- freely available
Appl. Sci. 2018, 8(10), 1959; https://doi.org/10.3390/app8101959
- We first investigate the problem of joint optimization of UAVs’ caching contents and service locations with the goal of balancing the tradeoff between user’s service probability and transmission overhead under the consideration of the share of neighbor UAVs’ caching contents.
- The problem is modeled as a UAV caching game, which intends to find the optimal solution that maximizes UAVs’ performance indicators, i.e., the ratio of user’s service probability and transmission overhead. In addition, the proposed game is proved to be a potential game with at least one pure-strategy NE. Meanwhile, the optimal solution can be achieved by its optimal NE.
- The log-linear caching algorithm is proposed to achieve the desirable solution in joint caching contents and service locations optimization problem. The simulation results show that the proposed algorithm can converge to a great NE solution and guarantee the great performance of UAV-assisted networks, which demonstrates the algorithm’s validity and effectiveness.
2. System Model and Problem Formulation
2.1. System Model
2.2. Problem Formulation
3. UAV-Assisted Caching Game
3.1. Game Model
3.2. Analysis of NE
- The potential game has one pure strategy NE at least.
- Local or global maxima of potential function constitutes a pure strategy NE.
3.3. Log-Linear Caching Algorithm
|Algorithm 1: Log-linear caching algorithm (LCA)|
| 1. Initialization|
(1) Initialize randomly users’ locations, file popularity and file requests , .
Initialize randomly and , satisfying to and .
(2) Moreover, set k = 0 as the round count and as the maximum round.
2. Repeat Round
k = k + 1
Choose UAV n randomly.
(1) Calculate by taking the action .
(2) Generate randomly from its strategy space
(3) Calculate by taking the action and keep other UAVs’ actions.
(4) Update strategy:
where , and is the learning parameter.
Meanwhile, all other UAVs keep their actions, and .
(5) until or ,
3. End rounds
4. Simulation and Numerical Results and Discussion
4.1. Simulation Scenario
4.2. Convergence Behavior
4.3. Performance Analysis
4.3.1. Performance of UAV-Assisted Networks versus the Number of System Files
4.3.2. Performance of UAV-Assisted Networks versus the Number of Cache Space
4.3.3. Performance of UAV-Assisted Networks versus the Number of UAVs
Conflicts of Interest
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|M||Number of ground users|
|N||Number of unmanned aerial vehicles (UAVs)|
|f||Set of system files|
|i-th requested file|
|Probability of requesting the i-th file|
|Parameter of a Zipf Distribution|
|Overhead of user m to obtain files|
|Set of user m’s interested files|
|Action of UAV n|
|Location of UAV n|
|Caching contents of UAV n|
|Communication thresholds between UAVs|
|Physical distance from UAV i to j|
|Neighbor UAVs by one hop|
|Overhead that User m gets file k|
|UAV that is serving for User m|
|UAV that caches file k|
|Overhead per file per distance from UAV to user m|
|Successful communication probability from UAV n to user m|
|Channel power gain from UAV to user m|
|Distance from UAV to user m|
|Set of files that UAV caches|
|Transmit power of UAV n|
|Path loss exponent|
|Additional attenuation factor due to the non-line-of-sight (NLOS) link|
|Height of UAV|
|Set of ground users that UAV n serves|
|Strategy space of player n|
|Utility function of player n|
|R||Overhead of all ground users|
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