Scaling up MIMO: Opportunities and Challenges with Very Large Arrays

This paper surveys recent advances in the area of very large MIMO systems. With very large MIMO, we think of systems that use antenna arrays with an order of magnitude more elements than in systems being built today, say a hundred antennas or more. Very large MIMO entails an unprecedented number of antennas simultaneously serving a much smaller number of terminals. The disparity in number emerges as a desirable operating condition and a practical one as well. The number of terminals that can be simultaneously served is limited, not by the number of antennas, but rather by our inability to acquire channel-state information for an unlimited number of terminals. Larger numbers of terminals can always be accommodated by combining very large MIMO technology with conventional time- and frequency-division multiplexing via OFDM. Very large MIMO arrays is a new research field both in communication theory, propagation, and electronics and represents a paradigm shift in the way of thinking both with regards to theory, systems and implementation. The ultimate vision of very large MIMO systems is that the antenna array would consist of small active antenna units, plugged into an (optical) fieldbus.Comment: Accepted for publication in the IEEE Signal Processing Magazine, October 201

Impact of User Mobility on Optimal Linear Receivers in Cellular Networks

We consider the uplink of non-cooperative multi-cellular systems deploying multiple antenna elements at the base stations (BS), covering both the cases of conventional and very large number of antennas. Given the inevitable pilot contamination and an arbitrary path-loss for each link, we address the impact of time variation of the channel due to the relative movement between users and BS antennas, which limits system's performance even if the number antennas is increased, as shown. In particular, we propose an optimal linear receiver (OLR) maximizing the received signal-to-interference-plus-noise (SINR). Closed-form lower and upper bounds are derived as well as the deterministic equivalent of the OLR is obtained. Numerical results reveal the outperformance of the proposed OLR against known linear receivers, mostly in environments with high interference and certain user mobility, as well as that massive MIMO is preferable even in time-varying channel conditions.Comment: 3 figures, 6 pages, accepted in ICC 201

Energy and spectral efficiency tradeoff in wireless communication

In the wireless communication world, a significant number of new user equipments is connecting to the network each and every day, and day after day this amount is increasing with no known bounds. Diverse quality of service (QoS) along with better system throughput are the crying needs at present. With the advancement in the field of massive multiple-input multiple-output (MMIMO) and Internet-of-things (IoT), the QoS is provided smoothly with the limited spectrum by the wireless operator. Hundreds of antenna elements in the digital arrays are set up at the base station in order to provide the smooth coverage and the best throughput within these spectra. However, implementing hundreds of antenna elements with associated a huge number of RF chains for digital beamforming consumes too much energy. Energy efficiency optimization has become a requirement at the present stage of wireless infrastructure. Due to the conflicting nature between the energy efficiency and the spectral efficiency, it is hard to make a balance. This thesis investigates how to achieve a good tradeoff between the energy and the spectral efficiency with maximum throughput outcomes from MMIMO, with the help of existing topologies and a futuristic perspective. Although the signal noise power is less in massive MIMO than the conventional cellular system, it still needs to be decreased and at the same time, the average channel gain per user equipment must be increased. Fixed power requirement for control signaling and load-independent power of backhaul infrastructure must be cut at least by a factor two as well as the power amplifier efficiency has to increase by 10% than LTE networks. The minimum mean square error (MMSE) estimator can be a possible solution in terms of the energy and the spectral efficiency despite having computational complexity which can be solved with the aid of Moore’s law and it is proposed by the non-profit research organization IMEC, which has developed an online web tool for observing and predicting contemporary as well as futuristic cellular base station’s power consumption. It supports various types of base stations with a wide range of operating conditions. The multicell minimum mean square error (M-MMSE) scheme can perform better than other existing schemes and showcase satisfactory tradeoff with frequency reuse factor higher than 2, where regularized zero-forcing (RZF) and maximum ratio (MR) combining fall down their capabilities for performing. With the precipitous rising of IoT, the Narrowband Internet-of-things (NB-IoT) may play an efficient supportive role if we can collaborate it with MMIMO. With its low power, wide area topologies combining with MMIMO technologies can show better tradeoffs. Due to its narrow bandwidth, the signal noise power would be less compared to the existent wideband topologies, and the average channel gain of active user equipment would be higher too. Hence it will give a great impact in terms of the tradeoff between energy and the spectral efficiency which is addressed in this thesis

