CN111835388A - Communication adjustment method, terminal, device, base station and medium in MIMO system - Google Patents

Communication adjustment method, terminal, device, base station and medium in MIMO system Download PDF

Info

Publication number
CN111835388A
CN111835388A CN201910318098.8A CN201910318098A CN111835388A CN 111835388 A CN111835388 A CN 111835388A CN 201910318098 A CN201910318098 A CN 201910318098A CN 111835388 A CN111835388 A CN 111835388A
Authority
CN
China
Prior art keywords
decision
communication
mimo system
transmission rate
representing
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.)
Granted
Application number
CN201910318098.8A
Other languages
Chinese (zh)
Other versions
CN111835388B (en
Inventor
罗喜良
朱晗宇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
ShanghaiTech University
Original Assignee
ShanghaiTech University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by ShanghaiTech University filed Critical ShanghaiTech University
Priority to CN201910318098.8A priority Critical patent/CN111835388B/en
Publication of CN111835388A publication Critical patent/CN111835388A/en
Application granted granted Critical
Publication of CN111835388B publication Critical patent/CN111835388B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0426—Power distribution
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/18—Negotiating wireless communication parameters
    • H04W28/20—Negotiating bandwidth
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/18—Negotiating wireless communication parameters
    • H04W28/22—Negotiating communication rate

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Quality & Reliability (AREA)
  • Power Engineering (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Radio Transmission System (AREA)

Abstract

According to the communication adjustment method, the terminal, the equipment, the base station and the medium in the MIMO system, the beam forming direction is determined by acquiring the current channel information; based on MAB learning mechanism and using Dual-UCB algorithm to dynamically adjust transmission rate and beam width, and satisfy long-term constraint of low throughput ratio. According to the method and the device, on the premise of ensuring the undersize throughput ratio, the proper transmission rate and the proper beam width are learned and selected in the multi-antenna system, so that the expected communication throughput of the MIMO system is maximized.

