3,884 research outputs found
Joint and Competitive Caching Designs in Large-Scale Multi-Tier Wireless Multicasting Networks
Caching and multicasting are two promising methods to support massive content
delivery in multi-tier wireless networks. In this paper, we consider a random
caching and multicasting scheme with caching distributions in the two tiers as
design parameters, to achieve efficient content dissemination in a two-tier
large-scale cache-enabled wireless multicasting network. First, we derive
tractable expressions for the successful transmission probabilities in the
general region as well as the high SNR and high user density region,
respectively, utilizing tools from stochastic geometry. Then, for the case of a
single operator for the two tiers, we formulate the optimal joint caching
design problem to maximize the successful transmission probability in the
asymptotic region, which is nonconvex in general. By using the block successive
approximate optimization technique, we develop an iterative algorithm, which is
shown to converge to a stationary point. Next, for the case of two different
operators, one for each tier, we formulate the competitive caching design game
where each tier maximizes its successful transmission probability in the
asymptotic region. We show that the game has a unique Nash equilibrium (NE) and
develop an iterative algorithm, which is shown to converge to the NE under a
mild condition. Finally, by numerical simulations, we show that the proposed
designs achieve significant gains over existing schemes.Comment: 30 pages, 6 pages, submitted to IEEE GLOBECOM 2017 and IEEE Trans.
Commo
Decentralized Fair Scheduling in Two-Hop Relay-Assisted Cognitive OFDMA Systems
In this paper, we consider a two-hop relay-assisted cognitive downlink OFDMA
system (named as secondary system) dynamically accessing a spectrum licensed to
a primary network, thereby improving the efficiency of spectrum usage. A
cluster-based relay-assisted architecture is proposed for the secondary system,
where relay stations are employed for minimizing the interference to the users
in the primary network and achieving fairness for cell-edge users. Based on
this architecture, an asymptotically optimal solution is derived for jointly
controlling data rates, transmission power, and subchannel allocation to
optimize the average weighted sum goodput where the proportional fair
scheduling (PFS) is included as a special case. This solution supports
decentralized implementation, requires small communication overhead, and is
robust against imperfect channel state information at the transmitter (CSIT)
and sensing measurement. The proposed solution achieves significant throughput
gains and better user-fairness compared with the existing designs. Finally, we
derived a simple and asymptotically optimal scheduling solution as well as the
associated closed-form performance under the proportional fair scheduling for a
large number of users. The system throughput is shown to be
, where is the
number of users in one cluster, is the number of subchannels and is
the active probability of primary users.Comment: 29 pages, 9 figures, IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL
PROCESSIN
Queue-Aware Distributive Resource Control for Delay-Sensitive Two-Hop MIMO Cooperative Systems
In this paper, we consider a queue-aware distributive resource control
algorithm for two-hop MIMO cooperative systems. We shall illustrate that relay
buffering is an effective way to reduce the intrinsic half-duplex penalty in
cooperative systems. The complex interactions of the queues at the source node
and the relays are modeled as an average-cost infinite horizon Markov Decision
Process (MDP). The traditional approach solving this MDP problem involves
centralized control with huge complexity. To obtain a distributive and low
complexity solution, we introduce a linear structure which approximates the
value function of the associated Bellman equation by the sum of per-node value
functions. We derive a distributive two-stage two-winner auction-based control
policy which is a function of the local CSI and local QSI only. Furthermore, to
estimate the best fit approximation parameter, we propose a distributive online
stochastic learning algorithm using stochastic approximation theory. Finally,
we establish technical conditions for almost-sure convergence and show that
under heavy traffic, the proposed low complexity distributive control is global
optimal.Comment: 30 pages, 7 figure
A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning
In this tutorial paper, a comprehensive survey is given on several major
systematic approaches in dealing with delay-aware control problems, namely the
equivalent rate constraint approach, the Lyapunov stability drift approach and
the approximate Markov Decision Process (MDP) approach using stochastic
learning. These approaches essentially embrace most of the existing literature
regarding delay-aware resource control in wireless systems. They have their
relative pros and cons in terms of performance, complexity and implementation
issues. For each of the approaches, the problem setup, the general solution and
the design methodology are discussed. Applications of these approaches to
delay-aware resource allocation are illustrated with examples in single-hop
wireless networks. Furthermore, recent results regarding delay-aware multi-hop
routing designs in general multi-hop networks are elaborated. Finally, the
delay performance of the various approaches are compared through simulations
using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201
Improving spatial resolution of confocal Raman microscopy by super-resolution image restoration
A new super-resolution image restoration confocal Raman microscopy method (SRIR-RAMAN) is proposed for improving the spatial resolution of confocal Raman microscopy. This method can recover the lost high spatial frequency of the confocal Raman microscopy by using Poisson-MAP super-resolution imaging restoration, thereby improving the spatial resolution of confocal Raman microscopy and realizing its super-resolution imaging. Simulation analyses and experimental results indicate that the spatial resolution of SRIR-RAMAN can be improved by 65% to achieve 200 nm with the same confocal Raman microscopy system. This method can provide a new tool for high spatial resolution micro-probe structure detection in physical chemistry, materials science, biomedical science and other areas
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