Power Allocation in Multibeam Satellite Systems: A Two-Stage Multi-Objective Optimization

Aravanis, Alexis I.; Shankar, M. R. Bhavani; Arapoglou, Pantelis-Daniel; Danoy, Grégoire; Cottis, Panayotis G.; Ottersten, Björn

doi:10.1109/twc.2015.2402682

Cited by 139 publications

(94 citation statements)

References 24 publications

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“…In the paper a phased array antenna is assumed at the satellite and call-admission control schemes are investigated. Differently from the approaches adopted in the papers mentioned above, the studies presented in [12], [13] explore the benefits of power allocation. In particular, a two-stages sub-optimal algorithm is applied to solve a non-convex optimization problem, the solution of which gives some insights about the relations between power allocation and offered traffic on the forward link of satellite networks.…”

Section: Introductionmentioning

confidence: 99%

Radio Resource Management Optimization of Flexible Satellite Payloads for DVB-S2 Systems

Cocco

Cola

Angelone

et al. 2018

IEEE Trans. on Broadcast.

134

147

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The increasing demand for high-rate broadcast and multicast services over satellite networks has pushed for the development of High Throughput Satellites characterized by a large number of beams (e.g., more than 100). This, together with the variable distribution of data traffic request across beams and over time, has called for the design of a new generation of satellite payloads, able to flexibly allocate bandwidth and power. In this context, this paper studies the problem of radio resource allocation in the forward link of multibeam satellite networks adopting the Digital Video Broadcasting -Satellite -Second Generation (DVB-S2) standard. We propose a novel objective function with the aim to meet as close as possible the requested traffic across the beams while taking fairness into account. The resulting non-convex optimization problem is solved using a modified version of the simulated annealing algorithm, for which a detailed complexity analysis is presented. Simulation results obtained under realistic conditions confirm the effectiveness of the proposed approach and shed some light on possible payload design implications.

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Section: Introductionmentioning

confidence: 99%

Radio Resource Management Optimization of Flexible Satellite Payloads for DVB-S2 Systems

Cocco

Cola

Angelone

et al. 2018

IEEE Trans. on Broadcast.

134

147

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“…Consequently, a trade-off between the USC and total power consumption (which is proportional to the total radiated power) can be achieved for a specific value of w. In particular, w = 0 corresponds to USC minimization. Moreover, it can be proved that problem (3) is NP-hard by following similar arguments as in [8]. Nevertheless, as will be seen later, we can obtain a stationary point of the equivalent differentiable problem with reasonable complexity.…”

Section: Problem Formulation and Transformationmentioning

confidence: 72%

“…Unlike previous works, a multi-objective approach that minimizes the USC together with the satellite power consumption is presented in [8]. In particular, a two-stage optimization is proposed to attain a set of Pareto optimal solutions using C. N. Efrem metaheuristics.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Energy-Efficient Power Allocation in Multibeam Satellite Systems

Efrem

Panagopoulos

2020

IEEE Wireless Commun. Lett.

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Power consumption is a major limitation in the downlink of multibeam satellite systems, since it has a significant impact on the mass and lifetime of the satellite. In this context, we study a new energy-aware power allocation problem that aims to jointly minimize the unmet system capacity (USC) and total radiated power by means of multi-objective optimization. First, we transform the original nonconvex-nondifferentiable problem into an equivalent nonconvex-differentiable form by introducing auxiliary variables. Subsequently, we design a successive convex approximation (SCA) algorithm in order to attain a stationary point with reasonable complexity. Due to its fast convergence, this algorithm is suitable for dynamic resource allocation in emerging on-board processing technologies. In addition, we formally prove a new result about the complexity of the SCA method, in the general case, that complements the existing literature where the complexity of this method is only numerically analyzed.

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“…Many research work has studied DPA techniques in multibeam satellite systems . Choi and Chan optimize the power allocation in order to adapt to channel conditions and meet traffic demands through Lagrange multiplier method.…”

Section: Introductionmentioning

confidence: 99%

“…In Choi and Chan, the consideration of CCI gives rise to a numerical approach for the interference mitigation problem, since the closed form of the relevant solution is hard to obtain. Further study is taken in Aravanis et al, which proves the NP‐hardness of power allocation with CCI and decomposes the problem into independent color‐based subproblems rendering the proposed two‐stage genetic algorithm and simulated annealing (GA‐SA) metaheuristics algorithm. To avoid the tendency of the SA to local optimum due to the behavior of stochastic optimization algorithm with nonconvex objective functions, Cocco et al proposes a modified version of SA‐based dynamic power allocation algorithm .…”

Section: Introductionmentioning

confidence: 99%

AG‐DPA: Assignment game–based dynamic power allocation in multibeam satellite systems

Liu

Fan

et al. 2019

Satell Commun Network

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Summary Dynamic power allocation is the key technology to maintain the link quality and improve the system throughput in multibeam satellite systems. Many numerical optimization algorithms have been proposed to optimize the power allocation schemes among beams. However, current metaheuristic algorithms, most of which are off‐line iterative methods, are not appropriate in nonuniform traffic demands and time‐varying channels due to the high computational complexity. To solve this problem, an assignment game–based dynamic power allocation (AG‐DPA) is proposed to achieve the suboptimality with low complexity in multibeam satellite systems. The key idea of the proposed AG‐DPA is to model the DPA problem into an assignment game model where the competitive equilibrium is achieved. Further, an adaptive price factor is introduced to make a trade‐off between algorithm performance and complexity. Simulation results show the effectiveness of the proposed AG‐DPA algorithm.

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Power Allocation in Multibeam Satellite Systems: A Two-Stage Multi-Objective Optimization

Cited by 139 publications

References 24 publications

Radio Resource Management Optimization of Flexible Satellite Payloads for DVB-S2 Systems

Radio Resource Management Optimization of Flexible Satellite Payloads for DVB-S2 Systems

Dynamic Energy-Efficient Power Allocation in Multibeam Satellite Systems

AG‐DPA: Assignment game–based dynamic power allocation in multibeam satellite systems

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