GPU-supported simulation for ABEP and QoS analysis of a combined macro diversity system in a gamma-shadowed k-μ fading channel

Abstract

In this paper we have analyzed macro-diversity (MD) system with one macro SC diversity (MD SC) receiver and two micro MRC (mD MRC) receivers over correlated Gamma-shadowed k-? fading channel. The average bit error probability (ABEP) is calculated using the moment generating function (MGF) approach for BDPSK and BPSK modulations. Graphical representation of the results illustrates the effects of different parameters of the system on its performance as well as the improvements due to the benefits of a combined micro and macro diversity. The obtained analytical expressions are used for the GPU-enabled mobile network modeling, planning and simulation environment to determine the value of Quality of Service (QoS) parameter. Finally, linear optimization is proposed as an approach to improve the QoS parameter of the fading-affected system observed in this paper.

Model-Driven Approach to Fading-Aware Wireless Network Planning Leveraging Multiobjective Optimization and Deep Learning

“…e following aspects are considered [104,105]: (1) infrastructure of the service provider (represented as base stations); (2) environmental impact and communication channel properties; (3) service consumers (various types of devices and vehicles); and (4) areas of interest where base stations are about to be deployed within the smart city.…”

“…Previously, in [104,105], we already adopted singleobjective linear optimization using AMPL language and CPLEX solver for optimal allocation of base stations at given smart city locations. However, in the current paper, the improvement regarding resource allocation is in extension, which adds multiobjective optimization approach, which aims to increase the overall benefits, even in the case of conflicting factors, such as the case of network performance maximization, energy consumption, and cost reduction at the same time.…”

“…e following commonly adopted measures are covered by the metamodel: outage probability, average bit error probability, level crossing rate, average fade duration, and channel capacity. ese values are calculated as the output of the GPU-enabled fading calculation engine [104,105] and assigned to each (station, location) pair.…”

“…While CC and LCR are maximized, as their increase denotes the improvement of performance, the values of Pout, ABEP, and AFD have to be minimized, as a greater number means worse performance in their case. For that reason, the two additional objective functions take the following form: In our case, it is assumed that all the optimization goals are equally weighted in order to make the performance and results comparable to our previous works considering single-objective allocation in the context of network planning [104,105]. Fine-tuning of both the objective function and performance indicator impact coefficients automatically is outside the scope of this paper and considered as possible future work.…”

“…In this case, the improvement is calculated with respect to QoS value expressed as the sum of fading-related performance metrics with positive impact containing plus sign, while those that reduce the performance are taken with minus. Finally, the last column shows the speed-up regarding the processing time, achieved compared to our previous work [105].…”