Efficient power allocation algorithm in downlink cognitive radio networks

Abdulghafoor, Omar B.; Shaat, Musbah; Shayea, Ibraheem; Mahmood, Farhad E.; Nordin, Rosdiadee; Lwas, Ali Khadim

doi:10.4218/etrij.2021-0013

Cited by 3 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a downlink OFDM scenario, the optimal subcarrier allocation is attained by assigning subcarrier to users holding maximum SINR as shown in [31]. However, this method is not effective in CR-uplink scenario due to per-user power constraints.…”

Section: Subcarrier Allocation Algorithmmentioning

confidence: 99%

Efficient Resource Allocation Algorithm in Uplink OFDM-Based Cognitive Radio Networks

Abdulghafoor¹,

Shaat²,

Shayea³

et al. 2023

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

The computational complexity of resource allocation processes, in cognitive radio networks (CRNs), is a major issue to be managed. Furthermore, the complicated solution of the optimal algorithm for handling resource allocation in CRNs makes it unsuitable to adopt in real-world applications where both cognitive users, CRs, and primary users, PUs, exist in the identical geographical area. Hence, this work offers a primarily price-based power algorithm to reduce computational complexity in uplink scenarios while limiting interference to PUs to allowable threshold. Hence, this paper, compared to other frameworks proposed in the literature, proposes a two-step approach to reduce the complexity of the proposed mathematical model. In the first step, the subcarriers are assigned to the users of the CRN, while the cost function includes a pricing scheme to provide better power control algorithm with improved reliability proposed in the second stage. The main contribution of this paper is to lessen the complexity of the proposed algorithm and to offer flexibility in controlling the interference produced to the users of the primary networks, which has been achieved by including a pricing function in the proposed cost function. Finally, the performance of the proposed power and subcarrier algorithm is confirmed for orthogonal frequency-division multiplexing (OFDM). Simulation results prove that the performance of the proposed algorithm is better than other algorithms, albeit with a lesser complexity of O(NM) + O(Nlog(N)).

show abstract

Section: Subcarrier Allocation Algorithmmentioning

confidence: 99%

Efficient Resource Allocation Algorithm in Uplink OFDM-Based Cognitive Radio Networks

Abdulghafoor¹,

Shaat²,

Shayea³

et al. 2023

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…Omar Abdulghafoor et al 21 had proposed power based pricing algorithm (PPA) for minimizing computational complexity with the application of underlay downlink Cognitive Radio Network (CRN). For improving the flexibility of power distribution to sub carriers, the pricing approach was proposed based on game theory.…”

Section: Related Workmentioning

confidence: 99%

ICA-ResNet-based signal detection and optimal power allocation for CRN-NOMA system

Kumar

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2

View full text Add to dashboard Cite

Non-orthogonal multiple access (NOMA) system is one of the assuring solutions for the upcoming wireless communication systems for achieving better spectral efficiency. The NOMA based wireless communication system is mainly transformed with cognitive radio network (CRN) to improve the performance. This research work focuses on signal detection and optimal power allocation for uplink CRN-NOMA system using optimal deep learning (DL) model. This model combines the channel state estimation process and signal detection. In the proposed work, the signal detection is carried out on the basis of allocated power to the user. Initially, the power allocation process is carried out by the Bisection water filling algorithm to achieve optimal power allocation. Then, the signal detection process is enabled by the DL model ResNet-152. Then, the metaheuristic optimization using Imperialist Competitive Algorithm (ICA) is exploited to provide optimal solution for better signal detection. The noisy channel's information like Rayleigh fading and AWGN are set for wireless transmission medium to transmit a signal. The combined model of power allocation and signal detection is carried out on MATLAB platform. The performance measures like spectral efficiency, energy efficiency, BER and average response rate of the proposed techniques are compared with the previous research works. When the value of mean harvested energy is 4.5 J  , the energy efficiency of the proposed model, deep Q learning, conventional TDMA and conventional NOMA/ TDMA are 0.74 [bit/s/Hz], 0.71 [bit/s/Hz], 0.67 [bit/s/Hz] and 0.57 [bit/s/Hz] respectively. It has been hence proved that the performance of this methodology is accurate and effective than the existing models.

show abstract

CEAR: A cooperation based energy aware reward scheme for next generation green cognitive radio networks

Srivastava

Kaur

2023

Physical Communication

View full text Add to dashboard Cite

Efficient power allocation algorithm in downlink cognitive radio networks

Cited by 3 publications

References 31 publications

Efficient Resource Allocation Algorithm in Uplink OFDM-Based Cognitive Radio Networks

Efficient Resource Allocation Algorithm in Uplink OFDM-Based Cognitive Radio Networks

ICA-ResNet-based signal detection and optimal power allocation for CRN-NOMA system

CEAR: A cooperation based energy aware reward scheme for next generation green cognitive radio networks

Contact Info

Product

Resources

About