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One of the downsides of the massive multiple-input-multiple-output (M-MIMO) system is its computational complexity. Considering that techniques and different algorithms proposed in the literature applied to conventional MIMO may not be well suited or readily applicable to M-MIMO systems, in this paper, the application of different formulations inside the convex optimization framework is investigated. This paper is divided into two parts. In the first part, linear programming, quadratic programming (QP), and semidefinite programming are explored in an M-MIMO environment with high-order modulation and under realistic channel conditions, i.e., considering spatial correlation, error in the channel estimation, as well as different system loading. The bit error rate is evaluated numerically through Monte Carlo simulations. In the second part, algorithms to solve the QP formulation are explored, and computational complexity in terms of floating-point operations (flops) is compared with linear detectors. Those algorithms have interesting aspects when applied to our specific problem (M-MIMO detection formulated as QP), such as the exploitation of the structure of the problem (simple constraints) and the improvement of the rate of convergence due to the well-conditioned Gram matrix (channel hardening). The number of iterations is higher when the number of users K becomes similar to the number of base station antennas M (i.e., K ≈ M) than the case K M ; the number of iterations increases slowly as the number of users K and base station antennas M increases while keeping a low system loading. The QP with projected algorithms presented better performance than minimum mean square error detector when K ≈ M and promising computational complexity for scenarios with increasing K and low system loading.

In this paper, we analyze the performance of evolutionary heuristic‐aided linear detectors deployed in multiple‐input–multiple‐output (MIMO) orthogonal frequency‐division multiplexing (OFDM) systems, considering realistic operating scenarios. Hybrid linear‐heuristic detectors under different initial solutions provided by linear detectors are considered, namely, differential evolution and particle swarm optimization. Numerical results demonstrated the applicability of hybrid detection approach, which can improve considerably the performance of minimum mean‐square error and matched filter detectors. Furthermore, we discuss how the complexity of the presented algorithms scales with the number of antennas, besides of verifying the spatial correlation effects on MIMO‐OFDM performance assisted by linear, heuristic, and hybrid detection schemes. The influence of the initial point in the performance improvement and complexity reduction is evaluated numerically.

This work analyzes the performance of implementable detectors for the multiple‐input multiple‐output (MIMO) orthogonal frequency division multiplexing (OFDM) technique under specific and realistic operation system conditions, including antenna correlation and array configuration. A time‐domain channel model was used to evaluate the system performance under realistic communication channel and system scenarios, including different channel correlation, modulation order, and antenna array configurations. Several MIMO‐OFDM detectors were analyzed for the purpose of achieving high performance combined with high capacity systems and manageable computational complexity. Numerical Monte Carlo simulations demonstrate the channel selectivity effect, while the impact of the number of antennas, adoption of linear against heuristic‐based detection schemes, and the spatial correlation effect under linear and planar antenna arrays are analyzed in the MIMO‐OFDM context.

The Orthogonal Frequency Division Multiplexing (OFDM) system is already used in commercial applications and is capable to deal with Intersymbolic Interference (ISI) caused by multipath channels. This system gained popularity after the application of the Fast Fourier Transform (FFT) and its inverse (IFFT) to modulate the signal in many subcarriers. This paper discusses implementation aspects of an OFDM system; such system is characterized by considering real constraints, including the memory consumption and the processing time. The OFDM modulator, channel samples and OFDM demodulator were implemented entirely in the DSP TMS320C6678 platform. As a proof-of-concept, a 256-QAM OFDM BER performance is compared with theoretical values. Moreover, the memory size is not demanding, consuming very few resources. It was observed a very high number of DSP clock cycles needed for the OFDM signal modulation, corresponding to more than 4 times the number used in demodulating the signal.