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 propose a method to increase the capacity achieved by uniform prior in discrete memoryless channels (DMC) with high input cardinality. It consists in appropriately reducing the input set. Different design criteria of the input subset are discussed. We develop an efficient algorithm to solve this problem based on the maximization of the cut-off rate. The method is applied to a mono-bit transceiver MIMO system, and it is shown that the capacity can be approached within tenths of a dB by employing standard binary codes while avoiding the use of distribution shapers.

Achieving near-capacity on large discrete memoryless channels with uniform distributed selected input

We present a unified model for connected antenna arrays with a large number of tightly integrated (i.e., coupled) antennas in a compact space within the context of massive multiple-input multiple-output (MIMO) communication. We refer to this system as tightly-coupled massive MIMO. From an information-theoretic perspective, scaling the design of tightly-coupled massive MIMO systems in terms of the number of antennas, the operational bandwidth, and form factor was not addressed in prior art. We investigate this open research problem using a physically consistent modeling approach for far-field (FF) MIMO communication based on multi-port circuit theory. In doing so, we turn mutual coupling (MC) from a foe to a friend of MIMO systems design, thereby challenging a basic percept in antenna systems engineering that promotes MC mitigation/compensation. We show that tight MC widens the operational bandwidth of antenna arrays thereby unleashing a missing MIMO gain that we coin"bandwidth gain". Furthermore, we derive analytically the asymptotically optimum spacing-to-antenna-size ratio by establishing a condition for tight coupling in the limit of large-size antenna arrays with quasi-continuous apertures. We also optimize the antenna array size while maximizing the achievable rate under fixed transmit power and inter-element spacing. Then, we study the impact of MC on the achievable rate of MIMO systems under line-of-sight (LoS) and Rayleigh fading channels. These results reveal new insights into the design of tightly-coupled massive antenna arrays as opposed to the widely-adopted"disconnected"designs that disregard MC by putting faith in the half-wavelength spacing rule.

Super-Wideband Massive MIMO

As larger bandwidths are used in multiple antenna wireless systems, the frequency selectivity of the antenna arrays starts to impact rate. Motivated by optimizing the achievable rate in compact antenna arrays, we present a system model that incorporates the effects of mutual coupling (MC) of wideband physically realizable single-input multiple-output (SIMO) and multiple-input single-output (MISO) antenna systems. For the SIMO system setup, we extract the noise correlation matrices for two different antenna array configurations (parallel and co-linear). We optimize the inter-element spacing in each alignment while maximizing the achievable rate and fixing the transmit power. Then, we compare the two compact antenna designs to a perfectly matched single omni-directional antenna while accounting for MC. Likewise, for the MISO antenna system, we derive the optimal beamformer that maximizes the achievable rate using the same antenna configurations as the SIMO system. Then, we study the impact of MC and develop a new single-port matching technique for wideband antenna arrays. Finally, we provide reciprocity plots to compare the performance of the SIMO-MISO systems using different channel models.

Optimizing the Mutual Information of Frequency-Selective Multi-Port Antenna Arrays in the Presence of Mutual Coupling

In this work, we present two design methods of the root raised cosine (RRC) pulse shaper in the transmitter of a Nyquist wavelength division multiplexing (WDM) system. Spectral shaping is one of the techniques needed to meet the requirement for high spectral efficiency in high bandwidth applications. A frequency domain (FD) design and a time domain (TD) design of the RRC filter are given and compared based on their length and their spectral shape. The spectral shape of a frequency domain RRC filter desgined with double degrees of freedom as the time domain finite impulse response (FIR) RRC filter has side lobes with less power. Additionally, the root sum squared values of the intersymbol interference (ISI) with an FD design is lower than with a TD FIR design.

Frequency Domain vs. Time Domain Filter Design of RRC Pulse Shaper for Spectral Confinement in High Speed Optical Communications

We perform CD compensation in the frequency domain through non-maximally decimated DFT filter bank with one-tap per sub-channel equalizer. Larger CD values are tolerated at the cost of slightly increased complexity compared to overlap-and-discard method.

Amine Mezghani

Papers

Achieving near-capacity on large discrete memoryless channels with uniform distributed selected input

Super-Wideband Massive MIMO

Optimizing the Mutual Information of Frequency-Selective Multi-Port Antenna Arrays in the Presence of Mutual Coupling

Frequency Domain vs. Time Domain Filter Design of RRC Pulse Shaper for Spectral Confinement in High Speed Optical Communications

CD equalization with non-maximally decimated DFT filter bank