Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization

Ultra-Wide Bandwidth (UWB) and mm-wave radio systems can resolve specular multipath components (SMCs) from estimated channel impulse response measurements. A geometric model can describe the delays, angles-of-arrival, and angles-of-departure of these SMCs, allowing for a prediction of these channel features. For the modeling of the amplitudes of the SMCs, a data-driven approach has been proposed recently, using Gaussian Process Regression (GPR) to map and predict the SMC amplitudes. In this paper, the applicability of the proposed multipath-resolved, GPR-based channel model is analyzed by studying features of the propagation channel from a set of channel measurements. The features analyzed include the energy capture of the modeled SMCs, the number of resolvable SMCs, and the ranging information that could be extracted from the SMCs. The second contribution of the paper concerns the potential applicability of the channel model for a multipath-resolved, single-anchor positioning system. The predicted channel knowledge is used to evaluate the measurement likelihood function at candidate positions throughout the environment. It is shown that the environmental awareness created by the multipath-resolved, GPR-based channel model yields higher robustness against position estimation outliers.

A dynamic Q-learning beamforming method for inter-cell interference mitigation in 5G massive MIMO networks

Beamforming is an essential technology in 5G Massive Multiple-Input Multiple-Output (MMIMO) communications, which are subject to many impairments due to the nature of wireless transmission channel. The Inter-Cell Interference (ICI) is one of the main obstacles faced by 5G communications due to frequency-reuse technologies. However, finding the optimal beamforming parameter to minimize the ICI requires infeasible prior network or channel information. In this paper, we propose a dynamic Q-learning beamforming method for ICI mitigation in the 5G downlink that does not require prior network or channel knowledge. Compared with a traditional beamforming method and other industrial Reinforcement Learning (RL) methods, the proposed method has lower computational complexity and better convergence efficiency. Performance analysis shows the quality of service improvement in terms of Signal-to-Interference-plus-Noise-Ratio (SINR) and the robustness towards different environments.

Resource Allocation Techniques for SC-FDMA Networks

Single carrier frequency division multiple access (SC-FDMA) is a promising uplink transmission technique that has the characteristic of low peak to average power ratio. The mobile terminal uplink transmission depends on the batteries with limited power budget. Moreover, the increasing number of mobile users needs to be accommodated in the limited available radio spectrum. Therefore, efficient resource allocation schemes are essential for optimizing the energy consumption and improving the spectrum efficiency. This chapter presents a comprehensive and systematic survey of resource allocation in SC-FDMA networks. The survey is carried out under two major categories that include centralized and distributed approaches. The schemes are also classified under various rubrics including optimization objectives and constraints considered, single-cell and multi-cell scenarios, solution types, and perfect/imperfect channel knowledge-based schemes. The advantages and limitations pertaining to these categories/rubrics have been highlighted, and directions for future research are identified.