arXiv subjects

Narges Gholipoor

Publications and source records attributed to Narges Gholipoor.

8 recordsLinked to original sources

arXiv2024

Strategic Utilization of Cellular Operator Energy Storages for Smart Grid Frequency Regulation

The innovative use of cellular operator energy storage enhances smart grid resilience and efficiency. Traditionally used to ensure uninterrupted operation of cellular base stations (BSs) during grid outages, these storages can now dynamically participate in the energy flexibility market. This dual utilization enhances the economic viability of BS storage systems and supports sustainable energy management. In this paper, we explore the potential of BS storages for supporting grid ancillary services by allocating a portion of their capacity while ensuring Ultra Reliable Low Latency (URLLC) requirements, such as meeting delay and reliability requirements. This includes feeding BS stored energy back into the grid during high-demand periods or powering BSs to regulate grid frequency. We investigate the impacts of URLLC requirements on grid frequency regulation, formulating a joint resource allocation problem. This problem maximizes total revenues of cellular networks, considering both the total sum rate in the communication network and BS storages participation in frequency regulation, while considering battery aging and cycling constraints. Simulation results show that a network with 1500 BSs can increase power vacancy compensation from 31% to 46% by reducing reliability from 10^(-8) to 10^(-3). For a power vacancy of -30 MW, this varies from 9.3 MW to 13.5 MW, exceeding a wind turbines capacity.

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arXiv2022

Liquid State Machine-Empowered Reflection Tracking in RIS-Aided THz Communications

Passive beamforming in reconfigurable intelligent surfaces (RISs) enables a feasible and efficient way of communication when the RIS reflection coefficients are precisely adjusted. In this paper, we present a framework to track the RIS reflection coefficients with the aid of deep learning from a time-series prediction perspective in a terahertz (THz) communication system. The proposed framework achieves a two-step enhancement over the similar learning-driven counterparts. Specifically, in the first step, we train a liquid state machine (LSM) to track the historical RIS reflection coefficients at prior time steps (known as a time-series sequence) and predict their upcoming time steps. We also fine-tune the trained LSM through Xavier initialization technique to decrease the prediction variance, thus resulting in a higher prediction accuracy. In the second step, we use ensemble learning technique which leverages on the prediction power of multiple LSMs to minimize the prediction variance and improve the precision of the first step. It is numerically demonstrated that, in the first step, employing the Xavier initialization technique to fine-tune the LSM results in at most 26% lower LSM prediction variance and as much as 46% achievable spectral efficiency (SE) improvement over the existing counterparts, when an RIS of size 11x11 is deployed. In the second step, under the same computational complexity of training a single LSM, the ensemble learning with multiple LSMs degrades the prediction variance of a single LSM up to 66% and improves the system achievable SE at most 54%.

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arXiv2021

Learning based E2E Energy Efficient in Joint Radio and NFV Resource Allocation for 5G and Beyond Networks

In this paper, we propose a joint radio and core resource allocation framework for NFV-enabled networks. In the proposed system model, the goal is to maximize energy efficiency (EE), by guaranteeing end-to-end (E2E) quality of service (QoS) for different service types. To this end, we formulate an optimization problem in which power and spectrum resources are allocated in the radio part. In the core part, the chaining, placement, and scheduling of functions are performed to ensure the QoS of all users. This joint optimization problem is modeled as a Markov decision process (MDP), considering time-varying characteristics of the available resources and wireless channels. A soft actor-critic deep reinforcement learning (SAC-DRL) algorithm based on the maximum entropy framework is subsequently utilized to solve the above MDP. Numerical results reveal that the proposed joint approach based on the SAC-DRL algorithm could significantly reduce energy consumption compared to the case in which R-RA and NFV-RA problems are optimized separately.

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arXiv2020

Cost-Effective Radio and NFV Resource Allocation: E2E QoS Provision

To fend off network ossification and support high degrees of flexibility and various services, network virtualization and slicing are introduced for the next-generation wireless cellular networks. These two technologies allow diversifying attributes of the future inter-networking and time-varying workloads based resource management paradigms. In this paper, we propose an end-to-end (E2E) resource allocation framework for future networks considering radio and core network by leveraging network function virtualization (NFV). We aim to minimize the network cost defined based on the utilized energy and spectrum while providing E2E quality of service (QoS) for diverse services with stringent QoS requirements. This goal is realized by formulating a novel optimization problem which performs the power and spectrum allocation in radio, and service function chaining and scheduling in the NFV environment while guaranteeing the distinct QoS constraints of the requested services. The proposed optimization problem is mixed-integer non-linear programming, which is a non-convex and NP-hard problem. To solve it, we adopt an iterative algorithm with novel admission control and a greedy-based heuristic algorithm, which is shown to have a polynomial order of complexity with 13:66% global optimality gap on average for a small scaled network. To validate the proposed framework, simulation results are carried out by considering different values of the network parameters and topologies. Moreover, our proposed framework and solution algorithm are assessed and compared with the existing works. Simulation results demonstrate that the proposed heuristic algorithm and framework outperforms the existing ones by 34% on average in cost reduction.

