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

This study formulates a novel precoding criterion for multiuser MIMO systems based on the minimization of the symbol error probability at the users. Unlike previous formulations that require the symbols to belong to a QPSK modulation the proposed criterion allows the utilization of PSK modulation in general. Based on the proposed minimum symbol error probability criterion a discrete programming problem is derived. Using a sophisticated branch-and-bound method the proposed precoding problem is optimally solved. Numerical results show that the proposed precoding method outperforms the state-of-the-art techniques for all examined SNR values in terms of symbol-error-rate.

Minimum Union Bound Symbol Error Probability Precoding for PSK Modulation and Phase Quantization

We consider a controllable multi-user wireless communications system based on reconfigurable intelligent surface (RIS) and investigate the problem of optimizing active and passive beamforming jointly, with the presence of mutual coupling effects. Due to the sub-wavelength structure, mutual coupling between RIS elements is unavoidable. We investigate the effect of mutual coupling among RIS elements resulting in multiple reflection effects which are ignored in conventional (approximative) RIS models. We propose a novel method to optimize the RIS phase shifters considering the effect of multiple reflections using a more physically-consistent model (exact RIS model). Numerical results show that the inherent multiple reflection effects along with the optimized phase shifters and the active precoder can improve the performance of RIS-aided wireless communications systems. 

Beamforming Optimization in RIS-Aided Mimo Systems Under Multiple-Reflection Effects

We present a channel-matched coding method for the compensation of dispersion in coarsely quantized coherent optical communication systems. The approach consists in optimizing a subset of equiprobable input symbols for each channel state, which is robust against dispersion and coarse quantization. This codebook optimization is solved efficiently using an iterative algorithm. The performance of the channel-matched codes is simulated for a 42.8Gbit/s dual-polarization coherent optical fiber system with QPSK modulation and single-bit analog-todigital (A/D) conversion. Apart from the optimization, the proposed coding method does not increase the encoding/decoding complexity, but yields a considerable coding gain.

Channel-Matched Coding for Coarsely Quantized Coherent Optical Communication Systems

We introduce the bilinear generalized vector approximate message passing (BiG-VAMP) algorithm which jointly recovers rank-deficient matrices from their noisy product through a probabilistic observation model. BiG-VAMP provides computationally efficient approximate implementations of both max-sum and sum-product loopy belief propagation (BP). We show how the proposed BiG-VAMP recovers different types of structured matrices and overcomes the fundamental limitations of other state-of-the-art techniques 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 that involve a general form of structured matrices. Numerical results on applications such as dictionary learning and real- valued/quantized matrix completion demonstrate the effectiveness of the proposed BiG-VAMP algorithm and its superiority over state-of-the-art techniques.

BiG-VAMP: The Bilinear Generalized Vector Approximate Message Algorithm

Delivering wireless ultrahigh-speed access at wider coverage is becoming considerably challenging due to the prohibitive investment costs per user and the necessary shift to range-limited millimeter-wave (mm Wave) transmissions. Reconfigurable intelligent surfaces (RIS) are expected to extend the reach of mm Wave and TeraHz signals more cost-effectively in situations where fiber backhaul and fronthaul are not accessible or infrastructure densification is costly. This paper investigates some challenges facing this technology, particularly in terms of scalability and the question of what type of RIS configurations would be appropriate for mmWave networks and what design strategies can be adopted to optimize the performance and minimize the signaling overhead. We conclude that the RIS-based wireless infrastructure should be mainly based on redirective (angular selective) rather than reflective RIS configurations but some issues around scalability need to be addressed.

Amine Mezghani

Papers

Minimum Union Bound Symbol Error Probability Precoding for PSK Modulation and Phase Quantization

Beamforming Optimization in RIS-Aided Mimo Systems Under Multiple-Reflection Effects

Channel-Matched Coding for Coarsely Quantized Coherent Optical Communication Systems

BiG-VAMP: The Bilinear Generalized Vector Approximate Message Algorithm

Reconfigurable Intelligent Surfaces for Quasi-Passive mmWave and THz Networks: Should They be Reflective or Redirective?