Power allocation and linear precoding for wireless communications with finite-alphabet inputs

This dissertation proposes a new approach to maximizing data rate/throughput of practical communication system/networks through linear precoding and power allocation. First, the mutual information or capacity region is derived for finite-alphabet inputs such as phase-shift keying (PSK), pulse-amplitude modulation (PAM), and quadrature amplitude modulation (QAM) signals. This approach, without the commonly used Gaussian input assumptions, complicates the mutual information analysis and precoder design but improves performance when the designed precoders are applied to practical systems and networks. Second, several numerical optimization methods are developed for multiple-input multiple-output (MIMO) multiple access channels, dual-hop relay networks, and point-to-point MIMO systems. In MIMO multiple access channels, an iterative weighted sum rate maximization algorithm is proposed which utilizes an alternating optimization strategy and gradient descent update. In dual-hop relay networks, the structure of the optimal precoder is exploited to develop a two-step iterative algorithm based on convex optimization and optimization on the Stiefel manifold. The proposed algorithm is insensitive to initial point selection and able to achieve a near global optimal precoder solution. The gradient descent method is also used to obtain the optimal power allocation scheme which maximizes the mutual information between the source node and destination node in dual-hop relay networks. For point-to-point MIMO systems, a low complexity precoding design method is proposed, which maximizes the lower bound of the mutual information with discretized power allocation vector in a non-iterative fashion, thus reducing complexity. Finally, performances of the proposed power allocation and linear precoding schemes are evaluated in terms of both mutual information and bit error rate (BER). Numerical results show that at the same target mutual information or sum rate, the proposed approaches achieve 3-10dB gains compared to the existing methods in the medium signal-to-noise ratio region. Such significant gains are also indicated in the coded BER systems --Abstract, page iv-v

Using Distributed Agents to Create University Course Timetables Addressing Essential & Desirable Constraints and Fair Allocation of Resources

In this study, the University Course Timetabling Problem (UCTP) has been investigated. This is a form of Constraint Satisfaction Problem (CSP) and belongs to the NP-complete class. The nature of a such problem is highly descriptive, a solution therefore involves combining many aspects of the problem. Although various timetabling algorithms have been continuously developed for nearly half a century, a gap still exists between the theoretical and practical aspects of university timetabling. This research is aimed to narrow the gap. We created an agent-based model for solving the university course timetabling problem, where this model not only considers a set of essential constraints upon the teaching activities, but also a set of desirable constraints that correspond to real-world needs. The model also seeks to provide fair allocation of resources. The capabilities of agents are harnessed for the activities of decision making, collaboration, coordination and negotiation by embedding them within the protocol designs. The resulting set of university course timetables involve the participation of every element in the system, with each agent taking responsibility for organising of its own course timetable, cooperating together to resolve problems. There are two types of agents in the model; these are Year-Programme Agent and Rooms Agent. In this study, we have used four different principles for organising the interaction between the agents: First-In-First-Out & Sequential (FIFOSeq), First-In-First-Out & Interleaved (FIFOInt), Round-Robin & Sequential (RRSeq) and Round-Robin & Interleaved (RRInt). The problem formulation and data instances of the third track of the Second International Timetabling Competition (ITC-2007) have been used as benchmarks for validating these implemented timetables. The validated results not only compare the four principles with each other; but also compare them with other timetabling techniques used for ITC-2007. The four different principles were able to successfully schedule all lectures in different periods, with no instances of two lectures occupying the same room at the same time. The lectures belonging to the same curriculum or taught by the same teacher do not conflict. Every lecture has been assigned a teacher before scheduling. The capacity of every assigned room is greater than, or equal to, the number of students in that course. The lectures of each course have been spread across the minimum number of working days with more than 98 percent success, and for more than 75 percent of the lectures under the same curriculum, it has been possible to avoid isolated deliveries. We conclude that the RRInt principle gives the most consistent likelihood of ensuring that each YPA in the system gets the best and fairest chance to obtain its resources

