CN109861773A - A kind of multi-user and multi-channel network dynamic frequency spectrum access method based on on-line study - Google Patents
A kind of multi-user and multi-channel network dynamic frequency spectrum access method based on on-line study Download PDFInfo
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Abstract
The invention discloses a kind of multi-user and multi-channel network dynamic frequency spectrum access method based on on-line study, comprising the following steps: (10) generate Complete Bipartite Graph；(20) secondary user's are randomly ordered；(30) secondary user's transmission rate-transmission duration binary group estimation；(40) channel selection and data are transmitted；(50) transfer of virtual income calculation；(60) weight updates.Transmission rate and transmission duration when the present invention by accessing channel to secondary user's every time carry out on-line study, and select access that secondary user's throughput of transmissions is made it is expected maximum channel, improve secondary user's throughput of transmissions.
Description
Technical Field
The invention belongs to the technical field of wireless data transmission, and particularly relates to a multi-user multi-channel network dynamic spectrum access method based on online learning.
Background
Because the management of the authorized frequency band is full of the reality of low spectrum utilization rate, the cognitive radio network is widely concerned by the industry and academia as a solution to the problem of low spectrum efficiency. In cognitive radio networks, secondary users may access channels used by unauthorized users. Dynamic spectrum access allows secondary users to determine the current clear channels and opportunistically use those clear channels.
Aiming at a multi-user multi-channel cognitive radio network, each channel in the network is independently orthogonal, when a plurality of secondary users access the channel, each channel can be accessed by only one secondary user, otherwise, transmission failure can be caused due to collision problems. In the existing method, when a secondary user selects to access a transmission channel, it is generally assumed that the available transmission time length and the transmission rate of the channel follow the same statistical distribution, and the expected benefits obtained when different users access different channels are the same. However, the transmission of the secondary users is affected by frequency, communication distance, etc., and the transmission gain thereof on different channels dynamically changes. Each time a secondary user accesses a channel, there is no a priori knowledge of the transmission performance of each available channel, either by probing the channel to estimate the expectation of the transmission throughput, or by selecting a channel for data transmission based on existing information. When the secondary user selects the access channel, if the estimated available transmission time length is less than the actual available time length, the waste of the transmission time slot is caused, and if the estimated available transmission time length is greater than the actual available time length, the transmission failure of the secondary user is caused by the regression of the authorized user. Meanwhile, when the secondary users access different channels, the transmission rates which can be achieved are also different.
In summary, the prior art has the following problems: the transmission performance of the secondary user selecting the access channel is dynamically changed under the influence of factors such as frequency, communication distance and the like, and the transmission performance of the channel cannot be reflected in time under the assumption that the channel characteristics obey a statistical distribution rule, so that the transmission throughput of the secondary user is easily reduced.
Disclosure of Invention
The invention aims to provide a multi-user multi-channel network dynamic spectrum access method based on online learning, which improves the transmission throughput of secondary users.
The technical solution for realizing the purpose of the invention is as follows:
a multi-user multi-channel network dynamic spectrum access method based on online learning comprises the following steps:
(10) generating a full bipartite graph, generating a bipartite graph G ═ Ν ∪ K, E, from a set of secondary users Ν and a set of orthogonal channels K available in the network;
(20) secondary user random ordering: randomly generating a user rank o ═ { o ] from the secondary user number1,...,oNIn which o isj∈{1,...,N}，ojI denotes that the secondary user i is in the jth bit of the rank;
(30) secondary user transmission rate-transmission duration binary estimation: the secondary user calculates the probability distribution of the transmission rate-transmission time length binary group according to the weight of the transmission rate-transmission time length binary group of the access channel, and estimates the reachable transmission rate and the available transmission time length of the secondary user according to the probability distribution;
(40) channel selection and data transmission: according to the estimated transmission rate B of the access channeltAnd a transmission duration DtThe secondary user estimates the throughput which can be obtained by accessing each channel, selects the channel with the maximum throughput to access and transmits data;
(50) and (3) transmission virtual profit calculation: each secondary user calculates the normalized transmission throughput obtained on the currently selected channel according to the actual transmission rate and the available transmission duration in the data transmission process, and calculates the transmission virtual gain according to the normalized throughput;
(60) and (3) updating the weight: and calculating the weight and the weight sum of the transmission rate-transmission duration binary group according to the transmission virtual gain to be used as the weight for estimating the transmission rate and the transmission duration when the next selected channel is accessed.
Compared with the prior art, the invention has the following remarkable advantages: the invention improves the transmission throughput of the secondary user by online learning the transmission rate and the transmission duration of the secondary user when accessing the channel each time and selecting the channel which is accessed to enable the transmission throughput of the secondary user to be expected to be maximum.
