CN112597388B - Cache-enabled D2D communication joint recommendation and caching method - Google Patents
Cache-enabled D2D communication joint recommendation and caching method Download PDFInfo
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Abstract
The invention discloses a cache-enabled D2D communication joint recommendation and cache method, which comprises the steps of constructing an optimization function by taking the minimum file acquisition cost of a user as an optimization target under the conditions of limited cache capacity and limited recommended quantity of the user, obtaining a recommendation scheme for each user by adopting an annealing algorithm, obtaining the cache scheme of each user by using a knapsack algorithm, performing iterative optimization on the cache scheme and the recommendation scheme, obtaining the optimal solution of the recommendation scheme and the cache scheme, and realizing the minimum average cost of the user. The invention combines recommendation and caching, reduces the influence of different preferences of users on caching, can reduce the average cost of the users by the proposed recommendation and caching scheme, stimulates the users to use D2D communication, and reduces network pressure.
Description
Technical Field
The invention relates to a recommendation and caching method, in particular to a cache-enabled D2D communication joint recommendation and caching method.
Background
The explosive growth of intelligent mobile devices and intelligent applications has led to a dramatic increase in mobile data traffic, and traditional cellular networks have been greatly challenged. Device-to-Device (D2D) technology, one of the key technologies for fifth generation mobile communication, allows direct communication between nearby devices in a communication network without the help of infrastructure such as core devices or center devices, thereby greatly reducing data pressure on the core network of the communication system. The caching technology is to store file content items (including videos, web pages and the like) required by a user in a caching entity in advance, so that the requirement of the user on the content can be effectively realized, the transmission delay is greatly reduced, and the energy loss caused by repeated transmission is reduced. Combining D2D with caching is an effective way to cope with the huge traffic of the network.
However, there are some problems in cache-enabled D2D communication, such as cellular networks generally have smaller cache capacities compared to wired networks, and users 'preferences for different contents vary, and the gain brought to the network by caching a part of the contents in advance in the limited cache capacity depends on the same degree of the user's preferences. If the user's preferences for content are very different, it is difficult to get significant gains even if the caching strategy is optimized. The recommendation system is introduced to reshape the request probability of the user for different contents to a certain extent, so that the user preference distribution tends to be uniform, but if the recommended contents are low in user preference degree, the cache contents cannot be fully utilized, and the problems of resource shortage and serious base station load are caused. The existing combined recommendation and caching method considers that the recommendation is utilized to remodel the request probability of the user for the content, and then files are cached at a base station end, so that the number of times of using a backhaul link is reduced. However, even if the request probability is reformed through recommendation, the content requested by the user still has a certain difference, only caching part of the file at the base station end cannot meet the content requests of all users, and when the user is far away from the base station, the user still needs more cost to obtain the required content.
Disclosure of Invention
The invention aims to: aiming at the defects of the prior art, the invention provides a cache-enabled D2D communication joint recommendation and caching method, which is used for designing a recommendation and caching strategy under the condition of small caching capacity, reducing the average cost of a user, stimulating the user to use D2D communication and relieving the pressure on a network.
The technical scheme is as follows: the invention relates to a cache-enabled D2D communication joint recommendation and cache method, which aims to establish the minimum average cost of users, decomposes a problem into two sub-problems of recommendation and cache by taking the cache capacity and the system recommendation number of the users as an optimization function of a constraint condition, solves the sub-problems of recommendation by applying an annealing algorithm, solves the sub-problems of cache by applying a knapsack algorithm, and finally circularly iterates the two sub-problems to obtain an optimal recommendation and cache scheme i Each user can cache the total size not exceeding C k Characterized in that said method comprises the steps of:
(1) Calculating the request probability of each user for the content;
(2) Constructing an optimization function taking the minimum average cost of the files acquired by the system as an optimization target;
(3) A buffer scheme is randomly initialized for each user, namely the total size of the random buffer is notOver C k A set of contents of (a);
(4) Based on the caching scheme in the step (3), caching conditions of other users in the communication range of the user k can be obtained, the average cost of the current user k is calculated through an objective function according to the content request probability of the user k and the caching files of the surrounding users, and an optimization function is solved through an annealing algorithm to obtain a recommendation scheme with the minimum average cost of the user k;
(5) Calculating the new request probability of each user for the content based on the recommendation scheme in the step (4), fixing the cache schemes of other users, and solving an optimization function for each user by using a knapsack algorithm in sequence to obtain the cache scheme with the minimum total average cost of the system;
(6) And (5) replacing the caching scheme obtained in the step (5) into the step (3), circularly performing the steps (4) and (5), and stopping iteration when the recommended scheme and the caching scheme are not updated any more.
