The Theory of Matrices. By F R. Gantmacher. Two volumes, pp. 374 and 276. 1959. (Translated from the Russian by K. A. Hirsch; Chelsea Publishing Company, New York)

Matrix Differential Calculus with Applications in Statistics and Econometrics

Fast Fourier Transform

The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

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Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

Radio access technologies for cellular mobile communications are typically characterized by multiple access schemes, e.g., frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and OFDMA. In the 4th generation (4G) mobile communication systems such as Long-Term Evolution (LTE) (Au et al., Uplink contention based SCMA for 5G radio access. Globecom Workshops (GC Wkshps), 2014. doi:10.1109/GLOCOMW.2014.7063547) and LTE-Advanced (Baracca et al., IEEE Trans. Commun., 2011. doi:10.1109/TCOMM.2011.121410.090252; Barry et al., Digital Communication, Kluwer, Dordrecht, 2004), standardized by the 3rd Generation Partnership Project (3GPP), orthogonal multiple access based on OFDMA or single carrier (SC)-FDMA is adopted. Orthogonal multiple access was a reasonable choice for achieving good system-level throughput performance with simple single-user detection. However, considering the trend in 5G, achieving significant gains in capacity and system throughput performance is a high priority requirement in view of the recent exponential increase in the volume of mobile traffic. In addition the proposed system should be able to support enhanced delay-sensitive high-volume services such as video streaming and cloud computing. Another high-level target of 5G is reduced cost, higher energy efficiency and robustness against emergencies.

Non-Orthogonal Multiple Access (NOMA) for cellular future radio access

Least mean square (LMS) based adaptive algorithms have been attracted much attention since their low computational complexity and robust recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods, e.g., zero-attracting LMS (ZA-LMS), reweighted zero-attracting LMS (RZA-LMS) and L p - norm sparse LMS (LP-LMS), have also been proposed. To take full advantage of channel sparsity, in this paper, we propose several improved adaptive sparse channel estimation methods using L p -norm normalized LMS (LP-NLMS) and L 0 -norm normalized LMS (L0-NLMS). Comparing with previous methods, effectiveness of the proposed methods is confirmed by computer simulations.

https://www.mobile.ecei.tohoku.ac.jp/paper/pdf/inter_2013/01_in_2013_gui.pdf

Improved adaptive sparse channel estimation based on the least mean square algorithm

In this paper, we investigate and analyze a hybrid ambient backscatter (AmBack) scheme with harvest-then-transmit protocols In this scheme, the whole transmission is divided into three phases (time slots): energy harvesting (EH), AmBack, and data transmission (DT) Depending on the type of the reflection coefficient (RC) for AmBack, two hybrid schemes are considered: variable RC (VRC) and fixed RC (FRC) In the VRC scheme, the RC changes with the channel state information, and the harvested energy in the first phase is totally used for DT On the other hand, in the FRC scheme, the RC is fixed to be one, and the harvested energy in the first phase is in part used to cover the tag circuit operation in the second phase and in part for DT in the third phase The resulting optimization problem is convex for the VRC scheme, but non-convex for the FRC scheme However, by utilizing the maximum principle in convex optimization, we are able to derive closed-form expressions for optimal solutions for both schemes In order to get more insights, we also analyze the upper bound performance of both schemes Simulation results demonstrate that the AmBack/EH-AmBack scheme can always achieve the optimal performance

Hybrid Ambient Backscatter Communication Systems With Harvest-Then-Transmit Protocols

Adaptive system identification ASI problems have attracted both academic and industrial attentions for a long time. As one of the classical approaches for ASI, performance of least mean square LMS is unstable in low signal-to-noise ratio SNR region. On the contrary, least mean fourth LMF algorithm is difficult to implement in practical system because of its high computational complexity in high SNR region, and hence it is usually neglected by researchers. In this paper, we propose an effective approach to identify unknown system adaptively by using combined LMS and LMF algorithms in different SNR regions. Experiment-based parameter selection is established to optimize the performance as well as to keep the low computational complexity. Copyright © 2013 John Wiley & Sons, Ltd.

Adaptive system identification using robust LMS/F algorithm

-Wideband wireless channel is a time dispersive channel and becomes strongly frequency-selective However, in most cases, the channel is composed of a few dominant taps and a large part of taps is approximately zero or zero To exploit the sparsity of multi-path channel (MPC), two methods have been proposed They are, namely, greedy algorithm and convex program Greedy algorithm is easy to be implemented but not stable; on the other hand, the convex program method is stable but difficult to be implemented as practical channel estimation problems In this paper, we introduce a novel channel estimation strategy using compressive sampling matching pursuit (CoSaMP) algorithm which was proposed in [1] This algorithm will combine the greedy algorithm with the convex program method The effectiveness of the proposed algorithm will be confirmed through comparisons with the existing methods 0 I I NTRODUCTION Coherent detection in wideband mobile communication systems often requires accurate channel state information at a receiver The study of channel estimation for the purposes of channel equalization has a long history In many studies, densely distributed channel impulse response was often assumed Under this assumption, it is necessary to use a long training sequence In addition, the linear channel estimation methods, such as least square (LS) algorithm, always lead to bandwidth inefficiency It is an interesting study to develop more bandwidth efficient method to acquire channel information Recently, the compressive sensi ng (CS) has been developed as a new technique It is regarded as an efficient signal acquisition framework for signals characterized as sparse or compressible in time or frequency domain One application of the CS technique is in channel estimation If the channel impulse response follows sparse distribution, we can apply the CS technique As a result, the training sequence length can be shortened compared with the linear estimation methods Recent measurements show that the sparse or approximate sparse distribution assumption is reasonable [2, 3] In other words, the wireless channels in real propagation environments are characterized as sparse or sparse clustered; these sparse or clustered channels are frequently termed as a sparse multi-path channel (SMPC) An example of SMPC impulse response channel is shown in Fig1 Recently, the study on SMPC has drawn a lot of attentions and concerning results can be found in literature [4-6] Correspondingl y, sparse channel estimation technique has also received considerable interest for its advantages in high bit rate transmissions over multipath channel [7]

Sparse Multipath Channel Estimation Using Compressive Sampling Matching Pursuit Algorithm

The massive MIMO channel is characterized by non-stationarity and fast variation, thereby the channel state information obtained by traditional methods will be outdated and the system performance will be degraded. In this paper, we propose a channel prediction algorithm in massive MIMO environments. First, considering the channel characteristics, we propose a first-order Taylor expansion-based predictive channel modeling method. Then, a channel prediction algorithm consisting of the estimation stage and prediction stage is proposed and the interval of effective prediction (IEP) is derived. The performance of the proposed algorithm is testified by numerical simulations. It is shown that, within the IEP, a reliable channel prediction can be obtained with low computational complexity.

Wei Peng

Papers

Improved adaptive sparse channel estimation based on the least mean square algorithm

Hybrid Ambient Backscatter Communication Systems With Harvest-Then-Transmit Protocols

Adaptive system identification using robust LMS/F algorithm

Sparse Multipath Channel Estimation Using Compressive Sampling Matching Pursuit Algorithm

Channel Prediction in Time-Varying Massive MIMO Environments