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

Filter Bank based Multicarrier (FBMC) with Offset-QAM (O-QAM) modulation is one of the strong candidates for the physical layer of 5-th generation mobile communication systems. The combination of FBMC/OQAM with multiple antennas in a multi-user scheme poses some challenges towards the design of equalizers and precoders due to the frequency overlapping of the sub-carriers, especially in environments with highly frequency selective channels. In this work we first calculate a fractionally spaced Minimum Mean Square Error (MMSE)-based equalizer design to accommodate multi-user (MU) Single Input Multiple Output (SIMO)-FBMC. Then, we apply the Mean Square Error (MSE)-duality to calculate the corresponding precoders for MU Multiple Input Single Output (MISO)-FBMC. Furthermore, we present four possibilities for the power allocation between users and sub-carriers. We finally show the performance superiority of the new method compared to a recently proposed method.

SIMO/MISO MSE-Duality for Multi-User FBMC with Highly Frequency Selective Channels

Computation of the impulse response and coding gain of a digital interconnection bus

Advanced multicarrier systems, like the Offset-QAM filter bank based (OQAM-FBMC) ones, are gaining importance as candidates for the physical layer of the 5-th generation of wireless communications. One of the main advantages of FBMC, when compared to traditional cyclic prefix based OFDM, is its higher spectral efficiency. However, this gain can be lost again if the problem of training based channel estimation is not tackled correctly. This is due to the memory inserted by the longer pulse shaping and the loss of orthogonality of overlapping subcarriers. In this paper we approach the problem of training based channel estimation for FBMC systems. We propose an iterative algorithm based on the expectation maximization (EM) maximum likelihood (ML) that reduces the overhead and consequently improves the spectral efficiency.

Enhancing spectral efficiency in advanced multicarrier techniques: A challenge

In this letter, the tradeoff between sensing and throughput is studied for cognitive radios by considering the energy consumed in the circuit. The total energy is minimized with respect to the power fractions of the power amplifier and the low noise amplifier, the bit resolution of the analog-to-digital converter, the input backoff of the power amplifier and the sensing time in spectrum sensing, for a fixed achievable number of bits per packet. Numerical results show that the optimal sensing time increases when the circuit energy is considered. They also show that the analog-to-digital converter consumes more energy and that it is advantageous to operate the power amplifier at high input backoffs in cognitive radios.

Circuit-Aware Cognitive Radios for Energy-Efficient Communications

The achievable rate of existing wireless systems is commonly studied based on mathematical models that disregard the physical limitations of antennas in terms of size, bandwidth, and efficiency. Antenna models are indeed essential to properly analyze the achievable rate as a key performance metric of wireless communication systems. In this work, we use ideas from Chu and Bode/Fano theories to establish the achievable rate under antenna-size constraint. Specifically, we characterize the maximum achievable rate over single-input single-output (SISO) wireless communication channels under a restriction on the antenna size at the receiver. By employing circuit-theoretic multiport models for radio communication systems, we derive the information-theoretic limits of compact antennas. We first describe an equivalent Chu's antenna circuit under the physical realizability conditions of its reflection coefficient. Such a design allows us to subsequently compute the achievable rate for a given size of the receive antenna thereby providing a realistic upper-bound on the system performance that we compare to the standard size-unconstrained Shannon capacity. We also determine the effective signal-to-noise ratio (SNR) which strongly depends on the antenna size and experiences an apparent finite-size performance degradation where only a small fraction of Shannon capacity can be achieved. We further determine the optimal signaling bandwidth which shows that impedance matching is essential in bandwidth-limited scenarios. We also examine the achievable rate in presence of interference showing that the size constraint is immaterial in interference-limited scenarios. Finally, our numerical results of the derived achievable rate as a function of the antenna size and the SNR reveal new insights into concepts for the physical design of radio systems.

Amine Mezghani

Papers

SIMO/MISO MSE-Duality for Multi-User FBMC with Highly Frequency Selective Channels

Computation of the impulse response and coding gain of a digital interconnection bus

Enhancing spectral efficiency in advanced multicarrier techniques: A challenge

Circuit-Aware Cognitive Radios for Energy-Efficient Communications

Achievable Rate with Antenna Size Constraint: Shannon meets Chu and Bode