PARAFAC-Based Multiuser Channel Parameter Estimation for MmWave Massive MIMO Systems over Frequency Selective Fading Channels

Channel estimation is crucial in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, especially with a few training sequences. To solve the problem of uplink channel estimation in mmWave massive MIMO systems, a PARAFAC-based algorithm is proposed for joint estimation of multiuser channels. The orthogonal frequency divisional multiplexing (OFDM) technique is exploited to combat the frequency selective fading channels. In this paper, the received signal at the base station (BS) is formulated as a third-order parallel factor (PARAFAC) tensor, and then a low-complexity algorithm is designed for fast estimation of the factor matrices related to channel parameters, thus leading to joint estimation of multiuser channel parameters via one-dimensional search. Moreover, the Cramér–Rao Bound (CRB) results for multiuser channel parameters are derived for evaluation. Theorical analysis and numerical results reveal that the algorithm performs well with a few training sequences. Compared with existing algorithms, the proposed algorithm has clear advantages both in estimation accuracy and computational complexity.

A Simple and Robust Equalization Algorithm for Variable Modulation Systems

This paper proposes a simple and robust variable modulation-decision-directed least mean square (VM-DDLMS) algorithm for reducing the complexity of conventional equalization algorithms and improving the stability of variable modulation (VM) systems. Compared to conventional adaptive equalization algorithms, known information was used as training sequences to reduce the bandwidth consumption caused by inserting training sequences; compared with conventional blind equalization algorithms, the parameters and decisions of the equalizer were determinate, which was conducive to a stable equalization performance. The simulation and implementation results show that the proposed algorithm has a better bit error rate (BER) performance than that of the constant modulus algorithm (CMA) and modified constant modulus algorithm (MCMA) while maintaining the same level of consumption of hardware resources. Compared to the conventional decision-directed least mean square (DDLMS) algorithm, the proposed algorithm only needs to make quadrature phase shift keying (QPSK) symbol decisions, which reduces the computational complexity. In parallel 11th-order equalization algorithms, the operating frequency of VM-DDLMS can reach up to 333.33 MHz.

Constructing benchmark test sets for biological sequence analysis using independent set algorithms

Statistical inference and machine learning methods are benchmarked on test data independent of the data used to train the method. Biological sequence families are highly non-independent because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in bench marking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new meth- ods for splitting sequence data into dissimilar training and test sets. These algo rithms input a sequence family and produce a split in which each test sequence is less than p % identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets.

Estimation and equalization strategies for fiber-wireless communications in a multiuser CDMA environment

Two major issues associated with fiber-wireless technology are the nonlinear distortion of the optical link and the multipath dispersion of the wireless channel. In order to limit the effects of these distortions, estimation, and subsequently equalization of the concatenated fiber-wireless channel needs to be done. This thesis addresses three scenarios in this regard, they are: uplink estimation using pseudonoise (PN) sequences, downlink estimation using Walsh codes, and uplink equalization using a decision feedback equalizer (DFE) and series reversion, all in the presence of both wireless and optical channel noise. The training sequences used in the identification are practically feasible. These training sequences have white noise-like properties which effectively decouples the identification of the linear and nonlinear channels. Correlation analysis is then applied to identify both systems. Furthermore, we propose an algorithm to mitigate the adverse effect of multiple access interference (MAI). Numerical evaluations show a good estimation of both the linear and nonlinear systems with 10 users for the uplink and 54 users for the downlink, both with a signal-to-noise ratio (SNR) of 25 dB. Chip error rate (CER) simulations show that the proposed MAI mitigation algorithm leaves only small residual MAI.

Estimation and equalization strategies for fiber-wireless communications in a multiuser CDMA environment

Two major issues associated with fiber-wireless technology are the nonlinear distortion of the optical link and the multipath dispersion of the wireless channel. In order to limit the effects of these distortions, estimation, and subsequently equalization of the concatenated fiber-wireless channel needs to be done. This thesis addresses three scenarios in this regard, they are: uplink estimation using pseudonoise (PN) sequences, downlink estimation using Walsh codes, and uplink equalization using a decision feedback equalizer (DFE) and series reversion, all in the presence of both wireless and optical channel noise. The training sequences used in the identification are practically feasible. These training sequences have white noise-like properties which effectively decouples the identification of the linear and nonlinear channels. Correlation analysis is then applied to identify both systems. Furthermore, we propose an algorithm to mitigate the adverse effect of multiple access interference (MAI). Numerical evaluations show a good estimation of both the linear and nonlinear systems with 10 users for the uplink and 54 users for the downlink, both with a signal-to-noise ratio (SNR) of 25 dB. Chip error rate (CER) simulations show that the proposed MAI mitigation algorithm leaves only small residual MAI.