Learning in Real-Time Search: A Unifying Framework

Real-time search methods are suited for tasks in which the agent is interacting with an initially unknown environment in real time. In such simultaneous planning and learning problems, the agent has to select its actions in a limited amount of time, while sensing only a local part of the environment centered at the agents current location. Real-time heuristic search agents select actions using a limited lookahead search and evaluating the frontier states with a heuristic function. Over repeated experiences, they refine heuristic values of states to avoid infinite loops and to converge to better solutions. The wide spread of such settings in autonomous software and hardware agents has led to an explosion of real-time search algorithms over the last two decades. Not only is a potential user confronted with a hodgepodge of algorithms, but he also faces the choice of control parameters they use. In this paper we address both problems. The first contribution is an introduction of a simple three-parameter framework (named LRTS) which extracts the core ideas behind many existing algorithms. We then prove that LRTA*, epsilon-LRTA*, SLA*, and gamma-Trap algorithms are special cases of our framework. Thus, they are unified and extended with additional features. Second, we prove completeness and convergence of any algorithm covered by the LRTS framework. Third, we prove several upper-bounds relating the control parameters and solution quality. Finally, we analyze the influence of the three control parameters empirically in the realistic scalable domains of real-time navigation on initially unknown maps from a commercial role-playing game as well as routing in ad hoc sensor networks

Operationalizing Declarative and Procedural Knowledge: A Benchmark on Logic Programming Petri Nets (LPPNs)

Modelling, specifying and reasoning about complex systems requires to process in an integrated fashion declarative and procedural aspects of the target domain. The paper reports on an experiment conducted with a propositional version of Logic Programming Petri Nets (LPPNs), a notation extending Petri Nets with logic programming constructs. Two semantics are presented: a denotational semantics that fully maps the notation to ASP via Event Calculus; and a hybrid operational semantics that process separately the causal mechanisms via Petri nets, and the constraints associated to objects and to events via Answer Set Programming (ASP). These two alternative specifications enable an empirical evaluation in terms of computational efficiency. Experimental results show that the hybrid semantics is more efficient w.r.t. sequences, whereas the two semantics follows the same behaviour w.r.t. branchings (although the denotational one performs better in absolute terms).Comment: draft version -- update

Distributed constraint satisfaction for coordinating and integrating a large-scale, heterogeneous enterprise

Market forces are continuously driving public and private organisations towards higher productivity, shorter process and production times, and fewer labour hours. To cope with these changes, organisations are adopting new organisational models of coordination and cooperation that increase their flexibility, consistency, efficiency, productivity and profit margins. In this thesis an organisational model of coordination and cooperation is examined using a real life example; the technical integration of a distributed large-scale project of an international physics collaboration. The distributed resource constraint project scheduling problem is modelled and solved with the methods of distributed constraint satisfaction. A distributed local search method, the distributed breakout algorithm (DisBO), is used as the basis for the coordination scheme. The efficiency of the local search method is improved by extending it with an incremental problem solving scheme with variable ordering. The scheme is implemented as central algorithm, incremental breakout algorithm (IncBO), and as distributed algorithm, distributed incremental breakout algorithm (DisIncBO). In both cases, strong performance gains are observed for solving underconstrained problems. Distributed local search algorithms are incomplete and lack a termination guarantee. When problems contain hard or unsolvable subproblems and are tightly or overconstrained, local search falls into infinite cycles without explanation. A scheme is developed that identifies hard or unsolvable subproblems and orders these to size. This scheme is based on the constraint weight information generated by the breakout algorithm during search. This information, combined with the graph structure, is used to derive a fail first variable order. Empirical results show that the derived variable order is 'perfect'. When it guides simple backtracking, exceptionally hard problems do not occur, and, when problems are unsolvable, the fail depth is always the shortest. Two hybrid algorithms, BOBT and BOBT-SUSP are developed. When the problem is unsolvable, BOBT returns the minimal subproblem within the search scope and BOBT-SUSP returns the smallest unsolvable subproblem using a powerful weight sum constraint. A distributed hybrid algorithm (DisBOBT) is developed that combines DisBO with DisBT. The distributed hybrid algorithm first attempts to solve the problem with DisBO. If no solution is available after a bounded number of breakouts, DisBO is terminated, and DisBT solves the problem. DisBT is guided by a distributed variable order that is derived from the constraint weight information and the graph structure. The variable order is incrementally established, every time the partial solution needs to be extended, the next variable within the order is identified. Empirical results show strong performance gains, especially when problems are overconstrained and contain small unsolvable subproblems

