence with any number, the row value of D corresponding to the sequence is the real channel complex gain, and the row values of D corresponding to other sequences are equivalent to zero values. Furthermore, if the kth device does not send a sequence, then the row value of D corresponding to that device is all equivalent to a zero value.

For the detection process, the problem can be generalized as: knowing the received signal Y and the observation matrix Φ, the sequence numbers of the non-zero rows of the equivalent channel matrix D are recovered. Although the number of rows of the observation matrix Φ is smaller than the number of columns (L<KN), i.e. the above formula representing the system model is a partial equation, a unique solution cannot be obtained. However, due to the characteristic of data intermittence of the internet of things, the number of active devices at the same time is often far smaller than the total number of devices, namely: k (K) _a =k. Due to the sparsity of the active devices, the equivalent channel matrix D is sparse, i.e.: only K _a When the row is a non-zero value, the network device can be guaranteed to detect the sequence number from the received signal. One typical approach is to detect using a simultaneous orthogonal matching pursuit (simultaneous orthogonal matching pursuit, SOMP) algorithm. Specifically, the SOMP algorithm is a greedy algorithm, and each iteration finds the row with the largest correlation value 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 sequence modulation information extraction. As shown in Table 2, step 1, row number of the equivalent channel matrix D with the largest correlation value is selectedStep 2 updating support setAnd step 3, restoring the channel elements at the corresponding positions of the support sets by using a least square method, step 4, updating residual errors according to the restored channel elements, terminating the iterative process if the normalized energy of the residual errors is smaller than the noise variance, and otherwise returning to step 1 to find a new support set. After the iteration is terminated, the set Ω is output ^t+1 The serial numbers of the non-zero lines of the equivalent channel matrix D are represented, and the serial numbers of the non-zero lines are corresponding to the serial numbers of corresponding equipment, so that information bits transmitted by the serial modulation can be obtained; output set Γ= [ Ω ] ^t+1 /N]The number of active devices, i.e. the devices corresponding to the non-zero rows of the equivalent channel matrix D, is indicated.

According to the embodiment of the application, the user detection and the sequence detection are performed based on the non-orthogonal sequence, so that access and information transmission of a large number of users are realized on the premise of guaranteeing the detection effect.

The method for providing information transmission according to the embodiment of the present application is described in detail above with reference to fig. 1 to 3. The following describes in detail the information transmission apparatus provided in the embodiments of the present application with reference to fig. 4 to 7.

Fig. 4 is a schematic block diagram of an apparatus for information transmission provided in 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 one possible design, the apparatus 10 may correspond to the terminal device in the above method embodiment. For example, it may be a user equipment or a chip configured in the user equipment.

In particular, the communication apparatus 10 may correspond to the terminal device in the method 100 and the method 300 according to embodiments of the present application, and the communication apparatus 10 may include a module for performing the method 100 in fig. 1 or the method performed by the terminal device in the method 300 in fig. 3. And, each unit in the communication device 10 and the other operations and/or functions described 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 10 is used to perform the method 100 in fig. 1, the transceiver module 11 may be used to perform the step S102 in the method 100, and the processing module 12 may be used to perform the step S102 in the method 100.

When the communication device 10 is used to perform the method 300 in fig. 3, the transceiver module 11 may be used to perform the step S302 in the method 300, and the processing module 12 may be used to perform the step S301 in the method 300.

Specifically, the processing module 12 is configured to determine a first sequence to be sent, where the first sequence belongs to a first sequence set, the first sequence set includes W sequences with a length of L, L < W, where L and W are positive integers, and the sequences in the first sequence set are related two by two; a transceiver module 11, configured to send the first sequence to a 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 comprises W sequences with the length of L, and the maximum cross-correlation value is the maximum value in correlation values between every two sequences in one sequence set.

The first sequence set is a sequence set with the minimum frequency of occurrence of the minimum maximum cross correlation value in a corresponding normalized correlation matrix in a sequence set with the minimum cross correlation value in the at least one second sequence set, and the normalized correlation matrix is a normalized matrix of an autocorrelation matrix of one sequence set.

The second sequence set is a set of W sequences with the length L in a third sequence set, the third sequence set comprises X sequences with the length Y, X is larger than or equal to W, Y is larger than or equal to 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.

As an example, when l=6, the first set of sequences includes some or all of the following sequences: {1,1,1,0,0,1} ^T ，{0,0,1,1,1,1} ^T ，{1,0,0,0,1,1} ^T ，{1,1,1,0,1,0} ^T ，{1,0,0,0,0,1} ^T ，{1,1,1,1,1,1} ^T ，{0,0,1,1,1,0} ^T ，{1,1,1,1,1,0} ^T ，{0,1,0,0,1,1} ^T ，{0,1,0,1,1,1} ^T ，{1,0,0,1,0,1} ^T ，{0,0,1,1,0,1} ^T ，{1,0,0,1,1,0} ^T ，{0,1,0,1,0,1} ^T ，{1,0,0,0,0,0} ^T ，{1,0,0,0,1,0} ^T ，{1,0,0,1,1,1} ^T ，{0,0,1,0,0,0} ^T ，{0,0,1,0,0,1} ^T ，{0,1,0,1,0,0} ^T ，{1,1,1,1,0,1} ^T ，{0,0,1,0,1,1} ^T ，{0,1,0,0,0,1} ^T ，{0,1,0,0,1,0} ^T ，{0,0,1,0,1,0} ^T ，{0,0,1,1,0,0} ^T ，{1,1,1,0,1,1} ^T ，{1,1,1,1,0,0} ^T ，{0,1,0,1,1,0} ^T ，{1,1,1,0,0,0} ^T ，{1,0,0,1,0,0} ^T ，{0,1,0,0,0,0} ^T Wherein { } ^T The representation vector is transposed.

As an example, when l=12, the first set of sequences includes some or all of the following sequences: {1,0,0,1,1,0,1,0,1,1,1,0} ^T ，{0,0,0,0,0,1,1,1,1,0,0,1} ^T ，{1,0,0,1,0,0,1,1,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,0,0} ^T ，{1,0,0,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,1,0,1,0,0,1,1} ^T ，{0,0,1,0,0,1,1,1,0,1,0,0} ^T ，{1,1,0,1,1,1,0,1,1,1,1,0} ^T ，{0,1,1,0,1,0,0,1,1,0,1,0} ^T ，{0,0,0,1,1,1,0,0,0,1,0,0} ^T ，{1,1,1,1,0,1,0,0,1,1,0,1} ^T ，{0,0,0,1,0,1,0,1,1,0,1,0} ^T ，{1,1,0,0,0,1,1,0,0,0,1,1} ^T ，{0,0,0,0,1,1,1,0,0,1,1,1} ^T ，{1,0,1,0,0,0,0,1,1,1,1,0} ^T ，{1,0,1,1,0,0,1,1,1,1,0,1} ^T ，{1,1,1,0,0,1,1,0,1,1,1,0} ^T ，{0,1,0,0,0,0,0,0,1,0,0,1} ^T ，{0,1,1,0,0,0,0,0,0,1,0,0} ^T ，{0,0,1,0,1,1,1,0,1,0,1,0} ^T ，{1,1,1,0,1,1,1,1,0,0,0,0} ^T ，{0,1,1,1,0,0,1,0,0,1,1,1} ^T ，{0,1,1,1,1,0,1,1,1,0,0,1} ^T ， {0,1,0,0,1,0,0,1,0,1,1,1} ^T ，{0,1,0,1,0,0,1,0,1,0,1,0} ^T ，{0,0,1,1,0,1,0,1,0,1,1,1} ^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 Wherein { } ^T The representation vector is transposed.

As an example, when l=24, the first set of sequences includes some or all of the following sequences: {1,0,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,1,1,1,1,0} ^T ，{0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,0,0,1} ^T ，{1,0,0,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1} ^T ，{1,0,0,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0} ^T ，{1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,1,1,1} ^T ，{0,0,1,1,0,0,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,0} ^T ，{1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0,1,1,0,1,0} ^T ，{0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1} ^T ，{0,0,1,0,1,1,0,1,1,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1} ^T ，{1,1,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,1,0,0,0} ^T ，{0,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,1,1,0,0,1,0} ^T ，{1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,0} ^T ，{0,0,0,0,1,0,1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1,1,0} ^T ，{1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,1,1,1,1} ^T ，{1,0,1,1,1,1,1,0,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0,0} ^T ，{1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1} ^T ，{0,1,1,0,1,0,1,0,1,1,1,0,0,1,0,1,0,0,1,1,0,1,0,1} ^T ，{0,1,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0} ^T ，{0,0,1,1,1,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,0,0,1,1,1,1,0,1,1,1,0,0,1,1,0,0,1,0,0} ^T ，{0,1,1,1,1,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1} ^T ，{0,1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0} ^T ，{0,1,1,0,0,0,0,1,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,0} ^T ，{0,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,0} ^T ，{0,0,0,1,0,1,1,0,0,1,1,1,1,1,0,1,0,1,1,0,1,1,1,1} ^T ，{1,0,1,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,1,1,1,0,1} ^T ，{1,1,0,0,1,0,0,1,0,1,0,1,1,0,1,1,0,1,1,1,1,0,0,1} ^T ，{0,0,0,1,1,1,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0} ^T ，{1,0,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,0,0,0,1,1} ^T ，{1,1,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,1} ^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 Wherein { } ^T The representation vector is transposed.

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

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

In particular, the communication apparatus 20 may correspond to the network device in the method 100 and the method 300 according to embodiments of the present application, and the communication apparatus 20 may include a module for performing the method 100 in fig. 1 or the method performed by the network device in the method 300 in fig. 3. And, each unit in the communication device 20 and the other operations and/or functions described 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 perform the method 100 in fig. 1, the transceiver module 21 may be used to perform step S102 in the method 100, and the processing module 22 may be used to perform step S103 in the method 100.

When the communication device 20 is used to perform the method 300 in fig. 3, the transceiver module 21 may be used to perform the step S302 in the method 300, and the processing module 22 may be used to perform the step S303 in the method 300.

Specifically, the transceiver module 21 is configured to receive signals; the processing module 22 is configured to perform sequence detection on the signal according to a first sequence set, so as to obtain at least one sequence, where the first sequence set includes W sequences with a length L, L < W, where L and W are positive integers, and the W sequences include the at least one sequence, and each column in the first sequence set is related two by two.

The first sequence set is a sequence set with the minimum maximum cross-correlation value corresponding to at least one second sequence set, the second sequence set comprises W sequences with the length of L, and the maximum cross-correlation value is the maximum value in correlation values between every two sequences in one sequence set.

The second sequence set is W sequences with the length L in a third sequence set, the third sequence set comprises X sequences with the length Y, X is more than or equal to W, Y is more than or equal to W, and the range of the maximum cross correlation value of the third sequence set is determined according to the number W of the sequences included in the second sequence set.

As an example, when l=6, theThe first set of sequences includes some or all of the following sequences: {1,1,1,0,0,1} ^T ，{0,0,1,1,1,1} ^T ，{1,0,0,0,1,1} ^T ，{1,1,1,0,1,0} ^T ，{1,0,0,0,0,1} ^T ，{1,1,1,1,1,1} ^T ，{0,0,1,1,1,0} ^T ，{1,1,1,1,1,0} ^T ，{0,1,0,0,1,1} ^T ，{0,1,0,1,1,1} ^T ，{1,0,0,1,0,1} ^T ，{0,0,1,1,0,1} ^T ，{1,0,0,1,1,0} ^T ，{0,1,0,1,0,1} ^T ，{1,0,0,0,0,0} ^T ，{1,0,0,0,1,0} ^T ，{1,0,0,1,1,1} ^T ，{0,0,1,0,0,0} ^T ，{0,0,1,0,0,1} ^T ，{0,1,0,1,0,0} ^T ，{1,1,1,1,0,1} ^T ，{0,0,1,0,1,1} ^T ，{0,1,0,0,0,1} ^T ，{0,1,0,0,1,0} ^T ，{0,0,1,0,1,0} ^T ，{0,0,1,1,0,0} ^T ，{1,1,1,0,1,1} ^T ，{1,1,1,1,0,0} ^T ，{0,1,0,1,1,0} ^T ，{1,1,1,0,0,0} ^T ，{1,0,0,1,0,0} ^T ，{0,1,0,0,0,0} ^T Wherein { } ^T The representation vector is transposed.