The step (1) comprises the following steps:
calculating the request probability of the user to the recommended R items of content as follows:
the probability of a request for the remaining content that is not recommended is:
wherein the content of the first and second substances,
indicating how well user k accepts the recommendation,
to the extent of the preference of user k for content i,
an increase in the probability that the R items of content are requested for recommendation;
the probability that end user k requests content i is:
wherein x k，i E {0,1} is an indicator variable of the recommended strategy, if x k，i A value of 1 indicates that content i is recommended to user k, and otherwise, no recommendation is made.
The optimization function in the step (2) is as follows:
x k，i ∈{0，1}， (4e)
y k，i ∈{0，1}， (4f)
the optimization function of the recommended scheme obtained based on the optimization function is as follows:
s.t.(4c)，(4d)，(4e)；
the optimization function of the caching scheme is as follows:
s.t.(4b)，(4f)
wherein d is kji Represents the charge of user k to obtain content i from user j, when i = b the user obtains content from base station, y k，i E {0,1} is an indicator variable of the cache policy if y k，i If the number is 1, caching the content i in the own cache space by the user k, otherwise, not caching; l is i Is the size of content i, C k Represents the buffer capacity of user k; when the user's preference degree for the content is larger than
When so, accepting the recommendation of the content; (4a) The formula is an objective function of the optimization problem, (4 b) is the buffer capacity limit of the user, (4 c) indicates that the number of contents recommended to the user by the system is R, (4 d) indicates that the contents recommended to the user by the system are the contents in which the user is interested, (4 e) and (4 f) indicate that the indicating variable is 0 or 1.
The step (4) comprises the following steps:
(41) Based on the caching scheme in the step (3), randomly recommending R items of content to the user k, and calculating the average cost C (k) of the user k;
(42) Randomly selecting one of R items of contents recommended to a user k, exchanging the R item of contents with non-recommended contents, and calculating new average cost C' (k);
(43) Judging whether C' (k) is smaller than C (k), if so, taking the recommended scheme of the step 42 as a new recommended scheme; if not, then the probability is used
Update the recommendation according to the step of step 42, wherein B c Is the boltzmann constant, and is,
is the minimum average cost of user k in the loop;
(44) Repeating the step (42) when the cycle number reaches t max Stopping the iteration of the recommendation strategy, and setting the cycle number as t max The R items of content are recommended to the user.
