CN110011775B - Method and system for jointly realizing active user detection and channel estimation - Google Patents
Method and system for jointly realizing active user detection and channel estimation Download PDFInfo
- Publication number
- CN110011775B CN110011775B CN201910221801.3A CN201910221801A CN110011775B CN 110011775 B CN110011775 B CN 110011775B CN 201910221801 A CN201910221801 A CN 201910221801A CN 110011775 B CN110011775 B CN 110011775B
- Authority
- CN
- China
- Prior art keywords
- active user
- equipment
- machine type
- active
- base station
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000001514 detection method Methods 0.000 title claims abstract description 24
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 29
- 238000004891 communication Methods 0.000 claims abstract description 29
- 230000004044 response Effects 0.000 claims description 14
- 230000008602 contraction Effects 0.000 claims description 5
- 239000011159 matrix material Substances 0.000 claims description 5
- 238000013475 authorization Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001413 cellular effect Effects 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/0202—Channel estimation
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/0202—Channel estimation
- H04L25/0224—Channel estimation using sounding signals
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L5/00—Arrangements affording multiple use of the transmission path
- H04L5/003—Arrangements for allocating sub-channels of the transmission path
- H04L5/0048—Allocation of pilot signals, i.e. of signals known to the receiver
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W74/00—Wireless channel access
- H04W74/08—Non-scheduled access, e.g. ALOHA
- H04W74/0833—Random access procedures, e.g. with 4-step access
Landscapes
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Power Engineering (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a method and a system for jointly realizing active user detection and channel estimation thereof, wherein the method for detecting active users in mass machine communication comprises the following steps: active user detection and active user channel estimation are modeled into sparse signal reconstruction in a compressive sensing theory, and an SLO reconstruction algorithm is used for estimating active users according to user channel states. The method provided by the invention can reduce the length of the pilot signal and can obtain the channel estimation performance superior to the least square method.
Description
Technical Field
The invention relates to an active user detection and channel estimation method in a Machine Type Communication (MTC) scene.
Background
With the increasing development of applications of internet of things such as smart home, smart transportation, smart medical treatment and the like, a communication network capable of providing interconnection and intercommunication for machine type equipment is urgently needed to be established at present. To achieve this goal, fifth generation mobile communication systems (5G) have identified massive machine-type communication, enhanced mobile broadband, and ultra-reliable low-latency communication as three services that need to be supported. Because of the large number of machines, 5G has a great challenge in providing timely network access and efficient data transmission for a large number of machines.
The cellular network is to let users compete for the transmission resource of the physical layer through the specialized random access control channel and adopt the authorized way at present, usually include four stages, first, every active user extracts a pilot signal and sends to the base transceiver station from the orthogonal pilot signal pool at random, notify the base transceiver station that there is data that needs to send this user; then, the base station sends a response signal for each received pilot signal, and authorization can continue to transmit information; then, the user receiving the authorization signal continues to send a connection request to the base station; and finally, the base station allocates resources for transmitting data to the user without pilot frequency conflict and authorizes the user to access the network, but does not respond to the user with pilot frequency conflict, and the user with access failure needs to wait for a time and then contend for access to the network again. This traditional authorized access scheme is not suitable for massive machine-type communication because: firstly, the coherence time and coherence bandwidth of the channel are limited, which determines that the length of the pilot signal is limited, so that the number of the pilot signals in the orthogonal pilot signal pool is limited, and in the face of massive access, the pilot collision rate is high, and the time consumption of equipment accessing the network is prolonged. Secondly, machine type communication is characterized in that burst type short data packet communication is performed, only a few bits of data information need to be sent sometimes, and if a traditional authorization type access scheme is adopted, the time occupied by an access phase far exceeds the time of a data transmission phase, so that the overall efficiency of a system is low. Therefore, massive machine-type communication cannot adopt the traditional authorized access scheme, and a non-orthogonal and authorization-free random access scheme needs to be developed. Each machine type device in the non-orthogonal and non-authorized access scheme arranges a fixed pilot signal, and if the machine type device has data to be sent at a certain moment, the device immediately and directly sends the pilot signal and the data information, so that pilot collision and long-time authorized access application can be avoided.