Description

Communication adjustment method, terminal, device, base station and medium in MIMO system
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method, a terminal, a device, a base station, and a medium for adjusting communications in a MIMO system.
Background
The wireless communication network is easy to deploy and can conveniently provide high-speed and stable wireless access service for wireless equipment, so that the application range of the wireless communication network is wider and wider, and the wireless communication network is an indispensable existence in daily life of people in the current information age.
In practical wireless communication systems, when there is no accurate channel information feedback, the selection of the transmission rate can only be done based on historical statistics of packet transmissions. In the prior art, such as the proposed ARF algorithm, the selection of the transmission rate is based on the statistical number of consecutive successes or failures of packet transmission; for another example, the proposed SampleRate algorithm is to count the average transmission time of data packets at each rate; the Minstrel algorithm is a transmission rate self-adaptive algorithm which is practically used in a widely-used WiFi system and dynamically adjusts the transmission rate according to the transmission throughput rate corresponding to each statistical rate.
With the development of wireless communication technology, Multiple Input Multiple Output (MIMO) technology is beginning to appear in the communication scene of practical application, and in this case, it is more challenging to implement effective transmission rate adaptation, and the prior art considers more complicated transmission rate and joint dynamic adjustment of MIMO mode. It should be noted that, after the MIMO technology is introduced, the difficulty of the transmission rate adaptation is not only due to the diversity of the MIMO mode, but also the use of the beamforming technology in the MIMO system makes the communication performance more sensitive to the influence of the channel information inaccuracy while improving the gain. In order to solve the problems of unstable transmission and low efficiency in the MIMO system caused by inaccurate channel information, a dynamic transmission rate adjustment method that maximizes the communication throughput on the premise of ensuring a certain transmission stability is required.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present application aims to provide a communication adjustment method, a terminal, a device, a base station and a medium in a MIMO system, so as to solve the influence caused by inaccurate channel information in the prior art.
To achieve the above and other related objects, the present application provides a communication adjustment method in a MIMO system, the method comprising: acquiring current channel information to determine a beamforming direction; based on MAB learning mechanism and using Dual-UCB algorithm to dynamically adjust transmission rate and beam width, and satisfy long-term constraint of low throughput ratio.
In an embodiment of the present application, a method for dynamically adjusting a transmission rate and a beam width by using a Dual-UCB algorithm based on an MAB learning mechanism includes: at time t, the decision is made according to the following equation:
Figure BDA0002033781580000021
wherein the content of the first and second substances,
Figure BDA0002033781580000022
an estimate value representing a corresponding expected composite gain when selecting decision d; representing a constraint on the cumulative penalty value. T represents the total number of transmissions; n is a radical ofdRepresenting the corresponding number of transmissions when decision d is selected;
Figure BDA0002033781580000023
an estimate representing a desired throughput rate at the time of the selection decision d;
Figure BDA0002033781580000024
representing an expected estimation value of the corresponding penalty value when the decision d is selected; v. oftIs a lagrange multiplier.
In an embodiment of the present application, the lagrange multiplier vtThe update formula of (2) is:
Figure BDA0002033781580000025
Figure BDA0002033781580000026
wherein R istAnd E represents the communication throughput at the time t, represents a preset throughput threshold, and is an updating step.
In an embodiment of the present application, the method for dynamically adjusting the transmission rate and the beam width based on the MAB learning mechanism and using the Dual-UCB algorithm further includes: decision space
Figure BDA0002033781580000027
All selectable decisions are included, for any one of which
Figure BDA0002033781580000028
Can be expressed as d ═ (r)d,wd) (ii) a Wherein r isdIndicating the corresponding transmission rate, w, at which decision d is selecteddRepresenting the corresponding beam width when selecting decision d; when decision d is selected, its corresponding expectationA penalty value and an expected throughput rate of
Figure BDA0002033781580000029
And mud。
In an embodiment of the application, the estimate of the expected penalty value and the estimate of the expected throughput rate are calculated according to statistical data of whether data packet transmission is successful or not.
To achieve the above and other related objects, the present application provides an electronic device, including: an obtaining module, configured to obtain current channel information to determine a beamforming direction; a processing module; the method is used for dynamically adjusting the transmission rate and the beam width based on an MAB learning mechanism and by utilizing a Dual-UCB algorithm, and meets the long-term constraint of low throughput ratio.
To achieve the above and other related objects, there is provided a terminal including: a memory, a processor, and a communicator; the memory is used for storing a computer program; the processor runs a computer program to realize the communication regulation method in the MIMO system; the communicator is used for communicating with an external device.
To achieve the above and other related objects, there is provided a base station, comprising: a memory, a processor, a receiver, and a transmitter; the memory is used for storing a computer program; the processor runs a computer program to realize the communication regulation method in the MIMO system; the receiver and the transmitter are used for receiving signals and transmitting signals.
To achieve the above and other related objects, the present application provides a computer storage medium storing a computer program which, when executed, performs a communication adjustment method in a MIMO system as described above.
To sum up, a communication adjusting method, a terminal, a device, a base station and a medium in the MIMO system of the present application. Determining a beamforming direction by acquiring the fed back current channel information; based on MAB learning mechanism and using Dual-UCB algorithm to dynamically adjust transmission rate and beam width, and satisfy long-term constraint of low throughput ratio.
Has the following beneficial effects:
on the premise of meeting the long-term constraint of low throughput ratio, the method can learn and select the proper transmission rate and beam width in the multi-antenna system so as to maximize the expected communication throughput of the MIMO system.
Drawings
Fig. 1 is a schematic diagram illustrating a MIMO system according to an embodiment of the present application.
Fig. 2 is a flowchart illustrating a communication adjustment method in a MIMO system according to an embodiment of the present invention.
Fig. 3 is a schematic view illustrating a communication adjustment method in a MIMO system according to an embodiment of the present invention.
Fig. 4 is a block diagram of an electronic device according to an embodiment of the present disclosure.
