Multi-Objective Resource Allocation for D2D and Enabled MC-NOMA Networks by Tchebycheff Method

Bayat, Siavash; Khalili, Ata; Zargari, Shayan; Mili, Mohammad Robat; Han, Zhu

doi:10.1109/tvt.2021.3068855

Cited by 14 publications

(8 citation statements)

References 17 publications

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“…Therefore, we resort to the Tchebycheff method to transform the MOOP into two single-objective optimization problems (SOOPs) instead of the widely used Dinklebach method [26]. This method can yield a complete Pareto optimal set [27], [28] compared to other approaches, such as -method, weighted product method, and exponentially weighted criterion [29]. • We exploit alternating optimization (AO) to split the first SOOP (throughput maximization) into two sub-problems.…”

Section: B Our Contributionsmentioning

confidence: 99%

“…The boundary achieved by all feasible points from the Pareto optimal set is called the Pareto optimal front (PF). Here, we adopt the weighted Tchebycheff method, which can yield the PF, even if the MOOP is nonconvex [27], [28]. It minimizes the maximum deviations of the objective values from their utopia values; e.g., one solves…”

Section: Equivalent Optimization Problemmentioning

confidence: 99%

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Energy-Efficient Hybrid Offloading for Backscatter-Assisted Wirelessly Powered MEC with Reconfigurable Intelligent Surfaces

Zargari,

Tellambura,

Herath

2022

Preprint

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Section: B Our Contributionsmentioning

confidence: 99%

Section: Equivalent Optimization Problemmentioning

confidence: 99%

Energy-Efficient Hybrid Offloading for Backscatter-Assisted Wirelessly Powered MEC with Reconfigurable Intelligent Surfaces

Zargari,

Tellambura,

Herath

2022

Preprint

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“…Nonetheless, in the existing MOOP frameworks in D2D communications underlying cellular networks such as refs. [17] and [18], the authors did not consider the impact of the IRS in D2D communication underlying cellular networks. In fact, since IRSs can be configured to effectively eliminate D2D interference and enhance desired signals, incorporating them into the existing MOOP framework in D2D communications can further increase spectral efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…A MOOP trade‐off between the D2D pairs and the CUs in D2D underlying non‐orthogonal multiple access (NOMA) cellular networks was studied in ref. [18] to maximize the sum rate of the D2D pairs and CUs simultaneously. This problem was first converted into a SOOP via the weighted Tchebycheff method.…”

Section: Introductionmentioning

confidence: 99%

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Resource allocation for multi‐IRS‐aided D2D communication underlying cellular networks

2022

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This paper investigates the resource allocation design in device-to-device (D2D) communication underlying cellular networks, which is assisted by multiple intelligent reflecting surfaces (IRSs) deployed at the cell boundary to enhance desired signals and mitigate interference between D2D pairs and CUs. In this regard, a multi-objective optimization problem (MOOP) framework is formulated to jointly maximize the downlink sum-rate of the D2D pairs and cellular users (CUs). To doing so, the MOOP is first converted into a single-objective optimization problem (SOOP) by invoking the weighted sum method. Next, to facilitate the high-coupled non-convex SOOP formulation, it is decomposed into two sub-problems through the alternative optimization method. For the first sub-problem, the inner approximation and successive convex approximations are adopted for jointly allocating D2D transmission power, subcarrier assignment, and transmit beamforming matrix at the BS. For the second sub-problem, an iterative algorithm based on the penalty-based method as well as Fenchel's duality approach is adopted to design the passive beamforming matrices at IRSs, which is guaranteed to rapidly converge to a stationary point. Simulation results reveal that a non-trivial trade-off between the total throughput of CUs and D2D pairs exist. Furthermore, the proposed framework significantly outperforms existing works addressed in the literature.

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Latency and energy aware rate maximization in MC-NOMA-based multi-access edge computing: A two-stage deep reinforcement learning approach

Nduwayezu

Yun

2022

Computer Networks

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Multi-Objective Resource Allocation for D2D and Enabled MC-NOMA Networks by Tchebycheff Method

Cited by 14 publications

References 17 publications

Energy-Efficient Hybrid Offloading for Backscatter-Assisted Wirelessly Powered MEC with Reconfigurable Intelligent Surfaces

Energy-Efficient Hybrid Offloading for Backscatter-Assisted Wirelessly Powered MEC with Reconfigurable Intelligent Surfaces

Resource allocation for multi‐IRS‐aided D2D communication underlying cellular networks

Latency and energy aware rate maximization in MC-NOMA-based multi-access edge computing: A two-stage deep reinforcement learning approach

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