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.
Bit-interleaved coded modulation (BICM) is a practical approach for reliable communication over the AWGN channel in the bandwidth limited regime. For a signal point constellation with 2 m points, BICM labels the signal points with bit strings of length m and then treats these m bits separately both at the transmitter and the receiver. BICM capacity is defined as the maximum of a certain achievable rate. Maximization has to be done over the probability mass functions (pmf) of the bits. This is a non-convex optimization problem. So far, the optimal bit pmfs were determined via exhaustive search, which is of exponential complexity in m. In this work, an algorithm called bit-alternating convex concave method (BACM) is developed. This algorithm calculates BICM capacity with a complexity that scales approximately as m 3 . The algorithm iteratively applies convex optimization techniques. BACM is used to calculate BICM capacity of 4, 8, 16, 32, and 64-PAM in AWGN. For PAM constellations with more than 8 points, the presented values are the first results known in the literature.
This paper presents a framework for studying amplify-and-forward Gaussian relay networks and optimizing the strategy of source/sink pairs. A typical communication scenario is that many pairs of users try to exchange information over the same network. Changing the strategy of the relays, in such a network, for each communication might be an overwhelming task. We consider the case where the behavior of the relays is fixed. By solely optimizing the strategy of the source and the sink, the goal is to maximize the mutual information between the transmitted signal and the received signal. We show a parallel between the multiple-input multiple-output (MIMO) single user channel and amplify-and-forward Gaussian relay networks and derive solutions for three different types of power constraints on the network, namely 1) source power constraint, 2) global network power constraint and 3) individual relay power constraints. Finally we provide numerical results for an example network.
Abstract-In this paper, we consider the problem of sensing a frequency spectrum in a distributed manner using as few measurements as possible while still guaranteeing a low detection error. To achieve this goal we use the newly developed technique of matrix completion which enables to recover a low rank matrix from a small subset of its entries. We model the sensed bandwidth at different cognitive radios as a spectrum matrix. It has been shown that in many cases the spectrum used by a primary user is underutilized. Therefore the spectrum matrix often has a low rank structure. By taking few measurements at several cognitive radios and reconstructing the matrix at a fusion center, we can dramatically reduce the required number of samples to reconstruct the utilization of the bandwidth. This is a key enabler for efficient and reliable spectrum reuse.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.