Spectral Efficiency Analysis in Presence of Correlated Gamma-Lognormal Desired and Interfering Signals

Spectral efficiency analysis in presence of correlated interfering signals is very important in modern generation wireless networks where there is aggressive frequency reuse with a dense deployment of access points. However, most works available in literature either address the effect of correlated interfering signals or include interferer activity, but not both. Further, available literature has also addressed the effect of large-scale fading (shadowing and distance-dependent path loss) only, however, has fallen short of including the composite effect of the line of sight and non-line of sight small-scale fading. The correlation of desired signals with interfering signals due to shadowing has also not been considered in existing literature. In this work, we present a comprehensive analytical signal to interference power ratio evaluation framework addressing all the above mentioned important components to the model in a holistic manner. In this analysis, we extend and apply the Moment Generating Function-matching method to such systems so that correlation and activity of lognormal random variables can be included with high accuracy. We compare the analytical results against realistic channel model based extensive Monte-Carlo simulation for mmWave and sub-6 GHz in both indoor and outdoor scenarios. the performance of the model is depicted in terms of mean, alpha-percentile outage spectral efficiency and Kullback-Leibler divergence and Kolmogorov-Smirnov distance.Comment: Published in IEEE Transactions on Vehicular Technolog

Performance Analysis of Joint Transmission Schemes in Ultra-Dense Networks - An Unified Approach

Ultra-dense network (UDN) is one of the enabling technologies to achieve 1000-fold capacity increase in 5G communication systems, and the application of joint transmission (JT) is an effective method to deal with severe inter-cell interferences in UDNs. However, most works done for performance analysis on JT schemes in the literature were based largely on simulation results due to the difficulties in quantitatively identifying the numbers of desired and interfering transmitters. In this work, we are motivated to propose an analytical approach to investigate the performance of JT schemes with a unified approach based on stochastic geometry, which is in particular useful for studying different JT methods and conventional transmission schemes without JT. Using the proposed approach, we can unveil the statistic characteristics (i.e., expectation, moment generation function, variance) of desired signal and interference powers of a given user equipment (UE), and thus system performances, such as average signal-to-interference-plus-noise ratio (SINR), and area spectral efficiency, can be evaluated analytically. The simulation results are used to verify the effectiveness of the proposed unified approach

Multi-Objective Framework for Dynamic Optimization of OFDMA Cellular Systems

Green cellular networking has become an important research area in recent years due to environmental and economical concerns. Switching off under-utilized BSs during off-peak traffic load conditions is a promising approach to reduce energy consumption in cellular networks. In practice, during initial cell planning, the BS locations and RAN parameters are optimized to meet the basic system design requirements like coverage, capacity, overlap, QoS etc. As these metrics are tightly coupled with each other due to co-channel interference, switching off certain BSs may affect the system requirements. Therefore, identifying a subset of large number of BSs which are to be put into sleep mode, is a challenging dynamic optimization problem. In this work, we develop a multiobjective framework for dynamic optimization framework for OFDMA based cellular systems. The objective is to identify the appropriate set of active sectors and RAN parameters that maximize coverage and area spectral efficiency while minimizing overlap and area power consumption without violating the QoS requirements for a given traffic demand density. The objective functions and constraints are obtained using appropriate analytical models which capture the traffic characteristics, propagation characteristics (pathloss, shadowing, and small scale fading) as well as load condition in neighbouring cells. A low complexity evolutionary algorithm is used for identifying the global Pareto optimal solutions at a faster convergence rate. The inter-relationships between the system objectives are studied and guidelines are provided to find an appropriate network configuration that provides the best achievable trade-offs. The results show that using the proposed framework, significant amount of energy saving can be achieved and with a low computational complexity while maintaining good trade-offs among the other objectives.Comment: To Appear (IEEE Access), 25 pages, 24 figures, 4 table