A d2d collaborative caching method based on incentive mechanism
An incentive mechanism and collaborative technology, applied in machine-to-machine/machine-type communication services, connection management, network traffic/resource management, etc., can solve user caching and transmission without incentives and no data requirements, and lack of caching users Incentives, effective joint design of user preference caching strategies, not considering user caching space constraints, etc.
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Embodiment 1
[0045] The schematic diagram of the D2D wireless caching system is as follows: figure 2 shown. There are N user equipment (User Equipment, UE) capable of D2D communication in the cell, called content requester terminals (Content Requester, CR), whose set is S CR ={CR n |n=1,2,...,N}. Some of the CRs have caching capabilities and are candidate caching helpers (Caching Helper, CH). Let the set of candidate cache helpers be S CH ={CH m |m=1,2,...,M}, CH m Owned cache space is S m . When m=n, CH m =CR n , that is, the same user is both CH and CR. If the Euclidean distance d between two users n,m is less than the target transmission distance R, the two users can communicate in D2D mode. with A=[a n,m ] N×N Indicates the user topological relationship, a n,m =1 indicates that user n and user m are within the D2D communication range, otherwise a n,m =0. CH m The neighbor CR set is defined as N CR,m ={CR n |a n,m = 1}, CR n The set of neighbor CHs is defined as N...
Embodiment 2
[0128] The user equipments in the system are subject to random distribution, and some user equipments are randomly selected as candidate caching servers with caching capabilities. User preferences are generated based on file popularity following the Zipf distribution. Some parameter settings are shown in Table 3. For the sake of fairness and rationality, the NO terminal flow pricing benchmark α should not be the same as the unloading flow unit price βλ mThe value difference is too large. In this embodiment, the value of α is set to 2, the value of β is 0.5 (that is, the scaling factor k=1 / 4), and the excitation factor λ m The value range is [1,4]. Monte Carlo simulation is used, 1000 random simulations are performed, and the final result is obtained by taking the average value of all the results.
[0129] Table 3: Simulation parameter settings
[0130]
[0131] (1) Comparison benchmark:
[0132] Most Popular Caching Policy (Most Popular Caching, MPC): All cache servic...
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