The exponential traffic growth of wireless communication networks gives rise to both the insufficient network capacity and excessive carbon emissions. Massive multiple-input multiple-output (MIMO) can improve the spectrum efficiency (SE) together with the energy efficiency (EE) and has been regarded as a promising technique for the next generation wireless communication networks. Channel model reflects the propagation characteristics of signals in radio environments and is very essential for evaluating the performances of wireless communication systems. The purpose of this paper is to investigate the state of the art in channel models of massive MIMO. First, the antenna array configurations are presented and classified, which directly affect the channel models and system performance. Then, measurement results are given in order to reflect the main properties of massive MIMO channels. Based on these properties, the channel models of massive MIMO are studied with different antenna array configurations, which can be used for both theoretical analysis and practical evaluation.

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The communication delay, which has great impact on vehicle-to-infrastructure (V2I) networks, faces a large challenge on account of complex network topology. In this paper, a novel architecture that integrates LTE networks with IEEE 802.11 based Vehicle Ad Hoc Networks (VANETs) is proposed, due to their high data transmission rates and wide coverage area, respectively. The proposed scheme aims at delay analysis, which is under stochastic network calculus. The service rate of LTE wireless channels is modeled as a Markov modulation process by the mobility process of vehicles, and the behavior of IEEE 802.11 communication is characterized by a service model with the derived single packet access delay. Based on the analytical principle of stochastic network calculus, the stochastic delay upper bounds of V2I communication networks are derived via the Moment Generating Function (MGF) method. Finally, analytical and simulation results are utilized to validate the accuracy of the proposed model.