Beamforming Design for Multiuser uRLLC With Finite Blocklength Transmission

He, Shiwen; An, Zhenyu; Zhu, Jianyue; Zhang, Jian; Huang, Yongming; Zhang, Yaoxue

doi:10.1109/twc.2021.3090197

Cited by 37 publications

(23 citation statements)

References 38 publications

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Section: A Weighted Sum Rate Maximization Problemmentioning

confidence: 99%

“…In this subsection, we would like to solve the WSRMax problem, i.e., the investigated problem (5) in [7] using UPGDNet. According to the Lemma 1 in [7], the WSRMax problem is solved using the uplink-downlink duality theory. For ease of notation, let q k be the transmitting power of the k-th user (UE), where k ∈ K {1, • • • , K} with K being the total number of UEs.…”

Section: A Weighted Sum Rate Maximization Problemmentioning

confidence: 99%

“…Although the proposed algorithm achieves good performance, but the computational complexity of it is relatively high, and there is still a certain distance from real-time applications. In order to propose a solution with lower computational complexity, we will design a learning model to solve problem (7) in the following subsection.…”

Section: A Weighted Sum Rate Maximization Problemmentioning

confidence: 99%

“…In this subsection, we focus on designing a learning model based on the UPGDNet and the analytical beamforming form to solve problem (7) via unrolling manner. Specifically, we unroll the alternating iteration algorithm proposed in [7] layer-by-layer. Each unrolled layer solves problem (10), followed with an analytical beamforming update model with (9) at the end of the unrolled layer.…”

Section: B Unrolled Srmax Network (Usrmnet)mentioning

confidence: 99%

“…They also proposed a particular class of conjugate beamforming for a cell free massive multiple-input multiple-output (MIMO) downlink uRLLC system to maintain the low computational complexity [6]. S. He et al focused on the beamforming design for the downlink multiuser uRLLC system [7]. They proposed three algorithms based on SCA to solve three different optimization problems subjected to some complicated constraints.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Unsupervised Deep Unrolling Framework for Constrained Optimization Problems in Wireless Networks

He¹,

Xiong²,

An³

et al. 2022

Preprint

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In wireless network, the optimization problems generally have complex constraints, and are usually solved via utilizing the traditional optimization methods that have high computational complexity and need to be executed repeatedly with the change of network environments. In this paper, to overcome these shortcomings, an unsupervised deep unrolling framework based on projection gradient descent, i.e., unrolled PGD network (UPGDNet), is designed to solve a family of constrained optimization problems. The set of constraints is divided into two categories according to the coupling relations among optimization variables and the convexity of constraints. One category of constraints includes convex constraints with decoupling among optimization variables, and the other category of constraints includes non-convex or convex constraints with coupling among optimization variables. Then, the first category of constraints is directly projected onto the feasible region, while the second category of constraints is projected onto the feasible region using neural network. Finally, an unrolled sum rate maximization network (USRMNet) is designed based on UPGDNet to solve the weighted SR maximization problem for the multiuser ultra-reliable low latency communication system. Numerical results show that USRMNet has a comparable performance with low computational complexity and an acceptable generalization ability in terms of the user distribution.

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Section: A Weighted Sum Rate Maximization Problemmentioning

confidence: 99%

Section: A Weighted Sum Rate Maximization Problemmentioning

confidence: 99%

Section: A Weighted Sum Rate Maximization Problemmentioning

confidence: 99%

Section: B Unrolled Srmax Network (Usrmnet)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Unsupervised Deep Unrolling Framework for Constrained Optimization Problems in Wireless Networks

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Xiong²,

An³

et al. 2022

Preprint

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A Survey of Scheduling in 5G URLLC and Outlook for Emerging 6G Systems

et al. 2023

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Future wireless communication is expected to be a paradigm shift from three basic service requirements of 5th Generation (5G) including enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency communication (URLLC) and the massive Machine Type Communication (mMTC). Integration of the three heterogeneous services into a single system is a challenging task. The integration includes several design issues including scheduling network resources with various services. Specially, scheduling the URLLC packets with eMBB and mMTC packets need more attention as it is a promising service of 5G and beyond systems. It needs to meet stringent Quality of Service (QoS) requirements and is used in time-critical applications. Thus through understanding of packet scheduling issues in existing system and potential future challenges is necessary. This paper surveys the potential works that addresses the packet scheduling algorithms for 5G and beyond systems in recent years. It provides state of the art review covering three main perspectives such as decentralised, centralised and joint scheduling techniques. The conventional decentralised algorithms are discussed first followed by the centralised algorithms with specific focus on single and multi-connected network perspective. Joint scheduling algorithms are also discussed in details. In order to provide an in-depth understanding of the key scheduling approaches, the performances of some prominent scheduling algorithms are evaluated and analysed. This paper also provides an insight into the potential challenges and future research directions from the scheduling perspective.

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Spectral and Energy Efficiency Maximization of MISO STAR-RIS-Assisted URLLC Systems

2023

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This paper proposes a general optimization framework for rate splitting multiple access (RSMA) in beyond diagonal (BD) reconfigurable intelligent surface (RIS) assisted ultra-reliable low-latency communications (URLLC) systems. This framework can solve a large family of optimization problems in which the objective and/or constraints are linear functions of the rates and/or energy efficiency (EE) of users. Using this framework, we show that RSMA and RIS can be mutually beneficial tools when the system is overloaded, i.e., when the number of users per cell is higher than the number of base station (BS) antennas. Additionally, we show that the benefits of RSMA increase when the packets are shorter and/or the reliability constraint is more stringent. Furthermore, we show that the RSMA benefits increase with the number of users per cell and decrease with the number of BS antennas. Finally, we show that RIS (either diagonal or BD) can highly improve the system performance, and BD-RIS outperforms regular RIS.

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Beamforming Design for Multiuser uRLLC With Finite Blocklength Transmission

Cited by 37 publications

References 38 publications

An Unsupervised Deep Unrolling Framework for Constrained Optimization Problems in Wireless Networks

An Unsupervised Deep Unrolling Framework for Constrained Optimization Problems in Wireless Networks

A Survey of Scheduling in 5G URLLC and Outlook for Emerging 6G Systems

Spectral and Energy Efficiency Maximization of MISO STAR-RIS-Assisted URLLC Systems

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