CN111866979A - Base station and channel dynamic allocation method based on multi-arm slot machine online learning mechanism - Google Patents

Base station and channel dynamic allocation method based on multi-arm slot machine online learning mechanism Download PDF

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CN111866979A
CN111866979A CN202010473494.0A CN202010473494A CN111866979A CN 111866979 A CN111866979 A CN 111866979A CN 202010473494 A CN202010473494 A CN 202010473494A CN 111866979 A CN111866979 A CN 111866979A
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channels
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CN111866979B (en
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李峰
于东晓
刘政阳
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Shandong University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/08—Reselecting an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/16—Performing reselection for specific purposes
    • H04W36/22—Performing reselection for specific purposes for handling the traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/30—Reselection being triggered by specific parameters by measured or perceived connection quality data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria

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Abstract

The invention belongs to the technical field of cognitive radio networks, and particularly relates to a base station and channel dynamic allocation method. The method comprises the following steps: initialization, for any user
Figure DDA0002515075240000011
Base station
Figure DDA0002515075240000012
And channel
Figure DDA0002515075240000013
Initializing gammai，j，k(0) 1 is ═ 1; enabling each user i to be sequentially connected with a base station j which is 1, 2, M and sequentially using a channel K which is 1, 2, …, K for data transmission; if the transmission is successful, ri，j，k(0) 1 is ═ 1; otherwise, ri，j，k(0) 0; order to
Figure DDA0002515075240000014
In each time slot T being 1, 2, … T, the dynamic allocation scheme of base station and channel is obtained by using the multi-arm slot machine online learning mechanism
Figure DDA0002515075240000015
And performs data transmission according to the scheme. The method of the invention has simple algorithm, improves the whole data transmission throughput of the secondary user as much as possible and realizes the dynamic allocation of the base station and the channel.

