Cloud Radio Access Network: Virtualizing Wireless Access for Dense Heterogeneous Systems

Simeone, Osvaldo,Maeder, Andreas,Peng, Mugen,Sahin, Onur,Yu, Wei The Korea Institute of Information and Commucation 2016 Journal of communications and networks Vol.18 No.2

Cloud radio access network (C-RAN) refers to the virtualization of base station functionalities by means of cloud computing. This results in a novel cellular architecture in which low-cost wireless access points, known as radio units or remote radio heads, are centrally managed by a reconfigurable centralized "cloud", or central, unit. C-RAN allows operators to reduce the capital and operating expenses needed to deploy and maintain dense heterogeneous networks. This critical advantage, along with spectral efficiency, statistical multiplexing and load balancing gains, make C-RAN well positioned to be one of the key technologies in the development of 5G systems. In this paper, a succinct overview is presented regarding the state of the art on the research on C-RAN with emphasis on fronthaul compression, baseband processing, medium access control, resource allocation, system-level considerations and standardization efforts.

Cloud Radio Access Network: Virtualizing Wireless Access for Dense Heterogeneous Systems

Osvaldo Simeone,Andreas Maeder,Mugen Peng,Onur Sahin,Wei Yu 한국통신학회 2016 Journal of communications and networks Vol.18 No.2

Cloud radio access network (C-RAN) refers to the virtualizationof base station functionalities by means of cloud computing. This results in a novel cellular architecture in which low-costwireless access points, known as radio units or remote radio heads,are centrally managed by a reconfigurable centralized “cloud”, orcentral, unit. C-RAN allows operators to reduce the capital andoperating expenses needed to deploy and maintain dense heterogeneousnetworks. This critical advantage, along with spectral effi-ciency, statistical multiplexing and load balancing gains, make CRANwell positioned to be one of the key technologies in the developmentof 5G systems. In this paper, a succinct overview is presentedregarding the state of the art on the research on C-RAN withemphasis on fronthaul compression, baseband processing, mediumaccess control, resource allocation, system-level considerations andstandardization efforts.

Fronthaul Compression and Precoding Design for C-RANs Over Ergodic Fading Channels

Kang, Jinkyu,Simeone, Osvaldo,Kang, Joonhyuk,Shamai, Shlomo IEEE 2016 IEEE Transactions on Vehicular Technology VT Vol.65 No.7

<P>This paper investigates the joint design of fronthaul compression and precoding for the downlink of cloud radio access networks (C-RANs). In a C-RAN, a central unit (CU) controls a cluster of radio units (RUs) through low-latency fronthaul links. Most previous works on the design of fronthaul compression and precoding assume constant channels and instantaneous channel state information (CSI) at the CU. This paper, in contrast, concentrates on a more practical scenario with block-ergodic channels and considers either instantaneous or stochastic CSI at the CU. Moreover, the analysis encompasses two types of CU-RU functional splits at the physical layer, which we refer to as compression-after-precoding (CAP) and compression-before-precoding (CBP). With the CAP approach, which is the standard C-RAN solution, all baseband processing is done at the CU. With the CBP scheme, channel encoding and precoding are instead performed at the RUs: The CU does not perform precoding but rather forwards separately the information messages of a subset of mobile stations (MSs) along with the compressed precoding matrices to each RU. Optimization algorithms over fronthaul compression and precoding for both CAP and CBP are proposed, which are based on a stochastic successive upper bound minimization (SSUM) approach. Numerical results yield insights into the optimal RU-CU functional split for C-RANs. As a general conclusion, the relative advantages of the two functional splits depend on the interplay between the enhanced interference management abilities of CAP, particularly for dense networks, and the lower fronthaul requirements of CBP in terms of precoding information overhead, particularly for large coherence periods and with stochastic, rather than instantaneous, CSI.</P>

Multivariate Fronthaul Quantization for Downlink C-RAN

Wonju Lee,Simeone, Osvaldo,Joonhyuk Kang,Shamai, Shlomo Institute of Electrical and Electronics Engineers 2016 IEEE transactions on signal processing Vol.64 No.19