The step (5) specifically comprises the following steps:
(51) Fixing the cache schemes of other users, updating the optimal cache scheme for the users 1 to K by using a knapsack algorithm in sequence, solving the knapsack problem of the user K by using dynamic programming, and specifically comprising the following steps of: s i，c Represents a subset of the file {1, 2.,. I }, 0 < I < I, the total size of the subset not exceeding C (0 < C < C) k ) And the total cost is minimal, f (i, c) denotes the use of the subset S i，c Total cost of the system, L i Denotes the size of the file i, denoted by f' (i +1, c)
The total system value of f (i, c) is given by the following equation:
all f (I, C), f (I, C) are obtained by the formula (7) k ) Corresponding to
Namely the caching scheme of the user k;
(52) Calculating the total average cost of all users before and after the update of the user k, if the total average cost after the update is reduced, keeping the update, otherwise, taking a cache scheme before the update;
(53) After the step (52) is performed on the user K, the next round of circulation is performed by returning to the step (51) until the total average cost in the whole round of circulation is not changed.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages: the optimal recommendation and cache strategy is obtained by jointly optimizing the recommendation and cache strategy, the influence of different preferences of users on the cache is reduced, the cache strategy is designed at a user side, the distance from the user side to a requesting user is short, the number of nodes is large, different files can be cached according to different request probabilities, the method is high in operation efficiency, and the pressure of the system is reduced.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 shows D2D communication radius R D2D =80m, method convergence diagrams under different buffer capacities;
FIG. 3 shows D2D communication radius R D2D =150m, method convergence diagrams under different buffer capacities;
FIG. 4 shows average user cost versus buffer capacity at D2D communication radius R D2D Pattern of change when =80 m;
FIG. 5 shows average user cost versus buffer size at D2D communication radius R D2D Pattern of change when =150 m.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
The invention designs a recommendation and cache method in a D2D communication system applying a cache technology, considers the conditions of limited user cache capacity and limited recommendation quantity, and optimizes the recommendation and cache strategy by taking the minimum average cost of the user for acquiring files as an optimization target. Dividing the problem into two sub-problems of a caching strategy and a recommendation strategy, and obtaining recommended contents for each user by adopting an annealing algorithm under the condition of fixing the caching strategy; then, a recommendation strategy is fixed, and a cache scheme of each user is obtained by using a knapsack algorithm. The two algorithms are iterated through recycling to minimize the average cost of the user.
First, the probability of each user's request for different content is calculated: the system consists of K users and I content items, with different users having different degrees of preference for different content items. The system recommends R items of content to each user, the size of the content item being L i Each user can cache the total size not exceeding C k The content of (1). Finally, calculating the request probability of the user to the recommended R items of content as follows:
the probability of a request for the remaining content that is not recommended is:
wherein the content of the first and second substances,
indicating how well user k accepts the recommendation,
to the extent of the preference of user k for content i,
to recommend an increase in the probability that the R items of content are requested. The probability that end user k requests content i is:
wherein x k，i E {0,1} is an indicator variable of the recommendation policy, if x k，i A value of 1 indicates that content i is recommended to user k, otherwise no recommendation is made.
Constructing an optimization function taking the minimum average cost of the files acquired by the user as an optimization target:
x k，i ∈{0，1}， (4e)
y k，i ∈{0，1}， (4f)
wherein d is kji Represents the cost of user k to obtain content i to user j, when i = b the user obtains content to the base station, y k，i E {0,1} is an indicator variable of the cache policy if y k，i If the value is 1, the user k caches the content i in the own cache space, otherwise, the content i is not cached. L is i Is the size of content i, C k Indicating the buffer capacity of user k. The user will not accept all recommended content, only if he prefers it more than
The recommendation of the content is accepted. (4a) The formula is an objective function of the optimization problem, (4 b) is the buffer capacity limit of the user, (4 c) indicates that the number of contents recommended to the user by the system is R, (4 d) indicates that the contents recommended to the user by the system are the contents in which the user is interested, (4 e) and (4 f) indicate that the indicating variable is 0 or 1.
The method comprises the following steps of decomposing a question into two sub-questions of a recommendation strategy and a cache strategy, wherein the recommendation sub-questions are as follows:
s.t.(4c)，(4d)，(4e)；
the cache sub-problem is:
s.t.(4b)，(4f).
a caching strategy is randomly initialized.
Designing a recommendation strategy of a user:
(41) The recommendation strategies of different users are independent from each other, so that the recommendation strategies can be formulated for each user respectively. Randomly recommending R items of content to a user k, and calculating the average cost C (k) of the user k according to an objective function:
(42) Randomly selecting one of the R items of content recommended to the user k, exchanging the R item of content with the unrecommended content, and then obtaining a new average cost C' (k);
(43) Judging whether C' (k) is smaller than C (k), if so, updating the recommendation strategy according to the content in the step 2; if not, then the probability P is used k Updating the recommendation strategy, probability P, according to the content in step 2 k Comprises the following steps:
wherein B is c Is a constant of boltzmann's constant,
is the minimum average cost of user k in the loop;
(44) Returning to the step 2, when the cycle number reaches t max And stopping circulation to obtain the recommendation strategy of the user k.
