AI & Digitalization Breakfast Seminar: Certifiably Optimal Anisotropic Rotation Averaging

Published: 2025-09-29

When: Wednesday October 15, 2025 at 09:00-10:00

Where: Control Seminar Room M:3170-73

Register: for free breakfast at the seminar, register no later than October 10th by using this link.

Abstract: Rotation averaging is a key subproblem in structure from motion. Semidefinite programming relaxations are often used to solve the problem, and there are theoretical results analyzing difficulty and optimality. However, previous methos focus on the isotropic setting, where the intrinsic uncertainties in the measurements are not fully incorporated into the resulting optimization task. Recent empirical results suggest that moving to an anisotropic framework, where these uncertainties are explicitly included, can result in an improvement of solution quality. However, global optimization for rotation averaging has remained a challenge in this scenario. In this talk we show how anisotropic costs can be incorporated in rotation averaging. We also demonstrate how existing solvers, designed for isotropic situations, fail in the anisotropic setting. Finally, we propose a stronger relaxation and empirically show that it recovers global optima on a number of tested datasets.

Speaker bio: Carl Olsson is a Professor at the Computer Vision and Machine Learning division at Centre for Mathematical Sciences, Lund University. He obtained his PhD at Lund University in 2009, and has since then been in Lund and at Chalmers. His research addresses large scale optimization methods with applications in computer vision. A particular interest is in mapping and navigation problems such as structure from motion. His research aims at improving reliability of algorithms by developing methods that provide globally optimal inference, independent of initialization.

The Breakfast Seminar Series is sponsored by the LTH Profile Area AI & Digitalization.

Thesis defense: Ashkan Sheikhi

Published: 2025-09-04

Title of thesis: Scaling massive MIMO with imperfect transceivers.

Link to thesis in Lund University Research Portal

Zoom link.
Zoom ID: 67836255687.

The number of users and the information transmitted over wireless networks have been growing constantly during the last decades. Nowadays, the pace of this growth is extremely sharp because of the new applications which heavily rely on wireless networks to meet users' demands. Wireless networks infrastructures are constantly developing to meet these demands. Massive multiple-input multiple-output (MIMO) and large intelligent surface (LIS) are two of the main technologies which are the key-enablers for the current and future wireless networks. The performance gains achieved from these system are mainly due to the large number of deployed transceiver chains, which enables serving more users by exploiting spatial domain multiplexing to meet the higher service requirements. The possibility to scale up these systems is a necessity to constantly meet the network demands. Deploying massive MIMO and LIS systems with non-ideal hardware components is of great importance to make the scalability of these systems feasible. In theory, the performance of these systems can grow unboundedly by scaling up the number of transceiver chains. However, assuming ideal hardware components for the transceivers is not realistic from a practical point of view, since the number of transceiver chains are in the order of hundreds to thousands, and the deployment cost, processing complexity, and power consumption can limit the scaling of such systems. 

This work presents an analysis of hardware quality, complexity, power consumption, versus performance of wireless communication systems, with a particular focus on massive MIMO and LIS architectures. We derive closed-form scaling laws that relate analogue front ends power consumption to key system and environmental parameters, such as bandwidth, signal-to-noise-plus-distortion-ratio (SNDR), and fading conditions, enabling informed decisions for low-power design. For massive MIMO systems, we explore both traditional and machine learning-based digital pre-distortion (DPD) strategies. In particular, we propose optimization of per-antenna DPD sizes under hardware constraints and adaptive neural DPD allocation strategies based on channel conditions, demonstrating substantial capacity improvements and system cost reductions. We further analyze the effects of non-ideal receiver chains on LIS, and propose efficient antenna and panel selection schemes to sustain LIS performance with fewer number of transceiver chains. Finally, we propose an over-the-air (OTA) method to jointly perform DPD and reciprocity calibration in massive MIMO and LIS systems, mitigating transmitter non-linearity and non-reciprocity without dedicated hardware or iterative algorithms. Collectively, these contributions provide new insights and tools for the design of energy- and cost-efficient wireless systems that remain robust under realistic hardware constraints.