Energy-Efficient Design for IRS-Assisted NOMA-Based Mobile Edge Computing

Xu, Zhiguo; Liu, Jianxin; Zou, Junwei; Wen, Zhigang

doi:10.1109/lcomm.2022.3172309

Cited by 24 publications

(7 citation statements)

References 16 publications

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“…Researchers have extensively studied IRS-enabled MEC networks. The research works in [6], [14]- [17] investigated the energy efficiency of the system. The problems in those studies are non-convex in nature due to interference terms and coupling decision variables.…”

Section: A Related Workmentioning

confidence: 99%

“…It is worthy noting that the previous studies did not investigate the performance of such systems. For instance, the literature in [5], [6], [10], [14]- [24] have considered diagonal IRS in MEC systems. Accordingly, the authors in [7], [9], [25]- [28] have proposed UAV communication in MEC networks while the research works in [12], [13], [29]- [32] have introduced BD-IRS in conventional networks and they do not consider MEC and UAV communication.…”

Section: B Motivation and Contributionsmentioning

confidence: 99%

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Optimizing Computational and Communication Resources for MEC Network Empowered UAV-RIS Communication

Mahmood

Khan

et al. 2022

2022 IEEE Globecom Workshops (GC Wkshps)

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With the technological evolution and new applications, user equipment (UEs) has become a vital part of our lives. However, limited computational capabilities and finite battery life bottleneck the performance of computationally demanding applications. A practical solution to enhance the quality of experience (QoE) is to offload the extensive computation to the mobile edge cloud (MEC). Moreover, the network's performance can be further improved by deploying an unmanned aerial vehicle (UAV) integrated with intelligent reflective surfaces (IRS): an effective alternative to massive antenna systems to enhance the signal quality and suppress interference. In this work, the MEC network architecture is assisted by UAV-IRS to provide computational services to the UEs. To do so, a cost minimization problem in terms of computing time and hovering energy consumption is formulated. Furthermore, to achieve an efficient solution to a formulated challenging problem, the original optimization problem is decoupled into sub-problems using the block-coordinate decent method. Moreover, numerical results are compared to baseline schemes to determine the effectiveness of the proposed scheme. Simulation results demonstrate that the optimal allocation of local computational resources results in minimizing tasks' computational time and hovering energy consumption.

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Section: A Related Workmentioning

confidence: 99%

Section: B Motivation and Contributionsmentioning

confidence: 99%

Optimizing Computational and Communication Resources for MEC Network Empowered UAV-RIS Communication

Mahmood

Khan

et al. 2022

2022 IEEE Globecom Workshops (GC Wkshps)

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“…In particular, a deep reinforcement learning algorithm for online computation offloading in wireless powered MEC systems based on the memory replay technique was studied in [30]. Furthermore, in [31], the authors investigated joint communication and computation resource allocation for non-orthogonal multiple access (NOMA) assisted MEC systems. However, the aforementioned resource allocation schemes [18]- [20], [22], [25]- [27], [30], [31] were based on Shannon's capacity formula for the additive white Gaussian noise (AWGN) channel.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, in [31], the authors investigated joint communication and computation resource allocation for non-orthogonal multiple access (NOMA) assisted MEC systems. However, the aforementioned resource allocation schemes [18]- [20], [22], [25]- [27], [30], [31] were based on Shannon's capacity formula for the additive white Gaussian noise (AWGN) channel. Since URLLC systems exploit a short frame structure and a small packet size to reduce latency, the relation between the achievable rate, decoding error probability, and transmission delay cannot be captured by Shannon's capacity formula which assumes infinite block length and zero error probability [32].…”

Section: Introductionmentioning

confidence: 99%

Optimal Resource Allocation for Multi-User OFDMA-URLLC MEC Systems

Ghanem¹,

Jamali

Schober³

2022

IEEE Open J. Commun. Soc.

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In this paper, we study resource allocation algorithm design for multi-user orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) in mobile edge computing (MEC) systems. To meet the stringent end-to-end delay and reliability requirements of URLLC MEC systems, we employ joint uplink-downlink resource allocation and finite blocklength transmission. Furthermore, we propose a partial time overlap between the uplink and downlink frames to minimize the end-to-end delay, which introduces a new time causality constraint. The proposed resource allocation algorithm is formulated as an optimization problem for minimization of the total weighted power consumption of the network under a constraint on the number of URLLC user bits computed within the maximum allowable computation time, i.e., the end-to-end delay of a computation task of each user. Despite the non-convexity and the complicated structure of the formulated optimization problem, we develop a globally optimal solution using a branch-and-bound approach based on discrete monotonic optimization theory. The branch-and-bound algorithm minimizes an upper bound on the total power consumption until convergence to the globally optimal value. Furthermore, to strike a balance between computational complexity and performance, we propose two efficient suboptimal algorithms. For the first suboptimal scheme, the optimization problem is reformulated in the canonical form of difference of convex programming. Then, successive convex approximation (SCA) is used to determine a locally optimal solution. For the second suboptimal scheme, we use a high signal-to-noise ratio approximation for the channel dispersion. Then, via novel transformations, we convert the nonconvex quality-of-service constraints of the original problem into equivalent second-order-cone constraints. Our simulation results reveal that the proposed resource allocation algorithm design facilitates URLLC in MEC systems, and yields significant power savings compared to three baseline schemes. Moreover, our simulation results show that the proposed suboptimal algorithms offer different trade-offs between performance and complexity and attain an excellent performance at comparatively low complexity.

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“…[35] investigated maximizing the energy efficiency (EE) of the IRS-MEC system while [36] attempted to minimize the sum energy consumption of the IRS-MEC system. Among the studies of the MEC time delay minimization, [37] optimized the latency of the IRS-MEC system in a continuous IRS phase shift scenario while [38] proposed a delay-optimal schedule strategy for a discrete IRS phase shift scenario.…”

Section: Research Combining Two Of the Three Technologies: Irs Mec An...mentioning

confidence: 99%

Intelligent reflecting surface aided wireless powered mobile edge computing

Lu¹

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I wish to express my sincere gratitude to my supervisor, Prof. Jun Zhao for his invaluable advice throughout my master's candidature. With his extensive experience in various cutting-edge topics in communication society, I have broadened my horizon as well as developed solid academic skills. Prof. Jun Zhao has given me trust in various tasks, which encourages me in many aspects and enables me to have all-around abilities and perseverance.I would also like to thank other members of our research lab for their support in academics and life. It is their kind help and support that made my study, research, and life in Singapore a wonderful time.Last but not least, I would like to thank my loving family and Anki for being my pillar of support throughout my master's candidature. Their love, encouragement and care made me stronger to face every difficulty throughout the past two years.

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Energy-Efficient Design for IRS-Assisted NOMA-Based Mobile Edge Computing

Cited by 24 publications

References 16 publications

Optimizing Computational and Communication Resources for MEC Network Empowered UAV-RIS Communication

Optimizing Computational and Communication Resources for MEC Network Empowered UAV-RIS Communication

Optimal Resource Allocation for Multi-User OFDMA-URLLC MEC Systems

Intelligent reflecting surface aided wireless powered mobile edge computing

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