122 research outputs found
Performance Analysis of Random Linear Network Coding in Two-Source Single-Relay Networks
This paper considers the multiple-access relay channel in a setting where two
source nodes transmit packets to a destination node, both directly and via a
relay node, over packet erasure channels. Intra-session network coding is used
at the source nodes and inter-session network coding is employed at the relay
node to combine the recovered source packets of both source nodes. In this
work, we investigate the performance of the network-coded system in terms of
the probability that the destination node will successfully recover the source
packets of the two source nodes. We build our analysis on fundamental
probability expressions for random matrices over finite fields and we derive
upper bounds on the system performance for the case of systematic and
non-systematic network coding. Simulation results show that the upper bounds
are very tight and accurately predict the decoding probability at the
destination node. Our analysis also exposes the clear benefits of systematic
network coding at the source nodes compared to non-systematic transmission.Comment: Proc. ICC 2015, Workshop on Cooperative and Cognitive Mobile Networks
(CoCoNet), to appea
The impact of 5G channel models on the performance of intelligent reflecting surfaces and decode-and-forward relaying
An intelligent reflecting surface (IRS) is an array of discrete elements with configurable scattering properties. It has the capability to beamform arriving radio waves to an intended receiver, making it an attractive candidate technology for fifth-generation (5G) communications. A recent study debunked the notion that IRSs can replace relays because a large number of IRS elements is required even to approach the performance of simple single-antenna decode-and-forward (DF) relays. The study introduced 4G channel models into a theoretical framework to obtain simulation results, based on which comparisons between the two schemes were carried out. In this paper, we consider 5G channel models, reflect on the revised results, and argue that IRSs and DF relays can complement each other's strengths and can both have a place in 5G and beyond 5G architectures
Resource Allocation Frameworks for Network-coded Layered Multimedia Multicast Services
The explosive growth of content-on-the-move, such as video streaming to
mobile devices, has propelled research on multimedia broadcast and multicast
schemes. Multi-rate transmission strategies have been proposed as a means of
delivering layered services to users experiencing different downlink channel
conditions. In this paper, we consider Point-to-Multipoint layered service
delivery across a generic cellular system and improve it by applying different
random linear network coding approaches. We derive packet error probability
expressions and use them as performance metrics in the formulation of resource
allocation frameworks. The aim of these frameworks is both the optimization of
the transmission scheme and the minimization of the number of broadcast packets
on each downlink channel, while offering service guarantees to a predetermined
fraction of users. As a case of study, our proposed frameworks are then adapted
to the LTE-A standard and the eMBMS technology. We focus on the delivery of a
video service based on the H.264/SVC standard and demonstrate the advantages of
layered network coding over multi-rate transmission. Furthermore, we establish
that the choice of both the network coding technique and resource allocation
method play a critical role on the network footprint, and the quality of each
received video layer.Comment: IEEE Journal on Selected Areas in Communications - Special Issue on
Fundamental Approaches to Network Coding in Wireless Communication Systems.
To appea
Binary Systematic Network Coding for Progressive Packet Decoding
We consider binary systematic network codes and investigate their capability
of decoding a source message either in full or in part. We carry out a
probability analysis, derive closed-form expressions for the decoding
probability and show that systematic network coding outperforms conventional
network coding. We also develop an algorithm based on Gaussian elimination that
allows progressive decoding of source packets. Simulation results show that the
proposed decoding algorithm can achieve the theoretical optimal performance.
Furthermore, we demonstrate that systematic network codes equipped with the
proposed algorithm are good candidates for progressive packet recovery owing to
their overall decoding delay characteristics.Comment: Proc. of IEEE ICC 2015 - Communication Theory Symposium, to appea
Rethinking the Intercept Probability of Random Linear Network Coding
This letter considers a network comprising a transmitter, which employs
random linear network coding to encode a message, a legitimate receiver, which
can recover the message if it gathers a sufficient number of linearly
independent coded packets, and an eavesdropper. Closed-form expressions for the
probability of the eavesdropper intercepting enough coded packets to recover
the message are derived. Transmission with and without feedback is studied.
