From the telecommunication companies' perspective, the preference for integrated sensing and communication (ISAC) in sixth-generation (6G) is to enhance existing infrastructure with sensing capabilities while minimizing network alterations and optimizing available resources. This prompts the investigation of ISAC leveraging the existing infrastructure of fifth-generation (5G) new radio (NR) signals as defined by the 3rd generation partnership project (3GPP). Additionally, improving spectral efficiency is crucial in scenarios with high demand for both communication and sensing applications to maintain the required quality of service (Qo$S$). Motivated by these, we propose the superposition of the physical downlink shared channel (PDSCH) for communication and the positioning reference signal (PRS) for sensing with proper power allocation. Furthermore, we propose a novel algorithm to reduce the interference for data decoding caused by PRS. Moreover, we introduce the joint exploitation of PRS and demodulation reference signal (DMRS) to prevent range ambiguity in the form of ghost targets. Through simulation analysis, we demonstrate the effectiveness of integrating PDSCH and PRS symbols within a unified resource grid. Our results show that the introduced approaches not only eliminate range ambiguity when sensing targets from gNodeBs (gNBs) but also enhance spectral efficiency by reducing interference between PRS and PDSCH. Simulation results show throughput enhancement and up to 57% improvement in bit error rate (BER). This work paves the way for supporting sensing applications in future network generations.

A major challenge for integrated sensing and communication (ISAC) systems is the degradation in localization accuracy caused by unfavorable propagation conditions, such as multipath effects and non-line-of-sight (NLoS) environments. These conditions can introduce outlier measurements that significantly affect localization performance. This paper explores ways to enhance target localization accuracy in multistatic ISAC systems under both line-of-sight (LoS) and NLoS conditions. We utilize the positioning reference signal (PRS) as the sensing signal. A new algorithm is proposed to improve localization accuracy by reducing the influence of outliers in range measurements and PRS range resolution. Finally, simulation results confirm the superiority of the proposed method compared to least squares (LS) method.

Integrated sensing and communication (ISAC) is anticipated to play a crucial role in sixth-generation (6G) mobile communication networks. A significant challenge in ISAC systems is the degradation of localization accuracy due to poor propagation conditions, such as multipath effects and non-line-of-sight (NLoS) scenarios. These conditions result in outlier measurements that can severely impact localization performance. This paper investigates the enhancement of target localization accuracy in multistatic ISAC systems under both line-of-sight (LoS) and NLoS conditions. We leverage positioning reference signal (PRS), which is currently employed in fifth-generation (5G) new radio (NR) for user equipment (UE) positioning, as the sensing signal. We introduce a novel algorithm to improve localization accuracy by mitigating the impact of outliers in range measurements, while also accounting for errors due to PRS range resolution. Eventually, through simulation results, we demonstrate the superiority of the proposed method over previous approaches. Indeed, we achieve up to 28 % and 20 % improvements in average localization error over least squares (LS) and iteratively reweighted least squares (IRLS) methods, respectively. Additionally, we observe up to 16 % and 13 % enhancements in the 90th percentile of localization error compared to LS and IRLS, respectively. Our simulation is based on 3rd Generation Partnership Project (3GPP) standards, ensuring the applicability of our results across diverse environments, including urban and indoor areas.

From the industrial standpoint on integrated sensing and communication (ISAC), the preference lies in augmenting existing infrastructure with sensing services while minimizing network changes and leveraging available resources. This paper investigates the potential of utilizing the existing infrastructure of fifth-generation (5G) new radio (NR) signals as defined by the 3rd generation partnership project (3GPP), particularly focusing on pilot signals for sensing within the ISAC framework. We propose to take advantage of the existing positioning reference signal (PRS) for sensing and the physical downlink shared channel (PDSCH) for communication, both readily available in 5G NR. However, the use of PRS for sensing poses challenges, leading to the appearance of ghost targets. To overcome this obstacle, we propose two innovative approaches for different PRS comb sizes within the ISAC framework, leveraging the demodulation reference signal (DMRS) within PDSCH to eliminate ghost targets. Subsequently, we formulate a resource allocation problem between PRS and PDSCH and determine the Pareto optimal point between communication and sensing without ghost targets. Through comprehensive simulation and analysis, we demonstrate that the joint exploitation of DMRS and PRS offers a promising solution for ghost target removal, while effective time and frequency resource allocation enables the achievement of Pareto optimality in ISAC.