Energy and spectral efficiency tradeoff in wireless communication

In the wireless communication world, a significant number of new user equipments is connecting to the network each and every day, and day after day this amount is increasing with no known bounds. Diverse quality of service (QoS) along with better system throughput are the crying needs at present. With the advancement in the field of massive multiple-input multiple-output (MMIMO) and Internet-of-things (IoT), the QoS is provided smoothly with the limited spectrum by the wireless operator. Hundreds of antenna elements in the digital arrays are set up at the base station in order to provide the smooth coverage and the best throughput within these spectra. However, implementing hundreds of antenna elements with associated a huge number of RF chains for digital beamforming consumes too much energy. Energy efficiency optimization has become a requirement at the present stage of wireless infrastructure. Due to the conflicting nature between the energy efficiency and the spectral efficiency, it is hard to make a balance. This thesis investigates how to achieve a good tradeoff between the energy and the spectral efficiency with maximum throughput outcomes from MMIMO, with the help of existing topologies and a futuristic perspective. Although the signal noise power is less in massive MIMO than the conventional cellular system, it still needs to be decreased and at the same time, the average channel gain per user equipment must be increased. Fixed power requirement for control signaling and load-independent power of backhaul infrastructure must be cut at least by a factor two as well as the power amplifier efficiency has to increase by 10% than LTE networks. The minimum mean square error (MMSE) estimator can be a possible solution in terms of the energy and the spectral efficiency despite having computational complexity which can be solved with the aid of Moore’s law and it is proposed by the non-profit research organization IMEC, which has developed an online web tool for observing and predicting contemporary as well as futuristic cellular base station’s power consumption. It supports various types of base stations with a wide range of operating conditions. The multicell minimum mean square error (M-MMSE) scheme can perform better than other existing schemes and showcase satisfactory tradeoff with frequency reuse factor higher than 2, where regularized zero-forcing (RZF) and maximum ratio (MR) combining fall down their capabilities for performing. With the precipitous rising of IoT, the Narrowband Internet-of-things (NB-IoT) may play an efficient supportive role if we can collaborate it with MMIMO. With its low power, wide area topologies combining with MMIMO technologies can show better tradeoffs. Due to its narrow bandwidth, the signal noise power would be less compared to the existent wideband topologies, and the average channel gain of active user equipment would be higher too. Hence it will give a great impact in terms of the tradeoff between energy and the spectral efficiency which is addressed in this thesis

Similarity Renormalization Group Evolution of Chiral Effective Nucleon-Nucleon Potentials in the Subtracted Kernel Method Approach

Methods based on Wilson's renormalization group have been successfully applied in the context of nuclear physics to analyze the scale dependence of effective nucleon-nucleon ($NN$) potentials, as well as to consistently integrate out the high-momentum components of phenomenological high-precision $NN$ potentials in order to derive phase-shift equivalent softer forms, the so called $V_{low-k}$ potentials. An alternative renormalization group approach that has been applied in this context is the Similarity Renormalization Group (SRG), which is based on a series of continuous unitary transformations that evolve hamiltonians with a cutoff on energy differences. In this work we study the SRG evolution of a leading order (LO) chiral effective $NN$ potential in the $^1 S_0$ channel derived within the framework of the Subtracted Kernel Method (SKM), a renormalization scheme based on a subtracted scattering equation.Comment: Published versio

A Generalized Spatial Correlation Model for 3D MIMO Channels based on the Fourier Coefficients of Power Spectrums

Previous studies have confirmed the adverse impact of fading correlation on the mutual information (MI) of two-dimensional (2D) multiple-input multiple-output (MIMO) systems. More recently, the trend is to enhance the system performance by exploiting the channel's degrees of freedom in the elevation, which necessitates the derivation and characterization of three-dimensional (3D) channels in the presence of spatial correlation. In this paper, an exact closed-form expression for the Spatial Correlation Function (SCF) is derived for 3D MIMO channels. This novel SCF is developed for a uniform linear array of antennas with nonisotropic antenna patterns. The proposed method resorts to the spherical harmonic expansion (SHE) of plane waves and the trigonometric expansion of Legendre and associated Legendre polynomials. The resulting expression depends on the underlying arbitrary angular distributions and antenna patterns through the Fourier Series (FS) coefficients of power azimuth and elevation spectrums. The novelty of the proposed method lies in the SCF being valid for any 3D propagation environment. The developed SCF determines the covariance matrices at the transmitter and the receiver that form the Kronecker channel model. In order to quantify the effects of correlation on the system performance, the information-theoretic deterministic equivalents of the MI for the Kronecker model are utilized in both mono-user and multi-user cases. Numerical results validate the proposed analytical expressions and elucidate the dependence of the system performance on azimuth and elevation angular spreads and antenna patterns. Some useful insights into the behaviour of MI as a function of downtilt angles are provided. The derived model will help evaluate the performance of correlated 3D MIMO channels in the future.Comment: Accepted in IEEE Transactions on signal processin