Fig. 5 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Fig. 6 is a schematic structural diagram of a base station in an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application is provided by way of specific examples, and other advantages and effects of the present application will be readily apparent to those skilled in the art from the disclosure herein. The present application is capable of other and different embodiments and of being practiced or being carried out in various ways, and it is capable of other various modifications and changes without departing from the spirit of the present application. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Embodiments of the present application will be described in detail below with reference to the accompanying drawings so that those skilled in the art can easily implement the embodiments. The present application may be embodied in many different forms and is not limited to the embodiments described herein.
In order to clearly explain the present application, components that are not related to the description are omitted, and the same reference numerals are given to the same or similar constituent elements throughout the specification.
Throughout the specification, when a component is referred to as being "connected" to another component, this includes not only the case of being "directly connected" but also the case of being "indirectly connected" with another element interposed therebetween. In addition, when a component is referred to as "including" a certain constituent element, unless otherwise stated, it means that the component may include other constituent elements, without excluding other constituent elements.
When an element is referred to as being "on" another element, it can be directly on the other element, or intervening elements may also be present. When a component is referred to as being "directly on" another component, there are no intervening components present.
Although the terms first, second, etc. may be used herein to describe various elements in some instances, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, the first interface and the second interface, etc. are described. Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used in this specification, specify the presence of stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, steps, operations, elements, components, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C ". An exception to this definition will occur only when a combination of elements, functions, steps or operations are inherently mutually exclusive in some way.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, singular forms also include plural forms as long as the statement does not explicitly state the opposite meaning. The term "comprises/comprising" when used in this specification is taken to specify the presence of stated features, regions, integers, steps, operations, elements, and/or components, but does not exclude the presence or addition of other features, regions, integers, steps, operations, elements, and/or components.
Terms indicating "lower", "upper", and the like relative to space may be used to more easily describe a relationship of one component with respect to another component illustrated in the drawings. Such terms are intended to have other meanings or operations of the device in use, not only the meanings indicated in the drawings. For example, if the device in the figures is turned over, elements described as "below" other elements would then be oriented "above" the other elements. Thus, the exemplary terms "under" and "beneath" all include above and below. The device may be rotated 90 or other angles and the terminology representing relative space is also to be interpreted accordingly.
Although not defined differently, including technical and scientific terms used herein, all terms have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. Terms defined in commonly used dictionaries are to be interpreted as having meanings consistent with those of related art documents and the contents of the present prompts, and should not be interpreted excessively as having ideal or very formulaic meanings unless defined otherwise.
MIMO (Multiple-Input Multiple-Output), Multiple-Input Multiple-Output technology, which uses Multiple transmitting antennas and Multiple receiving antennas at a transmitting end and a receiving end, respectively, so that signals are transmitted and received through the Multiple antennas at the transmitting end and the receiving end, thereby improving communication quality. The multi-antenna multi-transmission multi-receiving system can fully utilize space resources, realizes multi-transmission and multi-reception through a plurality of antennas, can improve the system channel capacity by times under the condition of not increasing frequency spectrum resources and antenna transmitting power, shows obvious advantages and is regarded as the core technology of next generation mobile communication. As shown in fig. 1 as a schematic view of its scenario.
As mentioned above, the use of beamforming in MIMO systems increases the gain and makes the communication performance more sensitive to the effect of channel information inaccuracy.
With the development of wireless communication technology and the increase of communication demand, the number of base stations is greatly demanded, and the cost of installing the base stations is relatively high.
Beamforming does not require the use of special antennas, and does not add other wireless subsystems, can improve performance, and is more efficient and several times higher than other digital signal processing techniques, such as the introduction of space-time block codes (STBC) and low density parity check codes (LDPC). The method is applicable to both family and enterprise environments.
Beamforming (BF) techniques can be divided into adaptive beamforming, fixed Beam and switched beamforming techniques. The fixed beam, i.e., the antenna pattern, IS fixed, dividing the three 120 sectors in IS-95 into fixed beams. The switched beam is an extension of the fixed beam, each 120 ° sector is subdivided into a plurality of smaller sectors, each sector has a fixed beam, and when a user moves within a sector, the switched beam mechanism can automatically switch the beam to the sector containing the strongest signal, but the fatal weakness of the switched beam mechanism is that the ideal signal and the interference signal cannot be distinguished.
The adaptive beam former can optimally form a directional diagram according to different paths of user signals in space propagation, different antenna gains are given in different arrival directions, narrow beams are formed in real time to align the user signals, side lobes are reduced as much as possible in other directions, and directional reception is adopted, so that the capacity of the system is improved. Due to the mobility of the mobile station and the scattering environment, the direction of arrival of signals received at the base station is time varying, signals of close frequencies but spatially separated can be separated using an adaptive beamformer, and these signals are tracked and the weights of the antenna array adjusted so that the beams of the antenna array point in the direction of the desired signal. A key technique for adaptive beamforming is how to obtain the channel parameters more accurately.
Based on the beam forming technology in the MIMO system, the degree that the communication performance of the MIMO system is influenced by the inaccuracy of the channel information is obviously improved. Therefore, the application provides a communication adjustment method, a terminal, a device, a base station and a medium in an MIMO system, so as to solve the influence caused by inaccurate channel information and maximize the dynamic transmission rate and beam width adjustment of communication throughput on the premise of meeting the long-term constraint of low throughput ratio.
Fig. 2 is a flow chart of a communication adjustment method in a MIMO system according to an embodiment of the present application. As shown, the method comprises:
