Multiple Parameter Estimation With Quantized Channel Output

We present a general problem formulation for optimal parameter estimation based on quantized observations, with application to antenna array communication and processing (channel estimation, time-of-arrival (TOA) and direction-of-arrival (DOA) estimation). The work is of interest in the case when low resolution A/D-converters (ADCs) have to be used to enable higher sampling rate and to simplify the hardware. An Expectation-Maximization (EM) based algorithm is proposed for solving this problem in a general setting. Besides, we derive the Cramer-Rao Bound (CRB) and discuss the effects of quantization and the optimal choice of the ADC characteristic. Numerical and analytical analysis reveals that reliable estimation may still be possible even when the quantization is very coarse.Comment: 9 pages, 9 figures, International ITG Workshop on Smart Antennas - WSA 2010, Bremen, German

Achieving Distributed Consensus in UWB Sensor Networks: A Low Sampling Rate Scheme with Quantized Measurements

Distributed consensus in sensor networks has received great attention in the last few years. Most of the research activity has been devoted to study the sensor interactions that allow the convergence of distributed consensus algorithms toward a globally optimal decision. On the other hand, the problem of designing an appropriate radio interface enabling such interactions has received little attention in the literature. Motivated by the above consideration, in this work an ultrawideband sensor network is considered and a physical layer scheme is designed, which allows the active sensors to achieve consensus in a distributed manner without the need of any admission protocol. We focus on the class of the so-called quantized distributed consensus algorithms in which the local measurements or current states of each sensor belong to a finite set. Particular attention is devoted to address the practical implementation issues as well as to the development of a receiver architecture with the same performance of existing alternatives based on an all-digital implementation but with a much lower sampling frequency on the order of MHz instead of GHz

Array Architectures and Physical Layer Design for Millimeter-Wave Communications Beyond 5G

Ever increasing demands in mobile data rates have resulted in exploration of millimeter-wave (mmW) frequencies for the next generation (5G) wireless networks. Communications at mmW frequencies is presented with two keys challenges. Firstly, high propagation loss requires base stations (BSs) and user equipment (UEs) to use a large number of antennas and narrow beams to close the link with sufficient received signal power. Consequently, communications using narrow beams create a new challenge in channel estimation and link establishment based on fine angular probing. Current mmW system use analog phased arrays that can probe only one angle at the time which results in high latency during link establishment and channel tracking. It is desirable to design low latency beam training by exploring both physical layer designs and array architectures that could replace current 5G approaches and pave the way to the communications for frequency bands in higher mmW band and sub-THz region where larger antenna arrays and communications bandwidth can be exploited. To this end, we propose a novel signal processing techniques exploiting unique properties of mmW channel, and show both theoretically, in simulation and experiments its advantages over conventional approaches. Secondly, we explore different array architecture design and analyze their trade-offs between spectral efficiency and power consumption and area. For comprehensive comparison, we have developed a methodology for optimal design of system parameters for different array architecture candidates based on the spectral efficiency target, and use these parameters to estimate the array area and power consumption based on the circuits reported in the literature. We show that the hybrid analog and digital architectures have severe scalability concerns in radio frequency signal distribution with increased array size and spatial multiplexing levels, while the fully-digital array architectures have the best performance and power/area trade-offs.The developed approaches are based on a cross-disciplinary research that combines innovation in model based signal processing, machine learning, and radio hardware. This work is the first to apply compressive sensing (CS), a signal processing tool that exploits sparsity of mmW channel model, to accelerate beam training of mmW cellular system. The algorithm is designed to address practical issues including the requirement of cell discovery and synchronization that involves estimation of angular channel together with carrier frequency offset and timing offsets. We have analyzed the algorithm performance in the 5G compliant simulation and showed that an order of magnitude saving is achieved in initial access latency for the desired channel estimation accuracy. Moreover, we are the first to develop and implement a neural network assisted compressive beam alignment to deal with hardware impairments in mmW radios. We have used 60GHz mmW testbed to perform experiments and show that neural networks approach enhances alignment rate compared to CS. To further accelerate beam training, we proposed a novel frequency selective probing beams using the true-time-delay (TTD) analog array architecture. Our approach utilizes different subcarriers to scan different directions, and achieves a single-shot beam alignment, the fastest approach reported to date. Our comprehensive analysis of different array architectures and exploration of emerging architectures enabled us to develop an order of magnitude faster and energy efficient approaches for initial access and channel estimation in mmW systems

