دانشگاه تربیت دبیر شهید رجائی
انتشارات دانشگاه تربیت دبیر شهید رجائی

  اخبار و اعلانات

 آمار

تعداد نشریات11
تعداد شماره‌ها208
تعداد مقالات2,090
تعداد مشاهده مقاله2,830,797
تعداد دریافت فایل اصل مقاله2,050,384
Danesh, N., Sheikhan, M., Mahboobi, B.. (1398). Interference Alignment Using Difference of Convex-based Beamformer Design for MIMO Interference Channels. فناوری آموزش, 7(2), 231-240. doi: 10.22061/jecei.2020.6108.280
N. Danesh; M. Sheikhan; B. Mahboobi. "Interference Alignment Using Difference of Convex-based Beamformer Design for MIMO Interference Channels". فناوری آموزش, 7, 2, 1398, 231-240. doi: 10.22061/jecei.2020.6108.280
Danesh, N., Sheikhan, M., Mahboobi, B.. (1398). 'Interference Alignment Using Difference of Convex-based Beamformer Design for MIMO Interference Channels', فناوری آموزش, 7(2), pp. 231-240. doi: 10.22061/jecei.2020.6108.280
Danesh, N., Sheikhan, M., Mahboobi, B.. Interference Alignment Using Difference of Convex-based Beamformer Design for MIMO Interference Channels. فناوری آموزش, 1398; 7(2): 231-240. doi: 10.22061/jecei.2020.6108.280

Interference Alignment Using Difference of Convex-based Beamformer Design for MIMO Interference Channels

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 11، دوره 7، شماره 2، مهر 2019، صفحه 231-240    اصل مقاله (1.06 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2020.6108.280
نویسندگان
N. Danesh1؛ M. Sheikhan* 1؛ B. Mahboobi2
1Department of Electrical Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
2Department of Electrical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
تاریخ دریافت: 27 مهر 1397،  تاریخ بازنگری: 01 فروردین 1398،  تاریخ پذیرش: 07 اردیبهشت 1398 
چکیده
Background and Objectives: To achieve significant throughput, interference alignment (IA) is an encouraging technique for wireless interference networks. In this study, we design an aligned beamformer based on the interference leakage minimization (ILM) method to reduce the interference power for a multiple-input multiple-output interference channel (MIMO-IC).
Methods: To deal with the non-convexity of ILM problem, we used a non-convex programming method (i.e., difference of convex [DC]). In this way, the interference leakage function is reformulated to a DC function including difference of two convex terms. Then, an additive function is defined that includes the DC objective function and a penalty function.
Results: We propose a novel DC-based IA algorithm that uses solutions of an upper bound of the additive function in each iteration; as the initial state for the next iteration. Through an iterative manner and for the large values of the penalty factor, the solutions of upper bound function converge to the solutions of the original DC objective function (i.e., interference leakage function).
Conclusion: In contrast to the frequent IA methods, the proposed DC-based IA algorithm updates transmit- and receive-beamformers in each iteration jointly (not alternately). Simulation results indicate that the proposed method outperforms some competitive IA algorithms by providing more throughputs and less interference leakage.
کلیدواژه‌ها
Interference؛ Alignment؛ Difference of convex؛ Beamformer؛ Penalty function
مراجع
[1] S. W. Peters, R. W. Heath, Jr., “Interference alignment via alternating minimization,” presented at the IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Taipei, Taiwan, 19-24, 2009.

[2] K. Gomadam, V. R. Cadambe, S. A. Jafar, “A distributed numerical approach to interference alignment and applications to wireless interference networks,” IEEE Trans. Information Theory, 57(6): 3309-3322, Jun. 2011.

[3] R. F. Guiazon, K. K. Wong, M. Fitc, “Coverage probability of cellular networks using interference alignment under imperfect CSI,” Digital Communications and Networks, 2(4): 162-166, 2016.

[4] N. Zhao, B. Chen, “Joint optimization of power splitting and allocation for SWIPT in interference alignment networks,” Physical Communication, 29: 67-77, 2018.

[5] H. Y. Lu, “Cyclic interference alignment for MIMO interference channels: A hybrid approach of MTLI and PSO,” Applied Soft Computing, 50): 158-165, Jan. 2017.

[6] P. G. Sudheesh, M. Magarini, P. Muthuchidambaranathan, “Interference alignment with iterative channel estimation for the reciprocal M×2 MIMO X Network,” Physical Communication, 27: 188-196, 2018.