As an example, when l=12, the first set of sequences includes some or all of the following sequences: {1,0,0,1,1,0,1,0,1,1,1,0} ^T ，{0,0,0,0,0,1,1,1,1,0,0,1} ^T ，{1,0,0,1,0,0,1,1,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,0,0} ^T ，{1,0,0,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,1,0,1,0,0,1,1} ^T ，{0,0,1,0,0,1,1,1,0,1,0,0} ^T ，{1,1,0,1,1,1,0,1,1,1,1,0} ^T ，{0,1,1,0,1,0,0,1,1,0,1,0} ^T ，{0,0,0,1,1,1,0,0,0,1,0,0} ^T ，{1,1,1,1,0,1,0,0,1,1,0,1} ^T ，{0,0,0,1,0,1,0,1,1,0,1,0} ^T ，{1,1,0,0,0,1,1,0,0,0,1,1} ^T ，{0,0,0,0,1,1,1,0,0,1,1,1} ^T ，{1,0,1,0,0,0,0,1,1,1,1,0} ^T ，{1,0,1,1,0,0,1,1,1,1,0,1} ^T ，{1,1,1,0,0,1,1,0,1,1,1,0} ^T ，{0,1,0,0,0,0,0,0,1,0,0,1} ^T ，{0,1,1,0,0,0,0,0,0,1,0,0} ^T ，{0,0,1,0,1,1,1,0,1,0,1,0} ^T ，{1,1,1,0,1,1,1,1,0,0,0,0} ^T ，{0,1,1,1,0,0,1,0,0,1,1,1} ^T ，{0,1,1,1,1,0,1,1,1,0,0,1} ^T ，{0,1,0,0,1,0,0,1,0,1,1,1} ^T ，{0,1,0,1,0,0,1,0,1,0,1,0} ^T ，{0,0,1,1,0,1,0,1,0,1,1,1} ^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 Wherein { } ^T The representation vector is transposed.

As an example, when l=24, the first set of sequences includes some or all of the following sequences: {1,0,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,1,1,1,1,0} ^T ，{0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,0,0,1} ^T ，{1,0,0,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1} ^T ，{1,0,0,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0} ^T ，{1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,1,1,1} ^T ，{0,0,1,1, 0,0,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,0} ^T ，{1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0,1,1,0,1,0} ^T ，{0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1} ^T ，{0,0,1,0,1,1,0,1,1,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1} ^T ，{1,1,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,1,0,0,0} ^T ，{0,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,1,1,0,0,1,0} ^T ，{1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,0} ^T ，{0,0,0,0,1,0,1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1,1,0} ^T ，{1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,1,1,1,1} ^T ，{1,0,1,1,1,1,1,0,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0,0} ^T ，{1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1} ^T ，{0,1,1,0,1,0,1,0,1,1,1,0,0,1,0,1,0,0,1,1,0,1,0,1} ^T ，{0,1,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0} ^T ，{0,0,1,1,1,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,0,0,1,1,1,1,0,1,1,1,0,0,1,1,0,0,1,0,0} ^T ，{0,1,1,1,1,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1} ^T ，{0,1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0} ^T ，{0,1,1,0,0,0,0,1,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,0} ^T ，{0,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,0} ^T ，{0,0,0,1,0,1,1,0,0,1,1,1,1,1,0,1,0,1,1,0,1,1,1,1} ^T ，{1,0,1,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,1,1,1,0,1} ^T ，{1,1,0,0,1,0,0,1,0,1,0,1,1,0,1,1,0,1,1,1,1,0,0,1} ^T ，{0,0,0,1,1,1,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0} ^T ，{1,0,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,0,0,0,1,1} ^T ，{1,1,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,1} ^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 Wherein { } ^T The representation vector is transposed.

Fig. 6 is a schematic diagram of an apparatus 30 for information transmission provided in an embodiment of the present application, and as shown in fig. 6, the apparatus 30 may be a terminal device, including various handheld devices with wireless communication functions, vehicle apparatuses, wearable apparatuses, computing apparatuses, or other processing apparatuses connected to a wireless modem, and various types of terminals, mobile stations, terminals, user apparatuses, soft terminals, and so on, or may be a chip or a chip system located on the terminal device.

The apparatus 30 may include a processor 31 (i.e., an example of a processing module) and a memory 32. The memory 32 is configured to store instructions, and the processor 31 is configured to execute the instructions stored in the memory 32, to cause the apparatus 30 to implement steps performed by a terminal device in a corresponding method as shown in fig. 1 or fig. 3.

Further, the device 30 may also include an input port 33 (i.e., one example of a transceiver module) and an output port 34 (i.e., another example of a transceiver module). Further, the processor 31, memory 32, input port 33 and output port 34 may communicate with each other via internal communication paths to communicate control and/or data signals. The memory 32 is used for storing a computer program, and the processor 31 may be used for calling and running the computer program from the memory 32 to control the input port 33 to receive signals and the output port 34 to send signals, so as to complete the steps of the terminal device in the method. The memory 32 may be integrated in the processor 31 or may be provided separately from the processor 31.

Alternatively, 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. Wherein the receiver and the transmitter may be the same or different physical entities. Which are the same physical entities, may be collectively referred to as transceivers.

Alternatively, 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, the functions of the input port 33 and the output port 34 may be considered to be implemented by a transceiving circuit or a dedicated chip for transceiving. The processor 31 may 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 manner of using a general purpose computer may be considered to implement the apparatus provided in the embodiments of the present application. I.e. program code that implements the functions of the processor 31, the input port 33 and the output port 34 is 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 code in the memory 32.

The modules or units in the apparatus 30 may be configured to perform the actions or processes performed by the device (e.g., terminal device) performing random access in the above method, and detailed descriptions thereof are omitted herein for avoiding redundancy.

The concepts related to the technical solutions provided in the embodiments of the present application, explanation and detailed description of the concepts related to the device 10 and other steps are referred to in the foregoing methods or descriptions related to the other embodiments, and are not repeated herein.

Fig. 7 is a schematic diagram of an apparatus 40 for information transmission according to an embodiment of the present application, as shown in fig. 7, the apparatus 40 may be a network device, including a network element having an information transmission function, such as a base station.