Drawings
Fig. 1 is a main flow chart of the dynamic spectrum access method based on online learning according to the present invention.
Fig. 2 is an example of generating a complete bipartite graph from secondary users and available channels.
Fig. 3 is a flow chart of the secondary user transmission rate-transmission duration binary estimation step in fig. 1.
Fig. 4 is a flowchart of the channel selection and data transmission steps of fig. 1.
Fig. 5 is a flowchart of the transmission virtual profit calculation step in fig. 1.
Detailed Description
As shown in fig. 1, the present invention provides a multi-user multi-channel network dynamic spectrum access method based on online learning, which includes the following steps:
(10) generating a full bipartite graph, generating a bipartite graph G ═ (Ν ∪ K, E) from a set of secondary users Ν and a set of orthogonal channels K available in the network, where Ν { 1., N } represents the set of all secondary users in the network, K ═ { 1., K } represents the set of all available channels in the network, E represents the set of edges in bipartite graph G fig. 2 is an example of generating a bipartite graph from users and available channels.
(20) Secondary user random ordering: n secondary users in the network are respectively distributed with a serial number, and the user sequence is defined as o ═ o1,...,oNIn which o isj∈{1,...,N}，ojI denotes that the secondary user i is in the j-th bit of the rank order.
(30) Secondary user transmission rate-transmission duration binary estimation: the secondary user calculates the probability distribution of the transmission rate-transmission time length binary group according to the weight of the transmission rate-transmission time length binary group of the access channel, and estimates the reachable transmission rate and the available transmission time length of the secondary user according to the probability distribution;
as shown in fig. 3, the (30) secondary user transmission rate-available transmission duration binary estimation step includes:
(31) transmission rate-transmission duration probability distribution calculation: when the secondary user accesses the channel for the t time, the probability distribution of the transmission rate-transmission duration binary group is,
where B denotes the channel transmission rate, D denotes the transmission duration available to the secondary user, ωB,D(t-1) represents a transmission rate-transmission duration binary group (B) of the t-1 th access channel of the secondary userD), W (t-1) represents the weight sum of the transmission rate-transmission duration binary group when the secondary user accesses the channel t-1 time, BmaxIndicating the maximum transmission rate, DmaxIndicating maximum transmission duration, parameterFor adjusting the compromise between using known gains for transmission rate-transmission duration doublets and detecting new transmission rate-transmission duration doublets, NΨ＝BmaxDmaxT represents the total iteration turn, all the transmission rate-transmission duration duplets are given the same weight during initialization,
(32) transmission rate-transmission duration estimation: the secondary users are based on the probability distribution of all possible transmission rate-transmission duration doublets,estimating the transmission rate B of the t-th access channeltAnd a transmission duration Dt.
(40) Channel selection and data transmission: according to the estimated transmission rate B of the access channeltAnd a transmission duration DtThe secondary user estimates the throughput which can be obtained by accessing each channel, selects the channel with the maximum throughput to access and transmits data;
as shown in fig. 4, the (40) channel accessing step includes:
(41) channel selection: according to the sequence in the user random sequencing o, the secondary users select the channel which enables the transmission throughput of the secondary users to expect the maximum in sequence;
(42) updating the bipartite graph: according to the channels already selected by the secondary users, deleting the corresponding users and the selected channels thereof, and all other edges connecting the users and the channels in the graph G;
(43) data transmission: each secondary user accesses the selected channel and transmits data on that channel
(50) And (3) transmission virtual profit calculation: each secondary user calculates the normalized transmission throughput obtained on the currently selected channel according to the actual transmission rate and the available transmission duration in the data transmission process, and calculates the transmission virtual gain according to the normalized throughput;
each time, the user transmits data on the selected corresponding channel until the next selection of the transmission channel. Accordingly, each secondary user can obtain the actual transmission rate and the available transmission duration, and calculate the normalized transmission throughput according to the actual transmission situation.
As shown in fig. 5, the (50) virtual profit calculating step includes:
(51) normalized transmission throughput calculation: when the secondary user selects the channel for transmission for the tth time, the normalized transmission throughput is calculated according to the transmission rate and the available transmission time length which are achieved when the selected channel is actually transmitted,
Brrepresenting the actual transmission rate, DrRepresenting the actual transmission duration.
(52) And (3) transmission virtual profit calculation: calculating a transmission virtual gain according to the normalized transmission throughput,
parameter(s)Is an error compensation parameter for compensating an estimation error in estimating a transmission virtual profit, and epsilon e (0, 1).
(60) And (3) updating the weight: and calculating the weight and the weight sum of the transmission rate-transmission duration binary group according to the transmission virtual gain to be used as the weight for estimating the transmission rate and the transmission duration when the next selected channel is accessed.
