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NOMA using intelligent reflecting surfaces with adaptive transmit power and multi-antennas energy harvesting

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Abstract

In this paper, we propose the use of Intelligent Reflecting Surfaces (IRS) between the secondary source and K secondary users. The secondary source transmits the combination of K symbols dedicated to K secondary Non Orthogonal Multiple Access (NOMA) users. A set \(I_i\) of IRS reflectors are dedicated to user \(U_i\) so that all reflections are in phase at \(U_i\). We derive the throughput when the secondary source harvests energy using \(n_r\) antennas and transmits with an adaptive transmit power to generate low interference at primary destination. We show that the use of IRS with 32 reflectors per user offers up to 45 and 50 dB gain with respect to NOMA and Orthogonal Multiple Access (OMA) without IRS.

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References

  1. Basar, E., Renzo, M. D., Rosny, J. D., Debbah, M., Alouini, M.-S., & Zhang, R. (2019). Wireless communications through reconfigurable intelligent surfaces. IEEE Access, 7, 142.

    Article  Google Scholar 

  2. Zhang, H., Di, Boya, Lingyang, S., & Han, Z. (2020). Reconfigurable intelligent surfaces assisted communications with limited phase shifts: how many phase shifts are enough? IEEE Transactions on Vehicular Technology, 69, 4.

    Article  Google Scholar 

  3. Renzo, MD. (2019). “6G Wireless: Wireless Networks Empowered by Reconfigurable Intelligent Surfaces”, 2019 25th Asia-Pacific Conference on Communications (APCC).

  4. Basar, E. (2020). reconfigurable intelligent surface-based index modulation: a new beyond MIMO paradigm for 6G. Early Access Article: IEEE Transactions on Communications, 68, 3187.

    Google Scholar 

  5. Wu, Q., & Zhang, R. (2020). Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network. IEEE Communications Magazine, 58(1), 106–112. https://doi.org/10.1109/MCOM.001.1900107

    Article  Google Scholar 

  6. Huang, C., Zappone, A., Alexandropoulos, George C., Debbah, M., & Yuen, C. (2019). Reconfigurable intelligent surfaces for energy efficiency in wireless communication. IEEE Transactions on Wireless Communications, 18, 8.

    Google Scholar 

  7. Alexandropoulos, George C., Vlachos, E. (2020). “A hardware architecture for reconfigurable intelligent surfaces with minimal active elements for explicit channel estimation”, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

  8. Guo, H, Liang, Y-C, Chen, J, Larsson, Erik G. (2020). “Weighted sum-rate maximization for reconfigurable intelligent surface aided wireless networks”, IEEE Transactions on Wireless Communications, Early Access Article

  9. Thirumavalavan, VC, Jayaraman, TS. (2020). “BER analysis of reconfigurable intelligent surface assisted downlink power domain NOMA system”, 2020 International Conference on COMmunication Systems and NETworkS (COMSNETS)

  10. Pradhan, C, Li, A, Song, L, Vucetic, B, Li, Y. (2020). “Hybrid precoding design for reconfigurable intelligent surface aided mmwave communication systems”, IEEE Wireless Communications Letters, Early Access Article

  11. Ying, K., Gao, Z., Lyu, S., Wu, Y., Wang, H., & Alouini, M.-S. (2020). GMD-based hybrid beamforming for large reconfigurable intelligent surface assisted millimeter-wave massive MIMO. IEEE Access, 8, 442.

    Google Scholar 

  12. Yang, L., Guo, W., & Imran, S. A. (2020). Mixed dual-hop FSO-RF communication systems through reconfigurable intelligent surface. Early Access Article: IEEE Communications Letters, 24, 1558.

    Google Scholar 

  13. Di, B., Zhang, H., Li, L., Song, L., Li, Y., & Han, Z. (2020). Practical hybrid beamforming with finite-resolution phase shifters for reconfigurable intelligent surface based multi-user communications. IEEE Transactions on Vehicular Technology, 69, 4.