Disclosure of Invention
The invention aims to provide a method for jointly realizing active user detection and channel estimation thereof in mass machine type communication, which solves the problem of long access time in the prior authorized access technology.
In order to achieve the above object, the present invention provides a method for detecting an active user in mass machine communication, including:
active user detection and active user channel estimation are modeled as sparse signal reconstruction in a compressed sensing theory, and an SLO (smooth L0 norm algorithm) reconstruction algorithm is applied to estimate active users according to user channel states.
Preferably, before modeling active user detection and active user channel estimation as sparse signal reconstruction in compressed sensing theory, comprising:
and randomly accessing a huge amount of machine type equipment to the base station in an unauthorized non-orthogonal mode.
Preferably, randomly accessing a huge amount of machine-type devices to the base station in an unlicensed non-orthogonal manner, includes:
directly sending pilot signals and data information of massive machine equipment to the base station; and each huge machine type device in the huge machine type devices is distributed with a pilot frequency sequence with a preset length, and the sequences do not need to satisfy the orthogonal relation.
Preferably, modeling active user detection and its channel estimation as sparse signal reconstruction in compressed sensing theory comprises:
a certain time slot, if there are K machines in K equipmentaIf a huge amount of machine equipment is in an active state, the base station receives the KaThe pilot signal from each active user can be expressed as:
wherein p (k) represents the number of the kth active user in all massive machine type equipment in the system; the base station and the massive machinery equipment are both provided with single antennas; h isp(k)Is the p (k) th massive machine-to-base station channel response; x is the number ofp(k)＝[xp(k),1,xp(k),2,…，xp(k),N]TIs the pilot sequence sent by the p (k) th huge machine type equipment, w is the mean value of 0 and the variance is sigma2Gaussian noise of (2);
when K isaWhen a huge amount of machine equipment is in an active state, K-KaThe huge machine type equipment is in a dormant state; a huge amount of machine type devices in a dormant state have their communication links not activated, and thus their channel responses are zero; if all the huge machines are consideredAlternatively, the received pilot signal may be equivalently represented by the following formula
Wherein h is a sparse vector containing K-KaElement of zero, KaA number of non-zero elements, and the position of the non-zero element in h corresponds to the number p (k) of the active user.
Preferably, the active users are estimated according to the user channel states by using an SLO (smoothed L0 norm algorithm) reconstruction algorithm, which includes:
step 121, inputting the observation signal y, the observation matrix X and the threshold value sigmaminA contraction factor rho, a step size mu and an iteration number L;
step 122, let
Wherein, the superscript symbol number
Representing a pseudo-inverse operation;
step 123, if σ is>σminSequentially executing (I) and (II); otherwise, the process proceeds to step 124,
(i) starting from the initial solution on the set of feasible solutions { h | y ═ Xh }
Starting with the L iterative steepest descent algorithm to maximize the objective function
(a) Setting the element value of the vector delta to
(b) Order to
Then pass through
Will be provided with
Projecting onto its feasible solution set;
(II) making σ ← ρ σ, and returning to step 123;
step 124, calculate
And find KaThe position serial number of the maximum element value, and the KaStoring the position serial number into the set I, and outputting the detected active user I and the active user channel state information
The invention also provides a system for detecting the active users in the mass machine type communication, which comprises:
active user detection and active user channel estimation are modeled as sparse signal reconstruction in a compressed sensing theory, and SLO (smooth L0 norm algorithm) reconstruction algorithm is applied to estimate equipment of active users according to user channel states.
Preferably, the method comprises the following steps:
and randomly accessing a huge amount of machine type equipment to the equipment of the base station in an unauthorized non-orthogonal mode.
Preferably, the device for randomly accessing a huge amount of machine type devices to the base station in an authorization-free non-orthogonal manner includes:
directly transmitting pilot signals and data information of massive machine equipment to the equipment of the base station; and each huge machine type device in the huge machine type devices is distributed with a pilot frequency sequence with a preset length, and the sequences do not need to satisfy the devices of an orthogonal relation.
