Anastasios Kakkavas

This work provides a comprehensive examination of Layer 1/Layer 2-triggered mobility (LTM) enhancements introduced in 3rd Generation Partnership Project (3GPP) Release (Rel.) 19, extending the foundational framework established in Rel. 18. The key advancement is the extension of LTM to inter-central unit (CU) scenarios, significantly broadening its applicability across diverse network deployments. This extension necessitated comprehensive standardization work addressing security key management, dual connectivity (DC) operations, and channel state information (CSI) resource coordination mechanisms. The measurement capabilities were improved by incorporating channel state information reference signal (CSI-RS)-based layer 1 (L1)-reference signal received power (RSRP) measurements and implementing event-triggered reporting to reduce signaling overhead. Additionally, conditional LTM enhances mobility robustness through autonomous user equipment (UE)-triggered cell switching, while early CSI acquisition procedures optimize post-handover throughput performance. The paper elaborates on the technical implementation of inter-CU LTM, covering network signaling architectures, security framework adaptations, and DC integration aspects. Furthermore, we explore Rel. 20 developments focusing on LTM integration with Carrier Aggregation and adaptive measurement configuration mechanisms, alongside initial considerations for 6G mobility evolution. To emphasize the importance of reducing the handover interruption time, our system-level simulation analysis quantifies the transport layer benefits of reduced interruption times, demonstrating that reducing the interruption duration in LTM to approximately 20 ms delivers significant transmission control protocol (TCP) throughput gains over conventional handover’s 100 ms interruption, especially when TCP re-transmission timeout (RTO) timers are set at 100 ms or below.

5-th generation (5G) has brought mobility robustness and interruption time reduction enhancements to mobility procedures. Advanced beam management procedures were also introduced in 5G, including beam-based mobility. In this paper, we summarize the procedure that fuses beam-based mobility with conventional mobility procedures, namely L1/L2-triggered mobility (LTM). We first provide the Release 18 LTM procedural summary, followed by a detailed explanation of the introduced technical enhancements and an analysis of their expected benefits. Through system-level simulations, we show that having the LTM as the mobility procedure brings two major improvements, namely interruption time reduction and mobility robustness. The lower interruption time is mainly the result of configuring the network and the user equipment (UE) well in advance of the handover, early configuration decoding, early synchronization, and triggering the handover with a lightweight medium access control (MAC) command. The mobility robustness increase is the result of the triggering of the handover based on the layer 1 (L1) measurements, which are more sensitive to channel degradation. This in turn further reduces the interruption time as the radio link failure and reestablishment are avoided. On the other hand, LTM can suffer from excess unnecessary handovers, where the UE is handed over back and forth between two or three cells. Besides, the increased measurement reporting of the L1 measurements can cause additional overhead. Finally, we give a short insight on the on-going Release 19 work that addresses some the known LTM issues.

The array design of multiple-input multiple-output (MIMO) systems in a line-of-sight (LoS) environment is investigated. Properly designed uniform array configurations at the transmitter (Tx) and receiver (Rx) can extract maximum spatial multiplexing gain only for a fixed transmit distance between the Tx and Rx arrays and for a fixed orientation of the arrays. However, such designs suffer from significant capacity variations when the position and/or orientation of the arrays is modified. To alleviate this, we examine robust, joint design of non-uniform Tx and Rx arrays, where the minimum capacity over a range of varying array positions and orientations is maximized. First, we show that, by leveraging convex relaxation, the joint Tx and Rx array design problem can be solved with convex optimization techniques in an iterative manner. Moreover, an alternative design method based on dynamic programming (DP) is proposed, which is shown to outperform the convex optimization approach. As the DP algorithm is quite demanding in terms of computational complexity, a modified DP-based algorithm is also proposed, where the Tx and Rx arrays are designed to have the same configuration. It is shown that the resulting non-uniform array configurations with the proposed designs outperform both uniform and non-uniform array designs of the literature in terms of the system robustness.

We consider a single-anchor multiple-input multiple-output orthogonal frequency-division multiplexing system with imperfectly synchronized transmitter (Tx) and receiver (Rx) clocks, where the Rx estimates its position based on the received reference signals. The Tx, having (imperfect) prior knowledge about the Rx location and the surrounding geometry, transmits reference signals based on a set of fixed beams. We develop strategies for the power allocation among the beams aiming to minimize the expected Cramér-Rao lower bound for Rx positioning. Additional constraints on the design are included to make the optimized power allocation robust to uncertainty on the line-of-sight (LOS) path direction. Furthermore, the effect of clock asynchronism on the proposed allocation strategies is studied. Our evaluation results show that, for non-negligible synchronization error, it is optimal to allocate a large fraction of the available power for the illumination of the non-LOS (NLOS) paths, which help resolve the clock offset. In addition, the complexity reduction achieved by our proposed suboptimal approach incurs only a small performance degradation. We also propose an off-grid compressed sensing-based position estimation algorithm, which exploits the information on the clock offset provided by NLOS paths, and show that it is asymptotically efficient.

In the context of positioning an agent with a single-anchor, this contribution focuses on the Fisher information about the position, orientation and clock offset of the agent provided by single-bounce reflections. The availability of prior knowledge of the agent's environment is taken into account via a prior distribution of the position of virtual anchors, and the rank, intensity and direction of provided information is studied. We show that when no prior knowledge is available, single-bounce reflections offer position information in the direction parallel to the reflecting surface, irrespective of the agent's and anchor's locations. We provide a geometrically intuitive explanation of the results and present numerical examples demonstrating their potential implications.

The fundamental limits of single-anchor multiantenna positioning are investigated. Exploiting the structure of the multiple input-multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) channel at millimeter-wave frequencies, we present geometrically intuitive asymptotic expressions for the Fisher information on position, orientation and velocity for large bandwidth and number of antennas. The effects of synchronization errors and mobility are studied and it is shown that non-line-of-sight (NLOS) paths can be used to estimate the synchronization error and drastically improve the positioning performance. We also find that, in the presence of line-of-sight (LOS), mobility has a small impact on the achievable positioning accuracy, but in the NLOS-only scenario it can significantly improve the achievable performance, depending on the variance of the synchronization error. Finally, considering a communication system with device-specific transmission and reception constraints, we compare the positioning accuracy between the downlink and the uplink and show that they are equivalent under the same signal-to-noise ratio (SNR).