Capacity bounds and estimates for the finite scatterers MIMO wireless channel

We consider the limits to the capacity of the multiple-input–multiple-output wireless channel as modeled by the finite scatterers channel model, a generic model of the multipath channel which accounts for each individual multipath component. We assume a normalization that allows for the array gain due to multiple receive antenna elements and, hence, can obtain meaningful limits as the number of elements tends to infinity. We show that the capacity is upper bounded by the capacity of an identity channel of dimension equal to the number of scatterers. Because this bound is not very tight, we also determine an estimate of the capacity as the number of transmit/receive elements tends to infinity which is asymptotically accurate

Dealing with Interference in Distributed Large-scale MIMO Systems: A Statistical Approach

This paper considers the problem of interference control through the use of second-order statistics in massive MIMO multi-cell networks. We consider both the cases of co-located massive arrays and large-scale distributed antenna settings. We are interested in characterizing the low-rankness of users' channel covariance matrices, as such a property can be exploited towards improved channel estimation (so-called pilot decontamination) as well as interference rejection via spatial filtering. In previous work, it was shown that massive MIMO channel covariance matrices exhibit a useful finite rank property that can be modeled via the angular spread of multipath at a MIMO uniform linear array. This paper extends this result to more general settings including certain non-uniform arrays, and more surprisingly, to two dimensional distributed large scale arrays. In particular our model exhibits the dependence of the signal subspace's richness on the scattering radius around the user terminal, through a closed form expression. The applications of the low-rankness covariance property to channel estimation's denoising and low-complexity interference filtering are highlighted.Comment: 12 pages, 11 figures, to appear in IEEE Journal of Selected Topics in Signal Processin

Estimation of Sparse MIMO Channels with Common Support

We consider the problem of estimating sparse communication channels in the MIMO context. In small to medium bandwidth communications, as in the current standards for OFDM and CDMA communication systems (with bandwidth up to 20 MHz), such channels are individually sparse and at the same time share a common support set. Since the underlying physical channels are inherently continuous-time, we propose a parametric sparse estimation technique based on finite rate of innovation (FRI) principles. Parametric estimation is especially relevant to MIMO communications as it allows for a robust estimation and concise description of the channels. The core of the algorithm is a generalization of conventional spectral estimation methods to multiple input signals with common support. We show the application of our technique for channel estimation in OFDM (uniformly/contiguous DFT pilots) and CDMA downlink (Walsh-Hadamard coded schemes). In the presence of additive white Gaussian noise, theoretical lower bounds on the estimation of SCS channel parameters in Rayleigh fading conditions are derived. Finally, an analytical spatial channel model is derived, and simulations on this model in the OFDM setting show the symbol error rate (SER) is reduced by a factor 2 (0 dB of SNR) to 5 (high SNR) compared to standard non-parametric methods - e.g. lowpass interpolation.Comment: 12 pages / 7 figures. Submitted to IEEE Transactions on Communicatio

MIMO channel model

Multiple-input multiple-output is a system that allows the technology to meet the growing demand for high data rates in wireless communications systems. The aim of this thesis is to investigate the capacity capability of the geometric channel model by using the one-ring and two-ring channel model and attempting to explain the differences and similarities between these two models.Simulations were performed on one-ring and two-ring channel models by using mat-lab code programming. These simulations indicate the ideal capacity results of one-ring and two-ring channel models. The simulation results show that capacity increased for both one-ring and two-ring channel model. However, in one-ring model, the mean capacity is more stable than the mean capacity of two-ring model, but the two-ring model has greater mean capacity than one-ring model.There are two types of conditions that have been applied: the equal power allocation and the water-filling. Mean capacity of both equal power and water-filling increased as the signal to noise ratio (SNR) is increased. The difference between these two capacities is that the equal power method has no knowledge about the channel of the transmitter and the transmit signal power is divided equally over the transmitting antennas. Water-filling assumes perfect knowledge about the channel which allows the total power to be divided in the most efficient way over different transmitters. Therefore, the results show the equal power has less capacity than the water-filling. Another element that affects the mean capacity is mutual coupling. The results show that the mutual coupling can be increased or decreased based on the close space between antennas

On the Outage Capacity of Orthogonal Space-time Block Codes Over Multi-cluster Scattering MIMO Channels

Multiple cluster scattering MIMO channel is a useful model for pico-cellular MIMO networks. In this paper, orthogonal space-time block coded transmission over such a channel is considered, where the effective channel equals the product of n complex Gaussian matrices. A simple and accurate closed-form approximation to the channel outage capacity has been derived in this setting. The result is valid for an arbitrary number of clusters n-1 of scatterers and an arbitrary antenna configuration. Numerical results are provided to study the relative outage performance between the multi-cluster and the Rayleigh-fading MIMO channels for which n=1.Comment: Added references; changes made in Section 3-