Energy efficient multi-connectivity algorithms for ultra-dense 5G networks

Poirot, Valentin; Ericson, M.; Nordberg, Mats; Andersson, Karl

doi:10.1007/s11276-019-02056-w

Cited by 35 publications

(23 citation statements)

References 31 publications

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“…Another device-level combination for two LR-WPAN technologies (ISA 100.11a and WirelessHart) is developed by Jecan et al [45] for industrial environments. Dual connectivity LTE/5G and WLAN for mobile devices intended for personal use is also frequent in the literature [46][47][48][49]. This paper presents a combined network at the device level, with an architecture based on clusters, whose main differences with respect to other work in the bibliography are summarized in Table 2.…”

Section: Related Workmentioning

confidence: 99%

Model of a Device-Level Combined Wireless Network Based on NB-IoT and IEEE 802.15.4 Standards for Low-Power Applications in a Diverse IoT Framework

García-Martín

Torralba

2021

Sensors

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With the development of the Internet of Things (IoT), Low Data Rate-Personal Area Networks (LR-WPAN) have been deployed for different applications. Now comes the need to integrate these networks in search of greater connectivity, performances, and geographic coverage. This integration is facilitated by the recent deployment of low power wide area networks (LPWAN) in the licensed bands, especially narrowband IoT (NB-IoT) and long-term evolution for machine-type communications (LTE-M), which are standardized technologies that will continue evolving as part of the fifth generation (5G) specifications. This paper proposes a design methodology for combined networks using LR-WPAN and LPWAN technologies. These networks are combined at the device level using a cluster-tree topology. An example is shown here, where an existing IEEE 802.15.4 network is combined with NB-IoT. To this end, new dual nodes are incorporated, acting as cluster heads. The paper discusses the different aspects of formation and operation of the combined network. A dynamic link selection (DLS) algorithm is also proposed, based on which cluster headers dynamically determine the preferred link, depending on link quality and type of traffic. Extensive simulations show that the DLS algorithm significantly increases battery life on dual nodes, which are the nodes with the highest power demands.

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Section: Related Workmentioning

confidence: 99%

Model of a Device-Level Combined Wireless Network Based on NB-IoT and IEEE 802.15.4 Standards for Low-Power Applications in a Diverse IoT Framework

García-Martín

Torralba

2021

Sensors

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“…For q = 2, (SCN), we adopted P SCNAN as the power consumption of the access network for SCN and P SCNBH as the power consumption of the backhaul network for SCN. The total power consumptions of SCN is defined in (6) while P SCNAN and P SCNBH are defined in (7) and (8)…”

Section: G Udn Modelmentioning

confidence: 99%

“…5G UDN deployments are envisaged to be heterogeneous and dense, primarily through the provisioning of small cells such as picocells and femtocells. In UDN, users can be within the vicinity of multiple cells, which implies high interferences if not managed [8]. In addition to interference issue, UDN will most likely face the problem of redundant small cells, SCs energy consumption, and limited backhaul capacity.…”

Section: Introductionmentioning

confidence: 99%

GPON and V-band mmWave in green backhaul solution for 5G ultra-dense network

Ajani¹,

Oduol

Adeyemo

2021

IJECE

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Ultra-dense network (UDN) is characterized by massive deployment of small cells which resulted into complex backhauling of the cells. This implies that for 5G UDN to be energy efficient, appropriate backhauling solutions must be provided. In this paper, we have evaluated the performance of giga passive optical network (GPON) and V-band millimetre wave (mmWave) in serving as green backhaul solution for 5G UDN. The approach was to first reproduce existing backhaul solutions in Very Dense Network (VDN) scenario which served as benchmark for the performance evaluation for the UDN scenario. The best two solutions, GPON and V-band solutions from the VDN were then deployed in 5G UDN scenario. The research was done by simulation in MATLAB. The performance metrics used were power consumption and energy efficiency against the normalized hourly traffic profile. The result revealed that GPON and V-band mmWave outperformed other solutions in VDN scenario. However, this performance significantly dropped in the UDN scenariodue to higher data traffic requirement of UDN compared to VDN. Thus, it can be concluded that GPON and V-band mmWave are not best suited to serve as green backhaul solution for 5G UDN necessitating further investigation of other available backhaul technologies.

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“…In [10], the authors study different degrees of multiconnectivity in 5G in the context of outage probability and associated spectral efficiency. In [11], solutions and enablers for 5G spectrum sharing in satellite and terrestrial multi-connectivity are discussed. Advanced architectures, protocols, and schemes for multi-connectivity, which improve the throughput and affect the latency, are proposed in [12]- [15].…”

Section: Related Workmentioning

confidence: 99%

Programmability of Connectivity Control

Atanasov¹,

Pencheva²

2021

ELEKTRON ELEKTROTECH

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Network programmability and edge computing as key features of next generation communications enable innovative services. While the programmability is focused on the core network of the fifth-generation system, the edge computing moves the network intelligence to the radio access network. This paper presents a study on the programmability of connectivity control as a function of radio access network using Multi-access Edge Computing. The capability of using more than one radio access technology simultaneously enhances reliability and increases the throughput, especially in dense networks. Opening the radio access network interfaces for programmability of multi-connectivity enables analytics applications to control the device connections to multiple radio links simultaneously based on information of radio conditions, user location or specific policies. The research novelty is in opening the radio access network interfaces for edge applications to access connectivity control.

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Energy efficient multi-connectivity algorithms for ultra-dense 5G networks

Cited by 35 publications

References 31 publications

Model of a Device-Level Combined Wireless Network Based on NB-IoT and IEEE 802.15.4 Standards for Low-Power Applications in a Diverse IoT Framework

Model of a Device-Level Combined Wireless Network Based on NB-IoT and IEEE 802.15.4 Standards for Low-Power Applications in a Diverse IoT Framework

GPON and V-band mmWave in green backhaul solution for 5G ultra-dense network

Programmability of Connectivity Control

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