You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Advances in MIMO Techniques for Mobile Communications—A Survey 
Chapter Title:  Using this MMS 1 
1. Abstract of Cited Paper (BIB001):  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.

Literature Review:
E estimate, the 1 st GA generation, y = containing a population of X individuals, is created. The x-th individual is a symbol vector denoted as 1 2 , , , where each element The GA search procedure shown in Figure  itiated , which involves several GA operations like mating, mutation, elitism etc. leading to the next generation. This process is repeated for Y generations and the individual with the highest fitness value is considered to be the detected L u 1 multiuser symbol vector for the corresponding OFDM subcarrier. All detected by the concatenated MMSE-GA MUD and therefore, no error propagation exists between the detected users. Enhanced G chnique called biased Q-function-based mutation (BQM) instead of the conventional uniform mutation (UM), and incorporating the iterative turbo trellis coded modulation (TTCM) scheme for FEC decoding, are also discussed in BIB002 . The BER performance comparis enarios where the no. of users exceeds the no. of BS antennas resulting in insufficient degrees of freedom for separating the users, is given in Figure 46 . Performance curves for L = 6, 7 and 8 users and P = 6 BS antennas are provided. The IGA MUD schemes still perform reasonably good with relatively small performance degradation. Figure 47 compares the complexity of the TTCM-MSE-GA and TTCM-ML MUDs in terms of the no. of = P. he complexity of the GA MUD increases very slowly O downlink channel is referred to as the C) BIB001 where the simultaneously tra-OF calculations, as the no. of users increase. The no. of BS antennas is kept equal to the no. of users i.e. L T with the number of users as compared to the ML MUD, resulting in a huge difference as more users are added to the system.