Dr. Vijey Thayananthan

1. Research Activitiesin 2017 Computer Science Department Faculty of Computing and Information TechnologyKing Abdul Aziz University Analysis of Non-Orthogonal Multiple Access (NOMA) for Future Directions of 5G System Dr. Vijey Thayananthan CS, FCIT, KAU

2. Outline • Basic Multiple Access (MA) and NOMA • Functionalities of 5G MA • Proposed research for enhancing energy efficiency and security provisioning in 5G system • Current research projects • Futureresearch direction of the 5G systems • Open research problems CS, FCIT, KAU

3. Basic Multiple Access (MA) and NOMA CDMA FDMA Combination of FDMA & TDMA NOMA OMA Noninterfering communication channels for each active link [1]. CS, FCIT, KAU

4. Comparison of OFDMA and NOMA/SOMA • OFDMA It is a flexible multiple-access technique that can accommodate many users with widely varying data rates. • NOMA It is recognized as a promising multiple access technique for the next generation cellular communication networks • SOMA In a given time-slot, some of the users are silent and appears orthogonal while some other users transmit nonorthogonally. CS, FCIT, KAU

5. NOMA • General framework: Bit Interleaver, Modulation, Resources, etc. • Types: MUSA, PD-NOMA, SCMA, IDMA etc. • Massive connectivity, Low latencyand high spectrum efficiency • NOMA via power domain multiplexing • Basic NOMA with a SIC Receiver • NOMA in Massive MIMO Systems • Network NOMA • NOMA via code domain multiplexing • Multi-User Shared Access (MUSA) - non-sparse matrix • Low-Density Spreading CDMA • Low-Density Spreading OFDMA • SCMA CS, FCIT, KAU

6. Illustration of SOMA transmission N CS, FCIT, KAU

7. PD-NOMA with SIC receiver in DL • PD-NOMA is more suitable for DL eMBB rather than for UL eMBBand UL mMTC • DL PD-NOMA has the feature of simplicity and many benefits • Spectral efficiency and system throughput, robust performance in high mobility scenarios and good affinity with MIMO technology. • UL PD-NOMA poses many challenges • Scheduling design and power control for eMBB, • Limits the scheduling flexibility for mMTC as there is no other means to differentiate data symbols from different data layers. CS, FCIT, KAU

8. Basic NOMA Scheme Transmitter and Receivers in Downlink CS, FCIT, KAU

9. Channel capacity comparison of uplink (left) and downlink (right) in an AWGN [5] • The uplink NOMA is able to achieve the capacity bound, while OMA schemes are in general suboptimal except at point C • Capacity region: NOMA achieves larger multi-user capacity compared to OMA CS, FCIT, KAU

10. Functionalities of 5G MA [1] Enhanced mobile broadband (eMBB) & ultra-reliable and low latency communications (URLLC) CS, FCIT, KAU

11. Proposed research for enhancing energy efficiency and security provisioning in 5G system • Aims • Analyzing the enhancement of energy efficiency (EE) through the understanding of NOMA which is key technique for Improving 5G system requirements • Enhancing security provisions using appropriate multiple access technology involved in the 5G systems • Objectives • Proposing Adaptive NOMA (ANOMA) which uses the efficient adaptive algorithm and design. • Enhancing EE and security provisions using ANOMA, adaptive LDPC (low density parity check) and massive MIMO with feedback and manifold techniques • Analyzing and comparing the expected results of 5G systems with proposed techniques CS, FCIT, KAU

12. Proposed Research: ANOMA • To improve overall performance, efficient adaptive algorithm will be employed with following concepts • Adaptive power control algorithms • Adaptive trellis coding (non binary (NB)-LDPC) • Adaptive modulation (NB-LDPC multi-dimensional modulation schemes) CS, FCIT, KAU

13. Proposed Research: ANOMA CS, FCIT, KAU

14. Why is ANOMA in 5G developments • Increases the EE because ANOMA reduces the overall hardware complexity • Reduces transmission latency, power consumption and signaling cost • Key idea of the ANOMA is to simplify the design with adaptive algorithms, exploit the power domain for multiple access and serve multiple users at the same time/frequency/code • Optimize the design using manifold techniques which increases the EE because it reduces the rank of the matrix based on massive MIMO-NOMA channel [3, 7] • Increases capacity by several magnitudes • Increases the EE in the network NOMA CS, FCIT, KAU

15. Interleave Division Multiple Access (IDMA) • Orthogonal Interleaver (OI), • Random Interleaver (RI), • Nested Interleaver (NI) • Shifting Interleavers (SI) and • Deterministic Interleaver (DI) • IDMA uses specific interleaves for user segregation • Interleaves generally utilize a less complex iterative multiuser identification concept at the receiver • Interleaverwhich improves the computational complexity, reduces the bandwidth consumption and the memory requirements of the system. CS, FCIT, KAU

