177 research outputs found
Rate Aware Instantly Decodable Network Codes
This paper addresses the problem of reducing the delivery time of data
messages to cellular users using instantly decodable network coding (IDNC) with
physical-layer rate awareness. While most of the existing literature on IDNC
does not consider any physical layer complications and abstract the model as
equally slotted time for all users, this paper proposes a cross-layer scheme
that incorporates the different channel rates of the various users in the
decision process of both the transmitted message combinations and the rates
with which they are transmitted. The consideration of asymmetric rates for
receivers reflects more practical application scenarios and introduces a new
trade-off between the choice of coding combinations for various receivers and
the broadcasting rate for achieving shorter completion time. The completion
time minimization problem in such scenario is first shown to be intractable.
The problem is, thus, approximated by reducing, at each transmission, the
increase of an anticipated version of the completion time. The paper solves the
problem by formulating it as a maximum weight clique problem over a newly
designed rate aware IDNC (RA-IDNC) graph. The highest weight clique in the
created graph being potentially not unique, the paper further suggests a
multi-layer version of the proposed solution to improve the obtained results
from the employed completion time approximation. Simulation results indicate
that the cross-layer design largely outperforms the uncoded transmissions
strategies and the classical IDNC scheme
Delivery Time Reduction for Order-Constrained Applications using Binary Network Codes
Consider a radio access network wherein a base-station is required to deliver
a set of order-constrained messages to a set of users over independent erasure
channels. This paper studies the delivery time reduction problem using
instantly decodable network coding (IDNC). Motivated by time-critical and
order-constrained applications, the delivery time is defined, at each
transmission, as the number of undelivered messages. The delivery time
minimization problem being computationally intractable, most of the existing
literature on IDNC propose sub-optimal online solutions. This paper suggests a
novel method for solving the problem by introducing the delivery delay as a
measure of distance to optimality. An expression characterizing the delivery
time using the delivery delay is derived, allowing the approximation of the
delivery time minimization problem by an optimization problem involving the
delivery delay. The problem is, then, formulated as a maximum weight clique
selection problem over the IDNC graph wherein the weight of each vertex
reflects its corresponding user and message's delay. Simulation results suggest
that the proposed solution achieves lower delivery and completion times as
compared to the best-known heuristics for delivery time reduction
Completion Time Reduction in Instantly Decodable Network Coding Through Decoding Delay Control
For several years, the completion time and decoding delay problems in
Instantly Decodable Network Coding (IDNC) were considered separately and were
thought to completely act against each other. Recently, some works aimed to
balance the effects of these two important IDNC metrics but none of them
studied a further optimization of one by controlling the other. In this paper,
we study the effect of controlling the decoding delay to reduce the completion
time below its currently best known solution. We first derive the
decoding-delay-dependent expressions of the users' and overall completion
times. Although using such expressions to find the optimal overall completion
time is NP-hard, we design a novel heuristic that minimizes the probability of
increasing the maximum of these decoding-delay-dependent completion time
expressions after each transmission through a layered control of their decoding
delays. Simulation results show that this new algorithm achieves both a lower
mean completion time and mean decoding delay compared to the best known
heuristic for completion time reduction. The gap in performance becomes
significant for harsh erasure scenarios
Centralized and Cooperative Transmission of Secure Multiple Unicasts using Network Coding
We introduce a method for securely delivering a set of messages to a group of
clients over a broadcast erasure channel where each client is interested in a
distinct message. Each client is able to obtain its own message but not the
others'. In the proposed method the messages are combined together using a
special variant of random linear network coding. Each client is provided with a
private set of decoding coefficients to decode its own message. Our method
provides security for the transmission sessions against computational
brute-force attacks and also weakly security in information theoretic sense. As
the broadcast channel is assumed to be erroneous, the missing coded packets
should be recovered in some way. We consider two different scenarios. In the
first scenario the missing packets are retransmitted by the base station
(centralized). In the second scenario the clients cooperate with each other by
exchanging packets (decentralized). In both scenarios, network coding
techniques are exploited to increase the total throughput. For the case of
centralized retransmissions we provide an analytical approximation for the
throughput performance of instantly decodable network coded (IDNC)
retransmissions as well as numerical experiments. For the decentralized
scenario, we propose a new IDNC based retransmission method where its
performance is evaluated via simulations and analytical approximation.