The industrial point of view towards integrated sensing and communication (ISAC), the preference is to leverage existing resources and fifth-generation (5G) infrastructure to minimize deployment costs and complexity. In this context, we explore the utilization of current 5G new radio (NR) signals aligned with 3rd generation partnership project (3GPP) standards. Positioning reference signals (PRS) for sensing and physical downlink shared channel (PDSCH) for communication have been chosen to form an ISAC framework. However, PRS-based sensing suffers from Doppler ambiguity when the Doppler frequency shift is severe. To address this challenge, we introduce a novel method within the ISAC system that leverages the demodulation reference signal (DMRS) present in PDSCH to eliminate Doppler ambiguity. Furthermore, we formulate a resource allocation problem between PRS and PDSCH to achieve a Pareto optimal point between communication and sensing without Doppler ambiguity. Through simulations and analysis, we demonstrate the effectiveness of our proposed method on joint DMRS-PRS exploitation in mitigating Doppler ambiguity and the efficiency of the resource allocation scheme in achieving Pareto optimality for ISAC within a 5G NR framework.

We have completed the collection of one of the richest accurately annotated mobile dataset of modes of transportation and locomotion. To do this, we developed a highly reliable Android application called DataLogger capable of recording multisensor data from multiple synchronized smartphones simultaneously. The application allows real-time data annotation. We explain how we designed the app to achieve high reliability and ease of use. We also present an evaluation of the application in a big-data collection (750 hours, 950 GB of data, 17 different sensor modalities), analysing the data loss (less than 0.4%) and battery consumption (≈ 6% on average per hour). The application is available as open source.

We explain how to obtain a highly versatile and precisely annotated dataset for the multimodal locomotion of mobile users. After presenting the experimental setup, data management challenges and potential applications, we conclude with the best practices for assuring data quality and reducing loss. The dataset currently comprises 7 months of measurements, collected by smartphone's sensors and a body-worn camera, while the 3 participants used 8 different modes of transportation. It comprises 950 GB of sensor data, which corresponds to 750 hours of labelled data. The obtained data will be useful for a wide range of research questions related to activity recognition, and will be made available to the community1.

In non-reciprocal wireless channels, the performance of link adaptation heavily deteriorates with outdated measurement reports. To provide an effective solution to this problem, we construct an accurate channel gain predictor by combining the Kalman Filter (KF) with Expectation-Maximization (EM). This joint design minimizes the mismatch between the autoregressive model of the KF and the true channel gain. After deriving the filter parameters in closed form, we compare the accuracy of our solution to two relevant alternatives in a 3GPP-compliant scenario. Impressive gains at low computational complexity make our solution a promising candidate to improve the next generation of link adaptation functions.

This demonstration shows a new type of HTTP adaptive streaming (HAS) for mobile users. We will present an anticipatory HAS policy that adapts video quality based on a prediction of the wireless channel state. By anticipating a poor channel state and by reducing video quality in advance, this policy maximizes the number of seconds in the user's playout buffer before coverage is lost. Even in difficult coverage situations such as tunnels and elevators, our approach reaches outstanding fluency without decreasing the average video bitrate. With this anticipatory buffering technique users require no channel resources at low channel state, which increases spectral efficiency. Our solution is based on an modified player application and will be demonstrated on off-the-shelf Smartphones in a conventional 4G network.

In wireless cellular networks, multiple scenarios involve the communication between devices located in the same vicinity. These links can be offloaded to a device to device (D2D) underlay which is controlled by the cellular infrastructure. Such an option alleviates the load on the base stations and saves spectrum resources for downlink connections. In this paper, we investigate the impact of D2D communications on the capacity of a cellular network from two directions: average link capacity and coverage. We present a stochastic geometry based framework which captures both cellular and device to device links. Based on this framework, we quantify the enhancements offered by D2D underlay in terms of system coverage, average link capacity and spectrum usage.

Power analysis attacks are non intrusive and easily mounted. As a consequence, there is a growing interest in efficient implementation of these attacks against block cipher algorithms such as Data Encryption Standard (DES) and Advanced Encryption Standard (AES). In our paper we propose a new technique based on the Kalman theory. We show how this technique could be useful for the cryptographic domain by making power analysis attacks faster. Moreover we prove that the Kalman filter is more powerful than the High Order Statistics technique.

In the realm of satellite communications, one of the great impairments to increase the spectral efficiency is multi-user interference in the reverse link (mobile to satellite). Standards techniques based on reuse factors and beam design, although interesting from an interference cancellation point of view, penalize dramatically the rate. In this contribution, we show how proper power allocation for successive interference cancellation can enhance the performance of satellite communications in the context of DVB-RCS systems with a high number of users. For a given user target rate, we provide a suitable algorithm based on minimum mean square error successive interference cancellation and assess the performance through various simulations.

This study proposes an efficient way of interference mitigation for ultra-wideband energy detection. A receiver for pulse position modulation systems is investigated. The inter-slot (i.e., intra-symbol) and inter-symbol interferences are studied and a probabilistic equalizer is derived. This energy equalizer is embedded into the loop of an iterative channel decoder. Computer simulations are performed on the channel models from the IEEE 802.15.3a task group.