Abolfazl Hajisami

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.

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.

Cocktail Party in the Cloud: Blind Source Separation for Co-Operative Cellular Communication in Cloud RAN

Due to the rapid growing popularity of mobile Internet, broadband cellular wireless systems are expected to offer higher and higher data rates even in high-mobility environments. Cloud Radio Access Network (C-RAN) is a new centralized paradigm for broadband wireless access that addresses efficiently the fluctuation in capacity demand through real-time inter-Base Station (BS) cooperation. An innovative Blind Source Separation (BSS)-based cellular communication solution for CRANs, Cloud-BSS, which leverages the inter-BS cooperation, is proposed. Cloud-BSS groups contiguous cells into clusters - sets of neighboring cells inside which mobile stations do not need to perform handovers - and allows them to use all of the frequency channels. The proposed solution is studied under different network topologies, and a novel strategy, called Channel-Select, to improve the Signal-to-Noise Ratio (SNR) is introduced. Cloud-BSS enhances the cluster spectral efficiency, decreases handovers, eliminates the need for bandwidth-consuming channel estimation techniques, and mitigates interference. Simulation results, which are discussed along with concepts, confirm these expectations.

Cloud-CFFR: Coordinated Fractional Frequency Reuse in Cloud Radio Access Network (C-RAN)

Fractional Frequency Reuse (FFR) and Coordinated Multi Point (CoMP) processing are two of the conventional methods to mitigate the Inter-Cell Interference (ICI) and to improve the average Signal-to-Interference-plus-Noise Ratio (SINR). However, FFR is associated with low system spectral efficiency and CoMP does not take any action to mitigate the inter-cluster interference. In the context of Cloud Radio Access Network (C-RAN) -- a new centralized paradigm for broadband wireless access that addresses efficiently the fluctuation in capacity demand through real-time Virtual Base Station (VBS) cooperation in the Cloud -- in this paper an innovative uplink solution, called Cloud-CFFR, is proposed to address the aforementioned problems. With respect to both FFR and CoMP, Cloud-CFFR decreases the complexity, delay, and ICI while increasing the system spectral efficiency. Since the system performance in cell-edge regions relies on the cooperation of different VBSs, there is no service interruption in handling handovers, moreover, in order to address the unanticipated change in capacity demand, Cloud-CFFR dynamically changes the sub-band boundaries based on the number of active users in the clusters. Simulation results confirm the validity of our analysis and show the benefits of this novel uplink solution.

Watermarking Based on Independent Component Analysis in Spatial Domain

This paper proposes an image watermarking scheme for copyright protection based on Independent Component Analysis (ICA). In the suggested scheme, embedding is carried out in cumulative form in spatial domain and ICA is used for watermark extraction. For extraction there is no need to access the original image or the watermark, and extraction is carried out only with two watermarked images. Experimental results show that the new method has better quality than famous methods [1], [2], [3] in spatial or frequency domain and is robust against various attacks. Noise addition, resizing, low pass filtering, multiple marks, gray-scale reduction, rotation, JPEG compression, and cropping are some attacks which a reconsidered in our extensive simulations to demonstrate the proposed algorithm performance.

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.

Kernel auto-encoder for semi-supervised hashing

Hashing-based approaches have gained popularity for large-scale image retrieval in recent years. It has been shown that semi-supervised hashing, which incorporates similarity/dissimilarity information into hash function learning could improve the hashing quality. In this paper, we present a novel kernel-based semi-supervised binary hashing model for image retrieval by taking into account auxiliary information, i.e., similar and dissimilar data pairs in achieving high quality hashing. The main idea is to map the data points into a highly non-linear feature space and then map the non-linear features into compact binary codes such that similar/dissimilar data points have similar/dissimilar hash codes. Empirical evaluations on three benchmark datasets demonstrate the superiority of the proposed method over several existing unsupervised and semi-supervised hash function learning methods.