52 research outputs found
Raptor Codes in the Low SNR Regime
In this paper, we revisit the design of Raptor codes for binary input
additive white Gaussian noise (BIAWGN) channels, where we are interested in
very low signal to noise ratios (SNRs). A linear programming degree
distribution optimization problem is defined for Raptor codes in the low SNR
regime through several approximations. We also provide an exact expression for
the polynomial representation of the degree distribution with infinite maximum
degree in the low SNR regime, which enables us to calculate the exact value of
the fractions of output nodes of small degrees. A more practical degree
distribution design is also proposed for Raptor codes in the low SNR regime,
where we include the rate efficiency and the decoding complexity in the
optimization problem, and an upper bound on the maximum rate efficiency is
derived for given design parameters. Simulation results show that the Raptor
code with the designed degree distributions can approach rate efficiencies
larger than 0.95 in the low SNR regime.Comment: Submitted to the IEEE Transactions on Communications. arXiv admin
note: text overlap with arXiv:1510.0772
Massive Non-Orthogonal Multiple Access for Cellular IoT: Potentials and Limitations
The Internet of Things (IoT) promises ubiquitous connectivity of everything
everywhere, which represents the biggest technology trend in the years to come.
It is expected that by 2020 over 25 billion devices will be connected to
cellular networks; far beyond the number of devices in current wireless
networks. Machine-to-Machine (M2M) communications aims at providing the
communication infrastructure for enabling IoT by facilitating the billions of
multi-role devices to communicate with each other and with the underlying data
transport infrastructure without, or with little, human intervention. Providing
this infrastructure will require a dramatic shift from the current protocols
mostly designed for human-to-human (H2H) applications. This article reviews
recent 3GPP solutions for enabling massive cellular IoT and investigates the
random access strategies for M2M communications, which shows that cellular
networks must evolve to handle the new ways in which devices will connect and
communicate with the system. A massive non-orthogonal multiple access (NOMA)
technique is then presented as a promising solution to support a massive number
of IoT devices in cellular networks, where we also identify its practical
challenges and future research directions.Comment: To appear in IEEE Communications Magazin
On the Fundamental Limits of Random Non-orthogonal Multiple Access in Cellular Massive IoT
Machine-to-machine (M2M) constitutes the communication paradigm at the basis
of Internet of Things (IoT) vision. M2M solutions allow billions of multi-role
devices to communicate with each other or with the underlying data transport
infrastructure without, or with minimal, human intervention. Current solutions
for wireless transmissions originally designed for human-based applications
thus require a substantial shift to cope with the capacity issues in managing a
huge amount of M2M devices. In this paper, we consider the multiple access
techniques as promising solutions to support a large number of devices in
cellular systems with limited radio resources. We focus on non-orthogonal
multiple access (NOMA) where, with the aim to increase the channel efficiency,
the devices share the same radio resources for their data transmission. This
has been shown to provide optimal throughput from an information theoretic
point of view.We consider a realistic system model and characterise the system
performance in terms of throughput and energy efficiency in a NOMA scenario
with a random packet arrival model, where we also derive the stability
condition for the system to guarantee the performance.Comment: To appear in IEEE JSAC Special Issue on Non-Orthogonal Multiple
Access for 5G System
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