In this work, we consider a heterogeneous network consisting in several macro nodes and pico nodes. Our goal is to associate users, belonging to this network, to one of the nodes, while maximizing the sum rate of all users. We also want to analyze the load balancing achieved by this association. Therefore, we develop a new theoretical framework to study cell association for the downlink of multi-cell networks and derive an upper bound on the achievable sum rate. We propose a dynamic cell association heuristic, which achieves performance close to optimal. Finally, we verify our results through numerical evaluations and implement the proposed heuristic in an LTE simulator to demonstrate its viability.
Abstract-This paper considers the problem of associating users, in an heterogeneous network, to either a macro node or a pico node within a tightly coordinated cell cluster. We introduce a new theoretical framework to model this problem for the downlink and derive upper bounds for achievable sum rate and minimum rate using convex optimization. Further we propose heuristics, consisting in dynamic cell association, enabling to achieve performance close to the upper bounds. Finally we implement these heuristics in an LTE simulator and show the potential of such dynamic cell association for a small LTE network.
Abstract-Consider the estimation of an unknown parameter vector in a linear measurement model. Centralized sensor selection consists in selecting a set of ks sensor measurements, from a total number of m potential measurements. The performance of the corresponding selection is measured by the volume of an estimation error covariance matrix. In this work, we consider the problem of selecting these sensors in a distributed or decentralized fashion. In particular, we study the case of two leader nodes that perform naive decentralized selections. We demonstrate that this can degrade the performance severely. Therefore, two heuristics based on convex optimization methods are introduced, where we first allow one leader to make a selection, and then to share a modest amount of information about his selection with the remaining node. We will show that both heuristics clearly outperform the naive decentralized selection, and achieve a performance close to the centralized selection.
In this paper, we consider a heterogeneous network with one macro node and one pico node. We are concerned with the problem of associating users in the macro cell, to either the macro node or the pico node, in order to maximize the sum rate in the downlink. We formulate a new theoretical framework to study this problem and derive an upper bound on the achievable sum rate using semidefinite relaxation. Furthermore, we propose a randomized heuristic to produce a feasible solution, and most importantly, give an analytic guarantee on its performance. Independently of the problem data, we can ensure a worst case performance for the randomization method. In practice, this guarantee is as good as the standard best SNR heuristic typically used in 3GPP LTE networks.
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