11,087 research outputs found
Ant colony system-based applications to electrical distribution system optimization
Chapter 16, February 201
Joint Symbol-Level Precoding and Reflecting Designs for IRS-Enhanced MU-MISO Systems
Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this article, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms
A Systematic Approach to Constructing Incremental Topology Control Algorithms Using Graph Transformation
Communication networks form the backbone of our society. Topology control
algorithms optimize the topology of such communication networks. Due to the
importance of communication networks, a topology control algorithm should
guarantee certain required consistency properties (e.g., connectivity of the
topology), while achieving desired optimization properties (e.g., a bounded
number of neighbors). Real-world topologies are dynamic (e.g., because nodes
join, leave, or move within the network), which requires topology control
algorithms to operate in an incremental way, i.e., based on the recently
introduced modifications of a topology. Visual programming and specification
languages are a proven means for specifying the structure as well as
consistency and optimization properties of topologies. In this paper, we
present a novel methodology, based on a visual graph transformation and graph
constraint language, for developing incremental topology control algorithms
that are guaranteed to fulfill a set of specified consistency and optimization
constraints. More specifically, we model the possible modifications of a
topology control algorithm and the environment using graph transformation
rules, and we describe consistency and optimization properties using graph
constraints. On this basis, we apply and extend a well-known constructive
approach to derive refined graph transformation rules that preserve these graph
constraints. We apply our methodology to re-engineer an established topology
control algorithm, kTC, and evaluate it in a network simulation study to show
the practical applicability of our approachComment: This document corresponds to the accepted manuscript of the
referenced journal articl
A Constant-Factor Approximation for Wireless Capacity Maximization with Power Control in the SINR Model
In modern wireless networks, devices are able to set the power for each
transmission carried out. Experimental but also theoretical results indicate
that such power control can improve the network capacity significantly. We
study this problem in the physical interference model using SINR constraints.
In the SINR capacity maximization problem, we are given n pairs of senders
and receivers, located in a metric space (usually a so-called fading metric).
The algorithm shall select a subset of these pairs and choose a power level for
each of them with the objective of maximizing the number of simultaneous
communications. This is, the selected pairs have to satisfy the SINR
constraints with respect to the chosen powers.
We present the first algorithm achieving a constant-factor approximation in
fading metrics. The best previous results depend on further network parameters
such as the ratio of the maximum and the minimum distance between a sender and
its receiver. Expressed only in terms of n, they are (trivial) Omega(n)
approximations.
Our algorithm still achieves an O(log n) approximation if we only assume to
have a general metric space rather than a fading metric. Furthermore, by using
standard techniques the algorithm can also be used in single-hop and multi-hop
scheduling scenarios. Here, we also get polylog(n) approximations.Comment: 17 page
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Combinatorial optimization and metaheuristics
Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods
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