2018
DOI: 10.1109/twc.2017.2777825
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Power-Aware Optimized RRH to BBU Allocation in C-RAN

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Cited by 37 publications
(15 citation statements)
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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%
“…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%
“…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%
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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%