High performance constraint satisfaction problem solving: State-recomputation versus state-copying.

Constraint Satisfaction Problems (CSPs) in Artificial Intelligence have been an important focus of research and have been a useful model for various applications such as scheduling, image processing and machine vision. CSPs are mathematical problems that try to search values for variables according to constraints. There are many approaches for searching solutions of non-binary CSPs. Traditionally, most CSP methods rely on a single processor. With the increasing popularization of multiple processors, parallel search methods are becoming alternatives to speed up the search process. Parallel search is a subfield of artificial intelligence in which the constraint satisfaction problem is centralized whereas the search processes are distributed among the different processors. In this thesis we present a forward checking algorithm solving non-binary CSPs by distributing different branches to different processors via message passing interface and execute it on a high performance distributed system called SHARCNET. However, the problem is how to efficiently communicate the state of the search among processors. Two communication models, namely, state-recomputation and state-copying via message passing, are implemented and evaluated. This thesis investigates the behaviour of communication from one process to another. The experimental results demonstrate that the state-recomputation model with tighter constraints obtains a better performance than the state-copying model, but when constraints become looser, the state-copying model is a better choice.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Y364. Source: Masters Abstracts International, Volume: 44-01, page: 0417. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Constraint programming on hierarchical multiprocessor systems

The work reported in this thesis is about constraint processing in the context of hierarchical multiprocessor systems, including distributed systems. More speci cally, it develops techniques and a system to help bringing the power available in today's multiprocessing networked systems into the constraint processing eld. Solving constraint speci ed problems is a process which lends itself naturally to parallelisation, as it usually implies going through very large search spaces, looking for a solution. Parallel constraint solving draws on the idea of dividing the search space among several workers, so the search may proceed faster, and thanks to the declarative nature of constraint programming, the parallelisation happens transparently as far as the user is concerned. However, to fully take advantage of the parallel computing power available, techniques must be developed to help ensure that the workers executing the search are kept busy at all times, which is an issue tackled by this work; RESUMO: Esta tese debruça-se sobre a programação por restrições no contexto dos sistemas multiprocessador hierárquicos, incluindo os sistemas distribuídos. Mais especificamente, o trabalho elaborado desenvolve as técnicas de resolução de problemas de satisfação de restrições recorrendo ao paralelismo. A actualidade do tema prende-se com a cada vez maior divulgação de que são objecto os sistemas multiprocessador que, juntamente com a omnipresença das redes de computadores, põe à nossa disposição uma capacidade de cálculo que necessita de ser posta a uso, o que tarda em acontecer. Nesta tese desenvolve-se um sistema que permite tirar partido desses recursos através do processamento de restrições A programação por restrições é um paradigma declarativo, em que o utilizador não tem de se preocupar com o controlo da computação, e a introdução de paralelismo nesta área pode realizar-se transparentemente. Por outro lado, o processo de pesquisa de soluções para problemas especificados por restrições adapta-se particularmente bem a ser paralelizado. Este tese apresenta uma abordagem _à resolução paralela de restrições, que junta paralelismo local, sob a forma de trabalhadores, com paralelismo distribuído, em que os actores são as equipas. O sistema construído, destinado a sistemas distribuídos de larga escala, que _é descrito e os seus resultados apresentados, inclui distribuição de trabalho, através de roubo de trabalho. Este funciona, localmente, sem a colaboração do roubado e, remotamente, com colaboração, num ambiente em que todas as equipas cooperam na procura da solução