Preferably, the apparatus for modeling active user detection and its channel estimation as sparse signal reconstruction in compressed sensing theory comprises:
a certain time slot, if there are K machines in K equipmentaIf a huge amount of machine equipment is in an active state, the base station receives the KaThe pilot signal from each active user can be expressed as:
wherein p (k) represents the number of the kth active user in all massive machine type equipment in the system; the base station and the massive machinery equipment are both provided with single antennas; h isp(k)Is the p (k) th massive machine-to-base station channel response; x is the number ofp(k)＝[xp(k),1,xp(k),2,…,xp(k),N]TIs the pilot sequence sent by the p (k) th huge machine type equipment, w is the mean value of 0 and the variance is sigma2Gaussian noise of (2);
when K isaWhen a huge amount of machine equipment is in an active state, K-KaThe huge machine type equipment is in a dormant state; a huge amount of machine type devices in a dormant state have their communication links not activated, and thus their channel responses are zero; if all massive machine-type devices are considered, the received pilot signal may be equivalently represented by the following formula
Wherein h is a sparse vector containing K-KaElement of zero, KaA number of non-zero elements, and the position of the non-zero element in h corresponds to the number p (k) of the active user.
Preferably, the apparatus for estimating active users according to user channel states by using SLO (smoothed L0 norm algorithm) reconstruction algorithm includes:
input device for inputting observation signal y, observation matrix X, threshold value sigmaminA contraction factor rho, a step size mu and an iteration number L;
order to
Wherein, the superscript symbol number
Representing a pseudo-inverse operation;
judging device, if σ>σminSequentially executing (I) and (II); otherwise, the output device is executed to work,
(i) starting from the initial solution on the set of feasible solutions { h | y ═ Xh }
Starting with the L iterative steepest descent algorithm to maximize the objective function
(a) Setting the element value of the vector delta to
(b) Order to
Then pass through
Will be provided with
Projecting onto its feasible solution set;
(II) enabling sigma ← rho sigma and returning to the judging device;
output device, calculate
And find KaThe position serial number of the maximum element value, and the KaStoring the position serial number into the set I, and outputting the detected active user I and the active user channel state information
Compared with the prior art, the method for detecting the active user and estimating the channel thereof provided by the invention uses the compressed sensing reconstruction algorithm SL0, and can greatly reduce the length of the pilot signal required by channel estimation. The method is low in calculation complexity and easy to implement.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart illustrating the steps of the method for jointly implementing active user detection and channel estimation thereof in mass machine type communication according to the present invention;
FIG. 2 is a comparison graph of the detection of active users and their channel estimation (labeled as the method provided by the present invention) by applying SL0 compressive sensing in combination with the least square method for detecting the accuracy of active users;
FIG. 3 is a normalized mean square error plot for estimating channel state information of active users using the method and least squares method of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
The invention provides a method for detecting active users in mass machine type communication, which comprises the following steps:
active user detection and active user channel estimation are modeled as sparse signal reconstruction in a compressed sensing theory, and an SLO (smooth L0 norm algorithm) reconstruction algorithm is applied to estimate active users according to user channel states.
Compared with the prior art, the method for detecting the active user and estimating the channel thereof provided by the invention uses the compressed sensing reconstruction algorithm SL0, and can greatly reduce the length of the pilot signal required by channel estimation. The method is low in calculation complexity and easy to implement.
For a better understanding of the contents of the embodiments of the present invention, a system model of the embodiments of the present invention will be described in detail first. A machine type device communication scene is considered, the machine type device communication scene comprises a base station and K machine type devices, the base station and the machine type devices are respectively provided with a single antenna, and a non-orthogonal and authorization-free multi-user access protocol is adopted. Suppose there is K within a certain coherence timeaIf a machine type device is transmitting a pilot signal to a base station, the pilot signal received by the base station can be represented as
Wherein p (k) represents the number of the k-th active user in all the machine type devices in the system; h isp(k)Is the channel response from the machine type equipment with the sequence number P (k) to the base station; x is the number ofp(k)＝[xp(k),1,xp(k),2,…,xp(k),N]TIs the pilot sequence sent by the p (k) th machine type equipment, w is the mean value of 0 and the variance is sigma2Gaussian noise.