<P>The cloud-radio access network (C-RAN) cellular architecture relies on the transfer of complex baseband signals to and from a central unit (CU) over digital fronthaul links to enable the virtualization of the baseband processing functionalities of distributed radio units (RUs). The standard design of digital fronthauling is based on either scalar quantization or on more sophisticated point-to-point compression techniques operating on baseband signals. Motivated by network-information theoretic results, techniques for fronthaul quantization and compression that improve over point-to-point solutions by allowing for joint processing across multiple fronthaul links at the CU have been recently proposed for both the uplink and the downlink. For the downlink, a form of joint compression, known in network information theory as multivariate compression, was shown to be advantageous under a nonconstructive asymptotic information-theoretic framework. In this paper, instead, the design of a practical symbol-by-symbol fronthaul quantization algorithm that implements the idea of multivariate compression is investigated for the C-RAN downlink. As compared to current standards, the proposed multivariate quantization (MQ) only requires changes in the CU processing while no modification is needed at the RUs. The algorithm is extended to enable the joint optimization of downlink precoding and quantization, reduced-complexity MQ via successive block quantization, and variable-length compression. Numerical results, which include performance evaluations over standard cellular models, demonstrate the advantages of MQ and the merits of a joint optimization with precoding.</P>

Mobile cloud computing with a UAV-mounted cloudlet: optimal bit allocation for communication and computation

Jeong, Seongah,Simeone, Osvaldo,Kang, Joonhyuk IET 2017 IET COMMUNICATIONS Vol.11 No.7

<P>Mobile cloud computing relieves the tension between computation-intensive mobile applications and battery-constrained mobile devices by enabling the offloading of computing tasks from mobiles to a remote processors. This study considers a mobile cloud computing scenario in which the 'cloudlet' processor that provides offloading opportunities to mobile devices is mounted on unmanned aerial vehicles (UAVs) to enhance coverage. Focusing on a slotted communication system with frequency division multiplexing between mobile and UAV, the joint optimisation of the number of input bits transmitted in the uplink by the mobile to the UAV, the number of input bits processed by the cloudlet at the UAV and the number of output bits returned by the cloudlet to the mobile in the downlink in each slot is carried out by means of dual decomposition under maximum latency constraints with the aim of minimising the mobile energy consumption. Numerical results reveal the critical importance of an optimised bit allocation in order to enable significant energy savings as compared with local mobile execution for stringent latency constraints.</P>

Optimal Fronthaul Quantization for Cloud Radio Positioning

Seongah Jeong,Simeone, Osvaldo,Haimovich, Alexander,Joonhyuk Kang IEEE 2016 IEEE Transactions on Vehicular Technology VT Vol.65 No.4

<P>Wireless positioning systems that are implemented by means of a cloud radio access network (C-RAN) may provide cost-effective solutions, particularly for indoor localization. In a C-RAN, baseband processing, including localization, is carried out at a centralized control unit (CU) based on quantized baseband signals received from the radio units (RUs) over finite-capacity fronthaul links. In this paper, the problem of maximizing the localization accuracy over fronthaul quantization/compression is formulated by adopting the Cramer-Rao bound (CRB) on the localization accuracy as the performance metric of interest and information-theoretic bounds on the compression rate. The analysis explicitly accounts for the uncertainty of parameters at the CU via a robust, or worst-case, optimization formulation. The proposed algorithm leverages the Charnes-Cooper transformation and difference-of-convex (DC) programming and is validated via numerical results.</P>

Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning

Jeong, Seongah,Simeone, Osvaldo,Kang, Joonhyuk IEEE 2018 IEEE Transactions on Vehicular Technology VT Vol.67 No.3

<P>Unmanned aerial vehicles (UAVs) have been recently considered as means to provide enhanced coverage or relaying services to mobile users (MUs) in wireless systems with limited or no infrastructure. In this paper, a UAV-based mobile cloud computing system is studied in which a moving UAV is endowed with computing capabilities to offer computation offloading opportunities to MUs with limited local processing capabilities. The system aims at minimizing the total mobile energy consumption while satisfying quality of service requirements of the offloaded mobile application. Offloading is enabled by uplink and downlink communications between the mobile devices and the UAV, which take place by means of frequency division duplex via orthogonal or nonorthogonal multiple access schemes. The problem of jointly optimizing the bit allocation for uplink and downlink communications as well as for computing at the UAV, along with the cloudlet's trajectory under latency and UAV's energy budget constraints is formulated and addressed by leveraging successive convex approximation strategies. Numerical results demonstrate the significant energy savings that can be accrued by means of the proposed joint optimization of bit allocation and cloudlet's trajectory as compared to local mobile execution as well as to partial optimization approaches that design only the bit allocation or the cloudlet's trajectory.</P>