16. Illustration example of PDMA technical framework in UL CS, FCIT, KAU

17. EE in 5G wireless network [4] CS, FCIT, KAU

18. Expected results of Downlink MIMO NOMA systems with manifolds (Pn-manifold) CS, FCIT, KAU

19. Enhancement of security provisions using appropriate MA technology • The security performance of the NOMA networks can be improved by invoking the protected zone and by generating artificial noise at the BS [9-11] • Adopta protected zone around the BS to establish an eavesdropper exclusion area with the aid of careful channel-ordering of the NOMA users • Artificial noise is generated at the BS for further improving the security of a beamforming-aided system • Secrecy diversity order increases with a number of antennas • SwDeMa: Securing control channels (when the packet is passed to the controller over the secure channel) • NOMA: Securing receivers (analyzing secrecy rate and outage probability of the channel) CS, FCIT, KAU

20. Proposed ANOMA for future security in 5G • New trust models for the future industries (NOMA, ANOMA, etc.) • New service models for evolving technologies (SwDeMa) • Dynamic approach of increasing privacy (ANOMA) • Evolved threat landscape (Infrastructure with all MA) CS, FCIT, KAU

21. Analysis of security gap approaches for future 5G CS, FCIT, KAU

22. Average secrecy sum rate (SSR) versus the transmit power [9] With increasing the number of users, SSR increases, the secrecy outage probability decreases and the connection outage probability increases. CS, FCIT, KAU

23. Comparisons of NOMA schemes CS, FCIT, KAU

24. Summary of performance of different NOMA schemes [1] CS, FCIT, KAU

25. Current Research • Adaptive Multiple Access Technology for Enhancing Energy Efficiency in 5G System • Wireless Inter-technology handover between Wi-Fi and 5G based cellular system with SDN • Analytical Cyber Security model based on lightweight Cryptography for IoT • Risk-based decision method for Vehicular ad hoc networks (KACST 2015) • Information Security against the big data breaches in cloud environment (DSR- 2016) CS, FCIT, KAU

26. Conclusions • Analysis of NOMA is investigated basic MIMO and massive MIMO • New model based on NOMA is designed for enhancing EE and security provisions • Uplink and down link NOMA schemes promises to improve the 5G system requirements (increases the capacity, improve the spectral and latency mechanism) while maintaining the security in physical layer. CS, FCIT, KAU

27. Future research direction of the 5G systems • Energy saving for next generation network (5G+) • Efficient multiple access technology (An important future direction is to study how MIMO-NOMA transmission can be realized with limited CSI feedback) • Combination with D2D and V2V multi-hop direct communication using dynamic graph and Stochastic geometry • EE optimization for the fading MIMO NOMA systems with statistical channel state information (CSI) at the transmitter. • Security for 5G based M2M communication • Enhancing secrecy rate • Dynamic security solutions • Mobile cyber security CS, FCIT, KAU

28. Network NOMA • Precoding solution increases the EE and SE. • To mitigate the inter-cell interference, joint precoding of NOMA users’ signals across neighboring cells can be considered. • The multi-user precoding used for single-cell NOMA maybe not be feasible for the network NOMA scenario • Multi-cell joint precoder is applied only to cell edge users CS, FCIT, KAU

29. Network NOMA with 2 cells and 4 users • EE can be achieved through the gains used between the users and base stations. Here, the single cell NOMA is extended to the network NOMA, with a zero-forcing (ZF) precoding scheme applied to users with weak channel conditions to efficiently mitigate the intercell interference. • Furthermore, the single cell NOMA is extended to network NOMA, with a distributed multi-user ZF precoding scheme applied to users with weak channel conditions. CS, FCIT, KAU

30. Illustration of a two-user NOMA network Cross-layer optimization is important to maximize the performance of NOMA in practice and meet the diversified demands of 5G, e.g, spectral efficiency, energy efficiency, massive connectivity, and low latency. CS, FCIT, KAU

31. Outage probability of the user pair CS, FCIT, KAU

32. NOMA in massive MIMO systems • The application of MIMO techniques to NOMA systems is important to enhance the performance gains of NOMA. • A general MIMO-NOMA framework is applicable to both downlink and uplink transmission • Alarger diversity gain can be achieved, e.g., for a scenario in which all nodes are equipped with M antennas, a diversity order of M is achievable. • The MIMO-NOMA framework is more general, and also applicable to the case where the users have fewer antennas than the base station. CS, FCIT, KAU