    Article  Google Scholar 

  14. Nadeem, Q.-U.-A., Kammoun, A., Chaaban, A., Debbah, M., & Alouini, M.-S. (2020). Asymptotic Max-Min SINR analysis of reconfigurable intelligent surface assisted MISO systems. IEEE Transactions on Wireless Communications, 17, 7748.

    Article  Google Scholar 

  15. Zhao, W., Wang, G., Atapattu, S., Tsiftsis, T. A., & Tellambura, C. (2020). Is backscatter link stronger than direct link in reconfigurable intelligent surface-assisted system? IEEE Communications Letters, 24, 1342.

    Article  Google Scholar 

  16. Li, S., Duo, B., Yuan, X., Liang, Y.-C., & Di Renzo, M. (2020). Reconfigurable intelligent surface assisted UAV communication: joint trajectory design and passive beamforming. IEEE Wireless Communications Letters, 9(5), 716–720. https://doi.org/10.1109/LWC.2020.2966705

    Article  Google Scholar 

  17. Dai, L, Wang, B, Wang, M, Yang, X, Tan, J, Bi, S, Xu, S, Yang, F, Chen, Z, Renzo, MD, Chae, C-B, Hanzo, L. (2020)P. “Reconfigurable intelligent surface-based wireless communications: antenna design, prototyping, and experimental results”, IEEE Access 8: 654

  18. Hua, S, Shi, Y. (2019). “Reconfigurable Intelligent Surface for Green Edge Inference in Machine Learning”, 2019 IEEE Globecom Workshops (GC Wkshps)

  19. Huang, C, Alexandropoulos, G C., Yuen, C, Debbah, M. (2019). “Indoor signal focusing with deep learning designed reconfigurable intelligent surfaces”, 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

  20. Manglayev, T. (2018). Refik Caglar Kizilirmak and Yau Hee Kho, “GPU accelerated successive interference cancellation for NOMA uplink with user clustering.” Wireless Personal Communications, 103, 2391–2400.

  21. Siva, B., & Reddy, K. (2021). Experimental validation of non-orthogonal multiple access (NOMA) technique using software defined radio. Wireless Personal Communications, 116, 3599–3612.

    Article  Google Scholar 

  22. Bathula Siva, K. R., & Kiran Mannem, J. K. (2021). Software defined radio based non-orthogonal multiple access (NOMA) systems. Wireless Personal Communications, 119, 1251–1273.

    Article  Google Scholar 

  23. Panda, S. (2020). Joint user patterning and power control optimization of MIMO-NOMA systems. Wireless Personal Communications, 112, 2557–2573.

    Article  Google Scholar 

  24. Narasimha Nayak, V., & Gurrala, Kiran K. (2021). A novel resource allocation for SWIPT-NOMA enabled AF relay based cooperative network. Wireless Personal Communications, 118, 2699–2716.

    Article  Google Scholar 

  25. Chen, Y-H, Chen, Y-F, Tseng, S-M. (Feb.2021). System performance analysis in cognitive radio-aided noma network: an application to vehicle-to-everything communications, Wireless Personal Communications, published online

  26. Abed, D., & Medjouri, A. (2021). CS-based near-optimal MUD for uplink grant-free NOMA. Wireless Personal Communications, 118, 3585–3594.

    Article  Google Scholar 

  27. Thirunavukkarasu, R., & Balasubramanian, R. (2021). An efficient code domain NOMA scheme with enhanced spectral and energy efficiency for networks beyond 5G. Wireless Personal Communications, 120(1), 353–377. https://doi.org/10.1007/s11277-021-08464-6

    Article  Google Scholar 

  28. Thi, Anh L. (2019). Hyung Yun Kong, “Evaluating the performance of cooperative NOMA with energy harvesting under physical layer security.” Wireless Personal Communications, 108, 1037–1054.

    Article  Google Scholar 

  29. Narasimha Nayak, V., & Gurrala, Kiran K. (2021). Enhanced physical layer security for cooperative noma network with hybrid-decode-amplify-forward relaying via power allocation assisted control jamming. Wireless Personal Communications, 120, 2473.

