Convex Restrictions for Outage Constrained MU-MISO Downlink under Imperfect Channel State Information
Authors: A. Preetha Priyadharshini, S. B. M. Priya
Abstract:
In this paper, we consider the MU-MISO downlink scenario, under imperfect channel state information (CSI). The main issue in imperfect CSI is to keep the probability of each user achievable outage rate below the given threshold level. Such a rate outage constraints present significant and analytical challenges. There are many probabilistic methods are used to minimize the transmit optimization problem under imperfect CSI. Here, decomposition based large deviation inequality and Bernstein type inequality convex restriction methods are used to perform the optimization problem under imperfect CSI. These methods are used for achieving improved output quality and lower complexity. They provide a safe tractable approximation of the original rate outage constraints. Based on these method implementations, performance has been evaluated in the terms of feasible rate and average transmission power. The simulation results are shown that all the two methods offer significantly improved outage quality and lower computational complexity.
Keywords: Imperfect channel state information, outage probability, multiuser- multi input single output.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1126183
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1118References:
[1] T. Yoo and A. Goldsmith, “On the optimality of multi antenna broadcast scheduling using zero-forcing beam forming,” IEEE J. Sel. Areas Commun., vol. 24, no. 3, pp. 528–541, Mar. 2006.
[2] J. Lee and N. Jindal, “High SNR analysis for MIMO broadcast channels: Dirty paper coding versus linear precoding,” IEEE Trans. Inf.Theory, vol. 53, no. 12, pp. 4787–4792, Dec. 2007.
[3] D. J. Love, R. W. Heath Jr., V. K. N. Lau, D. Gesbert, B. D. Rao, and M. Andrews, “An overview of limited feedback in wireless communication systems,” IEEE J. Sel. Areas Commun., vol. 26, no. 8, pp. 1341–1365, Oct. 2008.
[4] N. Jindal, “MIMO broadcast channels with finite-rate feedback,” IEEE Trans. Inf. Theory, vol. 52, no. 11, pp. 5045–5060, Nov. 2006.
[5] M. Kobayashi, N. Jindal, and G. Caire, “Training and feedback optimization for multiuser MIMO downlink,” IEEE Trans. Commun., vol.59, no. 8, pp. 2228–2240, Aug. 2011.
[6] Y. Xie, C. N. Georghiades, and A. Arapostathis, “Minimum outage probability transmission with imperfect feedback for MISO fading channels,” IEEE Trans. Wireless Commun., vol. 4, no. 3, pp. 1084–1091, May 2005.
[7] J. C. Roh and B. D. Rao, “Transmit beam forming in multiple-antenna systems with finite rate feedback: A VQ-based approach,” IEEE Trans. Inf. Theory, vol. 52, no. 3, pp. 1101–1112, Mar. 2006.
[8] M. B. Shenouda and T. N. Davidson, “Convex conic formulations of robust downlink precoder designs with quality of service constraints,” IEEE J. Sel. Topics Signal Process., vol. 1, no. 4, pp. 714–724, Dec. 2007.
[9] N. Vučić and H. Boche, “Robust QoS-constrained optimization of downlink multiuser MISO systems,” IEEE Trans. Signal Process., vol. 57, no. 2, pp. 714–725, Feb. 2009.
[10] M. B. Shenouda and T. N. Davidson, “Nonlinear and linear broadcasting with QoS requirements: Tractable approaches for bounded channel uncertainties,” IEEE Trans. Signal Process., vol. 57, no. 5, pp. 1936–1947, May 2009.
[11] G. Zheng, K.-K. Wong, and T.-S. Ng, “Robust linear MIMO in the downlink: A worst-case optimization with ellipsoidal uncertainty regions,” EURASIP J. Adv. Signal Process., vol. 2008, pp. 1–15, Jun. 2008.
[12] E. Song, Q. Shi, M. Sanjabi, R. Sun, and Z.-Q. Luo, “Robust SINR constrained MISO downlink beam forming: When is semi definite programming relaxation tight?,” EURASIP J. Wireless Commun. Netw., 2012:243, doi:10.1186/1687-1499-2012-243.
[13] Y. Yang, G. Scutari, and D. P. Palomar, “Parallel stochastic decomposition algorithms for multi-agent systems,” in Proc. IEEE SPAWC, Darmstadt, Germany, Jun. 16–19, 2013, pp. 180–184.
[14] M. B. Shenouda and T. N. Davidson, “Probabilistically-constrained approaches to the design of the multiple antenna downlink,” in Proc. IEEE Asilomar Conf., Pacific Grove, CA, USA, Oct. 26–29, 2008, pp. 1120–1124.
[15] M. B. Shenouda, T. N. Davidson, and L. Lampe, “Outage-based design of robust Tomlinson-Harashima transceivers for the MISO downlink with QoS requirements,” Signal Process., vol. 93, no. 12, pp. 3341–3352, Dec. 2013.
[16] N. Vucic and H. Boche, “A tractable method for chance-constrained power control in downlink multiuser MISO systems with channel uncertainty,” IEEE Signal Process. Lett., vol. 16, no. 5, pp. 346–349, Apr. 2009.
[17] F. Sohrabi and T. N. Davidson, “Coordinate update algorithms for robust power loading for the MISO downlink with outage constraints and Gaussian uncertainties,” in Proc. IEEE ICASSP, Vancouver, BC, Canada, May 26–31, 2013, pp. 4769–4773.
[18] Ben-Tal, L. E. Ghaoui, and A. Nemirovski, Robust Optimization, ser. Princeton Series in Applied Mathematics. Princeton, NJ: Princeton Univ. Press, 2009.
[19] Ben-Tal and A. Nemirovski, “Robust solutions of linear programming problems contaminated with uncertain data,” Math. Program., ser. A, vol. 88, no. 3, pp. 411–424, 2000.
[20] Bertsimas and M. Sim, “Tractable approximations to robust conic optimization problems,” Math. Program., ser. B, vol. 107, no. 1-2, pp. 5–36, 2006.