(5) Designing a caching strategy of a user:
(51) Fixing the cache schemes of other users, updating the optimal cache scheme for the users 1 to K by using a knapsack algorithm in sequence, and solving the knapsack problem of the user K by using dynamic programming, wherein the method comprises the following specific steps of: s i，c Represents a subset of the file {1, 2.,. I }, 0 < I < I, the total size of the subset not exceeding C (0 < C < C) k ) And the total cost is minimal, f (i, c) denotes the use of the subset S i，c Total cost of the system, L i Denotes the size of the file i, denoted by f' (i +1, c)
The total system value of f (i, c) is given by the following equation:
by passingEquation (7) yields all of f (I, C), f (I, C) k ) Corresponding to
Namely the caching scheme of the user k;
(52) Calculating the total average cost of all users before and after the update of the user k, if the total average cost after the update is reduced, keeping the update, otherwise, taking a cache scheme before the update;
(53) After the step (52) is performed on the user K, the next round of circulation is performed by returning to the step (51) until the total average cost in the whole round of circulation is not changed.
(6) And (5) returning to the step (5), and stopping iteration when the recommendation strategy and the cache strategy are not updated any more.
In summary, the invention decomposes the problem into two sub-problems of recommendation and cache by establishing an optimization function which takes the lowest average cost of the user as an objective function and the cache capacity and the system recommendation number of the user as constraint conditions, solves the sub-problems of recommendation by using an annealing algorithm, solves the sub-problems of cache by using a knapsack algorithm, and finally circularly iterates the two sub-problems to obtain the optimal recommendation and cache scheme.
As can be seen from FIGS. 2 and 3, the communication radius R in D2D D2D =80m, convergence can be achieved by two iterations when the buffer capacity is 5, convergence can be achieved by three iterations when the buffer capacities are 7, 9 and 11, respectively, and the communication radius R is D2D D2D When the length is not less than 80m, the convergence can be realized through two iterations, the method has high convergence speed, and the joint optimization method is proved to have higher calculation efficiency and reduce the operation burden of the system. Comparing fig. 2 and fig. 3, it can be seen that fig. 3 converges faster and has smaller average cost, which illustrates that the average cost can be further reduced by sharing content through D2D communication by more users and brings more gain to the system.
As can be seen from fig. 4 and 5, when the buffer capacity is smaller than the system recommended number 8, the average cost decreases rapidly, and as the buffer capacity increases, the average cost decreases; and when the cache capacity exceeds the system recommendation number, the average cost reduction speed tends to be flat, because when the cache capacity is large enough, the system can easily recommend the content which is interested in the user and is cached already. It can be seen that when the cache capacity is small, the average cost generated by the user adopting the cache scheme of the present invention is obviously lower than that of the cache scheme without recommendation, when the user preferences are different, the gain brought by optimizing the cache strategy alone is limited, and the combined recommendation cache method of the present invention effectively improves the network system performance.
