4,503 research outputs found
Feedforward data-aided phase noise estimation from a DCT basis expansion
This contribution deals with phase noise estimation from pilot symbols. The phase noise process is approximated by an expansion of discrete cosine transform (DCT) basis functions containing only a few terms. We propose a feedforward algorithm that estimates the DCT coefficients without requiring detailed knowledge about the phase noise statistics. We demonstrate that the resulting (linearized) mean-square phase estimation error consists of two contributions: a contribution from the additive noise, that equals the Cramer-Rao lower bound, and a noise independent contribution, that results front the phase noise modeling error. We investigate the effect of the symbol sequence length, the pilot symbol positions, the number of pilot symbols, and the number of estimated DCT coefficients it the estimation accuracy and on the corresponding bit error rate (PER). We propose a pilot symbol configuration allowing to estimate any number of DCT coefficients not exceeding the number of pilot Symbols, providing a considerable Performance improvement as compared to other pilot symbol configurations. For large block sizes, the DCT-based estimation algorithm substantially outperforms algorithms that estimate only the time-average or the linear trend of the carrier phase. Copyright (C) 2009 J. Bhatti and M. Moeneclaey
An Opportunistic Error Correction Layer for OFDM Systems
In this paper, we propose a novel cross layer scheme to lower power\ud
consumption of ADCs in OFDM systems, which is based on resolution\ud
adaptive ADCs and Fountain codes. The key part in the new proposed\ud
system is that the dynamic range of ADCs can be reduced by\ud
discarding the packets which are transmitted over 'bad' sub\ud
carriers. Correspondingly, the power consumption in ADCs can be\ud
reduced. Also, the new system does not process all the packets but\ud
only processes surviving packets. This new error correction layer\ud
does not require perfect channel knowledge, so it can be used in a\ud
realistic system where the channel is estimated. With this new\ud
approach, more than 70% of the energy consumption in the ADC can be\ud
saved compared with the conventional IEEE 802.11a WLAN system under\ud
the same channel conditions and throughput. The ADC in a receiver\ud
can consume up to 50% of the total baseband energy. Moreover, to\ud
reduce the overhead of Fountain codes, we apply message passing and\ud
Gaussian elimination in the decoder. In this way, the overhead is\ud
3% for a small block size (i.e. 500 packets). Using both methods\ud
results in an efficient system with low delay
Deep Reinforcement Learning for Resource Management in Network Slicing
Network slicing is born as an emerging business to operators, by allowing
them to sell the customized slices to various tenants at different prices. In
order to provide better-performing and cost-efficient services, network slicing
involves challenging technical issues and urgently looks forward to intelligent
innovations to make the resource management consistent with users' activities
per slice. In that regard, deep reinforcement learning (DRL), which focuses on
how to interact with the environment by trying alternative actions and
reinforcing the tendency actions producing more rewarding consequences, is
assumed to be a promising solution. In this paper, after briefly reviewing the
fundamental concepts of DRL, we investigate the application of DRL in solving
some typical resource management for network slicing scenarios, which include
radio resource slicing and priority-based core network slicing, and demonstrate
the advantage of DRL over several competing schemes through extensive
simulations. Finally, we also discuss the possible challenges to apply DRL in
network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201
Pervasive and mobile computing
The Pervasive and Mobile Computing Journal (PMC) is a professional, peer-reviewed journal that publishes high-quality scientific articles (both theory and practice) covering all aspects of pervasive computing and communications
Hierarchical Design Based Intrusion Detection System For Wireless Ad hoc Network
In recent years, wireless ad hoc sensor network becomes popular both in civil
and military jobs. However, security is one of the significant challenges for
sensor network because of their deployment in open and unprotected environment.
As cryptographic mechanism is not enough to protect sensor network from
external attacks, intrusion detection system needs to be introduced. Though
intrusion prevention mechanism is one of the major and efficient methods
against attacks, but there might be some attacks for which prevention method is
not known. Besides preventing the system from some known attacks, intrusion
detection system gather necessary information related to attack technique and
help in the development of intrusion prevention system. In addition to
reviewing the present attacks available in wireless sensor network this paper
examines the current efforts to intrusion detection system against wireless
sensor network. In this paper we propose a hierarchical architectural design
based intrusion detection system that fits the current demands and restrictions
of wireless ad hoc sensor network. In this proposed intrusion detection system
architecture we followed clustering mechanism to build a four level
hierarchical network which enhances network scalability to large geographical
area and use both anomaly and misuse detection techniques for intrusion
detection. We introduce policy based detection mechanism as well as intrusion
response together with GSM cell concept for intrusion detection architecture.Comment: 16 pages, International Journal of Network Security & Its
Applications (IJNSA), Vol.2, No.3, July 2010. arXiv admin note: text overlap
with arXiv:1111.1933 by other author
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