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 consider wireless communication systems with compact planar arrays having densely spaced antenna elements in conjunction with one-bit analog-to-digital and digital-to-analog converters (ADCs/DACs). We provide closed-form expressions for the achievable rates with simple linear processing techniques for the uplink as well as the downlink scenarios while taking into account the effects of antenna mutual coupling. In the downlink case, we introduce the concept of non-radiating dithering to combat correlations of the quantization errors. Under higher antenna element density, we show that the performance of the quantized system can be made close to the ideal performance regardless of the operating signal-to-noise ratio.

Massive MIMO with Dense Arrays and 1-bit Data Converters.

We introduce a mutual information based optimization for a two-port multiple-input single-output (MISO) antenna system. We develop a complete circuit-level analysis of a compact MISO system in the wideband regime. We design a physically realizable antenna array and study the impact of mutual coupling on the spectral efficiency. Then, we maximize the system’s mutual information by optimizing the beamformer under two different power constraints, namely the total dissipated power and the available power of the amplifiers. By varying the inter-element antenna spacing, we present results for the achievable spectral efficiency under different power amplifier constraints.

Beamforming optimization of wideband MISO systems in the presence of mutual coupling

This paper addresses the optimization of constrained stationary Markov input processes which achieve high information rates on intersymbol-interference (ISI) channels. The considered Markov processes define an optimized subset of equiprobable input symbols for each channel state and are thus uniquely described by the set of branches in a trellis section. We propose an iterative algorithm that efficiently solves the optimization problem. The algorithm successively removes the worst branches and thereby constructs a reduced trellis code that is matched to the channel. The second contribution of this paper are tight lower bounds for the mutual information rate (MIR) of Markov sources on ISI channels with finite input and output alphabets. The bounds can be evaluated within a small trellis window without using Monte Carlo methods.

Channel-matched trellis codes for finite-state intersymbol-interference channels

Nonlinear precoding and pulse shaping are jointly considered in multi-user massive multiple-input multiple-output (MIMO) systems with low-resolution D/A-converters (DACs) in terms of algorithmic approach as well as large system performance. Two design criteria are investigated: the mean {squared} error (MSE) with active constellation extension (ACE) and the symbol error rate (SER). Both formulations are solved based on a modified version of the generalized approximate message passing (GAMP) algorithm. Furthermore, theoretical performance results are derived based on the state evolution analysis of the GAMP algorithm. The MSE based technique is extended to jointly perform over-the-air (OTA) spectral shaping and precoding for frequency-selective channels, in which the spectral performance is characterized at the transmitter and at the receiver. Simulation and analytical results demonstrate that the MSE based approach yields the same performance as the SER based formulation in terms of uncoded SER. The analytical results provide good performance predictions up to medium SNR. Substantial improvements in detection, as well as spectral performance, are obtained from the proposed combined pulse shaping and precoding approach compared to standard linear methods.

Massive MIMO precoding and spectral shaping with low resolution DACs and active constellation extension.

A fully-digital wideband joint communication-radar (JCR) at the millimeter-wave (mmWave) band will simultaneously enable high communication and radar performances with enhanced design flexibility. In this paper, we present a measurement platform with a software-defined architecture to evaluate and demonstrate the performance of these JCR systems using real channel measurements. We develop this platform by extending a mmWave communication set-up with an additional full-duplex radar receiver and by capturing the MIMO JCR channel using a moving antenna on a sliding rail. To characterize the JCR performance, we conduct indoor experiments and apply traditional/advanced processing algorithms on the measured data. The results demonstrate that our testbed at 73 GHz with 2 GHz bandwidth can capture the JCR channel with high range/direction estimation accuracy. The comparison between the communication and radar channel shows the potential for improving JCR performance by exploiting the antenna diversity due to widely-separated communication and radar receivers.

Amine Mezghani

Papers

Massive MIMO with Dense Arrays and 1-bit Data Converters.

Beamforming optimization of wideband MISO systems in the presence of mutual coupling

Channel-matched trellis codes for finite-state intersymbol-interference channels

Massive MIMO precoding and spectral shaping with low resolution DACs and active constellation extension.

A MIMO Joint Communication-Radar Measurement Platform at the Millimeter-Wave Band : (Invited Paper)