Tuyen X. Tran

Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges

MEC is an emerging paradigm that provides computing, storage, and networking resources within the edge of the mobile RAN. MEC servers are deployed on a generic computing platform within the RAN, and allow for delay-sensitive and context-aware applications to be executed in close proximity to end users. This paradigm alleviates the backhaul and core network and is crucial for enabling low-latency, high-bandwidth, and agile mobile services. This article envisions a real-time, context-aware collaboration framework that lies at the edge of the RAN, comprising MEC servers and mobile devices, and amalgamates the heterogeneous resources at the edge. Specifically, we introduce and study three representative use cases ranging from mobile edge orchestration, collaborative caching and processing, and multi-layer interference cancellation. We demonstrate the promising benefits of the proposed approaches in facilitating the evolution to 5G networks. Finally, we discuss the key technical challenges and open research issues that need to be addressed in order to efficiently integrate MEC into the 5G ecosystem.

Elastic resource utilization framework for high capacity and energy efficiency in cloud RAN

Current radio access network architectures, characterized by a static configuration and deployment of base stations, have exposed their limitations in handling the temporal and geographical fluctuations of capacity demand. Moreover, small cell networks have exacerbated the problem of electromagnetic interference and decreased the energy efficiency. Although there are some solutions to alleviate these problems, they still suffer from static provisioning of BSs and lack of inter-BS communication. Cloud RAN is a new centralized paradigm based on virtualization technology that has emerged as a promising architecture and efficiently addresses such problems. C-RAN provides high energy efficiency together with gigabit-per-second data rates across software defined wireless networks. In this article, novel reconfigurable solutions based on C-RAN are proposed in order to adapt dynamically and efficiently to the fluctuations in per-user capacity demand. Co-location models for provisioning and allocation of virtual base stations are introduced, and pros and cons of different VBS architectures are studied. Also, the potential advantages of VBS clustering and consolidation to support recently proposed cooperative techniques like cooperative multipoint processing are discussed.

Collaborative multi-bitrate video caching and processing in Mobile-Edge Computing networks

Recently, Mobile-Edge Computing (MEC) has arisen as an emerging paradigm that extends cloud-computing capabilities to the edge of the Radio Access Network (RAN) by deploying MEC servers right at the Base Stations (BSs). In this paper, we envision a collaborative joint caching and processing strategy for on-demand video streaming in MEC networks. Our design aims at enhancing the widely used Adaptive BitRate (ABR) streaming technology, where multiple bitrate versions of a video can be delivered so as to adapt to the heterogeneity of user capabilities and the varying of network condition. The proposed strategy faces two main challenges: (i) not only the videos but their appropriate bitrate versions have to be effectively selected to store in the caches, and (ii) the transcoding relationships among different versions need to be taken into account to effectively utilize the processing capacity at the MEC servers. To this end, we formulate the collaborative joint caching and processing problem as an Integer Linear Program (ILP) that minimizes the backhaul network cost, subject to the cache storage and processing capacity constraints. Due to the NP-completeness of the problem and the impractical overheads of the existing offline approaches, we propose a novel online algorithm that makes cache placement and video scheduling decisions upon the arrival of each new request. Extensive simulations results demonstrate the significant performance improvement of the proposed strategy over traditional approaches in terms of cache hit ratio increase, backhaul traffic and initial access delay reduction.

Cooperative Hierarchical Caching in 5G Cloud Radio Access Networks

Over the last few years, C-RAN is proposed as a transformative architecture for 5G cellular networks that brings the flexibility and agility of cloud computing to wireless communications. At the same time, content caching in wireless networks has become an essential solution to lower the content- access latency and backhaul traffic loading, leading to user QoE improvement and network cost reduction. In this article, a novel cooperative hierarchical caching (CHC) framework in C-RAN is introduced where contents are jointly cached at the BBU and at the RRHs. Unlike in traditional approaches, the cache at the BBU, cloud cache, presents a new layer in the cache hierarchy, bridging the latency/capacity gap between the traditional edge-based and core-based caching schemes. Trace-driven simulations reveal that CHC yields up to 51 percent improvement in cache hit ratio, 11 percent decrease in average content access latency, and 18 percent reduction in backhaul traffic load compared to the edge-only caching scheme with the same total cache capacity. Before closing the article, we discuss the key challenges and promising opportunities for deploying content caching in C-RAN in order to make it an enabler technology in 5G ultra-dense systems.

