AUTHORS: Ana I. Perez-Neira, Miguel Angel Vazquez, Miguel Angel Lagunas

Download as PDF

ABSTRACT: An aggressive frequency reuse is expected within the next years in order to increase the spectral efficiency. Multiuser interference by all in-band transmitters can create a communication bottleneck and, therefore, it is compulsory to control it by means of radiated power regulations. In this work we consider received power as the main way to properly measure radiated power, serving at the same time as a spectrum sharing mechanism. Taking into account the constraints on the maximum total receive-power and maximum transmit-power, we first obtain the transmit powers that attain the Pareto-efficient rates in an uncoordinated network. Among these rates, we identify the maximum sum-rate point for noise-limited scenarios. Next, in order to reach this working point using as less power as possible, we design a novel beamformer under some practical considerations. This beamformer can be calculated in a non-iterative and distributed fashion (i.e. transmitters do not need to exchange information). We evaluate our design by means of Monte Carlo simulations, compare it with other non-iterative transmit beamformers and show its superior performance when the spectrum sharing receive-power constraints are imposed

KEYWORDS: -Beamforming, Spectrum Sharing, Cognitive beamforming, Interference Channel, Open Spectrum, Time Area Spectrum, Interference Management.

REFERENCES:

[1] Itu radio regulations. http://www.itu.int/, 2012.

[2] A.Fakoorian and A.L.Swindlehurst. Competing for secrecy in the MISO interference channel. IEEE Transactions on Signal Processing, 61(1):170–181, 2013.

[3] H. Al-Shatri and T. Weber. Optimizing power allocation in interference channels using D.C. programming. In Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt), Proceedings of the 8th International Symposium on, pages 360 –366, 2010.

[4] E. Bjornson, G. Zheng, M. Bengtsson, and B. Ottersten. Robust Monotonic Optimization Framework for Multicell MISO Systems. IEEE Transactions on Signal Processing, 60(5):2508– 2523, 2012.

[5] Emil Bjrnson, Eduard Jorswieck, Mrouane Debbah, and Bjrn Ottersten. Multi-Objective Signal Processing Optimization: The Way to Balance Conflicting Metrics in 5G Systems. IEEE Signal Processing Magazine, 31(6):14–23, 2014.

[6] P. Cao, E. A. Jorswieck, and S. Shi. Pareto Boundary of the Rate Region for SingleStream MIMO Interference Channels: Linear Transceiver Design. IEEE Transactions on Signal Processing, 61(20):4907–4922, 2013.

[7] M. Charafeddine and A. Paulraj. Maximum sum rates via analysis of 2-user interference channel achievable rates region. In Information Sciences and Systems, 2009. CISS 2009. 43rd Annual Conference on, pages 170 –174, 2009.

[8] M. Charafeddine, A. Sezgin, and A. Paulraj. Rate region frontiers for n-user interference channel with interference as noise. Proc of 45th Allerton, cs.IT(1):6, 2010.

[9] M. Chiang, P. Hande, T. Lan, and C. W. Tan. Power Control in Wireless Cellular Networks. Now Publishers Inc., 2008.

[10] M. Gastpar. On capacity under receive and spatial spectrum-sharing constraints. IEEE Transactions on Information Theory, 53(2):471–487, 2007.

[11] B. Gopalakrishnan and N.D. Sidiropoulos. Cognitive Transmit Beamforming From Binary CSIT. IEEE Transactions on Signal Processing, 14(2):895–906, 2015.

[12] M.H. Hassan, M. J. Hossain, and V. K. Bhargava. Cooperative beamforming for cognitiveradio-based broadcasting systems in presence of asynchronous interference. IEEE Transactions on Vehicular Technology, 66(3):2311 – 2323, 2016.

[13] Y. Huang and D. P. Palomar. Rank-constrained separable semidefinite programming with applications to optimal beamforming. IEEE TransWSEAS TRANSACTIONS on SYSTEMS and CONTROL Ana I. Perez-Neira, Miguel Angel Vazquez, Miguel Angel Lagunas E-ISSN: 2224-2856 313 Volume 13, 2018 actions on Signal Processing, 58(2):664–678, 2010.

[14] Jarkko Kaleva, Antti Tlli, and Markku Juntti. Decentralized beamforming for weighted sum rate maximization with rate constraints. In Proceedings of PIMRC Conference, pages 1–5, 2013.

[15] Jarkko Kaleva, Antti Tlli, and Markku Juntti. Rate constrained decentralized beamforming for mimo interfering broadcast channel. In Proceedings of PIMRC Conference, pages 1–5, 2015.

[16] S. Kim and G. Giannakis. Optimal resource allocation for mimo ad hoc cognitive radio networks. Journal of Mathematical Analysis and Applications, 49(2):430–468, 1975.

[17] E. Lagunas, S. Maleki, S. Chatzinotas, M. Soltanalian, A. Perez-Neira, and B. Ottersten. Power and rate allocation in cognitive satellite uplink networks. In Proceedings of ICC Conference, pages 1–5, 2016.

