Farhan Khalid

Coverage and capacity optimization (CCO) is critical for satisfying the high data rate and quality of service (QoS) demands in dense fifth-generation (5G) wireless networks. In this study, we propose a novel multi-agent deep reinforcement learning architecture based on proximal policy optimization (MADRL-PPO) to adjust the electronic antenna downtilt for CCO in 5G urban macro (UMa) deployments operating in the sub-6 GHz band. Based on the observations of spectral efficiency and interference, each macro base station (MBS) in the UMa scenario operates as an independent learning agent, dynamically adjusting its electronic antenna downtilt. To ensure fairness among users, a coverage-capacity fairness metric (CCFM) was introduced, which combines the average and edge spectral efficiencies. The reward function has an adaptive fairness coefficient that is tuned in real time based on the evolving state of the network. The proposed algorithm was implemented and evaluated in a 3GPP-compliant 5G UMa simulation environment developed using MATLAB. The simulation results demonstrate significant improvements in spectral efficiency and throughput, and that the proposed MADRL-PPO improves the average spectral efficiency, edge spectral efficiency, and throughput by up to 31%, 41%, and 30%, respectively, compared to fixed downtilt scenarios. The proposed MADRL-PPO is effective in creating scalable, distributed, self-organizing networks.

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.

Frequency modulated continuous wave (FMCW) multiple input multiple output (MIMO) radar systems employing Doppler division multiplexing (DDM) require calibrating the phase shifters to maintain optimal performance and minimize spurs all along the radar system's life cycle. Existing methods rely on a dedicated time slot to perform the phase shifters' calibration. Ideally, a method allowing for online estimation of the phase shifter imbalances without interrupting the radar operation would be desirable. In this work, a particular subset of the DDM coding scheme is analyzed, enabling such an online estimation. The proposed coding scheme and associated constellation imbalance estimator are detailed in this work and tested on the road, showing the robustness of the estimator.

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.

This letter presents a novel hybrid analog-digital beamforming design for massive multiuser MIMO downlink transmission in sparse millimeter wave (mm-wave) channels. The proposed hybrid regularized channel diagolnalization (HRCD) scheme combines analog (RF) phase-only beamforming with linear digital (baseband) precoding based on regularized channel diagonalization. Being a hybrid beamforming design utilizing simple non-iterative processing, HRCD provides a low complexity solution that is capable of outperforming several other hybrid schemes including those based on block diagonalization, as shown through simulations. Furthermore, it is also shown that the performance remains strong even with the use of finite-resolution phase shifters.