step S101: current channel information is acquired to determine a beamforming direction.
In some embodiments, the current channel information fed back is first obtained to determine the direction of the beam.
It should be noted that the channel information is obtained to determine the direction of the beam, because the beam direction must be known to perform the subsequent processing steps (beamforming techniques) of the method described in this application. The parameter to be adjusted in this application is not the beam direction, so the beam direction needs to be known in advance through the channel information.
Step S102: based on MAB learning mechanism and using Dual-UCB algorithm to dynamically adjust transmission rate and beam width, and satisfy long-term constraint of low throughput ratio.
In some embodiments, compared to the conventional optimization problem in the field of transmission rate adaptation, the communication adjustment method in the MIMO system described in the present application is mainly distinguished by two points, on one hand, the optimization variables include both transmission rate and beam width; another aspect is to ensure that the frequency of occurrences of too low throughput is within an acceptable range while maximizing the desired throughput. The problem to be solved is therefore to select a suitable strategy pi in case of long-term constraints of too low throughput ratio is met, such that the desired communication throughput is maximized.
In an embodiment of the present application, the step S102 is mainly implemented according to the following formula:
Figure BDA0002033781580000051
Figure BDA0002033781580000061
wherein the content of the first and second substances,
Figure BDA0002033781580000062
an estimate value representing a corresponding expected composite gain when selecting decision d; representing a constraint on the cumulative penalty value. T represents the total number of transmissions; n is a radical ofdRepresenting the corresponding number of transmissions when decision d is selected;
Figure BDA0002033781580000063
an estimate representing a corresponding expected throughput rate at the time of the selection decision d;
Figure BDA0002033781580000064
representing an expected estimation value of the corresponding penalty value when the decision d is selected; v. oftIs a lagrange multiplier.
In particular, a decision space
Figure BDA0002033781580000065
All selectable decisions are included, for any one of which
Figure BDA0002033781580000066
Can be expressed as d ═ (r)d,wd)；
Wherein r isdIndicating the corresponding transmission rate, w, at which decision d is selecteddRepresenting the corresponding beam width when selecting decision d; when decision d is selected, it corresponds to a desired penalty value and a desired throughput rate, respectively
Figure BDA0002033781580000067
And mud。
The estimation value of the expected penalty value and the estimation value of the expected throughput rate are obtained by calculation according to statistical data of whether data packet transmission is successful or not.
Figure BDA0002033781580000068
An estimate representing an expected composite gain;
Figure BDA0002033781580000069
an estimate representing a corresponding expected throughput rate at the time of the selection decision d;
Figure BDA00020337815800000610
representing the desired estimate of the corresponding penalty value when selecting decision d.
E is T represents the total transmission times; n is a radical ofdIndicating the corresponding number of transmissions when decision d is selected. v. oftIs Lagrange multiplier, and the updating formula is as follows:
Figure BDA00020337815800000611
Rtand E represents the communication throughput rate of the t-th transmission, represents a preset throughput rate threshold value, and is an updating step size.
In some embodiments, the following optimization problem is solved by selecting a proper parameter epsilon and adopting a Dual-UCB algorithm, so that the transmission rate expected value is maximized on the premise of meeting the long-term constraint of the over-low throughput ratio.
Figure BDA00020337815800000612
It should be noted that the MAB learning mechanism is a dobby slot machine learning mechanism. The mechanism is a balance problem for discussion exploration and utilization developed by the reward probability problem of the multi-arm slot machine.
The single-arm slot machine obtains the reward with a certain probability after pulling down the game arm. While a dobby slot machine needs to choose which game arm to pull to the bottom, the probability of winning a prize for each arm is different. A multiple-arm machine has a plurality of arms, and a gambler can randomly select one of the arms to rock after depositing a coin, each arm spits out a silver coin with a certain probability (i.e. the gambler's prize), but the probability that each arm will receive a coin is unknown to the gambler. The goal of the gambler is to maximize his own cumulative prize through a strategy that achieves as many money prizes as possible with a limited number of rockers. The dobby slot machine is well suited for discussing the balance of exploration and utilization. If a greedy algorithm is adopted each time, the game arms with the reward probability are selected, and the value of the behavior is completely utilized; if a non-known-best game arm is selected, it is explored. Generally, utilization can maximize a single reward, while exploration may yield a better long-term reward in the long term.
The problem of the dobby slot machine is to maximize the accumulated prize value of the rocker arm machine within a limited time. The core idea of the dobby slot machine problem is to search for a balance point by searching for more heuristics to acquire more knowledge and using the knowledge acquired currently, in the dobby slot machine, the search is to continue heuristics for handles with lower return and less pulling times, and the use is to select the best handle according to the return of different handles acquired currently, and the model is a decision method for finding how to balance the search and use in the search process.
The UCB (upper confidence interval) algorithm is a commonly used algorithm for calculating the multiple-arm slot machine problem. First, it is completely free of randomness; secondly, it considers a point in addition to the return of revenue, i.e. how high the confidence of this return of revenue is.
In some embodiments, based on the learning of the MAB learning mechanism, different decisions d are obtained, and each policy d corresponds to a corresponding transmission rate rdBeam width wdExpected penalty value
Figure RE-GDA0002075366220000071
Expected throughput rate mudAnd an expected penalty value
Figure RE-GDA0002075366220000072
With the expected throughput rate mudCorresponding estimated value
Figure RE-GDA0002075366220000073
And
Figure RE-GDA0002075366220000074
and further, by selecting a proper threshold belonging to the same E, the transmission rate expectation value is maximized on the premise of meeting the long-term constraint of the low throughput rate.
In some embodiments, the Dual-UCB algorithm is utilized to dynamically adjust the transmission rate and the beam width, which is dynamically adjusted to increase by a certain width.
As shown in the schematic view of the scenario in another embodiment shown in fig. 3. As shown in the figure, the best beam direction cannot be determined due to uncertainty of channel information, and when the beam is narrow, on the one hand, transmission is unstable, and on the other hand, even if r is the same2>r1Decision 1 may also result in a higher actual communication rate than decision 2.
Fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention. As shown, the electronic device 400 includes:
an obtaining module 401, configured to obtain current channel information to determine a beamforming direction;