The embodiment of the invention discloses a method for jointly realizing active user detection and channel estimation in mass machine communication, which mainly comprises the following steps:
step one, a huge amount of machine type equipment randomly accesses a system in an unauthorized non-orthogonal mode. Any machine type device is unauthorized to access the base station, that is, any active user (the device with data to be transmitted is called the active user) can directly transmit pilot signals and data information to the base station, an access application is not required to be transmitted to the base station in advance, and the pilot signals and the data information can be transmitted only after an instruction of access approval is received. In addition, each device is assigned a pilot sequence of length N, which does not require an orthogonal relationship to be satisfied.
Step twoAnd modeling active user detection and channel estimation thereof as sparse signal reconstruction in a compressive sensing theory. The system has K pieces of machine equipment in total, and K is set in a certain time periodaIf one machine is in active state, the rest K-KaThe machine class device is in a dormant state. The machine type device in the sleep state has no active communication link, and thus has zero channel response. If all the machines in the system are considered, the equation (1) can be equivalently expressed as
Wherein h is a sparse vector containing K-KaElement of zero, KaAnd the positions of the nonzero elements in h correspond to p (k) in the formula (1).
And step three, jointly detecting the active users and estimating the channel state information of the active users by using an SL0 compressive sensing reconstruction algorithm. According to the linear equation y ═ Xh + w in (2), the specific steps provided by the present invention for detecting active users and estimating their channel responses using the SL0 algorithm can be summarized as follows:
the present invention also provides a most preferred embodiment, the method comprising:
step 11, a large amount of machine equipment randomly accesses to a system in an unauthorized non-orthogonal mode;
step 12, modeling active user detection and channel estimation thereof as sparse signal reconstruction in the compressed sensing theory, and applying SL0(smoothed l)0Norm algorithm) reconstruction algorithm jointly detects and estimates active users and their channel state information.
It is further preferred that, in step 11,
any machine type device is unauthorized to access the base station, that is, any active user (the device with data to be transmitted is called the active user) can directly transmit pilot signals and data information to the base station, an access application is not required to be transmitted to the base station in advance, and the pilot signals and the data information can be transmitted only after an instruction of access approval is received. In addition, each device is assigned a pilot sequence of length N, which does not require an orthogonal relationship to be satisfied.
Further preferably, in step 12, modeling the active user detection and its channel estimation as a sparse signal reconstruction method in the compressive sensing theory includes:
a certain time slot, if the system has K in commonaIf the machine equipment is in active state, the base station receives the KaThe pilot signal from each active user can be expressed as:
wherein p (k) represents the number of the k-th active user in all the machine type devices in the system; the base station and the machine equipment are both provided with a single antenna hp(k)Is the channel response of the p (k) th machine type device to the base station; x is the number ofp(k)＝[xp(k),1,xp(k),2,…,xp(k),N]TIs the pilot sequence sent by the p (k) th machine type equipment, w is the mean value of 0 and the variance is sigma2Gaussian noise.
The system has K pieces of machine equipment in total, and K is set in a certain time periodaIf one machine is in active state, the rest K-KaThe machine class device is in a dormant state. The machine type device in the sleep state has no active communication link, and thus has zero channel response. If all the machine type devices in the system are considered, the received pilot signal can be equivalently expressed as
Wherein h is a sparse vector containing K-KaElement of zero, KaAnd the position of the non-zero element in h corresponds to the number p (k) of the active user in the system.
Further preferably, in step 12, the method for detecting active users and estimating their channel state information by using the SL0 reconstruction algorithm includes:
step 121, inputting the observation signal y, the observation matrix X and the threshold value sigmaminA contraction factor rho, a step size mu and an iteration number L;
step 122, let
Wherein the superscript symbol
Representing a pseudo-inverse operation;
step 123, if σ is>σminSequentially executing (I) and (II); otherwise, step 124 is performed.
