"Written by the pioneers of the concept, this is the first complete guide to the physical and engineering principles of Massive MIMO. Assuming only a basic background in communications and statisti ...

Fundamentals of Massive MIMO

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Linear And Nonlinear Programming

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.

/pdf/towards-6g-wireless-communication-networks-vision-enabling-2tj7a36yux.pdf

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

Intelligent reflecting surfaces (IRSs) constitute a disruptive wireless communication technique capable of creating a controllable propagation environment. In this paper, we propose to invoke an IRS at the cell boundary of multiple cells to assist the downlink transmission to cell-edge users, whilst mitigating the inter-cell interference, which is a crucial issue in multicell communication systems. We aim for maximizing the weighted sum rate (WSR) of all users through jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS subject to each BS’s power constraint and unit modulus constraint. Both the BSs and the users are equipped with multiple antennas, which enhances the spectral efficiency by exploiting the spatial multiplexing gain. Due to the non-convexity of the problem, we first reformulate it into an equivalent one, which is solved by using the block coordinate descent (BCD) algorithm, where the precoding matrices and phase shifts are alternately optimized. The optimal precoding matrices can be obtained in closed form, when fixing the phase shifts. A pair of efficient algorithms are proposed for solving the phase shift optimization problem, namely the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solutions. We also extend the proposed algorithms to the more general multiple-IRS and network MIMO scenarios. Finally, our simulation results confirm the advantages of introducing IRSs in enhancing the cell-edge user performance.

/pdf/multicell-mimo-communications-relying-on-intelligent-x5bnpnqnlw.pdf

Multicell MIMO Communications Relying on Intelligent Reflecting Surfaces

Space-Time Block Coding for Wireless Communications

In this paper, the tracking behaviors of the least mean M-estimate (LMM) and normalized LMM algorithms are analyzed in a unified manner in a non-stationary system described by the random-walk model. In the analysis, we consider the presence of impulsive noise and do not impose a specific distribution on the input signal. For predicting the steady-state performance, we provide analytical expressions for the algorithms. Unlike for the stationary case, the steady-state performance for the non-stationary case does not always improve as the step size decreases. As such, the optimal step size is also derived to reach the best steady-state performance. Simulation results support the theoretical findings.

Tracking Analyses Of M-Estimate Based LMS And NLMS Algorithms

This paper presents a set of transmit diversity selection algorithms based on discrete stochastic optimization for a two-phase, decode-and-forward, multi-relay cooperative MIMO system with a non-negligible direct path. Transmit diversity selection is performed jointly with channel estimation using discrete stochastic and continuous least squares optimization, respectively. Linear minimum mean square error receivers are used at the relay and destination nodes whilst no forward channel knowledge, precoding or inter-relay communication is required. Sets of candidate transmit diversity selections are generated and methods to optimize the selection whilst avoiding exhaustive searching are presented. The benefits of reducing the cardinality of these sets is shown and the performance of the proposed schemes are assessed via mean square error, bit-error rate and complexity comparisons. The performance and diversity achieved is shown to exceed that of standard multi-relay cooperative MIMO systems and random transmit diversity selection, and closely match that of the exhaustive solution.

MMSE transmit diversity selection for multi-relay cooperative MIMO systems using discrete stochastic gradient algorithms

Robust adaptive beamforming (RAB) plays a vital role in modern communications by ensuring the reception of high-quality signals. This article proposes a deep learning approach to robust adaptive beamforming. In particular, we propose a novel RAB approach where the sample covariance matrix (SCM) is used as the input of a deep 1D Complex-Valued Convolutional Neural Network (CVCNN). The network employs complex convolutional and pooling layers, as well as a Cartesian Scaled Exponential Linear Unit activation function to directly compute the nearly-optimum weight vector through the training process and without prior knowledge about the direction of arrival of the desired signal. This means that reconstruction of the interference plus noise (IPN) covariance matrix is not required. The trained CVCNN accurately computes the nearly-optimum weight vector for data not used during training. The computed weight vector is employed to estimate the signal-to-interference plus noise ratio. Simulations show that the proposed RAB can provide performance close to that of the optimal beamformer.

Robust Beamforming Based on Complex-Valued Convolutional Neural Networks for Sensor Arrays

Cloud-Aided Multi-Way Multiple-Antenna Relaying with Best-User Link Selection and Joint ML Detection.

Low-complexity precoding algorithms are proposed in this work to reduce the computational complexity and improve the performance of regularized block diagonalization (RBD) based precoding schemes for large multi-user MIMO (MU-MIMO) systems The proposed algorithms are based on a channel inversion technique, QR decompositions, and lattice reductions to decouple the MU-MIMO channel into equivalent SU-MIMO channels Simulation results show that the proposed precoding algorithms can achieve almost the same sum-rate performance as RBD precoding, substantial bit error rate (BER) performance gains, and a simplified receiver structure, while requiring a lower complexity

/pdf/low-complexity-lattice-reduction-aided-channel-inversion-309zr4njxv.pdf

Rodrigo C. de Lamare

Papers

Tracking Analyses Of M-Estimate Based LMS And NLMS Algorithms

MMSE transmit diversity selection for multi-relay cooperative MIMO systems using discrete stochastic gradient algorithms

Robust Beamforming Based on Complex-Valued Convolutional Neural Networks for Sensor Arrays

Cloud-Aided Multi-Way Multiple-Antenna Relaying with Best-User Link Selection and Joint ML Detection.

Low-complexity lattice reduction-aided channel inversion methods for large multi-User MIMO systems