The apparatus 40 may include a processor 41 (i.e., an example of a processing module) and a memory 42. The memory 42 is configured to store instructions, and the processor 41 is configured to execute the instructions stored in the memory 42, to cause the apparatus 40 to implement steps performed by a network device in a corresponding method as in fig. 1 or 3.

Further, the device 40 may also include an input port 43 (i.e., one example of a transceiver module) and an output port 44 (i.e., another example of a transceiver module). Further, the processor 41, memory 42, input port 43 and output port 44 may communicate with each other via internal communication paths to communicate control and/or data signals. The memory 42 is used for storing a computer program, and the processor 41 may be used for calling and running the computer program from the memory 42 to control the input port 43 to receive signals and the output port 44 to send signals, so as to complete the steps of the terminal device in the method. The memory 42 may be integrated in the processor 41 or may be provided separately from the processor 41.

Alternatively, if the apparatus 40 is a communication device, the input port 43 is a receiver and the output port 44 is a transmitter. Wherein the receiver and the transmitter may be the same or different physical entities. Which are the same physical entities, may be collectively referred to as transceivers.

Alternatively, 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, the functions of the input port 43 and the output port 44 may be considered to be implemented by a transceiving circuit or a dedicated chip for transceiving. The processor 41 may 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 manner of using a general purpose computer may be considered to implement the apparatus provided in the embodiments of the present application. I.e. program code that implements the functions of the processor 41, the input 43 and the output 44 is stored in the memory 42, and the general purpose processor implements the functions of the processor 41, the input 43 and the output 44 by executing the code in the memory 42.

The modules or units in the apparatus 40 may be configured to perform the actions or processes performed by the device (i.e., the access node) that accepts random access in the above method, and detailed descriptions thereof are omitted herein for avoiding redundancy.

The concepts related to the technical solutions provided in the embodiments of the present application, explanation, detailed description and other steps related to the device 40 refer to the descriptions related to the foregoing methods or other embodiments, and are not repeated herein.

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

It should also be appreciated that the memory in embodiments of the present application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. The volatile memory may be random access memory (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 RAM (SRAM), dynamic Random Access Memory (DRAM), synchronous Dynamic Random Access Memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), and direct memory bus 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 program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present application are all or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.

It should be understood that the term "and/or" is merely an association relationship describing the associated object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.

It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.

The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

A method of information transmission, comprising:

the method comprises the steps that terminal equipment determines a first sequence to be sent, wherein the first sequence belongs to a first sequence set, the first sequence set comprises W sequences with the length of L, L is smaller than W, L and W are positive integers, and the sequences in the first sequence set are related in pairs;

the first sequence is sent to a network device.
The method of claim 1, wherein the first set of sequences is a set of sequences having a smallest maximum cross-correlation value of at least one second set of sequences, the second set of sequences comprising W sequences of length L, the maximum cross-correlation value being a maximum of correlation values between every two sequences in one set of sequences.
The method of claim 2, wherein the first set of sequences is a set of sequences with a minimum maximum cross-correlation value of the at least one second set of sequences, and the corresponding normalized correlation matrix is a normalized matrix of autocorrelation matrices of a set of sequences with a minimum number of occurrences of the minimum maximum cross-correlation value.
A method according to claim 2 or 3, wherein the second set of sequences is a set of W sequences of length L in a third set of sequences, the third set of sequences comprising X sequences of length Y, X being ≡w, and wherein the range of maximum cross-correlation values for the third set of sequences is determined from the number W of sequences comprised by the second set of sequences.
The method according to any one of claim 1 to 4, wherein,

when l=6, the first set of sequences includes some or all of the following sequences:

{1,1,1,0,0,1} ^T ，{0,0,1,1,1,1} ^T ，{1,0,0,0,1,1} ^T ，{1,1,1,0,1,0} ^T ，{1,0,0,0,0,1} ^T ，{1,1,1,1,1,1} ^T ，{0,0,1,1,1,0} ^T ，{1,1,1,1,1,0} ^T ，{0,1,0,0,1,1} ^T ，{0,1,0,1,1,1} ^T ，{1,0,0,1,0,1} ^T ，{0,0,1,1,0,1} ^T ，{1,0,0,1,1,0} ^T ，{0,1,0,1,0,1} ^T ，{1,0,0,0,0,0} ^T ，{1,0,0,0,1,0} ^T ，{1,0,0,1,1,1} ^T ，{0,0,1,0,0,0} ^T ，{0,0,1,0,0,1} ^T ，{0,1,0,1,0,0} ^T ，{1,1,1,1,0,1} ^T ，{0,0,1,0,1,1} ^T ，{0,1,0,0,0,1} ^T ，{0,1,0,0,1,0} ^T ，{0,0,1,0,1,0} ^T ，{0,0,1,1,0,0} ^T ，{1,1,1,0,1,1} ^T ，{1,1,1,1,0,0} ^T ，{0,1,0,1,1,0} ^T ，{1,1,1,0,0,0} ^T ，{1,0,0,1,0,0} ^T ，{0,1,0,0,0,0} ^T ，

wherein { } ^T The representation vector is transposed.
The method according to any one of claim 1 to 4, wherein,

when l=12, the first set of sequences includes some or all of the following sequences:

{1,0,0,1,1,0,1,0,1,1,1,0} ^T ，{0,0,0,0,0,1,1,1,1,0,0,1} ^T ，{1,0,0,1,0,0,1,1,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,0,0} ^T ，{1,0,0,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,1,0,1,0,0,1,1} ^T ，{0,0,1,0,0,1,1,1,0,1,0,0} ^T ，{1,1,0,1,1,1,0,1,1,1,1,0} ^T ，{0,1,1,0,1,0,0,1,1,0,1,0} ^T ，{0,0,0,1,1,1,0,0,0,1,0,0} ^T ，{1,1,1,1,0,1,0,0,1,1,0,1} ^T ，{0,0,0,1,0,1,0,1,1,0,1,0} ^T ，{1,1,0,0,0,1,1,0,0,0,1,1} ^T ，{0,0,0,0,1,1,1,0,0,1,1,1} ^T ，{1,0,1,0,0,0,0,1,1,1,1,0} ^T ，{1,0,1,1,0,0,1,1,1,1,0,1} ^T ，{1,1,1,0,0,1,1,0,1,1,1,0} ^T ，{0,1,0,0,0,0,0,0,1,0,0,1} ^T ，{0,1,1,0,0,0,0,0,0,1,0,0} ^T ，{0,0,1,0,1,1,1,0,1,0,1,0} ^T ，{1,1,1,0,1,1,1,1,0,0,0,0} ^T ，{0,1,1,1,0,0,1,0,0,1,1,1} ^T ，{0,1,1,1,1,0,1,1,1, 0,0,1} ^T ，{0,1,0,0,1,0,0,1,0,1,1,1} ^T ，{0,1,0,1,0,0,1,0,1,0,1,0} ^T ，{0,0,1,1,0,1,0,1,0,1,1,1} ^T ，{1,0,0,0,1,0,0,0,1,1,0,1} ^T ，{1,1,0,0,1,1,1,1,1,1,0,1} ^T ，{0,0,1,1,1,1,0,0,1,0,0,1} ^T ，{1,0,1,1,1,0,1,0,0,0,1,1} ^T ，{1,1,0,1,0,1,0,0,0,0,0,0} ^T ，{0,1,0,1,1,0,1,1,0,1,0,0} ^T ，

