Joint Beamforming and Phase Shift Optimization for Multicell IRS-aided OFDMA-URLLC Systems

Ghanem, Walid R.; Jamali, Vahid; Schober, Robert

doi:10.1109/wcnc49053.2021.9417582

Cited by 40 publications

(30 citation statements)

References 17 publications

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“…x ; A (l−1) HW , x(l) are designed based on spectral-based graph convolutional NN, called HWGCNs, to generate gradient descent step-size vector η 4 . Specifically, the HWGCNs utilized in…”

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confidence: 99%

“…x(l,k) , respectively. 4 Due to the preliminary estimation stage, i.e., step 3 in Algorithm 1, is only related to two optimization variables, namely ϕ and θ, this paper only designs two gradient descent step sizes η1 and η2 for these two optimization variables, respectively.…”

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confidence: 99%

“…W. R. Ghanem et al investigated the optimal resource allocation algorithm design based on successive convex approximation (SCA) for broad-band multipleinput single-output (MISO) orthogonal frequency division multiplexing (OFDMA) uRLLC systems [3]. W. R Ghanem et al firstly studied the resource allocation prolem for intelligent reflecting surface (IRS) aided MISO OFDM uRLLC systems and proposed a suboptimal iterative optimization algorithm [4]. A.…”

Section: Introductionmentioning

confidence: 99%

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

He¹,

Xiong²,

An³

et al. 2022

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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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confidence: 99%

Section: H W H W H W H W H W H W H W H W a H W H W H W H W H W H Wmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

He¹,

Xiong²,

An³

et al. 2022

Preprint

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“…As a result, it is expected that IRS can play an important role for URLLC, especially for the deteriorating worst-case SNR of grouped users. In the literature, various prior works (e.g., [9], [10]) have been devoted to investigate the impacts of IRS on URLLC. For instance, the authors in [9] considered the multi-cell multiuser orthogonal frequency division multiple access (OFDMA) URLLC systems aided by an IRS and [10] studied a URLLC system with both an IRS and unmanned aerial vehicles (UAVs).…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, various prior works (e.g., [9], [10]) have been devoted to investigate the impacts of IRS on URLLC. For instance, the authors in [9] considered the multi-cell multiuser orthogonal frequency division multiple access (OFDMA) URLLC systems aided by an IRS and [10] studied a URLLC system with both an IRS and unmanned aerial vehicles (UAVs). However, these works did not consider the impacts of user grouping for URLLC and their results are not applicable to the system of our interest.…”

Section: Introductionmentioning

confidence: 99%

User Grouping and Reflective Beamforming for IRS-Aided URLLC

Xie

Liu

et al. 2021

Preprint

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This paper studies an intelligent reflecting surface (IRS)-aided downlink ultra-reliable and low-latency communication (URLLC) system, in which an IRS is dedicatedly deployed to assist a base station (BS) to send individual short-packet messages to multiple users. To enhance the URLLC performance, the users are divided into different groups and the messages for users in each group are encoded into a single codeword. By considering the time division multiple access (TDMA) protocol among different groups, our objective is to minimize the total latency for all users subject to their individual reliability requirements, via jointly optimizing the user grouping and block-length allocation at the BS together with the reflective beamforming at the IRS. We solve the latency minimization problem via the alternating optimization, in which the blocklengths and the reflective beamforming are optimized by using the techniques of successive convex approximation (SCA) and semi-definite relaxation (SDR), while the user grouping is updated by Kmeans and greedy-based methods. Numerical results show that the proposed designs can significantly reduce the communication latency, as compared to various benchmark schemes, which unveil the importance of user grouping and reflective beamforming optimization for exploiting the joint encoding gain and enhancing the worst-case user performance.

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