    Article  Google Scholar 

  30. Do, D.-T. (2021). Minh-Sang Van Nguyen “New look on device to device NOMA systems: With and without wireless power transfer modes.” Wireless Personal Communications, 116, 2485–2500.

    Article  Google Scholar 

  31. Tai-Jung, H. (2020). Theoretical analysis of NOMA within massive MIMO systems. Wireless Personal Communications, 112, 777–783.

    Article  Google Scholar 

  32. Tseng, S.-M., Chen, Y.-F., & Liu, K.-C. (2019). Cross layer power control and user pairing for DL multi-antenna NOMA. Wireless Personal Communications, 109, 1541–1556.

    Article  Google Scholar 

  33. Soumen, M., Sanjay, D. R., & Sumit, K. (2020). Partial relay selection in energy harvesting based NOMA network with imperfect CSI. Wireless Personal Communications, 120, 3153.

    Google Scholar 

  34. Thi, A. L. (2021). Hyung Yun Kong, “Effects of hardware impairment on the cooperative NOMA eh relaying network over nakagami-m fading channels.” Wireless Personal Communications, 116, 3577–3597.

    Article  Google Scholar 

  35. Prinick, R, Hrubos, M, Nemec, D, Mraev, T, Bozek, P. (December 2017). “Integration of Inertial Sensor Data into Control of the Mobile Platform”, Federal conference on Software Development and object Technologies

  36. Milan, S., Milan, V., & Peter, P. (2014). Chosen numerical algorithms for interval finite element analysis. Procedia Engineering, 96, 400.

    Article  Google Scholar 

  37. Rudolf, U., Martin, S., & Ivan, K. (2020). The use of onboard UAV GNSS navigation data for area and volume calculation. Acta Montanistica Slavia, 25, 3.

    Google Scholar 

  38. Raed, A., & Hatem, B. (2020). Throughput enhancement of cognitive radio networks-nonorthogonal multiple access with energy harvesting and adaptive transmit power. Transactions on Emerging Telecommunication Technology, 31, 8.

    Google Scholar 

  39. Gradshteyn, I. S., & Ryzhik, I. M. (1994). Table of integrals, series and products (5th ed.). San Diego: CA Academic.

    MATH  Google Scholar 

  40. Xi, Y., Burr, A., Wei, J. B., & Grace, D. (2011). A general upper bound to evaluate packet error rate over quasi-static fading channels. IEEE Transactions on Wireless Communications, 10(5), 1373–1377.

    Article  Google Scholar 

  41. Proakis, J. (2007). Digital communications (5th ed.). New York: Mac Graw-Hill.

    MATH  Google Scholar 

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The paper is the contribution of Prof. RA.

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Correspondence to Raed Alhamad.

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Appendix 1: Matlab codes

Appendix 1: Matlab codes

  • function NOMAR(NN)

  • NN is the number of Reflectors

  • x=0.1:0.01:10000;

  • Q=4;Constellation size

  • a1=0.9;

  • a2=0.1;

  • \(\begin{aligned} g & = 1 - (1 - 2*(1 - 1/sqrt(Q)) \\ & *erfc(sqrt(3*log2(Q)/2/(Q - 1)*x))).^{2} 00; \\ \end{aligned}\)

  • w0=sum(g)*.01;

  • alpha=1/2;

  • dspr=2;

  • dh=1;

  • \(lamdahs=dh^3;\)

  • \(rauspr=1/dspr^3\);

  • dsr=1/3;

  • \(rausr=1/dsr^3\);

  • d1=1.1;

  • \(rau1=1/d1^3\);

  • \(rau2=1/d2^3;\)

  • d2=1;

  • \(lamdasr1=d1^3;\)

  • \(lamdasr2=d2^3;\)

  • drpr=2;

  • \(raurpr=1/drpr^3;\)

  • i=1

  • T=10

  • plage=-30:2:30

  • for ebnodb=plage

  • \(ebno=10^(ebnodb/10);\)

  • N0=1/ebno

  • esno=log2(Q)*ebno;

