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.

https://www.nowpublishers.com/article/DownloadSummary/SIG-093

Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency

Digital Coding of Waveforms

We consider general multi-antenna fading channels with coarsely quantized outputs, where the channel is unknown to the transmitter and receiver. This analysis is of interest in the context of sensor network communication where low power and low cost are key requirements (e.g. standard IEEE 802.15.4 applications). This is also motivated by highly energy constrained communications devices where sampling the signal may be more energy consuming than processing or transmitting it. Therefore the analog-to-digital converters (ADCs) for such applications should be low-resolution, in order to reduce their cost and power consumption. In this paper, we consider the extreme case of only 1-bit ADC for each receive signal component. We derive asymptotics of the mutual information up to the second order in the signal-to-noise ratio (SNR) under average and peak power constraints and study the impact of quantization. We show that up to second order in SNR, the mutual information of a system with two-level (sign) output signals incorporates only a power penalty factor of almost 1.96 dB compared to the system with infinite resolution for all channels of practical interest. This generalizes a recent result for the coherent case.

Analysis of 1-bit Output Noncoherent Fading Channels in the Low SNR Regime

In multiple-antenna communications, as bandwidth and modulation order increase, system components must work with demanding tolerances. In particular, high resolution and high sampling rate analog-to-digital converters (ADCs) are often prohibitively challenging to design. Therefore ADCs for such applications should be low-resolution. This paper provides new insights into the problem of optimal signal detection based on quantized received signals for multiple-input multiple-output (MIMO) channels. It capitalizes on previous works [1], [2], [3], [4] which extensively analyzed the unquantized linear vector channel using graphical inference methods. In particular, a “loopy” belief propagation-like (BP) MIMO detection algorithm, operating on quantized data with low complexity, is proposed. In addition, we study the impact of finite receiver resolution in fading channels in the large-system limit by means of a state evolution analysis of the BP algorithm, which refers to the limit where the number of transmit and receive antennas go to infinity with a fixed ratio. Simulations show that the theoretical findings might give accurate results even with moderate number of antennas.

Belief propagation based MIMO detection operating on quantized channel output

We consider general multi-antenna fading channels with coarsely quantized outputs, where the channel is unknown to the transmitter and receiver. This analysis is of interest in the context of sensor network communication where low power and low cost are key requirements (e.g. standard IEEE 802.15.4 applications). This is also motivated by highly energy constrained communications devices where sampling the signal may be more energy consuming than processing or transmitting it. Therefore the analog-to-digital converters (ADCs) for such applications should be low-resolution, in order to reduce their cost and power consumption. In this paper, we consider the extreme case of only 1-bit ADC for each receive signal component. We derive asymptotics of the mutual information up to the second order in the signal-to-noise ratio (SNR) under average and peak power constraints and study the impact of quantization. We show that up to second order in SNR, the mutual information of a system with two-level (sign) output signals incorporates only a power penalty factor of almost π/2 (1.96 dB) compared to the system with infinite resolution for all channels of practical interest. This generalizes a recent result for the coherent case [1].

Analysis of 1-bit output noncoherent fading channels in the low SNR regime

We study the problem of downlink beamforming for the Weighted Sum Rate maximization (WSR) of Multi-User Multiple-Input-Single-Output systems with low-resolution Digital-to-Analog Converters (DACs) in a single-cell setup. The DACs, modeled as quantizers, are performing a nonlinear operation on the signals and are linearized using Bussgang decomposition and a linear approximation of the covariance of quantized signals. For the maximization of the WSR of the linearized system, we propose a gradient-based solution and a lower-complexity heuristic solution, based on the structure of the globally optimal solution. Through numerical simulations, we show that taking quantization into account in the filter design results in significant performance improvement when the number of transmit antennas is comparable to the number of users. When the number of transmit antennas becomes much larger than the number of users, it is found that the heuristic solution achieves near-optimal performance and that a quantization-aware design becomes less important.

Weighted Sum Rate Maximization for Multi-User MISO Systems with Low Resolution Digital to Analog Converters

In multiple-antenna communications, as bandwidth and modulation order increase, system components must work with demanding tolerances. In particular, high resolution and high sampling rate analog-to-digital converters (ADCs) are often prohibitively challenging to design. Therefore ADCs for such applications should be low-resolution. This paper provides new insights into the problem of optimal signal detection based on quantized received signals for multiple-input multiple-output (MIMO) channels. It capitalizes on previous works which extensively analyzed the unquantized linear vector channel using graphical inference methods. In particular, a "loopy" belief propagation-like (BP) MIMO detection algorithm, operating on quantized data with low complexity, is proposed. In addition, we study the impact of finite receiver resolution in fading channels in the large-system limit by means of a state evolution analysis of the BP algorithm, which refers to the limit where the number of transmit and receive antennas go to infinity with a fixed ratio. Simulations show that the theoretical findings might give accurate results even with moderate number of antennas.

Amine Mezghani

Papers

Analysis of 1-bit Output Noncoherent Fading Channels in the Low SNR Regime

Belief propagation based MIMO detection operating on quantized channel output

Analysis of 1-bit output noncoherent fading channels in the low SNR regime

Weighted Sum Rate Maximization for Multi-User MISO Systems with Low Resolution Digital to Analog Converters

Belief Propagation based MIMO Detection Operating on Quantized Channel Output