Furthermore, an optimization model that minimizes the intercept probability
under delay and reliability constraints is presented. Results validate the
proposed analysis and quantify the secrecy gain offered by a feedback link from
the legitimate receiver.Comment: IEEE Communications Letters, to appea
Decoding delay performance of random linear network coding for broadcast
Characterization of the delay profile of systems employing random linear network coding is important for the reliable provision of broadcast services. Previous studies focused on network coding over large finite fields or developed Markov chains to model the delay distribution but did not look at the effect of transmission deadlines on the delay. In this work, we consider generations of source packets that are encoded and transmitted over the erasure broadcast channel. The transmission of packets associated to a generation is taken to be deadline-constrained, that is, the transmitter drops a generation and proceeds to the next one when a predetermined deadline expires. Closed-form expressions for the average number of required packet transmissions per generation are obtained in terms of the generation size, the field size, the erasure probability and the deadline choice. An upper bound on the average decoding delay, which is tighter than previous bounds found in the literature, is also derived. Analysis shows that the proposed framework can be used to fine-tune the system parameters and ascertain that neither insufficient nor excessive amounts of packets are sent over the broadcast channel
Low Complexity Optimization of the Asymptotic Spectral Efficiency in Massive MIMO NOMA
Massive multiple-input multiple-output (MIMO) technology facilitates huge increases in the capacity of wireless channels, while non-orthogonal multiple access (NOMA) addresses the problem of limited resources in traditional orthogonal multiple access (OMA) techniques, promising enhanced spectral efficiency. This work uses asymptotic capacity computation results to reduce the complexity of a power allocation algorithm for small-scale MIMO-NOMA, so that it may be applied for systems with massive MIMO arrays. The proposed method maximizes the sum-capacity of the considered system, subject to power and performance constraints, and demonstrates greater accuracy than alternative approaches despite remaining low-complexity for arbitrarily large antenna arrays
Guessing Random Additive Noise Decoding of Network Coded Data Transmitted over Burst Error Channels
We consider a transmitter that encodes data packets using network coding and
broadcasts coded packets. A receiver employing network decoding recovers the
data packets if a sufficient number of error-free coded packets are gathered.
The receiver does not abandon its efforts to recover the data packets if
network decoding is unsuccessful; instead, it employs syndrome decoding (SD) in
an effort to repair erroneous received coded packets, and then reattempts
network decoding. Most decoding techniques, including SD, assume that errors
are independently and identically distributed within received coded packets.
Motivated by the guessing random additive noise decoding (GRAND) framework, we
propose transversal GRAND (T-GRAND): an algorithm that exploits statistical
dependence in the occurrence of errors, complements network decoding and
recovers all data packets with a higher probability than SD. T-GRAND examines
error vectors in order of their likelihood of occurring and altering the
transmitted packets. Calculation and sorting of the likelihood values of all
error vectors is a simple but computationally expensive process. To reduce the
complexity of T-GRAND, we take advantage of the properties of the likelihood
function and develop an efficient method, which identifies the most likely
error vectors without computing and ordering all likelihood values.Comment: 16 double-column pages, 10 figures, 1 table. Extended version of
paper presented at IEEE ISIT 2022. Submitted to IEEE Transactions on
Vehicular Technolog
Decoding probability analysis of network-coded data collection and delivery by relay drones
Relay drones in delay-tolerant applications are dispatched to remote locations in order to gather data transmitted by a source node. Collected data are stored on the drones and delivered to one or multiple bases. This paper considers two schemes for broadcasting data to drones when feedback channels are not available: a data carousel and systematic random linear network coding (RLNC). We propose a theoretical framework for the calculation of the probability that a base will fully or partially recover the transmitted data and the probability that all involved bases will successfully obtain the data, when the bases are either isolated or interconnected. Theoretical results are validated through simulations. Design considerations are also discussed, including the relationship among the field size used by RLNC, the number of relay drones and the requirement for full data recovery or the retrieval of at least part of the data
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