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

Dealing with nonlinear effects of the radio-frequency(RF) chain is a key issue in the realization of very large-scale multi-antenna (MIMO) systems. Achieving the remarkable gains possible with massive MIMO requires that the signal processing algorithms systematically take into account these effects. Here, we present a computationally efficient linear precoding method satisfying the requirements for low peak-to-average power ratio (PAPR) and low-resolution D/A-converters (DACs). The method is based on a sparse regularization of the precoding matrix and offers advantages in terms of precoded signal PAPR as well as processing complexity. Through simulation, we find that the method substantially improves conventional linear precoders.

Sparse Linear Precoders for Mitigating Nonlinearities in Massive MIMO

—Delivering wireless ultrahigh-speed access at wider coverage is becoming considerably challenging due to the pro-hibitive investment costs per user and the necessary shift to range-limited millimeter-wave (mmWave) transmissions. Reconﬁgurable intelligent surfaces (RIS) are expected to extend the reach of mmWave 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 RIS conﬁgurations for the wireless infrastructure likely need to be nonlocal (i.e., redirective, wavefront-selective) rather than local (i.e., reﬂective) to support communications and networking tasks such as integrated fronthaul and access (IFA) most efﬁciently.

Nonlocal Reconfigurable Intelligent Surfaces for Wireless Communication: Modeling and Physical Layer Aspects

This paper tackles the problem of joint active and passive beamforming optimization for an intelligent reflective surface (IRS)-assisted multi-user downlink multiple-input multiple-output (MIMO) communication system. We aim to maximize spectral efficiency of the users by minimizing the mean square error (MSE) of the received symbol. For this, a joint optimization problem is formulated under the minimum mean square error (MMSE) criterion. First, block coordinate descent (BCD) is used to decouple the joint optimization into two sub-optimization problems to separately find the optimal active precoder at the base station (BS) and the optimal matrix of phase shifters for the IRS. While the MMSE active precoder is obtained in a closed form, the optimal phase shifters are found iteratively using a modified version (also introduced in this paper) of the vector approximate message passing (VAMP) algorithm. We solve the joint optimization problem for two different models for IRS phase shifts. First, we determine the optimal phase matrix under a unimodular constraint on the reflection coefficients, and then under the constraint when the IRS reflection coefficients are modeled by a reactive load, thereby validating the robustness of the proposed solution. Numerical results are presented to illustrate the performance of the proposed method using multiple channel configurations. The results validate the superiority of the proposed solution as it achieves higher throughput compared to state-of-the-art techniques.

Joint Active and Passive Beamforming Design for IRS-Assisted Multi-User MIMO Systems: A VAMP-Based Approach

This study focuses on the development of low-resolution symbol-level precoding techniques for multiuser MIMO downlink systems with PSK modulation. While for QPSK the established minimum symbol error probability criterion is used, a criterion for PSK modulation, in general, is proposed based on the minimum union-bound symbol-error probability. Based on these criteria different low-resolution precoding approaches are proposed. First, suboptimal solutions are computed via a partial greedy search method. Then the suboptimal solutions are utilized as initialization for a novel branch-and-bound algorithm that can exploit knowledge of the system’s quality-of-service demands. Different than existing branch-and-bound approaches the proposed quality-of-service branch-and-bound method searches for a solution that attains a target symbol-error probability while going in the direction of the global optimal solution. In this sense, the proposed branch-and-bound method allows for tunable complexity performance trade-offs. Numerical results confirm that the proposed quality-of-service branch-and-bound algorithm yields reduced symbol-error probability with significantly smaller computational complexity than other state-of-the-art branch-and-bound designs. 

Minimum Symbol Error Probability Discrete Symbol Level Precoding for MU-MIMO Systems with PSK Modulation

Most research studies on deep learning (DL) applied to the physical layer of wireless communication do not put forward the critical role of the accuracy-generalization trade-off in developing and evaluating practical algorithms. To highlight the disadvantage of this common practice, we revisit a data decoding example from one of the first papers introducing DL-based end-to-end wireless communication systems to the research community and promoting the use of artificial intelligence (AI)/DL for the wireless physical layer. We then put forward two key trade-offs in designing DL models for communication, namely, accuracy versus generalization and compression versus latency. We discuss their relevance in the context of wireless communications use cases using emerging DL models including large language models (LLMs). Finally, we summarize our proposed evaluation guidelines to enhance the research impact of DL on wireless communications. These guidelines are an attempt to reconcile the empirical nature of DL research with the rigorous requirement metrics of wireless communications systems.

Amine Mezghani

Papers

Sparse Linear Precoders for Mitigating Nonlinearities in Massive MIMO

Nonlocal Reconfigurable Intelligent Surfaces for Wireless Communication: Modeling and Physical Layer Aspects

Joint Active and Passive Beamforming Design for IRS-Assisted Multi-User MIMO Systems: A VAMP-Based Approach

Minimum Symbol Error Probability Discrete Symbol Level Precoding for MU-MIMO Systems with PSK Modulation

From Multilayer Perceptron to GPT: A Reflection on Deep Learning Research for Wireless Physical Layer