Sau-Hsuan Wu

Bio: Sau-Hsuan Wu is an academic researcher from National Chiao Tung University. The author has contributed to research in topics: Fading & MIMO. The author has an hindex of 13, co-authored 82 publications receiving 554 citations. Previous affiliations of Sau-Hsuan Wu include Memorial Hospital of South Bend & University of Southern California.

Robust Hybrid Beamforming with Phased Antenna Arrays for Downlink SDMA in Indoor 60 GHz Channels

Abstract: A hybrid architecture is presented for downlink beamforming (BF) with phased antenna arrays (PAA) in indoor 60 GHz spatial division multiple access (SDMA) channels. To manage the multiple access and inter-symbol interferences (MAI/ISI) encountered in SDMA with limited feedbacks, a cost-effective time-domain hybrid BF (HBF) method is presented to exploit the directivity provided by PAA in radio frequency (RF) beam patterns and the spatial diversity offered by multiple baseband processing modules. To maintain signal qualities under unpredictable MAI/ISI in wireless multimedia streaming to which indoor 60 GHz radio mainly applies, robust beamformers are designed to maintain the signal to interference-plus-noise ratio (SINR) for each user with minimum total transmit power. The percentages in which the target SINRs can be satisfied with the proposed HBF schemes are found sensitive to uncertainties in the phase shifters of PAA. Two kinds of robust formulations are thus proposed to jointly combat the MAI, ISI and phase uncertainties. Robust beamformers with semi closed-form expressions can be obtained with a nonlinear kind of them, whose SINR satisfaction ratio can attain 80% or more by extensive simulations in an indoor two-user 60 GHz environment if RF beam patterns of the users do not highly overlap in space.

A cloud model and concept prototype for cognitive radio networks

Abstract: The FCC's approval for the first commercial operation in TV white space gives new momentum to the development of cognitive radio in TVWS. On the other hand, the rapid growth of Cloud computing makes it possible and more economical to build a CR metropolitan area network with commodity hardware. In view of the opportunity and challenges brought about by these two technologies, we propose a CR cloud networking model that is able to support CR access in TVWS. Making use of the flexible and vast computing capacity of the cloud, a database and a cooperative spectrum sensing algorithm that estimates the radio power map of licensed users are realized on a CR cloud implemented with Microsoft?s Windows Azure Cloud platform. The CRC can support CSS, dynamic spectrum access and mobility management. A medium access control protocol is also developed for this CRCN model to collect sensing reports and provide access to the TVWS and CRC services. Through this CRCN prototype, important network parameters such as the mean squared errors in CSS, the CR channel vacating delay, and the cloud-based handover time are measured for the design and deployment of the CRCN concept.

Cooperative spectrum sensing in TV White Spaces: When Cognitive Radio meets Cloud

Abstract: A Cognitive Radio Cloud Network (CRCN) in TV White Spaces (TVWS) is proposed in this paper. Under the infrastructure of CRCN, cooperative spectrum sensing (SS) and resource scheduling in TVWS can be efficiently implemented making use of the scalability and the vast storage and computing capacity of the Cloud. Based on the sensing reports collected on the Cognitive Radio Cloud (CRC) from distributed secondary users (SUs), we study and implement a sparse Bayesian learning (SBL) algorithm for cooperative SS in TVWS using Microsoft's Windows Azure Cloud platform. A database for the estimated locations and spectrum power profiles of the primary users are established on CRC with Microsoft's SQL Azure. Moreover to enhance the performance of the SBL-based SS on CRC, a hierarchical parallelization method is also implemented with Microsoft's dotNet 4.0 in a MapReduce-like programming model. Based on our simulation studies, a proper programming model and partitioning of the sensing data play crucial roles to the performance of the SBL-based SS on the Cloud.

Access point and communication system for resource allocation

Abstract: An access point (AP) and a communication system are provided. The access point comprises at least but not limited to a transceiver, a network connection unit, and a processing circuit. The processing circuit is configured for the following steps. The AP receives channel access requests of the user equipments (UEs) from the UEs. Next, the AP transmits a channel request data according to QoS requirements of plurality of channel access requests of the UEs to the server. Afterward, the AP receives resource allocation information associated with the channel request data from the server, wherein the resource allocation information comprises an allocated result of physical channels and transmission power configurations. Subsequently, the AP allocates the physical channels to the UEs according to the QoS requirements of the channel access requests of the UEs and the resource allocation information.

Cooperative Spectrum Sensing and Locationing: A Sparse Bayesian Learning Approach

Abstract: Based on the concept of sparse Bayesian learning, an expectation and maximization algorithm is proposed for cooperative spectrum sensing and locationing of the primary transmitters in cognitive radio systems. Different from typical approaches, not only the signal strength, but also the number and the radio power profiles of the primary transmitters are estimated, which greatly facilitates resource management in cognitive radio. Furthermore, the proposed algorithm can still roughly reconstruct the power propagation map of the primary transmitters even when the measurement rate is below the lower bound for which compressive sensing (CS) can reconstruct signals with the $\ell_1$-norm optimization method. Compared with the typical CS and Bayesian CS algorithms, simulation results show that average mean squared errors (MSE) of the estimated power propagation map are lower with the proposed algorithm. Besides, the computational complexity is also lower owing to bases pruning. The MSE of the location estimation are also shown to demonstrate the capability of the proposed algorithm.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Beamforming for Millimeter Wave Communications: An Inclusive Survey

Abstract: The remarkable growth of wireless data traffic in recent times has driven the need to explore suitable regions in the radio spectrum to meet the projected requirements. In pursuance of this, millimeter wave communications have received considerable attention in the research fraternity. Due to the high path and penetration losses at millimeter wavelengths, antenna beamforming assumes a pivotal role in establishing and maintaining a robust communication link. Beamforming for millimeter wave communications poses a multitude of diverse challenges due to the large channel bandwidth, unique channel characteristics, and hardware constraints. In this paper, we track the evolution and advancements in antenna beamforming for millimeter wave communications in the context of the distinct requirements for indoor and outdoor communication scenarios. We expand the scope of discussion by including the developments in radio frequency system design and implementation for millimeter wave beamforming. We explore the suitability of millimeter wave beamforming methods, both, existing and proposed till midyear 2015, and identify the exciting new prospects unfolding in this domain.

A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives

Abstract: The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers’ structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain.

Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications

Abstract: Due to the under-utilization problem of the allocated radio spectrum, cognitive radio (CR) communications have recently emerged as a reliable and effective solution. Among various network models, this survey paper focuses on the enabling techniques for interweave CR networks which have received great attention from standards perspective due to its reliability to achieve the required quality-of-service. Spectrum sensing provides the essential information to enable this interweave communications in which primary and secondary users are not allowed to access the medium concurrently. Several researchers have already considered various aspects to realize efficient techniques for spectrum sensing. In this direction, this survey paper provides a detailed review of the state-of-the-art related to the application of spectrum sensing in CR communications. Starting with the basic principles and the main features of interweave communications, this paper provides a classification of the main approaches based on the radio parameters. Subsequently, we review the existing spectrum sensing works applied to different categories such as narrowband sensing, narrowband spectrum monitoring, wideband sensing, cooperative sensing, practical implementation considerations for various techniques, and the recent standards that rely on the interweave network model. Furthermore, we present the latest advances related to the implementation of the legacy spectrum sensing approaches. Finally, we conclude this survey paper with some suggested open research challenges and future directions for the CR networks in next generation Internet-of-Things applications.