12,522 research outputs found
HARQ Buffer Management: An Information-Theoretic View
A key practical constraint on the design of Hybrid automatic repeat request
(HARQ) schemes is the size of the on-chip buffer that is available at the
receiver to store previously received packets. In fact, in modern wireless
standards such as LTE and LTE-A, the HARQ buffer size is one of the main
drivers of the modem area and power consumption. This has recently highlighted
the importance of HARQ buffer management, that is, of the use of buffer-aware
transmission schemes and of advanced compression policies for the storage of
received data. This work investigates HARQ buffer management by leveraging
information-theoretic achievability arguments based on random coding.
Specifically, standard HARQ schemes, namely Type-I, Chase Combining and
Incremental Redundancy, are first studied under the assumption of a
finite-capacity HARQ buffer by considering both coded modulation, via Gaussian
signaling, and Bit Interleaved Coded Modulation (BICM). The analysis sheds
light on the impact of different compression strategies, namely the
conventional compression log-likelihood ratios and the direct digitization of
baseband signals, on the throughput. Then, coding strategies based on layered
modulation and optimized coding blocklength are investigated, highlighting the
benefits of HARQ buffer-aware transmission schemes. The optimization of
baseband compression for multiple-antenna links is also studied, demonstrating
the optimality of a transform coding approach.Comment: submitted to IEEE International Symposium on Information Theory
(ISIT) 2015. 29 pages, 12 figures, submitted to journal publicatio
Sleep Period Optimization Model For Layered Video Service Delivery Over eMBMS Networks
Long Term Evolution-Advanced (LTE-A) and the evolved Multimedia Broadcast
Multicast System (eMBMS) are the most promising technologies for the delivery
of highly bandwidth demanding applications. In this paper we propose a green
resource allocation strategy for the delivery of layered video streams to users
with different propagation conditions. The goal of the proposed model is to
minimize the user energy consumption. That goal is achieved by minimizing the
time required by each user to receive the broadcast data via an efficient power
transmission allocation model. A key point in our system model is that the
reliability of layered video communications is ensured by means of the Random
Linear Network Coding (RLNC) approach. Analytical results show that the
proposed resource allocation model ensures the desired quality of service
constraints, while the user energy footprint is significantly reduced.Comment: Proc. of IEEE ICC 2015, Selected Areas in Communications Symposium -
Green Communications Track, to appea
Privacy-Constrained Remote Source Coding
We consider the problem of revealing/sharing data in an efficient and secure
way via a compact representation. The representation should ensure reliable
reconstruction of the desired features/attributes while still preserve privacy
of the secret parts of the data. The problem is formulated as a remote lossy
source coding with a privacy constraint where the remote source consists of
public and secret parts. Inner and outer bounds for the optimal tradeoff region
of compression rate, distortion, and privacy leakage rate are given and shown
to coincide for some special cases. When specializing the distortion measure to
a logarithmic loss function, the resulting rate-distortion-leakage tradeoff for
the case of identical side information forms an optimization problem which
corresponds to the "secure" version of the so-called information bottleneck.Comment: 10 pages, 1 figure, to be presented at ISIT 201
JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution
Recent years have witnessed a rapid growth of deep-network based services and
applications. A practical and critical problem thus has emerged: how to
effectively deploy the deep neural network models such that they can be
executed efficiently. Conventional cloud-based approaches usually run the deep
models in data center servers, causing large latency because a significant
amount of data has to be transferred from the edge of network to the data
center. In this paper, we propose JALAD, a joint accuracy- and latency-aware
execution framework, which decouples a deep neural network so that a part of it
will run at edge devices and the other part inside the conventional cloud,
while only a minimum amount of data has to be transferred between them. Though
the idea seems straightforward, we are facing challenges including i) how to
find the best partition of a deep structure; ii) how to deploy the component at
an edge device that only has limited computation power; and iii) how to
minimize the overall execution latency. Our answers to these questions are a
set of strategies in JALAD, including 1) A normalization based in-layer data
compression strategy by jointly considering compression rate and model
accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall
execution latency; and 3) An edge-cloud structure adaptation strategy that
dynamically changes the decoupling for different network conditions.
Experiments demonstrate that our solution can significantly reduce the
execution latency: it speeds up the overall inference execution with a
guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE
Optimized Network-coded Scalable Video Multicasting over eMBMS Networks
Delivery of multicast video services over fourth generation (4G) networks
such as 3GPP Long Term Evolution-Advanced (LTE-A) is gaining momentum. In this
paper, we address the issue of efficiently multicasting layered video services
by defining a novel resource allocation framework that aims to maximize the
service coverage whilst keeping the radio resource footprint low. A key point
in the proposed system mode is that the reliability of multicast video services
is ensured by means of an Unequal Error Protection implementation of the
Network Coding (UEP-NC) scheme. In addition, both the communication parameters
and the UEP-NC scheme are jointly optimized by the proposed resource allocation
framework. Numerical results show that the proposed allocation framework can
significantly increase the service coverage when compared to a conventional
Multi-rate Transmission (MrT) strategy.Comment: Proc. of IEEE ICC 2015 - Mobile and Wireless Networking Symposium, to
appea
Achievable Rate Regions for Two-Way Relay Channel using Nested Lattice Coding
This paper studies Gaussian Two-Way Relay Channel where two communication
nodes exchange messages with each other via a relay. It is assumed that all
nodes operate in half duplex mode without any direct link between the
communication nodes. A compress-and-forward relaying strategy using nested
lattice codes is first proposed. Then, the proposed scheme is improved by
performing a layered coding : a common layer is decoded by both receivers and a
refinement layer is recovered only by the receiver which has the best channel
conditions. The achievable rates of the new scheme are characterized and are
shown to be higher than those provided by the decode-and-forward strategy in
some regions.Comment: 27 pages, 13 figures, Submitted to IEEE Transactions on Wireless
Communications (October 2013
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