Resource efficiency is a key design principle for future 6G radio systems. To achieve resource efficiency, a high resource utilization must be accomplished. This can be realized by allocating exactly the number of resources to the various users, which are required by them. The number of required resources per user highly depends on the experienced channel conditions of a user, which can be determined by the Channel Quality Indicator (CQI) in the Downlink (DL). To determine the CQIs, reference signals are sent by the Base Station (BS) and the CQI is periodically reported by the User Equipemt (UE). CQI reporting can either be done for the entire Bandwidth Part (BWP), i.e., wideband CQI reporting, or for a subgroup of frequency resources, i.e., subband CQI reporting. In 5G Radio Access Networks (RANs), the unit of resource allocation in the frequency domain is a Resource Block Group (RBG), which consists of multiple Physical Resource Blocks (PRBs). The number of PRBs that make up a single RBG is fixed and depends on the employed Bandwidth Part (BWP) size [1]. Since the CQI determines how much data can be sent with a single RBG, it highly influences the number of required resources of a user. Hence, the accuracy and the granularity of the CQI in terms of the frequency spectrum impacts the overall resource utilization that can be achieved. Furthermore, the number of PRBs making up one RBG influences the achieved resource utilization, as the difference between the number of allocated resources and the number of required resources decreases with a decreasing level of granularity.   The goal of this thesis is to investigate the trade-off between a higher CQI reporting granularity, which comes with an increased signaling overhead, and the increased resource efficiency by achieving a better mapping between the actual channel conditions and the reported CQI value. Moreover, the impact of smaller RBG sizes should be compared against the increase in signaling overhead allowing for a more granular resource allocation. To this end, first, the student needs to gain a thorough understanding of the DL reference signals and CQI reporting in 5G as well as the DL resource allocation. This can be achieved by a detailed study of the ETSI 5G standards. Afterwards, the student is expected to conduct extensive simulations for different parameter settings to numerically evaluate the different trade-offs. Optionally, depending on the thesis progress, a similar analysis can be conducted for an UL setup or measurements can be conducted in a testbed setup that uses OpenAirInterface (OAI) [2].

References

[1] ETSI, “5G; NR; phyiscal layer procedures for data: 3GPP TS 38.214 version 17.5.0 release 17.” www.etsi.org, 2023. Technical Specification. [2] Open Air Interface, “OpenAirInterface | 5G software alliance for democratising wireless in- novation,” 2024. https://openairinterface.org [Accessed: January 31, 2024].

For 6G radio systems it is key to achieve high capacity, coverage and energy efficiency. Distributed massive MIMO (dMIMO) systems are one of the often proposed 6G concepts to help to achieve these challenging requirements. dMIMO is very similar to a JT CoMP system, which has been researched for many years even so the reported performance gains over classical mMIMO systems are typically small to moderate. There are a number of well known challenges like the high inter cooperation area interference, channel aging for a high number of involved radio channels, or, huge processing complexity due to the large size of the channel matrices for a high number of cooperating transmission points. To overcome at least some of these issues a novel so called ‘sparse’ dMIMO system has been proposed, where the conventional transmission of user data is replaced by a novel ‘start stop bit’ transmission scheme. The main feature is the sparse resource usage for data transmissions, which is interesting for dMIMO systems as it can help to significantly reduce the inter cooperation area interference by potentially 98%. Similarly, the complexity for the dMIMO precoding might be reduced by potentially 98% as most of the resource elements of a physical resource block will be set to zero, i.e., do not need any precoding. The scope of the master thesis is to evaluate the novel 6G sparse dMIMO concept, verify the claimed benefits, identify new challenges and potential new implementation concepts overcoming these challenges. Where useful, AI/ML based solutions should be applied to achieve highest performance with lowest complexity.