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State of the Art, Taxonomy, and Open Issues on Cognitive Radio Networks with NOMA

Fuhui Zhou, Yongpeng Wu, Ying-Chang Liang, Zan Li, Yuhao Wang, Kai-Kit Wong

arXiv:1801.01997v1cs.NIcs.IT

TL;DR

Growing wireless demand creates a need for higher spectral efficiency and massive connectivity, while integrating NOMA into CRNs introduces severe-interference challenges. This paper surveys CRNs with NOMA, organizes the literature through a taxonomy, and outlines challenges and open research issues.

  • Problem

    Growing mobile-device and wideband-service demand requires communication techniques that provide high spectral efficiency and massive connectivity under limited spectrum resources.

  • Method

    The paper surveys state-of-the-art CRNs with NOMA and classifies the literature by operation paradigms, enabling techniques, design objectives, and optimization characteristics.

  • Results

    The survey identifies CRNs with NOMA as having potential to improve spectral efficiency and increase the number of supported users, while requiring key challenges to be addressed.

  • Takeaways & Limitations

    The paper provides a consolidated basis for understanding CRNs with NOMA and presents open research issues as future research directions.

Abstract

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The explosive growth of mobile devices and the rapid increase of wideband wireless services call for advanced communication techniques that can achieve high spectral efficiency and meet the massive connectivity requirement. Cognitive radio (CR) and non-orthogonal multiple access (NOMA) are envisioned to be important solutions for the fifth generation wireless networks. Integrating NOMA techniques into CR networks (CRNs) has the tremendous potential to improve spectral efficiency and increase the system capacity. However, there are many technical challenges due to the severe interference caused by using NOMA. Many efforts have been made to facilitate the application of NOMA into CRNs and to investigate the performance of CRNs with NOMA. This article aims to survey the latest research results along this direction. A taxonomy is devised to categorize the literature based on operation paradigms, enabling techniques, design objectives and optimization characteristics. Moreover, the key challenges are outlined to provide guidelines for the domain researchers and designers to realize CRNs with NOMA. Finally, the open issues are discussed.

I. INTRODUCTION

The paper surveys CRNs with NOMA as a response to growing spectrum and connectivity demands, emphasizing their potential gains alongside interference and implementation challenges. It reviews existing work, proposes a taxonomy, and identifies challenges and open research issues.

  • Motivation: 5G networks require 1000 times higher system capacity, 10 times higher spectral efficiency, and 100 times higher connectivity density.Limited spectrum makes achieving capacity especially challenging and motivates advanced techniques for high spectral efficiency and massive connectivity.
  • Motivation: Cognitive radio enables secondary networks to access licensed bands through adaptive transmission while protecting primary-network QoS.This provides spectrum access for unlicensed users while preserving the quality of service of the licensed network.
  • Motivation: NOMA improves spectral efficiency and connectivity by allowing multiple users to share the same frequency band through non-orthogonal resources.Power levels and low-density spreading codes are examples of resources used to distinguish simultaneous users; dominant schemes include power-domain and code-domain NOMA.
  • Challenges: NOMA’s benefits incur mutual interference and receiver complexity, with detection complexity increasing as the number of NOMA users grows.Successive interference cancellation and user pairing are discussed as approaches for reducing interference and receiver complexity.
  • Benefits: CRNs with NOMA can improve spectral efficiency, user support, cooperation opportunities, and energy efficiency relative to corresponding OMA-based CRNs.Reported studies describe significant spectral-efficiency gains, simultaneous PU and SU performance improvements through cooperation, and higher energy efficiency.
  • Contributions: The paper reviews state-of-the-art CRN-with-NOMA research, develops a taxonomy, and outlines key challenges and open issues as future research directions.The taxonomy organizes literature by operation paradigms, enabling techniques, design objectives, and optimization characteristics.

II. STATE OF THE ART

Research on CRNs with NOMA has focused mainly on underlay and overlay operation, while interweave operation remains unexplored. Studies examine power-domain NOMA, MIMO, cooperative transmission, and related performance gains over OMA.

