# Mobile Communications, Computing and Caching Resources Optimization for Coded Caching with Device Computing

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Abstract:

Edge caching and computing have been regarded as an efficient approach to tackle the wireless spectrum crunch problem. In this paper, we design a general coded caching with device computing strategy for content computation, e.g., virtual reality (VR) rendering, to minimize the average transmission bandwidth with the caching capacity and...More

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Introduction

- Bandwidth saving is an eternal topic in wireless communications systems, especially in the era of shortage of wireless spectrum resource.
- Let N ⊆ F denote the set of computation tasks that are decided to be coded cached in each mobile device, i.e., cn = 1, for all n ∈ N .
- The input data of the computation tasks A, B, C is coded cached at each mobile device and N = F {A, B, C} where N = |F| = 3, that is c = {1, 1, 1} and d = 1.

Highlights

- Bandwidth saving is an eternal topic in wireless communications systems, especially in the era of shortage of wireless spectrum resource
- Bandwidth requirement in the wireless network has been greatly spurred by broadband applications and services, such as the immersive panoramic virtual reality (VR) video, high definition holographic gaming, and 8K/16K ultra-high definition video [1]
- The above studies reveal that the edge caching, e.g., at a base station (BS) or the mobile devices, has been regarded as a key enabling technology in future wireless networks to tackle the wireless spectrum crunch problem
- This paper proposes a coded caching with device computing strategy to minimize the average bandwidth consumption subject to the caching size and the energy of the mobile device, as well as the delay constraints
- The alternating direction method of multipliers (ADMM) algorithm is used to solve the computation programming, and we prove that the nonconvex problem can converge on monotropic program based on alternating direction method of multipliers, a stationary point of the computation programming is obtained
- Let N ⊆ F denote the set of computation tasks that are decided to be coded cached in each mobile device, i.e., cn = 1, for all n ∈ N

Results

- The mobile device k requests a computation task whose input data has been coded cached and decides to compute locally.
- Similar to Case 3,the task f in the mobile device k is computed by the MEC server, and has not been coded cached.
- Define BqI as the achievable bandwidth in the task request state Sq when the input data is decided to be coded cached, i.e., d = 1, and the expression is as follows
- Traditional Transmission: In the case, BS only multicasts the entire output data of a task to MDs, without using the caching and computing capability of MDs. Fig.
- It shows the proposed scheme achieves minimum bandwidth consumption over the baselines by making full use of the caching and computing resources of the mobile devices.
- The authors can see from Fig. 3 that even if the mobile device does not have the caching ability, i.e., C = 0, the proposed scheme can save the bandwidth compared with the traditional transmission scheme by using the local computing.
- Similar to [5], Fig. 5 evaluates the coded gain of the proposed scheme, compared with the uncoded caching with device computing scheme which the entire input data or the entire output data of a task is cached in a MD.

Conclusion

- The authors have studied the problem of how to save the average transmission bandwidth by exploiting the caching and computing resources of MDs in the MEC system.
- A coded caching with device computing strategy is proposed to minimize the average bandwidth under the delay of the computation tasks, the cache size and the average energy consumption of MDs. The formulated problem is a large-scale mix integer nonconvex and nonsmooth programming when the numbers of MDs and computation tasks get larger.
- There is no mobile device that requests the coded cached task when M q = 0, and the data size of coded multicast transmission is zero, i.e., bq (c, x) = 0.

Summary

- Bandwidth saving is an eternal topic in wireless communications systems, especially in the era of shortage of wireless spectrum resource.
- Let N ⊆ F denote the set of computation tasks that are decided to be coded cached in each mobile device, i.e., cn = 1, for all n ∈ N .
- The input data of the computation tasks A, B, C is coded cached at each mobile device and N = F {A, B, C} where N = |F| = 3, that is c = {1, 1, 1} and d = 1.
- The mobile device k requests a computation task whose input data has been coded cached and decides to compute locally.
- Similar to Case 3,the task f in the mobile device k is computed by the MEC server, and has not been coded cached.
- Define BqI as the achievable bandwidth in the task request state Sq when the input data is decided to be coded cached, i.e., d = 1, and the expression is as follows
- Traditional Transmission: In the case, BS only multicasts the entire output data of a task to MDs, without using the caching and computing capability of MDs. Fig.
- It shows the proposed scheme achieves minimum bandwidth consumption over the baselines by making full use of the caching and computing resources of the mobile devices.
- The authors can see from Fig. 3 that even if the mobile device does not have the caching ability, i.e., C = 0, the proposed scheme can save the bandwidth compared with the traditional transmission scheme by using the local computing.
- Similar to [5], Fig. 5 evaluates the coded gain of the proposed scheme, compared with the uncoded caching with device computing scheme which the entire input data or the entire output data of a task is cached in a MD.
- The authors have studied the problem of how to save the average transmission bandwidth by exploiting the caching and computing resources of MDs in the MEC system.
- A coded caching with device computing strategy is proposed to minimize the average bandwidth under the delay of the computation tasks, the cache size and the average energy consumption of MDs. The formulated problem is a large-scale mix integer nonconvex and nonsmooth programming when the numbers of MDs and computation tasks get larger.
- There is no mobile device that requests the coded cached task when M q = 0, and the data size of coded multicast transmission is zero, i.e., bq (c, x) = 0.

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