System throughput maximization in IRS-assisted phase cooperative NOMA networks

Sadia, Haleema; Hassan, Ahmad Kamal; Abbas, Ziaul Haq; Abbas, Ghulam

doi:10.1016/j.phycom.2023.102007

Cited by 15 publications

(5 citation statements)

References 116 publications

(89 reference statements)

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“…Higher than OMA and NOMA Higher spectral efficiency, Complex [13], [20], [84] IRS-OMA Improved BER over OMA Improved over conventional OMA Improved over OMA, Improved energy efficiency,Simple [13], [20], [35], [84] IRS-NOMA User specific performance, improved Improved over conventional NOMA Higher spectral efficiency and energy efficiency, Complex [20], [24], [35], [56] IRS-CNOMA User specific performance, improved Improved over conventional CNOMA Higher spectral efficiency and energy efficiency, More complex multiple IRS units and their multiple links distributed across a wide area. Interference Management: Managing interference between cooperative users and other users in the network can be challenging [69].…”

Section: Referencementioning

confidence: 99%

“…In traditional point-to-point communication, a sender communicates directly with a receiver. However, in cooperative communication, intermediate nodes, often referred to as relays, assist in transmitting the data from the sender to the receiver [6], [10], [11], [13], [36], [48], [49]. A cooperative communication system model is depicted in Fig.…”

Section: Principle Of Cooperative Communicationmentioning

confidence: 99%

“…This approach brings several advantages, including enhanced network reliability, reduced latency, improved coverage, and increased spectral efficiency of wireless networks. By intelligently sharing resources and information, cooperative communication technologies promise to unlock the full potential of 5G and future generations of wireless networks, enabling seamless connectivity, and low-latency applications [6], [9]- [11], [13]- [15]. In cooperative NOMA (CNOMA) multiple users simultaneously share the same time-frequency resources with distinct power levels and codebooks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Reflecting Surfaces (IRS)-Enhanced Cooperative NOMA: A Contemporary Review

Sharma,

Mishra,

Kumar

et al. 2024

IEEE Access

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The integration of intelligent reflecting surfaces (IRS) into cooperative non-orthogonal multiple access (NOMA) systems revolutionizes wireless networks by enhancing signal strength, mitigating interference, and optimizing spectral efficiency. The cooperative NOMA (CNOMA) framework, empowered by IRS technology, further promises enhanced performance, robustness, and scalability for next-generation wireless networks as compared to NOMA only systems. This paper explores the synergy between IRS and NOMA to leverage cooperative techniques for superior wireless system design. Fundamental principles, technological advancements, and potential applications of IRS-assisted CNOMA systems are discussed, highlighting existing works. Both underlay and overlay NOMA principles are examined in conjunction with IRS in the paper. Spatial modulation-aided CNOMA is explored for multiple-input multiple-output (MIMO) systems, along with its advantages and practical challenges. Additionally, the paper discusses fundamental principles and technological advancements of IRS-assisted CNOMA systems, emphasizing solutions to potential challenges and the role of machine learning (ML)/deep learning (DL) in resource optimization like transmit power and IRS phase settings. Simulation results are presented to highlight the benefits of IRS-aided CNOMA system design. Finally, the paper outlines future directions and potential research topics in IRSaided CNOMA.

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Section: Referencementioning

confidence: 99%

Section: Principle Of Cooperative Communicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intelligent Reflecting Surfaces (IRS)-Enhanced Cooperative NOMA: A Contemporary Review

Sharma,

Mishra,

Kumar

et al. 2024

IEEE Access

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“…Recently, the artificial intelligence (AI)-driven semantic communication paradigm has received much Given that IRS employs passive reflective elements devoid of signaling methods on received signals, the IRS demonstrates a capacity to reshape signals with minimal overhead [30]. Notably, a substantial body of research has concentrated on unraveling the extensive potential inherent in IRS technology [31][32][33][34][35][36]. The authors in [32] considered the IRS-assisted secure spectrum sharing network, where the IRS can significantly enhance the legal signal and suppress the eavesdropping at the eavesdroppers.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, it is noteworthy that only a limited number of studies have delved into the augmentation of semantic spectrum efficiency (S-SE) for task performance in the context of semantic spectrum sharing networks operating under low signal-to-noise ratios. Furthermore, a majority of the previously mentioned investigations [31,[33][34][35][36] have predominantly relied on conventional methodologies grounded in convex optimization, a less time-efficient paradigm when confronted with the complexities of large-scale connections. Consequently, there exists a compelling imperative to delve into and develop time-efficient intelligent resource allocation schemes [37].…”

Section: Introductionmentioning

confidence: 99%

A DRL-based resource allocation for IRS-enhanced semantic spectrum sharing networks

Zhang,

Li,

et al. 2024

EURASIP J. Adv. Signal Process.

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Semantic communication and spectrum sharing are pivotal technologies in addressing the perennial challenge of scarce spectrum resources for the sixth-generation (6G) communication networks. Notably, scant attention has been devoted to investigating semantic resource allocation within spectrum sharing semantic communication networks, thereby constraining the full exploitation of spectrum efficiency. To mitigate interference issues between primary users and secondary users while augmenting legitimate signal strength, the introduction of Intelligent Reflective Surfaces (IRS) emerges as a salient solution. In this study, we delve into the intricacies of resource allocation for IRS-enhanced semantic spectrum sharing networks. Our focal point is the maximization of semantic spectral efficiency (S-SE) for the secondary semantic network while upholding the minimum quality of service standards for the primary semantic network. This entails the joint optimization of parameters such as semantic symbol allocation, subchannel allocation, reflective coefficients of IRS elements, and beamforming adjustment of secondary base station. Recognizing computational intricacies and interdependence of variables in the non-convex optimization problem formulated, we present a judicious approach: a hybrid intelligent resource allocation approach leveraging dueling double-deep Q networks coupled with the twin-delayed deep deterministic policy. Simulation results unequivocally affirm the efficacy of our proposed resource allocation approach, showcasing its superior performance relative to baseline schemes. Our approach markedly enhances the S-SE of the secondary network, thereby establishing its prowess in advancing the frontiers of semantic spectrum sharing (S-SE).

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IRS-enabled NOMA communication systems: A network architecture primer with future trends and challenges

Sadia,

Hassan,

Abbas

et al. 2023

Digital Communications and Networks

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System throughput maximization in IRS-assisted phase cooperative NOMA networks

Cited by 15 publications

References 116 publications

Intelligent Reflecting Surfaces (IRS)-Enhanced Cooperative NOMA: A Contemporary Review

Intelligent Reflecting Surfaces (IRS)-Enhanced Cooperative NOMA: A Contemporary Review

A DRL-based resource allocation for IRS-enhanced semantic spectrum sharing networks

IRS-enabled NOMA communication systems: A network architecture primer with future trends and challenges

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