  • Enabling Techniques: CRNs with NOMA are presented as a platform for combining multiple advanced techniques, including MIMO, full-duplex, wireless charging, SWIPT, D2D, and M2M communication.These techniques are associated with high spectral or energy efficiency, long-time services, and diverse connectivity requirements.
  • Operation Paradigms: Most research investigates underlay and overlay CRNs with NOMA; no work is reported for the interweave mode.Underlay studies enforce tolerable interference to PUs, whereas overlay studies use cooperation to obtain spectrum access.
  • Underlay CRNs: Underlay studies introduced power-domain NOMA, examined user pairing and power allocation, and found benefits from selecting users with the best channel conditions.This pairing strategy differs from conventional NOMA, which pairs the best- and worst-channel users.
  • Underlay CRNs: MIMO techniques were reported to greatly improve CRN performance, while properly designed target rates and power coefficients enabled NOMA to outperform OMA.These findings concern underlay CRNs with NOMA and different power allocation strategies.
  • Overlay CRNs: Overlay studies developed cooperative NOMA schemes, including dynamic cooperation and Alamouti space-time block coded protocols, with gains for primary and secondary users.Reported comparisons considered performance gains, outage probability, and ergodic capacity relative to conventional OMA or superposition coding.

A. Resource Optimization in CRNs with NOMA

Resource optimization is important because it can improve SU performance while protecting PU QoS, but optimization research for CRNs with NOMA remains limited. Existing work includes energy-efficiency optimization and power-allocation algorithms.

  • Resource Optimization in CRNs with NOMA: Existing studies mainly analyze achievable performance using power allocation strategies, while NOMA characteristics have also been used to design optimal power allocation algorithms.The superiority of a proposed algorithm was verified through simulation results.
  • Resource Optimization in CRNs with NOMA: Resource optimization can improve SU performance and better protect PU QoS, but designing robust allocation schemes is challenging.The design depends on the optimization objective and the constraints of CRNs with NOMA.
  • Resource Optimization in CRNs with NOMA: An efficient sequential convex approximation algorithm was proposed to optimize energy efficiency in underlay CRNs with NOMA supporting an arbitrary number of PUs.Simulation results showed energy-efficiency gains compared with OMA.
  • Resource Optimization in CRNs with NOMA: The literature identifies resource optimization for CRNs with NOMA as very limited, leaving substantial work for future research.The stated research gap concerns the application of NOMA techniques into CRNs.

III. TAXONOMY

The paper’s taxonomy summarizes CRN-with-NOMA research using four dimensions: operation paradigms, enabling techniques, design objectives, and optimization characteristics.

  • III. TAXONOMY: The taxonomy is constructed by summarizing studies from the surveyed literature on CRNs with NOMA.It is presented in Fig. 4.
  • III. TAXONOMY: The classification parameters are operation paradigms, enabling techniques, design objectives, and optimization characteristics.These dimensions organize the surveyed research results.

A. Operation Paradigms

CRNs with NOMA use interweave, overlay, or underlay spectrum-sharing paradigms, each imposing different sensing, cooperation, interference, and resource-allocation requirements.

  • Paradigm Selection: The choice of operation paradigm depends on implementation complexity, PU QoS requirements, and the cooperative ability between primary and secondary networks.The paper gives TV bands as an interweave example and strong-channel SUs as an overlay cooperation example.
  • Underlay Mode: Underlay operation allows simultaneous coexistence when SU interference remains tolerable to PUs, using a data-transmission slot without separate sensing.This mode is presented as appropriate when tolerable interference is high.
  • Overlay Mode: Overlay operation lets the secondary network cooperate with PUs and superimpose PU signals onto SU signals to obtain spectrum access.Cooperation may include transmitting PU information or transferring energy to PUs.
  • NOMA-Specific Design: Power-domain NOMA allocates high power to a poor-channel SU so a good-channel SU can decode and cancel the poorer user’s interference.CRNs with NOMA serve multiple SUs over non-orthogonal resources and require multi-user detection at SU receivers.

B. Enabling Techniques

Enabling techniques for CRNs with NOMA target interference management, spectral and energy efficiency, wideband access, energy harvesting, and secure transmission. These techniques introduce challenges involving sensing validity, receiver complexity, and hardware implementation.

  • Spectrum sensing: Spectrum sensing algorithms developed under independent-sample and Gaussian assumptions can be invalid when primary-user signals are NOMA signals.Using non-orthogonal resources creates correlations that require revised analysis and sensing methods.
  • Energy-efficient techniques: Massive MIMO can serve multiple secondary users with low transmitted power, making primary-user interference tolerable while improving energy efficiency through precoding.The cognitive base station uses a large antenna array, for example more than 100 antennas.
  • Energy harvesting: Energy harvesting uses wireless charging or SWIPT; SWIPT simultaneously transfers information and energy but requires higher hardware complexity and signal splitting.Time-domain, power-domain, and antenna-domain protocols can realize signal splitting.
  • Secure transmission: Physical-layer security is presented as an alternative for protecting confidential information in CRNs with NOMA under limited resources.It exploits wireless-channel characteristics such as multipath fading and propagation delay.

