Antonio Forenza

Design and Evaluation of a Reconfigurable Antenna Array for MIMO Systems

New reconfigurable antenna array is demonstrated for multiple input multiple output (MIMO) communication systems that improves link capacity in closely spaced antenna arrays. The antenna system consists of an array of two printed dipoles separated by a distance of a quarter wavelength. Each of the dipoles can be reconfigured in length using PIN diode switches. The switch configuration can be modified in a manner adaptive to changes in the environment. The configuration of switches effects the mutual coupling between the array elements, and subsequently, the radiation pattern of each antenna, leading to different degrees of pattern diversity which can be used to improve link capacity. The PIN diode-based reconfigurable antenna solution is first motivated through a capacity analysis of the antenna in a clustered MIMO channel model. A new definition of spatial correlation coefficient is introduced to include the effects of antenna mismatch and radiation efficiency when quantifying the benefit of pattern diversity. Next, the widespread applicability of the proposed technique is demonstrated, relative to conventional half wavelength printed dipoles, using computational electromagnetic simulation in an outdoor and indoor environment and field measurements in an indoor laboratory environment. It is shown for the 2 times 2 system considered in this paper, that an average improvement of 10% and 8% is achieved in link capacity for a signal to noise ratio (SNR) respectively of 10 dB and 20 dB in an indoor environment compared to a system employing non reconfigurable antenna arrays.

Adaptive MIMO Transmission for Exploiting the Capacity of Spatially Correlated Channels

We consider a novel low-complexity adaptive multiple-input multiple-output (MIMO) transmission technique. The approach is based on switching between low-complexity transmission schemes, including statistical beamforming, double space-time transmit diversity, and spatial multiplexing, depending on the changing channel statistics, as a practical means of approaching the spatially correlated MIMO channel capacity. We first derive new ergodic capacity expressions for each MIMO transmission scheme in spatially correlated channels. Based on these results, we demonstrate that adaptive switching between MIMO schemes yields significant capacity gains over fixed transmission schemes. We also derive accurate analytical approximations for the optimal signal-to-noise-ratio switching thresholds, which correspond to the crossing-points of the capacity curves. These thresholds are shown to vary, depending on the spatial correlation, and are used to identify key switching parameters. Finally, we propose a practical switching algorithm that is shown to yield significant spectral efficiency improvements over nonadaptive schemes for typical channel scenarios

Simplified Spatial Correlation Models for Clustered MIMO Channels With Different Array Configurations

An approximate spatial correlation model for clustered multiple-input multiple-output (MIMO) channels is proposed in this paper. The two ingredients for the model are an approximation for uniform linear and circular arrays to avoid numerical integrals and a closed-form expression for the correlation coefficients that is derived for the Laplacian azimuth angle distribution. A new performance metric to compare parametric and nonparametric channel models is proposed and used to show that the proposed model is a good fit to the existing parametric models for low angle spreads (i.e., smaller than 10deg). A computational-complexity analysis shows that the proposed method is a numerically efficient way of generating the spatially correlated MIMO channels.

Opportunistic Space-Division Multiple Access With Beam Selection

In this paper, a novel transmission technique for the multiple-input multiple-output (MIMO) broadcast channel is proposed that allows simultaneous transmission to multiple users under a limited feedback requirement. During a training phase, the base station modulates a training sequence on multiple sets of randomly generated orthogonal beamforming vectors. Then, based on the users feedback, the base station opportunistically selects the users and corresponding orthogonal vectors that maximize the sum capacity. From theoretical analysis, the optimal amount of training to maximize the sum capacity is derived as a function of the system parameters. The main advantage of the proposed system is that it provides throughput gains for the MIMO broadcast channel with a small feedback overhead, and provides these gains even with a small number of active users. Numerical simulations show that a 20% gain in sum capacity is achieved (for a small number of users) over conventional opportunistic space division multiple access, and a 100% gain (for a large number of users) over conventional opportunistic beamforming.

Impact of antenna geometry on MIMO communication in indoor clustered channels

In this paper, we study the impact of different array configurations in indoor propagation environments. We measure the capacity/diversity gain attainable by different array geometries with seven elements and fixed interelement spacing. To make the discussion more concrete, we base our simulations on the IEEE 802.11n Technical Group (TC) clustered channel model. Our results show that averaged with respect to cluster location, uniform linear arrays often yield the highest capacity/diversity gains. However, in poor scatterer environments, and for compact arrays, the Star configuration provides the best system performance.