(i) starting from the initial solution on the set of feasible solutions { h | y ═ Xh }
Starting with the L iterative steepest descent algorithm to maximize the objective function
(a) Setting the element value of the vector delta to
(b) Order to
Then pass through
Will be provided with
Projecting onto its feasible solution set;
(II) making σ ← ρ σ, and returning to step 123;
step 124, calculate
And find KaThe position serial number of the maximum element value, and the KaStoring a position serial number in the set I, and outputting the detected active user I and the channel state information thereof
Compared with the prior art, the method for detecting the active user and estimating the channel thereof provided by the invention uses the compressed sensing reconstruction algorithm SL0, and can greatly reduce the length of the pilot signal required by channel estimation. The method is low in calculation complexity and easy to implement.
To verify the effectiveness of the method of the present invention versus the advantages over prior methods, the following simulation comparative tests were performed. The scene parameters considered are: the number K of the machine type devices is 200, and the number K of the active usersa40; parameter value of SL0 algorithm variable is sigmaminρ is 0.001, μ is 2, and L is 3. Fig. 2 is a comparison graph of the detection accuracy of the active users by using SL0 compressive sensing and the channel estimation method (labeled as the method provided by the present invention) in comparison with the conventional least square method using pilot signals with different lengths. Fig. 3 shows that the normalized mean square error curve of the channel estimation using pilot signals of different lengths is obtained by using the method and the least square method provided by the present invention, and it can be seen from the graph that the channel estimation method provided by the present invention can reduce the pilot length by 50% and has high estimation accuracy.
The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (6)
1. A method for detecting active users in mass machine type communication is characterized in that the method for detecting active users in mass machine type communication comprises the following steps:
modeling active user detection and active user channel estimation as sparse signal reconstruction in a compressed sensing theory, and estimating active users according to user channel states by using an SLO (simultaneous SLO) reconstruction algorithm;
the active user is estimated according to the user channel state by applying an SLO reconstruction algorithm, and the method comprises the following steps:
step 121, inputting the observation signal y, the observation matrix X and the threshold value sigmaminA contraction factor rho, a step size mu and an iteration number L;
step 122, let
Wherein, the superscript symbol number
Representing a pseudo-inverse operation;
step 123, if σ > σminSequentially executing (I) and (II); otherwise, the process proceeds to step 124,
(I) from the initial solution on the feasible solution set { h | y ═ Xh }
Starting with the L iterative steepest descent algorithm to maximize the objective function
(a) Setting the element value of the vector delta to
(b) Order to
Then pass through
Will be provided with
Projecting onto its feasible solution set;
(II) let σ ← ρ σ, and return to step 123;
step 124, calculate
And find KaThe position serial number of the maximum element value, and the KaStoring the position serial number into the set I, and outputting the detected active user I and the active user channel state information
Modeling active user detection and channel estimation thereof as sparse signal reconstruction in a compressive sensing theory, comprising:
a certain time slot, if there are K machines in K equipmentaIf a huge amount of machine equipment is in an active state, the base station receives the KaThe pilot signal from each active user can be expressed as:
wherein p (k) represents the number of the kth active user in all massive machine type equipment in the system; the base station and the massive machinery equipment are both provided with single antennas; h isp(k)Is the p (k) th massive machine-to-base station channel response; x is the number ofp(k)＝[xp(k)，1，xp(k)，2，…，xp(k)，N]TIs the pilot sequence sent by the p (k) th huge machine type equipment, w is the mean value of 0 and the variance is sigma2Gaussian noise of (2);
when K isaWhen a huge amount of machine equipment is in an active state, K-KaThe huge machine type equipment is in a dormant state; a huge amount of machine type devices in a dormant state have their communication links not activated, and thus their channel responses are zero; if all massive machine-type devices are considered, the received pilot signal may be equivalently represented by the following formula
Wherein h is a sparse vector containing K-KaElement of zero, KaA number of non-zero elements, and the position of the non-zero element in h corresponds to the number p (k) of the active user;
2. the method of detecting active users in mass machine type communication according to claim 1, before modeling active user detection and active user channel estimation as sparse signal reconstruction in compressive sensing theory, comprising:
and randomly accessing a huge amount of machine type equipment to the base station in an unauthorized non-orthogonal mode.
