Abstract
This chapter is concerned with the maximization of the weighted sum-rate (WSR) in the multicell MIMO multiple access channel (MAC). Considered is the uplink transmission in a multicell network with multiple mobile stations (MS) per cell. Assuming the interference coordination mode, the uplink precoders are jointly optimized at MSs in order to maximize the network WSR. Since this WSR maximization problem is shown to be nonconvex, obtaining its globally optimal solution is rather computationally complex. Thus, the focus of this chapter is on proposing a low-complexity algorithm to obtain at least a locally optimal solution. Specifically, based on successive convex approximation, the original nonconvex problem is decomposed into a sequence of simpler convex optimization problems, which can be solved optimally and separately at each MS. The approach shall then reveal the structure of the optimal uplink precoders. In addition, the proposed algorithm can be implemented in a distributed manner across the coordinated cells. Simulation results show a significant improvement in the network sum-rate by the proposed algorithm, compared to the case with no interference coordination.
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References
An, L.: D.C. programming for solving a class of global optimization problems via reformulation by exact penalty. In: C. Bliek, C. Jermann, A. Neumaier (eds.) Global Optimization and Constraint Satisfaction, Lecture Notes in Computer Science, vol. 2861, pp. 87–101. Springer Berlin / Heidelberg (2003)
An, L.T.H., Tao, P.D.: The DC (difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems. Annals of Operations Research 133
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, United Kingdom (2004)
Goldsmith, A.: Wireless Communications. Cambridge University Press, Cambridge, U.K. (2004)
Horst, R., Thoai, N.V.: DC programming: Overview. J. Optim. Theory Appl. 103(1), 1–43 (1999)
Kim, S.J., Giannakis, G.B.: Optimal resource allocation for MIMO ad hoc cognitive radio networks. IEEE Trans. Inform. Theory 57(5), 3117–3131 (2011)
Scutari, G., Palomar, D.P., Barbarossa, S.: Competitive design of multiuser MIMO system based on game theory: a unified view. IEEE J. Select. Areas in Commun. 26(9), 1089–1102 (2008)
Shi, C., Berry, R.A., Honig, M.L.: Monotonic convergence of distributed interference pricing in wireless networks. In: Proc. IEEE Int. Symp. Inform. Theory, pp. 1619–1623. Seoul, Republic of Korea (2009)
Shi, Q., Razaviyayn, M., Luo, Z.Q., He, C.: An iteratively weighted MMSE approach to distributed sum-utility maximization for MIMO interfering broadcast channel. IEEE Trans. Signal Process. 59(9), 4331–4340 (2011)
Ye, S., Blum, R.S.: Optimized signaling for MIMO interference systems with feedback. IEEE Trans. Signal Process. 51(11), 2839–2848 (2003)
Yu, W., Rhee, W., Boyd, S., Cioffi, J.M.: Iterative water-filing for Gaussian multiple-access channels. IEEE Trans. Inform. Theory 50(1), 145–152 (2004)
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Nguyen, D.H.N., Le-Ngoc, T. (2014). Sum-Rate Maximization for Multicell MIMO-MAC with IC. In: Wireless Coordinated Multicell Systems. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-06337-9_6
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DOI: https://doi.org/10.1007/978-3-319-06337-9_6
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