Adaptive MIMO transmission techniques for broadband wireless communication systems [Topics in Wireless Communications]

Link adaptation is a way to increase data rates in wireless systems by adapting transmission parameters such as the modulation and coding rate. While link adaptation in single antenna systems is now mature, its application to multiple-input multiple-output communication links, presented in several emerging wireless standards, has been challenging. The main reason is that the space-time transmission strategy can also be adjusted in MIMO communication links, introducing a new dimension for adaptation. This means that practical MIMO link adaptation algorithms must also provide a dynamic adaptation between diversity and multiplexing modes of operation. This article reviews a recently proposed framework for adaptive MIMO architectures and shows how to use this framework to reduce adaptive control overhead. We also discuss practical implementation issues. Simulations in an IEEE 802.16e (mobile WiMAX) system illustrate the frame-works potential improvements in data rates.

Adaptive MIMO transmission scheme: exploiting the spatial selectivity of wireless channels

We present a novel adaptive transmission technique for MIMO systems with the aim to enhancing the spectral efficiency for a target error rate performance and transmit power. This adaptive method employs the condition number of the spatial correlation matrix as an indicator of the spatial selectivity of the MIMO channel. The distribution of the condition number is then used to identify the prevailing channel environment. Depending on the identified channel state, our adaptive algorithm chooses the MIMO transmission method, among spatial multiplexing, D-STTD, and beamforming, which maximizes the spectral efficiency. Performance results show significant gains in throughput and reduced error rate compared to conventional fixed transmission schemes.

Switching Between OSTBC and Spatial Multiplexing with Linear Receivers in Spatially Correlated MIMO Channels

We present a low complexity adaptive transmission approach for spatially correlated MIMO channels. The proposed scheme adaptively switches between orthogonal space-time block codes (OSTBC) and spatial multiplexing (SM), depending of the channel correlation and SNR. We derive an exact closed-form expression and tight upper bound on the OSTBC capacity in double-correlated channels, and examine the relative capacity of OSTBC and SM in terms of the spatial correlation. We then derive efficient closed-form BER expressions for practical OSTBC transmission employing bit-interleaved coded modulation (BICM). Based on these results, we propose a practical adaptive algorithm which selects the combination of MIMO transmission scheme (OSTBC or SM), and BICM mode, which achieves the highest spectral efficiency whilst satisfying a pre-defined BER

Long range channel prediction for adaptive OFDM systems

In this paper, different techniques for long-range channel prediction for OFDM systems are investigated. Frequency domain channel prediction on each OFDM data subcarrier is first explored and it is shown that the optimum prediction filter depends only on the time-domain channel statistics for the wide-sense stationary uncorrelated scattering (WSSUS) wireless channel. Frequency domain prediction on the pilot subcarriers is investigated next, where the optimum prediction filter is determined for each pilot subcarrier and is reused for all the nearby data subcarriers. Finally, time-domain channel prediction on the multipath taps is explored. It is shown that frequency domain prediction on the pilot subcarriers performs almost identically to prediction using all subcarriers. Furthermore, it is also shown that time-domain prediction outperforms the frequency domain prediction methods.

A low complexity algorithm to simulate the spatial covariance matrix for clustered MIMO channel models

The capacity and error rate performance of a multiple-input multiple-output (MIMO) communication system depend strongly on the spatial correlation properties introduced by clustering in the propagation environment. Simulating correlated channels, using, for example, the common correlated Rayleigh fading model, requires numerically complex calculations of the transmit and receive spatial correlation matrices as a function of the cluster size and location. The paper proposes a numerically efficient way of generating these correlation matrices for indoor clustered channel models. The method makes use of a uniform linear array approximation to avoid numerical integrals and derives a closed-form expression for the correlation coefficients, assuming a Laplacian angle distribution. Simulations show that the approximate correlation model exhibits good fit for moderate angle spreads. Complexity calculations show that this approach takes about 1/200 of the time to compute the spatial correlation matrices compared to existing methods.