74,598 research outputs found
On the Computational Power of Radio Channels
Radio networks can be a challenging platform for which to develop distributed algorithms, because the network nodes must contend for a shared channel. In some cases, though, the shared medium is an advantage rather than a disadvantage: for example, many radio network algorithms cleverly use the shared channel to approximate the degree of a node, or estimate the contention. In this paper we ask how far the inherent power of a shared radio channel goes, and whether it can efficiently compute "classicaly hard" functions such as Majority, Approximate Sum, and Parity.
Using techniques from circuit complexity, we show that in many cases, the answer is "no". We show that simple radio channels, such as the beeping model or the channel with collision-detection, can be approximated by a low-degree polynomial, which makes them subject to known lower bounds on functions such as Parity and Majority; we obtain round lower bounds of the form Omega(n^{delta}) on these functions, for delta in (0,1). Next, we use the technique of random restrictions, used to prove AC^0 lower bounds, to prove a tight lower bound of Omega(1/epsilon^2) on computing a (1 +/- epsilon)-approximation to the sum of the nodes\u27 inputs. Our techniques are general, and apply to many types of radio channels studied in the literature
Optimized Artificial Neural Network Using Differential Evolution for Prediction of RF Power in VHF/UHF TV and GSM 900 Bands for Cognitive Radio Networks
Cognitive radio (CR) technology has emerged as
a promising solution to many wireless communication problems
including spectrum scarcity and underutilization. The knowledge
of Radio Frequency (RF) power (primary signals and/ or interfering
signals plus noise) in the channels to be exploited by CR
is of paramount importance, not just the existence or absence of
primary users. If a channel is known to be noisy, even in the
absence of primary users, using such channels will demand large
quantities of radio resources (transmission power, bandwidth, etc)
in order to deliver an acceptable quality of service to users.
Computational Intelligence (CI) techniques can be applied to
these scenarios to predict the required RF power in the available
channels to achieve optimum Quality of Service (QoS). While
most of the prediction schemes are based on the determination
of spectrum holes, those designed for power prediction use known
radio parameters such as signal to noise ratio (SNR), bandwidth,
and bit error rate. Some of these parameters may not be available
or known to cognitive users. In this paper, we developed a time
domain based optimized Artificial Neural Network (ANN) model
for the prediction of real world RF power within the GSM 900,
Very High Frequency (VHF) and Ultra High Frequency (UHF)
TV bands. The application of the models produced was found to
increase the robustness of CR applications, specifically where the
CR had no prior knowledge of the RF power related parameters.
The models used implemented a novel and innovative initial
weight optimization of the ANNâs through the use of differential
evolutionary algorithms. This was found to enhance the accuracy
and generalization of the approac
Data Multiplexing in Radio Interferometric Calibration
New and upcoming radio interferometers will produce unprecedented amounts of
data that demand extremely powerful computers for processing. This is a
limiting factor due to the large computational power and energy costs involved.
Such limitations restrict several key data processing steps in radio
interferometry. One such step is calibration where systematic errors in the
data are determined and corrected. Accurate calibration is an essential
component in reaching many scientific goals in radio astronomy and the use of
consensus optimization that exploits the continuity of systematic errors across
frequency significantly improves calibration accuracy. In order to reach full
consensus, data at all frequencies need to be calibrated simultaneously. In the
SKA regime, this can become intractable if the available compute agents do not
have the resources to process data from all frequency channels simultaneously.
In this paper, we propose a multiplexing scheme that is based on the
alternating direction method of multipliers (ADMM) with cyclic updates. With
this scheme, it is possible to simultaneously calibrate the full dataset using
far fewer compute agents than the number of frequencies at which data are
available. We give simulation results to show the feasibility of the proposed
multiplexing scheme in simultaneously calibrating a full dataset when a limited
number of compute agents are available.Comment: MNRAS Accepted 2017 November 28. Received 2017 November 28; in
original form 2017 July 0
Low-complexity joint user and power scheduling in downlink NOMA over fading channels
Non-orthogonal multiple access (NOMA) has been considered one of the most
promising radio access techniques for next-generation cellular networks. In
this paper, we study the joint user and power scheduling for downlink NOMA over
fading channels. Specifically, we focus on a stochastic optimization problem to
maximize the weighted average sum rate while ensuring given minimum average
data rates of users. To address this problem, we first develop an opportunistic
user and power scheduling algorithm (OUPS) based on the duality and stochastic
optimization theory. By OUPS, the stochastic problem is transformed into a
series of deterministic ones for the instantaneous weighted sum rate
maximization for each slot. Thus, we additionally develop a heuristic algorithm
with very low computational complexity, called user selection and power
allocation algorithm (USPA), for the instantaneous weighted sum rate
maximization problem. Via simulation results, we demonstrate that USPA provides
near-optimal performance with very low computational complexity, and OUPS well
guarantees given minimum average data rates.Comment: 7 pages, 5 figure
A power and time efficient radio architecture for LDACS1 air-to-ground communication
L-band Digital Aeronautical Communication System (LDACS) is an emerging standard that aims at enhancing air traffic management by transitioning the traditional analog aeronautical communication systems to the superior and highly efficient digital domain. The standard places stringent requirements on the communication channels to allow them to coexist with critical L-band systems, requiring complex processing and filters in baseband. Approaches based on cognitive radio are also proposed since this allows tremendous increase in communication capacity and spectral efficiency. This requires high computational capability in airborne vehicles that can perform the complex filtering and masking, along with tasks associated with cognitive radio systems like spectrum sensing and baseband adaptation, while consuming very less power. This paper proposes a radio architecture based on new generation FPGAs that offers advanced capabilities like partial reconfiguration. The proposed architecture allows non-concurrent baseband modules to be dynamically loaded only when they are required, resulting in improved energy efficiency, without sacrificing performance. We evaluate the case of non-concurrent spectrum sensing logic and transmission filters on our cognitive radio platform based on Xilinx Zynq, and show that our approach results in 28.3% reduction in DSP utilisation leading to lower energy consumption at run-time
Cognitive Radio for Emergency Networks
In the scope of the Adaptive Ad-hoc Freeband (AAF) project, an emergency network built on top of Cognitive Radio is proposed to alleviate the spectrum shortage problem which is the major limitation for emergency networks. Cognitive
Radio has been proposed as a promising technology to solve
todayĂą?~B??~D?s spectrum scarcity problem by allowing a secondary user in the non-used parts of the spectrum that aactully are assigned to primary services. Cognitive Radio has to work in different frequency bands and various wireless channels and supports multimedia services. A heterogenous reconfigurable System-on-Chip (SoC) architecture is proposed to enable the evolution from the traditional software defined radio to Cognitive Radio
Adaptive Bayesian decision feedback equalizer for dispersive mobile radio channels
The paper investigates adaptive equalization of time dispersive mobile ratio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator (MLSE). In terms of computational complexity, the adaptive Bayesian DFE is slightly more complex than the conventional DFE but is much simpler than the adaptive MLSE. In terms of error rate in symbol detection, the adaptive Bayesian DFE outperforms the conventional DFE dramatically. Moreover, for severely fading multipath channels, the adaptive MLSE exhibits significant degradation from the theoretical optimal performance and becomes inferior to the adaptive Bayesian DFE
Cognitive node selection and assignment algorithms for weighted cooperative sensing in radar systems
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