340 research outputs found
Error Rate Analysis of Cognitive Radio Transmissions with Imperfect Channel Sensing
This paper studies the symbol error rate performance of cognitive radio
transmissions in the presence of imperfect sensing decisions. Two different
transmission schemes, namely sensing-based spectrum sharing (SSS) and
opportunistic spectrum access (OSA), are considered. In both schemes, secondary
users first perform channel sensing, albeit with possible errors. In SSS,
depending on the sensing decisions, they adapt the transmission power level and
coexist with primary users in the channel. On the other hand, in OSA, secondary
users are allowed to transmit only when the primary user activity is not
detected. Initially, for both transmission schemes, general formulations for
the optimal decision rule and error probabilities are provided for arbitrary
modulation schemes under the assumptions that the receiver is equipped with the
sensing decision and perfect knowledge of the channel fading, and the primary
user's received faded signals at the secondary receiver has a Gaussian mixture
distribution. Subsequently, the general approach is specialized to rectangular
quadrature amplitude modulation (QAM). More specifically, optimal decision rule
is characterized for rectangular QAM, and closed-form expressions for the
average symbol error probability attained with the optimal detector are derived
under both transmit power and interference constraints. The effects of
imperfect channel sensing decisions, interference from the primary user and its
Gaussian mixture model, and the transmit power and interference constraints on
the error rate performance of cognitive transmissions are analyzed
Maximum-Likelihood Sequence Detection of Multiple Antenna Systems over Dispersive Channels via Sphere Decoding
Multiple antenna systems are capable of providing high data rate transmissions over wireless channels. When the channels are dispersive, the signal at each receive antenna is a combination of both the current and past symbols sent from all transmit antennas corrupted by noise. The optimal receiver is a maximum-likelihood sequence detector and is often considered to be practically infeasible due to high computational complexity (exponential in number of antennas and channel memory). Therefore, in practice, one often settles for a less complex suboptimal receiver structure, typically with an equalizer meant to suppress both the intersymbol and interuser interference, followed by the decoder. We propose a sphere decoding for the sequence detection in multiple antenna communication systems over dispersive channels. The sphere decoding provides the maximum-likelihood estimate with computational complexity comparable to the standard space-time decision-feedback equalizing (DFE) algorithms. The performance and complexity of the sphere decoding are compared with the DFE algorithm by means of simulations
Bit error rate performance of MIMO MMSE receivers in correlated rayleigh flat-fading channels
This paper analyzes the average bit error rate (BER) of multiple-input-multiple-output (MIMO) systems in transmit-correlated Rayleigh flat-fading channels. The receiver scheme is based on the minimum mean square error (MMSE) criterion, and the input may be precoded to optimize the communication. Accurate closed-form formulations for the average BER are derivedThis work was
supported in part by Project TEC2008-06327-C03-02, Project CSD2008-
00010, and Project CCG08-UC3M/TIC-4069Publicad
New Results On the Sum of Two Generalized Gaussian Random Variables
We propose in this paper a new method to compute the characteristic function
(CF) of generalized Gaussian (GG) random variable in terms of the Fox H
function. The CF of the sum of two independent GG random variables is then
deduced. Based on this results, the probability density function (PDF) and the
cumulative distribution function (CDF) of the sum distribution are obtained.
These functions are expressed in terms of the bivariate Fox H function. Next,
the statistics of the distribution of the sum, such as the moments, the
cumulant, and the kurtosis, are analyzed and computed. Due to the complexity of
bivariate Fox H function, a solution to reduce such complexity is to
approximate the sum of two independent GG random variables by one GG random
variable with suitable shape factor. The approximation method depends on the
utility of the system so three methods of estimate the shape factor are studied
and presented
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