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Establishing Large-Scale MIMO Communication: Coding for Channel Estimation

Shabara, Yahia
http://orcid.org/0000-0002-1809-3570
http://rave.ohiolink.edu/etdc/view?acc_num=osu1618578732285999

Abstract Details

2021, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
The surge in mobile broadband data demands is expected to surpass the available spectrum capacity below 6 GHz. This expectation has prompted the exploration of millimeter-wave (mmWave) frequency bands as a candidate technology for next-generation wireless networks, like 5G-NR and WiFi ad/ay. However, numerous challenges to deploying mmWave communication systems, including channel estimation, need to be met before practical deployments are possible. The channel estimation problem is particularly complex due to the large antenna arrays, i.e., large-MIMO, used in mmWave transceivers. Large-MIMO antennas offer significant performance gains in terms of improved spectral efficiency, superior spatial multiplexing capabilities, as well as the ability to deliver high transmit signal power, which is crucial for compensating for the severe attenuation of high-frequency signals. However, large-MIMO channel estimation is complex since it entails the discovery of large-sized channel matrices, which is a daunting task and may necessitate a large number of measurements. Channel estimation is especially challenging for ``initial link establishment'', where limited prior knowledge about the channel is available. Reducing the number of necessary measurements thus holds the key to faster link establishment. For sparse MIMO channels, such reduction is possible due to the prior knowledge that the channel can be represented in a domain in which most of its components are negligibly small. The problem of "Fast Link Establishment" is the focus of this dissertation. In particular, we focus on the development and evaluation of sparse channel estimation algorithms that only require a small number of measurements. We divide this dissertation into three research objectives, as follows: First: We seek to develop a reliable channel estimation framework that: (1) requires a limited number of measurements (compared to the channel dimensions), and (2) operates using energy-efficient transceiver architectures. Our channel estimation solution is in turn divided into two separate sub-problems, namely, Measurement Design and Measurement Interpretation: - Measurement Design is the problem of finding the set of measurements that best preserves the information contained in the channel. We treat the sparse channel estimation problem as that of beam discovery in the angular domain. Our proposed framework extends from an analogy we draw from the problem of error discovery in "Linear Channel Coding". Specifically, we show that locating strong channel paths is analogous to locating errors in linear block codes. As a result, we are able to i) provide rigorous criteria for solving the channel estimation problem, ii) significantly decrease the number of required measurements, and iii) utilize a fairly simple and energy-efficient transceiver architecture. - Measurement Interpretation is the problem of mapping the acquired measurements to a corresponding channel estimate. Due to the sheer complexity of joint measurement processing, we break up the complex measurement decoding problem into smaller sub-problems, which estimate the rows and columns of the channel separately. The sub-problems can be run in parallel, which significantly enhances the speed of computation. We propose three different methods to solve the aforementioned sub-problems, namely, the Look-up Table method, the Search method and a machine learning, DNN-based method. DNN decoding offers the best tradeoff between reliability and computational complexity. Our proposed binary coding solution framework outperforms state-of-the-art compressed sensing methods as well as the IEEE 802.11ad beam alignment scheme. Second: we study the fundamentals limits governing the number of measurements achievable by our proposed framework. More specifically, we study the \textit{lower bound} on the number of measurements that perfectly preserve the information contained in the channel, when measurement encoding is based on binary codes. The channel coding analogy does not naturally lend itself to characterizing this lower bound. Thus, we turn to a Binary Source Coding analogy, which is more directly related to reducing the number of measurements. Treating channel estimation as binary source compression leaves the nature of the solution unchanged but allows us to derive clear-cut lower bounds on the required number of measurements. Third: we try to understand the relationship between the lower measurement bound of our binary-coding-based solution vs. the general measurement bound that works for any type of solution. This allows us to better understand the capabilities of our framework. To the best of our knowledge, the tightest known general asymptotic lower bound is far smaller than our derived bound (in our second research objective). We show that this aforementioned general lower bound is too loose since it does not account for the limitations of the MIMO channel estimation problem. We then derive a generalized tight asymptotic lower bound, which scales exactly as the bound for our binary coding framework. We argue the tightness of our general bound, by showing that, under a mild constraint on channel sparsity, there exists a solution whose number of measurements achieves such lower bounds.
Eylem Ekici (Advisor)
C. Emre Koksal (Advisor)
Ness Shroff (Committee Member)
145 p.
Communication; Computer Science; Electrical Engineering
Sparse Channel Estimation; Path Discovery; Sparse Recovery; Compressed Sensing; Millimeter-Wave; Linear Block Coding; Source Coding; Measurement Lower Bound; Machine Learning

Recommended Citations

Citations

  • Shabara, Y. (2021). Establishing Large-Scale MIMO Communication: Coding for Channel Estimation [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1618578732285999

    APA Style (7th edition)

  • Shabara, Yahia. Establishing Large-Scale MIMO Communication: Coding for Channel Estimation. 2021. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1618578732285999.

    MLA Style (8th edition)

  • Shabara, Yahia. "Establishing Large-Scale MIMO Communication: Coding for Channel Estimation." Doctoral dissertation, Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1618578732285999

    Chicago Manual of Style (17th edition)

osu1618578732285999
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This open access ETD is published by The Ohio State University and OhioLINK.