Multilayer Network Optimization for 5G &amp; 6G

Ramírez-Arroyo, Alejandro; Cano, Pablo H. Zapata; Palomares‐Caballero, Ángel; Carmona-Murillo, Javier; Luna-Valero, Francisco; Valenzuela-Valdés, Juan F.

doi:10.1109/access.2020.3036744

Cited by 17 publications

(25 citation statements)

References 43 publications

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“…Global trends in the telecommunication market show that wireless communication networks are becoming one of the key elements in the implementation of the Internet of Things (IoT) paradigm [2]. Due to the rapid growth of mobile data traffic, the popularity of IoT, and M2M, mobile operators are constantly focused on improving the quality of service (QoS) provision, developing 4G networks into the future software-defined heterogeneous 5G/6G networks based on Long-Term Evolution (LTE) technology [3][4][5][6]. LTE is now seen as the best technology to support MTC due to its Internet compatibility, high capacity, flexibility in radio resource management, and scalability.…”

Section: Introductionmentioning

confidence: 99%

QoS‐Aware Optimal Radio Resource Allocation Method for Machine‐Type Communications in 5G LTE and beyond Cellular Networks

Beshley

Medvetskyi

et al. 2021

Wireless Communications and Mobile Computing

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In this paper, we consider the saturation problem in the 3GPP LTE cellular system caused by the expected huge number of machine-type communication (MTC) devices, leading to a significant impact on both machine-to-machine (M2M) and human-to-machine H2H traffic. M2M communications are expected to dominate traffic in LTE and beyond cellular networks. In order to address this problem, we proposed an advanced architecture designed for 5G LTE networks to enable the coexistence of H2H/M2M traffic, supported by different priority strategies to meet QoS for each traffic. The queuing strategy is implemented with an M2M gateway that manages four queues allocated to different types of MTC traffic. The optimal radio resource allocation method in LTE and beyond cellular networks was developed. This method is based on adaptive selection of channel bandwidth depending on the QoS requirements and priority traffic aggregation in the M2M gateway. Additionally, a new simulation model is proposed which can help in studying and analyzing the mutual impact between M2M and H2H traffic coexistence in 5G networks while considering high and low priority traffics for both M2M and H2H devices. This simulator automates the proposed method of optimal radio resource allocation between the M2M and H2H traffic to ensure the required QoS. Our simulation results proved that the proposed method improved the efficiency of radio resource utilization to 13% by optimizing the LTE frame formation process.

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Section: Introductionmentioning

confidence: 99%

QoS‐Aware Optimal Radio Resource Allocation Method for Machine‐Type Communications in 5G LTE and beyond Cellular Networks

Beshley

Medvetskyi

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Therefore, it is not surprising that the monthly mobile traffic attained 38 Exabytes in 2019. Moreover, this traffic is expected to reach 160 Exabytes per month by 2025 [2,3]. This scenario results in new challenges to develop efficient and robust wireless communications networks, which should provide enhanced mobile broadband (eMBB) and ultra-reliable and low latency communications (URLLC).…”

Section: Palavras-chavementioning

confidence: 99%

“…where the coefficient τ defines the periodic extension of the constellation and depends on the modulation alphabet and the power allocation scheme 3 . The modulo processing is equivalent to adding a perturbation vector d p to the transmit data s, i.e.,š = s+d p .…”

Section: Tomlinson-harashima Precodingmentioning

confidence: 99%

“…. The matrixV (0),k contains the last N t −L k zero singular values, thus forming an orthogonal basis for the null space ofH k and satisfying (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17). It follows that the first precoder is given by…”

mentioning

confidence: 99%