Application of this method is not limited to our special problem and can be
generalized to a new class of problems introduced in this paper as the
cooperative index coding problem
A Graph Model for Opportunistic Network Coding
Recent advancements in graph-based analysis and solutions of instantly
decodable network coding (IDNC) trigger the interest to extend them to more
complicated opportunistic network coding (ONC) scenarios, with limited increase
in complexity. In this paper, we design a simple IDNC-like graph model for a
specific subclass of ONC, by introducing a more generalized definition of its
vertices and the notion of vertex aggregation in order to represent the storage
of non-instantly-decodable packets in ONC. Based on this representation, we
determine the set of pairwise vertex adjacency conditions that can populate
this graph with edges so as to guarantee decodability or aggregation for the
vertices of each clique in this graph. We then develop the algorithmic
procedures that can be applied on the designed graph model to optimize any
performance metric for this ONC subclass. A case study on reducing the
completion time shows that the proposed framework improves on the performance
of IDNC and gets very close to the optimal performance
Throughput Maximization in Cloud Radio Access Networks using Network Coding
This paper is interested in maximizing the total throughput of cloud radio
access networks (CRANs) in which multiple radio remote heads (RRHs) are
connected to a central computing unit known as the cloud. The transmit frame of
each RRH consists of multiple radio resources blocks (RRBs), and the cloud is
responsible for synchronizing these RRBS and scheduling them to users. Unlike
previous works that consider allocating each RRB to only a single user at each
time instance, this paper proposes to mix the flows of multiple users in each
RRB using instantly decodable network coding (IDNC). The proposed scheme is
thus designed to jointly schedule the users to different RRBs, choose the
encoded file sent in each of them, and the rate at which each of them is
transmitted. Hence, the paper maximizes the throughput which is defined as the
number of correctly received bits. To jointly fulfill this objective, we design
a graph in which each vertex represents a possible user-RRB association,
encoded file, and transmission rate. By appropriately choosing the weights of
vertices, the scheduling problem is shown to be equivalent to a maximum weight
clique problem over the newly introduced graph. Simulation results illustrate
the significant gains of the proposed scheme compared to classical coding and
uncoded solutions.Comment: 7 pages, 7 figure
Delay Minimization for Instantly Decodable Network Coding in Persistent Channels with Feedback Intermittence
In this paper, we consider the problem of minimizing the multicast decoding
delay of generalized instantly decodable network coding (G-IDNC) over
persistent forward and feedback erasure channels with feedback intermittence.
In such an environment, the sender does not always receive acknowledgement from
the receivers after each transmission. Moreover, both the forward and feedback
channels are subject to persistent erasures, which can be modelled by a two
state (good and bad states) Markov chain known as Gilbert-Elliott channel
(GEC). Due to such feedback imperfections, the sender is unable to determine
subsequent instantly decodable packets combination for all receivers. Given
this harsh channel and feedback model, we first derive expressions for the
probability distributions of decoding delay increments and then employ these
expressions in formulating the minimum decoding problem in such environment as
a maximum weight clique problem in the G-IDNC graph. We also show that the
problem formulations in simpler channel and feedback models are special cases
of our generalized formulation. Since this problem is NP-hard, we design a
greedy algorithm to solve it and compare it to blind approaches proposed in
literature. Through extensive simulations, our adaptive algorithm is shown to
outperform the blind approaches in all situations and to achieve significant
improvement in the decoding delay, especially when the channel is highly
persisten
Instantly Decodable Network Coding for Real-Time Scalable Video Broadcast over Wireless Networks
In this paper, we study a real-time scalable video broadcast over wireless
networks in instantly decodable network coded (IDNC) systems. Such real-time
scalable video has a hard deadline and imposes a decoding order on the video
layers.We first derive the upper bound on the probability that the individual
completion times of all receivers meet the deadline. Using this probability, we
design two prioritized IDNC algorithms, namely the expanding window IDNC
(EW-IDNC) algorithm and the non-overlapping window IDNC (NOW-IDNC) algorithm.
These algorithms provide a high level of protection to the most important video
layer before considering additional video layers in coding decisions. Moreover,
in these algorithms, we select an appropriate packet combination over a given
number of video layers so that these video layers are decoded by the maximum
number of receivers before the deadline. We formulate this packet selection
problem as a two-stage maximal clique selection problem over an IDNC graph.
Simulation results over a real scalable video stream show that our proposed
EW-IDNC and NOW-IDNC algorithms improve the received video quality compared to
the existing IDNC algorithms
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