2,000 research outputs found
Optimization of Information Rate Upper and Lower Bounds for Channels with Memory
We consider the problem of minimizing upper bounds and maximizing lower
bounds on information rates of stationary and ergodic discrete-time channels
with memory. The channels we consider can have a finite number of states, such
as partial response channels, or they can have an infinite state-space, such as
time-varying fading channels. We optimize recently-proposed information rate
bounds for such channels, which make use of auxiliary finite-state machine
channels (FSMCs). Our main contribution in this paper is to provide iterative
expectation-maximization (EM) type algorithms to optimize the parameters of the
auxiliary FSMC to tighten these bounds. We provide an explicit, iterative
algorithm that improves the upper bound at each iteration. We also provide an
effective method for iteratively optimizing the lower bound. To demonstrate the
effectiveness of our algorithms, we provide several examples of partial
response and fading channels, where the proposed optimization techniques
significantly tighten the initial upper and lower bounds. Finally, we compare
our results with an improved variation of the \emph{simplex} local optimization
algorithm, called \emph{Soblex}. This comparison shows that our proposed
algorithms are superior to the Soblex method, both in terms of robustness in
finding the tightest bounds and in computational efficiency. Interestingly,
from a channel coding/decoding perspective, optimizing the lower bound is
related to increasing the achievable mismatched information rate, i.e., the
information rate of a communication system where the decoder at the receiver is
matched to the auxiliary channel, and not to the original channel.Comment: Submitted to IEEE Transactions on Information Theory, November 24,
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A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles
In recent years, there has been a dramatic increase in the use of unmanned
aerial vehicles (UAVs), particularly for small UAVs, due to their affordable
prices, ease of availability, and ease of operability. Existing and future
applications of UAVs include remote surveillance and monitoring, relief
operations, package delivery, and communication backhaul infrastructure.
Additionally, UAVs are envisioned as an important component of 5G wireless
technology and beyond. The unique application scenarios for UAVs necessitate
accurate air-to-ground (AG) propagation channel models for designing and
evaluating UAV communication links for control/non-payload as well as payload
data transmissions. These AG propagation models have not been investigated in
detail when compared to terrestrial propagation models. In this paper, a
comprehensive survey is provided on available AG channel measurement campaigns,
large and small scale fading channel models, their limitations, and future
research directions for UAV communication scenarios
Low-Complexity Detection/Equalization in Large-Dimension MIMO-ISI Channels Using Graphical Models
In this paper, we deal with low-complexity near-optimal
detection/equalization in large-dimension multiple-input multiple-output
inter-symbol interference (MIMO-ISI) channels using message passing on
graphical models. A key contribution in the paper is the demonstration that
near-optimal performance in MIMO-ISI channels with large dimensions can be
achieved at low complexities through simple yet effective
simplifications/approximations, although the graphical models that represent
MIMO-ISI channels are fully/densely connected (loopy graphs). These include 1)
use of Markov Random Field (MRF) based graphical model with pairwise
interaction, in conjunction with {\em message/belief damping}, and 2) use of
Factor Graph (FG) based graphical model with {\em Gaussian approximation of
interference} (GAI). The per-symbol complexities are and
for the MRF and the FG with GAI approaches, respectively, where
and denote the number of channel uses per frame, and number of transmit
antennas, respectively. These low-complexities are quite attractive for large
dimensions, i.e., for large . From a performance perspective, these
algorithms are even more interesting in large-dimensions since they achieve
increasingly closer to optimum detection performance for increasing .
Also, we show that these message passing algorithms can be used in an iterative
manner with local neighborhood search algorithms to improve the
reliability/performance of -QAM symbol detection
A Framework for Uplink Intercell Interference Modeling with Channel-Based Scheduling
This paper presents a novel framework for modeling the uplink intercell
interference (ICI) in a multiuser cellular network. The proposed framework
assists in quantifying the impact of various fading channel models and
state-of-the-art scheduling schemes on the uplink ICI. Firstly, we derive a
semianalytical expression for the distribution of the location of the scheduled
user in a given cell considering a wide range of scheduling schemes. Based on
this, we derive the distribution and moment generating function (MGF) of the
uplink ICI considering a single interfering cell. Consequently, we determine
the MGF of the cumulative ICI observed from all interfering cells and derive
explicit MGF expressions for three typical fading models. Finally, we utilize
the obtained expressions to evaluate important network performance metrics such
as the outage probability, ergodic capacity, and average fairness numerically.
Monte-Carlo simulation results are provided to demonstrate the efficacy of the
derived analytical expressions.Comment: IEEE Transactions on Wireless Communications, 2013. arXiv admin note:
substantial text overlap with arXiv:1206.229
Maximizing Energy Efficiency in Multiple Access Channels by Exploiting Packet Dropping and Transmitter Buffering
Quality of service (QoS) for a network is characterized in terms of various
parameters specifying packet delay and loss tolerance requirements for the
application. The unpredictable nature of the wireless channel demands for
application of certain mechanisms to meet the QoS requirements. Traditionally,
medium access control (MAC) and network layers perform these tasks. However,
these mechanisms do not take (fading) channel conditions into account. In this
paper, we investigate the problem using cross layer techniques where
information flow and joint optimization of higher and physical layer is
permitted. We propose a scheduling scheme to optimize the energy consumption of
a multiuser multi-access system such that QoS constraints in terms of packet
loss are fulfilled while the system is able to maximize the advantages emerging
from multiuser diversity. Specifically, this work focuses on modeling and
analyzing the effects of packet buffering capabilities of the transmitter on
the system energy for a packet loss tolerant application. We discuss low
complexity schemes which show comparable performance to the proposed scheme.
The numerical evaluation reveals useful insights about the coupling effects of
different QoS parameters on the system energy consumption and validates our
analytical results.Comment: in IEEE trans. Wireless communications, 201
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