Abstract-In this paper we introduce an optimized Markov Chain Monte Carlo (MCMC) technique for solving the integer least-squares (ILS) problems, which include Maximum Likelihood (ML) detection in Multiple-Input Multiple-Output (MIMO) systems. Two factors contribute to the speed of finding the optimal solution by the MCMC detector: the probability of the optimal solution in the stationary distribution, and the mixing time of the MCMC detector. Firstly, we compute the optimal value of the "temperature" parameter, in the sense that the temperature has the desirable property that once the Markov chain has mixed to its stationary distribution, there is polynomially small probability (1 poly(N ), instead of exponentially small) of encountering the optimal solution. This temperature is shown to be at most O( √ SNR ln(N )) 1 , where SNR is the signal-to-noise ratio, and N is the problem dimension. Secondly, we study the mixing time of the underlying Markov chain of the proposed MCMC detector. We find that, the mixing time of MCMC is closely related to whether there is a local minimum in the lattice structures of ILS problems. For some lattices without local minima, the mixing time of the Markov chain is independent of SNR, and grows polynomially in the problem dimension; for lattices with local minima, the mixing time grows unboundedly as SNR grows, when the temperature is set, as in conventional wisdom, to be the standard deviation of noises. Our results suggest that, to ensure fast mixing for a fixed dimension N , the temperature for MCMC should instead be set as Ω( √ SNR) in general. Simulation results show that the optimized MCMC detector efficiently achieves approximately ML detection in MIMO systems having a huge number of transmit and receive dimensions.

Massive MIMO systems have made significant progress in increasing spectral and energy efficiency over traditional MIMO systems by exploiting large antenna arrays. In this paper we consider the joint maximum likelihood (ML) channel estimation and data detection problem for massive SIMO (single input multiple output) wireless systems. Despite the large number of unknown channel coefficients for massive SIMO systems, we improve an algorithm to achieve the exact ML non-coherent data detection with a low expected complexity. We show that the expected computational complexity of this algorithm is linear in the number of receive antennas and polynomial in channel coherence time. Simulation results show the performance gain of the optimal non-coherent data detection with a low computational complexity.

To whom is always with me.To all my family.ii ACKNOWLEDGEMENTSI would like to thank my advisor, Professor Weiyu Xu for his limitless support and inspiration. His immense input brings this piece of work to be compatible and comprehensive. I am indebted to his insights and enthusiasm which carried my research to logical conclusions. I would also like to extend my gratitude to my committee members, in particular, Professor Soura Dasgupta and Professor Anton Kruger for all the knowledge that I earned from their lectures and their comments toward my research. In addition, I would like to thank Cathy Kern and Dina Blanc who always go way beyond their duties to help me, whether it was inside or outside the ECE Department. I extend my profound gratitude to all my friends and lab mates for their brotherly love, support, and company during my time in the university of Iowa. A special appreciation goes to my parents, my wife, and my big family in Iraq for believing in me and for all the values that they raised me with. I couldn't have come this far without you all.iii ABSTRACTIn the past decade there has been a significant growth in the number of devices consuming data traffics. Billions of mobile data devices are now connected to the global wireless network. Real-time audio, video, and virtual reality applications require reliable wireless communications with high data throughput. One way to meet these requirements is increasing the number of transmit and/or receive antennas of the wireless communication systems. Massive multiple-input multiple-output (MIMO) has emerged as a promising candidate technology for the next generation (5G) wireless communications. Massive MIMO increases the spatial multiplexing gain and diversity gain by adding a large number of antennas to the base stations (BS) of wireless communication systems. However, designing efficient algorithms to decode transmitted signal with low complexity is a big challenge in massive MIMO. In this dissertation, we design and analyze novel algorithms to achieve near-optimal or optimal performance for coherent data detection, and joint channel estimation and signal detection in massive MIMO systems.The dissertation consists of three parts depending on the number of users at the transmitter side.In the first part, we assume the channel state information is known at the receiver. We introduce a probabilistic approach to solve the problem of coherent signal detection using an optimized Markov Chain Monte Carlo (MCMC) algorithm. Two factors contribute to the speed of finding the optimal solution by the iv MCMC detector: The probability of encountering the optimal solution when the Markov chain converges to the stationary distribution, and the mixing time of the MCMC detector. First, we compute the optimal value of the "temperature" parameter such that the MC encounters the optimal solution in a polynomially small probability. Second, we study the mixing time of the underlying Markov chain of the proposed MCMC detector.In the second part, we consider optimal non-coherent ...

