Delay-Optimal Distributed Scheduling in Multi-User Multi-Relay Cellular Wireless Networks

Moghadari, Mohammad; Hossain, Ekram; Le, Long Bao

doi:10.1109/tcomm.2013.020413.120457

Cited by 24 publications

(12 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…for each decision epoch k = 0, 1, 2, · · · do k ← k + 1 for each action a k ∈ A s k do Determine the post-decision global system states k ← f (s k , a k ) by (5) Determine the post-decision local system state vector {s n,k } N n=1 ← {f n (s n,k , a k )} N n=1 by (15) for each IoT device n ∈ N do Encodes k in the input vector xs n,k Determine the feature φs n,k (s k ) by (20), (21), (22) Determine the local reward functiong n (s k ) by (17) end for Determine the value of the RHS of (25) end for Select the action according to (25) end for…”

Section: Algorithm 1 Determine the Optimal Actionmentioning

confidence: 99%

Multiuser Resource Control With Deep Reinforcement Learning in IoT Edge Computing

Lei

Xiong

et al. 2019

IEEE Internet Things J.

View full text Add to dashboard Cite

By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing. However, due to the resource constraint of IoT devices and wireless network, both the communications and computation resources need to be allocated and scheduled efficiently for better system performance. In this paper, we propose a joint computation offloading and multi-user scheduling algorithm for IoT edge computing system to minimize the long-term average weighted sum of delay and power consumption under stochastic traffic arrival. We formulate the dynamic optimization problem as an infinite-horizon average-reward continuous-time Markov decision process (CTMDP) model. One critical challenge in solving this MDP problem for the multi-user resource control is the curse-of-dimensionality problem, where the state space of the MDP model and the computation complexity increase exponentially with the growing number of users or IoT devices. In order to overcome this challenge, we use the deep reinforcement learning (RL) techniques and propose a neural network architecture to approximate the value functions for the post-decision system states. The designed algorithm to solve the CTMDP problem supports semi-distributed auction-based implementation, where the IoT devices submit bids to the BS to make the resource control decisions centrally. Simulation results show that the proposed algorithm provides significant performance improvement over the baseline algorithms, and also outperforms the RL algorithms based on other neural network architectures.

show abstract

Section: Algorithm 1 Determine the Optimal Actionmentioning

confidence: 99%

Multiuser Resource Control With Deep Reinforcement Learning in IoT Edge Computing

Lei

Xiong

et al. 2019

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…Without loss of generality, we focus our investigation on one sector of the cell and the same analysis can be applied to the rest of the sectors. An amplify-and-forward relay node is installed at a fixed distance from the BS to serve the cell-edge users in each sector [30]. The users in a sector are divided into two groups based on their distances from the BS and the relay node.…”

Section: A Relay-based Wireless-powered Uplink Cellular Networkmentioning

confidence: 99%

On Multiuser Resource Allocation in Relay-Based Wireless-Powered Uplink Cellular Networks

Lohani

Loodaricheh

Hossain

et al. 2016

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

We propose relay-based wireless-powered uplink cellular networks in which users first harvest energy from RF transmissions of base station/relay nodes and then use that energy for uplink transmission. Given the limited total transmission time and available energy at the relay node, we propose different resource allocation frameworks for the proposed relay-based networks considering two different relay-based harvest-then-transmit scenarios. We first propose iterative algorithm to determine time and relay node power allocation (for downlink wireless charging and uplink data transmission/relaying) for both scenarios. We then perform joint optimal time and power allocation for one scenario. Resource allocation results show that most of the available resources (transmission time and relay node energy) are allocated for wireless energy harvesting which is similar to that observed for downlink simultaneous wireless information and power transfer (SWIPT) in the existing literature. Simulation results on comparison of different relay-based scenarios reveal interesting insights and demonstrate remarkable improvement of different performance metrics of wireless-powered cellular networks in the presence of relay node.

show abstract

“…One feasible solution can be obtained by enumeration, but it cannot be distributed. In order to obtain the scheduling policy in a distributed manner, motivated by the observation in [18], we split the optimization problem into two stages, which correspond to the BS-RS link and AP-users link, respectively. In the first stage, CSs solve a local MDP problem to determine the scheduling actions in the BS-RS link.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Delay-Aware Online Service Scheduling in High-Speed Railway Communication Systems

Zhu

Shen

et al. 2014

Mathematical Problems in Engineering

View full text Add to dashboard Cite

We investigate the downlink service scheduling problem in relay-assisted high-speed railway (HSR) communication systems, taking into account stochastic packet arrivals and quality-of-service (QoS) requirements. The scheduling problem is formulated as an infinite-horizon average cost constrained Markov decision process (MDP), where the scheduling actions depend on the channel state information (CSI) and the queue state information (QSI). Our objective is to find a policy that minimizes the average end-to-end delay through scheduling actions under the service delivery ratio constraints. To address the challenge of centralized control and high complexity of traditional MDP approaches, we propose a distributed online scheduling algorithm based on approximate MDP and stochastic learning, where the scheduling policy is a function of the local CSI and QSI only. Numerical experiments are carried out to show the performance of the proposed algorithm.

show abstract

Delay-Optimal Distributed Scheduling in Multi-User Multi-Relay Cellular Wireless Networks

Cited by 24 publications

References 24 publications

Multiuser Resource Control With Deep Reinforcement Learning in IoT Edge Computing

Multiuser Resource Control With Deep Reinforcement Learning in IoT Edge Computing

On Multiuser Resource Allocation in Relay-Based Wireless-Powered Uplink Cellular Networks

Delay-Aware Online Service Scheduling in High-Speed Railway Communication Systems

Contact Info

Product

Resources

About