## 3 State-of-the-Art MIMO-SDR Systems
### MIMO-OFDM Block Enhancements
#### 3.3.1 Channel Estimation
The nominated comb-type pilot-based technique was used within a space-time block coding (STBC). Conventionally, the comb-type pilot is the most used for the relatively small MIMO-OFDM architectures [116]. However, with more complex architectures, the amount of overhead increase and the choice of pilot sequence become a more important point when avoiding the pilot contamination problem (the case of using the same pilot sequence). Thus,  
Figure 17: Pilot arrangement possible situations: (**a**) block type; (**b**) comb type; (**c**) 2D-grid type. Figure 18: Pilot-based channel estimation scheme. several authors [117, 119] have opted to use the Zadoff Chu (or generalized Chirp-like) sequences, due to their orthogonality characteristics. In [117], the estimation was performed using a DFT-based method in the time domain; the interpolation step was completely omitted. [PERSON], [PERSON] and [PERSON] [117] took advantage of the fact that a delay in the time domain is transposed as a phase shift in the frequency domain. Therefore, they inserted the pilots in the frequency domain, while the estimation was performed in the time domain. In [118], a 2D grid-type scheme with a DFT-based time interpolator block was chosen for a massive MIMO system, due to its lower complexity. The nominated block incorporates a 2-point DFT (instead of the N-point DFT in conventional algorithm) process to optimize the use of the storage resources. The algorithm was simulated in various Rayleigh fading channel conditions with the universal threshold truncation technique [141]. Another training scheme where the pilots are spread in both time and frequency directions was designated in [119]. The novel time-frequency training OFDM scheme (TFT-OFDM) performs, jointly, a time estimation and an N-point DFT-based frequency estimation. The two estimators are unified under the same framework. However, in [120], the authors investigate a different pilot-symbol scheme which adopts an ML estimator followed by a Wiener filtering approach for an LTE MIMO-OFDM system. The conducted study proved that a denser pilot combined with a partial knowledge of some statistics of the channel could attenuate the BER affected by interpolation error accumulation. [PERSON], [PERSON] and [PERSON] [121] consider a robust least-squares estimation for a channel with frequency offset and phase noise, while [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [122] choose a novel receiver structure based on pilot symbols and an iterative soft-estimate. In [122], the estimator is based on an LS time-domain interpolator. The soft estimate was realized through an improved maximum a posteriori (MAP) decoder. Despite the traditional LS- and LMMSE-based estimators that have a relatively limited performance with the existence of strong interferences, novel innovative approaches based on machine learning are being introduced [123, 124, 142]. A sparse Bayesian learning-based estimator was designated in an orthogonal space-time block coded MIMO-OFDM system for an approximatively sparse channel type [123]. This technique updates the channel coefficients within a maximization formulation based on an ML algorithm for the estimation of the hyper parameters of the channel. Moreover, in [124] a pilot selection idea was implemented using a cat swarm optimization to enhance the channel estimation process. More recently, the need to provide full secrecy in sensitive data applications has spurred research in physical layer secure communications. The usage of MIMO by increasing the number of parallel and independent channels in a rich scattering environment has been recognized to be a tool to increase the uncertainty about the channel and therefore increase the equivocation rate at an illegitimate receiver (e.g., [143, 144] and references therein).