Energy efficient radio resource management strategies for green radio

Han, Congzheng; Armour, Simon

doi:10.1049/iet-com.2011.0113

Cited by 28 publications

(26 citation statements)

References 15 publications

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“…Selection is realized by comparing the objective function values of target vector and trial vector. For minimization problem, if the trial vector has better value of the objective function, then it replaces the updated one as per (7).…”

Section: Selectionmentioning

confidence: 99%

Optimal Power Allocation for Green Cognitive Radio using Differential Evolution Approach

Raj¹,

Kaushik²,

Kumar³

2017

IJERT

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Abstract-In this study, the problem of determining the power allocation that maximizes the energy efficiency of cognitive radio network is investigated using differential evolution algorithm with constraint handling technique. The energy-efficient fractional objective is defined in terms of bits per Joule per Hertz. The proposed constrained fractional programming problem is a non-linear nonconvex optimization problem. Nature inspired algorithms like Differential Evolution (DE) can describe and resolve complex relationships from intrinsically very simple initial conditions with little or no knowledge of the search space. In simulation results, the effect of different system parameters (interference threshold level, number of primary users and number of secondary users) on the performance of the proposed algorithm is investigated.Index Terms-Green cognitive radio, power allocation, energy efficiency, differential evolution 1 INTRODUCTION Energy efficiency plays a key role in designing wireless communication networks. The energy-efficient wireless networks help in saving the battery life and reduction in global warming. Massive growth and demand of high data rate wireless devices and applications cause significant increase in the greenhouse gas emissions and crowdedness in available frequency spectrum. The main goal of green communication is to develop wireless networks, protocols and devices that jointly maximize the high data rate and minimize the greenhouse gas emissions, that is, minimize the transmit power. The maximum data rate transfer with minimum transmit power is the key of green communication.

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Section: Selectionmentioning

confidence: 99%

Optimal Power Allocation for Green Cognitive Radio using Differential Evolution Approach

Raj¹,

Kaushik²,

Kumar³

2017

IJERT

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“…CQI specifies the SINR of each UE on every sub-carrier. LA selects the modulation and coding scheme (MCS) based on the channel quality measurement, Table 5 presents the LA table for the 2 × 2 SFBC MIMO with WINNER II multipath and path loss channel models obtained from link level simulations at a block error rate (BLER) of 10 21 [27]. AC selects the users to be passed to the packet scheduler.…”

Section: System Model and Simulation Assumptionsmentioning

confidence: 99%

“…Considering the HFR factor 1 as the benchmark, the median RH-ERGs at a low-offered load are 34, 27,22,21,16,14 and 220 for SGC/RRM, HFR factor 1/3, FFR 5/8, FFR 2/3, HFR 1/3 inverted, FFR 3/4 and SFR power factor 2, respectively. At a high-offered load the RH-ERGs are 45, 39, 17, 13, 21 15 and 25 for SGC/RRM, HFR factor 1/3, FFR 5/8, FFR 2/3, HFR 1/3 inverted, FFR 3/4 and SFR power factor 2, respectively.…”

Section: Rh Energy Performancementioning

confidence: 99%

Energy efficient coordinated radio resource management: a two player sequential game modelling for the long-term evolution downlink

2012

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Inter-cell interference (ICI) is a key limiting factor to the radio frequency (RF) energy performance of a multi-cell multi-user radio access network (RAN). The channel quality of the cell edge users is greatly impaired by ICI owing to the fact that cell edge users are furthest away from their serving base stations (BSs) and closest to the interfering BSs. Consequently the BS is compelled to allocate more physical resource blocks [PRBs (In LTE a PRB spans 12 sub-carriers each with a bandwidth of 15 kHz over a 0.5 ms time slot)] to the cell edge users in order to meet their quality-of-service (QoS) targets. The study proposes a novel ICI management technique that mitigates the effects of ICI through a sequential game play between cells in the E-UTRAN [Evolved Universal Mobile Telecommunications Systems (UMTS) terrestrial radio access network] based on the instantaneous cell offered load. The proposed technique is shown to produce greater user channel quality improvements of 4 dB greater than the state-of-the-art ICI management techniques. The channel quality improvements result in a utilisation of less PRBs, RF and radio head energy which translate to energy reduction gains of 34 and 45% at low-and high-offered loads, respectively. In addition the proposed scheme does not require a central processing entity such as a radio network controller and can be implemented in unplanned self-organising networks.

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“…In [11], the authors propose a score based scheduling algorithm and evaluate the performance of their scheme in terms of energy consumption and throughput. In [12], the authors propose a radio resource allocation (RRA) strategy to reduce the power consumption of BS in terms of Joule/bit.…”

Section: Introductionmentioning

confidence: 99%

Energy efficient and quality of service aware resource block allocation in OFDMA systems

et al. 2015

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This study investigates energy efficient allocation of radio resource blocks in orthogonal frequency division multiple access (OFDMA) systems while considering the status of the users' data buffers to reduce packet dropping rate by the downlink scheduler. The proposed scheme exploits the fluctuations of traffic load to efficiently schedule users' data packets by reducing the overall energy consumption of the system whenever the status of data buffers permits. From information theory point of view, the proposed scheme exploits the fundamental trade-off between energy efficiency and spectral efficiency to perform scheduling. First, the problem is formulated as an optimisation problem and then a novel solution, based on dynamic programming, is applied. By comparing the analytical solution with the ones obtained by exhaustive search, it is demonstrated that the proposed scheme is close to the optimal solution, with low computational complexity. In addition, comprehensive simulations are conducted to evaluate the performance of the suggested algorithm. Both analytical and simulation results demonstrate the superiority of the proposed algorithm compared with the well-known benchmark schemes in terms of energy efficiency and packet dropping rate.

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Energy efficient radio resource management strategies for green radio

Cited by 28 publications

References 15 publications

Optimal Power Allocation for Green Cognitive Radio using Differential Evolution Approach

Optimal Power Allocation for Green Cognitive Radio using Differential Evolution Approach

Energy efficient coordinated radio resource management: a two player sequential game modelling for the long-term evolution downlink

Energy efficient and quality of service aware resource block allocation in OFDMA systems

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