8,529 research outputs found
Approximate Decoding Approaches for Network Coded Correlated Data
This paper considers a framework where data from correlated sources are
transmitted with help of network coding in ad-hoc network topologies. The
correlated data are encoded independently at sensors and network coding is
employed in the intermediate nodes in order to improve the data delivery
performance. In such settings, we focus on the problem of reconstructing the
sources at decoder when perfect decoding is not possible due to losses or
bandwidth bottlenecks. We first show that the source data similarity can be
used at decoder to permit decoding based on a novel and simple approximate
decoding scheme. We analyze the influence of the network coding parameters and
in particular the size of finite coding fields on the decoding performance. We
further determine the optimal field size that maximizes the expected decoding
performance as a trade-off between information loss incurred by limiting the
resolution of the source data and the error probability in the reconstructed
data. Moreover, we show that the performance of the approximate decoding
improves when the accuracy of the source model increases even with simple
approximate decoding techniques. We provide illustrative examples about the
possible of our algorithms that can be deployed in sensor networks and
distributed imaging applications. In both cases, the experimental results
confirm the validity of our analysis and demonstrate the benefits of our low
complexity solution for delivery of correlated data sources
High-Rate Space-Time Coded Large MIMO Systems: Low-Complexity Detection and Channel Estimation
In this paper, we present a low-complexity algorithm for detection in
high-rate, non-orthogonal space-time block coded (STBC) large-MIMO systems that
achieve high spectral efficiencies of the order of tens of bps/Hz. We also
present a training-based iterative detection/channel estimation scheme for such
large STBC MIMO systems. Our simulation results show that excellent bit error
rate and nearness-to-capacity performance are achieved by the proposed
multistage likelihood ascent search (M-LAS) detector in conjunction with the
proposed iterative detection/channel estimation scheme at low complexities. The
fact that we could show such good results for large STBCs like 16x16 and 32x32
STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in
excess of 20 bps/Hz (even after accounting for the overheads meant for pilot
based training for channel estimation and turbo coding) establishes the
effectiveness of the proposed detector and channel estimator. We decode perfect
codes of large dimensions using the proposed detector. With the feasibility of
such a low-complexity detection/channel estimation scheme, large-MIMO systems
with tens of antennas operating at several tens of bps/Hz spectral efficiencies
can become practical, enabling interesting high data rate wireless
applications.Comment: v3: Performance/complexity comparison of the proposed scheme with
other large-MIMO architectures/detectors has been added (Sec. IV-D). The
paper has been accepted for publication in IEEE Journal of Selected Topics in
Signal Processing (JSTSP): Spl. Iss. on Managing Complexity in Multiuser MIMO
Systems. v2: Section V on Channel Estimation is update
Optimal Population Coding, Revisited
Cortical circuits perform the computations underlying rapid perceptual decisions within a few dozen milliseconds with each neuron emitting only a few spikes. Under these conditions, the theoretical analysis of neural population codes is challenging, as the most commonly used theoretical tool – Fisher information – can lead to erroneous conclusions about the optimality of different coding schemes. Here we revisit the effect of tuning function width and correlation structure on neural population codes based on ideal observer analysis in both a discrimination and reconstruction task. We show that the optimal tuning function width and the optimal correlation structure in both paradigms strongly depend on the available decoding time in a very similar way. In contrast, population codes optimized for Fisher information do not depend on decoding time and are severely suboptimal when only few spikes are available. In addition, we use the neurometric functions of the ideal observer in the classification task to investigate the differential coding properties of these Fisher-optimal codes for fine and coarse discrimination. We find that the discrimination error for these codes does not decrease to zero with increasing population size, even in simple coarse discrimination tasks. Our results suggest that quite different population codes may be optimal for rapid decoding in cortical computations than those inferred from the optimization of Fisher information
Lossless and near-lossless source coding for multiple access networks
A multiple access source code (MASC) is a source code designed for the following network configuration: a pair of correlated information sequences {X-i}(i=1)(infinity), and {Y-i}(i=1)(infinity) is drawn independent and identically distributed (i.i.d.) according to joint probability mass function (p.m.f.) p(x, y); the encoder for each source operates without knowledge of the other source; the decoder jointly decodes the encoded bit streams from both sources. The work of Slepian and Wolf describes all rates achievable by MASCs of infinite coding dimension (n --> infinity) and asymptotically negligible error probabilities (P-e((n)) --> 0). In this paper, we consider the properties of optimal instantaneous MASCs with finite coding dimension (n 0) performance. The interest in near-lossless codes is inspired by the discontinuity in the limiting rate region at P-e((n)) = 0 and the resulting performance benefits achievable by using near-lossless MASCs as entropy codes within lossy MASCs. Our central results include generalizations of Huffman and arithmetic codes to the MASC framework for arbitrary p(x, y), n, and P-e((n)) and polynomial-time design algorithms that approximate these optimal solutions