Abstract-In this paper, we propose an efficient optimal joint channel estimation and data detection algorithm for massive MIMO wireless systems. Our algorithm is optimal in terms of the generalized likelihood ratio test (GLRT). For massive MIMO systems, we show that the expected complexity of our algorithm grows polynomially in the channel coherence time. Simulation results demonstrate significant performance gains of our algorithm compared with suboptimal non-coherent detection algorithms. To the best of our knowledge, this is the first algorithm which efficiently achieves GLRT-optimal non-coherent detections for massive MIMO systems with general constellations.

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There are many problems that restrict the work of the Mach-Zehnder modulator (MZM), including the lack of using various windows of wavelengths, the large half-wave voltage, and the small optical confinement factor. In this paper, a mathematical model for MZM based on lithium niobate (LN) is designed to solve these problems. In this model, a wide window of optical wavelength from visible-to-infrared (632.8-to-1560 nm) was utilized. Moreover, it achieved a better modulation with lower attenuation and a lower dispersion by the window (1550-1560) nm. The other window of optical wavelength is about (632.8-to-634 nm), and (646-to-647 nm) which can be used for short-haul applications to reduce attenuation and dispersion. Furthermore, a small length of the arm, about 2-3 mm, was utilized to accomplish a large change of the refractive index and lower applied voltage of up to 250 V. The small operation half-wave voltage achieved about 1.2 V leading to better switching of the MZM. In addition, a large optical confinement factor of ≤1 unitless was obtained. Even better performance of MZM was attained by using a suitable length arm of MZM of about 2 mm, along with an electric field of about 175 V/mm and 233 V/mm using poling at 100 V.

Deformation correction and recovery of dynamic magnetic resonance images (DMRI) with low complexity algorithms without compromising image quality is a challenging problem. We proposed a motion estimation deformation-correction compressive sensing (DC-CS) scheme to recover dynamic images from its undersampled measurements. We simplify the complex optimization problem into three sub-problems. The contributions of this research are: introducing a global search strategy instead of the DC registration step, guaranteeing a non-explicit motion estimation that avoids any spatial alignment or registration of the images, and lowering the computational cost to the minimum by using PatchMatch (PM). The simulation result shows that the PM algorithm accelerates the recovery time without losing the quality in comparison with the DC algorithm.

In the current scenario, wireless sensor networks (WSNs) are embedded in the “Internet of Things (IoT) ” platform where sensor nodes automatically link and use the Internet to communicate and execute their activities. WSNs are well suited for the collection of long-term IoT representation environmental data. The WSNs includes wireless communication capabilities, computation process, and nodes with sensing capabilities. Data dissemination methods, power management, and many routing procedures have been mainly designed for WSNs integrated IoT platform. Also, we consider load and bandwidth consumption as an essential issue in our design. Hence, this paper introduces a data disseminated energy-efficient clustering algorithm using multiple parameter decision-making for selecting an optimal clustering algorithm. For the cluster head selection process, we consider different kinds of parameters such as Initial Energy, Average Energy of the Network, Energy Consumption Rate, and Residual Energy. By considering these factors, nodes are continually monitored, and the cluster header is selected according to the maximum energy value. The respective cluster members are chosen in the cluster coverage area using the swarming techniques. In other words, we used swarm techniques as a cluster head selection process to avoid load and bandwidth consumption. The excellence of the system is evaluated using simulation results which show that this introduced method is more effective in terms of preventing bandwidth and load consumption. In this context, we use network simulator 2 (NS2) to simulate different kinds of metrics such as a packet delivery ratio, network lifetime, and energy consumption.