Description

Base station and channel dynamic allocation method based on multi-arm slot machine online learning mechanism
Technical Field
The invention belongs to the technical field of cognitive radio networks, and particularly relates to a dynamic allocation method of a base station and a channel.
Background
Cognitive Radio (CR) technology may utilize a dynamic channel access technology to improve the utilization efficiency of limited spectrum resources. In a Cognitive Radio Network (CRNs), users authorized to obtain spectrum become Primary users (Primary users), and those users not authorized become Secondary users (Secondary users). The primary user enjoys the priority of using the spectrum resources, not only can use the spectrum resources at any time, but also can give up the idle spectrum to the secondary users, so that the secondary users can share the spectrum resources on the premise of not influencing the primary user.
In a cognitive radio network, a core problem is how to improve the utilization efficiency of frequency band resources by secondary users (hereinafter, referred to as "users" without ambiguity). We consider a typical cognitive radio network system: a group of users is connected to a given group of base stations for data transmission in an access (orthogonal) channel. Then the problem is how to dynamically allocate base stations and channels to these users in order to achieve the goal of improving the overall data throughput of the users. Due to the wide distribution of base stations and users, the transmission links between a base station and (secondary) users may be close to different primary users, so that even the quality of the same channel on different transmission links is different. Furthermore, it is not always possible to measure the quality distribution of a channel over different transmission links in advance. At the same time, the base stations usually have different capacities, i.e. the maximum number of users that can be connected for parallel transmission of data. Although some existing channel allocation strategies propose to solve the problem of uncertainty of channel quality by using a machine learning technology (such as a Multi-arm slot machine (Multi-arm band) method, most existing strategies only consider the problem of matching between channels and users and cannot be directly applied to the problem of allocation of base stations and channels.
Disclosure of Invention
Aiming at the technical problems, the invention provides a dynamic allocation method of a base station and a channel based on a multi-arm slot machine online learning mechanism, which is oriented to a cognitive radio network, dynamically allocates the base station and the channel to users by learning the quality distribution of the channel on different user-base station transmission links through the multi-arm slot machine mechanism, improves the overall throughput rate of the users as much as possible, and meets the following constraints: 1) one user can only link one base station and access one channel at any time; 2) only one channel can be accessed by one base station at any time; 3) the number of users linked by a base station at any one time cannot exceed its capacity.
In order to solve the technical problem, the invention adopts the technical scheme that: a base station and channel dynamic allocation method based on a multi-arm slot machine online learning mechanism comprises the following steps:
s101: initialization, for any user
Figure BDA0002515075220000021
Base station
Figure BDA0002515075220000022
And channel
Figure BDA0002515075220000023
Initializing gammai，j，k(0) 1 is ═ 1; enabling each user i to be sequentially connected with a base station j which is 1, 2, M and sequentially using a channel K which is 1, 2, …, K for data transmission; if the transmission is successful, ri，j，k(0) 1 is ═ 1; otherwise, ri，j，k(0) 0; order to
Figure BDA0002515075220000024
Wherein, γi，j，k(t) indicates the user up to time slot t
Figure BDA0002515075220000025
Through a base station
Figure BDA0002515075220000026
Access channel
Figure BDA0002515075220000027
The number of times of (c); r isi，j，k(t) is belonged to {0, 1} to indicate whether the user i successfully transmits data through the base station j and the channel k in the time slot t;
Figure BDA0002515075220000028
indicating the times of successful data transmission of the user i by accessing the channel k through the base station j until the time slot t;
s102: in each time slot T-1, 2, … T, the following steps are performed:
1021. for each user
Figure BDA0002515075220000029
Base station
Figure BDA00025150752200000210
Channel with a plurality of channels
Figure BDA00025150752200000211
Calculate its weight ui，j，k(t)；
Figure BDA00025150752200000212
1022. Using a greedy algorithm based on
Figure BDA00025150752200000213
Calculating an allocation scheme:
Figure BDA00025150752200000214
wherein,
Figure BDA00025150752200000215
indicating the base station to which user i is to connect in time slot t,
Figure BDA00025150752200000216
representing user i and base station bi(t) channels accessed in time slot t;
1023. let each user i connect to base station bi(t) and using channel ci(t) transmitting data, and updating r according to the transmission resulti，j，k(t): i.e. when the transmission is successful, ri，j，k(t) ═ 1; otherwise, ri，j，k(t)＝0；
1024. For each user
Figure BDA00025150752200000217
Base station
Figure BDA00025150752200000218
Channel with a plurality of channels
Figure BDA00025150752200000219
Update it
Figure BDA00025150752200000220
And gammai，j，k(t): if (i, j, k) e.phi,
Figure BDA0002515075220000031
if not, then,
Figure BDA0002515075220000032
and jumps to step 1021.
As a preferred aspect of the present invention, in step 1022, the method for calculating the allocation scheme Φ (t) specifically includes:
(3) initialization
Figure BDA0002515075220000033
(4) If it is
Figure BDA0002515075220000034
If the number is null, outputting a distribution scheme phi (t); otherwise, the following steps are executed:
a. For any base station
Figure BDA0002515075220000035