The (60) weight updating step specifically includes:
the weight of the transmission rate-transmission duration doublet is updated by,
wherein,to control the learning speed control parameter of the algorithm learning speed, and the sum of the weights of the Tth time after accessing the channel and the transmission rate-transmission duration binary is updated to,
finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (5)
1. A multi-user multi-channel network dynamic spectrum access method based on online learning is characterized by comprising the following steps:
(10) generating a complete bipartite graph, generating a complete bipartite graph G ═ (Ν ∪ K, E) from the set of secondary users Ν and the set of orthogonal channels K available in the network;
(20) secondary user random ordering: randomly generating user sequence o ═ o according to secondary user sequence number1,...,oNIn which o isj∈{1,...,N}，ojI denotes that the secondary user i is in the jth bit of the rank;
(30) secondary user transmission rate-transmission duration binary estimation: the secondary user calculates the probability distribution of the transmission rate-transmission time length binary group according to the weight of the transmission rate-transmission time length binary group of the access channel, and estimates the reachable transmission rate and the available transmission time length of the secondary user according to the probability distribution;
(40) channel selection and data transmission: according to the estimated transmission rate B of the access channeltAnd a transmission duration DtThe secondary user estimates the throughput which can be obtained by accessing each channel, selects the channel with the maximum throughput to access and transmits data;
(50) and (3) transmission virtual profit calculation: each secondary user calculates the normalized transmission throughput obtained on the currently selected channel according to the actual transmission rate and the available transmission duration in the data transmission process, and calculates the transmission virtual gain according to the normalized throughput;
(60) and (3) updating the weight: and calculating the weight and the weight sum of the transmission rate-transmission duration binary group according to the transmission virtual gain to be used as the weight for estimating the transmission rate and the transmission duration when the next selected channel is accessed.
2. The method for accessing dynamic spectrum of multi-user multi-channel network based on online learning of claim 1, wherein the step of estimating the secondary user transmission rate-transmission duration binary set (30) comprises:
(31) transmission rate-transmission duration probability distribution calculation: when the secondary user accesses the channel for the t time, the probability distribution of the transmission rate-transmission duration binary group is,
where B denotes the channel transmission rate, D denotes the transmission duration available to the secondary user, ωB,D(t-1) represents the weight of the transmission rate-transmission duration binary group (B, D) when the secondary user accesses the channel t-1 time, and W (t-1) represents the transmission rate-transmission time when the secondary user accesses the channel t-1 timeSum of weights of long doublets, BmaxIndicating the maximum transmission rate, DmaxIndicating maximum transmission duration, parameterFor adjusting the compromise between using known gains for transmission rate-transmission duration doublets and detecting new transmission rate-transmission duration doublets, NΨ＝BmaxDmaxT represents the total iteration turn, all the transmission rate-transmission duration duplets are given the same weight during initialization,
(32) transmission rate-transmission duration estimation: the secondary users are based on the probability distribution of all possible transmission rate-transmission duration doublets,estimating the transmission rate B of the t-th access channeltAnd a transmission duration Dt。
3. The method for multi-user multi-channel network dynamic spectrum access based on online learning of claim 1, wherein the channel selection and data transmission step (40) comprises:
(41) channel selection: according to the sequence in the user random sequencing o, the secondary users select the channel which enables the transmission throughput of the secondary users to expect the maximum in sequence;
(42) updating the bipartite graph: according to the channels already selected by the secondary users, deleting the corresponding users and the selected channels thereof, and all other edges connecting the users and the channels in the graph G;
(43) data transmission: each secondary user accesses the selected channel and transmits data on that channel.
4. The method for multi-user multi-channel network dynamic spectrum access based on online learning of claim 1, wherein the step of (50) calculating the transmission virtual profit comprises:
(51) normalized transmission throughput calculation: when the secondary user selects the channel for transmission for the tth time, the normalized transmission throughput is calculated according to the transmission rate and the available transmission time length which are achieved when the selected channel is actually transmitted,
Brrepresenting the actual transmission rate, DrRepresenting the actual transmission duration.
(52) And (3) transmission virtual profit calculation: calculating a transmission virtual gain according to the normalized transmission throughput,
parameter(s)Is an error compensation parameter for compensating an estimation error in estimating a transmission virtual profit, and epsilon e (0, 1).
5. The method for accessing dynamic spectrum of multi-user multi-channel network based on online learning according to claim 1, wherein the step (60) of updating the weight specifically comprises:
the weight of the transmission rate-transmission duration doublet is updated by,
wherein,to control the learning speed control parameter of the algorithm learning speed, and the sum of the weights of the Tth time after accessing the channel and the transmission rate-transmission duration binary is updated to,
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