96016
Fast algorithm for large-scale UMTS coverage planning with soft handover consideration
Coverage planning by means of controlling cell Common Pilot Channel (CPICH) power is an important task in deploying UMTS networks. In addition to determining coverage, CPICH power heavily influences the amount of Soft Handover (SHO). Non-uniform cell CPICH power allows for significant power saving and thereby higher capacity for traffic channels. However, optimizing non-uniform CPICH is challenging in terms of computational effort, if the resulting coverage pattern is required to satisfy a desired level of SHO. We present a very fast algorithm for this planning problem. The algorithm has utilized the fact that SHO is strongly correlated with cell overlap. Using overlap as a very good approximation of SHO, the algorithm can compute a near optimal coverage pattern within a few seconds even for large networks. Next, the solution is further polished to deal with SHO accurately. Simulation results show that our solution strategy is able to perform non-uniform CPICH optimization very time-efficiently, making it possible to tackle large scale UMTS coverage planning under SHO consideration.
616754
Automated Planning of CPICH Power for Enhancing HSDPA Performance at Cell Edges with Preserved Control of R99 Soft Handover
We present and demonstrate a novel approach for automated planning of common pilot channel (CPICH) power in mobile networks with co-existing HSDPA and R99 services. CPICH power allocation greatly influences the cell coverage pattern. A conventional strategy is to uniformly allocate a constant proportion of the total power to CPICH. We study non-uniform CPICH and optimize its allocation for enhancing HSDPA performance. We focus on HSDPA performance at cell edges, where user throughput is typically very low. Our approach is based on a linear-integer mathematical model; solving the model results in a power allocation that both ensures CPICH coverage and optimizes HSDPA performance at cell edges. Moreover, the model allows for precise control of soft handover (SHO) regions for R99 service. Experimental results show that our approach yields significant enhancement of HSDPA performance at cell edges with preserved R99 SHO control.
321115
Compressed pattern matching in DNA sequences
We propose derivative Boyer-Moore (d-BM), a new compressed pattern matching algorithm in DNA sequences. This algorithm is based on the Boyer-Moore method, which is one of the most popular string matching algorithms. In this approach, we compress both DNA sequences and patterns by using two bits to represent each A, T, C, G character. Experiments indicate that this compressed pattern matching algorithm searches long DNA patterns (length > 50) more than 10 times faster than the exact match routine of the software package Agrep, which is known as the fastest pattern matching tool. Moreover, compression of DNA sequences by this method gives a guaranteed space saving of 75%. In part the enhanced speed of the algorithm is due to the increased efficiency of the Boyer-Moore method resulting from an increase in alphabet size from 4 to 256.
16858369
Efficient Processing of Metric Skyline Queries
Skyline query is of great importance in many applications, such as multi-criteria decision making and business planning. In particular, a skyline point is a data object in the database whose attribute vector is not dominated by that of any other objects. Previous methods to retrieve skyline points usually assume static data objects in the database (i.e. their attribute vectors are fixed), whereas several recent work focus on skyline queries with dynamic attributes. In this paper, we propose a novel variant of skyline queries, namely metric skyline, whose dynamic attributes are defined in the metric space (i.e. not limited to the Euclidean space). We illustrate an efficient and effective pruning mechanism to answer metric skyline queries through a metric index. Most importantly, we formalize the query performance of the metric skyline query in terms of the pruning power, by a cost model, in light of which we construct an optimized metric index aiming to maximize the pruning power of metric skyline queries. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed pruning techniques as well as the constructed index in answering metric skyline queries.
6331812
Fine-Grained Power Gating Based on the Controlling Value of Logic Elements
Leakage power consumption of logic elements has become a serious problem, especially in the sub-100-nanometer process. In this paper, a novel power gating approach by using the controlling value of logic elements is proposed. In the proposed method, sleep signals of the power-gated blocks are extracted completely from the original circuits without any extra logic element. A basic algorithm and a probability-based heuristic algorithm have been developed to implement the basic idea. The steady maximum delay constraint has also been introduced to handle the delay issues. Experiments on the ISCAS'85 benchmarks show that averagely 15-36% of logic elements could be power gated at a time for random input patterns, and 3-31% of elements could be stopped under the steady maximum delay constraints. We also show a power optimization method for AND/OR tree circuits, in which more than 80% of gates can be power-gated.
13418028
Discovering matching dependencies
Matching dependencies (MDs) are recently proposed for various data quality applications such as detecting the violation of integrity constraints and duplicate object identification. In this paper, we study the problem of discovering matching dependencies for a given database instance. First, we formally define the measures, support and confidence, for evaluating the utility of MDs in the given database instance. Then, we study the discovery of MDs with certain utility requirements of support and confidence. Exact algorithms are developed, together with pruning strategies to improve the time performance. Finally, our experimental evaluation demonstrates the efficiency of the proposed methods.
14817991
A General Cost Model for Dimensionality Reduction in High Dimensional Spaces
Similarity search usually encounters a serious problem in the high dimensional space, known as the "curse of dimensionality". In order to speed up the retrieval efficiency, previous approaches usually reduce the dimensionality of the entire data set to a fixed lower value before building indexes (referred to as global dimensionality reduction (GDR)). More recent works focus on locally reducing the dimensionality of data to different values (called the local dimensionality reduction (LDR)). However, so far little work has formally evaluated the effectiveness and efficiency of both GDR and LDR for range queries. Motivated by this, in this paper, we propose a general cost model for both GDR and LDR, in light of which we introduce a novel LDR method, PRANS. It can achieve high retrieval efficiency with the guarantee of optimality given by the formal model. Finally, a B+-tree index is constructed over the reduced partitions for fast similarity search. Extensive experiments validate the correctness of our cost model on both real and synthetic data sets, and demonstrate the efficiency and effectiveness of the proposed PRANS method.