Multi-resource allocation in cloud radio access networks

The following topics are dealt with: MIMO communication; array signal processing; radiofrequency interference; probability; wireless channels; iterative methods; OFDM modulation; optimisation; channel estimation; precoding.

In recent years, the Cloud Radio Access Network (CRAN) has become a promising solution for increasing network capacity in terms of high data rates and low latencies for fifth-generation (5G) cellular networks. In CRAN, the traditional base stations (BSs) are decoupled into remote radio heads (RRHs) and base band units (BBUs) that are respectively responsible for radio and baseband functionalities. The RRHs are geographically proximated whereas the the BBUs are pooled in a centralized cloud named BBU pool. This virtualized architecture facilitates the system to offer high computation and communication loads from the impetuous rise of mobile devices and applications. Heterogeneous service requests from the devices to different RRHs are now sent to the BBUs to process centrally. Meeting the baseband processing of heterogeneous requests while keeping their Quality-of-Service (QoS) requirements with the limited computational resources as well as enhancing service provider profit is a challenging multi-constraint problem. In this work, a multi-objective non-linear programming solution to the Quality-of-Experience (QoE) and Profit-aware Resource Allocation problem is developed which makes a trade-off in between the two. Two computationally viable scheduling algorithms, named First Fit Satisfaction and First Fit Profit algorithms, are developed to focus on maximization of user QoE and profit, respectively, while keeping the minimum requirement level for the other one. The simulation environment is built on a relevant simulation toolkit. The experimental results demonstrate that the proposed system outperforms state-of-the-art works well across the requests QoS, average waiting time, user QoE, and service provider profit.

Cloud Radio Access Network (C-RAN) or centralized RAN could be seen as a promising solution to deal the 5G requirements. Traditional C-RANs are organized as a three-element network, that contains Base Band Unit (BBU) pool, Radio Radio Remote Unit (RRU) and the network interconnecting BBUs and RRUs called fronthaul. The BBU provides baseband signal processing functions and the RRU provides Radio Frequency signal transmission and reception functions. In this paper we propose procedures for the dimensioning of the transport network resources in the following scenario: i) all of the physical layer functions are implemented in the BBU and the signal samples are transported on Common Public Radio Interface (CPRI) flows; ii) Ethernet traffic is also transported for the use of traditional Radio Base Stations (RBS); iii) bandwidth saving techniques are applied by means of a reconfiguration of CPRI line bit rate so as to reduce the number of circuits of the transport network in the low traffic scenario. We show how the application of the introduced techniques allows for a saving of bandwidth in the order of 80%.

In the Cloud Radio Access Network (C-RAN) architecture, the baseband signals from multiple remote radio heads are processed in a centralized baseband unit (BBU) pool. This architecture allows network operators to adapt the BBU's computational resources to the aggregate access load experienced at the BBU, which can change in every air-interface access frame. The degree of savings that can be achieved by adapting the resources is a tradeoff between savings, adaptation frequency, and increased queuing time. If the time scale for adaptation of the resource multiplexing is greater than the access frame duration, then this may result in additional access latency and limit the energy savings. In this paper we investigate the tradeoff by considering two extreme time-scales for the resource multiplexing: (i) long-term, where the computational resources are adapted over periods much larger than the access frame durations; (ii) short-term, where the adaption is below the access frame duration. We develop a general C-RAN queuing model that describes the access latency and show, for Poisson arrivals, that long-term multiplexing achieves savings comparable to short-term multiplexing, while offering low implementation complexity.

A novel architecture design for Radio Access Networks (RANs) to address critical elements in resource management and to achieve the 5G mobile network requirements is needed. Cloud Radio Access Network (C-RAN) and Software Defined Networking (SDN) / Network Function Virtualization (NFV) technologies are recognized as key enabling solutions for the future mobile networks. C-RAN is a new mobile network architecture, characterized by a redistribution of functionalities. More specifically, the baseband processing resources are centralized in remote locations, known as Base Band Unit (BBU) Pool, whereas the radio functionalities, known as Remote Radio Head (RRH), remain in edge locations. SDN focuses on decoupling control and data plane, while NFV performs the functionalities abstraction from the underlying hardware. In this paper, a hierarchical layered software-defined architecture for future 5G networks is proposed. We deploy a Next Generation Fronthaul Interface (NGFI) based architecture, in order to overcome issues related to massive MIMO deployment. Furthermore, in order to implement dynamically different functional splitting options, a virtualized Remote Aggregation Unit (RAU) is proposed. As further contribution, our solution aims to provide new features that facilitate SDN/NFV integration in wireless/mobile environments.

This paper studies the downlink of a cloud radio access network (C-RAN) that incorporates a baseband central processor (CP), multiple remote radio units (RUs), and a network of wireless fronthaul links that connect the RUs to the CP. The fronthaul network utilizes dedicated point-to-point free-space optical (FSO) links along with a broadcast radio frequency (RF) channel. The spectrum of the RF channel is also utilized for downlink transmission from the RUs to the mobile users. That is, the available RF spectrum is time-shared (in a half-duplex manner) among the fronthaul and downlink. The data symbols intended for different users are linearly precoded at the CP in the form of quantized in-phase and quadrature (IQ) samples. These samples are compressed then delivered via the fronthaul network to the corresponding RUs. The RUs, in turn, perform decompression and IQ modulation, before broadcasting their RF signals to the users. We focus on the joint design of the linear precoders, quantizers, and capacity of the RF fronthaul links, along with the time allocation of the RF spectrum, in order to maximize the weighted sum-rate of the users, subject to power constraints and capacity limitations of the hybrid fronthaul network. The resulting problem is nonconvex and difficult to handle. Therefore, we propose a computationally-tractable algorithm that utilizes line search and alternating convex optimization in order to obtain a high-quality suboptimal solution. We provide numerical examples to demonstrate the performance of the proposed algorithm under different weather conditions. We also show the performance gain of hybrid RF/FSO fronthaul, as compared to FSO-only fronthaul, during unfavorable weather conditions.

With large available continuous bandwidth, millimeter wave (mmWave) bands hold promise as a carrier frequency for fifth generation (5G) wireless communications. Moreover, mmWave bands also play an important role in radar detections. Based on this observation, we propose an mmWave wireless communication and radar detection integrated network architecture for railways, not only to increase the capacity of railway wireless communication systems, but also to realize train operation environment detection to enhance train operation safety. To overcome the aggravated path loss in mmWave bands, directional beamforming is generally used to concentrate signal radiation energy both in wireless communications and radar detections. Nevertheless, for wireless communications, the signaling blind zone of directional beamforming makes it less effective in wireless link establishment and maintenance. Therefore, in this proposed integrated network, two frequency bands are employed, where omnidirectionally radiated licensed lower frequency bands carry critical signaling and data, and mmWave bands are time-division multiplexed to transmit large-volume communication data for trains, or to perform environment detection for enhancing train operation safety. To mitigate handovers and to increase baseband processing resource utilization in railway scenarios, the proposed integrated network is deployed based on the cloud radio access network (C-RAN) architecture, where both licensed lower frequency band radio remote units (RRUs) and mmWave band RRUs are connected to a building baseband unit (BBU) pool through high-speed backhauls. Besides, physical (PHY) frame structures and up/downlink communication signaling procedures are designed for this proposed integrated network. Performance analysis results have demonstrated that the proposed integrated network can highly increase the capacity for railway wireless communication systems and achieve high distance and angular resolution for radar detections.

With the emergence of cloud radio access network (C-RAN) architecture, latency in fronthaul (FH) network is a critical performance metric especially for ultra-reliable and low-latency communication applications. The stringent FH capacity and latency requirements of C-RAN can be relaxed by offloading some baseband functionalities to remote radio unit (RRU), referred to as functional splitting. This allows packetized FH network solutions such as ubiquitous Ethernet. In this paper, we calculate the FH latency in the uplink of a C-RAN system with massive MIMO-based RRUs and 3GPP functional Split 7, wherein MIMO equalization is done at the RRU. We derive tractable, closed-form expressions for the steady-state probabilities of queue length and sojourn time distribution at the output port of an Ethernet switch in the FH network. We first present these results for Poisson file arrivals from users in the network and exponential file size distribution. We then extend the results to general file size distribution. The numerical results show that the file size and spectral efficiency of the users are critical in determining the FH latency. Further, results show that switch speed can be decreased without incurring significant increase in FH latency revealing the possibility for statistical multiplexing gains.

To meet the 5G requirements in terms of high capacity and enhanced quality of service, the Cloud Radio Access Network (C-RAN) relies on the network densification and coordination. The main idea behind the C- RAN is to split the base station into two parts: the first component is the Remote Radio Head (RRH) which will be responsible for all Radio transmissions and the second one is the Base Band Unit (BBU) which will be responsible for the processing and control. Only the RRH will be densified to offer more capacity. The processing resources will be pooled and clustered so that many RRHs will be managed by one BBU pool which will reduce the related CAPEX and OPEX of the network's deployment, operation and maintenance. The main goal of this paper is to propose an optimized clustering algorithm able to improve interference management between BBU clusters and decrease the network mobility signaling cost. In this work, we will be interested by the implementation and optimization of local search algorithm (Tabu Search) and evolution algorithm (Genetic Algorithm) as well as the combination of both methods in order to enhance the convergent solution.

Cloud radio access networks (C-RAN) is a potential solution to meet the traffic demand of the future network, which migrates the baseband units (BBU) to the BBU pool in the cloud. The connection between BBU and remote radio head (RRH) can be dynamically changed and the resources can be allocated more flexible according to some factor, e.g., traffic demand of RRHs. Therefore, the resource allocation scheme in C-RAN is redefined, which regards the BBUs as a sharing resource. Existing studies execute the mapping scheme with the consideration of the single BBU but not the whole network, e.g., the BBU with high utilization switches the supported RRH to other BBU to reduce the resource usage. To achieve this goal, this paper makes use of the concept of cloud resources and centralized management to fully utilize the resources and allocate the resources more centralized. The main contribution of this paper is to propose a dynamic BBU–RRH mapping scheme using a borrow-and-lend approach to dynamically allocate the resources to RRHs in a clever and efficient way. With the centralized management, the max busy group borrows the resource for RRH switching, whereas the min helper group lends the resource to handle the switching RRHs. Finally, simulation results illustrate the proposed mechanism in C-RAN significantly reduces the waste of the resource usage and improves the throughput.

Cloud radio access network architecture has become increasingly important in meeting the growing demand of high data rate services as well as managing interference among base station sites. However, this architecture is associated with high fronthaul link latencies that result into increased overall network latencies. This paper proposes a placement method for baseband units so as to lower the fronthaul link latencies. This is achieved by formulating the problem as a nonlinear optimization problem that is solved by fuzzy c-means clustering and a heuristic genetic based algorithm. The paper further investigates the user response time comparing it with the scenario before optimizing location of baseband units in edge clouds. Simulation results show that the proposed scheme greatly reduces overall fronthaul link latencies and cost of ownership in cloud radio access networks. We also show that there is an optimal number of edge clouds with baseband units for different cloud radio access network sizes. On the side of the users, the response time is greatly reduced when the baseband units are optimally placed.

Cloud radio-access network (C-RAN) has the most promising network architecture to support the new fifth generation (5G) mobile network. The C-RAN witnesses a revolutionary base station functionality split, where it is composed of simplified remote radio heads (RRH) and several baseband units (BBUs) pools in network backbone. The C-RAN assists in employing several functionalities more efficiently. It has privileges over the existing radio-access networks (RANs) in case of global resource management, interference management, and radio-access technology (RAT) selection to guarantee higher system throughput and lower energy consumption. In this paper, we demonstrate a C-RAN system-level simulator that performs centralized user scheduling, edge-user joint transmission (JT), and global per-antenna carrier aggregation (CA). In addition, we demonstrate the capability of our system-level simulator to investigate the bottleneck effect of the fronthaul-link capacity as the system expands or C-RAN coordinated facilities increase. Our analysis shows throughput enhancement compared to the traditional RAN; however, with high fronthaul links utilization rates.

In this work, we study a full-duplex (FD) cloud radio access network (C- RAN) where the central unit utilizes FD remote radio units (RU)s belonging to the same operator, i.e., the trusted RUs, as well as the RUs belonging to other operators or private owners, i.e., untrusted RUs. In order to provide information secrecy against the untrusted RUs and external eavesdroppers in the physical layer, the known method of artificial noise (AN)-based jamming is utilized where the trusted RUs may act as AN transmitters. Moreover, we employ the fronthaul quantization noise shaping in the DL such that it is simultaneously used to comply with the limited capacity of the fronthaul links, as well as to degrade decoding capability of the individual eavesdropper and the untrusted RUs for both the UL and DL communications. A method for joint optimization of the UL and DL transmission covariance, fronthaul compression and jamming strategies are proposed to maximize network weighted sum secrecy rate (WSSR). Numerical evaluations show that,for a wide range of practical system conditions, the optimized utilization of the downlink quantization shaping is sufficient to provide an almost-optimal secrecy rate and thereby renders additional AN-based jamming transmission as unnecessary.

Cloud radio access network and the subsequent next-generation fronthaul interface are the key solutions for the upcoming 5G deployment. In both architectures, the baseband units (BBUs) are centralized, and remote radio units (RRUs) are separately allocated, where the BBUs and RRUs are connected through a fronthaul network with common public radio interface (CPRI) and its subsequent eCPRI. Today, a cost-effective fronthaul transport network is paramount for 5G deployment. In this paper, we proposed a comprehensive optical mobile fronthaul network (COMF) to meet the various fronthaul requirements. The COMF design has the features of low-cost high-speed transmission, bandwidth reduction, low link latency, high signal fidelity and flexible connection. To support both the CPRI and eCPRI interfaces, several low-cost high-bitrate optical transmission techniques are comparatively studied. Meanwhile, time-division multiplexing-based analog radio over fiber solution is also discussed. Furthermore, to achieve flexible fronthaul delivering, optical switching technologies based on arrayed waveguide grating router and wavelength selective switch are presented, respectively. In the end, the novel strategies for low-delay resource management are investigated.

Cloud Radio Access Network (C-RAN) architecture has been anticipated as an effective approach to overcoming next generation (5G) wireless challenges by combining RAN and cloud networking. C-RAN demands a new paradigm of energy consumption and optimized its resources. In this paper, a C-RAN with dynamic Base Band Unit (BBU) allocation is presented, and based on this allocation we propose a power model aiming to decrease the total network power consumption. We also propose a cost model to reduce the total cost of the C-RAN. This paper also develops analytical models that enable to compare traditional RAN (T-RAN) design with the emerging Cloud RAN paradigm. Numerical results come about based on simulation settings recommend that the proposed C-RAN has better performance than the traditional network in terms of both power consumption and system cost.