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 provide a maximum likelihood formulation for the blind estimation of massive mmWave MIMO channels while taking into account their underlying sparse structure. The main advantage of this approach is the fact that the overhead due to pilot sequences can be reduced dramatically especially when operating at low SNR per antenna. Thereby, the sparsity in the angular domain is exploited as a key property to enable the unambiguous blind separation between user's channels. On the other hand, as only the sparsity is assumed, the proposed method is robust with respect to the statistical properties of the channel and data and allows the estimation in rapidly time-varying scenarios and eventually the separation of interfering users from adjacent base stations. Additionally, a performance limit is derived based on the clairvoyant Cramer Rao lower bound. Simulation results demonstrate that this maximum likelihood formulation yields superior estimation accuracy with reasonable computational complexity and limited model assumptions.

Blind Estimation of Sparse Multi-User Massive MIMO Channels

We consider a multi-user (MU) multiple-input-single-output (MISO) downlink system with M single-antenna users and N transmit antennas with a nonlinear power amplifier (PA) at each antenna. Instead of emitting constant envelope (CE) signals from the antennas to have highly power efficient PAs, we relax the CE constraint and allow the transmit signals to have instantaneous power less than or equal to the available power at each PA. The PA power efficiency decreases but simulation results show that the same performance in terms of bit-error-ratio (BER) can be achieved with less transmitted power and less PA power consumption. We propose a linear and a nonlinear precoder design to mitigate the multi-user interference (MUI) under the constraint of a maximal instantaneous per-antenna peak power.

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Precoding under instantaneous per-antenna peak power constraint

Compressed sensing (CS) algorithms can be used to perform beam alignment in mmWave phased arrays with fewer channel measurements by exploiting the sparsity of mmWave channels. Unfortunately, several efficient CS matrices cannot be realized in phased arrays due to hardware constraints. In this work, we propose to use shifted Zadoff-Chu (ZC) sequences in the antenna domain to realize efficient CS matrices for channel estimation or beam alignment. We prove that the shifted ZC-based CS matrix satisfies the restricted isometry property with high probability. Furthermore, CS algorithms that use the proposed training can exploit the fast Fourier transform and are computationally efficient over those that use pseudo-random phase shifts. Using simulations, we show that the proposed CS matrix achieves better beam alignment performance than the commonly used random phased shift-based CS matrices.

Spatial Zadoff-Chu Modulation for Rapid Beam Alignment in mmWave Phased Arrays

Recently, it has been shown that dirty paper coding (DPC) achieves the sum rate capacity of the Gaussian multi-user multiple-input single-output (MU-MISO) broadcast channel of a single isolated cell. However, when considering a multi- cell scenario, i.e., a cellular network, the optimal strategy to maximize the sum rate capacity in each of the cells is still unknown. Nevertheless, based on a game-theoretic framework DPC can be applied at each cell as a decentralized strategy in a cellular network, in order to maximize the sum broadcast capacity of the network. By treating the cells in the network as players in a strategic cooperative game, simultaneous iterative waterfilling can be performed, i.e., every cell computes its optimal beamforming vectors according to DPC and by considering the intercell interference generated in the previous iteration. At each iteration the beamforming vectors for each user in each cell are updated with the gradient projection algorithm in order to maximize the sum network broadcast capacity. The algorithm is repeated until it converges, i.e., a local maximum is achieved. This theoretic result approaches the maximum rate that can be transmitted in the downlink of a network. Additionally, in order to introduce some fairness into the network, we consider in a similar way as the previous problem, the task of minimizing the sum of the mean square errors of all the users in the network.

On maximizing the sum network MISO broadcast capacity

We investigate in this work the optimal control strategies of a receiver that is powered by the ambient energy it harvests over time. Two scenarios are mainly considered, the single-antenna system with non-fading channel, and the multiantenna system with fading channel. The throughput achieved on the limited time slot [ 0, T ] is maximized with respect to the resolution of the A/D converter and the transmission bandwidth/ number of antennas. To this end, the receiver is assumed to have full, non-causal knowledge about the energy arriving process, and the transmitter is assumed to be in full cooperation with the receiver. Convexity of the formulated optimizations is discussed, and the optimal control theory is applied for finding the optimal state trajectories of the system. Numerical simulations are performed for the analysis of the optimal control variables, as well as for the comparison of throughput achieved with different channel gains and optimizations.

Amine Mezghani

Papers

Blind Estimation of Sparse Multi-User Massive MIMO Channels

Precoding under instantaneous per-antenna peak power constraint

Spatial Zadoff-Chu Modulation for Rapid Beam Alignment in mmWave Phased Arrays

On maximizing the sum network MISO broadcast capacity

Throughput Maximization for Energy Harvesting Receivers