Claims (2)
1. For a system consisting of K users and I content items, R content items are recommended to each user according to preference degrees of different users to different content items, wherein the size of each content item is L i Each user can cache the total size not exceeding C k Characterized in that said method comprises the steps of:
(1) Calculating the request probability of each user for the content;
(2) Constructing an optimization function taking the minimum average cost of the files acquired by the system as an optimization target;
(3) A buffer scheme is randomly initialized for each user, namely the total random buffer size does not exceed C k A set of contents of (a);
(4) Obtaining the caching conditions of other users in the communication range of the user k based on the caching scheme in the step (3), calculating the average cost of the current user k through an objective function according to the content request probability of the user k and the caching files of the surrounding users, and solving an optimization function by using an annealing algorithm to obtain a recommendation scheme with the minimum average cost of the user k;
(5) Calculating the new request probability of each user for the content based on the recommendation scheme in the step (4), fixing the cache schemes of other users, and solving an optimization function for each user by using a knapsack algorithm in sequence to obtain the cache scheme with the minimum total average cost of the system;
(6) Replacing the caching scheme obtained in the step (5) into the step (3), circularly performing the steps (4) and (5), stopping iteration when the recommended scheme and the caching scheme are not updated any more,
the optimization function is:
x k，i ∈{0，1}， (e)
y k，i ∈{0，1}， (f)
the optimization function of the recommended scheme obtained based on the optimization function is as follows:
s.t.(c)，(d)，(e)；
the optimization function of the caching scheme is as follows:
s.t.(b)，(f)
wherein d is kji Represents the charge of user k to obtain content i from user j, when i = b the user obtains content from base station, x k，i E {0,1} is an indicator variable of the recommendation policy, y k，i E {0,1} is an indicator variable of the cache policy, if y k，i If the number is 1, the user k caches the content i in the own cache space, otherwise, the content is not cachedStoring; l is a radical of an alcohol i Is the size of content i, C k Representing the buffer capacity of user k; when the user's preference degree for the content is larger than
When so, accepting the recommendation of the content; formula (a) is an objective function of the optimization problem, (b) is a cache capacity limit of the user, (c) represents that the number of contents recommended to the user by the system is R, (d) represents that the contents recommended to the user by the system are contents in which the user is interested, (e) and (f) represent that the indicating variable is 0 or 1,
the step (4) comprises the following steps:
(41) Based on the caching scheme in the step (3), randomly recommending R items of content to the user k, and calculating the average cost C (k) of the user k;
(42) Randomly selecting one of the R items of content recommended to the user k, exchanging the selected one with the unrecommended content, and calculating new average cost C' (k);
(43) Judging whether C' (k) is smaller than C (k), if so, taking the recommended scheme of the step (42) as a new recommended scheme; if not, then the probability is used
Updating the recommendation according to the step of step (42), wherein Bc is the Boltzmann constant,
is the minimum average cost of user k in the loop;
(44): repeating the step (42) when the cycle number reaches t max Stopping the iteration of the recommendation strategy, and setting the cycle number as t max The R items of content at the time are recommended to the user k,
the step (5) specifically comprises the following steps:
(51) Fixing the cache schemes of other users, updating the optimal cache scheme for the users 1 to k by using a knapsack algorithm in sequence, solving the knapsack problem of the user k by using dynamic programming, and specifically comprising the following steps of: s i，c Represents a subset of the file {1, 2.,. I }, 0 < I < I, the total size of this subsetSmall not more than C (0 < C < C) k ) And the total cost is minimal, f (i, c) denotes the use of the subset S i，c Total cost of the system, L i Denotes the size of the file i, denoted by f' (i +1, c)
The total system value of f (i, c) is given by the following equation:
all f (I, C), f (I, C) are obtained by the formula (7) k ) Corresponding to
Namely the caching scheme of the user k;
(52) Calculating the total average cost of all users before and after the updating of the user k, if the total average cost after the updating is reduced, keeping the updating, and if not, taking a cache scheme before the updating;
(53) After the step (52) is performed on the user K, the next round of circulation is performed by returning to the step (51) until the total average cost in the whole round of circulation is not changed.
2. The cache-enabled D2D communication joint recommendation and caching method according to claim 1, wherein the step (1) comprises the following steps:
calculating the request probability of the user to the recommended R items of content as follows:
the probability of a request for the remaining content that is not recommended is:
wherein, the first and the second end of the pipe are connected with each other,
indicating how well user k accepts the recommendation,
to the extent of the preference of user k for content i,
an increase in the probability that the R items of content are requested for recommendation;
the probability that end user k requests content i is:
wherein x k，i E {0,1} is an indicator variable of the recommendation policy, if x k，i A value of 1 indicates that content i is recommended to user k, and otherwise, no recommendation is made.
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| CN111860595A (en) * | 2020-06-17 | 2020-10-30 | 南京邮电大学 | Heterogeneous network cache decision method based on user preference prediction |
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