A Stochastic Channel Model With Dual Mobility for 5G Massive Networks

Abstract

In this paper, a new stochastic channel model (SCM) is proposed for fifth-generation (5G) systems. By means of the sum-of-sinusoids (SoS) method to generate spatially consistent random variables (SCRVs), the proposed model extends the 3rd Generation Partnership Project (3GPP)-SCM by considering three important features for accurate simulations in 5G, i.e., support for dual mobility, spatial correlation at both ends of the link and considerable reductions of the required memory consumption when compared with existing models. A typical problem presented in existing channel models, namely the generation of uncorrelated large scale parameters (LSPs) and small scale parameters (SSPs) for close base stations (BSs), is solved, then allowing for more realistic numerical evaluations in most of the 5G scenarios characterized by a large density of BSs and user equipments (UEs) per unit of area, such as ultra-dense networks (UDNs), indoor environments, device-to-device (D2D) and vehicular-to-vehicular (V2V). The proposed model emerges as the first SCM, and therein lower complexity when compared with ray-tracing (RT)based models, that comprises all the following features: support for single and dual mobility with spatial consistency, smooth time evolution, dynamic modeling, large antenna array, frequency range up 100 GHz and bandwidth up to 2 GHz. Some of the features are calibrated for single mobility in selected scenarios and have shown a good agreement with the calibration results found in the literature.

Joint Resource Allocation and Transceiver Design for Sum-Rate Maximization under Latency Constraints in Multicell MU-MIMO Systems

“…In other words, we assume that the channel matrix is perfectly known at transmitters and receivers without channel estimation errors. Moreover, we consider that the channel matrices are generated using a realistic channel modeling based on the 3GPP's stochastic channel model with spatial, frequency and time correlations [39], [40].…”

“…The BS and UEs heights are 25 m and 1.5 m, respectively, and the UEs speed is equal to 3 km/h. The 5G stochastic radio channel for dual mobility (5G-StoRM) [39], [40], assuming the urban micro (UMi) scenario, is used for all links, considering a carrier frequency of 2 GHz and that each sub-channel has a bandwidth of 180 kHz. More details about the channel generation can be found in [39], [40].…”

“…The 5G stochastic radio channel for dual mobility (5G-StoRM) [39], [40], assuming the urban micro (UMi) scenario, is used for all links, considering a carrier frequency of 2 GHz and that each sub-channel has a bandwidth of 180 kHz. More details about the channel generation can be found in [39], [40]. For all analyzed scenarios, the power budget is 𝑃 𝑏 = 35 dBm, ∀𝑏 ∈ B and the step size, 𝜌 (𝑘) , is fixed to be equal to 0.01.…”

A Comprehensive Study on Simulation Techniques for 5G Networks: State of the Art Results, Analysis, and Future Challenges

“…The existing GSCM can be further classified into two categories: the Winner type ones and the COST ones. For the first case, various examples can be found in literature, such as the 3GPP spatial channel model (SCM), extended SCM (SCME) [29], Winner (WIM1), Winner II (WIM2) [30], Winner+ (WIM+), and QuaDRiGa [31]. Their main characteristic consists of the definition of the scatterers based on the angles of departure and angles of arrival, i.e., from a terminal perspective point of view.…”

Evaluating the Mobility Impact on the Performance of Heterogeneous Wireless Networks Over η – μ Fading Channels

“…The received signal strength indications (RSSI) play a vital role in designing the channel assignment, handover, and power control and have been considered instrumental in enhancing the capacity and performance of wireless networks [3]. In existing studies, the statistical models assume the static characteristic of wireless networks [4]. However, with the advent of communication technologies such as IoT, 5G, and 6G, the network characteristic is becoming increasingly dynamic and complex [5]- [7].…”