General knowledge and detailed memory benefit from different training sequences

Real-world learning contexts sometimes require the use of general knowledge, whereas others depend on recalling detailed information about individual events. By combining category learning with trial-unique source information, we examined how different learning sequences (blocked vs. interleaved) impact the acquisition of generalized (category-level) and detailed (exemplar-specific) knowledge. Participants were trained to identify paintings by different artists, half of which were studied in a sequence blocked by artist and the remainder interleaved between artists. Participants were tested on general knowledge (category induction) and detailed memory (source recall), both immediately after learning and a 1-week delay. We found that interleaved learning improved general knowledge, but blocked learning improved detailed memory. Furthermore, we found that general knowledge remained stable whereas detailed memory performance declined after a delay. Our results indicate that optimal training conditions differ based on the goals of learning such as enhancing general knowledge or improving memory of individual event details.

Joint Channel Estimation and Synchronization Techniques for Time-Interleaved Block-Windowed Burst OFDM

From a conceptual perspective, beyond-5G technologies promise to deliver very low latency, even higher data rates, and ultrareliable connections for future generations of communication systems. Modulation schemes based on orthogonal frequency-domain multiplexing (OFDM) can accommodate these requirements for wireless systems. Several hybrid OFDM-based systems, such as the time-interleaved block-windowed burst–OFDM (TIBWB–OFDM), are capable of achieving even better spectral confinement and power efficiency. This paper addresses the implementation of the TIBWB–OFDM system in more realistic and practical wireless link scenarios by addressing the challenges of proper and reliable channel estimation and frame synchronization. We propose to incorporate a preamble formed by optimal correlation training sequences such as the Zadoff–Chu (ZC) sequences. The added ZC preamble sequence is used to jointly estimate the frame beginning through signal-correlation strategies and a threshold decision device, and acquire channel-state information (CSI) by employing estimators on the basis of the preamble sequence and transmitted data. The employed receiver estimators show that it is possible to detect the TIBWB–OFDM frame beginning and highlight the robustness of the TIBWB–OFDM technique to imperfect channel estimations by showing that it can provide comparatively close BER performance to the one where the CSI is perfectly known.

Infinite use of finite means? Evaluating the generalization of center embedding learned from an artificial grammar

Human language is often assumed to make "infinite use of finite means" - that is, to generate an infinite number of possible utterances from a finite number of building blocks. From an acquisition perspective, this assumed property of language is interesting because learners must acquire their languages from a finite number of examples. To acquire an infinite language, learners must therefore generalize beyond the finite bounds of the linguistic data they have observed. In this work, we use an artificial language learning experiment to investigate whether people generalize in this way. We train participants on sequences from a simple grammar featuring center embedding, where the training sequences have at most two levels of embedding, and then evaluate whether participants accept sequences of a greater depth of embedding. We find that, when participants learn the pattern for sequences of the sizes they have observed, they also extrapolate it to sequences with a greater depth of embedding. These results support the hypothesis that the learning biases of humans favor languages with an infinite generative capacity.

An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications

By modulating the optical power of the light-emitting diode (LED) in accordance with the electrical source and using a photodetector to convert the corresponding optical variation back into electrical signals, visible light communication (VLC) has been developed to achieve lighting and communications simultaneously, and is now considered one of the promising technologies for handling the continuing increases in data demands, especially indoors, for next generation wireless broadband systems. During the process of electrical-to-optical conversion using LEDs in VLC, however, signal distortion occurs due to LED nonlinearity, resulting in VLC system performance degradation. Artificial neural networks (ANNs) are thought to be capable of achieving universal function approximation, which was the motivation for introducing ANN predistortion to compensate for LED nonlinearity in this paper. Without using additional training sequences, the related parameters in the proposed ANN predistorter can be adaptively updated, using a feedback replica of the original electrical source, to track the LED time-variant characteristics due to temperature variation and aging. Computer simulations and experimental implementation were carried out to evaluate and validate the performance of the proposed ANN predistorter against existing adaptive predistorter schemes, such as the normalized least mean square predistorter and the Chebyshev polynomial predistorter.