Positioning via direct localisation in C-RAN systems

Jeong, Seongah,Simeone, Osvaldo,Haimovich, Alexander,Kang, Joonhyuk IET 2016 IET COMMUNICATIONS Vol.10 No.16

<P>Cloud radio access network (C-RAN) is a prominent architecture for fifth generation wireless cellular system that is based on the centralisation of baseband processing for multiple distributed radio units (RUs) at a control unit (CU). In this study, it is proposed to leverage the C-RAN architecture to enable the implementation of direct localisation of the position of mobile devices from the received signals at distributed RUs. With ideal connections between the CU and the RUs, direct localisation is known to outperform traditional indirect localisation, whereby the location of a source is estimated from intermediary parameters estimated at the RUs. However, in a C-RAN system with capacity limited fronthaul links, the advantage of direct localisation may be offset by the distortion caused by the quantisation of the received signal at the RUs. In this study, the performance of direct localisation is studied by accounting for the effect of fronthaul quantisation with or without dithering. An approximate maximum likelihood localisation is developed. Then, the Cramer-Rao bound on the squared position error of direct localisation with quantised observations is derived. Finally, the performance of indirect localisation and direct localisation with or without dithering is compared via numerical results.</P>

HARQ Buffer Management: An Information-Theoretic View

Wonju Lee,Simeone, Osvaldo,Joonhyuk Kang,Rangan, Sundeep,Popovski, Petar IEEE 2015 IEEE TRANSACTIONS ON COMMUNICATIONS Vol.63 No.11

<P>A key practical constraint on the design of hybrid automatic repeat request (HARQ) schemes is the size of the on-chip buffer that is available at the receiver to store previously received packets. In fact, in modern wireless standards such as LTE and LTE-A, the HARQ buffer size is one of the main drivers of the modem area and power consumption. This has recently highlighted the importance of HARQ buffer management, that is, of the use of buffer-aware transmission schemes and of advanced compression policies for the storage of received data. This work investigates HARQ buffer management by leveraging information-theoretic achievability arguments based on random coding. Specifically, standard HARQ schemes, namely Type-I, Chase Combining, and Incremental Redundancy, are first studied under the assumption of a finite-capacity HARQ buffer by considering both coded modulation, via Gaussian signaling, and Bit Interleaved Coded Modulation (BICM). The analysis sheds light on the impact of different compression strategies, namely the conventional compression log-likelihood ratios and the direct digitization of baseband signals, on the throughput. The optimization of coding blocklength is also investigated, highlighting the benefits of HARQ buffer-aware transmission scheme.</P>

Optimization of Massive Full-Dimensional MIMO for Positioning and Communication

Jeong, Seongah,Simeone, Osvaldo,Kang, Joonhyuk IEEE 2018 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS Vol.17 No.9

<P>Massive full-dimensional multiple-input multiple-output (FD-MIMO) base stations (BSs) have the potential to bring multiplexing and coverage gains by means of three-dimensional (3D) beamforming. The key technical challenges for their deployment include the presence of limited-resolution front ends and the acquisition of channel state information (CSI) at the BSs. This paper investigates the use of FD-MIMO BSs to provide simultaneously high-rate data communication and mobile 3D positioning in the downlink. The analysis concentrates on the problem of beamforming design by accounting for imperfect CSI acquisition via time division duplex-based training and for the finite resolution of analog-to-digital converter and digital-to-analog converter at BSs. Both <I>unstructured beamforming</I> and a low-complexity <I>Kronecker beamforming</I> solution are considered, where for the latter the beamforming vectors are decomposed into separate azimuth and elevation components. The proposed algorithmic solutions are based on the Bussgang theorem, rank-relaxation, and successive convex approximation (SCA) methods. Comprehensive numerical results demonstrate that the proposed schemes can effectively cater to both data communication and positioning services, providing only minor performance degradations compared to the conventional cases in which either only the data communication or only positioning is implemented. Moreover, the proposed low-complexity Kronecker beamforming is seen to guarantee a limited performance loss in the presence of a large number of BS antennas.</P>