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      User-Centric Joint Access-Backhaul Design for Full-Duplex Self-Backhauled Wireless Networks

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          Abstract

          Full-duplex self-backhauling is promising to provide cost-effective and flexible backhaul connectivity for ultra-dense wireless networks, but also poses a great challenge to resource management between the access and backhaul links. In this paper, we propose a user-centric joint access-backhaul transmission framework for full-duplex self-backhauled wireless networks. In the access link, user-centric clustering is adopted so that each user is cooperatively served by multiple small base stations (SBSs). In the backhaul link, user-centric multicast transmission is proposed so that each user's message is treated as a common message and multicast to its serving SBS cluster. We first formulate an optimization problem to maximize the network weighted sum rate through joint access-backhaul beamforming and SBS clustering when global channel state information (CSI) is available. This problem is efficiently solved via the successive lower-bound maximization approach with a novel approximate objective function and the iterative link removal technique. We then extend the study to the stochastic joint access-backhaul beamforming optimization with partial CSI. Simulation results demonstrate the effectiveness of the proposed algorithms for both full CSI and partial CSI scenarios. They also show that the transmission design with partial CSI can greatly reduce the CSI overhead with little performance degradation.

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          Applications of second-order cone programming

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            Multi-Cell MIMO Cooperative Networks: A New Look at Interference

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              The concave-convex procedure.

              The concave-convex procedure (CCCP) is a way to construct discrete-time iterative dynamical systems that are guaranteed to decrease global optimization and energy functions monotonically. This procedure can be applied to almost any optimization problem, and many existing algorithms can be interpreted in terms of it. In particular, we prove that all expectation-maximization algorithms and classes of Legendre minimization and variational bounding algorithms can be reexpressed in terms of CCCP. We show that many existing neural network and mean-field theory algorithms are also examples of CCCP. The generalized iterative scaling algorithm and Sinkhorn's algorithm can also be expressed as CCCP by changing variables. CCCP can be used both as a new way to understand, and prove the convergence of, existing optimization algorithms and as a procedure for generating new algorithms.
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                Author and article information

                Journal
                16 December 2018
                Article
                1812.07516
                66fd34e0-946e-403f-800c-0f12579cfd18

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                Part of this paper was presented in IEEE GLOBECOM 2018
                eess.SP cs.IT math.IT

                Numerical methods,Information systems & theory,Electrical engineering
                Numerical methods, Information systems & theory, Electrical engineering

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