33. Downlink MIMO NOMA systems with statistical CSI • An important future direction is to study how MIMO-NOMA transmission can be realized with limited CSI feedback • However, in practice, the perfect CSI is usually hard to obtain in fading channels, the long term power control schemes (LTPC) with statistical CSI is preferred to reduce the CSI feedback overhead. CS, FCIT, KAU

34. Expected results of Future NOMA with manifold CS, FCIT, KAU

35. Security taxonomy for 5G-based IoTmiddleware [6] Big Data Security and Lightweight Approaches User Privacy and Data Protection Devices and Applications Protection Communication Channels Protection CS, FCIT, KAU

36. Security provisioning in NOMA Secrecy outage probability captures the probability of both reliability and secrecy for one transmission We focus on secure beamforming and power allocation design optimization problem which maximizes sum achievable secrecy rate of central users subject to transmit power constraint at base station and transmission rate requirements at cell-edge users. CS, FCIT, KAU

37. Generalized Semi-Orthogonal Multiple-Access (GSOMA) for Massive MIMO The advantage of GSOMA with respect to the conventional TDD is that it schedules more groups, which enhances the aggregate rate. Zero forcing receiver (ZF) removes the inter-user interference in each group due the data transmission which is one of the advantages of GSOMA with respect to SOMA CS, FCIT, KAU

38. Open Research Problems • Analysis of energy efficiency (EE) with resource allocation under imperfect CSI • Analysis of EE with NOMA for the perfect CSI because perfect CSI is usually hard to obtain in fading channels • New network protocols using energy-efficient NOMA • Cyber security solution using software define multiple access (SoDeMa) or SDMA • Security provisioning in NOMA • SDN provides simple abstractions to describe the components, the functions they provide, and the protocol to manage the forwarding plane from a remote controller via a secure channel. All layer security issues using SDN concepts • Uses of NOMA for dynamic security in next-generation mobile networks CS, FCIT, KAU

39. References Wang, Yingmin, Bin Ren, Shaohui Sun, Shaoli Kang, and Xinwei Yue. "Analysis of non-orthogonal multiple access for 5G." China Communications 13, no. Supplement2 (2016): 52-66. Liu, Yuanwei, Zhiguo Ding, MagedElkashlan, and Jinhong Yuan. "Nonorthogonal Multiple Access in Large-Scale Underlay Cognitive Radio Networks." IEEE Transactions on Vehicular Technology 65, no. 12 (2016): 10152-10157 Thayananthan, Vijey, and Fahd Bahazaq. "Analytical Model of Energy Saving Approach in Wireless Sensor Network." In Computer Modelling and Simulation (UKSim), 2014 UKSim-AMSS 16th International Conference on, Cambridge University, pp. 505-510. IEEE, 2014. Agiwal, Mamta, Abhishek Roy, and NavratiSaxena. "Next generation 5G wireless networks: A comprehensive survey." IEEE Communications Surveys & Tutorials 18, no. 3 (2016): 1617-1655. Dai, Linglong, Bichai Wang, Yifei Yuan, Shuangfeng Han, I. Chih-Lin, and Zhaocheng Wang. "Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends." IEEE Communications Magazine 53, no. 9 (2015): 74-81. CS, FCIT, KAU

40. References Tiburski, Ramão Tiago, Leonardo AlbernazAmaral, and Fabiano Hessel. "Security Challenges in 5G-Based IoT Middleware Systems." In Internet of Things (IoT) in 5G Mobile Technologies, pp. 399-418. Springer International Publishing, 2016. J. Chen, X. Chen, W. H. Gerstacker, and D. W. K. Ng, “Resource allocation for a massive mimo relay aided secure communication,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 8, pp. 1700–1711, Aug. 2016. Y. Wu, R. Schober, D. W. K. Ng, C. Xiao, and G. Caire, “Secure massive mimo transmission with an active eavesdropper,” IEEE Trans. Inf. Theory, vol. 62, no. 7, pp. 3880–3900, Jul. 2016. Y. Zhang, H.-M. Wang, Q. Yang, and Z. Ding, “Secrecy sum rate maximization in non-orthogonal multiple access,” IEEE Commun. Lett., vol. 20, no. 5, pp. 930-933, May 2016. Li, Yiqing, Miao Jiang, Qi Zhang, Quanzhong Li, and Jiayin Qin. "Secure Beamforming in Downlink MISO Non-Orthogonal Multiple Access Systems." IEEE Transactions on Vehicular Technology (2017). Liu, Yuanwei, Zhijin Qin, MagedElkashlan, Yue Gao, and Lajos Hanzo. "Enhancing the Physical Layer Security of Non-orthogonal Multiple Access in Large-Scale Networks." arXiv preprint arXiv:1612.03901 (2016). CS, FCIT, KAU