Network vector quantization
We present an algorithm for designing locally optimal vector quantizers for general networks. We discuss the algorithm's implementation and compare the performance of the resulting "network vector quantizers" to traditional vector quantizers (VQs) and to rate-distortion (R-D) bounds where available. While some special cases of network codes (e.g., multiresolution (MR) and multiple description (MD) codes) have been studied in the literature, we here present a unifying approach that both includes these existing solutions as special cases and provides solutions to previously unsolved examples
Speeding Multicast by Acknowledgment Reduction Technique (SMART)
We present a novel feedback protocol for wireless broadcast networks that
utilize linear network coding. We consider transmission of packets from one
source to many receivers over a single-hop broadcast erasure channel. Our
method utilizes a predictive model to request feedback only when the
probability that all receivers have completed decoding is significant. In
addition, our proposed NACK-based feedback mechanism enables all receivers to
request, within a single time slot, the number of retransmissions needed for
successful decoding. We present simulation results as well as analytical
results that show the favorable scalability of our technique as the number of
receivers, file size, and packet erasure probability increase. We also show the
robustness of this scheme to uncertainty in the predictive model, including
uncertainty in the number of receiving nodes and the packet erasure
probability, as well as to losses of the feedback itself. Our scheme, SMART, is
shown to perform nearly as well as an omniscient transmitter that requires no
feedback. Furthermore, SMART, is shown to outperform current state of the art
methods at any given erasure probability, file size, and numbers of receivers
Performance Modelling and Optimisation of Multi-hop Networks
A major challenge in the design of large-scale networks is to predict and optimise the
total time and energy consumption required to deliver a packet from a source node to a
destination node. Examples of such complex networks include wireless ad hoc and sensor
networks which need to deal with the effects of node mobility, routing inaccuracies, higher
packet loss rates, limited or time-varying effective bandwidth, energy constraints, and the
computational limitations of the nodes. They also include more reliable communication
environments, such as wired networks, that are susceptible to random failures, security
threats and malicious behaviours which compromise their quality of service (QoS) guarantees.
In such networks, packets traverse a number of hops that cannot be determined
in advance and encounter non-homogeneous network conditions that have been largely
ignored in the literature. This thesis examines analytical properties of packet travel in
large networks and investigates the implications of some packet coding techniques on both
QoS and resource utilisation.
Specifically, we use a mixed jump and diffusion model to represent packet traversal
through large networks. The model accounts for network non-homogeneity regarding
routing and the loss rate that a packet experiences as it passes successive segments of a
source to destination route. A mixed analytical-numerical method is developed to compute
the average packet travel time and the energy it consumes. The model is able to capture
the effects of increased loss rate in areas remote from the source and destination, variable
rate of advancement towards destination over the route, as well as of defending against
malicious packets within a certain distance from the destination. We then consider sending
multiple coded packets that follow independent paths to the destination node so as to
mitigate the effects of losses and routing inaccuracies. We study a homogeneous medium
and obtain the time-dependent properties of the packet’s travel process, allowing us to
compare the merits and limitations of coding, both in terms of delivery times and energy
efficiency. Finally, we propose models that can assist in the analysis and optimisation
of the performance of inter-flow network coding (NC). We analyse two queueing models
for a router that carries out NC, in addition to its standard packet routing function. The
approach is extended to the study of multiple hops, which leads to an optimisation problem
that characterises the optimal time that packets should be held back in a router, waiting
for coding opportunities to arise, so that the total packet end-to-end delay is minimised
Precoded Integer-Forcing Universally Achieves the MIMO Capacity to Within a Constant Gap
An open-loop single-user multiple-input multiple-output communication scheme
is considered where a transmitter, equipped with multiple antennas, encodes the
data into independent streams all taken from the same linear code. The coded
streams are then linearly precoded using the encoding matrix of a perfect
linear dispersion space-time code. At the receiver side, integer-forcing
equalization is applied, followed by standard single-stream decoding. It is
shown that this communication architecture achieves the capacity of any
Gaussian multiple-input multiple-output channel up to a gap that depends only
on the number of transmit antennas.Comment: to appear in the IEEE Transactions on Information Theor
- …