Reliability-Guaranteed Uplink Resource Management in Proactive Mobile Network for Minimal Latency Communications

Wang, Yingze; Chen, Kwang-Cheng; Gong, Zhenzhen; Cui, Qimei; Zhang, Ning; Zhang, Ping

doi:10.1109/twc.2022.3231319

Cited by 6 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the specific data transmission and resource management method, refs. [ 6 , 10 , 16 ] provide uplink and downlink solutions, respectively. The core challenge in the uplink is to ensure transmission reliability when the network is in passive service without control interaction.…”

Section: Related Workmentioning

confidence: 99%

“…Refs. [ 6 , 16 ] design a dual reinforcement learning iterative technique in a shared environment that realizes the reliability guarantee of uplink transmission in PMNs via free control interaction. Ref.…”

Section: Related Workmentioning

confidence: 99%

“…This novel approach replaces conventional direct interaction control methods with historical data mining and perception of the surrounding environment to obtain relevant and necessary information. Consequently, a proactive mobile network (PMN) architecture is proposed [ 5 , 6 , 7 ]. The PMN architecture is considered to have significant theoretical value and holds the potential for deployment in future 6G networks [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the channel resources available to different smart machines (SMs) are not independent, which limits efficiency within a restricted frequency bandwidth. This situation is further exacerbated by the impact of virtual cell technology, which introduces substantial inter–user interference and compounds the difficulty of ensuring reliable capacity in the PMN [ 6 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

RIS-Aided Proactive Mobile Network Downlink Interference Suppression: A Deep Reinforcement Learning Approach

Wang

Sun

Cui

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

A proactive mobile network (PMN) is a novel architecture enabling extremely low-latency communication. This architecture employs an open-loop transmission mode that prohibits all real-time control feedback processes and employs virtual cell technology to allocate resources non-exclusively to users. However, such a design also results in significant potential user interference and worsens the communication’s reliability. In this paper, we propose introducing multi-reconfigurable intelligent surface (RIS) technology into the downlink process of the PMN to increase the network’s capacity against interference. Since the PMN environment is complex and time varying and accurate channel state information cannot be acquired in real time, it is challenging to manage RISs to service the PMN effectively. We begin by formulating an optimization problem for RIS phase shifts and reflection coefficients. Furthermore, motivated by recent developments in deep reinforcement learning (DRL), we propose an asynchronous advantage actor–critic (A3C)-based method for solving the problem by appropriately designing the action space, state space, and reward function. Simulation results indicate that deploying RISs within a region can significantly facilitate interference suppression. The proposed A3C-based scheme can achieve a higher capacity than baseline schemes and approach the upper limit as the number of RISs increases.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%