Recently, Cloud-based Radio Access Network (C-RAN) has been proposed as a potential solution to reduce energy cost in cellular networks. C-RAN centralizes the baseband processing capabilities of Base Stations (BSs) in a cloud computing platform in the form of BaseBand Unit (BBU) pool. In C-RAN, power consumed by the traditional BS system is distributed as wireless transmission power of the Remote Radio Heads (RRHs) and baseband processing power of the BBU pool. Different from previous work where wireless transmission power and baseband processing power are optimized individually and independently, this paper focuses on joint optimization of allocation for these two kinds of power and attempts to minimize the total power consumption subject to Quality of Service (QoS) requirements from users in terms of data rates. First, we exploit the load coupling model to express the coupling relations among power, load and user data rates. Based on the load coupling mode, we formulate the joint power optimization problem in C-RAN over both wireless transmission power and baseband processing power. Second, we prove that operating at full load may not be optimal in minimizing the total power consumption in C-RAN. Finally, we propose an efficient iterative algorithm to solve the target problem. Simulations have been performed to validate our theoretical and algorithmic work. The results show that the proposed algorithm outperforms existing schemes (without joint power optimization) in terms of power consumption.
There also exists some work on optimizing baseband processing power in C-RANs. By virtualization, the baseband processing resources in the BBU pool are dynamically shared among all RRHs. In @cite_8 , a dynamic programming scheme is proposed to minimize the baseband processing power consumption in the case of dynamic cell traffic load. However, the computational complexity of the proposed scheme is exponential. In order to reduce the computational complexity, the authors in @cite_19 propose a BBU virtualization scheme that minimizes the baseband processing power consumption with a linear computational complexity order.
@cite_19 @cite_8
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In cloud radio access networks (C-RANs), the baseband processing units (BBUs) from traditional base stations (BSs) are virtualized and moved into a single centralized location. By virtualization, the computing resources of BBUs can be dynamically shared among all cells, enabling a significant improvement in computing resource utilization and power efficiency. In this letter, we propose a BBUs virtualization scheme that minimizes the power consumption with a linear computational complexity order. The scheme is based on a heuristic simulated annealing (HSA) algorithm, which combines a bin packing algorithm with SA. Simulation results show that the HSA effectively decreases system power consumption when compared to standard approaches. A cloud radio access network (Cloud-RAN) is a network architecture that holds the promise of meeting the explosive growth of mobile data traffic. In this architecture, all the baseband signal processing is shifted to a single baseband unit (BBU) pool, which enables efficient resource allocation and interference management. Meanwhile, conventional powerful base stations can be replaced by low-cost low-power remote radio heads (RRHs), producing a green and low-cost infrastructure. However, as all the RRHs need to be connected to the BBU pool through optical transport links, the transport network power consumption becomes significant. In this paper, we propose a new framework to design a green Cloud-RAN, which is formulated as a joint RRH selection and power minimization beamforming problem. To efficiently solve this problem, we first propose a greedy selection algorithm, which is shown to provide near-optimal performance. To further reduce the complexity, a novel group sparse beamforming method is proposed by inducing the group-sparsity of beamformers using the weighted -norm minimization, where the group sparsity pattern indicates those RRHs that can be switched off. Simulation results will show that the proposed algorithms significantly reduce the network power consumption and demonstrate the importance of considering the transport link power consumption. MIMO and cloud radio access network (C-RAN) are promising techniques for implementing future wireless communication systems, where a large number of antennas are deployed either being co-located at the base station or totally distributed at separate sites called remote radio heads (RRHs), both to achieve enormous spectrum efficiency and energy efficiency gains. Here, we consider a general antenna deployment method for wireless networks, termed multi-antenna C-RAN, where a flexible number of antennas can be equipped at each RRH to more effectively balance the performance and fronthaul complexity tradeoff beyond the conventional massive MIMO and single-antenna C-RAN. To coordinate and control the fronthaul traffic over multi-antenna RRHs, under the uplink communication setup, we propose a new “spatial-compression-and-forward (SCF)” scheme, where each RRH first performs a linear spatial filtering to denoise and maximally compress its received signals from multiple users to a reduced number of dimensions, then conducts uniform scalar quantization over each of the resulting dimensions in parallel, and finally