Primary Component Carrier Selection and Physical Cell ID Assignment are two important self-configuration problems pertinent to LTE-Advanced. In this work, we investigate the possibility to solve these problems in a distributive manner using a graph coloring approach. Algorithms based on real-valued interference pricing of conflicts converge rapidly to a local optimum, whereas algorithms with binary interference pricing have a chance to find a global optimum. We apply both local search algorithms and complete algorithms such as Asynchronous Weak-Commitment Search. For system level performance evaluation, a picocellular scenario is considered, with indoor base stations in office houses placed in a Manhattan grid. We investigate a growing network, where neighbor cell lists are generated using practical measurement and reporting models. Distributed selection of conflict-free primary component carriers is shown to converge with 5 or more component carriers, while distributed assignment of confusion-free physical cell IDs is shown to converge with less than 15 IDs. The results reveal that the use of binary pricing of interference with an attempt to find a global optimum outperforms real-valued pricing.

This paper addresses robust link adaptation for a precoded downlink multiple input single output (MISO) system, for guaranteeing ultra-reliable (99.999%) transmissions to mobile users served by a small cell network (e.g. slowly moving machines in a factory). Effects of inaccurate channel state information (CSI) caused by user mobility and varying precoders in neighboring cells are mitigated. Both of these impairments translate to changes of received signal-to-noise plus interference ratios (SINRs), leading to CSI mispredictions and potentially erroneous transmissions. Knowing the statistics of the propagation channels and the precoder variation, backoff values can be selected to guarantee robust link adaptation. Combining this with information on the current channel state, transmissions can be adapted to have a desired reliability. Theoretical analysis accompanied by simulation results show that the proposed approach is suitable for attaining 5G ultra-reliability targets in realistic settings.

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This paper discusses novel joint (intra-cell and inter-cell) resource allocation algorithms for self-organized interference coordination in multi-carrier multiple-input multipleoutput (MIMO) small cell networks (SCNs). The proposed algorithms enable interference coordination autonomously, over multiple degrees of freedom, such as base station transmit powers, transmit precoders, and user scheduling weights. A generic α-fair utility maximization framework is considered to analyze performance-fairness trade-off, and to quantify the gains achievable in interference-limited networks. The proposed scheme involves limited inter-base station signaling in the form of two step (power and precoder) pricing. Based on this decentralized coordination, autonomous power and precoder update decision rules are considered, leading to algorithms with different characteristics in terms of user data rates, signaling load, and convergence speed. Simulation results in a practical setting show that the proposed pricing-based self-organization can achieve up to 100% improvement in cell-edge data rates, when compared to baseline optimization strategies. Furthermore, the convergence of the proposed algorithms is also proved theoretically.Index Terms-Self-organizing networks, autonomous algorithms, interference coordination, resource allocation, multipleinput multiple-output, co-channel interference, small cell networks, network utility maximization.

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