Advances in MIMO Techniques for Mobile Communications—A Survey <s> Introduction <s> This paper presents an overview of progress in the area of multiple input multiple output (MIMO) space-time coded wireless systems. After some background on the research leading to the discovery of the enormous potential of MIMO wireless links, we highlight the different classes of techniques and algorithms proposed which attempt to realize the various benefits of MIMO including spatial multiplexing and space-time coding schemes. These algorithms are often derived and analyzed under ideal independent fading conditions. We present the state of the art in channel modeling and measurements, leading to a better understanding of actual MIMO gains. Finally, the paper addresses current questions regarding the integration of MIMO links in practical wireless systems and standards. <s> BIB001 </s> Advances in MIMO Techniques for Mobile Communications—A Survey <s> Introduction <s> In recent years, considerable attention has been paid to the joint optimal transceiver design for multi-input multi-output (MIMO) communication systems. In this paper, we propose a joint transceiver design that combines the geometric mean decomposition (GMD) with either the conventional zero-forcing VBLAST decoder or the more recent zero-forcing dirty paper precoder (ZFDP). Our scheme decomposes a MIMO channel into multiple identical parallel subchannels, which can make it rather convenient to design modulation/demodulation and coding/decoding schemes. Moreover, we prove that our scheme is asymptotically optimal for (moderately) high SNR in terms of both channel throughput and bit error rate (BER) performance. This desirable property is not shared by any other conventional schemes. We also consider the subchannel selection issues when some of the subchannels are too poor to be useful. Our scheme can also be combined with orthogonal frequency division multiplexing (OFDM) for intersymbol interference (ISI) suppression. The effectiveness of our approaches has been validated by both theoretical analyses and numerical simulations. <s> BIB002 </s> Advances in MIMO Techniques for Mobile Communications—A Survey <s> Introduction <s> In this letter, a new orthogonal frequency-division multiplexing (OFDM) technique for multiple-input multiple output (MIMO) channels is proposed to reduce interchannel interference (ICI) caused by high-speed mobiles in cellular environments. After analyzing the ICI caused by high-speed mobile channels using a simple curve fitting technique, the weighting factor for group transmission is optimized. Then, a new MIMO-OFDM technique, based on the weighting factor optimization, is proposed for reducing ICI caused by time-varying channels. Performances of the proposed technique are verified by using the I-METRA channel, proposed for an MIMO channel to 3GPP, and a MIMO-OFDM simulator designed for macrocellular mobile communication. It is shown by computer simulation that the proposed MIMO-OFDM technique is effective in reducing ICI and noise as well as in obtaining diversity gain even under highly-correlated fast fading channels, compared with the conventional MIMO-OFDM schemes. <s> BIB003 </s> Advances in MIMO Techniques for Mobile Communications—A Survey <s> Introduction <s> 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 nu- merous 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 for employment in multiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems. <s> BIB004
Multiple-input multiple-output (MIMO) wireless systems employ multiple transmit and receive antennas to increase the transmission data rate through spatial multiplexing or to improve system reliability in terms of bit error rate (BER) performance using space-time codes (STCs) for diversity maximization BIB001 . MIMO systems exploit multipath propagation to achieve these benefits, without the expense of additional bandwidth. More recent MIMO techniques like the geometric mean decomposition (GMD) technique proposed in BIB002 aim at combining the diversity and data rate maximization aspects of MIMO in an optimal manner. These advantages make MIMO a very attractive and promising option for future mobile communication systems especially when combined with the benefits of orthogonal frequency-division multiplexing (OFDM) BIB004 BIB003 . The capacity of an M u N single-user MIMO (SU-MIMO) system with M transmit and N receive antennas, in terms of the spectral efficiency i.e. bits per second per Hz, is given by BIB001  where H is the N u M MIMO channel matrix and ȡ is the signal to noise ratio (SNR) at any receive antenna. Equation BIB001 assumes that the M information sources are uncorrelated and have equal power. Expressed in terms of the eigenvalues, Equation (1) can be written as BIB001 ¦ · BIB002 where Ȝ i represent the nonzero eigenvalues of HH H or H H H for N M and M < N respectively and m = min (M, N) . Therefore, MIMO systems are capable of achieving several-fold increase in system capacity as compared to single-input single-output (SISO) systems by transmitting on the spatial eigenmodes of the MIMO channel. Equation (2) also shows that the performance of MIMO systems is dependent on the channel eigenvalues. Very low eigenvalues indicate weak transmission channels which may make it difficult to recover the information from the received signals. Optimal power allocation based on the water-filling algorithm can be used to maximize the system capacity subject to a total transmit power constraint. Water-filling provides substantial capacity gain when the eigenvalue spread, i.e., the condition number Ȝ max /Ȝ min is sufficiently large. The MIMO concept becomes even more attractive in multiuser scenarios where the network capacity can be increased by simultaneously accommodating several users without the expense of valuable frequency resources. This paper is arranged as follows: Section 2 provides an overview of the current wireless standards which support MIMO technologies. Sections 3 and 4 include detailed discussion and performance analysis of various important SU-MIMO and multiuser MIMO (MU-MIMO) techniques respectively that are proposed for the next generation wireless communication systems. In-depth description of several MU-MIMO uplink and downlink schemes is given in Section 4 followed by a brief discussion of the MU-MIMO capacity. Section 5 provides an overview of convex optimization which has become an important tool for designing optimal MIMO beamforming systems. Section 6 concludes this work and identifies the areas for future research.