This Advanced Seminar provides a comprehensive overview of the research landscape of employing Large Language Models (LLMs) for Urban Mobility applications. The presented work in this seminar is categorized based on how LLMs are employed to serve various urban mobility applications. This goes from employing LLMs as a black box with a bit of prompt engineering, to fine-tuning LLMs to fit urban mobility applications, to completely retrain a vanilla LLM architecture with urban mobility data, to modifying the internal LLM loss function to fit urban mobility applications. The seminar concludes by presenting a set of benchmarking and evaluation work while pointing out to research gaps, open problems, and future research directions for employing LLMs to urban mobility applications.

Though polar scientists entertain having huge amounts of publicly available datasets, they face the challenge that working with such data is a cumbersome process that requires downloading tons of unnecessary data and writing various scripts on top of it. This hinders their ability to perform any kind of interactive analysis. This paper presents Polaris; a novel open-source system infrastructure for Polar science that is highly Interactive and Scalable. Polaris is designed based on three observations that distinguish the query workload of polar scientists, namely, all queries are spatio-temporal, not all data are equal, and the large majority of queries are aggregates. Polaris is equipped with a hierarchical spatio-temporal index structure that stores precomputed aggregates for data of interest. Experimental results with a real Polaris prototype and real scientific data show that it achieves highly interactive and scalable data access, enabling interactive analysis of polar science data.

This demonstration presents Polaris; a novel open-source system infrastructure for Polar science that is highly Interactive and Scalable. Polaris is designed based on three observations that distinguish the query workload of polar scientists, namely, all queries are spatio-temporal, not all data are equal, and the large majority of queries are aggregates. With this, Polaris is equipped with a hierarchical spatio-temporal index structure that stores precomputed aggregates for data of interest. Audience will be able to experience Polaris through various scenarios that show the interactivity and scalability as well as Polaris optimized query processes.

This tutorial provides a comprehensive overview of the research landscape of employing Large Language Models (LLMs) to spatial analysis queries. The tutorial categorizes the research in this area based on how LLMs are employed to serve such queries. This goes from employing LLMs as is, to fine-tuning LLMs, to completely retrain LLM architectures, to modifying the LLM internals to fit spatial queries. The tutorial concludes by a set of benchmarks and pointing out to research gaps and future research directions.

In this paper, we investigate the performance of a distributed Reconfigurable Intelligent Surface (RIS) system as a solution to RIS-aided Joint Spatial Division and Multiplexing (JSDM) in Multi-User Multiple Input Single Output (MU-MISO) scenarios. JSDM is a well-studied massive MIMO (mMIMO) scheme in the literature, where two-stage beamforming at the base station (BS) is used to decrease the channel estimation overhead and mitigate interference. We consider a situation where several users with obstructed direct BS channels are being served through RIS-created links, giving rise to larger channel dimensions than conventional mMIMO systems. JSDM comes in handy in this case to overcome the increased estimation overhead. However, RIS-aided links suffer from rank deficiency which will be prohibitive in serving multiple users solely through these channels. The downlink of a single-cell MU-MISO system employing JSDM is investigated, where a distributed RISs set-up installed between the BS and the users is proposed as a solution to overcome the rank deficiency of the provided alternative link, making it reliable to serve several users through spatial multiplexing. Phase shift configuration of the RISs is optimized through a projected gradient ascent algorithm maximizing the deterministic equivalent of the sum rate expression, providing a low-overhead algorithm dependent on statistical channel state information (CSI) only. Monte Carlo simulations and analytical deterministic equivalent results show that despite the higher double reflection path-loss, distributed RISs systems with increased rank outperform the one RIS rank-deficient system, along with reduced CSI overhead and complexity as a consequence of JSDM.

Joint Spatial Division and Multiplexing (JSDM) is a massive MIMO (mMIMO) scheme in which the beamforming is split into two stages, decreasing the channel estimation overhead and mitigating the interference. Moreover, Reconfigurable Intelligent Surfaces (RIS) have emerged as a hot research topic in recent years, being widely advocated as a candidate technology for next-generation wireless communications. These surfaces passively alter the behavior of propagation environments ameliorating the performance of wireless communication systems. However, this gives rise to even larger channel dimensions than conventional mMIMO systems, causing a surge in the estimation overhead. In this paper, we investigate the downlink of a single-cell Multi-User MIMO (MU-MIMO) system employing JSDM with the aid of a RIS installed between the base station (BS) and the users having a weak direct BS link. The RIS provides an alternative communication link, and JSDM reduces the channel estimation overhead. We briefly explain the concepts of JSDM and spatial signatures. Then, we introduce a novel similarity measure suitable for clustering the users in the presence of a RIS. Furthermore, we propose to add a precoding stage to the overall JSDM precoder to remove undesired effects induced by the use of a RIS in the considered MU-MIMO scheme. Simulation results show the improved performance of RIS-aided JSDM in comparison to the conventional scheme when the RIS is large enough for a given transmit power with a gain in the average sum rate, reduced effective channel dimensions and increased cell coverage.

Reconfigurable Intelligent Surfaces (RIS) have been a hot research topic in recent years, being widely advocated to represent a promising technology for beyond 5G cellular networks. In this paper, we propose a modulation scheme called RIS-IM in which Index Modulation (IM) is applied using RIS. However, to overcome the strong channel correlation caused by the small separation distance between the RIS elements a precoding stage based on the concept of signature constellations is added at the transmitter. The precoding stage is shown to counteract the degradations caused by these correlations providing interesting gains. We further improve the performance by passive beamforming, optimizing the phase shifts of the RIS elements using Semidefinite Programming (SDP) to improve the receiver SNR. We then provide performance analysis for this IM scheme using computer simulations. A comparison of RIS-IM with Spatial Modulation (SM), which is another member of the IM family, is also included, showing that RIS-IM provides substantial SNR gains.

The initial access in 5G New Radio (NR) standalone millimeter wave (mmWave) uses the techniques of beam sweeping to Figure out appropriate directions of transmission and reception. Beam sweeping refers to the transmission of cell specific signals by the base station (BS) while switching beam direction in a sequential manner in order to cover the whole cell. The conventional beam sweeping consists of transmitting sequentially and periodically the beams, according to a fixed pattern, which results in an initial access latency. The aim of this paper is to minimize the overall number of timeslots needed to connect all users to the BS by allowing using multiple beams in some slots and then activating the beams in a prioritized and controlled manner rather than sequentially and periodically as in the conventional scheme. The problem is formulated as a combinatorial optimization problem, for which a Semidefinite Relaxation-based (SDR-based) solving approach is proposed. Numerical results demonstrate that our scheme outperforms significantly the conventional beam sweeping.