Resource Allocation in 5G: NR Sidelink Mode 2 & Wi-Fi 6/7

Abstract

5G/5G advanced era brings about two emerging wireless technologies: New Radio (NR) Sidelink (SL) and Wi-Fi 6/7. First, 5G New Radio (NR) Sidelink (SL) has demonstrated the promising capability for infrastructure-less cellular coverage. Understanding the fundamentals of the NR SL channel access mechanism, Semi-Persistent Scheduling (SPS), which is specified by the 3rd Generation Partnership Project (3GPP), is a necessity to enhance the NR SL Packet Reception Ratio (PRR). However, most existing works fail to account for the new SPS features introduced in NR SL, which might be out-of-date for comprehensively describing the NR SL PRR. The existing models ignore the relationships between SPS parameters and, therefore, do not provide sufficient insights into the PRR of SPS. This work proposes a novel SPS PRR model incorporating MAC collisions based on new features in NR SL. We extend our model by loosening several simplifying assumptions made in our initial modeling. The extended models illustrate how the PRR is affected by various SPS parameters. The computed results are validated via simulations using the network simulator (ns-3), which provides important guidelines for future NR SL enhancement work. Second, new features brought by 5G New Radio V2X (NR-V2X) support multiple vehicular communication types (unicast, groupcast, and broadcast) to coexist in road scenarios. However, the current standard does not specify the resource scheduling approach for groupcast to support such a new feature, which may severely degrade its packet delivery performance impacted by other communication types. In this paper, we investigate the scheduling and resource allocation approaches for a groupcast-based application, vehicle platooning, under the environment characterizing this new feature. We first analyze two baseline resource allocation approaches, i.e., Semi-persistent Scheduling (SPS) stated in the 3rd generation partnership project (3GPP) and Random Selection (RS), based on the metric of packet collision probability. Subsequently, considering the issues from baseline approaches, we develop an Improved Random Selection (IRS) scheme to decrease the collision probability. We further propose to employ Deep Deterministic Policy Gradient (DDPG) algorithm to overcome the impact of inter-vehicle collaboration in the platoon based on local information. A Monte Carlo simulator is then used to verify the analytical models' results. The numerical results show that IRS significantly mitigates the packet collision probability compared with the baselines. Meanwhile, DDPG outperforms IRS in terms of the packet collision probability as well as average scheduled delay and is more robust to the change in the environment. Third, Downlink (DL) Multi-User (MU) Multiple Input Multiple Output (MU-MIMO) is a key technology that allows multiple concurrent data transmissions from an Access Point (AP) to a selected sub-set of clients for higher network efficiency in Wi-Fi 6 (IEEE 802.11ax). However, DL MU-MIMO feature is typically turned off as the default setting in AP vendors' products, that is, turning on the DL MU-MIMO may not help increase the network efficiency, which is counter-intuitive. In this thesis, we provide a sufficiently deep understanding of the interplay between the various underlying factors, i.e., channel state information (CSI) overhead and spatial correlation, which result in negative results when turning on the DL MU-MIMO. Furthermore, we provide a fundamental guideline as a function of operational scenarios to address the fundamental question ``when the DL MU-MIMO should be turned on/off".