92,064 research outputs found
Clustering based space-time network coding
Abstract—Many-to-one communication is a challenging prob-lem in practice due to channel fading and multi-user interfer-ences. In this work, a new protocol that leverages spatial diversity through space-time network coding is proposed. The N source nodes are first divided into K clusters, each having Q nodes, and the clusters send data successively in a time-division multiple access way. Each node behaves as a decode-and-forward relay to other clusters, and uses linear coding to combine the local symbol and the relayed symbols. To separate the multi-source signals, each node has a unique signature waveform, and linear decorrelator is used at the receivers. Both the exact Symbol Error Rate (SER) and the asymptotic SER at high signal-to-noise ratios of the M-ary phase-shift keying signal are studied then. It is shown that a diversity order of (N − Q + 1) can be achieved with a low transmission delay of K time slots, which is more bandwidth efficient than the existing protocols. Simulation results also justify the performance gains. I
Weightless: Lossy Weight Encoding For Deep Neural Network Compression
The large memory requirements of deep neural networks limit their deployment
and adoption on many devices. Model compression methods effectively reduce the
memory requirements of these models, usually through applying transformations
such as weight pruning or quantization. In this paper, we present a novel
scheme for lossy weight encoding which complements conventional compression
techniques. The encoding is based on the Bloomier filter, a probabilistic data
structure that can save space at the cost of introducing random errors.
Leveraging the ability of neural networks to tolerate these imperfections and
by re-training around the errors, the proposed technique, Weightless, can
compress DNN weights by up to 496x with the same model accuracy. This results
in up to a 1.51x improvement over the state-of-the-art
Scalable Compression of Deep Neural Networks
Deep neural networks generally involve some layers with mil- lions of
parameters, making them difficult to be deployed and updated on devices with
limited resources such as mobile phones and other smart embedded systems. In
this paper, we propose a scalable representation of the network parameters, so
that different applications can select the most suitable bit rate of the
network based on their own storage constraints. Moreover, when a device needs
to upgrade to a high-rate network, the existing low-rate network can be reused,
and only some incremental data are needed to be downloaded. We first
hierarchically quantize the weights of a pre-trained deep neural network to
enforce weight sharing. Next, we adaptively select the bits assigned to each
layer given the total bit budget. After that, we retrain the network to
fine-tune the quantized centroids. Experimental results show that our method
can achieve scalable compression with graceful degradation in the performance.Comment: 5 pages, 4 figures, ACM Multimedia 201
Distributed Space Time Coding for Wireless Two-way Relaying
We consider the wireless two-way relay channel, in which two-way data
transfer takes place between the end nodes with the help of a relay. For the
Denoise-And-Forward (DNF) protocol, it was shown by Koike-Akino et. al. that
adaptively changing the network coding map used at the relay greatly reduces
the impact of Multiple Access interference at the relay. The harmful effect of
the deep channel fade conditions can be effectively mitigated by proper choice
of these network coding maps at the relay. Alternatively, in this paper we
propose a Distributed Space Time Coding (DSTC) scheme, which effectively
removes most of the deep fade channel conditions at the transmitting nodes
itself without any CSIT and without any need to adaptively change the network
coding map used at the relay. It is shown that the deep fades occur when the
channel fade coefficient vector falls in a finite number of vector subspaces of
, which are referred to as the singular fade subspaces. DSTC
design criterion referred to as the \textit{singularity minimization criterion}
under which the number of such vector subspaces are minimized is obtained.
Also, a criterion to maximize the coding gain of the DSTC is obtained. Explicit
low decoding complexity DSTC designs which satisfy the singularity minimization
criterion and maximize the coding gain for QAM and PSK signal sets are
provided. Simulation results show that at high Signal to Noise Ratio, the DSTC
scheme provides large gains when compared to the conventional Exclusive OR
network code and performs slightly better than the adaptive network coding
scheme proposed by Koike-Akino et. al.Comment: 27 pages, 4 figures, A mistake in the proof of Proposition 3 given in
Appendix B correcte
Measuring spike train synchrony
Estimating the degree of synchrony or reliability between two or more spike
trains is a frequent task in both experimental and computational neuroscience.
In recent years, many different methods have been proposed that typically
compare the timing of spikes on a certain time scale to be fixed beforehand.
Here, we propose the ISI-distance, a simple complementary approach that
extracts information from the interspike intervals by evaluating the ratio of
the instantaneous frequencies. The method is parameter free, time scale
independent and easy to visualize as illustrated by an application to real
neuronal spike trains obtained in vitro from rat slices. In a comparison with
existing approaches on spike trains extracted from a simulated Hindemarsh-Rose
network, the ISI-distance performs as well as the best time-scale-optimized
measure based on spike timing.Comment: 11 pages, 13 figures; v2: minor modifications; v3: minor
modifications, added link to webpage that includes the Matlab Source Code for
the method (http://inls.ucsd.edu/~kreuz/Source-Code/Spike-Sync.html
- …