Dynamic Optimization for Resource Allocation in Relay-Aided OFDMA Systems Under Multiservice

Li, Wei; Lei, Jing; Wang, Tao; Xiong, Congcong; Wei, Jibo

doi:10.1109/tvt.2015.2410832

Cited by 24 publications

(21 citation statements)

References 26 publications

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“…Li et al [239] applied a discrete version of PSO to the resource allocation problem in a cooperative OFDMA system to maximize the average utility of all mobile stations under multiservice. The correlation between adjacent frames is exploited to transfer knowledge.…”

Section: Discrete Applicationsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

475

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Discrete Applicationsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

475

213

View full text Add to dashboard Cite

show abstract

“…In Fig. 8 we compare FREE with the other two related works in [41] and [44]. Lagrange dual decomposition method and discrete particle swarm optimization method are adopted respectively by [41] and [44] for resource allocation in cooperative relay networks, i.e., to pursue…”

Section: Simulation Resultsmentioning

confidence: 99%

“…For fair comparison, we assume that the BSs in [41] and [44] are also powered by hybrid energy. We assume there is a heuristic renewable energy management scheme for [41] and [44]. With the heuristic scheme, the harvested energy is always charged into the battery as long as there is enough space, and the BS always uses the saved renewable energy with priority.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Energy-Efficient Resource Allocation for Time-Varying OFDMA Relay Systems With Hybrid Energy Supplies

Yang

Shen

Han

et al. 2018

IEEE Systems Journal

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Abstract-This paper investigates the energy efficient resource allocation for orthogonal frequency division multiple access (OFDMA) relay systems, where the system is supplied by the conventional utility grid and a renewable energy generator equipped with a storage device. The optimal usage of radio resource depends on the characteristics of the renewable energy generation and the mobile traffic, which exhibit both temporal and spatial diversities. Lyapunov optimization method is used to decompose the problem into the joint flow control, radio resource allocation and energy management without knowing a priori knowledge of system statistics. It is proven that the proposed algorithm can result in close-to-optimal performance with capacity limited data buffer and storage device. Simulation results show that the flexible tradeoff between the system utility and the conventional energy consumption can be achieved. Compared with other schemes, the proposed algorithm demonstrates better performance.

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“…The user positions are generated by a uniform distribution via Monte Carlo simulation. For OFDMA [40,41] as (58) shows, when the transmitted power is fixed, the power allocation using a water-filling strategy and fixed power equipartition are denoted as (59) and (60), respectively. or NOMA strategies, this subsection discusses the approximate function property between the original target of (61) and our alternative target of (62).…”

Section: Comparisons Between Original and Alternative Targetsmentioning

confidence: 99%

Performance analysis of gain ratio power allocation strategies for non-orthogonal multiple access in indoor visible light communication networks

Tao

et al. 2018

J Wireless Com Network

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Non-orthogonal multiple access (NOMA) is a promising method for enhancing the throughput in visible light communication (VLC) networks. In NOMA, signal power domain control, called gain ratio power allocation (GRPA), can significantly improve the user sum rate with full-time frequency resource utilization. In indoor NOMA-VLC networks, the scenario in which users are covered by VLC illuminants on the ceiling is typical. First, this paper proposes a novel GRPA strategy for a single VLC cell. Second, due to the difficulty of direct comparisons between our and previously reported GRPA strategies, this paper presents an alternative lower bound for comparability. Third, in two-and threeuser cases, this paper analytically demonstrates that our NOMA-VLC GRPA strategy outperforms the aforementioned strategy, when successive interference cancellation (SIC) is used. Moreover, in the multi-user case, experimental results show that the average user data rate (AUDR) of multi-user cases under our strategy is better than that under the previous strategy. Finally, our paper provides both analytic and numerical results to prove that VLC single-cell system throughput under our strategy is better than that under the previous strategy. The proposed alternative lower bound is also proved to fit the original NOMA-VLC GRPA target asymptotically based on indoor VLC channels. Furthermore, to achieve the same AUDR, our strategy supports worse received conditions than does the previous strategy.

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Dynamic Optimization for Resource Allocation in Relay-Aided OFDMA Systems Under Multiservice

Cited by 24 publications

References 26 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Energy-Efficient Resource Allocation for Time-Varying OFDMA Relay Systems With Hybrid Energy Supplies

Performance analysis of gain ratio power allocation strategies for non-orthogonal multiple access in indoor visible light communication networks

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