[7] S. Mollaebrahim, P. M. Ghari, M. S. Fazel, M. A. Imran, “Designing precoding and receive matrices for interference alignment in MIMO interference channels,” presented at the IEEE Global Telecommunications Conference (GLOBECOM),Singapore, 2017.

[8] K. Gomadam, V. R. Cadambe, S. A. Jafar, “Approaching the capacity of wireless networks through distributed interference alignment,” presented at the IEEE Global Telecommunications Conference (GLOBECOM), New Orleans, USA, 2008.

[9] S. W. Peters, R. W. Heath, “Cooperative algorithms for MIMO interference channels,” IEEE Trans. Vehicular Technology, 60(1): 206-218, 2011.

 [10] D. S. Papailiopoulos, A. G. Dimakis, “Interference alignment as a rank constrained rank minimization,” IEEE Trans. Signal Processing, 60(8): 4278-4288, 2012.

[11] L. Ruan, M. Z. Win, V. K. N. Lau, “Designing interference alignment algorithms by algebraic geometry analysis,” in Proc. IEEE Global Telecommunications Conference (GLOBECOM): 1796-1801, 2013.

[12] S. M. Razavi, T. Ratnarajah, “Adaptive LS-and MMSE-based beamformer design for multiuser MIMO interference channels,” IEEE Transactions on Vehicular Technology, 65(1): 132-144, 2016.

[13] C. Le, S. Moghaddamnia, J. Peissig, “A hybrid optimization approach for interference alignment in multi-user MIMO relay networks under different CSI,” IEEE Trans. Wireless Communications, 16: 7834-7847, 2017.

[14] G. Sridharan, W. Yu, “Linear beamformer design for interference alignment via rank minimization,” IEEE Trans. Signal Processing, 63(22): 5910-5923, 2015.

[15] S. M. Razavi, “Beamformer design for MIMO interference broadcast channels with semi-definite programming,” IEEE Trans. Signal Processing, 66(17): 4504-4515, 2018.

[16] O. González, C. Beltrán, I. Santamar´ıa, “A feasibility test for linear interference alignment in MIMO channels with constant coefficients,” IEEE Trans. Information Theory, 60(3): 1840-1856, 2014.

[17] D. C. G. Bresler, D. Tse, “Feasibility of interference alignment for the MIMO interference channel,” IEEE Trans. Information Theory, 60(9, p. 5573-5586, 2014.

[18] H. A. L. Thi, T. P. Dinh, “The DC (difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems,” Annals Operations Research, 133(1-4): 23-46, 2005.

[19] H. H. M. Tam, E. Che, H. D. Tuan, “Optimized linear precoder in MIMO interference channel using D.C. programming,” presented at the IEEE 7th Int. Conf. Signal Processing and Communication Systems (ICSPCS), Carrara, Australia, 2013.

[20] X. Chen, S. H. Song, K. B. Letaief, “Interference alignment in dual-hop MIMO interference channel,” IEEE Trans. Wireless Communications, 13(3): 1274-1283, 2014.

[21] T. Ketseoglou, E. Ayanoglu, “Zero-forcing per-group precoding (ZF-PGP) for robust optimized downlink massive MIMO performance,” IEEE Trans. Communications, 67(10): 6816-6828, 2019.

[22] T. Ketseoglou, E. Ayanoglu, “Downlink precoding for massive MIMO systems exploiting virtual channel model sparsity,” IEEE Trans. Communications, 66(5): 1925-1939, 2018.

[23] A. Hjorungnes, D. Gesbert, “Complex-valued matrix differentiation: Techniques and key results,” IEEE Trans. Signal Processing, 55(6): 2740-2746, 2007.

[24] S. Boyd, L. Vandenberghe, Convex optimization, New York: Cambridge, 2004.

[25] B. Mahboobi, E. Soleimani-Nasab, M. Ardebilipour, “Outage probability based robust distributed beam-forming in multi-user cooperative networks with imperfect CSI,” Wireless Personal Communications, 77(3): 1629-1658, 2014.

[26] CVX research, “Matlab software for disciplined convex programming, version 2.0 beta,” 2012.

[27] M. C. Grant, S. P. Boyed, “Graph implementations for Non-smooth convex programs,” in Recent Advances in Learning and Control, Springer: 95-110, 2008.

[28] B. S. Grant, “CVX: Matlab software for disciplined convex programming (web page and software),” 2009.

آمار
تعداد مشاهده مقاله: 330
تعداد دریافت فایل اصل مقاله: 304


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب