Darlan C. Moreira

Performance of Joint Antenna and User Selection Schemes for Interference Alignment in MIMO Interference Channels

In multiuser MIMO systems, the transmitter can select a subset of antennas and/or users which have good channel conditions to maximize the system throughput using various selection criteria. Furthermore, the Interference Alignment (IA) method, which is based on the concept of precoding, can provide interference free dimensions. It offers different trade-offs between complexity and performance. The basic idea of Interference Alignment consists in precoding the transmitted signals such that they are aligned at the receiver where they constitute interference, while at the same time disjointed from the desired signal. We analyze the performance of antenna selection and multiuser diversity together in order to allow opportunistic IA using chordal and Fubini-Study distances. Analyses and simulations verify the behavior of the proposed scheme.

Filtered Delay-Subspace Approach for Pilot Assisted Channel Estimation in OFDM Systems

In this paper we propose a pilot-based OFDM channel estimator based on the combination of low-pass filtering and delay-subspace projection. The proposed estimator, which we abbreviate ST-LP, is robust in the sense it does not require prior statistical knowledge of the channel. The only assumptions are the least-square (LS) estimates have limited spectrum and the channel follows the tapped delay line (TDL) model, which are commonly taken in practice. Since it is desirable slow delay variations operability acceptance, the delay-subspace is tracked by a subspace tracking (ST) algorithm. The ST-LP estimator can be implemented by two filtering structures, which provide a trade-off between accuracy and complexity. Simulation results confirm the superior performance of the ST-LP estimator when compared to methods already reported in the literature

Convergence analysis of iterative Interference Alignment algorithms

Interference Alignment (IA) is more appropriated for the high Signal-to-Noise Ratio (SNR) regime and it is considered suboptimal for low to middle SNR values, where mixed criteria algorithms that also take the direct channel into consideration are expected to be better. In this paper we investigate the performance of some of the most well known iterative algorithms for IA in the low and high SNR regimes, the Min Leakage algorithm, which is a pure IA algorithm, and the Max Signal-to-Interference-plus-Noise Ratio (SINR) and Minimum Mean-Square Error (MMSE) algorithms, which also take the direct channel into account. We illustrate that although the Max SINR and MMSE algorithms can find solutions close to IA at the high SNR regime, they are not as stable in that regime and a pure IA algorithm might perform better. To obtain good performance in both low and high SNR regimes we propose to initialize the mixed criteria algorithms with a pure IA solution, which does not degrade performance at low SNR values but increases stability and performance at the high SNR regime.

Analysis of interference alignment for wireless communication with external interference

In order to better cope with the increasing levels of interference in wireless cellular systems, interference mitigation techniques that consider a certain degree of coordination/cooperation among cells have been recently employed, such as Joint Processing (JP) and Interference Alignment (IA). In this paper we evaluate the performance of different IA-based algorithms in the presence of uncoordinated external interference, with JP schemes used as a benchmark. Sum rate and Bit Error Rate (BER) results are presented for different external interference scenarios and it is shown that there is trade-off between IA and JP, and that for high external interference levels IA algorithms modified to handle this external interference can achieve better BER performances.

Interference Alignment, Concepts and Algorithms for Wireless Systems

This chapter describes several aspects of the Interference Alignment (IA) technique with a focus on the spatial domain. The basic idea of Interference Alignment consists in precoding the transmitted signals such that they are aligned at the receiver where they constitute interference, while at the same time disjointed from the desired signal. This alignment limits the generated interference to a subspace at each receiver, which in turn leaves the remaining dimensions free from any interference. Most of the literature on IA considers an idealized scenario with perfect CSI available at the transmitter. In this chapter some well known IA algorithms from the literature are described, also with simulation results for this idealized scenario. Afterwards, some insights are provided on the impact of imperfections, where CSI error and transmit antenna correlation are included within the channel model. Finally, a systemic context is considered where IA is analyzed from a system-level point-of-view, for which some insights on complexity issues are also provided

Block diagonalization with subspace based external interference mitigation

In this work we propose a variation to the Block Diagonalization (BD) algorithm described that mitigates the interference from an external interference source which does not participate in the joint transmission inherent to the BD algorithm. The idea consists of precoding the signal at the transmission side so that the received signal at each receiver lies in a subspace orthogonal to the subspace containing the external interference. It is similar to how interference alignment algorithms work, but here instead of limiting the caused interference at the unintended receivers to a specific subspace, we are limiting the desired signal at the intended receivers. Since this “limiting” aspect reduces the dimensions available for the useful signal, a trade-off is involved between how many useful streams can be transmitted and how much of the external interference energy can be canceled. The proposed algorithm is analyzed and we show different metrics that can be used to determine the “good point” in this trade-off.

Link Adaptation for MIMO-OFDM Systems

The paradigm of the design of a wireless system has changed. Since the use of the dimensioning for the “worst case”, which means to design the system to work on the fading margin available when the channel has its poorest behavior, the driver of the optimization has evolved to a more suitable use of the available resources for performing a reliable communication. This approach is then called link adaptation (LA), when the system chooses the parameters which are the most suitable for usage in a certain channel condition. The always increasing demand for higher data rates, lower energy consumption, etc., requires that the system resources are utilized as efficiently as possible and LA techniques are already a reality in any modern wireless communication systems to achieve that goal. While many aspects of LA, such as usage of different modulations and code rates for providing better “protection” to data streams according to the channel condition, have already been understood, each system has a different set of “interesting parameters” to be adapted in multiple dimensions and the trade-off between LA gains and signaling overhead still provides challenges to be answered. Typical dimensions used in LA procedure are modulation and coding. The choice of the modulation allows the system to improve/decrease the spectral efficiency and the code rate impacts the amount of redundancy inserted for error protection into data frames. However, it is possible to envisage the exploitation of other features of the wireless system, for instance the spatial and frequency domains. This chapter describes the use of transmission modes considering parameters which are important to the performance of a wireless system, in particular the extension of LA to the MIMO-OFDM case in fourth generation (4G) systems. The rest of the chapter is organized as follows. The fundamentals of multipleinput multiple-output (MIMO) systems are presented in Section 10.2, where classical MIMO schemes are described. Section 10.3 discusses the trade-off between the diversity and multiplexing gains that can be extracted from the MIMO channel