sends the total quantized bits via a finite-rate fronthaul link to the baseband unit (BBU) for joint information decoding. Under this scheme, we maximize the minimum SINR of all users at the BBU by a joint resource allocation over the wireless transmission and fronthaul links. Specifically, each RRH determines its own spatial filtering solution in a distributed manner to reduce the signaling overhead with the BBU, while the BBU jointly optimizes the users’ transmit power, the RRHs’ fronthaul bits allocation, and the BBU’s receive beamforming with fixed spatial filters at individual RRHs. Numerical results show that, given a total number of antennas to be deployed, multi-antenna C-RAN with the proposed SCF and joint optimization significantly outperforms both massive MIMO and single-antenna C-RAN under practical fronthaul capacity constraints. The cloud radio access network (Cloud-RAN) has recently been proposed as one of the cost-effective and energy-efficient techniques for 5G wireless networks. By moving the signal processing functionality to a single baseband unit (BBU) pool, centralized signal processing and resource allocation are enabled in cloud-RAN, thereby providing the promise of improving the energy efficiency via effective network adaptation and interference management. In this paper, we propose a holistic sparse optimization framework to design green cloud-RAN by taking into consideration the power consumption of the fronthaul links, multicast services, as well as user admission control. Specifically, we first identify the sparsity structures in the solutions of both the network power minimization and user admission control problems, which call for adaptive remote radio head (RRH) selection and user admission. However, finding the optimal sparsity structures turns out to be NP-hard, with the coupled challenges of the @math -norm-based objective functions and the nonconvex quadratic QoS constraints due to multicast beamforming. In contrast to the previous works on convex but nonsmooth sparsity inducing approaches, e.g., the group sparse beamforming algorithm based on the mixed @math -norm relaxation, we adopt the nonconvex but smoothed @math -minimization ( @math @math $ -minimization problem from the SDR technique. We illustrate the effectiveness of the proposed algorithms with extensive simulations for network power minimization and user admission control in multicast cloud-RAN. The cloud radio access network (C-RAN) is a promising network architecture for future mobile communications, and one practical hurdle for its large scale implementation is the stringent requirement of high capacity and low latency fronthaul connecting the distributed remote radio heads (RRH) to the centralized baseband pools (BBUs) in the C-RAN. To improve the scalability of C-RAN networks, it is very important to take the fronthaul loading into consideration in the signal detection, and it is very desirable to reduce the fronthaul loading in C-RAN systems. In this paper, we consider uplink C-RAN systems and we propose a distributed fronthaul compression scheme at the distributed RRHs and a joint recovery algorithm at the BBUs by deploying the techniques of distributed compressive sensing (CS). Different from conventional distributed CS, the CS problem in C-RAN system needs to incorporate the underlying effect of multi-access fading for the end-to-end recovery of the transmitted signals from the users. We analyze the performance of the proposed end-to-end signal recovery algorithm and we show that the aggregate measurement matrix in C-RAN systems, which contains both the distributed fronthaul compression and multiaccess fading, can still satisfy the restricted isometry property with high probability. Based on these results, we derive tradeoff results between the uplink capacity and the fronthaul loading in C-RAN systems. Featured by centralized processing and cloud based infrastructure, Cloud Radio Access Network (C-RAN) is a promising solution to achieving an unprecedented system capacity in future wireless cellular networks. The huge capacity gain mainly comes from the centralized and coordinated signal processing at the cloud server. However, full-scale coordination in a large-scale C-RAN requires the processing of very large channel matrices, leading to high computational complexity and channel estimation overhead. To tackle this challenge, we establish a unified theoretical framework for dynamic clustering by exploiting the near-sparsity of large C-RAN channel matrices. Based on this framework, we propose a dynamic nested clustering (DNC) algorithm that greatly improves the system scalability in terms of baseband-processing and channel-estimation complexity. With the proposed DNC algorithm, we show that the computational complexity (i.e., the computation time with serial processing) for the optimal linear detector is significantly reduced from @math to @math , where @math is the number of remote radio heads (RRHs) in the C-RAN. Moreover, the proposed DNC algorithm is also amenable to parallel processing, which further reduces the computation time to @math . In order to support high-data-rate wireless communication links, millimeter wave (mmWave) systems need to overcome considerable propagation attenuation. In mmWave systems, the small wavelength enables pre-processing exploiting large antenna arrays to provide the required gain. Generally, in traditional microwave