Reinforcement Learning Based Latency Minimization in Secure NOMA-MEC Systems With Hybrid SIC

Wang, Kaidi; Li, Haodong; Ding, Zhiguo; Xiao, Pei

doi:10.1109/twc.2022.3194685

Cited by 29 publications

(9 citation statements)

References 33 publications

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“…In [33], authors investigated hybrid SIC decoding and PLS in a NOMA assisted MEC framework in the presence of ED. A RL based approach for latency reduction was developed where combined computing resource allocation, task assignment, and PA were taken into consideration in order to address the PA challenges for NOMA and orthogonal multiple access offloading schemes.…”

Section: A Related Workmentioning

confidence: 99%

Sum Capacity-Based Modeling and Secrecy Analysis of MISO-NOMA Cooperative IoT Framework

Zaman,

Munawar,

Moinuddin

et al. 2024

IEEE Open J. Commun. Soc.

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Non-orthogonal multiple access (NOMA) has emerged as an effective technology , aiding in latency reduction, improving security and reliability, and enhancing the spectral efficiency and capacity in Internet of Things (IoT)-enabled communication systems. In this work, an implementation of co-operative secure multiple-input single-output NOMA IoT framework is proposed and examined in a two-fold manner. First, under the consideration of standalone links, a user selection criteria which relies on the characterization of sum ergodic capacity (SEC) is investigated in the presence of an eavesdropper (ED) and beamforming constraints. Next, using the optimized beamformers, a user selection criteria which relies on the outage probability analysis is performed under cooperative relaying considerations. We demonstrate that our optimization approach achieves reduced capacity and coverage for the ED while maximizing the overall SEC and coverage of the network under multiple network configurations. The proposed optimization solution demonstrates superior performance compared to the benchmarked schemes. Derived closed-form analytical expressions are validated through simulation means.

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Section: A Related Workmentioning

confidence: 99%

Sum Capacity-Based Modeling and Secrecy Analysis of MISO-NOMA Cooperative IoT Framework

Zaman,

Munawar,

Moinuddin

et al. 2024

IEEE Open J. Commun. Soc.

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“…Specifically, in [24], a power allocation algorithm has been provided to achieve an optimal secure data rate and reduce the whole task latency of both communication and computation. The authors in [25] have investigated PLS in a NOMA-based MEC system with hybrid SIC decoding. A latency minimization problem has been formulated by jointly designing computational resource allocation, task assignment and power allocation.…”

Section: ) Secure Mec Systemsmentioning

confidence: 99%

Delay Minimization for Massive MIMO Based Cooperative Mobile Edge Computing System With Secure Offloading

Yilmaz

Özbek

Mumtaz³

2023

IEEE Open J. Veh. Technol.

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Mobile edge computing (MEC) has been envisioned as a promising technology for enhancing the computational capacities of mobile devices by enabling task offloading. In this paper, we present a novel framework for a cooperative MEC system by employing Massive Multiple-Input Multiple-Output (MIMO) and non-orthogonal multiple access (NOMA) technologies, including security aspects. Specifically, in the proposed cooperative MEC system, there is no strong direct transmission link between the cell-edge user and the MEC server; consequently, the user sends their tasks to the MEC server through the helpers at the cell-centers. In the proposed framework, we minimize the overall delay, including secure offloading under the constraints of computing capability and transmit power. The proposed algorithm minimizes the overall delay in downlink and uplink transmission while satisfying security constraints to solve the formulated problem. The simulation results show that Massive MIMO based NOMA improves the performance of the secure MEC system by employing more than one helper.INDEX TERMS Secure offloading, delay minimization, massive multiple input multiple output (MIMO), mobile edge computing (MEC), non-orthogonal multiple access (NOMA)

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“…In [21], local computation resource, offloading ratio, uplink transmission time and power and subcarrier assignment were jointly optimized to minimize the sum of weighted energy consumed by users in NOMA-MEC networks, and some effective iterative algorithms were designed for single-user and multi-user cases. In [24], joint task offloading, power allocation and computing resource allocation were optimized to achieve delay minimization using a deep reinforcement learning (DRL) algorithm in NOMA-MEC networks. In [25], the joint optimization of offloading decisions, local and edge computing resource allocation and power and subchannel allocation were realized to minimize energy consumption in heterogeneous NOMA-MEC networks, and an effective iterative algorithm was designed.…”

Section: Related Workmentioning

confidence: 99%

Computing Offloading Based on TD3 Algorithm in Cache-Assisted Vehicular NOMA–MEC Networks

Zhou,

Xu,

Qin

et al. 2023

Sensors

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In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA–MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm.

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Reinforcement Learning Based Latency Minimization in Secure NOMA-MEC Systems With Hybrid SIC

Cited by 29 publications

References 33 publications

Sum Capacity-Based Modeling and Secrecy Analysis of MISO-NOMA Cooperative IoT Framework

Sum Capacity-Based Modeling and Secrecy Analysis of MISO-NOMA Cooperative IoT Framework

Delay Minimization for Massive MIMO Based Cooperative Mobile Edge Computing System With Secure Offloading

Computing Offloading Based on TD3 Algorithm in Cache-Assisted Vehicular NOMA–MEC Networks

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