wherein { } ^T The representation vector is transposed.
The method according to any one of claim 1 to 4, wherein,

when l=24, the first set of sequences includes some or all of the following sequences:

{1,0,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,1,1,1,1,0} ^T ，{0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,0,0,1} ^T ，{1,0,0,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1} ^T ，{1,0,0,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0} ^T ，{1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,1,1,1} ^T ，{0,0,1,1,0,0,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,0} ^T ，{1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0,1,1,0,1,0} ^T ，{0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1} ^T ，{0,0,1,0,1,1,0,1,1,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1} ^T ，{1,1,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,1,0,0,0} ^T ，{0,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,1,1,0,0,1,0} ^T ，{1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,0} ^T ，{0,0,0,0,1,0,1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1,1,0} ^T ，{1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,1,1,1,1} ^T ，{1,0,1,1,1,1,1,0,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0,0} ^T ，{1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1} ^T ，{0,1,1,0,1,0,1,0,1,1,1,0,0,1,0,1,0,0,1,1,0,1,0,1} ^T ，{0,1,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0} ^T ，{0,0,1,1,1,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,0,0,1,1,1,1,0,1,1,1,0,0,1,1,0,0,1,0,0} ^T ，{0,1,1,1,1,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1} ^T ，{0,1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0} ^T ，{0,1,1,0,0,0,0,1,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,0} ^T ，{0,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,0} ^T ，{0,0,0,1,0,1,1,0,0,1,1,1,1,1,0,1,0,1,1,0,1,1,1,1} ^T ，{1,0,1,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,1,1,1,0,1} ^T ，{1,1,0,0,1,0,0,1,0,1,0,1,1,0,1,1,0,1,1,1,1,0,0,1} ^T ，{0,0,0,1,1,1,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0} ^T ，{1,0,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,0,0,0,1,1} ^T ，{1,1,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,1} ^T ，{0,1,0,0,0,1,1,1,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1} ^T ，

wherein { } ^T The representation vector is transposed.
A method of information transmission, comprising:

the network equipment receives the signal;

and performing sequence detection on the signal according to a first sequence set to obtain at least one sequence, wherein the first sequence set comprises W sequences with the length of L, L is less than W, L and W are positive integers, the W sequences comprise the at least one sequence, and each column in the first sequence set is related in pairs.
The method of claim 8, wherein the first set of sequences is a set of sequences having a minimum maximum cross-correlation value corresponding to at least one second set of sequences, the second set of sequences comprising W sequences of length L, the maximum cross-correlation value being a maximum of correlation values between every two sequences in one set of sequences.
The method of claim 9, wherein the first set of sequences is a set of sequences with a minimum maximum cross-correlation value of the at least one second set of sequences, and the corresponding normalized correlation matrix is a normalized matrix of autocorrelation matrices of a set of sequences with a minimum number of occurrences of the minimum maximum cross-correlation value.
The method according to claim 9 or 10, wherein the second set of sequences is W sequences of length L in a third set of sequences, the third set of sequences comprising X sequences of length Y, X being ≡w, Y being ≡w, the range of the maximum cross-correlation value of the third set of sequences being determined according to the number W of sequences comprised by the second set of sequences.
The method according to any one of claims 8 to 11, wherein,

When l=6, the first set of sequences includes some or all of the following sequences:

{1,1,1,0,0,1} ^T ，{0,0,1,1,1,1} ^T ，{1,0,0,0,1,1} ^T ，{1,1,1,0,1,0} ^T ，{1,0,0,0,0,1} ^T ，{1,1,1,1,1,1} ^T ，{0,0,1,1,1,0} ^T ，{1,1,1,1,1,0} ^T ，{0,1,0,0,1,1} ^T ，{0,1,0,1,1,1} ^T ，{1,0,0,1,0,1} ^T ，{0,0,1,1,0,1} ^T ，{1,0,0,1,1,0} ^T ，{0,1,0,1,0,1} ^T ，{1,0,0,0,0,0} ^T ，{1,0,0,0,1,0} ^T ，{1,0,0,1,1,1} ^T ，{0,0,1,0,0,0} ^T ，{0,0,1,0,0,1} ^T ，{0,1,0,1,0,0} ^T ，{1,1,1,1,0,1} ^T ，{0,0,1,0,1,1} ^T ，{0,1,0,0,0,1} ^T ，{0,1,0,0,1,0} ^T ，{0,0,1,0,1,0} ^T ，{0,0,1,1,0,0} ^T ，{1,1,1,0,1,1} ^T ，{1,1,1,1,0,0} ^T ，{0,1,0,1,1,0} ^T ，{1,1,1,0,0,0} ^T ，{1,0,0,1,0,0} ^T ，{0,1,0,0,0,0} ^T ，

wherein { } ^T The representation vector is transposed.
The method according to any one of claims 8 to 11, wherein,

when l=12, the first set of sequences includes some or all of the following sequences:

