An Energy-Efficient User Association Scheme Based on Robust Optimization in Ultra-Dense Networks

Jin

Concurrency and Computation

2022

Summary With the development of computing technology and the popularization of application in 5G network, mobile‐edge computing (MEC), which can effectively reduce time delay and save energy consumption, has attracted extensive attention from the academic community. How to make a suitable allocation decision for tasks becomes one of the critical issues in MEC systems. In order to yield the most benefit to a newly arriving task, by placing a decision making module (DMM) in a mobile device, a MEC system architecture is presented. Based on the net benefit of a newly arriving task, the DMM makes a decision of dropping the task, allocating the task to the local execution system, or offloading the task to the MEC server via the transmission system. From the observable perspective of task individuals, a pure threshold strategy is proposed to show that a Nash equilibrium is always allowed. By constructing a two‐dimensional continuous‐time Markov chain, a social optimal threshold strategy is proposed. Numerical results show that threshold under the pure threshold strategy is always greater than that under the social optimal threshold strategy. For this, a charging policy is presented to coincide the pure threshold strategy with the social optimal threshold strategy.

Section: Related Workmentioning

confidence: 99%

Allocation strategy for time‐sensitive tasks in mobile edge computing: An observable perspective

Jin

Concurrency and Computation

2022

“…Small cell deployment in UDN is considered to lift the traffic load from macrocells [80]. However, based on the received signal, most of the users associate to macrocells result in affecting the small cell splitting gain and energy efficiency [81]. Additionally, users with high data rate requirements prefer to use small cells.…”

Section: ) Resource Allocationmentioning

confidence: 99%

Energy-Efficient Ultra-Dense 5G Networks: Recent Advances, Taxonomy and Future Research Directions

Mughees¹,

Tahir

Sheikh

et al. 2021

IEEE Access

The global surge of connected devices and multimedia services necessitates increased capacity and coverage of communication networks. One approach to address the unprecedented rise in capacity and coverage requirement is deploying several small cells to create ultra-dense networks. This, however, exacerbates problems with energy consumption and network management due to the density and unplanned nature of the deployment. This review discusses various approaches to solving energy efficiency problems in ultra-dense networks, ranging from deployment to optimisation. Based on the review, we propose a taxonomy, summarise key findings, and discuss operational and implementation details of past research contributions. In particular, we focus on popular approaches such as machine learning, game theory, stochastic and heuristic techniques in the ultra-dense network from an energy perspective due to their promise in addressing the issue in future networks. Furthermore, we identify several challenges for improving energy efficiency in an ultra-dense network. Finally, future research directions are outlined for improving energy efficiency in ultra-dense networks in 5G and beyond 5G networks.

“…Authors in [10] propose combining conventional user association technique based on the highest signal strength with time bases user scheduling to balance BS loads in HCN. In [11], user association based on energy optimization is proposed for heavily congested network. The focus of the study is to improve energy consumption of the BS while catering a huge volume of users.…”

Section: User-centric Learning For Multiple Access Selectionsmentioning

confidence: 99%

“…From selected pair of state and action, a reward is calculated. The rewards are accumulated over time once each pair of state and action is tested, yield: (10) Given that the policy is defined, and the rewardaccumulated over time, the state value function is evaluated as shown in ( 11): (11) The valuation in (10) can be improved to understand the state and action relationship while accumulating the rewards. This is presented by (12): (12) To find the optimal decision, (12) is utilised (13) Putting ( 12) into (13), we have ( 14) in a simplified version (14) Finally, the equation is further expanded for model free QL as shown in ( 14):…”

Section: Reinforcement Learningmentioning

confidence: 99%

User-Centric Learning for Multiple Access Se-lections

Dzulkifly

Hashim²,

Ismail

et al. 2019

IJEAT

We are in the age where business growth is based on how user-centric your services or goods is. Current research on wireless system is more focused on ensuring that user could achieve optimal throughput with minimal delay, disregarding what user actually wants from the services. Looking from connectivity point of view, especially in urban areas these days, there are multiple mobile and wireless access that user could choose to get connected to. As people are looking toward machine automation, we understand that the same could be done for allowing users to choose services based on their own requirement. This paper looks into unconventional, non-disruptive approach to provide mobile services based on user requirements. The first stage of this study is to look for user association from three new perspectives. The second stage involved utilizing a reinforcement learning algorithm known as q-learning, to learn from feedbacks to identify optimal decision in reaching user-centric requirement goal. The outcome from the proposed deployment has shown significant improvement in user association with learning aware solution.