  • lamda=max(w0*N0/(a1-a2*w0),N0*w0/a2);

  • betaa=max(w0*N0/(a1-a2*w0),0)

  • alpha=0.5;

  • \(\begin{gathered} [FY2(i)FY2s(i)] = outageRISATPEHFinal \hfill \\ (lamda,NN,1/d1^{3} ,alpha); \hfill \\ \end{gathered}\)

  • \(\begin{gathered} [FY1(i)FY1s(i)] = outageRISATPEHFinal \hfill \\ (betaa,NN,1./d2^{3} ,alpha); \hfill \\ \end{gathered}\)

  • i=i+1;

  • end

  • PEP1=FY1

  • PEP2=FY2;

  • PEP1s=FY1s;

  • PEP2s=FY2s;

  • \(Th1=log2(Q)*(1-PEP1)*(1-alpha);\)

  • \(Th1s=log2(Q)*(1-PEP1s)*(1-alpha);\)

  • \(Th2=log2(Q)*(1-PEP2)*(1-alpha);\)

  • \(Th2s=log2(Q)*(1-PEP2s)*(1-alpha);\)

  • \(Th=Th1+Th2;\)

  • \(Ths=Th1s+Th2s;\)

  • plot(plage,Th1,’k-’)

  • hold on

  • plot(plage,Th1s,’kp’)

  • figure

  • plot(plage,Th2,’r–’)

  • hold on

  • plot(plage,Th2s,’rs’)

  • figure

  • plot(plage,Th,’-.’)

  • hold on

  • plot(plage,Ths,’o’)

  • function [F Fs]=outageRISATPEH(x,NN,asnr,alpha)

  • ple=3;

  • D=1;

  • \(Delta=1/D^ple;\)

  • m=NN*pi/4;

  • \(sig=sqrt(NN*(1-pi^2/16));\)

  • N0=1/asnr;

  • K=10000;

  • a=1;

  • n=2;

  • gAS=(randn(n,K)+j*randn(n,K))*sqrt(a/2);

  • mu=0.5*alpha/(1-alpha);

  • \(Emax=mu*sum(abs(gAS).^2,1);\)

  • T=1;dspr=1;

  • \(aspr=dspr^ple;\)

  • \(hSPR=(randn(1,K)+j*randn(1,K))*sqrt(aspr/2);\)

  • \(E=min(Emax,T./abs(hSPR).^2);\)

  • A=m+randn(1,K)*sig;

  • \(SNR=A.^2.*E/N0;\)

  • \(Fs=length(find(SNR!`x))/K;\)

  • pas=.01;

  • y=.0001:pas:1000;

  • JJ=zeros(1,length(y));

  • for pp=0:n-1

  • \(JJ=JJ+(x*N0./y).^pp/factorial(pp)/mu^pp/a^(2*pp);\)

  • end

  • \(\begin{aligned} {\text{F3}} & {\text{ = 1 - 1/a*exp( - 1/a*x*N0}}{\text{./y/mu)}}{\text{.*JJ + 1/a*1/aspr}} \\ & {\text{*exp( - 1/a*x*N0}}{\text{./y/mu)}}{\text{.*exp( - 1/aspr*T*y/x/N0)}}{\text{.*JJ;}} \\ \end{aligned}\)

  • \(\begin{aligned} f2 & = 1./sqrt(8*pi*sig^{2} *asnr*y*Delta). \\ & *exp( - (sqrt(y/asnr/Delta) - m).^{2} /2/sig^{2} ) \\ & + 1./sqrt(8*pi*sig^{2} *asnr*y*Delta). \\ & *exp( - (sqrt(y/asnr/Delta) + m).^{2} /2/sig^{2} ); \\ \end{aligned}\)

  • F=sum(f2.*F3)*pas;

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Alhamad, R. NOMA using intelligent reflecting surfaces with adaptive transmit power and multi-antennas energy harvesting. Wireless Netw 28, 2365–2374 (2022). https://doi.org/10.1007/s11276-022-02970-6

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  • DOI: https://doi.org/10.1007/s11276-022-02970-6

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