Distributed space-time block coding in cooperative relay networks with application in cognitive radio

Spatial diversity is an effective technique to combat the effects of severe fading in wireless environments. Recently, cooperative communications has emerged as an attractive communications paradigm that can introduce a new form of spatial diversity which is known as cooperative diversity, that can enhance system reliability without sacrificing the scarce bandwidth resource or consuming more transmit power. It enables single-antenna terminals in a wireless relay network to share their antennas to form a virtual antenna array on the basis of their distributed locations. As such, the same diversity gains as in multi-input multi-output systems can be achieved without requiring multiple-antenna terminals. In this thesis, a new approach to cooperative communications via distributed extended orthogonal space-time block coding (D-EO-STBC) based on limited partial feedback is proposed for cooperative relay networks with three and four relay nodes and then generalized for an arbitrary number of relay nodes. This scheme can achieve full cooperative diversity and full transmission rate in addition to array gain, and it has certain properties that make it alluring for practical systems such as orthogonality, flexibility, low computational complexity and decoding delay, and high robustness to node failure. Versions of the closed-loop D-EO-STBC scheme based on cooperative orthogonal frequency division multiplexing type transmission are also proposed for both flat and frequency-selective fading channels which can overcome imperfect synchronization in the network. As such, this proposed technique can effectively cope with the effects of fading and timing errors. Moreover, to increase the end-to-end data rate, this scheme is extended for two-way relay networks through a three-time slot framework. On the other hand, to substantially reduce the feedback channel overhead, limited feedback approaches based on parameter quantization are proposed. In particular, an optimal one-bit partial feedback approach is proposed for the generalized D-O-STBC scheme to maximize the array gain. To further enhance the end-to-end bit error rate performance of the cooperative relay system, a relay selection scheme based on D-EO-STBC is then proposed. Finally, to highlight the utility of the proposed D-EO-STBC scheme, an application to cognitive radio is studied

Spectrum sensing for cognitive radios: Algorithms, performance, and limitations

Inefficient use of radio spectrum is becoming a serious problem as more and more wireless systems are being developed to operate in crowded spectrum bands. Cognitive radio offers a novel solution to overcome the underutilization problem by allowing secondary usage of the spectrum resources along with high reliable communication. Spectrum sensing is a key enabler for cognitive radios. It identifies idle spectrum and provides awareness regarding the radio environment which are essential for the efficient secondary use of the spectrum and coexistence of different wireless systems. The focus of this thesis is on the local and cooperative spectrum sensing algorithms. Local sensing algorithms are proposed for detecting orthogonal frequency division multiplexing (OFDM) based primary user (PU) transmissions using their autocorrelation property. The proposed autocorrelation detectors are simple and computationally efficient. Later, the algorithms are extended to the case of cooperative sensing where multiple secondary users (SUs) collaborate to detect a PU transmission. For cooperation, each SU sends a local decision statistic such as log-likelihood ratio (LLR) to the fusion center (FC) which makes a final decision. Cooperative sensing algorithms are also proposed using sequential and censoring methods. Sequential detection minimizes the average detection time while censoring scheme improves the energy efficiency. The performances of the proposed algorithms are studied through rigorous theoretical analyses and extensive simulations. The distributions of the decision statistics at the SU and the test statistic at the FC are established conditioned on either hypothesis. Later, the effects of quantization and reporting channel errors are considered. Main aim in studying the effects of quantization and channel errors on the cooperative sensing is to provide a framework for the designers to choose the operating values of the number of quantization bits and the target bit error probability (BEP) for the reporting channel such that the performance loss caused by these non-idealities is negligible. Later a performance limitation in the form of BEP wall is established for the cooperative sensing schemes in the presence of reporting channel errors. The BEP wall phenomenon is important as it provides the feasible values for the reporting channel BEP used for designing communication schemes between the SUs and the FC