Journal
IEEE TRANSACTIONS ON COMMUNICATIONS
Volume 68, Issue 3, Pages 1746-1759Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCOMM.2018.2881464
Keywords
Resource management; Programming; Clustering algorithms; Interference; Femtocell networks; Linear programming; 5G mobile communication; Femto cell; clustering; resource allocation; user association; mmWave; D; C; programming; multiplicative penalty function
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In millimeter wave (mmW) dense femto-networks, major challenges are overcoming the performance loss imposed by the channel and managing the co-channel interference. The former is due to the mmW susceptibility to pathloss and shadowing and the latter is due to density of the network. We cope with both challenges by a clustering method designed for mmW environment. In our approach, the femto access points (FAP) and femto users (FU) are clustered based on having the most line of sight connectivity. We modify this binary optimization problem into a continuous problem using deductive penalty functions and solve it by difference of two convex functions (D.C.) programming. Our clustering algorithm achieves higher data rate compared to the foremost clustering method. We also propose a technique to assign FUs to FAPs in each cluster which has near-optimal performance and polynomial time complexity. We solve mixed integer nonlinear programming of power and sub-channel allocation by D.C. programming. Instead of using the deductive penalty terms in D.C. programming, we penalize the objective function in a multiplicative manner. Thus, the penalty term depends on both constraint violation and objective function. Our scheme achieves around 10% higher data rate compared to the method using deductive penalty terms.
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