46 research outputs found
Interference reduction in multiuser relay networks
In future multiuser wireless systems, the limited system resources have to be extensively reused for serving several users. This results in received interferences at the users which limit the performance of the system. A scenario with several source-destination node pairs communicating unidirectionally through a shared medium is considered. The communication among the nodes is assisted by some relays and takes place in two time slots. The present dissertation focuses on investigating how the relay and the filter coefficients can be smartly adjusted such that the system performance is enhanced.In zukünftigen Mehrbenutzerfunksystemen müssen die begrenzten Systemressourcen intensiv wiederverwendet werden. Dadurch empfangen die Benutzer Interferenzsignale, sodass die Performanz des Funksystems begrenzt wird. Es wird ein Szenario, bestehend aus mehreren Paaren von Quell- und Zielknoten, betrachtet. Die Knotenpaare kommunizieren unidirektional miteinander durch ein Relay. Diese Dissertation konzentriert sich auf die Untersuchung, wie die Relay- und die Filterkoeffizienten intelligent angepasst werden können, sodass die Performanz des Funksystems erhöht wird
Cost Sharing Games for Energy-Efficient Multi-Hop Broadcast in Wireless Networks
We study multi-hop broadcast in wireless networks with one source node and
multiple receiving nodes. The message flow from the source to the receivers can
be modeled as a tree-graph, called broadcast-tree. The problem of finding the
minimum-power broadcast-tree (MPBT) is NP-complete. Unlike most of the existing
centralized approaches, we propose a decentralized algorithm, based on a
non-cooperative cost-sharing game. In this game, every receiving node, as a
player, chooses another node of the network as its respective transmitting node
for receiving the message. Consequently, a cost is assigned to the receiving
node based on the power imposed on its chosen transmitting node. In our model,
the total required power at a transmitting node consists of (i) the transmit
power and (ii) the circuitry power needed for communication hardware modules.
We develop our algorithm using the marginal contribution (MC) cost-sharing
scheme and show that the optimum broadcast-tree is always a Nash equilibrium
(NE) of the game. Simulation results demonstrate that our proposed algorithm
outperforms conventional algorithms for the MPBT problem. Besides, we show that
the circuitry power, which is usually ignored by existing algorithms,
significantly impacts the energy-efficiency of the network.Comment: 33 pages including references, figures, and table
Maximizing the Sum Rate in Cellular Networks Using Multi-Convex Optimization
In this paper, we propose a novel algorithm to maximize the sum rate in
interference-limited scenarios where each user decodes its own message with the
presence of unknown interferences and noise considering the
signal-to-interference-plus-noise-ratio. It is known that the problem of
adapting the transmit and receive filters of the users to maximize the sum rate
with a sum transmit power constraint is non-convex. Our novel approach is to
formulate the sum rate maximization problem as an equivalent multi-convex
optimization problem by adding two sets of auxiliary variables. An iterative
algorithm which alternatingly adjusts the system variables and the auxiliary
variables is proposed to solve the multi-convex optimization problem. The
proposed algorithm is applied to a downlink cellular scenario consisting of
several cells each of which contains a base station serving several mobile
stations. We examine the two cases, with or without several half-duplex
amplify-and-forward relays assisting the transmission. A sum power constraint
at the base stations and a sum power constraint at the relays are assumed.
Finally, we show that the proposed multi-convex formulation of the sum rate
maximization problem is applicable to many other wireless systems in which the
estimated data symbols are multi-affine functions of the system variables.Comment: 24 pages, 5 figure
Multi-state analysis functional models using Bayesian networks
Multilevel Flow Modeling (MFM) model maps functionality of components in a system through logical interconnections and is effective in predicting success rates of tasks undertaken. However, the output of this model is binary, which is taken at its extrema, i.e., success and failure, while in reality, the operational status of plant components often spans between these end. In this paper, a multi-state model is proposed by adding probabilistic information to the modelling framework. Using a heat exchanger pilot plant as a case study, the MFM model is transformed into its fault tree [1] equivalent to incorporate failure probability information. To facilitate computations, the FT model is transformed into Bayesian Network model, and applied for fault detection and diagnosis problems. The results obtained illustrate the effectiveness and feasibility of the proposed method
Optimizing Power Allocation in Interference Channels Using D.C. Programming
International audiencePower allocation is a promising approach for optimizing the performance of mobile radio systems in interference channels. In the present paper, the non-convex objective function of the power allocation problem aiming at maximizing the sum rate with a total power constraint is reformulated as a difference of two concave functions. A global optimum power allocation is found by applying a branch and bound based algorithm to the new formulation. The algorithm basically splits the feasible region consecutively into subregions where for every subregion the objective function is upper and lower bounded. For a certain partition of the feasible region, a power allocation corresponding to the highest lower bound which is upper bounded by the highest upper bound with some insignificant difference is found as the global optimum. A convex maximization formulation of the optimization problem with a piecewise linearly outer approximated feasible region is essentially applied for finding an upper bound which only requires solving a linear program problem. The simulation results show a significant improvement in the sum rate of the proposed algorithm over the conventional suboptimal techniques
A cross-layer resource allocation scheme for spatial multiplexing-based MIMO-OFDMA systems
<p>Abstract</p> <p>We investigate the resource allocation problem for the downlink of a multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) system. The sum rate maximization itself cannot cope with fairness among users. Hence, we address this problem in the context of the utility-based resource allocation presented in earlier papers. This resource allocation method allows to enhance the efficiency and guarantee fairness among users by exploiting multiuser diversity, frequency diversity, as well as time diversity. In this paper, we treat the overall utility as the quality of service indicator and design utility functions with respect to the average transmission rate in order to simultaneously provide two services, real-time and best-effort. Since the optimal solutions are extremely computationally complex to obtain, we propose a suboptimal joint subchannel and power control algorithm that converges very fast and simplifies the MIMO resource allocation problem into a single-input single-output resource allocation problem. Simulation results indicate that using the proposed method achieves near-optimum solutions, and the available resources are distributed more fairly among users.</p