Impact Of Cluster Size Research Articles

Performance Analysis of CoMP in Ultra-Dense Networks with Limited Backhaul Capacity

Coordinated multipoint (CoMP) transmission provides an effective way to mitigate inter-cell interference as well as improve network performance in ultra-dense networks (UDNs). However, applying coordination among access points (APs) requires high backhaul capacity (BC), which becomes a bottleneck for employing CoMP. Considering a downlink UDN with limited BC, this paper conducts performance analysis of two CoMP schemes including joint transmission (JT) and coordinated scheduling/beamforming (CS/CB) from three aspects: a typical user's perspective, a typical AP's perspective and a network perspective. Firstly, the coverage probability (CP) and ergodic capacity (EC) of a typical user are characterized. Secondly, per-AP backhaul consumption is explicitly quantified and the successful serving probability (SSP) of a typical AP is proposed to capture its robustness to limited BC. Additionally, we propound effective ergodic capacity (EEC) to consider performance gain and backhaul consumption simultaneously from a global perspective. Further, the approximated expressions of all the performance metrics are given so that the impact of cluster size and AP density can be overtly observed. Numerical results illustrate that JT outperforms CS/CB in the case of high BC threshold, small cluster size or high AP density and a comparative smaller size is preferable for both two schemes for practice.

Small sample GEE estimation of regression parameters for longitudinal data.

Longitudinal (clustered) response data arise in many bio-statistical applications which, in general, cannot be assumed to be independent. Generalized estimating equation (GEE) is a widely used method to estimate marginal regression parameters for correlated responses. The advantage of the GEE is that the estimates of the regression parameters are asymptotically unbiased even if the correlation structure is misspecified, although their small sample properties are not known. In this paper, two bias adjusted GEE estimators of the regression parameters in longitudinal data are obtained when the number of subjects is small. One is based on a bias correction, and the other is based on a bias reduction. Simulations show that the performances of both the bias-corrected methods are similar in terms of bias, efficiency, coverage probability, average coverage length, impact of misspecification of correlation structure, and impact of cluster size on bias correction. Both these methods show superior properties over the GEE estimates for small samples. Further, analysis of data involving a small number of subjects also shows improvement in bias, MSE, standard error, and length of the confidence interval of the estimates by the two bias adjusted methods over the GEE estimates. For small to moderate sample sizes (N ≤50), either of the bias-corrected methods GEEBc and GEEBr can be used. However, the method GEEBc should be preferred over GEEBr, as the former is computationally easier. For large sample sizes, the GEE method can be used.