Heterogeneous Computational Resource Allocation for NOMA: Toward Green Mobile Edge-Computing Systems

Mohajer, Amin; Daliri, Mahya Sam; Mirzaei, Abbas; Ziaeddini, A.; Nabipour, Mohammad; Bavaghar, Maryam

doi:10.1109/tsc.2022.3186099

Cited by 99 publications

(17 citation statements)

References 32 publications

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“…The total allocation of CPU cycles for computing resources is set at 8 GHz. Notably, in a reference [26], the processing densities for speech recognition algorithms and 400-frame videos are measured at 31680 and 2640 cycles per bit, respectively. Consequently, it is reasonable to attribute a processing density of 31680, 31680, and 264 cycles per bit to resource-consuming robots 3, 4, and 5, respectively.…”

Section: Simulation and Discussionmentioning

confidence: 99%

Research on Price-Based Autonomous Group Robot Resource Allocation Strategy in Emergency Scenarios

Yi,

Xiao

2023

Preprint

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In unknown and dynamic emergency scenarios, achieving the collaboration of autonomous group robots for search and rescue operations can be regarded as resource allocation among robots at the micro-level. The resource allocation problem for autonomous group robots can be abstracted as a non-cooperative game, and in a dynamically changing environment, pricing becomes a critical factor for effective resource allocation.This paper starts from the perspectives of uniform pricing strategy and differential pricing strategy, respectively. It establishes master-slave game models for these two pricing strategies to describe resource allocation between resource providers and resource consumers. Furthermore, the paper utilizes game theory to model the competition for computational resources between resource-providing robots and resource-consuming robots, and solves for their Nash equilibrium solution, demonstrating its existence and uniqueness.Additionally, performance analysis and numerical analysis are conducted on both the uniform pricing model and the differential pricing model, thereby highlighting the advantages and disadvantages of different pricing models for dynamic adjustment of pricing strategies. Meanwhile, the differential pricing model introduces a fairness factor to enhance collaboration between robots and prevent resource accumulation. Simulation results indicate that under the same CPU cycle, the average processing time is shorter in the uniform pricing model, while in the differential pricing model, the resource-providing robots yield higher profits. Hence, the suitable pricing strategy can be chosen based on specific requirements.Simultaneously, as the CPU cycle increases, the cost for resource-consuming robots decreases, average processing time reduces, and the payment enthusiasm of resource-consuming robots increases. Therefore, the CPU cycle is related to the overall well-being of the autonomous group robot system.

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Section: Simulation and Discussionmentioning

confidence: 99%

Research on Price-Based Autonomous Group Robot Resource Allocation Strategy in Emergency Scenarios

Yi,

Xiao

2023

Preprint

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“…The intensive deployment of small cell network is the key feature of the next-generation wireless access network, which aims to provide the necessary capacity increase. In reference [15], a dynamic optimization model is proposed, which applies the subgradient method to compute resource allocation, and uses continuous convex approximation (SCA) and dual decomposition method to solve the max-min fairness problem. The simulation results show that in addition to ensuring the fairness requirements, the EE of various traffic models is significantly improved.…”

Section: Introductionmentioning

confidence: 99%

5G communication resource allocation strategy for mobile edge computing based on deep deterministic policy gradient

Jun

2023

The Journal of Engineering

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Distributed base station deployment, limited server resources and dynamically changing end users in mobile edge networks make the design of computing offloading schemes extremely challenging. Considering the advantages of deep reinforcement learning (DRL) in dealing with dynamic complex problems, this paper designs an optimal computing offloading and resource allocation strategy. Firstly, the authors consider a multi-user mobile edge network scenario consisting of Macro-cell Base Station (MBS), Small-cell Base Station (SBS) and multiple terminal devices, the communication overhead and calculation overhead generated are formulated and described in detail. Besides, combined with the deterministic delay of tasks, the optimization objective of this paper is clarified to comprehensively consider system energy consumption. Then, a learning algorithm based on Deep Deterministic Policy Gradient (DDPG) is proposed to minimize system energy consumption. Finally, simulation experiments show that the authors' proposed DDPG algorithm can effectively optimize the target value, and the total system energy consumption is only 15.6 J, which is better than other compared algorithms. It is also proved that the proposed algorithm has excellent communication resource allocation ability.

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“…This paper compensates for this shortcoming of the previous protocol by providing a multilayered routing algorithm considering data link constraints and auxiliary intermediate nodes in order to decrease the total power utilization. The authors in [23] proposed an efficient smart control plan for the dynamic transmission in the wireless sensor networks using cooperative protocols. This algorithm supports the dynamic operations of block data and thirdparty public validation to provide high security against data forgery and replacement.…”

Section: -Introductionmentioning

confidence: 99%

A Novel Approach for Establishing Connectivity in Partitioned Mobile Sensor Networks using Beamforming Techniques

Mirzaei¹,

Zandian²

2022

jist

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Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomenon may deprive a large number of alive nodes of sending their important time critical data to the sink. The application of data caching in mobile sensor networks is exponentially increasing as a highspeed data storage layer. This paper presents a deep learning-based beamforming approach to find the optimal transmission strategies for cache-enabled backhaul networks. In the proposed scheme, the sensor nodes in isolated partitions work together to form a directional beam which significantly increases their overall communication range to reach out a distant relay node connected to the main part of the network. The proposed methodology of cooperative beamforming-based partition connectivity works efficiently if an isolated cluster gets partitioned with a favorably large number of nodes. We also present a new cross-layer method for link cost that makes a balance between the energy used by the relay. By directly adding the accessible auxiliary nodes to the set of routing links, the algorithm chooses paths which provide maximum dynamic beamforming usage for the intermediate nodes. The proposed approach is then evaluated through simulation results. The simulation results show that the proposed mechanism achieves up to 30% energy consumption reduction through beamforming as partition healing in addition to guarantee user throughput.

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Heterogeneous Computational Resource Allocation for NOMA: Toward Green Mobile Edge-Computing Systems

Cited by 99 publications

References 32 publications

Research on Price-Based Autonomous Group Robot Resource Allocation Strategy in Emergency Scenarios

Research on Price-Based Autonomous Group Robot Resource Allocation Strategy in Emergency Scenarios

5G communication resource allocation strategy for mobile edge computing based on deep deterministic policy gradient

A Novel Approach for Establishing Connectivity in Partitioned Mobile Sensor Networks using Beamforming Techniques

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