Optimum power and beam allocation based on traffic demands and channel conditions over satellite downlinks

Choi, Jihwan P.; Chan, Vincent W. S.

doi:10.1109/twc.2005.858365

Cited by 125 publications

(99 citation statements)

References 8 publications

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“…Specifically, the Jain Index of the ceiled satisfaction index 10 It is common practice to set a maximum number of iterations per temperature together with additional stopping conditions that trigger the cooling event before N it iterations are performed. N it can thus be regarded to as an upper bound on the number of iterations per temperature.…”

Section: Performance Analysismentioning

confidence: 99%

See 1 more Smart Citation

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

Cocco

Cola

Angelone

et al. 2018

IEEE Trans. on Broadcast.

127

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: Performance Analysismentioning

confidence: 99%

“…In [9], [10] the advantages of multi-beam with respect to single beam satellite systems are studied considering different performance metrics. Specifically, the optimal power allocation is derived for two different objective functions, one leading to throughput maximization and the other related to fairness.…”

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.

127

147

View full text Add to dashboard Cite

show abstract

“…In [26], the authors consider the whole multibeam satellite system design and they propose to allocate different bandwidth and power to each beam according to the asymmetrical traffic demand among the beams. In [27], the issue of multi-beam power allocation is solved considering both traffic demands and channel conditions over satellite downlinks. Carrier frequency assignment for military SatCom in which balance between spectral efficiency and resilience is taken into account was presented in [28].…”

Section: A Cognitive Satellite Downlinkmentioning

confidence: 99%

Resource Allocation for Cognitive Satellite Communications With Incumbent Terrestrial Networks

Lagunas

Sharma

Maleki

et al. 2015

IEEE Trans. Cogn. Commun. Netw.

197

136

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Abstract-The lack of available unlicensed spectrum together with the increasing spectrum demand by multimedia applications has resulted in a spectrum scarcity problem, which affects Satellite Communications (SatCom) as well as terrestrial systems. The goal of this paper is to propose Resource Allocation (RA) techniques, i.e. carrier, power and bandwidth allocation, for a cognitive spectrum utilization scenario where the satellite system aims at exploiting the spectrum allocated to terrestrial networks as the incumbent users without imposing harmful interference to them. In particular, we focus on the microwave frequency bands 17.7 − 19.7 GHz for the cognitive satellite downlink and 27.5 − 29.5 GHz for the cognitive satellite uplink, although the proposed techniques can be easily extended to other bands. In the first case, assuming that the satellite terminals are equipped with multiple Low Block Noise Converters (LNB), we propose a joint beamforming and carrier allocation scheme to enable cognitive Space-to-Earth communications in the shared spectrum where Fixed Service (FS) microwave links have priority of operation. In the second case, however, the cognitive satellite uplink should not cause harmful interference to the incumbent FS system. For the latter, we propose a Joint Power and Carrier Allocation (JPCA) strategy followed by a bandwidth allocation scheme which guarantees protection of the terrestrial FS system while maximizing the satellite total throughput. The proposed cognitive satellite exploitation techniques are validated with numerical simulations considering realistic system parameters. It is shown that the proposed cognitive exploitation framework represents a promising approach for enhancing the throughput of conventional satellite systems.

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“…Let us denote the hopping decision period (defined as Beam Hopping Window latter) comprised of fixed equal-length slots, and assume the visiting time for each cell (all cells must be visited at least one time in a period) is integer multiples of slots. The existing studies of the time assignment methods could be classified into two categories according to its decision epoch, namely the per-slot assignments [12,13] and the per-period assignments [9]. In [12], Neely et al consider it a server allocation problem, and design a 'Choose-the-K-LargestConnected-Queue' policy with the stabilization goal.…”

Section: Introductionmentioning

confidence: 99%

“…In [12], Neely et al consider it a server allocation problem, and design a 'Choose-the-K-LargestConnected-Queue' policy with the stabilization goal. Choi and Chan [13] prove that cell with the highest attenuationweighted demand should be served first per slot in order to balance the traffic demands and the channel capacity when the beam number is less than the cell number. In [9], the author carries out the time slot allocation following Fairness Cost Function and nth Order Difference Cost Function.…”

Section: Introductionmentioning

confidence: 99%

QoS-equilibrium slot allocation for beam hopping in broadband satellite communication systems

Han

Zheng

Huang³

et al. 2015

Wireless Netw

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In broadband satellite communication systems, beam hopping (BH) is more and more welcome to compensate the low-power-efficiency wide beam and the lowspectrum-efficiency multiple spot beams simultaneously. As a flexible coverage method, BH demands efficient hopping strategy to reach the desired quality of service (QoS). In this paper, we consider the QoS equilibrium, in particular the delay fairness, among different cells illuminated by one hopping beam through designing the hopping strategy on the condition of capacity satisfaction. Due to the physical limitation that hopping speed is much slower than packet arrival speed, the ideal first-come-first-serve packet scheduling is not realizable, which compels us to pursuit the optimal slot scheduling instead. In this paper, we consider a long-term delay fairness problem by perperiod slot allocation for BH, using instantaneous and statistical information. To deal with the complex stochastic programming problem, we introduce the time-sharing principle to uncouple the variables and adopt stochastic gradient theory to deduce the suboptimal close-form solution. Our contributions are in three folds: firstly, it is the first time to consider the delay fairness not the traditional capacity fairness for BH; secondly, a general per-period decision scheme is proposed for the unique slot allocation in the perspective of queuing; thirdly, a suboptimal closeform solution for the per-period slot allocation is obtained, which could also cope with burst traffic cases. In simulation, we verify our method's advantage in terms of delay fairness comparing with the existing similar works.

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Optimum power and beam allocation based on traffic demands and channel conditions over satellite downlinks

Cited by 125 publications

References 8 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

Resource Allocation for Cognitive Satellite Communications With Incumbent Terrestrial Networks

QoS-equilibrium slot allocation for beam hopping in broadband satellite communication systems

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