SINR Analysis and Interference Management of Macrocell Cellular Networks in Dense Urban Environments

Banday, Yusra; Rather, Ghulam Mohammad; Begh, Gh. Rasool

doi:10.1007/s11277-019-06947-1

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…polynomial-time algorithm for it. Several researches investigated capacity of 5G mm-wave networks in different architectures along with outdoor cellular network architecture [9][10][11]. In the paper 27 and 32 efficiency of spectrum in mm-wave cellular systems has been reached to 5G LTE systems by using high directional antennas.…”

Section: The Literature Reviewsmentioning

confidence: 99%

MIMO channel modeling and Cell size optimization for 5G dense urban macro cell environment

Tripathi,

Tiwari,

Prakash

et al. 2023

Preprint

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The frequency spectrum is integral part of the 5G communication which facilitate different user requirements. As a result of the surge in data consumption, 5G capacity in developed areas might theoretically run out by 2023. As a result, 5G Millimeter Wave (mm-Wave) would become a vital resource for the continuous provision of upgraded mobile broadband services. In the present manuscript, the heuristic optimization technique along with SVD signal processing technique are applied to optimize the cell size and channel modeling for handling the large number of signals evaluated in MATLAB. The results demonstrate that the proposed technique improves the performance. Further the proposed work is in depicts decrease in interference and increase of signal strength for urban as well as rural scenarios; it found that proposed model is as par the state of arts.

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Section: The Literature Reviewsmentioning

confidence: 99%

MIMO channel modeling and Cell size optimization for 5G dense urban macro cell environment

Tripathi,

Tiwari,

Prakash

et al. 2023

Preprint

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“…Therefore, the uplink signal-to-interference-plus-noise ratio (SINR) can be expressed as follows [40].…”

Section: Sdn-based Seamless Handover Communication Model In Mec Environmentmentioning

confidence: 99%

Deep reinforcement learning-based resource allocation and seamless handover in multi-access edge computing based on SDN

Zhang

Luo

2021

Knowl Inf Syst

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With the access devices that are densely deployed in multi-access edge computing environments, users frequently switch access devices when moving, which causes the imbalance of network load and the decline of service quality. To solve the problems above, a seamless handover scheme for wireless access points based on perception is proposed. First, a seamless handover model based on load perception is proposed to solve the unbalanced network load, in which a seamless handover algorithm for wireless access points is used to calculate the access point with the highest weight, and a software-defined network controller controls the switching process. A joint allocation method of communication and computing resources based on deep reinforcement learning is proposed to minimize the terminal energy consumption and the system delay. A resource allocation model is based on minimizing terminal energy consumption, and system delay is built. The optimal value of task offloading decision and resource allocation vector are calculated with deep reinforcement learning. Experimental results show that the proposed method can reduce the network load and the task execution cost.

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“…Additionally, there is no packet loss when data is transmitted since the BLER value remains unchanged. The obtained data were compared with simulation experiments and data from real-world equipment reviewed in [45]. Despite using different frequency bands, data obtained at the start time are similar in appearance in both works.…”

Section: Using 3gpp Propagation Loss Modelmentioning

confidence: 99%

Implementation and Evaluation of Nodal Distribution and Movement in a 5G Mobile Network

et al. 2021

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The determining factor in the accelerated pace of informatization is the increase in the speed and reliability of data transmission networks. In this regard, new and existing standards are developed and modernized. A lot of organizations are constantly working on the development and implementation of new generation communication networks. This article provides an overview of available software solutions that allow us to investigate and evaluate the behavior of data networks. In particular, tools suitable for mobile communication systems were determined, having sufficient built-in functionality and allowing us to add our own implementations. NS3 has been chosen as a suitable network simulator. Apart from the review, a solution for this tool was developed. It allows estimating the reliability of data transmission from the start movement of a network node at all times during its removal from a base station.

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SINR Analysis and Interference Management of Macrocell Cellular Networks in Dense Urban Environments

Cited by 7 publications

References 11 publications

MIMO channel modeling and Cell size optimization for 5G dense urban macro cell environment

MIMO channel modeling and Cell size optimization for 5G dense urban macro cell environment

Deep reinforcement learning-based resource allocation and seamless handover in multi-access edge computing based on SDN

Implementation and Evaluation of Nodal Distribution and Movement in a 5G Mobile Network

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