An Efficient 3-D Ray Tracing Method: Prediction of Indoor Radio Propagation at 28 GHz in 5G Network

Hossain, Ferdous; Geok, Tan Kim; Rahman, Tharek Abd; Hindia, Mohammad Nour; Dimyati, Kaharudin; Ahmed, Sharif; Tso, C.P.; Rahman, Noor Ziela Abd

doi:10.3390/electronics8030286

Cited by 54 publications

(34 citation statements)

References 41 publications

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“…Most of the cases in the 3D SB RT method took a higher computational time compared to the 3D smart method. For this comparison purpose, both methods used the same high configured server with graphics card [15,16]. The 3D SB RT and 3D smart methods belong to the site-specific model family.…”

Section: Results Validation and Discussionmentioning

confidence: 99%

A Smart 3D RT Method: Indoor Radio Wave Propagation Modelling at 28 GHz

et al. 2019

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This paper describes a smart ray-tracing method based on the ray concept. From the literature review, we observed that there is still a research gap on conventional ray-tracing methods that is worthy of further investigation. The herein proposed smart 3D ray-tracing method offers an efficient and fast way to predict indoor radio propagation for supporting future generation networks. The simulation data was verified by measurements. This method is advantageous for developing new ray-tracing algorithms and simulators to improve propagation prediction accuracy and computational speed.

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Section: Results Validation and Discussionmentioning

confidence: 99%

A Smart 3D RT Method: Indoor Radio Wave Propagation Modelling at 28 GHz

et al. 2019

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“…In this framework, two main channel representations can be found in literature: Stochastic models, and deterministic ones. In the first case, these models employ ray tracing techniques in order to compute delay and other associated parameters, using geographical information [26,27]. In the second case, these parameters are calculated based on parametric models using well-known probability density functions, by means of statistical analyses of measurement data collected during extensive measurement campaigns [28].…”

Section: Channel Modeling For Mimo Wireless Orientationsmentioning

confidence: 99%

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

2020

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Ιn this review article, a comprehensive study is provided regarding the latest achievements in simulation techniques and platforms for fifth generation (5G) wireless cellular networks. In this context, the calculation of a set of diverse performance metrics, such as achievable throughput in uplink and downlink, the mean Bit Error Rate, the number of active users, outage probability, the handover rate, delay, latency, etc., can be a computationally demanding task due to the various parameters that should be incorporated in system and link level simulations. For example, potential solutions for 5G interfaces include, among others, millimeter Wave (mmWave) transmission, massive multiple input multiple output (MIMO) architectures and non-orthogonal multiple access (NOMA). Therefore, a more accurate and realistic representation of channel coefficients and overall interference is required compared to other cellular interfaces. In addition, the increased number of highly directional beams will unavoidably lead to increased signaling burden and handovers. Moreover, until a full transition to 5G networks takes place, coexistence with currently deployed fourth generation (4G) networks will be a challenging issue for radio network planning. Finally, the potential exploitation of 5G infrastructures in future electrical smart grids in order to support high bandwidth and zero latency applications (e.g., semi or full autonomous driving) dictates the need for the development of simulation environments able to incorporate the various and diverse aspects of 5G wireless cellular networks.

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“…Different routing and medium access control decisions are evaluated in different multihop and relay interactions among the users. An efficient three-dimensional ray tracing (ETRT) method for the prediction of indoor radio propagation is proposed in [14] and compared with conventional shooting bouncing ray tracing (SBRT) method. Simulation results are tallied with actual measurement using path loss and received signal strength indication.…”

Section: State Of the Artmentioning

confidence: 99%

A novel mapping technique for ray tracer to system-level simulation

Khan

Busari

Huq

et al. 2020

Computer Communications

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Simulations have become remarkably useful in evaluating the performance of new techniques and algorithms in communication networks. This is due to its comparative cost, time and complexity advantage over the analytical and field trial approaches. For large-scale networks, system-level simulators (SLS) are used to assess the performance of the systems. The SLS typically employs statistical channel models to characterize the propagation environment. However, the communication channels can be more accurately modeled using the deterministic ray tracing tools, though at the cost of higher complexity. In this work, we present a novel framework for a hybrid system that integrates both the ray tracer and the SLS. In the hybrid system, the channel strength in terms of the signal-to-noise ratio (SNR) is fed from the ray tracer to the SLS which then uses the values for further tasks such as resource allocation and the consequent performance evaluation. Using metrics such as user throughput and spectral efficiency, our results show that the hybrid system predicts the system performance more accurately than the baseline SLS without ray tracing. The hybrid system will thus facilitate the accurate assessment of the performance of next-generation wireless systems.

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An Efficient 3-D Ray Tracing Method: Prediction of Indoor Radio Propagation at 28 GHz in 5G Network

Cited by 54 publications

References 41 publications

A Smart 3D RT Method: Indoor Radio Wave Propagation Modelling at 28 GHz

A Smart 3D RT Method: Indoor Radio Wave Propagation Modelling at 28 GHz

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

A novel mapping technique for ray tracer to system-level simulation

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