Dynamic base station formation for solving NLOS problem in 5G millimeter-wave communication

Millimeter-wave communication is one of the enabling technologies to meet high data-rate requirements of 5G wireless systems. Millimeter-wave systems due large available bandwidth enable gigabit-per-second data rates for line-of-sight (LOS) transmissions in short distances. However, for non-line-of-sight (NLOS) transmissions, millimeter-wave systems suffers performance degradation because the received signal strengths at user equipments (UEs) are not satisfactory. In this paper, the NLOS problem in millimeter-wave systems is treated from SoftAir (a wireless software-defined networking architecture) perspective. In particular, a so-called dynamic base station (BS) formation is introduced, which adaptively coordinates BSs and their multiple antennas to always satisfy UEs' quality-of-service (QoS) requirements in NLOS cases. First, the architecture for software-defined millimeter-wave system is introduced, where remote radio heads (RRHs) coordination is explained and millimeter-wave channel model between RRHs and UEs is analyzed. A ubiquitous millimeter-wave coverage problem is formulated, which jointly optimizes RRH-UE associations and beamforming weights of RRHs to maximize the UE sum-rate while guaranteeing QoS and system-level constraints. After proving the np-hardness of the coverage optimization problem with non-convex constraints, an iterative algorithm is developed for dynamic BS formation that achieves ubiquitous coverage with high data rates in LOS and NLOS cases. Through successive convex approximations, the proposed dynamic BS formation algorithm transforms the original mixed-integer nonlinear programming into a mixed-integer second-order cone programming, which is efficiently solved by convex tools. Simulations validate the efficacy of our solution that completely solves NLOS problem by facilitating ubiquitous coverage in 5G millimeter-wave systems.

[1]  Ian F. Akyildiz,et al.  Wireless software-defined networks (W-SDNs) and network function virtualization (NFV) for 5G cellular systems: An overview and qualitative evaluation , 2015, Comput. Networks.

[2]  Robert W. Heath,et al.  Asymptotic SINR for millimeter wave massive MIMO cellular networks , 2015, 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[3]  Theodore S. Rappaport,et al.  Millimeter Wave Mobile Communications for 5G Cellular: It Will Work! , 2013, IEEE Access.

[4]  F AkyildizIan,et al.  Wireless software-defined networks (W-SDNs) and network function virtualization (NFV) for 5G cellular systems , 2015 .

[5]  Min Luo,et al.  Control traffic balancing in software defined networks , 2016, Comput. Networks.

[6]  Ian F. Akyildiz,et al.  5G roadmap: 10 key enabling technologies , 2016, Comput. Networks.

[7]  Long Bao Le,et al.  Coordinated Multipoint ( CoMP ) Transmission Design for Cloud-RANs with Limited Fronthaul Capacity Constraints , 2015 .

[8]  Marco Di Renzo,et al.  Stochastic Geometry Modeling and Analysis of Multi-Tier Millimeter Wave Cellular Networks , 2014, IEEE Transactions on Wireless Communications.

[9]  Ian F. Akyildiz,et al.  SoftAir: A software defined networking architecture for 5G wireless systems , 2015, Comput. Networks.

[10]  Theodore S. Rappaport,et al.  Millimeter Wave Channel Modeling and Cellular Capacity Evaluation , 2013, IEEE Journal on Selected Areas in Communications.

[11]  Thomas L. Marzetta,et al.  Pilot Contamination and Precoding in Multi-Cell TDD Systems , 2009, IEEE Transactions on Wireless Communications.

[12]  Markku J. Juntti,et al.  Decentralized coordinated beamforming for weighted sum energy efficiency maximization in multi-cell MISO downlink , 2015, 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[13]  Amir Beck,et al.  A sequential parametric convex approximation method with applications to nonconvex truss topology design problems , 2010, J. Glob. Optim..

[14]  Ronald L. Rivest,et al.  Introduction to Algorithms, Second Edition , 2001 .

[15]  Yong Cheng,et al.  Joint Network Optimization and Downlink Beamforming for CoMP Transmissions Using Mixed Integer Conic Programming , 2013, IEEE Transactions on Signal Processing.

[16]  Robert W. Heath,et al.  Coverage and capacity of millimeter-wave cellular networks , 2014, IEEE Communications Magazine.

[17]  Federico Boccardi,et al.  A dynamic joint clustering scheduling algorithm for downlink CoMP systems with limited CSI , 2012, 2012 International Symposium on Wireless Communication Systems (ISWCS).