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arXiv2020

E2E Migration Strategies Towards 5G: Long-term Migration Plan and Evolution Roadmap

After freezing the first phase of the fifth generation of wireless networks (5G) standardization, it finally goes live now and the rollout of the commercial launch (most in fixed 5G broadband services) and migration has been started. However, some challenges are arising in the deployment, integration of each technology, and the interoperability in the network of the communication service providers (CSPs). At the same time, the evolution of 5G is not clear and many questions arise such as whether 5G has long-term evolution or when 5G will change to a next-generation one. This paper provides long-term migration options and paths towards 5G considering many key factors such as the cost, local/national data traffic, marketing, and the standardization trends in the radio access network (RAN), the transport network (TN), the core network (CN), and E2E network. Moreover, we outline some 5G evolution road maps emphasizing on the technologies, standards, and service time lines. The proposed migration paths can be the answer to some CSPs concerns about how to do long-term migration to 5G and beyond.

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arXiv2019

Cloud-based Queuing Model for Tactile Internet in Next Generation of RAN

Ultra-low latency is the most important requirement of the Tactile Internet (TI), which is one of the proposed services for the next-generation wireless network (NGWN), e.g., fifth generation (5G) network. In this paper, a new queuing model for the TI is proposed for the cloud radio access network (CRAN) architecture of the NGWN by applying power domain non-orthogonal multiple access (PD-NOMA) technology. In this model, we consider both the radio remote head (RRH) and baseband processing unit (BBU) queuing delays for each end-to-end (E2E) connection between a pair of tactile users. In our setup, to minimize the transmit power of users subject to guaranteeing an acceptable delay of users, and fronthaul and access constraints, we formulate a resource allocation (RA) problem. Furthermore, we dynamically set the fronthaul and access links to minimize the total transmit power. Given that the proposed RA problem is highly non-convex, in order to solve it, we utilize diverse transformation techniques such as successive convex approximation (SCA) and difference of two convex functions (DC). Numerical results show that by dynamic adjustment of the access and fronthaul delays, transmit power reduces in comparison with the fixed approach per each connection. Also, energy efficiency of orthogonal frequency division multiple access (OFDMA) and PD-NOMA are compared for our setup.

cs.NI↗

arXiv2019

E2E Delay Guarantee for the Tactile Internet via joint NFV and Radio Resource Allocation

The Tactile Internet (TI) is one of the next generation wireless network services with end to end (E2E) delay as low as 1~ms. Since this ultra low E2E delay cannot be met in the current 4G network architecture, it is necessary to investigate this service in the next generation wireless network by considering new technologies such as network function virtualization (NFV). On the other hand, given the importance of E2E delay in the TI service, it is crucial to consider the delay of all parts of the network, including the radio access part and the NFV core part. In this paper, for the first time, we investigate the joint radio resource allocation (R-RA) and NFV resource allocation (NFV-RA) in a heterogeneous network where queuing delays, transmission delays, and delays resulting from virtual network function (VNF) execution are jointly considered. For this setup, we formulate a new resource allocation (RA) problem to minimize the total cost function subject to guaranteeing E2E delay of each user. Since the proposed optimization problem is highly non-convex, we exploit alternative search method (ASM), successive convex approximation (SCA), and heuristic algorithms to solve it. Simulation results reveal that in the proposed scheme can significantly reduce the network costs compared to the case where the two problems are optimized separately.

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arXiv2019

Resource Management and Admission Control for Tactile Internet in Next Generation of RAN

In this paper, a new queuing model for the Tactile Internet (TI) is proposed for the cloud radio access network (C-RAN) architecture of the next generation wireless networks, e.g., 5G, assisted via orthogonal frequency division multiple access (OFDMA) technology. This model includes both the radio remote head (RRH) and baseband processing unit (BBU) queuing delays and reliability for each end to end (E2E) connection between each pair of tactile users. For this setup, with the aim to minimize the transmit power of users subject to guaranteeing tolerable delay of users, and fronthaul and access limitations, we formulate a resource allocation problem. Since the proposed optimization problem is highly non-convex, to solve it in an efficient manner, we utilize diverse transformation techniques such as successive convex approximation (SCA) and difference of two convex functions (DC). In addition, we propose an admission control (AC) algorithm to make problem feasible. In our proposed system model, we dynamically adjust the fronthaul and access links to minimize the transmit power. Simulation results reveal that by dynamic adjustment of the access and fronthaul delays, transmit power can be saved compared to the case of fixed approach per each transmission session. Moreover, the number of rejected users in the network is significantly reduced and more users are accepted.

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