Power Allocation Using Geometric Water Filling for OFDM-Based Cognitive Radio Networks

Sultana, Ajmery; Zhao, Lian; Fernando, Xavier

doi:10.1109/vtcfall.2016.7881172

Cited by 7 publications

(11 citation statements)

References 10 publications

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“…Note that, in all cases, ξ max and ξ mean assumed smaller values than ξ c , which was already expected. Additionally, we note that increasing Γ causes data rate reduction, as the latter is directly proportional to the number of allocated bits, which according to (18) is inversely proportional to Γ.…”

Section: Case Studymentioning

confidence: 90%

“…Such characteristic results in smaller constellations with less energy for subchannels associated with intermediate nSNR values and can even result in the avoidance of allocation to subchannels associated with very low nSNR values. This effect becomes more clear as Γ increases, since according to (18) and (21), the number of bits and the incremental transmission power are, respectively, inversely and directly proportional to Γ. Therefore, we can conclude that the resource allocation to subchannels associated with low nSNR values is not prioritized and may even not occur.…”

Section: Case Studymentioning

confidence: 94%

“…According to (18), the gap Γ from the Shannon capacity curve and the nSNR associated with the q th element of D i , Algorithm # 1: Levin-Campello algorithm based on the RA criterion.…”

Section: B Considerations On the Gapmentioning

confidence: 99%

“…In this figure, we can see that the SNR presents different shape from the nSNR, with the allocated power P D,q being the difference between these two quantities. This occurs because the number of bits allocated to the elements of D i is quantized, which, according to (18), causes the SNR to assume quantized values. 5 shows the allocated power P D,q to the q th element of D i that was obtained with the solution of the resource allocation problem.…”

Section: Case Studymentioning

confidence: 99%

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Resource Allocation in HS-OFDM-based PLC Systems: A Tutorial

Oliveira

Colen

Vinck

et al. 2018

JCIS

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This work organizes and discusses important information on resource allocation in power line communication (PLC) systems based on the Hermitian symmetric orthogonal frequency-division multiplexing scheme. In the context of this work, the term resource refers to bits and transmission power, which can be optimally or sub-optimally allocated to subcarriers on the basis of the knowledge of the normalized signal-tonoise ratio (nSNR) of power line subchannels. In this sense, we thoroughly discuss the formulation of the resource allocation for PLC systems and accordingly derive expressions for the nSNR as well as bit and transmission power allocation in all subchannels. Additionally, we precisely detail greedy algorithms to assist practitioners and researchers in the implementation of the resource allocation procedure. Considering a frequency-selective PLC channel corrupted by additive colored Gaussian noise as well as M-ary pulse amplitude modulation and quadrature amplitude modulation as modulation schemes, the influence of the gap from the Shannon capacity curve on symbol error rate probability is discussed. On top of the carried out discussion, numerical results are presented in order to emphasize the usefulness of a proper comprehension of this subject.

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Section: Case Studymentioning

confidence: 90%

Section: Case Studymentioning

confidence: 94%

“…According to (18), the gap Γ from the Shannon capacity curve and the nSNR associated with the q th element of D i , Algorithm # 1: Levin-Campello algorithm based on the RA criterion.…”

Section: B Considerations On the Gapmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

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Resource Allocation in HS-OFDM-based PLC Systems: A Tutorial

Oliveira

Colen

Vinck

et al. 2018

JCIS

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“…We consider the cases of random and quasi-random traffic in the downlink of an orthogonal frequency division multiplexing (OFDM)-based cell which provides service to calls from many service-classes. OFDM is a dominant technology in 4th generation (4G) networks and can also be considered as a candidate technology in 5th generation (5G) networks [17][18][19][20][21] and in cognitive radio networks [22]. The analysis of this OFDM-based cell relies on the loss models of [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

An Analytical Framework in OFDM Wireless Networks Servicing Random or Quasi-Random Traffic

et al. 2019

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We consider the downlink of an orthogonal frequency division multiplexing (OFDM)-based cell that services calls from many service-classes. The call arrival process is random (Poisson) or quasi-random, i.e., calls are generated by an infinite or a finite number of sources, respectively. In order to determine congestion probabilities and resource utilization, we model the cell as a multirate loss model. Regarding the call admission, we consider the restricted accessibility, the bandwidth reservation (BR), and the complete sharing (CS) policies. In a system of restricted accessibility, a new call may be blocked even if resources do exist. In a BR system, subcarriers can be reserved in favor of calls of high subcarrier requirements. Finally, in a CS system, a new call is blocked due to resource unavailability. In all three policies, we show that there exist recursive formulas for the determination of the various performance measures. Based on simulation, the accuracy of the proposed formulas is found to be quite satisfactory.

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