Joint Uplink-Downlink Resource Allocation for OFDMA-URLLC MEC Systems

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

doi:10.1109/globecom42002.2020.9348027

Cited by 9 publications

(20 citation statements)

References 18 publications

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“…We note that this paper expands the corresponding conference version [1] in several directions. First, the formulated optimization problem targets joint local computing and edge offloading, while only edge offloading was considered in [1].…”

Section: Introductionmentioning

confidence: 75%

“…All transceivers have single antennas. The system employs frequency division duplex (FDD) 1 . Thereby, the total bandwidth W is divided into two bands for uplink and downlink transmission having bandwidths W u and W d , respectively.…”

Section: A System Modelmentioning

confidence: 99%

“…Optimization problem ( 51) is a non-linear convex problem which can be Algorithm 3 Successive Convex Approximation 1: Initialize: Random initial points α (1) , s u(1) , s d (1) . Set iteration index i = 1, maximum number of iterations I max , and penalty factors, η 1 > 0, η 2 > 0, and η 3 > 0.…”

Section: ) Suboptimal Algorithmmentioning

confidence: 99%

“…Recently, significant attention has been devoted to studying and developing resource allocation algorithms for URLLC. In particular, optimal power allocation in a multi-user time division multiple access (TDMA) This paper will be presented in part at IEEE GLOBECOM 2020 [1].…”

Section: Introductionmentioning

confidence: 99%

“…By substituting the right hand sides of ( 46)-( 48) into (44), we obtain the following optimization problem: minimize 49) is a convex optimization problem. To facilitate the application of CVX for solving problem (49), we reformulate the cubic function f 3 k appearing in the cost function and Algorithm 2 Successive Convex Approximation 1: Initialize: Random initial points s u (1) , s d (1) , pu(1) , pd(1) , α (1) . Set iteration index i = 1, maximum number of iterations I max , and penalty factors η 1 > 0, η 2 > 0, and η 3 > 0.…”

Section: ) Big-m Formulation: Let Us First Introduce the New Optimiza...mentioning

confidence: 99%

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Resource Allocation for Multi-User Downlink URLLC-OFDMA Systems

Ghanem

Jamali

Sun

et al. 2019

2019 IEEE International Conference on Communications Workshops (ICC Workshops)

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In this paper, we study resource allocation algorithm design for secure multi-user downlink ultra-reliable low latency communication (URLLC). To enhance physical layer security (PLS), the base station (BS) is equipped with multiple antennas and artificial noise (AN) is injected by the BS to impair the eavesdroppers' channels. To meet the stringent delay requirements in secure URLLC systems, short packet transmission (SPT) is adopted and taken into consideration for resource allocation design. The resource allocation algorithm design is formulated as an optimization problem for minimization of the total transmit power, while guaranteeing quality-of-service (QoS) constraints regarding the URLLC users' number of transmitted bits, packet error probability, information leakage, and delay. Due to the nonconvexity of the optimization problem, finding a global solution entails a high computational complexity. Thus, we propose a low-complexity algorithm based successive convex approximation (SCA) to find a sub-optimal solution. Our simulation results show that the proposed resource allocation algorithm design ensures the secrecy of the URLLC users' transmissions, and yields significant power savings compared to a baseline scheme.

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Section: Introductionmentioning

confidence: 75%

Section: A System Modelmentioning

confidence: 99%

Section: ) Suboptimal Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: ) Big-m Formulation: Let Us First Introduce the New Optimiza...mentioning

confidence: 99%

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Resource Allocation for Multi-User Downlink URLLC-OFDMA Systems

Ghanem

Jamali

Sun

et al. 2019

2019 IEEE International Conference on Communications Workshops (ICC Workshops)

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Refiner GAN Algorithmically Enabled Deep-RL for Guaranteed Traffic Packets in Real-Time URLLC B5G Communication Systems

et al. 2022

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Ultra-reliable and Low-latency Communications (URLLC) is expected to be one of the most critical characteristics in Beyond fifth-Generation (B5G) cellular networks with stringent low latency and high-reliability requirements. The Deep Reinforcement Learning (deep-RL) framework has been applied to predict the optimization of a Resource Block (RB) and minimize Power Allocation (PA) to guarantee a high End-to-End (E2E) reliability and low E2E latency under rate constraints. This paper proposes a novel Policy Gradient-based Actor-Critic Learning (PGACL) algorithm to optimize the policy gradient for optimal rate allocation to solve the RB, minimize power, and guarantee a solution for URLLC scheduling. The purpose of a PGACL algorithm is to provide a good policy with a closer convergence rate and a low computational cost depending on the reduced action space for every user. URLLC systems need to operate in highly reliable systems and account for extreme network conditions. Therefore, we proposed the refiner Generative Adversarial Networks (GANs) that apply enough extreme events for the deep-RL agent to generate synthetic data with high reliability similar to real data based on the regulated number of extreme events in the dataset. This refiner GAN method enables a deep-RL approach to generate large amounts of data practically used in real-time operation. Simulation results showed that the proposed deep-RL for refiner-GAN can omit the transient training time and develop deep-learning based on a controlled set of unlabeled real traffic at a relatively short time. Furthermore, the refiner GAN demonstrated 99.9999% reliability and E2E latency of less than 1.4ms.INDEX TERMS URLLC, beyond fifth-generation, end-to-end, generative adversarial networks.

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New Reward-Clipping Mechanism in Deep -Learning Enabled Internet of Things in 6G to Improve Intelligent Transmission Scheduling

Alhartomi

2023

2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC)

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Joint Uplink-Downlink Resource Allocation for OFDMA-URLLC MEC Systems

Cited by 9 publications

References 18 publications

Resource Allocation for Multi-User Downlink URLLC-OFDMA Systems

Resource Allocation for Multi-User Downlink URLLC-OFDMA Systems

Refiner GAN Algorithmically Enabled Deep-RL for Guaranteed Traffic Packets in Real-Time URLLC B5G Communication Systems

New Reward-Clipping Mechanism in Deep -Learning Enabled Internet of Things in 6G to Improve Intelligent Transmission Scheduling

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