{1,0,0,1,1,0,1,0,1,1,1,0} ^T ，{0,0,0,0,0,1,1,1,1,0,0,1} ^T ，{1,0,0,1,0,0,1,1,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,0,0} ^T ，{1,0,0,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,1,0,1,0,0,1,1} ^T ，{0,0,1,0,0,1,1,1,0,1,0,0} ^T ，{1,1,0,1,1,1,0,1,1,1,1,0} ^T ，{0,1,1,0,1,0,0,1,1,0,1,0} ^T ，{0,0,0,1,1,1,0,0,0,1,0,0} ^T ，{1,1,1,1,0,1,0,0,1,1,0,1} ^T ，{0,0,0,1,0,1,0,1,1,0,1,0} ^T ，{1,1,0,0,0,1,1,0,0,0,1,1} ^T ，{0,0,0,0,1,1,1,0,0,1,1,1} ^T ，{1,0,1,0,0,0,0,1,1,1,1,0} ^T ，{1,0,1,1,0,0,1,1,1,1,0,1} ^T ，{1,1,1,0,0,1,1,0,1,1,1,0} ^T ，{0,1,0,0,0,0,0,0,1,0,0,1} ^T ，{0,1,1,0,0,0,0,0,0,1,0,0} ^T ，{0,0,1,0,1,1,1,0,1,0,1,0} ^T ，{1,1,1,0,1,1,1,1,0,0,0,0} ^T ，{0,1,1,1,0,0,1,0,0,1,1,1} ^T ，{0,1,1,1,1,0,1,1,1,0,0,1} ^T ，{0,1,0,0,1,0,0,1,0,1,1,1} ^T ，{0,1,0,1,0,0,1,0,1,0,1,0} ^T ，{0,0,1,1,0,1,0,1,0,1,1,1} ^T ，{1,0,0,0,1,0,0,0,1,1,0,1} ^T ，{1,1,0,0,1,1,1,1,1,1,0,1} ^T ，{0,0,1,1,1,1,0,0,1,0,0,1} ^T ，{1,0,1,1,1,0,1,0,0,0,1,1} ^T ，{1,1,0,1,0,1,0,0,0,0,0,0} ^T ，{0,1,0,1,1,0,1,1,0,1,0,0} ^T ，

wherein { } ^T The representation vector is transposed.
The method according to any one of claims 8 to 11, wherein,

when l=24, the first set of sequences includes some or all of the following sequences:

{1,0,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,1,1,1,1,0} ^T ，{0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,0,0,1} ^T ，{1,0,0,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1} ^T ，{1,0,0,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0} ^T ，{1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,1,1,1} ^T ，{0,0,1,1,0,0,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,0} ^T ，{1,1,1,0,1,1,1,1,0,1,0, 0,1,1,0,1,1,1,0,1,1,0,1,0} ^T ，{0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1} ^T ，{0,0,1,0,1,1,0,1,1,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1} ^T ，{1,1,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,1,0,0,0} ^T ，{0,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,1,1,0,0,1,0} ^T ，{1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,0} ^T ，{0,0,0,0,1,0,1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1,1,0} ^T ，{1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,1,1,1,1} ^T ，{1,0,1,1,1,1,1,0,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0,0} ^T ，{1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1} ^T ，{0,1,1,0,1,0,1,0,1,1,1,0,0,1,0,1,0,0,1,1,0,1,0,1} ^T ，{0,1,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0} ^T ，{0,0,1,1,1,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,0,0,1,1,1,1,0,1,1,1,0,0,1,1,0,0,1,0,0} ^T ，{0,1,1,1,1,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1} ^T ，{0,1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0} ^T ，{0,1,1,0,0,0,0,1,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,0} ^T ，{0,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,0} ^T ，{0,0,0,1,0,1,1,0,0,1,1,1,1,1,0,1,0,1,1,0,1,1,1,1} ^T ，{1,0,1,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,1,1,1,0,1} ^T ，{1,1,0,0,1,0,0,1,0,1,0,1,1,0,1,1,0,1,1,1,1,0,0,1} ^T ，{0,0,0,1,1,1,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0} ^T ，{1,0,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,0,0,0,1,1} ^T ，{1,1,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,1} ^T ，{0,1,0,0,0,1,1,1,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1} ^T ，

wherein { } ^T The representation vector is transposed.
An apparatus for information transmission, comprising:

the processing module is used for determining a first sequence to be sent, wherein the first sequence belongs to a first sequence set, the first sequence set comprises W sequences with the length of L, L is less than W, L and W are positive integers, and the sequences in the first sequence set are related in pairs;

and the receiving and transmitting module is used for transmitting the first sequence to the network equipment.
The apparatus of claim 15, wherein the first set of sequences is a set of sequences having a smallest maximum cross-correlation value of at least one second set of sequences, the second set of sequences comprising W sequences of length L, the largest cross-correlation value being a largest of correlation values between every two sequences in one set of sequences.
The apparatus of claim 16, wherein the first set of sequences is a set of sequences having a smallest maximum cross-correlation value of the at least one second set of sequences, and wherein the smallest number of times the smallest maximum cross-correlation value occurs in a corresponding normalized correlation matrix, the normalized correlation matrix being a normalized matrix of autocorrelation matrices of one set of sequences.
The apparatus of claim 16 or 17, wherein the second set of sequences is a set of W sequences of length L in a third set of sequences, the third set of sequences comprising X sequences of length Y, x≡w, the range of maximum cross-correlation values for the third set of sequences being determined from the number W of sequences comprised by the second set of sequences.
The device according to any one of claims 15 to 18, wherein,

when l=6, the first set of sequences includes some or all of the following sequences:

{1,1,1,0,0,1} ^T ，{0,0,1,1,1,1} ^T ，{1,0,0,0,1,1} ^T ，{1,1,1,0,1,0} ^T ，{1,0,0,0,0,1} ^T ，{1,1,1,1,1,1} ^T ，{0,0,1,1,1,0} ^T ，{1,1,1,1,1,0} ^T ，{0,1,0,0,1,1} ^T ，{0,1,0,1,1,1} ^T ，{1,0,0,1,0,1} ^T ，{0,0,1,1,0,1} ^T ，{1,0,0,1,1,0} ^T ，{0,1,0,1,0,1} ^T ，{1,0,0,0,0, 0} ^T ，{1,0,0,0,1,0} ^T ，{1,0,0,1,1,1} ^T ，{0,0,1,0,0,0} ^T ，{0,0,1,0,0,1} ^T ，{0,1,0,1,0,0} ^T ，{1,1,1,1,0,1} ^T ，{0,0,1,0,1,1} ^T ，{0,1,0,0,0,1} ^T ，{0,1,0,0,1,0} ^T ，{0,0,1,0,1,0} ^T ，{0,0,1,1,0,0} ^T ，{1,1,1,0,1,1} ^T ，{1,1,1,1,0,0} ^T ，{0,1,0,1,1,0} ^T ，{1,1,1,0,0,0} ^T ，{1,0,0,1,0,0} ^T ，{0,1,0,0,0,0} ^T ，

wherein { } ^T The representation vector is transposed.
The device according to any one of claims 15 to 18, wherein,

when l=12, the first set of sequences includes some or all of the following sequences:

{1,0,0,1,1,0,1,0,1,1,1,0} ^T ，{0,0,0,0,0,1,1,1,1,0,0,1} ^T ，{1,0,0,1,0,0,1,1,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,0,0} ^T ，{1,0,0,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,1,0,1,0,0,1,1} ^T ，{0,0,1,0,0,1,1,1,0,1,0,0} ^T ，{1,1,0,1,1,1,0,1,1,1,1,0} ^T ，{0,1,1,0,1,0,0,1,1,0,1,0} ^T ，{0,0,0,1,1,1,0,0,0,1,0,0} ^T ，{1,1,1,1,0,1,0,0,1,1,0,1} ^T ，{0,0,0,1,0,1,0,1,1,0,1,0} ^T ，{1,1,0,0,0,1,1,0,0,0,1,1} ^T ，{0,0,0,0,1,1,1,0,0,1,1,1} ^T ，{1,0,1,0,0,0,0,1,1,1,1,0} ^T ，{1,0,1,1,0,0,1,1,1,1,0,1} ^T ，{1,1,1,0,0,1,1,0,1,1,1,0} ^T ，{0,1,0,0,0,0,0,0,1,0,0,1} ^T ，{0,1,1,0,0,0,0,0,0,1,0,0} ^T ，{0,0,1,0,1,1,1,0,1,0,1,0} ^T ，{1,1,1,0,1,1,1,1,0,0,0,0} ^T ，{0,1,1,1,0,0,1,0,0,1,1,1} ^T ，{0,1,1,1,1,0,1,1,1,0,0,1} ^T ，{0,1,0,0,1,0,0,1,0,1,1,1} ^T ，{0,1,0,1,0,0,1,0,1,0,1,0} ^T ，{0,0,1,1,0,1,0,1,0,1,1,1} ^T ，{1,0,0,0,1,0,0,0,1,1,0,1} ^T ，{1,1,0,0,1,1,1,1,1,1,0,1} ^T ，{0,0,1,1,1,1,0,0,1,0,0,1} ^T ，{1,0,1,1,1,0,1,0,0,0,1,1} ^T ，{1,1,0,1,0,1,0,0,0,0,0,0} ^T ，{0,1,0,1,1,0,1,1,0,1,0,0} ^T ，

Wherein { } ^T The representation vector is transposed.
The device according to any one of claims 15 to 18, wherein,

when l=24, the first set of sequences includes some or all of the following sequences:

{1,0,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,1,1,1,1,0} ^T ，{0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,0,0,1} ^T ，{1,0,0,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1} ^T ，{1,0,0,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0} ^T ，{1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,1,1,1} ^T ，{0,0,1,1,0,0,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,0} ^T ，{1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0,1,1,0,1,0} ^T ，{0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1} ^T ，{0,0,1,0,1,1,0,1,1,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1} ^T ，{1,1,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,1,0,0,0} ^T ，{0,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,1,1,0,0,1,0} ^T ，{1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,0} ^T ，{0,0,0,0,1,0,1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1,1,0} ^T ，{1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,1,1,1,1} ^T ，{1,0,1,1,1,1,1,0,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0,0} ^T ，{1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1} ^T ，{0,1,1,0,1,0,1,0,1,1,1,0,0,1,0,1,0,0,1,1,0,1,0,1} ^T ，{0,1,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0} ^T ，{0,0,1,1,1,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,0,0,1,1,1,1,0,1,1,1,0,0,1,1,0,0,1,0,0} ^T ，{0,1,1,1,1,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1} ^T ，{0,1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0} ^T ，{0,1,1,0,0,0,0,1,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,0} ^T ，{0,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,0} ^T ，{0,0,0,1,0,1,1,0,0,1,1, 1,1,1,0,1,0,1,1,0,1,1,1,1} ^T ，{1,0,1,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,1,1,1,0,1} ^T ，{1,1,0,0,1,0,0,1,0,1,0,1,1,0,1,1,0,1,1,1,1,0,0,1} ^T ，{0,0,0,1,1,1,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0} ^T ，{1,0,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,0,0,0,1,1} ^T ，{1,1,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,1} ^T ，{0,1,0,0,0,1,1,1,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1} ^T ，

wherein { } ^T The representation vector is transposed.
An apparatus for information transmission, comprising:

the receiving and transmitting module is used for receiving signals;

the processing module is used for carrying out sequence detection on the signals according to a first sequence set to obtain at least one sequence, the first sequence set comprises W sequences with the length of L, L is smaller than W, L and W are positive integers, the W sequences comprise the at least one sequence, and each sequence in the first sequence set is related in pairs.
The apparatus of claim 22, wherein the first set of sequences is a corresponding set of sequences having a minimum maximum cross-correlation value of at least one second set of sequences, the second set of sequences comprising W sequences of length L, the maximum cross-correlation value being a maximum of correlation values between every two sequences in one set of sequences.
The apparatus of claim 23, wherein the first set of sequences is a set of sequences with a minimum cross-correlation value of a maximum of the at least one second set of sequences, and wherein the minimum number of times the minimum maximum cross-correlation value occurs in a corresponding normalized correlation matrix, the normalized correlation matrix being a normalized matrix of autocorrelation matrices of a set of sequences.
The apparatus of claim 23 or 24, wherein the second set of sequences is a set of W sequences of length L in a third set of sequences, the third set of sequences comprising X sequences of length Y, x≡w, y≡w, the range of the maximum cross-correlation value of the third set of sequences being determined from the number of sequences W comprised by the second set of sequences.
The device according to any one of claims 22 to 25, wherein,

when l=6, the first set of sequences includes some or all of the following sequences:

{1,1,1,0,0,1} ^T ，{0,0,1,1,1,1} ^T ，{1,0,0,0,1,1} ^T ，{1,1,1,0,1,0} ^T ，{1,0,0,0,0,1} ^T ，{1,1,1,1,1,1} ^T ，{0,0,1,1,1,0} ^T ，{1,1,1,1,1,0} ^T ，{0,1,0,0,1,1} ^T ，{0,1,0,1,1,1} ^T ，{1,0,0,1,0,1} ^T ，{0,0,1,1,0,1} ^T ，{1,0,0,1,1,0} ^T ，{0,1,0,1,0,1} ^T ，{1,0,0,0,0,0} ^T ，{1,0,0,0,1,0} ^T ，{1,0,0,1,1,1} ^T ，{0,0,1,0,0,0} ^T ，{0,0,1,0,0,1} ^T ，{0,1,0,1,0,0} ^T ，{1,1,1,1,0,1} ^T ，{0,0,1,0,1,1} ^T ，{0,1,0,0,0,1} ^T ，{0,1,0,0,1,0} ^T ，{0,0,1,0,1,0} ^T ，{0,0,1,1,0,0} ^T ，{1,1,1,0,1,1} ^T ，{1,1,1,1,0,0} ^T ，{0,1,0,1,1,0} ^T ，{1,1,1,0,0,0} ^T ，{1,0,0,1,0,0} ^T ，{0,1,0,0,0,0} ^T ，

wherein { } ^T The representation vector is transposed.
The device according to any one of claims 22 to 25, wherein,

when l=12, the first set of sequences includes some or all of the following sequences:

{1,0,0,1,1,0,1,0,1,1,1,0} ^T ，{0,0,0,0,0,1,1,1,1,0,0,1} ^T ，{1,0,0,1,0,0,1,1,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,0,0} ^T ，{1,0,0,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,1,0,1,0,0,1,1} ^T ，{0,0,1,0,0,1,1,1,0,1,0,0} ^T ，{1,1,0,1,1,1,0,1,1,1,1,0} ^T ，{0,1,1,0,1,0,0,1,1,0,1,0} ^T ，{0,0,0,1,1,1,0,0,0,1,0,0} ^T ，{1,1,1,1,0,1,0,0,1,1,0,1} ^T ，{0,0,0,1,0,1,0,1,1,0,1,0} ^T ，{1,1,0,0,0,1,1,0,0,0,1,1} ^T ，{0,0,0,0,1,1,1,0,0,1,1,1} ^T ，{1, 0,1,0,0,0,0,1,1,1,1,0} ^T ，{1,0,1,1,0,0,1,1,1,1,0,1} ^T ，{1,1,1,0,0,1,1,0,1,1,1,0} ^T ，{0,1,0,0,0,0,0,0,1,0,0,1} ^T ，{0,1,1,0,0,0,0,0,0,1,0,0} ^T ，{0,0,1,0,1,1,1,0,1,0,1,0} ^T ，{1,1,1,0,1,1,1,1,0,0,0,0} ^T ，{0,1,1,1,0,0,1,0,0,1,1,1} ^T ，{0,1,1,1,1,0,1,1,1,0,0,1} ^T ，{0,1,0,0,1,0,0,1,0,1,1,1} ^T ，{0,1,0,1,0,0,1,0,1,0,1,0} ^T ，{0,0,1,1,0,1,0,1,0,1,1,1} ^T ，{1,0,0,0,1,0,0,0,1,1,0,1} ^T ，{1,1,0,0,1,1,1,1,1,1,0,1} ^T ，{0,0,1,1,1,1,0,0,1,0,0,1} ^T ，{1,0,1,1,1,0,1,0,0,0,1,1} ^T ，{1,1,0,1,0,1,0,0,0,0,0,0} ^T ，{0,1,0,1,1,0,1,1,0,1,0,0} ^T ，

wherein { } ^T The representation vector is transposed.
The device according to any one of claims 22 to 25, wherein,

when l=24, the first set of sequences includes some or all of the following sequences:

{1,0,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,1,1,1,1,0} ^T ，{0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,0,0,1} ^T ，{1,0,0,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1} ^T ，{1,0,0,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0} ^T ，{1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0} ^T ，{1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,1,1,1} ^T ，{0,0,1,1,0,0,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,0} ^T ，{1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0,1,1,0,1,0} ^T ，{0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1} ^T ，{0,0,1,0,1,1,0,1,1,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1} ^T ，{1,1,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,1,0,0,0} ^T ，{0,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,1,1,0,0,1,0} ^T ，{1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,0} ^T ，{0,0,0,0,1,0,1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1,1,0} ^T ，{1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,1,1,1,1} ^T ，{1,0,1,1,1,1,1,0,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0,0} ^T ，{1,1,1,1,0,0,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,1} ^T ，{0,1,1,0,1,0,1,0,1,1,1,0,0,1,0,1,0,0,1,1,0,1,0,1} ^T ，{0,1,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0} ^T ，{0,0,1,1,1,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,0,1,1} ^T ，{1,1,1,1,1,0,0,1,1,1,1,0,1,1,1,0,0,1,1,0,0,1,0,0} ^T ，{0,1,1,1,1,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1} ^T ，{0,1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0} ^T ，{0,1,1,0,0,0,0,1,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,0} ^T ，{0,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,0} ^T ，{0,0,0,1,0,1,1,0,0,1,1,1,1,1,0,1,0,1,1,0,1,1,1,1} ^T ，{1,0,1,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,1,1,1,0,1} ^T ，{1,1,0,0,1,0,0,1,0,1,0,1,1,0,1,1,0,1,1,1,1,0,0,1} ^T ，{0,0,0,1,1,1,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,0} ^T ，{1,0,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,0,0,0,1,1} ^T ，{1,1,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,1} ^T ，{0,1,0,0,0,1,1,1,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1} ^T ，

wherein { } ^T The representation vector is transposed.
A communication device, comprising:

a processor and a memory;

the memory is used for storing a computer program;

The processor for executing a computer program stored in the memory to cause the communication device to perform the method of any one of claims 1 to 7 or to perform the method of any one of claims 8 to 14.
A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when run on a computer, causes the computer to perform the method according to any of claims 1 to 7 or to perform the method according to any of claims 8 to 14.