Dynamic Radio Cooperation for Downlink Cloud-RANs with Computing Resource Sharing

A novel dynamic radio-cooperation strategy is proposed for Cloud Radio Access Networks (C-RANs) consisting of multiple Remote Radio Heads (RRHs) connected to a central Virtual Base Station (VBS) pool. In particular, the key capabilities of C-RANs in computing-resource sharing and real-time communication among the VBSs are leveraged to design a joint dynamic radio clustering and cooperative beam forming scheme that maximizes the downlink weighted sum-rate system utility (WSRSU). Due to the combinatorial nature of the radio clustering process and the non-convexity of the cooperative beam forming design, the underlying optimization problem is NP-hard, and is extremely difficult to solve for a large network. Our approach aims for a suboptimal solution by transforming the original problem into a Mixed-Integer Second-Order Cone Program (MI-SOCP), which can be solved efficiently using a proposed iterative algorithm. Numerical simulation results show that our low-complexity algorithm provides close-to-optimal performance in terms of WSRSU while significantly outperforming conventional radio clustering and beam forming schemes. Additionally, the results also demonstrate the significant improvement in computing-resource utilization of C-RANs over traditional RANs with distributed computing resources.

Deep Learning with Edge Computing for Localization of Epileptogenicity Using Multimodal rs-fMRI and EEG Big Data

Epilepsy is a chronic brain disorder characterized by the occurrence of spontaneous seizures of which about 30 percent of patients remain medically intractable and may undergo surgical intervention; despite the latter, some may still fail to attain a seizure-free outcome. Functional changes may precede structural ones in the epileptic brain and may be detectable using existing noninvasive modalities. Functional connectivity analysis through electroencephalography (EEG) and resting state-functional magnetic resonance imaging (rs-fMRI), complemented by diffusion tensor imaging (DTI), has provided such meaningful input in cases of temporal lobe epilepsy (TLE). Recently, the emergence of edge computing has provided competent solutions enabling context-aware and real-time response services for users. By leveraging the potential of autonomic edge computing in epilepsy, we develop and deploy both noninvasive and invasive methods for the monitoring, evaluation and regulation of the epileptic brain, with responsive neurostimulation (RNS; Neuropace). First, an autonomic edge computing framework is proposed for processing of big data as part of a decision support system for surgical candidacy. Second, an optimized model for estimation of the epileptogenic network using independently acquired EEG and rs-fMRI is presented. Third, an unsupervised feature extraction model is developed based on a convolutional deep learning structure for distinguishing interictal epileptic discharge (IED) periods from nonIED periods using electrographic signals from electrocorticography (ECoG). Experimental and simulation results from actual patient data validate the effectiveness of the proposed methods.

Dynamic Radio Cooperation for User-Centric Cloud-RAN With Computing Resource Sharing

A novel dynamic radio-cooperation strategy is proposed for a Cloud Radio Access Network (Cloud-RAN) consisting of multiple Remote Radio Heads connected to a central Virtual Base Station (VBS) pool. In particular, the key capabilities of Cloud-RAN in computing-resource sharing and real-time communication among the VBSs are leveraged to design a joint dynamic radio clustering and cooperative beamforming scheme that maximizes the downlink Weighted Sum-Rate System Utility (WSRSU). Due to the combinatorial nature of the radio clustering process and to the non-convexity of the cooperative beamforming design, the underlying optimization problem is NP-hard, and is extremely difficult to solve for a large network. The proposed approach aims for a suboptimal solution by transforming the original problem into a Mixed-Integer Second-Order Cone Program (MI-SOCP) and applying Sequential Convex Approximation (SCA) to derive a novel iterative algorithm. Numerical simulation results show that our low-complexity algorithm provides near-optimal performance in terms of WSRSU while significantly outperforming conventional radio clustering and beamforming schemes. Additionally, the results also demonstrate the significant improvement in computing-resource utilization of Cloud-RAN over a traditional RAN with distributed computing resources.