3. The method of claim 1, wherein randomly accessing the macro machine device to the base station in an unlicensed non-orthogonal manner comprises:
directly sending pilot signals and data information of massive machine equipment to the base station; and each huge machine type device in the huge machine type devices is distributed with a pilot frequency sequence with a preset length, and the sequences do not need to satisfy the orthogonal relation.
4. A system for detecting active users in mass machine type communication, the system comprising:
modeling active user detection and active user channel estimation as sparse signal reconstruction in a compressive sensing theory, and estimating equipment of an active user according to a user channel state by using an SLO (simultaneous SLO) reconstruction algorithm;
the equipment for estimating the active user according to the user channel state by applying the SLO reconstruction algorithm comprises the following steps:
input device for inputting observation signal y, observation matrix X, threshold value sigmaminA contraction factor rho, a step size mu and an iteration number L;
order to
Wherein, the superscript symbol
Representing a pseudo-inverse operation;
a judging device, if σ > σminSequentially executing (I) and (II); otherwise, the output device is executed to work,
(I) on the set of feasible solutions { h | y ═ Xh }, from the initial solution
Starting with the L iterative steepest descent algorithm to maximize the objective function
(a) Setting the element value of the vector delta to
(b) Order to
Then pass through
Will be provided with
Projecting onto its feasible solution set;
(II) making sigma ← rho sigma and returning to the judging device;
output device, calculate
And find KaThe position serial number of the maximum element value, and the KaStoring the position serial number into the set I, and outputting the detected active user I and the active user channel state information
Apparatus for modeling active user detection and channel estimation thereof as sparse signal reconstruction in compressive sensing theory, comprising:
a certain time slot, if there are K machines in K equipmentaIf a huge amount of machine equipment is in an active state, the base station receives the KaThe pilot signal from each active user can be expressed as:
wherein p (k) indicates that the k active user isNumbering in all massive machine equipment in the system; the base station and the massive machinery equipment are both provided with a single antenna; h isp(k)Is the p (k) th massive machine-to-base station channel response; x is the number ofp(k)＝[xp(k)，1，xp(k)，2，…，xp(k)，N]TIs the pilot sequence sent by the p (k) th huge machine type equipment, w is the mean value of 0 and the variance is sigma2Gaussian noise of (2);
when K isaWhen a huge amount of machine equipment is in an active state, K-KaThe huge machine type equipment is in a dormant state; a huge amount of machine type devices in a dormant state have their communication links not activated, and thus their channel responses are zero; if all massive machine-type devices are considered, the received pilot signal may be equivalently represented by the following formula
Wherein h is a sparse vector containing K-KaElement of zero, KaA number of non-zero elements, and the position of the non-zero element in h corresponds to the number p (k) of the active user;
5. the system for detecting active users in mass machine type communication of claim 4, comprising:
and randomly accessing a huge amount of machine type equipment to the equipment of the base station in an unauthorized non-orthogonal mode.