[18] M. Lagunas, J. Vidal, and A.I. Perez-Neira. Joint array combining and MLSE for single-user receivers in multipath Gaussian multiuser channels. Selected Areas in Communications, IEEE Journal on, 18(11):2252–2259, 2000.

[19] M.A. Lagunas, A. Perez-Neira, and X. Artiga. Array factor directivity for interference scenarios. In Proceedings of the 19th International Conference on Systems, pages 105–111, 2015.

[20] M.A. Lagunas, A. Perez-Neira, and M.A. Vazquez. Regulation and research on wireless communications. In IEEE Proceedings of Applied Electromagnetics Conference (AEMC), pages 1–4, 2011.

[21] U. Lakmal, M. Codreanu, M. Latva-aho, and A. Ephremides. A robust beamformer design for underlay cognitive radio networks using worst case optimization. EURASIP Journal on Wireless Communications and Networking, 37:1 –16, 2014.

[22] E. Larsson, D. Danev, and E. Jorswieck. Asymptotically optimal transmit strategies for the multiple antenna interference channel. In Communication, Control, and Computing, 2008 46th Annual Allerton Conference on, pages 708 –714, 2008.

[23] Z. Luo, W. Ma, A.M.-C. So, Y. Ye, and S. Zhang. Semidefinite relaxation of quadratic optimization problems. Signal Processing Magazine, IEEE, 27(3):20 –34, 2010.

[24] Marja Matinmikko, Matti Latva-aho, Petri Ahokangas, Seppo Yrjl, and Timo Koivumki. Micro Operators to Boost Local Service Delivery in 5G. Wireless Personal Communications, pages 1–14, 2017.

[25] R. Mochaourab and E. A. Jorswieck. Cooperative interference management with MISO beamforming. IEEE Transactions on Signal Processing, 58(10):5450 – 5458, 2010.

[26] R. Mochaourab and E. A. Jorswieck. Optimal Beamforming in Interference Networks with Perfect Local Channel Information. IEEE Transactions on Signal Processing, 59(3):1128 – 1141, 2010.

[27] M. Mueck, S. Srikanteswara, and B. Badic. Spectrum Sharing: Licensed Shared Access (LSA) and Spectrum Access System (SAS). In Intel Report, October 2015.

[28] F. Negro, M. Cardone, I. Ghauri, and D. Slock. SINR balancing and beamforming for the MISO interference channel. In Proceedings of PIMRC Conference, pages 1–5, 2011.

[29] D. P. Palomar and Y. Eldar. Convex Optimization in Signal Processing and Communications. Cambridge University Press, 2010.

[30] A. Perez-Neira, J. M.Veciana, M.A. Vazquez, and E. Lagunas. Distributed power control with received power constraints for time-areaspectrum licenses. Signal Processing, Elsevier, 120:141 – 155, 2016.

[31] M. Sadek, A. Tarighat, and A.H. Sayed. A leakage-based precoding scheme for downlink multi-user MIMO channels. IEEE Transactions on Wireless Communications, 6(5):1711–1721, 2007.

[32] E. Van, C. Meyers, D.J. O’Hara, and R. C. Scott. A property system for market allocation of the electromagnetic spectrum: A legaleconomic-engineering study. Stanford Law Review, 21(6):1499, 1969.

[33] M.A. Vazquez, A. Perez-Neira, and M.A. Lagunas. Generalized Eigenvector for Decentralized Transmit Beamforming in the MISO Interference Channel. IEEE Transactions on Signal Processing, 61(4):878–882, 2013.

[34] F. Wang and W. Wang. Robust beamforming and power control for multiuser cognitive radio network. In Proceedings of Globecom Conference, pages 1–6, 2010.

[35] Y. Ye and S. Zhang. New results on quadratic minimization. SIAM J. OPTIM., 14(1)(1):245– 267, 2003.

[36] S. Yiu, M. Vu, and V. Tarokh. Interference reduction by beamforming in cognitive networks. In Proceedings of Globecom Conference, pages 1–6, 2008.

[37] R. Zakhour and D. Gesbert. Coordination on the miso interference channel using the virtual sinr framework. In Workshop on Smart Antennas (WSA), volume 17, page 18, 2009.

[38] R. Zakhour and D. Gesbert. Distributed Multicell-MISO Precoding Using the Layered Virtual SINR Framework. Wireless Communications, IEEE Transactions on, 9(8):2444–2448, 2010.

[39] L. Zhang, Y.-C. Liang, Y. Xin, and H. V. Poor. Robust cognitive beamforming with partial channel state information. IEEE Transactions on Wireless Communications, 8(8):2415– 2419, 2008.

[40] R. Zhang, F. Gao, and Y.-C. Liang. Cognitive beamforming made practical: Effective interference channel and learning-throughput tradeoff. IEEE Transactions on Communications, 58(2):706–718, 2010.