Joint Task Offloading and Resource Allocation for Space–Air–Ground Collaborative Network
Abstract
:1. Introduction
- How to properly control the transmission power of ground devices and the HAP drone? In this system, distinct communication methods are employed for the interactions between devices and the HAP drone, as well as between the HAP drone and LEO satellites. Consequently, it is imperative to develop specific power control strategies tailored to each transmission method.
- How to reasonably allocate the computing resources of each edge server? In this system, the HAP drone can directly obtain the computing capability of edge nodes (deployed on LEO satellites) and the computing requirements of GDs. Based on this information, HAP drones need to allocate a reasonable size of computing resources for each task.
- How to make a task-offloading decision? Considering a partial offloading model, each task can be split into two parts: the first part of it is processed at the HAP drone and the rest of it is offloaded to LEO satellite for processing. Therefore, the task-splitting strategy needs to be appropriately made. In addition, there are multiple LEO satellites in the field view of the HAP drone, and it is necessary to determine the target LEO satellite for each task offloading.
- (1)
- Considering the limited energy and resources of nodes in the system, we formulate an optimization problem of joint task offloading and resource allocation, aiming to minimize the weighted total energy consumption of the system. This problem is a mixed-integer non-linear programming (MINLP) problem.
- (2)
- We propose a low-complexity iterative algorithm based on a block coordinate descent (BCD) method to solve this MINLP problem, which reduces the complexity of the original problem by converting the original problem into two subproblems for the iterative solution. For the first subproblem, we transform the problem into a convex optimization problem and solve it with the convex algorithm. For the second subproblem, we convert this to a continuous variable problem by using a penalty-based transformation, and then we solve it by a concave–convex procedure (CCP)-based algorithm.
- (3)
- The simulation experiments have verified the convergence of the proposed algorithm in this paper. Furthermore, compared to the other two benchmark algorithms, the algorithm proposed in this paper consistently achieves a smaller overall system-weighted energy consumption under the same conditions.
2. Related Works
2.1. Space–Air–Ground Collaborative Edge Computing
2.2. NOMA-Assisted Edge Computing
3. System Model
3.1. NOMA-Based Communication Model
3.1.1. GD-HAP Drone Uplink Communication Model
3.1.2. Consideration of SIC Decoding
3.2. FDMA-Based Communication Model
3.3. Task Offloading and Computation Model
3.3.1. GD-HAP Drone Task Offloading Model
3.3.2. HAP Drone Transmission and Computation Model
3.3.3. LEO Satellite Computation Model
3.4. Overall Delay and Energy Consumption
4. Strategy Design and Problem Formulation
4.1. Strategy Design
- Information collection: in this step, the HAP drone collects information from LEO satellites within its visual range, and information from GDs connected to the HAP drone (including computational resources, channel information, etc.).
- Task-offloading request: In this step, the GDs connected to the HAP drone send a task-offloading request to the HAP drone, which includes specific information about the task, such as the data size, required CPU cycles per bit of data processing, and the maximum processing tolerance delay.
- Strategy-making and distribution: After the HAP drone collects information from each node and receives task-offloading requests from the GDs, the HAP drone makes an appropriate strategy for resource allocation and task offloading based on this information. The resource allocation and task-offloading strategy will be sent to the respective GDs and LEO satellites via C-band and Ka-band.
- Task processing: After receiving the resource allocation and task-offloading strategy from the HAP drone, the GDs send the task to the HAP drone according to the strategy, and then the HAP drone and LEO satellites process the tasks based on the resource allocation and task-offloading strategy.
4.2. Problem Formulation
5. Algorithm Design for
5.1. Algorithm Design for
5.2. Algorithm Design for
Algorithm 1: Joint Task-Offloading and Resource Allocation Algorithm for solving |
1: : maximum tolerance , constant parameter , where , the maximum number of iterations , initial feasible point . 2: to 3: Update the communication and computation resource allocation strategy by solving based on . 4: Update variables with fixed variables based on 5: Obtain optimal by solving with given . 6: Update penalizing coefficient by 7:
8: break. 9: 10:
11: : The optimal policy and optimal energy system |
5.3. Complexity Analysis
6. Numerical Result
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Mei, C.; Gao, C.; Wang, H.; Xing, Y.; Ju, N.; Hu, B. Joint Task Offloading and Resource Allocation for Space–Air–Ground Collaborative Network. Drones 2023, 7, 482. https://doi.org/10.3390/drones7070482
Mei C, Gao C, Wang H, Xing Y, Ju N, Hu B. Joint Task Offloading and Resource Allocation for Space–Air–Ground Collaborative Network. Drones. 2023; 7(7):482. https://doi.org/10.3390/drones7070482
Chicago/Turabian StyleMei, Chengli, Cheng Gao, Heng Wang, Yanxia Xing, Ningyao Ju, and Bo Hu. 2023. "Joint Task Offloading and Resource Allocation for Space–Air–Ground Collaborative Network" Drones 7, no. 7: 482. https://doi.org/10.3390/drones7070482