Blind Transmitter Localization Using Deep Learning: A Scalability Study

This work presents an investigation on the scalability of a deep leaning (DL)-based blind transmitter positioning system for addressing the multi transmitter localization (MLT) problem. The proposed approach is able to estimate relative coordinates of non-cooperative active transmitters based solely on received signal strength measurements collected by a wireless sensor network. A performance comparison with two other solutions of the MLT problem are presented for demonstrating the benefits with respect to scalability of the DL approach. Our investigation aims at highlighting the potential of DL to be a key technique that is able to provide a low complexity, accurate and reliable transmitter positioning service for improving future wireless communications systems.Comment: Published in: 2023 IEEE Wireless Communications and Networking Conference (WCNC

Experimental Performance of Blind Position Estimation Using Deep Learning

Accurate indoor positioning for wireless communication systems represents an important step towards enhanced reliability and security, which are crucial aspects for realizing Industry 4.0. In this context, this paper presents an investigation on the real-world indoor positioning performance that can be obtained using a deep learning (DL)-based technique. For obtaining experimental data, we collect power measurements associated with reference positions using a wireless sensor network in an indoor scenario. The DL-based positioning scheme is modeled as a supervised learning problem, where the function that describes the relation between measured signal power values and their corresponding transmitter coordinates is approximated. We compare the DL approach to two different schemes with varying degrees of online computational complexity. Namely, maximum likelihood estimation and proximity. Furthermore, we provide a performance comparison of DL positioning trained with data generated exclusively based on a statistical path loss model and tested with experimental data.Comment: Published in: GLOBECOM 2022 - 2022 IEEE Global Communications Conferenc

On the Analysis and Optimization of Fast Conditional Handover with Hand Blockage for Mobility

Although frequency range 2 (FR2) systems are an essential part of 5G-Advanced and future 3GPP releases, the mobility performance of multi-panel user equipment (MPUE) with hand blockage is still an area open for research and standardization. In this article, a comprehensive study on the mobility performance of MPUE with hand blockage is performed for conditional handover (CHO) and its potential enhancement denoted by fast conditional handover (FCHO). In contrast to CHO, in FCHO the MPUE can reuse earlier target cell preparations after each handover to autonomously execute subsequent handovers. This saves both the signaling overhead associated with the reconfiguration and re-preparation of target cells after each handover and reduces mobility failures. Results have shown that FCHO offers considerable mobility performance gains as compared to CHO for different hand blockage cases that are dependent on the hand position around the MPUE. For the worst-case hand blockage scenario, it is seen that mobility failures reduce by 10.5% and 19.3% for the 60 km/h and 120 km/h mobility scenarios, respectively. This gain comes at the expense of reserving the handover resources of an MPUE for a longer time given that the target cell configurations are not necessarily released after each handover. In this article, the longer resource reservation problem in FCHO is analysed and three different resource reservation optimization techniques are introduced. Results have shown that these optimization techniques not only reduce the resource reservation time but also significantly reduce the signaling overhead at the possible expense of a tolerable degradation in mobility performance.Comment: Submitted to IEEE Access for possible publicatio

Reduced Complexity Window Decoding Schedules for Coupled LDPC Codes

Window decoding schedules are very attractive for message passing decoding of spatially coupled LDPC codes. They take advantage of the inherent convolutional code structure and allow continuous transmission with low decoding latency and complexity. In this paper we show that the decoding complexity can be further reduced if suitable message passing schedules are applied within the decoding window. An improvement based schedule is presented that easily adapts to different ensemble structures, window sizes, and channel parameters. Its combination with a serial (on-demand) schedule is also considered. Results from a computer search based schedule are shown for comparison

Non-Uniform Window Decoding Schedules for Spatially Coupled LDPC Codes

Spatially coupled low-density parity-check codes can be decoded using a graph-based message passing algorithm applied across the total length of the coupled graph. However, considering practical constraints on decoding latency and complexity, a sliding window decoding approach is normally preferred. In order to reduce decoding complexity compared with standard parallel decoding schedules, serial schedules can be applied within a decoding window. However, uniform serial schedules within a window do not provide the expected reduction in complexity. Hence, we propose non-uniform schedules (parallel and serial) based on measured improvements in the estimated bit error rate (BER). We show that these non-uniform schedules result in a significant reduction in complexity without any loss in performance. Furthermore, based on observations made using density evolution, we propose a non-uniform pragmatic decoding schedule (parallel and serial) that does not require any additional calculations (e.g., BER estimates) within the decoding process