and the processing module 402 is configured to dynamically adjust the transmission rate and the beam width based on the MAB learning mechanism and by using a Dual-UCB algorithm, and satisfy the long-term constraint of the low throughput ratio.
It should be noted that, because the contents of information interaction, execution process, and the like between the modules/units of the apparatus are based on the same concept as the method embodiment described in the present application, the technical effect brought by the contents is the same as the method embodiment of the present application, and specific contents can be referred to the description in the foregoing method embodiment of the present application, and are not repeated herein.
It should be further noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these units can be implemented entirely in software, invoked by a processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the processing module 402 may be a separate processing element, or may be integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the functions of the processing module 402. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, the steps of the method or the modules may be implemented by hardware integrated logic circuits in a processor element or instructions in software.
For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when one of the above modules is implemented in the form of a Processing element scheduler code, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
Fig. 5 is a schematic structural diagram of a terminal according to an embodiment of the present application. As shown, the terminal 500 includes: a memory 501, a processor 502, and a communicator 503; the memory 501 is used for storing a computer program; the processor 502 runs a computer program to implement the communication adjustment method in the MIMO system as described in fig. 2.
In some embodiments, the number of the memory 501 in the terminal 500 may be one or more, the number of the processor 502 may be one or more, the number of the communicator 503 may be one or more, and fig. 5 is taken as an example.
In an embodiment of the present application, the processor 502 in the terminal 500 loads one or more instructions corresponding to the application program process into the memory 501 according to the steps described in fig. 2, and the processor 502 executes the application program stored in the memory 502, thereby implementing various functions in the communication adjustment method in the MIMO system described in fig. 2.
In some embodiments, the terminal may be a smart phone, a tablet computer, a palmtop computer, or the like.
The Memory 501 may include a Random Access Memory (RAM), and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The memory 501 stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an expanded set thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic services and for handling hardware-based tasks.
The Processor 502 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, or discrete hardware components.
The communicator 503 is used to implement communication connection between the database access device and other devices (such as client, read-write library and read-only library). The communicator 503 may include one or more sets of modules of different communication manners, for example, a CAN communication module communicatively connected to a CAN bus. The communication connection may be one or more wired/wireless communication means and combinations thereof. The communication method comprises the following steps: any one or more of the internet, CAN, intranet, Wide Area Network (WAN), Local Area Network (LAN), wireless network, Digital Subscriber Line (DSL) network, frame relay network, Asynchronous Transfer Mode (ATM) network, Virtual Private Network (VPN), and/or any other suitable communication network. For example: any one or a plurality of combinations of WIFI, Bluetooth, NFC, GPRS, GSM and Ethernet.
In some specific applications, the various components of the terminal 500 are coupled together by a bus system that may include a power bus, a control bus, a status signal bus, etc., in addition to a data bus. But for clarity of explanation the various busses are shown in fig. 5 as a bus system.
Fig. 6 is a schematic structural diagram of a base station according to an embodiment of the present invention. As shown, the base station 600 includes: a memory 601, a processor 602, a receiver 603, and a transmitter 604; the memory 601 is used for storing computer programs; the processor 602 runs a computer program to implement the communication adjustment method in the MIMO system as described in fig. 2; the receiver 603 and the transmitter 604 are used for receiving signals and transmitting signals.
In an embodiment of the present application, the base station 600 includes a generalized base station and a narrow base station.
The generalized Base Station is an abbreviation of Base Station Subsystem (BSS). Taking a GSM network as an example, it includes a base transceiver station (BST) and a Base Station Controller (BSC). A base station controller may control tens to tens of base transceiver stations. While in WCDMA and like systems similar concepts are referred to as NodeB and RNC.
The narrow base station, i.e., the common mobile communication base station, is a form of a radio station, which refers to a radio transceiver station for information transfer with a mobile phone terminal through a mobile communication switching center in a certain radio coverage area.
It should be noted that, because the contents of information interaction, execution process, and the like between the devices/units of the base station are based on the same concept as the terminal embodiment described in the present application, the technical effect brought by the contents is the same as the method embodiment of the present application, and specific contents can be referred to the description in the terminal embodiment shown in the foregoing of the present application, and are not repeated herein.
In some embodiments, the various components of the base station 600 are coupled together by a bus system, which may include a power bus, a control bus, a status signal bus, and the like, in addition to a data bus. But for clarity of illustration the various buses have been referred to as a bus system in figure 6.
In an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and the computer program, when executed by a processor, implements the communication adjusting method in the MIMO system as described in fig. 2.
It is emphasized that the method described herein can be applied to the terminal described herein, and also to the base station described herein.
The computer-readable storage medium, as will be appreciated by one of ordinary skill in the art: the embodiment for realizing the functions of the system and each unit can be realized by hardware related to computer programs. The aforementioned computer program may be stored in a computer readable storage medium. When the program is executed, the embodiment including the functions of the system and the units is executed; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
To sum up, the communication adjusting method, the terminal, the device, the base station and the medium in the MIMO system provided by the present application determine a beamforming direction by acquiring current channel information; based on MAB learning mechanism and using Dual-UCB algorithm to dynamically adjust transmission rate and beam width, and satisfy long-term constraint of low throughput ratio.
The application effectively overcomes various defects in the prior art and has high industrial utilization value.
The above embodiments are merely illustrative of the principles and utilities of the present application and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications or changes which can be accomplished by those skilled in the art without departing from the spirit and technical spirit of the present invention shall be covered by the claims of the present application.

