2,729 research outputs found
On the Throughput of Large-but-Finite MIMO Networks using Schedulers
This paper studies the sum throughput of the {multi-user}
multiple-input-single-output (MISO) networks in the cases with large but finite
number of transmit antennas and users. Considering continuous and bursty
communication scenarios with different users' data request probabilities, we
derive quasi-closed-form expressions for the maximum achievable throughput of
the networks using optimal schedulers. The results are obtained in various
cases with different levels of interference cancellation. Also, we develop an
efficient scheduling scheme using genetic algorithms (GAs), and evaluate the
effect of different parameters, such as channel/precoding models, number of
antennas/users, scheduling costs and power amplifiers' efficiency, on the
system performance. Finally, we use the recent results on the achievable rates
of finite block-length codes to analyze the system performance in the cases
with short packets. As demonstrated, the proposed GA-based scheduler reaches
(almost) the same throughput as in the exhaustive search-based optimal
scheduler, with substantially less implementation complexity. Moreover, the
power amplifiers' inefficiency and the scheduling delay affect the performance
of the scheduling-based systems significantly
Interference Alignment for Cognitive Radio Communications and Networks: A Survey
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe
Energy and bursty packet loss tradeoff over fading channels: a system-level model
Energy efficiency and quality of service (QoS) guarantees are the key design goals for the 5G wireless communication systems. In this context, we discuss a multiuser scheduling scheme over fading channels for loss tolerant applications. The loss tolerance of the application is characterized in terms of different parameters that contribute to quality of experience (QoE) for the application. The mobile users are scheduled opportunistically such that a minimum QoS is guaranteed. We propose an opportunistic scheduling scheme and address the cross-layer design framework when channel state information (CSI) is not perfectly available at the transmitter and the receiver. We characterize the system energy as a function of different QoS and channel state estimation error parameters. The optimization problem is formulated using Markov chain framework and solved using stochastic optimization techniques. The results demonstrate that the parameters characterizing the packet loss are tightly coupled and relaxation of one parameter does not benefit the system much if the other constraints are tight. We evaluate the energy-performance tradeoff numerically and show the effect of channel uncertainty on the packet scheduler design
Distributed space time block coding and application in cooperative cognitive relay networks
The design and analysis of various distributed space time block coding
schemes for cooperative relay networks is considered in this thesis.
Rayleigh frequency flat and selective fading channels are assumed to
model the links in the networks, and interference suppression techniques
together with an orthogonal frequency division multiplexing (OFDM)
type transmission approach are employed to mitigate synchronization
errors at the destination node induced by the different delays through
the relay nodes.
Closed-loop space time block coding is first considered in the context
of decode-and-forward (regenerative) networks. In particular, quasi orthogonal
and extended orthogonal coding techniques are employed for
transmission from four relay nodes and parallel interference cancellation
detection is exploited to mitigate synchronization errors. Availability
of a direct link between the source and destination nodes is studied.
Outer coding is then added to gain further improvement in end-to-end
performance and amplify-and-forward (non regenerative) type networks
together with distributed space time coding are considered to reduce
relay node complexity. A novel detection scheme is then proposed
for decode-and-forward and amplify-and-forward networks with closed-loop
extended orthogonal coding and closed-loop quasi-orthogonal coding
which reduce the computational complexity of the parallel interference cancellation. The near-optimum detector is presented for relay
nodes with single or dual antennas. End-to-end bit error rate simulations
confirm the potential of the approach and its ability to mitigate
synchronization errors
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