12 research outputs found
Live Prefetching for Mobile Computation Offloading
The conventional designs of mobile computation offloading fetch user-specific
data to the cloud prior to computing, called offline prefetching. However, this
approach can potentially result in excessive fetching of large volumes of data
and cause heavy loads on radio-access networks. To solve this problem, the
novel technique of live prefetching is proposed in this paper that seamlessly
integrates the task-level computation prediction and prefetching within the
cloud-computing process of a large program with numerous tasks. The technique
avoids excessive fetching but retains the feature of leveraging prediction to
reduce the program runtime and mobile transmission energy. By modeling the
tasks in an offloaded program as a stochastic sequence, stochastic optimization
is applied to design fetching policies to minimize mobile energy consumption
under a deadline constraint. The policies enable real-time control of the
prefetched-data sizes of candidates for future tasks. For slow fading, the
optimal policy is derived and shown to have a threshold-based structure,
selecting candidate tasks for prefetching and controlling their prefetched data
based on their likelihoods. The result is extended to design close-to-optimal
prefetching policies to fast fading channels. Compared with fetching without
prediction, live prefetching is shown theoretically to always achieve reduction
on mobile energy consumption.Comment: To appear in IEEE Trans. on Wireless Communicatio
Feedback-Topology Designs for Interference Alignment in MIMO Interference Channels
Interference alignment (IA) is a joint-transmission technique that achieves
the capacity of the interference channel for high signal-to-noise ratios
(SNRs). Most prior work on IA is based on the impractical assumption that
perfect and global channel-state information(CSI) is available at all
transmitters. To implement IA, each receiver has to feed back CSI to all
interferers, resulting in overwhelming feedback overhead. In particular, the
sum feedback rate of each receiver scales quadratically with the number of
users even if the quantized CSI is fed back. To substantially suppress feedback
overhead, this paper focuses on designing efficient arrangements of feedback
links, called feedback topologies, under the IA constraint. For the
multiple-input-multiple-output (MIMO) K-user interference channel, we propose
the feedback topology that supports sequential CSI exchange (feedback and
feedforward) between transmitters and receivers so as to achieve IA
progressively. This feedback topology is shown to reduce the network feedback
overhead from a cubic function of K to a linear one. To reduce the delay in the
sequential CSI exchange, an alternative feedback topology is designed for
supporting two-hop feedback via a control station, which also achieves the
linear feedback scaling with K. Next, given the proposed feedback topologies,
the feedback-bit allocation algorithm is designed for allocating feedback bits
by each receiver to different feedback links so as to regulate the residual
interference caused by the finite-rate feedback. Simulation results demonstrate
that the proposed bit allocation leads to significant throughput gains
especially in strong interference environments.Comment: 28 pages; 11 figures ; submitted to IEEE Trans. on Signal Processin
Pre-coder design over two-symbol extension for K-user cyclic interference channels
The authors consider K-user cyclic single-input–single-output (SISO) interference channel (IC) where each receiver is interfered with from only one neighbouring transmitter. In the K-user cyclic SISO IC, K/2 sum degrees of freedom can be achieved when two-symbol extension is applied even without channel state information at the transmitter. They derive an approximation of the ergodic sum rate as a function of pre-coders and propose designs of pre-coders to maximise the ergodic sum rate