2,041 research outputs found
Adaptive Cut Generation Algorithm for Improved Linear Programming Decoding of Binary Linear Codes
Linear programming (LP) decoding approximates maximum-likelihood (ML)
decoding of a linear block code by relaxing the equivalent ML integer
programming (IP) problem into a more easily solved LP problem. The LP problem
is defined by a set of box constraints together with a set of linear
inequalities called "parity inequalities" that are derived from the constraints
represented by the rows of a parity-check matrix of the code and can be added
iteratively and adaptively. In this paper, we first derive a new necessary
condition and a new sufficient condition for a violated parity inequality
constraint, or "cut," at a point in the unit hypercube. Then, we propose a new
and effective algorithm to generate parity inequalities derived from certain
additional redundant parity check (RPC) constraints that can eliminate
pseudocodewords produced by the LP decoder, often significantly improving the
decoder error-rate performance. The cut-generating algorithm is based upon a
specific transformation of an initial parity-check matrix of the linear block
code. We also design two variations of the proposed decoder to make it more
efficient when it is combined with the new cut-generating algorithm. Simulation
results for several low-density parity-check (LDPC) codes demonstrate that the
proposed decoding algorithms significantly narrow the performance gap between
LP decoding and ML decoding
Self-Sustaining Caching Stations: Towards Cost-Effective 5G-Enabled Vehicular Networks
In this article, we investigate the cost-effective 5G-enabled vehicular
networks to support emerging vehicular applications, such as autonomous
driving, in-car infotainment and location-based road services. To this end,
self-sustaining caching stations (SCSs) are introduced to liberate on-road base
stations from the constraints of power lines and wired backhauls. Specifically,
the cache-enabled SCSs are powered by renewable energy and connected to core
networks through wireless backhauls, which can realize "drop-and-play"
deployment, green operation, and low-latency services. With SCSs integrated, a
5G-enabled heterogeneous vehicular networking architecture is further proposed,
where SCSs are deployed along roadside for traffic offloading while
conventional macro base stations (MBSs) provide ubiquitous coverage to
vehicles. In addition, a hierarchical network management framework is designed
to deal with high dynamics in vehicular traffic and renewable energy, where
content caching, energy management and traffic steering are jointly
investigated to optimize the service capability of SCSs with balanced power
demand and supply in different time scales. Case studies are provided to
illustrate SCS deployment and operation designs, and some open research issues
are also discussed.Comment: IEEE Communications Magazine, to appea
The Buffered \pi-Calculus: A Model for Concurrent Languages
Message-passing based concurrent languages are widely used in developing
large distributed and coordination systems. This paper presents the buffered
-calculus --- a variant of the -calculus where channel names are
classified into buffered and unbuffered: communication along buffered channels
is asynchronous, and remains synchronous along unbuffered channels. We show
that the buffered -calculus can be fully simulated in the polyadic
-calculus with respect to strong bisimulation. In contrast to the
-calculus which is hard to use in practice, the new language enables easy
and clear modeling of practical concurrent languages. We encode two real-world
concurrent languages in the buffered -calculus: the (core) Go language and
the (Core) Erlang. Both encodings are fully abstract with respect to weak
bisimulations
Dynamic Relationships among Composite Property Prices of Major Chinese Cities: Contemporaneous Causality through Vector Error Corrections and Directed Acyclic Graphs
The present study is the first one that investigates dynamic relations among composite real estate price indices of ten different cities in China during the years from 2005 to 2021. Utilizing the data recorded on a monthly basis, we apply VECM (vector error-correction modeling) and DAGs (directed acyclic graphs) in order to characterize contemporaneous causal relations among the ten real estate price indices. We use the PC algorithm to identify a pattern with non-directed edges and the LiNGAM algorithm to determine the causal ordering, based on which we calculate the results of innovation accounting. The LiNGAM algorithm adopted here effectively utilizes non-normality for facilitating the arrival of complete causal orderings. Our results show that price dynamics revealed through processes of price adjustments due to shocks to prices are rather sophisticated and such dynamics are, in general, dominated by price indices of Shanghai and Shenzhen, which are two top-tier cities among the four top-tier cities in China. This indicates that policy design on composite property prices should be focusing on price indices of Shanghai and Shenzhen
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