Tesis Doctorado / doctoral Thesis
Optimizing Spectral Efficiency with Massive MIMO Systems for 5G NR Mobile Networks
Fecha
2020-12Registro en:
Carrera, D. F., (2020). Optimizing Spectral Efficiency with Massive MIMO Systems for 5G NR Mobile Networks. (Doctoral dissertation). Instituto Tecnológico y de Estudios Superiores de Monterrey).
591185
Autor
Carrera Moreno, Diego Fernando
Institución
Resumen
The fifth-generation (5G) of mobile communications defines two main operating bands: The frequency band below 6 GHz (sub-6 GHz), where the pilot contamination phenomenon limits the spectral efficiency, and the millimeter-wave (mmWave) band, where the link propagation is affected primarily with the path loss. For the sub-6 GHz band, it is studied a method to mitigate the pilot contamination effects in the channel estimation process in a multiple-input multiple-output (MIMO) receiver. To overcome the challenges in channel estimation and MIMO combining and precoding processing, it is studied the dynamic behavior of the use of a given estimator applied to different MIMO receivers using the 5G New Radio (NR) frame structure. For the mmWave band, it is proposed a statistical signal processing framework to increment the achievable spectral efficiency in multi-user scenarios affected primarily by path loss. It is proposed to characterize the massive MIMO scenario, defining a link-level radio communication with the cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) scheme. The proposed techniques must reduce the bit error rate (BER) and error vector magnitude (EVM) at the receiver, thereby incrementing the spectral efficiency, taking into account an adequate combination between complexity and accuracy of estimation/detection.
For the sub-6 GHz band, a comparative analysis is performed to study the BER and EVM achieved with the least-squares (LS), the minimum mean squared error (MMSE), and the Kalman filter (KF) channel estimators when these are applied to the maximum-ratio (MR) combiner and the regularised zero-forcing (RZF) receiver. The MSE achieved with the different channel estimators was also compared by varying the noise and interference power at the receiver. The proposed methodology relies on the characterisation of a massive MIMO channel with a quasi-deterministic radio channel generator and a cyclic prefix orthogonal frequency division multiplexing link-level radio simulation. The 5G NR frame structure was used to perform channel estimation and equalization for operation frequencies below 6 GHz. Numerical results show that the MRC receiver achieves its maximum performance with the KF estimator, especially at low signal-to-noise ratio scenarios, while the RZF receiver achieves its maximum performance with the LS estimation even in high interference scenarios.
The problem of optimizing the performance of multi-user millimeter-wave (mmWave) communications is addressed in three steps. The first one is given by the use of a new pilot mapping to reduce the inter-user interference effect and to perform more accurate channel estimation. In the second step, a hybrid receiver is proposed that, based on the accuracy of the channel state information, chooses between the MMSE and the multi-user regularized zero-forcing beamforming (RZFBF) receivers, to combine/precode the massive MIMO signal. In the third step, it is proposed to improve the beam direction with a slight change in the azimuth angle during the uplink communications to increase the multi-user efficiency and reduce inter-user interference. Numerical results show the performance increase using the proposed solutions in terms of the spectral efficiency by comparing the MMSE, RZFBF, and hybrid receivers.
Additionally, an extreme learning machine (ELM)-based receiver for multi-user massive MIMO systems is introduced. The proposed ELM combining method, defined in the complex plane, is designed to directly perform MIMO combining processing to the received uplink signals, based on the adoption of the pilot symbols as training data. Numerical results show that by appropriately setting the number of hidden neurons, the ELM achieves higher spectral efficiency and smaller BER, requiring fewer floating-point operations than the conventional linear MIMO receivers, namely the minimum mean squared error and maximum ratio receivers.