Date of Degree
PhD (Doctor of Philosophy)
Electrical and Computer Engineering
First Committee Member
Second Committee Member
Third Committee Member
Fourth Committee Member
Over the past few years there has been an extensive growth in data traffic consumption devices. Billions of mobile data devices are connected to the global wireless network. Customers demand revived services and up-to-date developed applications, like real-time video and games. These applications require reliable and high data rate wireless communication with high throughput network. One way to meet these requirements is by 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 communication. Massive MIMO increases the spatial multiplexing gain and the data rate by adding an excessive number of antennas to the base station (BS) terminals of wireless communication systems. However, building efficient algorithms able to decode a coherently or non-coherently large flow of transmitted signal with low complexity is a big challenge in massive MIMO. In this dissertation, we propose novel approaches to achieve optimal performance for joint channel estimation and signal detection for massive MIMO systems. The dissertation consists of three parts depending on the number of users at the receiver side.
In the first part, we introduce a probabilistic approach to solve the problem of coherent signal detection using the optimized Markov Chain Monte Carlo (MCMC) technique. Two factors contribute to the speed of finding the optimal solution by the 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.
We assume the channel state information is known in the first part of the dissertation; in the second part we consider non-coherent signal detection. We develop and design an optimal joint channel estimation and signal detection algorithms for massive (single-input multiple-output) SIMO wireless systems. We propose exact non-coherent data detection algorithms in the sense of generalized likelihood ratio test (GLRT). In addition to their optimality, these proposed tree based algorithms perform low expected complexity and for general modulus constellations. More specifically, despite the large number of the unknown channel coefficients for massive SIMO systems, we show that the expected computational complexity of these algorithms is linear in the number of receive antennas (N) and polynomial in channel coherence time (T). We prove that as $N \rightarrow \infty$, the number of tested hypotheses for each coherent block equals $T$ times the cardinality of the modulus constellation. Simulation results show that the optimal non-coherent data detection algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity.
In the part three, we consider massive MIMO uplink wireless systems with time-division duplex (TDD) operation. We propose an optimal algorithm in terms of GLRT to solve the problem of joint channel estimation and data detection for massive MIMO systems. We show that the expected complexity of our algorithm grows polynomially in the channel coherence time (T). The proposed algorithm is novel in two terms: First, the transmitted signal can be chosen from any modulus constellation, constant and non-constant. Second, the algorithm decodes the received noisy signal, which is transmitted a from multiple-antenna array, offering exact solution with polynomial complexity in the coherent block interval. Simulation results demonstrate significant performance gains of our approach compared with suboptimal non-coherent detection schemes. 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.
In the past decade there has been a significant growth in the number of de- vices 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 (JED) in massive MIMO systems. Our proposed algorithms decode the noisy received signal offering polynomial complexity in the coherent block interval. In addition, the transmitted signal can be chosen from any constellation including nonconstant-modulus constellations like 16-QAM. To the best of our knowledge, the proposed algorithms in this dissertation are the state- of-the-art to achieve JED for massive MIMO systems.
Coherent and non-coherent detection, Generalized ration test, Joint channel estimation and data detection, Massive MIMO, Tree search algorithms
xiii, 162 pages
Includes bibliographical references (pages 157-162).
Copyright © 2017 Haider Ali Jasim Alshamary
Alshamary, Haider Ali Jasim. "Coherent and non-coherent data detection algorithms in massive MIMO." PhD (Doctor of Philosophy) thesis, University of Iowa, 2017.