Deep Learning for Primary Sector Prediction in FR2 New Radio Systems

Aldalbahi, Adel; Jasim, Mohammed; Shahabi, Farzad; Mazin, Asim; Siasi, Nazli; Oliveira, Diogo

doi:10.1109/access.2021.3128432

Cited by 3 publications

(4 citation statements)

References 37 publications

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“…Here, the set of cached NFs is predetermined by content popularity and most requested NFs as per the study in [32] as depicted in Figure 2 (retrieved from the study in [32]). It shows the most popular NFs in the network, where f 1 and f 2 account for call-in and call-out, f 3 and f 4 account for sms-in and sms-out, and f 5 represents internet-access traffic, respectively.…”

Section: Cache Modelmentioning

confidence: 99%

Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications

et al. 2022

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Fog-radio access networks (F-RANs) alleviate fronthaul delays for cellular networks as compared to their cloud counterparts. This allows them to be suitable solutions for networks that demand low propagation delays. Namely, they are suitable for millimeter wave (mmWave) operations that suffer from short propagation distances and possess a poor scattering environment (low channel ranks). The F-RAN here is comprised of fog nodes that are collocated with radio remote heads (RRHs) to provide local processing capabilities for mobile station (MS) terminals. These terminals demand various network functions (NFs) that correspond to different service requests. Now, provisioning these NFs on the fog nodes also yields service delays due to the requirement for service migration from the cloud, i.e., offloading to the fog nodes. One solution to reduce this service delay is to provide cached copies of popular NFs in advance. Hence, it is critical to study function popularity and allow for content caching at the F-RAN. This is further a necessity given the limited resources at the fog nodes, thus requiring efficient resource management to enhance network capacity at reduced power and cost penalty. This paper proposes novel solutions that allocate popular NFs on the fog nodes to accelerate services for the terminals, namely, the clustered and distributed caching methods. The two methods are analyzed and compared against the baseline uncached provisioning schemes in terms of service delay, energy consumption, and cost.

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Section: Cache Modelmentioning

confidence: 99%

Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications

et al. 2022

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“…where D src, n i ð Þ is the path delay between the src node and any fog node n i and W(n i ) and W(e) are weight vectors assigned to every node and link respectively to indicate the relative utilization and occupancy levels. This also allows 23 for instantaneous load monitoring in the network, which allows for load balancing and avoids aggregate load saturation to one node.…”

Section: Cache Model At the Fog Nodementioning

confidence: 99%

“…The network can predict the content popularity of the incoming NFs using machine learning algorithms. Hence, this work considers the prediction outcomes of the long short‐term memory algorithm in Aldalbahi et al 23,24 in the caching model. These popular NFs are shown in Figure 2, which is taken from the study in Aldalbahi et al, 23 where the NF

f_{1}

represents the call‐in,

f_{2}

represents the call‐out,

f_{3}

accounts for the sms‐in,

f_{4}

accounts for the sms‐out, and

f_{5}

represents the internet traffic.…”

Section: System Modelmentioning

confidence: 99%

“…Hence, this work considers the prediction outcomes of the long short‐term memory algorithm in Aldalbahi et al 23,24 in the caching model. These popular NFs are shown in Figure 2, which is taken from the study in Aldalbahi et al, 23 where the NF

f_{1}

represents the call‐in,

f_{2}

represents the call‐out,

f_{3}

accounts for the sms‐in,

f_{4}

accounts for the sms‐out, and

f_{5}

represents the internet traffic. Following the content prediction, the set of popular NFs is locally cached on all the fog nodes.…”

Section: System Modelmentioning

confidence: 99%

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Caching of popular content at F‐RAN for mmWave new radio

Aldalbahi

Jasim

Bouzguenda

et al. 2022

Int J Communication

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Summary Fog computing allows for local data processing at the edge of the network. This allows for reduced link delays between mobile users and access points. Here, fog nodes are collocated with radio remote heads (RRHs) to provide local processing capabilities, that is, creating fog radio access network (F‐RAN) that can support ultralow latency for cellular networks that operate on millimeter wave (mmWave) bands. Here, mobile stations (MS) demand various network functions (NFs) that correspond to different service requests. Hence, it is critical to study function popularity and allow content caching at the F‐RAN. Given the limited resources at the fog nodes, it is important to efficiently manage the resources to improve network operations and enhance the capacity at reduced delays and cost. Therefore, caching in mmWave F‐RAN requires node allocation that can accommodate the highest number of cached functions. Hence, this paper proposes a novel node placement scheme that leverages Nelder–Mead meta‐heuristic. Results show that the proposed scheme yields in reduced delay, cost, and power and energy consumption.

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