Claims (9)

1. A method for adjusting communication in a MIMO system, the method comprising:
acquiring current channel information to determine a beamforming direction;
based on MAB learning mechanism and using Dual-UCB algorithm to dynamically adjust transmission rate and beam width, and satisfy long-term constraint of low throughput ratio.
2. The communication adjustment method in the MIMO system of claim 1, wherein the method for dynamically adjusting the transmission rate and the beam width based on the MAB learning mechanism and using the Dual-UCB algorithm comprises:
at time t, the decision is made according to the following equation:
Figure FDA0002033781570000011
Figure FDA0002033781570000012
wherein the content of the first and second substances,
Figure FDA0002033781570000013
an estimate value representing a corresponding expected composite gain when selecting decision d; representing a constraint on the cumulative penalty value. T represents the total number of transmissions; n is a radical ofdRepresenting the corresponding number of transmissions when decision d is selected;
Figure FDA0002033781570000014
an estimate representing a corresponding expected throughput rate at the time of the selection decision d;
Figure FDA0002033781570000015
representing an expected estimation value of the corresponding penalty value when the decision d is selected; v. oftIs a lagrange multiplier.
3. The method of claim 2, wherein the lagrangian multiplier v is a function of the received signal strengthtThe update formula of (2) is:
Figure FDA0002033781570000016
wherein R istAnd E represents the communication throughput at the time t, represents a preset throughput threshold, and is an updating step.
4. The method of claim 2, wherein the method for dynamically adjusting the transmission rate and the beam width based on the MAB learning mechanism and using the Dual-UCB algorithm further comprises:
decision space
Figure FDA0002033781570000017
All selectable decisions are included, for any one of which
Figure FDA0002033781570000018
Can be expressed as d ═ (r)d,wd)；
Wherein r isdIndicating the corresponding transmission rate, w, at which decision d is selecteddRepresenting the corresponding beam width when selecting decision d; when decision d is selected, it corresponds to a desired penalty value and a desired throughput rate, respectively
Figure FDA0002033781570000019
And mud。
5. The method of claim 4, wherein the estimate of the expected penalty value and the estimate of the expected throughput rate are calculated according to statistical data about whether packet transmission is successful.
6. An electronic device, comprising:
an obtaining module, configured to obtain current channel information to determine a beamforming direction;
a processing module; the method is used for dynamically adjusting the transmission rate and the beam width based on an MAB learning mechanism and by utilizing a Dual-UCB algorithm, and meets the long-term constraint of low throughput ratio.
7. A terminal, characterized in that the terminal comprises: a memory, a processor, and a communicator; the memory is used for storing a computer program; the processor runs a computer program to realize the communication adjusting method in the MIMO system according to any one of claims 1 to 5; the communicator is used for communicating with an external device.
8. A base station, characterized in that the base station comprises: a memory, a processor, a receiver, and a transmitter; the memory is used for storing a computer program; the processor runs a computer program to realize the communication adjusting method in the MIMO system according to any one of claims 1 to 5; the receiver and the transmitter are used for receiving signals and transmitting signals.
9. A computer storage medium, in which a computer program is stored, which when executed performs the communication adjustment method in the MIMO system according to any one of claims 1 to 5.
CN201910318098.8A 2019-04-19 2019-04-19 Communication adjustment method, terminal, device, base station and medium in MIMO system Active CN111835388B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910318098.8A CN111835388B (en) 2019-04-19 2019-04-19 Communication adjustment method, terminal, device, base station and medium in MIMO system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910318098.8A CN111835388B (en) 2019-04-19 2019-04-19 Communication adjustment method, terminal, device, base station and medium in MIMO system