And arbitrary channels
Figure BDA0002515075220000036
Let lj，k＝min{N′，τjH, calculate ui，j，k(t) the greatest weight of lj，kSet of individual users
Figure BDA0002515075220000037
b. Order of calculation
Figure BDA0002515075220000038
Base station with the largest value
Figure BDA0002515075220000039
And channel
Figure BDA00025150752200000310
Namely, it is
Figure BDA00025150752200000311
c. For each user
Figure BDA00025150752200000312
Allocation base station j*And channel k*I.e. by
Φ(t)←Φ(t)∪{(i，j*，k*)}
d. Updating
Figure BDA00025150752200000313
And jumping to the step (2).
Compared with the prior art, the base station and channel dynamic allocation method based on the multi-arm slot machine online learning mechanism of the invention utilizes the multi-arm slot machine mechanism to learn the quality distribution of the channels on different user-base station transmission links online, and improves the overall throughput rate of users as much as possible by dynamically allocating the base station and the channels to the users. The difference between the expected cumulative throughput achievable by the method of the present invention and the expected cumulative throughput achievable under off-line conditions (i.e., known channel quality distribution) is linear with the number of users, the number of base stations, and the number of channels, and increases at an exponential rate of o (logt) over a finite time T.
Drawings
Fig. 1 is an overall flowchart of a base station and a channel dynamic allocation method according to an embodiment of the present invention;
FIG. 2 is a diagram of utilizing weight sets in an embodiment of the present invention
Figure BDA0002515075220000041
A flow chart for calculating the base station and channel allocation scheme Φ (t).
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
On the contrary, the invention is intended to cover alternatives, modifications, equivalents and alternatives which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the following detailed description of the present invention, certain specific details are set forth in order to provide a better understanding of the present invention. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details.
One embodiment provided by the invention is as follows: the flow of the method for dynamically allocating base stations and channels based on the online learning mechanism of the multi-arm slot machine is shown in fig. 1, and specifically comprises the following steps:
order to
Figure BDA0002515075220000042
And
Figure BDA0002515075220000043
respectively representing a set of N (secondary) users, M base stations and K channels. Without loss of generality, assume M < K < N. Let τ bejIndicating a base station
Figure BDA0002515075220000044
The capacity of (c). Suppose that time is divided into a set of time slots T0, 1, 2, …, T. Let gamma be i，j，k(t) indicates the user up to time slot t
Figure BDA0002515075220000045
Through a base station
Figure BDA0002515075220000046
Access channel
Figure BDA0002515075220000047
Number of times of (r)i，j，k(t) e 0, 1 indicates whether user i successfully transmits data through base station j and channel k in time slot t,
Figure BDA0002515075220000048
Figure BDA0002515075220000049
and the number of times of successful data transmission by the user i accessing the channel k through the base station j until the time slot t is shown. The invention is realized by the following technical scheme:
s101, an initialization phase. For any user
Figure BDA00025150752200000410
Base station
Figure BDA00025150752200000411
And channel
Figure BDA00025150752200000412
Initializing gammai，j，k(0)＝1；
Each user i is sequentially connected to the base station j 1, 2.. times.m and performs data transmission using the channel K1, 2, …, K in turn. If the transmission is successful, ri，j，k(0) 1 is ═ 1; otherwise, ri，j，k(0) 0. Order to
Figure BDA0002515075220000051
S102, in each time slot T1, 2, … T, executing the following steps:
1021. for each user
Figure BDA0002515075220000052
Base station
Figure BDA0002515075220000053
Channel with a plurality of channels
Figure BDA0002515075220000054
Calculate its weight ui，j，k(t)；
Figure BDA0002515075220000055
1022. Using a greedy algorithm based on
Figure BDA0002515075220000056
Computing allocation schemes
Figure BDA0002515075220000057
Wherein,
Figure BDA0002515075220000058
indicating the base station to which user i is to connect in time slot t,
Figure BDA0002515075220000059
representing user i and base station bi(t) channels accessed in time slot t;
the method for calculating the base station and the channel allocation scheme Φ (t) is shown in fig. 2, and comprises the following specific steps:
(1) initialization
Figure BDA00025150752200000510
(2) If, if
Figure BDA00025150752200000511
If the number is null, outputting a distribution scheme phi (t); otherwise, the following steps are executed:
a. For any base station
Figure BDA00025150752200000512
And arbitrary channels
Figure BDA00025150752200000513
Let lj，k＝min{N′，τjH, calculate ui，j，k(t) the greatest weight of lj，kSet of individual users
Figure BDA00025150752200000514
b. Order of calculation
Figure BDA00025150752200000515
Base station with the largest value
Figure BDA00025150752200000516
And channel
Figure BDA00025150752200000517
Namely:
Figure BDA00025150752200000518
c. for each user
Figure BDA00025150752200000519
Allocation base station j*And channel k*Namely:
Φ(t)←Φ(t)∪{(i，j*，k*)}
d. updating
Figure BDA00025150752200000520
And jumping to the step (2).
1023. Let each user i connect to base station bi(t) and using channel ci(t) transmitting data, and updating r according to the transmission resulti，j，k(t): when the transmission is successful, ri，j，k(t) ═ 1; otherwise, ri，j，k(t)＝0)；
1024. For each user
Figure BDA0002515075220000061
Base station
Figure BDA0002515075220000062
Channel with a plurality of channels
Figure BDA0002515075220000063
Update it
Figure BDA0002515075220000064
And gammai，j，k(t): if (i, j, k) e.phi,
Figure BDA0002515075220000065
if not, then,
Figure BDA0002515075220000066
and jumps to step 1021.

