Information Theory and Reliable Communication

Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless communication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc networks, and wearables. Signal processing is critical for enabling the next generation of mmWave communication. Due to the use of large antenna arrays at the transmitter and receiver, combined with radio frequency and mixed signal power constraints, new multiple-input multiple-output (MIMO) communication signal processing techniques are needed. Because of the wide bandwidths, low complexity transceiver algorithms become important. There are opportunities to exploit techniques like compressed sensing for channel estimation and beamforming. This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies.

An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems

There is considerable pressure to define the key requirements of 5G, develop 5G standards, and perform technology trials as quickly as possible. Normally, these activities are best done in series but there is a desire to complete these tasks in parallel so that commercial deployments of 5G can begin by 2020. 5G will not be an incremental improvement over its predecessors; it aims to be a revolutionary leap forward in terms of data rates, latency, massive connectivity, network reliability, and energy efficiency. These capabilities are targeted at realizing high-speed connectivity, the Internet of Things, augmented virtual reality, the tactile internet, and so on. The requirements of 5G are expected to be met by new spectrum in the microwave bands (3.3-4.2 GHz), and utilizing large bandwidths available in mm-wave bands, increasing spatial degrees of freedom via large antenna arrays and 3-D MIMO, network densification, and new waveforms that provide scalability and flexibility to meet the varying demands of 5G services. Unlike the one size fits all 4G core networks, the 5G core network must be flexible and adaptable and is expected to simultaneously provide optimized support for the diverse 5G use case categories. In this paper, we provide an overview of 5G research, standardization trials, and deployment challenges. Due to the enormous scope of 5G systems, it is necessary to provide some direction in a tutorial article, and in this overview, the focus is largely user centric, rather than device centric. In addition to surveying the state of play in the area, we identify leading technologies, evaluating their strengths and weaknesses, and outline the key challenges ahead, with research test beds delivering promising performance but pre-commercial trials lagging behind the desired 5G targets.

5G : A tutorial overview of standards, trials, challenges, deployment, and practice

Massive multiple-input multiple-output MIMO is one of themost promising technologies for the next generation of wirelesscommunication networks because it has the potential to providegame-changing improvements in spectral efficiency SE and energyefficiency EE. This monograph summarizes many years ofresearch insights in a clear and self-contained way and providesthe reader with the necessary knowledge and mathematical toolsto carry out independent research in this area. Starting froma rigorous definition of Massive MIMO, the monograph coversthe important aspects of channel estimation, SE, EE, hardwareefficiency HE, and various practical deployment considerations.From the beginning, a very general, yet tractable, canonical systemmodel with spatial channel correlation is introduced. This modelis used to realistically assess the SE and EE, and is later extendedto also include the impact of hardware impairments. Owing tothis rigorous modeling approach, a lot of classic "wisdom" aboutMassive MIMO, based on too simplistic system models, is shownto be questionable.

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Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency

Digital Coding of Waveforms

We present iterative algorithms which efficiently compute an optimized subset of equiprobable input symbols that achieves near-capacity on a large asymmetric discrete memoryless channel (DMC). The optimized subset defines a delay-free channel-adapted inner code which can be combined with a standard code. The introduction of a dense data structure renders it possible to apply channel-adaptive coding to 2-bit quantized MIMO systems, which exhibit a very large output set. The coding approach is shown to be robust to imperfect channel state information (CSI). Finally, we present a binary index switching algorithm that minimizes the BER by optimizing the mapping from code bits to input symbols.

Channel-adaptive coding for coarsely quantized MIMO systems

In this contribution, we investigate a coarsely quantized Multi-User (MU)-Multiple Input Single Output (MISO) downlink communication system, where we assume 1-Bit Digital-to-Analog Converters (DACs) at the Base Station (BS) antennas. First, we analyze the achievable sum rate lower-bound using the Bussgang decomposition. In the presence of the non-linear quanization, our analysis indicates the potential merit of reconsidering traditional signal processing techniques in coarsely quantized systems, i.e., reconsidering transmit covariance matrices whose rank is equal to the rank of the channel. Furthermore, in the second part of this paper, we propose a linear precoder design which achieves the predicted increase in performance compared with a state of the art linear precoder design. Moreover, our linear signal processing algorithm allows for higher-order modulation schemes to be employed.

Reconsidering Linear Transmit Signal Processing in 1-Bit Quantized Multi-User MISO Systems

An efficient filter-tap calculation with minimized number of divisions is reported for training-aided 2 × 2 MIMO frequency domain fractionally-spaced equalizers. Performance evaluation is based on a 28-GBaud PDM-4QAM and PDM-16QAM optical transmission system.

Low-complexity training-aided 2×2 MIMO frequency domain fractionally-spaced equalization

Orthogonal frequency division multiplexing (OFDM) system, known to be very practical for multipath environments, suffers from signal waveform with high Peak-to-Average Power Ratio (PAPR). As a consequence nonlinear distortion are introduced by the radio frequency (RF) components, such as amplitude clipping due to the low noise amplifier (LNA) in low cost receivers. This can significantly affect the symbol error rate (SER) performance of such systems. In this article we propose a method to efficiently estimate the SER performance of OFDM receivers in an industrial test environment, where the transmitter is considered ideal and neither fading nor significant noise are observed. To this end importance sampling (IS) techniques are employed. The application of the proposed method shortens the simulation/measurement time up to three orders of magnitude compared to the time needed by intensive Monte Carlo (MC) simulations. We also compare the IS estimated SER with intensive MC simulations and analytically derived results.

Efficient SER measurement method for OFDM receivers with nonlinear distortion

We introduce the bilinear generalized vector approximate message passing (BiG-VAMP) algorithm which jointly recovers two matrices U and V from their noisy product through a probabilistic observation model. BiG-VAMP provides computationally efficient approximate implementations of both max-sum and sumproduct loopy belief propagation (BP). We show how the proposed BiG-VAMP algorithm recovers different types of structured matrices and overcomes the fundamental limitations of other state-of-the-art approaches to the bilinear recovery problem, such as BiG-AMP, BAd-VAMP and LowRAMP. In essence, BiG-VAMP applies to a broader class of practical applications which involve a general form of structured matrices. For the sake of theoretical performance prediction, we also conduct a state evolution (SE) analysis of the proposed algorithm and show its consistency with the asymptotic empirical mean-squared error (MSE). Numerical results on various applications such as matrix factorization, dictionary learning, and matrix completion demonstrate unambiguously the effectiveness of the proposed BiG-VAMP algorithm and its superiority over stateof-the-art algorithms. Using the developed SE framework, we also examine (as one example) the phase transition diagrams of the matrix completion problem, thereby unveiling a low detectability region corresponding to the low signal-to-noise ratio (SNR) regime.

Amine Mezghani

Papers

Channel-adaptive coding for coarsely quantized MIMO systems

Reconsidering Linear Transmit Signal Processing in 1-Bit Quantized Multi-User MISO Systems

Low-complexity training-aided 2×2 MIMO frequency domain fractionally-spaced equalization

Efficient SER measurement method for OFDM receivers with nonlinear distortion

Bilinear Generalized Vector Approximate Message Passing.