Publications (2)

Publication Number Publication Date
CN111835388A true CN111835388A (en) 2020-10-27
CN111835388B CN111835388B (en) 2022-02-11

Family

ID=72911692

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910318098.8A Active CN111835388B (en) 2019-04-19 2019-04-19 Communication adjustment method, terminal, device, base station and medium in MIMO system

Country Status (1)

Country Link
CN (1) CN111835388B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102918887A (en) * 2010-06-04 2013-02-06 微软公司 Dynamic channel and transmission rate selection
CN104022841A (en) * 2014-06-18 2014-09-03 重庆邮电大学 Communication method and system based on encryption transmission and wireless energy carrying
US20160013850A1 (en) * 2014-07-11 2016-01-14 Huawei Technologies Canada Co., Ltd. Systems and Methods for Optimized Beamforming and Compression for Uplink MIMO Cloud Radio Access Networks
US20160191216A1 (en) * 2014-03-07 2016-06-30 Huawei Technologies Co., Ltd. Systems and Methods for OFDM with Flexible Sub-Carrier Spacing and Symbol Duration
US9503164B1 (en) * 2015-06-09 2016-11-22 Hong Kong Applied Science and Technology Research Institute Company Limited Method and apparatus for channel estimation in massive MIMO systems with dynamic training design
US20170251392A1 (en) * 2016-02-25 2017-08-31 Kabushiki Kaisha Toshiba Wireless communication device and wireless communication method
CN107171701A (en) * 2017-04-19 2017-09-15 西安电子科技大学 The power distribution method that a kind of MassiveMIMO systems are gathered based on mixed tensor
CN109327252A (en) * 2018-10-30 2019-02-12 电子科技大学 It is a kind of based on on-line study beam selection method from the context

