Open-Loop Communications for Up-Link URLLC Under Clustered User Distribution

Zheng, Canjian; Zheng, Fu-Chun; Luo, Jijun; Feng, Daquan

doi:10.1109/tvt.2021.3110309

Cited by 8 publications

(8 citation statements)

References 33 publications

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“…3) Data Aggregation: In large-scale systems, devices may be clustered in local area networks, which then connect to the core networks through a gateway or aggregator [151], [157], [164], [165], [166], [167], [168]. Instead of a traditional spatial clustering relying on geometric distances and/or energy-related measures [151], [157], [164], [165], [166], [167], a more advisable approach in the context of large-scale URLLC lies in jointly considering traffic profiles (both in terms of traffic intensity and performance requirements) and network dynamics. Interestingly, Orim et al [168] have initially proposed a priority-based spatial aggregation algorithm that gives priority to URLLC devices through power ramping in the physical random access channel.…”

Section: B Clusteringmentioning

confidence: 99%

Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication—A Tutorial

López,

Mahmood,

Shehab

et al. 2023

Proc. IEEE

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Section: B Clusteringmentioning

confidence: 99%

Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication—A Tutorial

López,

Mahmood,

Shehab

et al. 2023

Proc. IEEE

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“…This section begins by demonstrating how to formulate problem (12) as a reinforcement learning problem. Furthermore, based on the characteristics of the problem's continuous high-dimensional decision variables, a scheme based on A3C is proposed to modify the RISs phase shifts and amplitude reflection coefficient.…”

Section: Deep Reinforcement Learning Approachmentioning

confidence: 99%

“…Various factors, such as the fluctuating network load, the state of wireless channels, and the transmission requirements of multiple devices, exhibit statistical patterns and state transition characteristics over time in practical network scenarios. From an engineering standpoint, the significance of (12) resides in its ability to guide decision making regarding the network-dependent behaviors of RISs. Given these conditions' inherent uncertainty and stochastic nature, numerous decision-making problems can be effectively addressed by transforming them into Markov decision process (MDP) problems and applying RL theory to maximize decision-making utility.…”

Section: Reinforcement Learning Problem Formulationmentioning

confidence: 99%

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

Wang

Sun

Cui

et al. 2023

Sensors

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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.

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“…For CP delay, the traditional close-loop mode may not satisfy low-latency requirements of mission-critical services due to feedback and retransmissions. As shown in Figure 9, to reduce the latency and alleviate signaling overhead, the open-loop communication (OLC) mode [16] can be adopted in NAFD scheme, which is feedback free. The CP delay components are listed as follows:…”

Section: Open-loop Communication Mode For Nafd Schemementioning

confidence: 99%

Network-Assisted Full-Duplex Enabled Ultrareliable and Low-Latency Communications

Zhu

et al. 2022

Wireless Communications and Mobile Computing

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The ultrareliable and low-latency communication (URLLC) is one of the key scenarios of the current 5G new radio (NR). The performance of the duplex technologies in URLLC to assist in meeting the needs of low-latency services is of great significance. However, time division duplex (TDD) has poor delay performance due to the extra data waiting delay caused by the frequent uplink/downlink switching. Besides, TDD systems cannot dynamically adapt to the real-time changes of user requirements due to the problem of frame structure configuration, while frequency division duplex (FDD) has more pilot overhead due to channel estimation and worse spectrum utilization which are great drawbacks in 5G NR enabled URLLC. In this paper, we further proposed the strengths of a novel duplex technology named network-assisted full-duplex (NAFD) in terms of URLLC latency performance compared with TDD and FDD. NAFD scheme exploits the resources of the time and spatial domains to form a special duplex mechanism where the central processing unit (CPU) can dynamically assign the modes of different access points (APs) as downlink transmission or uplink receiving in the same coherent time block. Some improvement of the existing NAFD scheme for better fitting for URLLC is also proposed in this paper. The simulation results and analysis showed that the NAFD scheme outperforms other duplex technologies under URLLC scenarios.

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Open-Loop Communications for Up-Link URLLC Under Clustered User Distribution

Cited by 8 publications

References 33 publications

Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication—A Tutorial

Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication—A Tutorial

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

Network-Assisted Full-Duplex Enabled Ultrareliable and Low-Latency Communications

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