Sau-Hsuan Wu

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

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

The FCCs 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 and Locationing: A Sparse Bayesian Learning Approach

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

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

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Cooperative Sensing of Wideband Cognitive Radio: A Multiple-Hypothesis-Testing Approach

-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.

Multistage MMSE receivers for ultra-wide bandwidth impulse radio communications

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 Microsofts Windows Azure Cloud platform. A database for the estimated locations and spectrum power profiles of the primary users are established on CRC with Microsofts SQL Azure. Moreover to enhance the performance of the SBL-based SS on CRC, a hierarchical parallelization method is also implemented with Microsofts 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.

Time Synchronization Protocol for Small-Scale Wireless Sensor Networks

A low-complexity decision fusion method is proposed for cooperative spectrum sensing (SS) of wideband cognitive radio (CR) using multiple-hypothesis testing. To maintain the quality of SS in multiple channels, performance indices of false-sensing rate and false-ignorance rate are defined for the Benjamini-Hochberg (BH) procedure to control the false-alarm ratio (FAR) and missing ratio (MR) of SS, respectively. Extended from these results, a double BH procedure is further studied in an attempt to simultaneously attain a low FAR and MR. In addition to identifying the availability of channels, a signal strength-estimation scheme that features multiple-hypothesis testing is also proposed to classify the level of the SNR for cooperative SS. Simulation results show that the double BH procedure can suppress both the FAR and the MR at a high SNR while maintaining a considerable FAR in the low-SNR regime. As such, the system can enjoy throughput enhancement by allowing more active cognitive access at a high SNR and still prevent introducing excessive multiple-access interference to the primary users when their SNRs are weak. On the other hand, for SNR classification, simulations also show that the SNR can be classified within one level, plus or minus the true strength with more than a 96% chance. This allows the system to employ more generic protocols such as concurrent transmissions for cognitive access.

Iterative Joint Channel Estimation and Multiuser Detection for DS-CDMA in Frequency-Selective Fading Channels

A reduced-rank receiver based on the multi-stage Wiener filter (MSWF) is proposed for multiple-access interference suppression for ultra wideband time-hopping spread spectrum radio in multipath channels. The proposed reduced-rank MSWF receiver provides performance superior to that of the classical RAKE receiver with similar complexity, and comparable to that of the full rank minimum mean squared error receiver with much lower complexity. The reduced rank MWSF also offers improved tracking performance in the presence of time-varying channels. Simulation and theoretical results confirm that the reduced rank scheme achieves a comparable output SINR to that of the full-rank scheme employing only 1/50th of the full-rank dimension. The superior tracking behavior of the reduced rank MWSF is also demonstrated.

Reduced-rank multistage receivers for DS-CDMA in frequency-selective fading channels

A Round-Robin Timing Exchange (RRTE) protocol is proposed for the distributed synchronization of wireless sensor network. Compared to the Timing-sync Protocol for Sensor Network (TPSN) and the Pairwise Broadcasting Synchronization (PBS) scheme, the power consumption for each sensor node (SN) of RRTE is much smaller than that of TPSN and is evenly distributed among each SN in contrast to the PBS method. In addition, the synchronization accuracy of RRTE falls within that of TPSN and PBS, and can be adjusted by controlling the number of SNs in one cycle of synchronization. This makes the synchronization protocol particularly useful for small-scale wireless sensor networks. Furthermore, to improve the accuracy of synchronization, a recursive second-order regression method is also proposed to smooth the timing adjustment of each synchronization step without requiring complicated computations.

Planar arrays hybrid beamforming for SDMA in millimeter wave applications

An iterative joint channel estimation, symbol detection, phase recovery and interference cancellation structure is proposed for asynchronous code-division multiple-access systems over frequency-selective fading channels. Based on the expectation maximization (EM) algorithm, a recursive channel estimator is developed for blind channel tracking, using a novel stochastic signal processing technique. To perform symbol detection given the phase ambiguities of the resultant EM channel estimates, a noncoherent scheme is developed to compute the a posteriori probabilities (APPs) of data symbols. Moreover, by incorporating the APPs into the proposed recursive channel estimator, phase ambiguity due to the EM channel estimation can be resolved, which enables soft multiple access interference cancellation for multiuser detection. Based on these new signal processing schemes, an iterative structure is proposed for joint channel estimation and multiuser detection over fast fading channels.