Tight Analytical and Asymptotic Upper Bound for the BER and FER of Linear Codes Over Exponentially Correlated Generalized-Fading Channels

2019 ◽  
Vol 67 (6) ◽  
pp. 3852-3864 ◽  
Author(s):  
Yassine Mouchtak ◽  
Faissal El Bouanani ◽  
Daniel Benevides da Costa
Keyword(s):  
Fading Channels ◽  
Upper Bound ◽  
Linear Codes ◽  
Asymptotic Upper Bound ◽  
Generalized Fading Channels

MGF-Based Effective Capacity for Generalized Fading Channels

2014 ◽  
Vol 519-520 ◽  
pp. 929-933 ◽  
Author(s):  
Zhe Ji ◽  
You Zheng Wang ◽  
Jian Hua Lu
Keyword(s):  
Closed Form ◽  
Fading Channels ◽  
Mathematical Expression ◽  
Moment Generating Function ◽  
Effective Capacity ◽  
Multiple Antenna ◽  
Method Of Moment ◽  
Single Antenna ◽  
Generalized Fading Channels

In this paper, we study the effective capacity (EC) which was proposed to measure the quality of service (QoS) for fading channels. A unified expression for the effective capacity based on the method of moment generating function (MGF) is proposed. The unified expression applies to various fading channels and is derived for both single antenna and multiple antenna diversity system. The mathematical expression is illustrated with Nakagami-m fading channels and closed form expressions are derived in this case. The simulation results verify the consistence of the closed-form expressions with numerical evaluations.

ances and covariances obtained from REML are normally distributed with expectation vector and variance-covariance matrix equal to the fol-low  ing, r  espectiv    ely,   When σˆ > 0.04, let νˆ = δˆ + σˆ + σˆ − 2ωˆ + σˆ − (1 + c (7.6) be an estimate for the (7.3) reference-scaled metric in accordance with FDA Guidance (2001) and using a REML UN model. Then (Patter-son, 2003; Patterson and Jones, 2002b), this estimate is asymptotically normally distributed and unbiased with E[νˆ ] = δ +σ − (1 + c and Var[νˆ ] = 4σ + l + 4l + (1 + c ) (l )+ 2l −2(1+c − 2(1+c +4(1+c −2(1+c . Similarly, for the constant-scaled metric, when σˆ ≤ 0.04, νˆ = δˆ + σˆ + σˆ − 2ωˆ + σˆ − σˆ − 0.04(c ) (7.7) E[νˆ ] = δ +σ − 0.04(c ) Var[νˆ ] = 4σ + l + 4l + 2l − 2l − 4l + 4l − 2l . The required asymptotic upper bound √ of the 90% confidence interval can √ then be calculated as νˆ + 1.645× V̂ ar[νˆ ] or νˆ + 1.645× V̂ ar[νˆ ], where the variances are obtained by ‘plugging in’ the estimated values of the variances and covariances obtained from SAS proc mixed into the formulae for Var[νˆ ] or Var[νˆ ]. The necessary SAS code to do this is given in Appendix B. The output reveals that σˆ = 0.0714 and the upper bound is−0.060 for log(AUC). For log(Cmax), σˆ = 0.1060 and the upper bound is −0.055. As both of these upper bounds are below zero, IBE can be claimed.

2003 ◽  
pp. 367-367
Keyword(s):  
Confidence Interval ◽  
Covariance Matrix ◽  
Upper Bound ◽  
Upper Bounds ◽  
Fda Guidance ◽  
Asymptotic Upper Bound ◽  
Variance Covariance Matrix ◽  
Normally Distributed
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