Joint User Pairing and Power Allocation With Compressive Sensing in NOMA Systems

Nasser, Ahmed; Muta, Osamu; Gacanin, Haris; Elsabrouty, Maha

doi:10.1109/lwc.2020.3023619

Cited by 20 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…15 A low-complex user pairing and power allocation technique using the compressive sensing theory in SISO case is investigated. 16 Dynamic user clustering in SISO system with a cluster size of 2, 3, and 4 users based on channel gain differences is investigated. 12 A low complex clustering algorithm with a cluster size of 2, based on channel gain difference and correlation for a multiple input single output (MISO) system is discussed, 13 where digital beamforming design is employed.…”

Section: Related Workmentioning

confidence: 99%

“…A fast proportional fairness scheduling‐based user pairing and power allocation algorithm is presented for the SISO case and compared with the exhaustive search method 15 . A low‐complex user pairing and power allocation technique using the compressive sensing theory in SISO case is investigated 16 . Dynamic user clustering in SISO system with a cluster size of 2, 3, and 4 users based on channel gain differences is investigated 12 …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic user clustering with hybrid beamforming in millimeter wave non‐orthogonal multiple access (NOMA) and power allocation using teaching learning based optimization (TLBO)

Sumathi

Ramesh

Ding

2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Non-orthogonal multiple access (NOMA) is a most promising candidate in future wireless networks to improve user fairness and spectral / energy efficiency in multiuser scenarios. In this work, we investigate a clustered multi-user millimeter wave (mmWave) downlink NOMA system with a hybrid beamforming architecture. In most existing research with dynamic user clustering, the number of users in each cluster varies from one to any number. A single user in a cluster corresponds to orthogonal multiple access (OMA) case, which is not beneficial. At the same time, clusters with more than one user employ the NOMA technique. However, the main limitation of NOMA with a large number of users is the successive interference cancellation (SIC) decoding complexity which increases with the number of users. Moreover, this also causes a delay in decoding the data at the users. This problem is addressed in our proposed algorithm, where each cluster has a minimum of two users to a maximum of a fixed number of users. Together with the dynamic clustering, a hybrid beamforming vector is also designed. Then, a single-step power allocation algorithm using teaching-learning based optimization (TLBO) to maximize sum spectral efficiency is proposed, which is less complex than the existing suboptimal methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic user clustering with hybrid beamforming in millimeter wave non‐orthogonal multiple access (NOMA) and power allocation using teaching learning based optimization (TLBO)

Sumathi

Ramesh

Ding

2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…In Ref. [11], a relaxed

l_{1}

‐norm problem is formulated to jointly tackle user pairing and power allocation, and then a compressive sensing‐based solution is applied. In Ref.…”

Section: Related Workmentioning

confidence: 99%

Cache‐enabled reinforcement learning scheme for power allocation and user selection in opportunistic downlink NOMA transmissions

Gendia

Muta

Nasser

2022

IEEJ Transactions Elec Engng

Self Cite

View full text Add to dashboard Cite

Non-orthogonal multiple access (NOMA) allows multiple user equipment (UE) to simultaneously share the same resource blocks using varying levels of transmit power at the base station (BS) side. Proper allocation of transmission power and selection of candidate users for pairing over the same resource block are critical for an efficient utilization of the available resources. Optimal UE selection and power splitting among paired UEs can be made through an exhaustive search over the space of all possible solutions. However, the cost incurred by such approach can render it practically infeasible. Reinforcement learning (RL) deploying double deep-Q networks (DDQN) is a promising framework that can be adopted for tackling the problem. In this article, an RL-based DDQN scheme is proposed for user pairing in opportunistic access to downlink NOMA systems with capacity-limited backhaul link connections. The proposed algorithm relies on proactive data caching to alleviate the throttling caused by backhaul bottlenecks, and optimized UE selection and power allocation are accomplished through the continuous interaction between an RL agent and the NOMA environment to increase the overall system throughput. Simulation results are presented to showcase the near-optimal strategy achieved by the proposed scheme.

show abstract

“…Joint power allocation and signal detection is NP-hard problem. The sub optimal solution is provided with Artificial intelligence (AI) based approaches 17 . The machine learning model provides efficient solution for joint problem and it requires complex computational steps.…”

Section: Introductionmentioning

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

Joint User Pairing and Power Allocation With Compressive Sensing in NOMA Systems

Cited by 20 publications

References 17 publications

Dynamic user clustering with hybrid beamforming in millimeter wave non‐orthogonal multiple access (NOMA) and power allocation using teaching learning based optimization (TLBO)

Dynamic user clustering with hybrid beamforming in millimeter wave non‐orthogonal multiple access (NOMA) and power allocation using teaching learning based optimization (TLBO)

Cache‐enabled reinforcement learning scheme for power allocation and user selection in opportunistic downlink NOMA transmissions

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

Contact Info

Product

Resources

About