Sixth Generation (6G) transceivers are envisioned to feature massively large antenna arrays compared to its predecessor. This will result in even higher spectral efficiency (SE) and multiplexing gains. However, immense concerns remain about the energy efficiency (EE) of such transceivers. This work focuses on partially connected hybrid architectures, with the primary aim of enhancing the EE of the system. To achieve this objective, the study proposes a combined approach of joint antenna selection and precoding, which holds the potential to further optimize the system’s EE while maintaining a satisfactory SE performance levels. The proposed approach incorporates antenna selection based on a meta-heuristic cyclic binary particle swarm optimization algorithm along with successive interference cancellation-based precoding. The results indicate that the proposed solution, in terms of SE and EE, performs very close to the optimal exhaustive search algorithm. This study also investigates the trade-off between SE and EE in a low and high signal-to-noise ratio (SNR) regimes. The robustness of the proposed scheme is also demonstrated when the channel state information is imperfect. In conclusion, this work presents a lower complexity approach to enhance EE in 6G transceivers while maintaining SE performance and along with a reduction in power consumption.

For future wireless communication, millimeter wave (mmWave) coupled with the massive multiple-input multiple-output (MIMO) are key technologies to overcome the huge data rate requirements. Although massive MIMO greatly improves the spectral efficiency (SE) of the system, the use of large antenna arrays not only increases the computational complexity it may also decrease the energy efficiency. Focusing on improvement in energy efficiency, we propose a low-complexity solution for joint transmit antenna selection and hybrid precoder design for multi-user mmWave Massive MIMO communication systems. Particularly, considering a partially connected hybrid architecture, binary particle swarm optimization and deep neural network (DNN) algorithms are employed for transmit antenna selection and analog precoder design, respectively. Results show that the proposed solution performs very close, in terms of spectral efficiency, to the optimal exhaustive search based antenna selection and singular value decomposition based precoder design with lower computational complexity. It is also shown that the proposed solution also improves the energy efficiency of the system. Finally, the proposed solution is not very sensitive to channel imperfections.

Hybrid pre-coding strategies are considered as a potential solution for combating path loss experienced by Massive MIMO systems operating at millimeter wave frequencies. The partially connected structure is preferred over the fully connected structure due to smaller computational complexity. In order to improve the spectral efficiency of a partially connected hybrid pre-coding architecture, which is one of the requirements of future 5G/B5G systems, this work proposes the application of evolutionary algorithms for joint computation of RF and the digital pre-coder. The evolutionary algorithm based scheme jointly evaluates the RF and digital pre-coder for a partially connected hybrid structure by taking into account the current RF chain for computations and therefore it is not based on interference cancellation from all other RF chains as in the case of successive interference cancellation (SIC). The evolutionary algorithm, i.e., Artificial Bee Colony (BEE) based pre-coding scheme outperforms other popular evolutionary algorithms as well as the SIC based pre-coding scheme in terms of spectral efficiency. In addition, the proposed algorithm is not overly sensitive to variations in channel conditions.