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 the maximization of the overall power efficiency when communicating over a noisy single-input single-output channel while satisfying a certain throughput constraint. The total power dissipation includes the transmit power as well as the analog circuit power consumption. In the context of battery operated short range communication, where low power, low cost and small size are key requirements (e.g. standard IEEE 802.15.4), this circuit aware system optimization is crucial given the growing importance of "Green Communication". In fact, the transmit power in such applications reaches values in the order of or is even smaller than the power dissipation of certain analog components along the signal path. Using an appropriate information-theoretic framework we derive the optimal bit-resolution of the Analog-to-Digital Converter (ADC), the optimal choice of the noise figure for the low noise amplifier (LNA) and the optimal operating input back-off (IBO) of the Power Amplifier (PA) as a function of the path-loss (i.e. the communication distance). This work is an extension to the work [1], where only the ADC and transmit power have been considered.

Circuit aware design of power-efficient short range communication systems

Millimeter wave (mmWave) massive MIMO cellular systems will be characterized by increased bandwidths and the ability to handle a large number of users. To cope with the power consumption problem due to an increased number of receive antennas, the idea of equipping one-bit ADCs at the base station has been proposed. The goal of this paper is to establish performance bounds on the channel estimation of one-bit mmWave massive MIMO receivers for different types of channel models. The Cramer-Rao bound (CRB) is a lower bound on the performance of unbiased estimators and sets a benchmark for the design of channel estimators. We consider both a structured channel model for a single user where the channel is composed of a superposition of multipaths characterized by path delays and directions-of-arrival (DOAs), and an unstructured channel model where the channel is a generic FIR filter. The Fisher information matrix (FIM) for these channel models are derived in closed form. The CRB is also extended to a dictionary-based channel model, where the path delays and DOAs are selected from small perturbations on a discrete grid, and a sparsity constraint applies to the vector of path loss components. We also derive the Bayesian CRB when the array response is imperfectly known and is affected by perturbations in the sensor pattern or position. The CRBs are evaluated numerically and the effects of various system parameters on the CRB are studied. The dependencies between channel parameters and the effect of array perturbations are also investigated.

Channel Estimation in One-Bit Massive MIMO Systems: Angular Versus Unstructured Models

We address the joint optimization of transmitter and receivers for a multi-user multiple-input multiple-output (MIMO) broadcast channel (BC) system under the assumption of perfect channel state information (CSI) at both transmitter and receivers. Tomlinson Harashima precoding (THP) is employed for interuser interference presubtraction and the mean square error (MSE) is minimized. Since the downlink problem is difficult to handle, we formulate an equivalent uplink problem by exploiting the duality between THP and decision feedback equalization (DFE). We present an iterative solution, which delivers suboptimum transmit and receive matrices as well as a suboptimum precoding order. The performance of the algorithm is studied theoretically and experimentally

Iterative THP Transceiver Optimization for Multi-User MIMO Systems Based on Weighted Sum-Mse Minimization

A long-haul transmission of 100 Gb/s without optical chromatic-dispersion (CD) compensation provides a range of benefits regarding cost effectiveness, power budget, and nonlinearity tolerance. The channel memory is largely dominated by CD in this case with an intersymbol-interference spread of more than 100 symbol durations. An efficient implementation of digital CD compensation is feasible by frequency-domain (FD) filtering. Still the large size of the Fourier transform requires a high gate-count and a large chip size. We propose a new FD filtering on the basis of a nonmaximally decimated discrete Fourier transform filter bank with a trivial prototype filter and a delayed single-tap equalizer per sub-band. This method, which can be regarded as an extension to the popular overlap-save method, allows us to increase the CD tolerance drastically. At the same time, the implementation complexity is not altered apart from adding simple memory elements realizing sub-band delays. With this technique, the uncompensated trans-Pacific transmission becomes feasible with the digital CD compensation.

Delayed Single-Tap Frequency-Domain Chromatic-Dispersion Compensation

We investigate the Direction of Arrival (DoA) estimation for smalland large-scale antenna arrays with a small and a large number of antenna elements, respectively. Two classes of algorithms are considered, namely subspaceand compressed sensing (CS)-based algorithms. We compare those algorithms in terms of both the DoA estimation performance and the computational complexity based on different parameters such as number of antenna elements, number of snapshots and quantization. From this comparison, we conclude that the subspace-based method ESPRIT is well suited for small-scale antenna systems while the CS-based method IHT is advantageous for large-scale antenna systems.

Amine Mezghani

Papers

Circuit aware design of power-efficient short range communication systems

Channel Estimation in One-Bit Massive MIMO Systems: Angular Versus Unstructured Models

Iterative THP Transceiver Optimization for Multi-User MIMO Systems Based on Weighted Sum-Mse Minimization

Delayed Single-Tap Frequency-Domain Chromatic-Dispersion Compensation

DoA Estimation Performance and Computational Complexity of Subspace- and Compressed Sensing-based Methods