Claims (2)

1. A base station and channel dynamic allocation method based on a multi-arm slot machine online learning mechanism is characterized by comprising the following steps:
s101: initialization, for any user
Figure FDA0002515075210000011
Base station
Figure FDA0002515075210000012
And channel
Figure FDA0002515075210000013
Initialization ri，j，k(0) 1 is ═ 1; enabling each user i to be sequentially connected with a base station j which is 1, 2, M and sequentially using a channel K which is 1, 2, …, K for data transmission; if the transmission is successful, ri，j，k(0) 1 is ═ 1; otherwise, ri，j，k(0) 0; order to
Figure FDA0002515075210000014
Wherein, γi，j，k(t) indicates the user up to time slot t
Figure FDA0002515075210000015
Through a base station
Figure FDA0002515075210000016
Access channel
Figure FDA0002515075210000017
Number of times of (r)i，j，k(t) e 0, 1 indicates whether user i successfully transmits data through base station j and channel k in time slot t,
Figure FDA0002515075210000018
Indicating the times of successful data transmission of the user i by accessing the channel k through the base station j until the time slot t;
s102: in each time slot T-1, 2, … T, the following steps are performed:
1021. for each user
Figure FDA0002515075210000019
Base station
Figure FDA00025150752100000110
Channel with a plurality of channels
Figure FDA00025150752100000111
Calculate its weight ui，j，k(t)；
Figure FDA00025150752100000112
1022. Using a greedy algorithm based on
Figure FDA00025150752100000113
Calculating an allocation scheme:
Figure FDA00025150752100000114
wherein,
Figure FDA00025150752100000115
indicating the base station to which user i is to connect in time slot t,
Figure FDA00025150752100000116
representing user i and base station bi(t) channels accessed in time slot t;
1023. let each user i connect to base station bi(t) and using channel ci(t) transmitting data, and updating r according to the transmission resulti，j，k(t): i.e. when the transmission is successful, ri，j，k(t) ═ 1; otherwise, ri，j，k(t)＝0；
1024. For each user
Figure FDA00025150752100000117
Base station
Figure FDA00025150752100000118
Channel with a plurality of channels
Figure FDA00025150752100000119
Update it
Figure FDA00025150752100000120
And ri，j，k(t): if (i, j, k) e.phi,
Figure FDA0002515075210000021
if not, then,
Figure FDA0002515075210000022
and jumps to step 1021.
2. The method for dynamically allocating base stations and channels based on the dobby slot machine online learning mechanism as claimed in claim 1, wherein the calculation method of the allocation scheme Φ (t) in the step 1022 comprises:
(1) initialization
Figure FDA0002515075210000023
(2) If it is
Figure FDA0002515075210000024
If the number is null, outputting a distribution scheme phi (t); otherwise, the following steps are executed:
a. for any base station
Figure FDA0002515075210000025
And arbitrary channels
Figure FDA0002515075210000026
Let lj，k＝min{N′，τjH, calculate ui，j，k(t) the greatest weight of l j，kSet of individual users
Figure FDA0002515075210000027
b. Order of calculation
Figure FDA0002515075210000028
Base station with the largest value
Figure FDA0002515075210000029
And channel
Figure FDA00025150752100000210
Namely, it is
Figure FDA00025150752100000211
c. For each user
Figure FDA00025150752100000212
Allocation base station j*And channel k*I.e. by
Φ(t)←Φ(t)∪{(i，j*，k*)}
d. Updating
Figure FDA00025150752100000213
And jumping to the step (2).
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