6. The system for detecting active users in mass machine type communication of claim 4, wherein the device for randomly accessing mass machine type devices to the base station in an unlicensed non-orthogonal manner comprises:
directly transmitting pilot signals and data information of massive machine equipment to the equipment of the base station; and each huge machine type device in the huge machine type devices is distributed with a pilot frequency sequence with a preset length, and the sequences do not need to satisfy the devices of an orthogonal relation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910221801.3A CN110011775B (en) | 2019-03-22 | 2019-03-22 | Method and system for jointly realizing active user detection and channel estimation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910221801.3A CN110011775B (en) | 2019-03-22 | 2019-03-22 | Method and system for jointly realizing active user detection and channel estimation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110011775A CN110011775A (en) | 2019-07-12 |
| CN110011775B true CN110011775B (en) | 2022-06-07 |
Family
ID=67167781
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910221801.3A Active CN110011775B (en) | 2019-03-22 | 2019-03-22 | Method and system for jointly realizing active user detection and channel estimation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110011775B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110784286B (en) * | 2019-11-01 | 2022-05-03 | 重庆邮电大学 | Multi-user detection method of non-orthogonal multiple access system based on compressed sensing |
| CN112929307B (en) * | 2021-02-26 | 2022-03-25 | 广东工业大学 | Distributed channel estimation method under authorization-free communication |
| CN113194499B (en) * | 2021-04-02 | 2022-12-09 | 西安交通大学 | Authorization-free self-adaptive transmission method based on user estimation |
| WO2023272475A1 (en) * | 2021-06-29 | 2023-01-05 | Nokia Shanghai Bell Co., Ltd. | Methods, apparatuses, and computer readable media for user equipment device activity detection |
| WO2023060370A1 (en) * | 2021-10-11 | 2023-04-20 | Nokia Shanghai Bell Co., Ltd. | Methods, devices, and computer readable media for adaptive user equipment device activity detection |
| CN115333709B (en) * | 2022-06-21 | 2023-06-23 | 北京邮电大学 | Data transmission method, device, electronic equipment and storage medium |
| WO2024108547A1 (en) * | 2022-11-25 | 2024-05-30 | Nokia Shanghai Bell Co., Ltd. | Enhancement on user activity detection |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102015208344A1 (en) * | 2015-05-06 | 2016-11-10 | Universität Bremen | Multi-carrier Compressed Sensing Multi-User System |
| CN106130938A (en) * | 2016-07-18 | 2016-11-16 | 安徽师范大学 | TDD extensive mimo system multi-user association channel estimation methods |
| CN106936556A (en) * | 2017-03-07 | 2017-07-07 | 西北工业大学 | A kind of time-frequency two-dimensional Sparse Code multiple access method towards arrowband Internet of Things |
| CN107248902A (en) * | 2017-07-21 | 2017-10-13 | 电子科技大学 | A kind of block compressed sensing non-orthogonal multiple system multi-user detection method |
| CN107743103A (en) * | 2017-10-26 | 2018-02-27 | 北京交通大学 | The multinode access detection of MMTC systems based on deep learning and channel estimation methods |
| CN109327850A (en) * | 2018-11-16 | 2019-02-12 | 安徽大学 | Multi-user detection method of non-orthogonal multiple access system based on gradient tracking and multi-step quasi-Newton method technology |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105554901B (en) * | 2015-12-11 | 2019-01-11 | 清华大学 | Accidental access method |
| US10033550B2 (en) * | 2016-03-21 | 2018-07-24 | Huawei Technologies Co., Ltd. | User equipment detection for uplink random access in dispersive fading environments |
| GB2557930A (en) * | 2016-12-16 | 2018-07-04 | Samsung Electronics Co Ltd | A method for detecting a number of active users in a communication system |
| CN106789770A (en) * | 2016-12-26 | 2017-05-31 | 电子科技大学 | The nonopiate multiple-user access methods of extensive MIMO based on compressed sensing |
| KR102329272B1 (en) * | 2017-08-04 | 2021-11-19 | 삼성전자주식회사 | Apparatus and method for uplink transmissions in wireless communication system |
| CN108259073A (en) * | 2018-01-30 | 2018-07-06 | 深圳清华大学研究院 | Uplink communication multiuser signal detection method and device based on generalized space modulation |
