Power-Aware Optimized RRH to BBU Allocation in C-RAN

Aqeeli, Emad; Moubayed, Abdallah; Shami, Abdallah

doi:10.1109/twc.2017.2777825

Cited by 38 publications

(15 citation statements)

References 21 publications

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“…Where θ is an experimental parameter, δ m denotes the signalto-interference-plus-noise ratio (SINR) of UE m and K 0 includes computing resources for FFT function that according to [12] imposes a constant base processing load on the system. Based on the experimental results of [13] we assume that, in each cell n, the computing resource requirements for coding, modulation and FFT are 50%,10% and 40%, respectively. Moreover, we assume that VNFs have first in first out (FIFO) queues where µ * is mean service rate for cloud processing and r m for wireless transmission rate which satisfies according to r m = B m log(1 + δ m ), where B m is wireless transmission bandwidth for UE m. In this respect, we further suppose that cloud processing and wireless transmission queues follow an exponential distribution with mean 1 µ * and 1 rm respectively [14].…”

Section: Computation Cost(k (T)mentioning

confidence: 99%

Continuous Multi-objective Zero-touch Network Slicing via Twin Delayed DDPG and OpenAI Gym

Rezazadeh

Chergui

Alonso

et al. 2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

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Artificial intelligence (AI)-driven zero-touch network slicing (NS) is a new paradigm enabling the automation of resource management and orchestration (MANO) in multi-tenant beyond 5G (B5G) networks. In this paper, we tackle the problem of cloud-RAN (C-RAN) joint slice admission control and resource allocation by first formulating it as a Markov decision process (MDP). We then invoke an advanced continuous deep reinforcement learning (DRL) method called twin delayed deep deterministic policy gradient (TD3) to solve it. In this intent, we introduce a multi-objective approach to make the central unit (CU) learn how to re-configure computing resources autonomously while minimizing latency, energy consumption and virtual network function (VNF) instantiation cost for each slice. Moreover, we build a complete 5G C-RAN network slicing environment using OpenAI Gym toolkit where, thanks to its standardized interface, it can be easily tested with different DRL schemes. Finally, we present extensive experimental results to showcase the gain of TD3 as well as the adopted multi-objective strategy in terms of achieved slice admission success rate, latency, energy saving and CPU utilization.

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Section: Computation Cost(k (T)mentioning

confidence: 99%

Continuous Multi-objective Zero-touch Network Slicing via Twin Delayed DDPG and OpenAI Gym

Rezazadeh

Chergui

Alonso

et al. 2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

View full text Add to dashboard Cite

show abstract

“…where J 1 and J 2 can be defined in (5), also and are weighting factors which can be selected arbitrarily to give almost equal weights for both objectives. In fact, the proper selection of and will enable the final solution to be very close to the best compromise solution between J 1 and J 2 .…”

Section: Implementation Of the Optimization Algorithmsmentioning

confidence: 99%

“…Recent advancements in cellular networks such as LTE and LTE‐advance offer a fast and high bandwidth connectivity delivered to the users. However, the massive growth in handheld device technology and an equally rapid increase in mobile applications have made the task daunting while introducing new obstacles such as the increasing signaling overhead and a tangible power consumption resulted in processing the baseband computational requirements . Signaling overhead is one of the vital issues that impact the cellular behavior causing a significant load in the core network, hence effecting the cellular network reliability.…”

Section: Introductionmentioning

confidence: 99%

“…However, the massive growth in handheld device technology and an equally rapid increase in mobile applications have made the task daunting while introducing new obstacles such as the increasing signaling overhead 3,4 and a tangible power consumption resulted in processing the baseband computational requirements. 5 Signaling overhead is one of the vital issues that impact the cellular behavior causing a significant load in the core network, hence effecting the cellular network reliability. One study predicts that the growth in the signaling overhead in the coming years will be faster than the growth of data traffic by 50%.…”

Section: Introductionmentioning

confidence: 99%

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Optimal location management in LTE networks using evolutionary techniques

Aqeeli

Hashim

Haque

et al. 2019

Int J Communication

Self Cite

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Summary Wireless network is considered a vital enabler in the world of information technology, specifically, LTE and LTE advanced networks, which are the latest technologies owing to their fast speed, robustness, and large bandwidth. However, in spite of the aforementioned advancements, signaling overhead poses critical challenges in terms of network availability, especially those caused by location management messages which are related to users’ mobility behavior. This paper seeks to address the problem of signaling overhead caused by the location management messages specifically, tracking area update (TAU) and paging by deploying three evolutionary algorithms, namely particle swarm optimization (PSO), artificial bee colony (ABC), and gravitational search algorithm (GSA). The deployed algorithms guarantee yielding the minimum values of the signaling overhead for TAU, paging, and the battery power consumption of the user. It is shown that ABC‐based algorithm has faster convergence and better signaling overhead when compared with other implemented algorithms. Moreover, the measured relative standard deviation (RSD) value of all algorithms shows low uncertainty of around 1% for the objective function and 3% for the paging, TAU, and power. Hence, the three applied optimization algorithms have proven to be efficient and reliable for solving the problem in a large‐scale environment.

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“…Quality of service (QoS) can be guaranteed, to the mobile users (MUs), via the C-RAN architecture by deploying a number of RRHs, each of capacity C radio resource units (RUs), and forming a common pool of BBUs. Such a BBU pooling reduces not only the necessary processors for baseband processing but also the operators' capital/operational expenditures as well as the power consumption compared to the traditional RAN architectures [3], [4]. To further benefit from network function virtualization [5], we consider virtualized BBU computational resources (V-BBU) which are connected to the RRHs with a lowlatency, high-capacity fronthaul, via the common public radio interface (CPRI) [6].…”

Section: Introductionmentioning

confidence: 99%

Multiservice Loss Models for Cloud Radio Access Networks

et al. 2021

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In this paper, a cloud radio access network (C-RAN) is considered where the remote radio heads (RRHs) are separated from the baseband units which form a common pool of computational resource units. Depending on their capacity, the RRHs may form one or more clusters. Each RRH accommodates multiservice traffic, i.e., calls from different service-classes with different radio and computational resource requirements. Arriving calls follow a Poisson process and simultaneously require radio and computational resource units in order to be accepted in the serving RRH. If their resource requirements cannot be met then calls are blocked and lost. Otherwise, calls remain in the serving RRH for a generally distributed service time. Assuming the single-cluster C-RAN, we model it as a multiservice loss system, prove that a product form solution exists for the steady-state probabilities and determine call blocking probabilities via an efficient convolution algorithm whose accuracy is validated via simulation. Furthermore, we generalize the previous multiservice loss model by considering the more complex multi-cluster case where RRHs of the same capacity are grouped in different clusters.

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Power-Aware Optimized RRH to BBU Allocation in C-RAN

Cited by 38 publications

References 21 publications

Continuous Multi-objective Zero-touch Network Slicing via Twin Delayed DDPG and OpenAI Gym

Continuous Multi-objective Zero-touch Network Slicing via Twin Delayed DDPG and OpenAI Gym

Optimal location management in LTE networks using evolutionary techniques

Multiservice Loss Models for Cloud Radio Access Networks

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