7,344 research outputs found
Carrier Aggregation in Multi-Beam High Throughput Satellite Systems
Carrier Aggregation (CA) is an integral part of current terrestrial networks.
Its ability to enhance the peak data rate, to efficiently utilize the limited
available spectrum resources and to satisfy the demand for data-hungry
applications has drawn large attention from different wireless network
communities. Given the benefits of CA in the terrestrial wireless environment,
it is of great interest to analyze and evaluate the potential impact of CA in
the satellite domain. In this paper, we study CA in multibeam high throughput
satellite systems. We consider both inter-transponder and intra-transponder CA
at the satellite payload level of the communication stack, and we address the
problem of carrier-user assignment assuming that multiple users can be
multiplexed in each carrier. The transmission parameters of different carriers
are generated considering the transmission characteristics of carriers in
different transponders. In particular, we propose a flexible carrier allocation
approach for a CA-enabled multibeam satellite system targeting a proportionally
fair user demand satisfaction. Simulation results and analysis shed some light
on this rather unexplored scenario and demonstrate the feasibility of the CA in
satellite communication systems
Multicast Multigroup Precoding and User Scheduling for Frame-Based Satellite Communications
The present work focuses on the forward link of a broadband multibeam
satellite system that aggressively reuses the user link frequency resources.
Two fundamental practical challenges, namely the need to frame multiple users
per transmission and the per-antenna transmit power limitations, are addressed.
To this end, the so-called frame-based precoding problem is optimally solved
using the principles of physical layer multicasting to multiple co-channel
groups under per-antenna constraints. In this context, a novel optimization
problem that aims at maximizing the system sum rate under individual power
constraints is proposed. Added to that, the formulation is further extended to
include availability constraints. As a result, the high gains of the sum rate
optimal design are traded off to satisfy the stringent availability
requirements of satellite systems. Moreover, the throughput maximization with a
granular spectral efficiency versus SINR function, is formulated and solved.
Finally, a multicast-aware user scheduling policy, based on the channel state
information, is developed. Thus, substantial multiuser diversity gains are
gleaned. Numerical results over a realistic simulation environment exhibit as
much as 30% gains over conventional systems, even for 7 users per frame,
without modifying the framing structure of legacy communication standards.Comment: Accepted for publication to the IEEE Transactions on Wireless
Communications, 201
A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning
In this tutorial paper, a comprehensive survey is given on several major
systematic approaches in dealing with delay-aware control problems, namely the
equivalent rate constraint approach, the Lyapunov stability drift approach and
the approximate Markov Decision Process (MDP) approach using stochastic
learning. These approaches essentially embrace most of the existing literature
regarding delay-aware resource control in wireless systems. They have their
relative pros and cons in terms of performance, complexity and implementation
issues. For each of the approaches, the problem setup, the general solution and
the design methodology are discussed. Applications of these approaches to
delay-aware resource allocation are illustrated with examples in single-hop
wireless networks. Furthermore, recent results regarding delay-aware multi-hop
routing designs in general multi-hop networks are elaborated. Finally, the
delay performance of the various approaches are compared through simulations
using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201
Sum Rate Maximizing Multigroup Multicast Beamforming under Per-antenna Power Constraints
A multi-antenna transmitter that conveys independent sets of common data to
distinct groups of users is herein considered, a model known as physical layer
multicasting to multiple co-channel groups. In the recently proposed context of
per-antenna power constrained multigroup multicasting, the present work focuses
on a novel system design that aims at maximizing the total achievable
throughput. Towards increasing the system sum rate, the available power
resources need to be allocated to well conditioned groups of users. A detailed
solution to tackle the elaborate sum rate maximization multigroup multicast
problem under per-antenna power constraints is therefore derived. Numerical
results are presented to quantify the gains of the proposed algorithm over
heuristic solutions. Besides Rayleigh faded channels, the solution is also
applied to uniform linear array transmitters operating in the far field, where
line-ofsight conditions are realized. In this setting, a sensitivity analysis
with respect to the angular separation of co-group users is included. Finally,
a simple scenario providing important intuitions for the sum rate maximizing
multigroup multicast solutions is elaborated.Comment: Submitted to IEEE GlobeCom 2014, Austin, TX. arXiv admin note:
substantial text overlap with arXiv:1406.7699, arXiv:1406.755
Weighted Fair Multicast Multigroup Beamforming under Per-antenna Power Constraints
A multi-antenna transmitter that conveys independent sets of common data to
distinct groups of users is considered. This model is known as physical layer
multicasting to multiple co-channel groups. In this context, the practical
constraint of a maximum permitted power level radiated by each antenna is
addressed. The per-antenna power constrained system is optimized in a maximum
fairness sense with respect to predetermined quality of service weights. In
other words, the worst scaled user is boosted by maximizing its weighted
signal-to-interference plus noise ratio. A detailed solution to tackle the
weighted max-min fair multigroup multicast problem under per-antenna power
constraints is therefore derived. The implications of the novel constraints are
investigated via prominent applications and paradigms. What is more, robust
per-antenna constrained multigroup multicast beamforming solutions are
proposed. Finally, an extensive performance evaluation quantifies the gains of
the proposed algorithm over existing solutions and exhibits its accuracy over
per-antenna power constrained systems.Comment: Under review in IEEE Transactions in Signal Processin
A Very Brief Introduction to Machine Learning With Applications to Communication Systems
Given the unprecedented availability of data and computing resources, there
is widespread renewed interest in applying data-driven machine learning methods
to problems for which the development of conventional engineering solutions is
challenged by modelling or algorithmic deficiencies. This tutorial-style paper
starts by addressing the questions of why and when such techniques can be
useful. It then provides a high-level introduction to the basics of supervised
and unsupervised learning. For both supervised and unsupervised learning,
exemplifying applications to communication networks are discussed by
distinguishing tasks carried out at the edge and at the cloud segments of the
network at different layers of the protocol stack
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