systems, pre-processing is done at the digital baseband. However, the cost and power consumption of a radio frequency (RF) chain, which carries out translation between RF and digital baseband, is too high. It is impossible to afford one for each antenna element. This hardware limitation places additional constraints on pre-processing design. In this paper, we use hybrid spatial processing architecture with a lower number of RF chains than the antenna elements. We propose a joint design of digital baseband pre-processing and post-processing based on a weighted minimum mean-squared error (MMSE) criterion subject to the transmit power constraint. Then, the optimization in the RF domain is specified into three criteria according to various error weights. For ease of hardware implementation, we develop a lower complexity transceiver in which pre-processing in RF domain after upconversion is implemented merely using analog phase shifters. Finally, we evaluate our proposed scheme by means of simulation. Massive multiple-input multiple-output (MIMO) is envisioned to offer considerable capacity improvement, but at the cost of high complexity of the hardware. In this paper, we propose a low-complexity hybrid precoding scheme to approach the performance of the traditional baseband zero-forcing (ZF) precoding (referred to as full-complexity ZF), which is considered a virtually optimal linear precoding scheme in massive MIMO systems. The proposed hybrid precoding scheme, named phased-ZF (PZF), essentially applies phase-only control at the RF domain and then performs a low-dimensional baseband ZF precoding based on the effective channel seen from baseband. Heavily quantized RF phase control up to 2 bits of precision is also considered and shown to incur very limited degradation. The proposed scheme is simulated in both ideal Rayleigh fading channels and sparsely scattered millimeter wave (mmWave) channels, both achieving highly desirable performance. Cloud-radio access network (C-RAN) is characterized by a hierarchical structure, in which the baseband-processing functionalities of remote radio heads (RRHs) are implemented by means of cloud computing at a central unit (CU). A key limitation of C-RANs is given by the capacity constraints of the fronthaul links connecting RRHs to the CU. In this letter, the impact of this architectural constraint is investigated for the fundamental functions of random access and active user equipment (UE) identification in the presence of a potentially massive number of UEs. In particular, the standard C-RAN approach based on quantize-and-forward and centralized detection is compared to a scheme based on an alternative CU-RRH functional split that enables local detection. Both techniques leverage Bayesian sparse detection. Numerical results illustrate the relative merits of the two schemes as a function of the system parameters. Millimeter wave (mmWave) multiple-input multipleoutput (MIMO) communication with large antenna arrays has been proposed to enable gigabit per second communication for next generation cellular systems and local area networks. A key difference relative to lower frequency solutions is that in mmWave systems, precoding combining can not be performed entirely at digital baseband, due to the high cost and power consumption of some components of the radio frequency (RF) chain. In this paper we develop a low complexity algorithm for finding hybrid precoders that split the precoding combining process between the analog and digital domains. Our approach exploits sparsity in the received signal to formulate the design of the precoder combiners as a compressed sensing optimization problem. We use the properties of the matrix containing the array response vectors to find first an orthonormal analog precoder, since sparse approximation algorithms applied to orthonormal sensing matrices are based on simple computations of correlations. Then, we propose to perform a local search to refine the analog precoder and compute the baseband precoder. We present numerical results demonstrate substantial improvements in complexity while maintaining good spectral efficiency. Precoding combining and large antenna arrays are essential in millimeter wave (mmWave) systems. In traditional MIMO systems, precoding combining is usually done digitally at baseband with one radio frequency (RF) chain and one analog-to-digital converter (ADC) per antenna. The high cost and power consumption of RF chains and ADCs at mmWave frequencies make an all-digital processing approach prohibitive. When only a limited number of RF chains is available, hybrid architectures that split the precoding combining processing into the analog and digital domains are attractive. A previously proposed hybrid solution employs phase shifters and mixers in the RF precoding combining stage. It obtains near optimal spectral efficiencies with a reduced number of RF channels. In this paper we propose a different hybrid architecture, which simplifies the hardware at the receiver by replacing the phase shifters with switches. We present a new approach for compressed sensing based channel estimation for the hybrid architectures. Given the channel estimate, we propose a novel algorithm that jointly designs the antenna subsets selected and the baseband combining. Using power consumption calculations and achievable rates, we compare the performance of hybrid combining with antenna switching and