This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also

Abstract — In this paper, a two stage hybrid interference cancellation and equalization framework is proposed for interference cancellation in the uplink of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. The first stage uses time domain equalization to suppress co-channel interference, mitigate asynchronism, and shorten the post-equalization channel response to be no longer than the length of the cyclic prefix. The second stage performs lowcomplexity single tap equalization and detection in the frequency domain. The framework is developed specifically for spatial multiplexing and is applied to multiuser MIMO-OFDM systems with asynchronism between users as well as to single-user MIMO-OFDM systems. Various equalizer design methods are proposed that determine the coefficients directly from the training data and are compared with methods based on channel estimates. The equalizer coefficients and post-equalization channel response are found by solving a joint optimization that maximizes the signal to interference-plus-noise ratio (SINR) in the frequency domain. Simulations compare various training based methods and show the proposed methods provide good bit error rate (BER) and SINR performance in a variety of interference scenarios.

In this paper, the system throughput of a wireless local-area-network (WLAN) based on multiple-input multipleoutput orthogonal frequency division multiplexing (MIMO OFDM) is studied. A broadband channel model is derived from indoor channel measurements. This model is used in simulations to evaluate the performance of different MIMO detection algorithms. It is concluded that a MIMO scheme combined with an additional antenna at the receiver side and the low computational complex minimum-mean-squared-error (MMSE) algorithm is preferable. Link-level results are used to calculate the throughput of a single-cell scenario containing multiple MIMO users using a semi-analytical framework. Results show that the throughput of a 22 extension of an IEEE 802.11a system is 1.5 to 2 times higher than that of its single antenna counterpart.

Layered architectures like the V-BLAST scheme are a promising candidate to exploit the capacity advantages of multiple antenna systems leading to practical wireless communication schemes with very high data rates. The combination with orthogonal frequency division multiplexing (OFDM), called MIMO–OFDM, with per-antenna-coding is one of the most likely implementations of these multilayer architectures in frequency selective environments. In this paper, we present a novel, computational efficient implementation of successive interference cancellation (SIC) for coded MIMO–OFDM. It utilises a parallelised version of the Sorted QR Decomposition (SQRD) to achieve the same optimised detection order for all subcarriers, in order to exploit the error correction capability of the forward error correction code within the SIC. In comparison to other schemes known from literature, our approach requires only a fraction of computational complexity with almost the same performance. Copyright © 2007 John Wiley & Sons, Ltd. 1.

The performance of OFDM based systems is seriously affected by imperfections in system implementation. To gain a beter understanding of the influence of these impairments on the performance of multiple antenna OFDM systems, this paper studies a zero-forcing based MIMO OFDM system with imperfections modeled as additive error sources in both transmitter (TX) and receiver (RX). Based on this model, expressions are derived for the probability of error of uncoded impaired MIMO systems in fading and non-fading environments. These results allow for insightful comparison between the influence of TX and RX impairments. It is concluded that the influence of RX imperfections decreases with an increasing number of RX branches, while this is not the case for TX deficiencies.

This paper studies the influence, estimation and digital compensation of IQ imbalance at both transmitter and receiver side of a multiple-input multiple-output (MIMO) OFDM system. Hereto a preamble is designed, which enables simultaneous estimation of the channel and imbalance parameters. New estimation approaches for TX, RX and joint TX and RX IQ imbalance in MIMO OFDM systems are proposed and evaluated. Results from a numerical study show that all three proposed compensation approaches are able to significantly suppress the influence of IQ mismatch.

ii iii ACKNOWLEDGMENTS I first thank Dr. Todor Cooklev for the support and leadership he provided me throughout the last two semesters. I am very thankful for the opportunity to work under the guidance and teachings of Dr. Cooklev, and I am better engineer for it. Next, I thank my graduate committee; Dr. Steven Walter, Dr. Carlos Raez and Dr. Tim Grove and thesis format director, Barbara Lloyd for the time and effort expended on my behalf. I thank Raytheon Company for supporting my efforts and desires to further my education. At times, managing the requirements of graduate school in conjunction with a demanding job can be very stressful and difficult to balance. As such, I am thankful to work for a company that fosters an environment where education is valued and the goals of its employees are supported. I thank my family for supporting my dreams and providing me every possible opportunity to reach this milestone. Last but not least, I thank my fiancé, Rebecca. Rebecca sacrificed a lot to be with me and has been nothing but supportive during the many hours I have put into graduate school above and beyond the duties of

The techniques proposed in the previous deliverable D4.1 [8] for synchronization, channel estimation and equalization in FBMC, are now completed and some additional results presented. The MIMO techniques for multiantenna radiocommunication systems have been analyzed and adapted to the FBMC context and new space diversity and multiplexing algorithms have been developed to exploit the particularities of FBMC. The performance of OFDM systems is taken as the reference to assess the performance of the FBMC schemes proposed. The main achievements are summarized at the end of the document. ICT-212887 2 Deliverable D4.2 TABLE OF CONTENTS 1 Introduction....................................................................................................................................... 3 2 Synchronization, channel estimation and equalization in MIMO-FBMC.................................. 3 2.1 Preamble-based CFO estimation.................................................................................................. 3 2.2 Auxiliary pilot based synchronization and channel estimation.................................................... 7