Joint Proportional Fairness Scheduling Using Iterative Search for mmWave Concurrent Transmission
Millimeter wave (mmWave) will play a significant role as a 5G candidate in facing the growing demand of enormous data rate in the near future. The conventional mmWave standard, IEEE 802.11ad, considers establishing only one mmWave link in wireless local area network (WLAN) to provide multi Gbps data rate. But, mmWave has a tenuous channel which hinders it from providing such rate. Hence, it's necessary to establish multiple mmWave links simultaneously by deploying a multiple number of mmWave access points (APs) in 5G networks. Unfortunately, applying conventional standard without any modifications for mmWave concurrent transmission impedes mmWave APs from selecting optimum mmWave concurrent links. Because IEEE 802.11ad standard associates the user equipment (UEs) to mmWave APs using the link that has the maximum received power without considering mutual interference between simultaneous links. In this paper, a joint proportional fairness scheduling (JPFS) optimization problem for establishing optimum mmWave concurrent transmission links is formulated. And, to find a solution to this non-polynomial (NP) time problem, we use exhaustive search (ES) scheme. Numerical simulation proves the effectiveness of using the ES scheme to improve the system performance.
Code (0)
등록된 구현이 없습니다.
Tasks
FairnessSchedulingSimilar Papers 제목 키워드 기반
Fairness-Aware Job Scheduling for Multi-Job Federated Learning
Federated learning (FL) enables multiple data owners (a.k.a. FL clients) to collaboratively train machine learning models without disclosing sensitive private data. Existing FL research mostly focuses on the monopoly sce…
FairnessFederated LearningSchedulingBuffer-aware Wireless Scheduling based on Deep Reinforcement Learning
In this paper, the downlink packet scheduling problem for cellular networks is modeled, which jointly optimizes throughput, fairness and packet drop rate. Two genie-aided heuristic search methods are employed to explore …
Deep Reinforcement LearningFairnessHeuristic Searchreinforcement-learning+3Multi-objective Optimization of Clustering-based Scheduling for Multi-workflow On Clouds Considering Fairness
Distributed computing, such as cloud computing, provides promising platforms to execute multiple workflows. Workflow scheduling plays an important role in multi-workflow execution with multi-objective requirements. Altho…
Cloud ComputingClusteringDistributed ComputingFairness+1On Fair Virtual Conference Scheduling: Achieving Equitable Participant and Speaker Satisfaction
The (COVID-19) pandemic-induced restrictions on travel and social gatherings have prompted most conference organizers to move their events online. However, in contrast to physical conferences, virtual conferences face a …
FairnessSchedulingProportional Fairness in Clustering: A Social Choice Perspective
We study the proportional clustering problem of Chen et al. [ICML'19] and relate it to the area of multiwinner voting in computational social choice. We show that any clustering satisfying a weak proportionality notion o…
ClusteringFairness