QuaRo: A Queue-Aware Robust Coordinated Transmission Strategy for Downlink C-RANs

A queue-aware robust (QuaRo) coordinated transmission strategy is proposed for Cloud Radio Access Networks (C-RANs) with a central BaseBand processing Unit (BBU) connected to multiple Remote Radio Heads (RRHs). Such QuaRo strategy is adaptive to both user-traffic urgency via Queue State Information (QSI) and wireless channel opportunity via the observed (yet imperfect) Channel State Information (CSI). This involves clustering the RRHs into virtual user-centric clusters and performing Coordinated Beamforming (CB) from each virtual cluster to the target user in the downlink. The underlying control policy is formulated via Lyapunov optimization to minimize the average total transmit power at the RRHs while ensuring the stability of the system. In particular, the designed control policy does not require a-priori knowledge of the probability distribution of data-traffic arrival and channel states, and is robust against the instantaneous channel estimation error in each time slot. Extensive simulation results are presented to illustrate performance gains and robustness of the proposed solutions.

On Achievable Rate and Ergodic Capacity of NAF Multi-Relay Networks with CSI

This paper investigates the achievable rate and ergodic capacity of a non-orthogonal amplify-and-forward (NAF) half-duplex multi-relay network where multiple relays exploit channel state information (CSI) to cooperate with a pair of source and destination. In the first step, for a given input covariance matrix at the source, we derive an optimal power allocation scheme among the relays via optimal instantaneous power amplification coefficients to maximize the achievable rate. Given the nature of broadcasting and receiving collisions in NAF, the considered problem in this step is non-convex. To overcome this drawback, we propose a novel method by evaluating the achievable rate in different sub-domains of the vector channels. It is then demonstrated that the globally optimal solution can be derived in closed-form. In the next step, we establish the ergodic channel capacity by jointly optimizing the input covariance matrix at the source and the power allocation among the relays. We show that this is a bi-level non-convex problem and solve it using Tammer decomposition method. This approach allows us to transform the original optimization problem into an equivalent master problem and a set of sub-problems having closed-form solutions derived in the first step. The channel capacity is then obtained using an iterative water-filling-based algorithm. Finally, we analyze the capacity-achieving input covariance matrix at the source in high and low signal-to-noise ratio (SNR) regimes. At sufficiently high SNRs, it is shown that the transmit power at the source should be equally distributed in all broadcasting and cooperative phases. On the other hand, in low SNR regions, the source should spend all its power in the broadcasting phase associated with a relay having the strongest cascaded source-relay and relay-destination channels.

Elastic-Net: Boosting Energy Efficiency and Resource Utilization in 5G C-RANs

Current Distributed Radio Access Networks (DRANs), which are characterized by a static configuration and deployment of Base Stations (BSs), have exposed their limitations in handling the temporal and geographical fluctuations of capacity demands. At the same time, each BSs spectrum and computing resources are only used by the active users in the cell range, causing idle BSs in some areas/times and overloaded BSs in other areas/times. Recently, Cloud Radio Access Network (CRAN) has been introduced as a new centralized paradigm for wireless cellular networks in which—through virtualization—the BSs are physically decoupled into Virtual Base Stations (VBSs) and Remote Radio Heads (RRHs). In this paper, a novel elastic framework aimed at fully exploiting the potential of C-RAN is proposed, which is able to adapt to the fluctuation in capacity demand while at the same time maximizing the energy efficiency and resource utilization. Simulation and testbed experiment results are presented to illustrate the performance gains of the proposed elastic solution against the current static deployment.