C. Objectives

CRNs with NOMA pursue objectives spanning resource use, interference, capacity, energy, fairness, energy transfer, and user experience. These objectives are constrained by primary-user QoS and secondary-user requirements.

  • The key objectives are cost minimization, interference minimization, SE maximization, EE maximization, energy-transfer efficiency, fairness, and SU QoE.Resource allocation and optimization techniques are important for realizing these objectives.
  • Cost minimization reduces CBS transmission power, while interference minimization limits interference to PUs subject to SU QoS.
  • SE maximization optimizes SU capacity while protecting PU QoS, and EE is defined as total capacity divided by total consumed power.
  • Energy-transfer efficiency maximization uses the ratio of harvested power to consumed power as its objective.
  • Fairness: Fairness objectives include max-min, proportional, and harmonic fairness, respectively emphasizing the worst-channel SU, fairness–sum-rate balance, and harmonic mean rate.
  • QoE: QoE improvement targets the overall acceptability of an application or service from SUs’ perspective and is generally evaluated using a mean opinion score.

D. Optimization Characteristic

Optimization in CRNs with NOMA is characterized by objective scope and CSI assumptions. Non-orthogonal resources make multi-user-detection constraints, conflicting objectives, imperfect CSI, and algorithmic complexity central concerns.

  • Single-objective optimization emphasizes one metric while treating others as constraints, but conflicting SE, EE, and energy-transfer objectives create tradeoffs.
  • Multi-objective optimization can provide tradeoffs among conflicting objectives, although Pareto-optimal solutions may require highly complex algorithms.
  • NOMA optimization is more challenging than OMA optimization because non-orthogonal resources require SINR constraints for successful SIC under a predefined decoding order.
  • CSI robustness: Non-robust optimization assumes perfect CSI and provides theoretical-limit analysis despite the impracticality of that assumption.
  • CSI robustness: Robust optimization addresses quantization errors, time delay, limited feedback, imperfect CSI, and imperfect SIC, increasing the difficulty of solving the problem.

IV. CHALLENGES AND FUTURE DIRECTIONS

The paper identifies open issues across sensing, massive MIMO, energy harvesting, mmW, cooperative transmission, hybrid access, and resource optimization. The recurring difficulties are non-orthogonality, imperfect information, hardware complexity, security, and conflicting objectives.

  • Spectrum sensing: Deriving detection probabilities under correlated samples and exploiting non-orthogonality for novel spectrum sensing algorithms remain open issues.
  • Massive MIMO: Massive MIMO requires blind channel-estimation algorithms for pilot contamination and robust precoding schemes under imperfect CSI and multi-user detection.
  • Energy harvesting: Energy harvesting faces energy-harvesting efficiency and security challenges, including resource allocation under nonlinear EH models and malicious energy-harvesting SUs.
  • Security: Physical-layer security for CRNs with NOMA needs further investigation because malicious energy-harvesting SUs may disguise themselves as licensed NOMA SUs.
  • mmW communications: mmW CRNs with NOMA require beamforming that protects PU QoS under shadowing and intermittent connectivity, plus multiple-user access mechanisms.
  • Cooperative transmission: Overlay operation requires selecting cooperative NOMA SUs and designing scheduling schemes that protect PU QoS while improving CRN spectral efficiency.
  • OMA&NOMA: Hybrid OMA–NOMA access can manage inter-network interference, but many SUs create high receiver complexity and require user-clustering designs.
  • Resource allocation and optimization: Resource allocation must jointly address conflicting objectives, multi-user-detection feasibility, and imperfect CSI, making robust designs important yet challenging.

V. CONCLUSION

Integrating NOMA into CRNs is presented as promising for higher spectral efficiency and service to more users, but practical deployment requires addressing associated challenges. The article reviews the research landscape, organizes it through a thematic taxonomy, outlines key challenges, and identifies open research issues.

  • CRNs with NOMA have the potential to significantly improve spectral efficiency and increase the number of users served.
  • The article outlines key challenges that must be addressed for NOMA techniques to be practically applied in CRNs.
  • The article reviews state-of-the-art research efforts aimed at enabling NOMA techniques in CRNs.
  • A thematic taxonomy is devised to categorize and classify the literature on CRNs with NOMA.
  • Several open research issues are presented as future research directions, while investigation of NOMA integration into CRNs is concluded to be in its infancy.
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