Performance Gains of Optimal Antenna Deployment for Massive MIMO Systems

We consider the uplink of a single-cell multi-user multiple-input multiple-output (MIMO) system with several single antenna transmitters/users and one base station with $N$ antennas in the $N\rightarrow\infty$ regime. The base station antennas are evenly distributed to $n$ admissable locations throughout the cell. First, we show that a reliable (per-user) rate of $O(\log n)$ is achievable through optimal locational optimization of base station antennas. We also prove that an $O(\log n)$ rate is the best possible. Therefore, in contrast to a centralized or circular deployment, where the achievable rate is at most a constant, the rate with a general deployment can grow logarithmically with $n$, resulting in a certain form of "macromultiplexing gain." Second, using tools from high-resolution quantization theory, we derive an accurate formula for the best achievable rate given any $n$ and any user density function. According to our formula, the dependence of the optimal rate on the user density function $f$ is curiously only through the differential entropy of $f$. In fact, the optimal rate decreases linearly with the differential entropy, and the worst-case scenario is a uniform user density. Numerical simulations confirm our analytical findings.Comment: GLOBECOM 201

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

Self organization of tilts in relay enhanced networks: a distributed solution

Despite years of physical-layer research, the capacity enhancement potential of relays is limited by the additional spectrum required for Base Station (BS)-Relay Station (RS) links. This paper presents a novel distributed solution by exploiting a system level perspective instead. Building on a realistic system model with impromptu RS deployments, we develop an analytical framework for tilt optimization that can dynamically maximize spectral efficiency of both the BS-RS and BS-user links in an online manner. To obtain a distributed self-organizing solution, the large scale system-wide optimization problem is decomposed into local small scale subproblems by applying the design principles of self-organization in biological systems. The local subproblems are non-convex, but having a very small scale, can be solved via standard nonlinear optimization techniques such as sequential quadratic programming. The performance of the developed solution is evaluated through extensive simulations for an LTE-A type system and compared against a number of benchmarks including a centralized solution obtained via brute force, that also gives an upper bound to assess the optimality gap. Results show that the proposed solution can enhance average spectral efficiency by up to 50% compared to fixed tilting, with negligible signaling overheads. The key advantage of the proposed solution is its potential for autonomous and distributed implementation