Datacenter Design for Future Cloud Radio Access Network.

Cloud radio access network (C-RAN), an emerging cloud service that combines the traditional radio access network (RAN) with cloud computing technology, has been proposed as a solution to handle the growing energy consumption and cost of the traditional RAN. Through aggregating baseband units (BBUs) in a centralized cloud datacenter, C-RAN reduces energy and cost, and improves wireless throughput and quality of service. However, designing a datacenter for C-RAN has not yet been studied. In this dissertation, I investigate how a datacenter for C-RAN BBUs should be built on commodity servers. I first design WiBench, an open-source benchmark suite containing the key signal processing kernels of many mainstream wireless protocols, and study its characteristics. The characterization study shows that there is abundant data level parallelism (DLP) and thread level parallelism (TLP). Based on this result, I then develop high performance software implementations of C-RAN BBU kernels in C++ and CUDA for both CPUs and GPUs. In addition, I generalize the GPU parallelization techniques of the Turbo decoder to the trellis algorithms, an important family of algorithms that are widely used in data compression and channel coding. Then I evaluate the performance of commodity CPU servers and GPU servers. The study shows that the datacenter with GPU servers can meet the LTE standard throughput with 4× to 16× fewer machines than with CPU servers. A further energy and cost analysis show that GPU servers can save on average 13× more energy and 6× more cost. Thus, I propose the C-RAN datacenter be built using GPUs as a server platform. Next I study resource management techniques to handle the temporal and spatial traffic imbalance in a C-RAN datacenter. I propose a “hill-climbing” power management that combines powering-off GPUs and DVFS to match the temporal C-RAN traffic pattern. Under a practical traffic model, this technique saves 40% of the BBU energy in a GPU-based C-RAN datacenter. For spatial traffic imbalance, I propose three workload distribution techniques to improve load balance and throughput. Among all three techniques, pipelining packets has the most throughput improvement at 10% and 16% for balanced and unbalanced loads, respectively.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120825/1/qizheng_1.pd

Polynomial matrix decomposition techniques for frequency selective MIMO channels

For a narrowband, instantaneous mixing multi-input, multi-output (MIMO) communications system, the channel is represented as a scalar matrix. In this scenario, singular value decomposition (SVD) provides a number of independent spatial subchannels which can be used to enhance data rates or to increase diversity. Alternatively, a QR decomposition can be used to reduce the MIMO channel equalization problem to a set of single channel equalization problems. In the case of a frequency selective MIMO system, the multipath channel is represented as a polynomial matrix. Thus conventional matrix decomposition techniques can no longer be applied. The traditional solution to this broadband problem is to reduce it to narrowband form by using a discrete Fourier transform (DFT) to split the broadband channel into N narrow uniformly spaced frequency bands and applying scalar decomposition techniques within each band. This describes an orthogonal frequency division multiplexing (OFDM) based system. However, a novel algorithm has been developed for calculating the eigenvalue decomposition of a para-Hermitian polynomial matrix, known as the sequential best rotation (SBR2) algorithm. SBR2 and its QR based derivatives allow a true polynomial singular value and QR decomposition to be formulated. The application of these algorithms within frequency selective MIMO systems results in a fundamentally new approach to exploiting spatial diversity. Polynomial matrix decomposition and OFDM based solutions are compared for a wide variety of broadband MIMO communication systems. SVD is used to create a robust, high gain communications channel for ultra low signal-to-noise ratio (SNR) environments. Due to the frequency selective nature of the channels produced by polynomial matrix decomposition, additional processing is required at the receiver resulting in two distinct equalization techniques based around turbo and Viterbi equalization. The proposed approach is found to provide identical performance to that of an existing OFDM scheme while supporting a wider range of access schemes. This work is then extended to QR decomposition based communications systems, where the proposed polynomial approach is found to not only provide superior bit-error-rate (BER) performance but significantly reduce the complexity of transmitter design. Finally both techniques are combined to create a nulti-user MIMO system that provides superior BER performance over an OFDM based scheme. Throughout the work the robustness of the proposed scheme to channel state information (CSI) error is considered, resulting in a rigorous demonstration of the capabilities of the polynomial approach

Analysis and Implementation of PAPR reduction algorithms for C-OFDM signals

Nowadays multicarrier modulation has become a key technology for communication systems; for example C-OFDM schemes are used in wireless LAN (802.11a/g/n), terrestrial digital television (DVB-T) and audio broadcaster (DAB) in Europe, and discrete multitone (DMT) in x.DSL systems. The principal difficulty with OFDM is the occurrence of the coherent alignment of the time domain parallel signals at the transmitted side which forces system designer to introduce either additional hard computationally device or a suitable power back-off at the high power amplifier in order to cope with the large magnitude signal fluctuation. This leads to a significant increment in computational cost in the former case whereas in a worse allowable power utilization in the latter case with respect to the original system. However since both allowable power and computational cost are subject to a design as well as regulatory limit others solution must be accomplished. Peak reduction techniques reduce maximum-to-mean amplitude fluctuations nominating as a feasible solution. Peak-to-average power ratio is the key metric to measure this amplitude fluctuations at transmitter and to give a clear figure of merit for comparison among different techniques

Synchronization algorithms and architectures for wireless OFDM systems

Orthogonal frequency division multiplexing (OFDM) is a multicarrier modulation technique that has become a viable method for wireless communication systems due to the high spectral efficiency, immunity to multipath distortion, and being flexible to integrate with other techniques. However, the high-peak-to-average power ratio and sensitivity to synchronization errors are the major drawbacks for OFDM systems. The algorithms and architectures for symbol timing and frequency synchronization have been addressed in this thesis because of their critical requirements in the development and implementation of wireless OFDM systems. For the frequency synchronization, two efficient carrier frequency offset (CFO) estimation methods based on the power and phase difference measurements between the subcarriers in consecutive OFDM symbols have been presented and the power difference measurement technique is mapped onto reconfigurable hardware architecture. The performance of the considered CFO estimators is investigated in the presence of timing uncertainty conditions. The power difference measurements approach is further investigated for timing synchronization in OFDM systems with constant modulus constellation. A new symbol timing estimator has been proposed by measuring the power difference either between adjacent subcarriers or the same subcarrier in consecutive OFDM symbols. The proposed timing metric has been realized in feedforward and feedback configurations, and different implementation strategies have been considered to enhance the performance and reduce the complexity. Recently, multiple-input multiple-output (MIMO) wireless communication systems have received considerable attention. Therefore, the proposed algorithms have also been extended for timing recovery and frequency synchronization in MIMO-OFDM systems. Unlike other techniques, the proposed timing and frequency synchronization architectures are totally blind in the sense that they do not require any information about the transmitted data, the channel state or the signal-to-noise-ratio (SNR). The proposed frequency synchronization architecture has low complexity because it can be implemented efficiently using the three points parameter estimation approach. The simulation results confirmed that the proposed algorithms provide accurate estimates for the synchronization parameters using a short observation window. In addition, the proposed synchronization techniques have demonstrated robust performance over frequency selective fading channels that significantly outperform other well-established methods which will in turn benefit the overall OFDM system performance. Furthermore, an architectural exploration for mapping the proposed frequency synchronization algorithm, in particular the CFO estimation based on the power difference measurements, on reconfigurable computing architecture has been investigated. The proposed reconfigurable parallel and multiplexed-stream architectures with different implementation alternatives have been simulated, verified and compared for field programmable gate array (FPGA) implementation using the Xilinx’s DSP design flow.EThOS - Electronic Theses Online ServiceMinistry of Higher Education and Scientific Research (MOHSR) of IraqGBUnited Kingdo