phase shifting, showing that antenna selection is preferred in a range of operating conditions. An F-RAN is presented in this article as a promising paradigm for the fifth generation wireless communication system to provide high spectral and energy efficiency. The core idea is to take full advantage of local radio signal processing, cooperative radio resource management, and distributed storing capabilities in edge devices, which can decrease the heavy burden on fronthaul and avoid large-scale radio signal processing in the centralized baseband unit pool. This article comprehensively presents the system architecture and key techniques of F-RANs. In particular, key techniques and their corresponding solutions, including transmission mode selection and interference suppression, are discussed. Open issues in terms of edge caching, software-defined networking, and network function virtualization are also identified. Power-aware scheduling problem has been a recent issue in cluster systems not only for operational cost due to electricity cost, but also for system reliability. As recent commodity processors support multiple operating points under various supply voltage levels, Dynamic Voltage Scaling (DVS) scheduling algorithms can reduce power consumption by controlling appropriate voltage levels. In this paper, we provide power-aware scheduling algorithms for bag-of-tasks applications with deadline constraints on DVS-enabled cluster systems in order to minimize power consumption as well as to meet the deadlines specified by application users. A bag-of-tasks application should finish all the sub-tasks before the deadline, so that the DVS scheduling scheme should consider the deadline as well. We provide the DVS scheduling algorithms for both time-shared and space-shared resource sharing policies. The simulation results show that the proposed algorithms reduce much power consumption compared to static voltage schemes. Coarse quantization at the base station (BS) of a massive multi-user (MU) multiple-input multiple-output (MIMO) wireless system promises significant power and cost savings. Coarse quantization also enables significant reductions of the raw analog-to-digital converter data that must be transferred from a spatially separated antenna array to the baseband processing unit. The theoretical limits as well as practical transceiver algorithms for such quantized MU-MIMO systems operating over frequency-flat, narrowband channels have been studied extensively. However, the practically relevant scenario where such communication systems operate over frequency-selective, wideband channels is less well understood. This paper investigates the uplink performance of a quantized massive MU-MIMO system that deploys orthogonal frequency-division multiplexing (OFDM) for wideband communication. We propose new algorithms for quantized maximum a posteriori channel estimation and data detection, and we study the associated performance quantization tradeoffs. Our results demonstrate that coarse quantization (e.g., four to six bits, depending on the ratio between the number of BS antennas and the number of users) in massive MU-MIMO-OFDM systems entails virtually no performance loss compared with the infinite-precision case at no additional cost in terms of baseband processing complexity. We study the problem of non-preemptive scheduling to minimize energy consumption for devices that allow dynamic voltage scaling. Specifically, consider a device that can process jobs in a non-preemptive manner. The input consists of (i) the set R of available speeds of the device, (ii) a set J of jobs, and (iii) a precedence constraint Π among J. Each job j in J, defined by its arrival time aj, deadline dj, and amount of computation cj, is supposed to be processed by the device at a speed in R. Under the assumption that a higher speed means higher energy consumption, the power-saving scheduling problem is to compute a feasible schedule with speed assignment for the jobs in J such that the required energy consumption is minimized. This paper focuses on the setting of weakly dynamic voltage scaling, i.e., speed change is not allowed in the middle of processing a job. To demonstrate that this restriction on many portable power-aware devices introduces hardness to the power-saving scheduling problem, we prove that the problem is NP-hard even if aj = aj ′ and dj = dj ′ hold for all j,j ′∈ Jand |R|=2. If |R|<∞, we also give fully polynomial-time approximation schemes for two cases of the general NP-hard problem: (a) all jobs share a common arrival time, and (b) Π = ∅ and for any j,j ′ ∈ J, aj ≤ aj ′ implies dj ≤ dj ′. To the best of our knowledge, there is no previously known approximation algorithm for any special case of the NP-hard problem. Due to the high cost and power consumption of radio frequency (RF) chains, millimeter wave (mm-wave) communica- tion systems equipped with large antenna arrays typically employ less RF chains than the antenna elements. This leads to the use of a hybrid MIMO processor consisting of a RF beamformer and a baseband MIMO processor in mm-wave communications. In this paper, we consider amplify-and-forward (AF) relay-assisted mm- wave systems with the hybrid MIMO processors over frequency- selective channels. We develop an iterative algorithm for jointly designing the receive transmit (Rx Tx) RF baseband processors of the relay based on the orthogonal matching pursuit (OMP) algorithm for sparse approximation, while assuming orthogonal frequency division multiplexing (OFDM) signaling. Simulation results show that the proposed method outperforms the conven- tional method that designs the baseband processor after steering the RF beams. Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method level computation offloading. Advancing on previous works, it focuses on the elasticity and scalability of the server side and enhances the power of mobile cloud computing by parallelizing method execution using multiple Virtual Machine (VM) images. We evaluate the system using a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for the N-queens puzzle and one order of magnitude for a face detection and a virus scan application, using cloud offloading. We then show that if a task is parallelizable, the user can request more than one VM to execute it, and these VMs will be provided dynamically. In fact, by exploiting parallelization, we achieve a greater reduction on the execution time and energy consumption for the previous applications. Finally, we use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements. Network Function Virtualization (NFV) has revolutionized service provisioning in cloud datacenter networks. It enables the complete decoupling of Network Functions (NFs) from the physical hardware middle boxes that network operators deploy for implementing service-specific and strictly ordered NF chains. Precisely, NFV allows for dispatching NFs as instances of plain software called virtual network functions (VNFs) running on virtual machines hosted by one or more industry standard physical machines. Nevertheless, NF softwarization introduces processing vulnerability ( e.g. , failures caused by hardware or software, and so on). Since any failure of VNFs could break down an entire service chain, thus interrupting the service, the functionality of an NFV-enabled network will require a higher reliability compared with traditional networks. This paper encloses an in-depth investigation of a reliability-aware joint VNF chain placement and flow routing optimization. In order to guarantee the required reliability, an incremental approach is proposed to determine the number of required VNF backups. Through illustration, it is shown herein that the formulated single path routing model can be easily extended to support resource sharing between adjacent backup VNF instances. This paper advocates the absolute existence of a share-resource-based VNF assignment strategy that is capable of trading off all of the reliability, bandwidth, and computing resources consumption of a given service chain. A heuristic is proposed to work around the complexity of the presently formulated integer linear programming (ILP). Thorough numerical analysis and simulations are conducted in order to verify and assert the validity, correctness, and effectiveness of this proposed heuristic reflecting its ability to achieve very close results to those obtained through the resolution of the complex ILP within a negligible amount of time. Above and beyond, the proposed resource-sharing-based VNF placement scheme outperforms existing resource-sharing agnostic schemes by 15.6 and 14.7 in terms of bandwidth and CPU utilization respectively. Nowadays, virtualization is present in almost all computing infrastructures. Thanks to server consolidation and VM migration, virtualization helps in power reduction. However, modern powerful computers with higher processor frequency, multiple cores and multiple CPUs constitute the main factor contributing to the continuously increase of energy consumption in numerous computing infrastructures. In this context, energy management takes a critical importance. A hardware technology, called Dynamic Voltage and Frequency Scaling (DVFS), serves to dynamically modify the processor frequency (according to the CPU needs) in order to achieve less energy consumption. However, lowering frequency also generates poor virtual machine (VM) performance. In this paper, we propose a solution consisting of an extended VM scheduler and DVFS, and report some experiments based on this proposal. This enhanced scheduler, according to VM CPU load, dynamically scales processor frequency in order to save energy. The idea is to adapt the current VM scheduler to analyze CPU load, and modify the current processor frequency to the lowest possible, but still support the guaranteed VM performance. The algorithm is designed and simulated on a web server as the sample application and Xen as the virtualization platform. Test results and performance evaluations prove our design and implementation. Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method-level computation offloading. Advancing on previous work, it focuses on the elasticity and scalability of the cloud and enhances the power of mobile cloud computing by parallelizing method execution using multiple virtual machine (VM) images. We implement ThinkAir and evaluate it with a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for a N-queens puzzle application and one order of magnitude for a face detection and a virus scan application. We then show that a parallelizable application can invoke multiple VMs to execute in the cloud in a seamless and on-demand manner such as to achieve greater reduction on execution time and energy consumption. We finally use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements.