arXiv subjects

Muris Sarajlic

Publications and source records attributed to Muris Sarajlic.

9 recordsLinked to original sources

arXiv2026

Estimating Timing Advance for Sub-THz Distributed Systems from Sub-10 GHz Channel State Information

Dual-band wireless architectures transmit the control information over the sub-10 GHz while reserving sub-THz for high data rate links, offering notable capacity gains. However, a critical bottleneck in such systems is timing synchronization. Due to the narrow beams of the sub-THz radio units (RUs), when the dual-band user equipment (UE) rotates or moves, it becomes necessary to switch the transmission between the sub-THz RUs. This switching requires recalibrating the timing of uplink (UL) and downlink (DL) transmissions to prevent communication disruptions. Moreover, for sub-THz RUs, the method introduces significant overhead and latency, especially when switches are frequent. Leveraging the reliable sub-10 GHz band offers greater resilience to UE mobility, making it suitable for control signalling. Thus, in this paper, we propose a deep learning-based algorithm that infers the propagation delay from the sub-THz RUs to the UE using sub-10 GHz channel characteristics. The inferred delay is used for calculating the timing advance for UL transmissions without the need for two-way synchronization. Simulation results show that the RU switch can be made seamless at the physical layer, without incurring any synchronization-related latency.

eess.SP↗

arXiv2025

Distributed Deployment and Dual-Frequency Concepts to Strengthen Sub-THz Wireless Systems

The vast bandwidth available at sub-THz frequencies holds great promise for high-speed wireless access, precise localization, and advanced sensing applications. However, fundamental physical constraints and technological limitations make the deployment of reliable sub-THz networks challenging. We propose a new paradigm for sub-THz coverage by transmitting the RF signals over polymer microwave fibers (PMFs) that interconnect low-complexity radio units (RUs) in a daisy-chain configuration. The distributed architecture ensures that user equipments (UEs) connect to RUs in their proximity, reducing path loss and mitigating blocking. The RUs leverage low-complexity, compact integrated antenna modules. Additionally, dual-frequency tandem operation is proposed, integrating the sub-THz system with a sub-10 GHz system that provides control signalling and a robust fallback solution for the sub-THz system. This proposed tandem architecture can open up the full potential of sub-THz technology and paves the way to cost- and energy-efficient, high-performance, real-time connectivity in dynamic environments.

eess.SP↗

arXiv2025

Deep Learning for sub-THz Radio Unit Selection using sub-10 GHz Channel Information and Inferred Device Beamforming

The dense and distributed deployment of sub-THz radio units (RUs) alongside sub-10 GHz access point (AP) is a promising approach to provide high data rate and reliable coverage for future 6G applications. However, beam search or RU selection for the sub-THz RUs incurs significant overhead and high power consumption. To address this, we introduce a method that leverages deep learning to infer a suitable sub-THz RU candidate from a set of sub-THz RUs using the sub-10 GHz channel characteristics. A novel aspect of this work is the consideration of inter-band beam configuration (IBBC), defined as the broadside angle between the low-band and high-band antenna patterns of the user equipment (UE). Since IBBC indicates the beamforming information or UE's orientation, it is typically not shared with the network as a part of signalling. Therefore, we propose a solution strategy to infer a suitable sub-THz RU even when UEs do not share their IBBC information. Simulation results illustrate the performance of the inferred sub-THz RU and highlights the detrimental impact of neglecting UE orientation on the systems performance.

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arXiv2023

Machine Learning (ML)-assisted Beam Management in millimeter (mm)Wave Distributed Multiple Input Multiple Output (D-MIMO) systems

Beam management (BM) protocols are critical for establishing and maintaining connectivity between network radio nodes and User Equipments (UEs). In Distributed Multiple Input Multiple Output systems (D-MIMO), a number of access points (APs), coordinated by a central processing unit (CPU), serves a number of UEs. At mmWave frequencies, the problem of finding the best AP and beam to serve the UEs is challenging due to a large number of beams that need to be sounded with Downlink (DL) reference signals. The objective of this paper is to investigate whether the best AP/beam can be reliably inferred from sounding only a small subset of beams and leveraging AI/ML for inference of best beam/AP. We use Random Forest (RF), MissForest (MF) and conditional Generative Adversarial Networks (c-GAN) for demonstrating the performance benefits of inference.

