Waveforming Optimizations for Time-Reversal Cloud Radio Access Networks

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.

We consider downlink transmission in cloud radio access networks with multiple cochannel multicasting groups served by a group of remote radio heads (RRHs), which receive information from a base band unit via finite-capacity fronthaul links. Our aim is to jointly design RRH selection and beamforming vectors such that the minimum weighted data rate among users is maximized under the constraints of maximum transmit power and fronthaul capacity at each specific RRH. The problem is intractable due to the numerical difficulties of combination and nonconvex functions. Based on a semidefinite relaxation technique, bisection search, and a branch-and-bound method, we develop an upper bound, which is also the optimal solution to the original problem if the relaxation is tight. More importantly, we propose a heuristic low-complexity iterative procedure for practical applications based on the state-of-the-art sequential convex approximation. Subsequently, we modify the proposed methods for the uncertain channel state information case. To be specific, the upper bound and its suboptimal solution are altered based on the $\mathcal {S}$ -lemma while the low-complexity algorithm is tailored by using two different approximations of intractable robust counterpart. The validity of the proposed methods in the region of limited fronthaul capacity is confirmed by numerical results.

This paper studies a resource allocation problem where a set of users within a specific region is served by cloud radio access network (C-RAN) structure consisting of a set of base-band units (BBUs) connected to a set of radio remote heads (RRHs) equipped with a large number of antennas via limited capacity front-haul links. User association to each RRH, BBU and front-haul link is essential to achieve high rates for cell-edge users under network limitations. We introduce two types of optimization variables to formulate this resource allocation problem: (i) C-RAN user association factor (UAF) including RRH, BBU and front-haul allocation for each user and (ii) power allocation vector. The formulated optimization problem is non-convex with high computational complexity. An efficient two-level iterative approach is proposed. The higher level consists of two steps where, in each step, one of these two optimization variables is fixed to derive the other. At the lower level, by applying different transformations and convexification techniques, the optimization problem in each step is broken down into a sequence of geometric programming (GP) problems to be solved by the successive convex approximation (SCA). Simulation results reveal the effectiveness of the proposed approach to increase the total throughput of network, specifically for cell-edge users. It outperforms the traditional user association approach, in which, each user is first assigned to the RRH with the largest average value of signal strength, and then, based on this fixed user association, front-haul link association and power allocation are optimized.

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 (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.

In this paper we consider a cloud radio access network (C-RAN) where the remote radio heads (RRHs) are separated from the baseband signal processing servers, named baseband units (BBUs). The latter forms a centralized pool of high-performance data center resources. To benefit from network function virtualization, we consider virtualized BBU (V-BBU) resources where the BBU functionality and services have been virtualized in the form of virtual network functions. All RRHs in the C-RAN form a single cluster. Each RRH of such a cluster may accommodate random or quasi-random traffic. That means that new calls in a RRH can be generated by an infinite number of mobile users (random traffic) or by a finite number of mobile users (quasi-random traffic). An arriving call requires a radio resource unit from the serving RRH and a computational resource unit from the V-BBU. If these resource units are available, then the call is accepted and remains in the system for a generally distributed service time. Otherwise, the call is blocked and lost. In order to analyze this C-RAN we model it as a loss system and study two cases: (i) all RRHs accommodate quasi-random traffic and (ii) some RRHs accommodate random traffic and the rest accommodate quasi-random traffic. In both cases, we show that the steady state probabilities have a product form solution and propose convolution algorithms for the accurate determination of the main teletraffic performance measure which is congestion probability. The accuracy of these algorithms is verified via simulation.

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.

Computational resource allocation is a critical issue for the baseband unit (BBU) pool in a cloud radio access network (C-RAN). There are multiple resources in a BBU, including CPU, memory, disk, etc. The virtual machines (VMs) have diverse requirements along these resources to handle the baseband signal processing of corresponding remote radio units (RRUs). Consolidating VMs to BBUs based on single resource incurs over-allocation of the resources that are not explicitly allocated. Therefore, we study the multi-resource allocation problem in CRANs, which aims to minimize the number of active BBUs that are required to serve all users in the network. Since the RRU can be set to an idle state when its traffic is low, the number of VMs and their resource demands are all adjustable. We propose an efficient algorithm to solve this problem. This algorithm selects active RRUs and associates users with these RRUs in an iterative way. It adapts a heuristic for the multi-dimensional bin packing problem to assign VMs to BBUs. Our proposed method can significantly reduce the number of required active BBUs, while satisfying the VMs' demands for multiple computational resources. Simulation results demonstrate the effectiveness of our proposed algorithm.

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.

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

Cloud Radio Access Network (C-RAN) is widely considered a significant part of fifth generation (5G) networks. This paper offers flexible Software Defined Radio (SDR) testbed for C-RAN researchers and developers to verify network function and evaluate system performance. In 5G C-RAN architecture, a Baseband Unit (BBU) of C-RAN is split into a Centralized Unit (CU) and a Distributed Unit (DU). As a consequence, a SDR testbed based on OpenAirInterface is built and extended to support the CU/DU split. In order to support communications between the CU and DU units, a succinct and effective interface protocol is designed. Specifically, the CU/DU control plane split at the RRC/RLC point is primarily addressed, so that User Equipment (UE) can complete attach UE and detach UE procedures through the decoupled CU and DU units. Besides, the CU/DU user plane split at the PDCP/RLC point is accomplished. Therefore, two independent units, CU and DU, can substitute for BBU. Finally, this paper evaluates the testbed using a real network setup, a commercial Evolved Packet Core (EPC), a remote radio head (RRU) and several commercial UEs. The results show that the testbed can successfully attach UE, detach UE and transmit data to commercial UEs over the air.

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 Network (C-RAN) is a key architecture for 5G cellular wireless network that aims at improving spectral and energy efficiency of the network by merging RAN and cloud computing together. In this paper, a novel resource allocation scheme that optimizes the network energy efficiency of a C-RAN is designed. First, an energy consumption model that characterizes the computation energy of the BaseBand Unit (BBU) is introduced based on empirical results collected from a programmable C-RAN testbed. Then, an optimization problem is formulated to maximize the energy efficiency of the network, subject to practical constraints including Quality of Service (QoS) requirement, radio remote head transmit power, and fronthaul capacity limits. The introduced Network Energy Efficiency Maximization (NEEM) problem jointly considers the tradeoff among the network accumulated data rate, BBU power consumption, fronthaul cost, and beamforming design. To deal with the non-convexity and mixed-integer nature of the problem, we utilize successive convex approximation methods to transform the original problem into the equivalent Weighted Sum-Rate (WSR) maximization problem. We then propose a provably-convergent iterative method to solve the resulting WSR problem. Extensive simulation results coupled with real-time experiments on a small-scale C-RAN testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.

Cloud Radio Access Network (C-RAN) is a key architecture for 5G cellular wireless network that aims at improving spectral and energy efficiency of the network by uniting traditional RAN with cloud computing. In this paper, a novel resource allocation scheme that optimizes the network energy efficiency of a C-RAN is designed. First, an energy consumption model that characterizes the computation energy of the BaseBand Unit (BBU) is introduced based on empirical results collected from a programmable C-RAN testbed. Then, an optimization problem is formulated to maximize the energy efficiency of the network, subject to practical constraints including Quality of Service (QoS) requirement, radio remote head transmit power, and fronthaul capacity limits. The formulated Network Energy Efficiency Maximization (NEEM) problem jointly considers the tradeoff among the network accumulated data rate, BBU power consumption, fronthaul cost, and beamforming design. To deal with the non-convexity and mixed-integer nature of the problem, we utilize successive convex approximation methods to transform the original problem into the equivalent Weighted Sum-Rate (WSR) maximization problem. We then propose a provably-convergent iterative method to solve the resulting WSR problem. Extensive simulation results coupled with real-time experiments on a small-scale C-RAN testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.