327 research outputs found
Improved Decoding of Expander Codes
We study the classical expander codes, introduced by Sipser and Spielman [M. Sipser and D. A. Spielman, 1996]. Given any constants 0 < ?, ? < 1/2, and an arbitrary bipartite graph with N vertices on the left, M < N vertices on the right, and left degree D such that any left subset S of size at most ? N has at least (1-?)|S|D neighbors, we show that the corresponding linear code given by parity checks on the right has distance at least roughly {? N}/{2 ?}. This is strictly better than the best known previous result of 2(1-?) ? N [Madhu Sudan, 2000; Viderman, 2013] whenever ? < 1/2, and improves the previous result significantly when ? is small. Furthermore, we show that this distance is tight in general, thus providing a complete characterization of the distance of general expander codes.
Next, we provide several efficient decoding algorithms, which vastly improve previous results in terms of the fraction of errors corrected, whenever ? < 1/4. Finally, we also give a bound on the list-decoding radius of general expander codes, which beats the classical Johnson bound in certain situations (e.g., when the graph is almost regular and the code has a high rate).
Our techniques exploit novel combinatorial properties of bipartite expander graphs. In particular, we establish a new size-expansion tradeoff, which may be of independent interests
Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity
Spatial crowdsourcing (SC) engages large worker pools for location-based
tasks, attracting growing research interest. However, prior SC task allocation
approaches exhibit limitations in computational efficiency, balanced matching,
and participation incentives. To address these challenges, we propose a
graph-based allocation framework optimized for massive heterogeneous spatial
data. The framework first clusters similar tasks and workers separately to
reduce allocation scale. Next, it constructs novel non-crossing graph
structures to model balanced adjacencies between unevenly distributed tasks and
workers. Based on the graphs, a bidirectional worker-task matching scheme is
designed to produce allocations optimized for mutual interests. Extensive
experiments on real-world datasets analyze the performance under various
parameter settings
Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets
We investigate a data quality-aware dynamic client selection problem for
multiple federated learning (FL) services in a wireless network, where each
client offers dynamic datasets for the simultaneous training of multiple FL
services, and each FL service demander has to pay for the clients under
constrained monetary budgets. The problem is formalized as a non-cooperative
Markov game over the training rounds. A multi-agent hybrid deep reinforcement
learning-based algorithm is proposed to optimize the joint client selection and
payment actions, while avoiding action conflicts. Simulation results indicate
that our proposed algorithm can significantly improve training performance.Comment: 6 pages,8 figure
Seamless Service Provisioning for Mobile Crowdsensing: Towards Integrating Forward and Spot Trading Markets
The challenge of exchanging and processing of big data over Mobile
Crowdsensing (MCS) networks calls for the new design of responsive and seamless
service provisioning as well as proper incentive mechanisms. Although
conventional onsite spot trading of resources based on real-time network
conditions and decisions can facilitate the data sharing over MCS networks, it
often suffers from prohibitively long service provisioning delays and
unavoidable trading failures due to its reliance on timely analysis of complex
and dynamic MCS environments. These limitations motivate us to investigate an
integrated forward and spot trading mechanism (iFAST), which entails a new
hybrid service trading protocol over the MCS network architecture. In iFAST,
the sellers (i.e., mobile users with sensing resources) can provide long-term
or temporary sensing services to the buyers (i.e., sensing task owners). iFast
enables signing long-term contracts in advance of future transactions through a
forward trading mode, via analyzing historical statistics of the market, for
which the notion of overbooking is introduced and promoted. iFAST further
enables the buyers with unsatisfying service quality to recruit temporary
sellers through a spot trading mode, upon considering the current
market/network conditions. We analyze the fundamental blocks of iFAST, and
provide a case study to demonstrate its superior performance as compared to
existing methods. Finally, future research directions on reliable service
provisioning for next-generation MCS networks are summarized
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