1,569 research outputs found
Optimal Power Allocation over Multiple Identical Gilbert-Elliott Channels
We study the fundamental problem of power allocation over multiple
Gilbert-Elliott communication channels. In a communication system with time
varying channel qualities, it is important to allocate the limited transmission
power to channels that will be in good state. However, it is very challenging
to do so because channel states are usually unknown when the power allocation
decision is made. In this paper, we derive an optimal power allocation policy
that can maximize the expected discounted number of bits transmitted over an
infinite time span by allocating the transmission power only to those channels
that are believed to be good in the coming time slot. We use the concept belief
to represent the probability that a channel will be good and derive an optimal
power allocation policy that establishes a mapping from the channel belief to
an allocation decision.
Specifically, we first model this problem as a partially observable Markov
decision processes (POMDP), and analytically investigate the structure of the
optimal policy. Then a simple threshold-based policy is derived for a
three-channel communication system. By formulating and solving a linear
programming formulation of this power allocation problem, we further verified
the derived structure of the optimal policy.Comment: 10 pages, 7 figure
The Noise Reduction over Wireless Channel Using Vector Quantization Compression and Filtering
The transmission of compressed data over wireless channel conditions represents a big challenge. The idea of providing robust transmission gets a lot of attention in field of research. In this paper we study the effect of the noise over wireless channel. We use the model of Gilbert-Elliot to represent the channel. The parameters of the model are selected to represent three cases of channel. As data for transmission we use images in gray level size 512x512. To minimize bandwidth usage we compressed the image with vector quantization also in this compression technique we study the effect of the codebook in the robustness of transmission so we use different algorithms to generate the codebook for the vector quantization finally we study the restoration efficiency of received image using filtering and indices recovery technique
Graded quantization for multiple description coding of compressive measurements
Compressed sensing (CS) is an emerging paradigm for acquisition of compressed
representations of a sparse signal. Its low complexity is appealing for
resource-constrained scenarios like sensor networks. However, such scenarios
are often coupled with unreliable communication channels and providing robust
transmission of the acquired data to a receiver is an issue. Multiple
description coding (MDC) effectively combats channel losses for systems without
feedback, thus raising the interest in developing MDC methods explicitly
designed for the CS framework, and exploiting its properties. We propose a
method called Graded Quantization (CS-GQ) that leverages the democratic
property of compressive measurements to effectively implement MDC, and we
provide methods to optimize its performance. A novel decoding algorithm based
on the alternating directions method of multipliers is derived to reconstruct
signals from a limited number of received descriptions. Simulations are
performed to assess the performance of CS-GQ against other methods in presence
of packet losses. The proposed method is successful at providing robust coding
of CS measurements and outperforms other schemes for the considered test
metrics
Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications
From its introduction to its quindecennial, network coding has built a strong reputation for enhancing packet recovery and achieving maximum information flow in both wired and wireless networks. Traditional studies focused on optimizing the throughput of the system by proposing elaborate schemes able to reach the network capacity. With the shift toward distributed computing on mobile devices, performance and complexity become both critical factors that affect the efficiency of a coding strategy. Instantly decodable network coding presents itself as a new paradigm in network coding that trades off these two aspects. This paper review instantly decodable network coding schemes by identifying, categorizing, and evaluating various algorithms proposed in the literature. The first part of the manuscript investigates the conventional centralized systems, in which all decisions are carried out by a central unit, e.g., a base-station. In particular, two successful approaches known as the strict and generalized instantly decodable network are compared in terms of reliability, performance, complexity, and packet selection methodology. The second part considers the use of instantly decodable codes in a device-to-device communication network, in which devices speed up the recovery of the missing packets by exchanging network coded packets. Although the performance improvements are directly proportional to the computational complexity increases, numerous successful schemes from both the performance and complexity viewpoints are identified
On Applications of Machine Learning In IRS NOMA Architectures For 5G and Beyond
L'abstract è presente nell'allegato / the abstract is in the attachmen
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