22,557 research outputs found
Simple implementations of mutually orthogonal complementary sets of sequences
This paper presents simple software and hardware implementations for a class of mutually orthogonal complementary sets of sequences based on its closed-form construction formula. Following a brief review of the Golay-paired Hadamard matrix concept, the flow graph for constructing mutually orthogonal Golay-paired Hadamard matrices, which represent the scalable complete complementary sets of sequences, is proposed. Then, their superb scalability and completeness are summarized. Finally, the C and Matlab functions and a logic schematic diagram are given to easily generate these complementary sequences
Low-Complexity Generation of Scalable Complete Complementary Sets of Sequences
This paper presents extremely low-complexity boolean logic for the generation of coefficients suitable for filtering or correlation of scalable complete complementary sets of sequences (SCCSS). As the unique auto- and cross-correlation properties of SCCSS are of broad interest, the simplicity of the proposed coefficient generation technique allows arbitrarily long SCCSS to be used in resource constrained applications
Bandwidth efficient multi-station wireless streaming based on complete complementary sequences
Data streaming from multiple base stations to a client is recognized as a robust technique for multimedia streaming. However the resulting transmission in parallel over wireless channels poses serious challenges, especially multiple access interference, multipath fading, noise effects and synchronization. Spread spectrum techniques seem the obvious choice to mitigate these effects, but at the cost of increased bandwidth requirements. This paper proposes a solution that exploits complete complementary spectrum spreading and data compression techniques jointly to resolve the communication challenges whilst ensuring efficient use of spectrum and acceptable bit error rate. Our proposed spreading scheme reduces the required transmission bandwidth by exploiting correlation among information present at multiple base stations. Results obtained show 1.75 Mchip/sec (or 25%) reduction in transmission rate, with only up to 6 dB loss in frequency-selective channel compared to a straightforward solution based solely on complete complementary spectrum spreading
Knowledge-aware Complementary Product Representation Learning
Learning product representations that reflect complementary relationship
plays a central role in e-commerce recommender system. In the absence of the
product relationships graph, which existing methods rely on, there is a need to
detect the complementary relationships directly from noisy and sparse customer
purchase activities. Furthermore, unlike simple relationships such as
similarity, complementariness is asymmetric and non-transitive. Standard usage
of representation learning emphasizes on only one set of embedding, which is
problematic for modelling such properties of complementariness. We propose
using knowledge-aware learning with dual product embedding to solve the above
challenges. We encode contextual knowledge into product representation by
multi-task learning, to alleviate the sparsity issue. By explicitly modelling
with user bias terms, we separate the noise of customer-specific preferences
from the complementariness. Furthermore, we adopt the dual embedding framework
to capture the intrinsic properties of complementariness and provide geometric
interpretation motivated by the classic separating hyperplane theory. Finally,
we propose a Bayesian network structure that unifies all the components, which
also concludes several popular models as special cases. The proposed method
compares favourably to state-of-art methods, in downstream classification and
recommendation tasks. We also develop an implementation that scales efficiently
to a dataset with millions of items and customers
Functional Dependencies Unleashed for Scalable Data Exchange
We address the problem of efficiently evaluating target functional
dependencies (fds) in the Data Exchange (DE) process. Target fds naturally
occur in many DE scenarios, including the ones in Life Sciences in which
multiple source relations need to be structured under a constrained target
schema. However, despite their wide use, target fds' evaluation is still a
bottleneck in the state-of-the-art DE engines. Systems relying on an all-SQL
approach typically do not support target fds unless additional information is
provided. Alternatively, DE engines that do include these dependencies
typically pay the price of a significant drop in performance and scalability.
In this paper, we present a novel chase-based algorithm that can efficiently
handle arbitrary fds on the target. Our approach essentially relies on
exploiting the interactions between source-to-target (s-t) tuple-generating
dependencies (tgds) and target fds. This allows us to tame the size of the
intermediate chase results, by playing on a careful ordering of chase steps
interleaving fds and (chosen) tgds. As a direct consequence, we importantly
diminish the fd application scope, often a central cause of the dramatic
overhead induced by target fds. Moreover, reasoning on dependency interaction
further leads us to interesting parallelization opportunities, yielding
additional scalability gains. We provide a proof-of-concept implementation of
our chase-based algorithm and an experimental study aiming at gauging its
scalability with respect to a number of parameters, among which the size of
source instances and the number of dependencies of each tested scenario.
Finally, we empirically compare with the latest DE engines, and show that our
algorithm outperforms them
A Systematic Framework for the Construction of Optimal Complete Complementary Codes
The complete complementary code (CCC) is a sequence family with ideal
correlation sums which was proposed by Suehiro and Hatori. Numerous literatures
show its applications to direct-spread code-division multiple access (DS-CDMA)
systems for inter-channel interference (ICI)-free communication with improved
spectral efficiency. In this paper, we propose a systematic framework for the
construction of CCCs based on -shift cross-orthogonal sequence families
(-CO-SFs). We show theoretical bounds on the size of -CO-SFs and CCCs,
and give a set of four algorithms for their generation and extension. The
algorithms are optimal in the sense that the size of resulted sequence families
achieves theoretical bounds and, with the algorithms, we can construct an
optimal CCC consisting of sequences whose lengths are not only almost arbitrary
but even variable between sequence families. We also discuss the family size,
alphabet size, and lengths of constructible CCCs based on the proposed
algorithms
The STRESS Method for Boundary-point Performance Analysis of End-to-end Multicast Timer-Suppression Mechanisms
Evaluation of Internet protocols usually uses random scenarios or scenarios
based on designers' intuition. Such approach may be useful for average-case
analysis but does not cover boundary-point (worst or best-case) scenarios. To
synthesize boundary-point scenarios a more systematic approach is needed.In
this paper, we present a method for automatic synthesis of worst and best case
scenarios for protocol boundary-point evaluation.
Our method uses a fault-oriented test generation (FOTG) algorithm for
searching the protocol and system state space to synthesize these scenarios.
The algorithm is based on a global finite state machine (FSM) model. We extend
the algorithm with timing semantics to handle end-to-end delays and address
performance criteria. We introduce the notion of a virtual LAN to represent
delays of the underlying multicast distribution tree. The algorithms used in
our method utilize implicit backward search using branch and bound techniques
and start from given target events. This aims to reduce the search complexity
drastically. As a case study, we use our method to evaluate variants of the
timer suppression mechanism, used in various multicast protocols, with respect
to two performance criteria: overhead of response messages and response time.
Simulation results for reliable multicast protocols show that our method
provides a scalable way for synthesizing worst-case scenarios automatically.
Results obtained using stress scenarios differ dramatically from those obtained
through average-case analyses. We hope for our method to serve as a model for
applying systematic scenario generation to other multicast protocols.Comment: 24 pages, 10 figures, IEEE/ACM Transactions on Networking (ToN) [To
appear
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