eess.SP↗

arXiv2022

6G Radio Requirements to Support Integrated Communication, Localization, and Sensing

6G will be characterized by extreme use cases, not only for communication, but also for localization, and sensing. The use cases can be directly mapped to requirements in terms of standard key performance indicators (KPIs), such as data rate, latency, or localization accuracy. The goal of this paper is to go one step further and map these standard KPIs to requirements on signals, on hardware architectures, and on deployments. Based on this, system solutions can be identified that can support several use cases simultaneously. Since there are several ways to meet the KPIs, there is no unique solution and preferable configurations will be discussed.

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arXiv2020

Decentralized Massive MIMO Processing Exploring Daisy-chain Architecture and Recursive Algorithms

Algorithms for Massive MIMO uplink detection and downlink precoding typically rely on a centralized approach, by which baseband data from all antenna modules are routed to a central node in order to be processed. In the case of Massive MIMO, where hundreds or thousands of antennas are expected in the base-station, said routing becomes a bottleneck since interconnection throughput is limited. This paper presents a fully decentralized architecture and an algorithm for Massive MIMO uplink detection and downlink precoding based on the Stochastic Gradient Descent (SGD) method, which does not require a central node for these tasks. Through a recursive approach and very low complexity operations, the proposed algorithm provides a good trade-off between performance, interconnection throughput and latency. Further, our proposed solution achieves significantly lower interconnection data-rate than other architectures, enabling future scalability.

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arXiv2018

Fully Decentralized Massive MIMO Detection Based on Recursive Methods

Algorithms for Massive MIMO uplink detection typically rely on a centralized approach, by which baseband data from all antennas modules are routed to a central node in order to be processed. In case of Massive MIMO, where hundreds or thousands of antennas are expected in the base-station, this architecture leads to a bottleneck, with critical limitations in terms of interconnection bandwidth requirements. This paper presents a fully decentralized architecture and algorithms for Massive MIMO uplink based on recursive methods, which do not require a central node for the detection process. Through a recursive approach and very low complexity operations, the proposed algorithms provide a sequence of estimates that converge asymptotically to the zero-forcing solution, without the need of specific hardware for matrix inversion. The proposed solution achieves significantly lower interconnection data-rate than other architectures, enabling future scalability.

eess.SP↗

arXiv2018

Impact of Relay Cooperation on the Performance of Large-scale Multipair Two-way Relay Networks

We consider a multipair two-way relay communication network, where pairs of user devices exchange information via a relay system. The communication between users employs time division duplex, with all users transmitting simultaneously to relays in one time slot and relays sending the processed information to all users in the next time slot. The relay system consists of a large number of single antenna units that can form groups. Within each group, relays exchange channel state information (CSI), signals received in the uplink and signals intended for downlink transmission. On the other hand, per-group CSI and uplink/downlink signals (data) are not exchanged between groups, which perform the data processing completely independently. Assuming that the groups perform zero-forcing in both uplink and downlink, we derive a lower bound for the ergodic sumrate of the described system as a function of the relay group size. By close observation of this lower bound, it is concluded that the sumrate is essentially independent of group size when the group size is much larger than the number of user pairs. This indicates that a very large group of cooperating relays can be substituted by a number of smaller groups, without incurring any significant performance reduction. Moreover, this result implies that relay cooperation is more efficient (in terms of resources spent on cooperation) when several smaller relay groups are used in contrast to a single, large group.

cs.IT↗

arXiv2017

Deep Convolutional Neural Networks for Massive MIMO Fingerprint-Based Positioning

This paper provides an initial investigation on the application of convolutional neural networks (CNNs) for fingerprint-based positioning using measured massive MIMO channels. When represented in appropriate domains, massive MIMO channels have a sparse structure which can be efficiently learned by CNNs for positioning purposes. We evaluate the positioning accuracy of state-of-the-art CNNs with channel fingerprints generated from a channel model with a rich clustered structure: the COST 2100 channel model. We find that moderately deep CNNs can achieve fractional-wavelength positioning accuracies, provided that an enough representative data set is available for training.

stat.ML↗