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102918887A (en) * 2010-06-04 2013-02-06 微软公司 Dynamic channel and transmission rate selection
US20160191216A1 (en) * 2014-03-07 2016-06-30 Huawei Technologies Co., Ltd. Systems and Methods for OFDM with Flexible Sub-Carrier Spacing and Symbol Duration
CN104022841A (en) * 2014-06-18 2014-09-03 重庆邮电大学 Communication method and system based on encryption transmission and wireless energy carrying
US20160013850A1 (en) * 2014-07-11 2016-01-14 Huawei Technologies Canada Co., Ltd. Systems and Methods for Optimized Beamforming and Compression for Uplink MIMO Cloud Radio Access Networks
US9503164B1 (en) * 2015-06-09 2016-11-22 Hong Kong Applied Science and Technology Research Institute Company Limited Method and apparatus for channel estimation in massive MIMO systems with dynamic training design
US20170251392A1 (en) * 2016-02-25 2017-08-31 Kabushiki Kaisha Toshiba Wireless communication device and wireless communication method
CN107171701A (en) * 2017-04-19 2017-09-15 西安电子科技大学 The power distribution method that a kind of MassiveMIMO systems are gathered based on mixed tensor
CN109327252A (en) * 2018-10-30 2019-02-12 电子科技大学 It is a kind of based on on-line study beam selection method from the context

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
CEM TEKIN等: ""Online Learning of Rested and Restless Bandits"", 《IEEE TRANSACTIONS ON INFORMATION THEORY 》 *
JEAN-YVES AUDIBERT等: "Exploration-exploitation tradeoff using variance estimates in multi-armed bandits", 《THEORETICAL COMPUTER SCIENCE》 *
SIMON BEGASHAW等: ""Enhancing Blind Interference Alignment with Reinforcement Learning"", 《2016 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)》 *
金魁: "新型第四代移动通信天线研究", 《中国优秀博硕士学位论文全文数据库（博士）信息科技辑》 *

Also Published As

Publication number Publication date
CN111835388B (en) 2022-02-11

Similar Documents

Publication Publication Date Title
Yoshida et al. MAB-based client selection for federated learning with uncertain resources in mobile networks
CN111698789B (en) Scheduling method, apparatus and storage medium in communication system
US10652898B2 (en) Control node and method thereof
US11290163B2 (en) Downlink user equipment selection
EP3915200B1 (en) Design and adaptation of hierarchical codebooks
US20240072848A1 (en) Method and system for controlling phase shifts of intelligent reflecting surface
CN112886985B (en) Bluetooth signal receiving/transmitting method, device, chip and equipment
US20200304194A1 (en) Wireless communication apparatus for adaptive beamforming and method of operation thereof
US7286845B2 (en) System, and associated method, for scheduling weighted transmissions from multiple antennas
Cai et al. Reconfigurable intelligent surface assisted D2D underlay communications: A two-timescale optimization design
US11647468B2 (en) Transmission power allocation method based on user clustering and reinforcement learning
Ganjalizadeh et al. Saving energy and spectrum in enabling URLLC services: A scalable RL solution
CN112751595B (en) Beam adjustment method, device, storage medium and source base station
CN102740325B (en) Method, device for acquiring channel information and method, device for optimizing beam forming
KR102465448B1 (en) Method and Apparatus for Beam Forming in MIMO System
CN111835388B (en) Communication adjustment method, terminal, device, base station and medium in MIMO system
EP4231458A1 (en) Reflective surface adjustment method and related apparatus
CN113644946B (en) Deep learning dynamic beam forming method for user-oriented central network
CN101989875B (en) Multi-cell interference suppression method and base station controller
CN116488747A (en) Information interaction method and device and communication equipment
CN116998175A (en) Reinforcement learning for SON parameter optimization
Thomas et al. Massive MISO IBC Beamforming‐a Multi-Antenna Stochastic Geometry Perspective
Almasi et al. Reconfigurable intelligent surface-aided NOMA with limited feedback
WO2024050667A1 (en) Communication method and related apparatus
Shi et al. SINR constrained beamforming for a MIMO multi-user downlink system

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