| CN108540410B (en) * | 2018-03-06 | 2020-11-03 | 东南大学 | Joint active user detection and channel estimation method for scheduling-free transmission system |
| CN108768585B (en) * | 2018-04-27 | 2021-03-16 | 南京邮电大学 | Multi-user detection method of uplink signaling-free non-orthogonal multiple access (NOMA) system based on deep learning |
-
2019
- 2019-03-22 CN CN201910221801.3A patent/CN110011775B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102015208344A1 (en) * | 2015-05-06 | 2016-11-10 | Universität Bremen | Multi-carrier Compressed Sensing Multi-User System |
| CN106130938A (en) * | 2016-07-18 | 2016-11-16 | 安徽师范大学 | TDD extensive mimo system multi-user association channel estimation methods |
| CN106936556A (en) * | 2017-03-07 | 2017-07-07 | 西北工业大学 | A kind of time-frequency two-dimensional Sparse Code multiple access method towards arrowband Internet of Things |
| CN107248902A (en) * | 2017-07-21 | 2017-10-13 | 电子科技大学 | A kind of block compressed sensing non-orthogonal multiple system multi-user detection method |
| CN107743103A (en) * | 2017-10-26 | 2018-02-27 | 北京交通大学 | The multinode access detection of MMTC systems based on deep learning and channel estimation methods |
| CN109327850A (en) * | 2018-11-16 | 2019-02-12 | 安徽大学 | Multi-user detection method of non-orthogonal multiple access system based on gradient tracking and multi-step quasi-Newton method technology |
Non-Patent Citations (1)
| Title |
|---|
| 非正交多址接入中基于压缩感知的多用户检测技术研究;赵晓娟;《中国优秀硕士学位论文全文数据库》;20190131;正文2.2.1、3.1节 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110011775A (en) | 2019-07-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110011775B (en) | Method and system for jointly realizing active user detection and channel estimation | |
| Shahin et al. | Cognitive backoff mechanism for IEEE802. 11ax high-efficiency WLANs | |
| CN116326093A (en) | Method and apparatus for performing sensing in wireless LAN system | |
| CN116248223B (en) | Concealed communication optimization method and system based on unmanned aerial vehicle network | |
| WO2022206328A1 (en) | Communication collaboration method and apparatus | |
| Zorzi | Mobile radio slotted ALOHA with capture and diversity | |
| US20090055909A1 (en) | Data transmitting method with multiple token mechanism in wireless token ring protocol | |
| Interdonato et al. | Intra-slot interference cancellation for collision resolution in irregular repetition slotted ALOHA | |
| Abebe et al. | Multi-sequence spreading random access (MSRA) for compressive sensing-based grant-free communication | |
| US8837478B1 (en) | System and method for interference protection in downlink multi-user multiple-input, multiple-output communication | |
| Lim et al. | Hybrid active user detection for massive machine-type communications in IoT | |
| CN111683023A (en) | Model-driven large-scale equipment detection method based on deep learning | |
| KR101874259B1 (en) | Method and system for performing random access based on compressive sensing for machine type communications considering tradeoff between link performance and latency | |
| Arun et al. | Design and performance analysis of a MAC protocol for wireless LANs supporting multipacket reception | |
| CN103368702A (en) | Method for fluxion self adaption in wireless local area network distributed interference and alignment | |
| Sriprasert et al. | A Discrete-Time Simulation for Throughput Performance Evaluation of IEEE802. 11 with Hidden Stations | |
| Qureshi et al. | Resource allocation for a multi‐device URLLC network | |
| CN116057970A (en) | Method and apparatus for performing clustering for sensing in a wireless local area network system | |
| CN106850246A (en) | The recognition methods of facility information and device | |
| KR101766415B1 (en) | Apparatus for iterative order recursive least square estimation-based algorithm for join detection of activity and data from group-wise compressive sensing signal and method thereof | |
| Zhang et al. | Performance analysis of a random access packet radio system with joint network-spatial diversity | |
| Losavio et al. | Performance analysis of 802.15. 4 wireless standard | |
| CN115842701B (en) | Channel estimation method, device, communication equipment and storage medium | |
| Plodchinda et al. | A Discrete-Time Simulation for Throughput Performance Evaluation of IEEE802. 11 with Hidden Stations | |
| CN110233762B (en) | Method for improving throughput of full-duplex CSMA network by using MAB |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |