arXiv subjects

Adeel Malik

Publications and source records attributed to Adeel Malik.

10 recordsLinked to original sources

arXiv2025

TDoA-Based Self-Supervised Channel Charting with NLoS Mitigation

Channel Charting (CC) has emerged as a promising framework for data-driven radio localization, yet existing approaches often struggle to scale globally and to handle the distortions introduced by non-line-of-sight (NLoS) conditions. In this work, we propose a novel CC method that leverages Channel Impulse Response (CIR) data enriched with practical features such as Time Difference of Arrival (TDoA) and Transmission Reception Point (TRP) locations, enabling a TDoA-based self-supervised localization function on a global scale. The proposed framework is further enhanced with short-interval User Equipment (UE) displacement measurements, which improve the continuity and robustness of the learned positioning function. Our algorithm incorporates a mechanism to identify and mask NLoS-induced noisy measurements, leading to significant performance gains. We present the evaluation of our proposed models in a real 5G testbed, benchmarked against centimeter-accurate Real-Time Kinematic (RTK) positioning, in an O-RAN-based 5G network using OpenAirInterface (OAI) software at EURECOM. It demonstrates results that outperform state-of-the-art semi-supervised and self-supervised CC approaches in a real-world scenario. The results show localization accuracies of 2--4 meters in 90\% of cases, across varying NLoS ratios. Furthermore, we provide public datasets of CIR recordings, along with the true position labels used in this paper's evaluation.

cs.NI↗

arXiv2025

Experimental Insights from OpenAirInterface 5G positioning Testbeds: Challenges and solutions

5G New Radio (NR) is a key enabler of accurate positioning in smart cities and smart factories. This paper presents the experimental results from three 5G positioning testbeds running open-source OpenAirInterface (OAI) gNB and Core Network (CN), using Uplink Time Difference of Arrival (UL-TDoA) with the newly integrated Location Management Function (LMF). The testbeds are deployed across both indoor factories and outdoor scenarios with O-RAN Radio Units (RUs), following a 3GPP-compliant system model. The experiments highlight the impact of synchronization impairments, multipath propagation, and deployment geometry on positioning accuracy. To address these challenges, we propose tailored ToA and TDoA filtering as well as a novel position estimation method based on Particle Swarm Optimization (PSO) within the LMF pipeline. Moreover, we show a beyond-5G framework that leverages non-conventional measurements such as Channel Impulse Response (CIR) to train and test Artificial Intelligence and Machine Learning (AI/ML) models for data-driven positioning. The results demonstrate the feasibility of achieving 1-2 meter positioning accuracy in 90% of cases in different testbeds, offering practical insights for the design of robust 5G positioning systems. Moreover, we publicly release the datasets collected in this work to support the research within the 5G positioning community.

cs.NI↗

arXiv2024

From Concept to Reality: 5G Positioning with Open-Source Implementation of UL-TDoA in OpenAirInterface

This paper presents, for the first time, an open-source implementation of the 3GPP Uplink Time Difference of Arrival (UL-TDoA) positioning method using the OpenAirInterface (OAI) framework. UL-TDoA is a critical positioning technique in 5G networks, leveraging the time differences of signal arrival at multiple base stations to determine the precise location of User Equipment (UE). This implementation aims to democratize access to advanced positioning technology by integrating UL-TDoA capabilities into both the Radio Access Network (RAN) and Core Network (CN) components of OAI, providing a comprehensive and 3GPP-compliant solution. The development includes the incorporation of essential protocol procedures, message flows, and interfaces as defined by 3GPP standards. Validation is conducted using two distinct methods: an OAI-RF simulator-based setup for controlled testing and an O-RAN-based Localization Testbed at EURECOM in real-world conditions. The results demonstrate the viability of this open-source UL-TDoA implementation, enabling precise positioning in various environments. By making this implementation publicly available, the study paves the way for widespread research, development, and innovation in the field of 5G positioning technologies, fostering collaboration and accelerating the advancement of cellular network positioning.

cs.IT↗

arXiv2022

LTC-SUM: Lightweight Client-driven Personalized Video Summarization Framework Using 2D CNN

This paper proposes a novel lightweight thumbnail container-based summarization (LTC-SUM) framework for full feature-length videos. This framework generates a personalized keyshot summary for concurrent users by using the computational resource of the end-user device. State-of-the-art methods that acquire and process entire video data to generate video summaries are highly computationally intensive. In this regard, the proposed LTC-SUM method uses lightweight thumbnails to handle the complex process of detecting events. This significantly reduces computational complexity and improves communication and storage efficiency by resolving computational and privacy bottlenecks in resource-constrained end-user devices. These improvements were achieved by designing a lightweight 2D CNN model to extract features from thumbnails, which helped select and retrieve only a handful of specific segments. Extensive quantitative experiments on a set of full 18 feature-length videos (approximately 32.9 h in duration) showed that the proposed method is significantly computationally efficient than state-of-the-art methods on the same end-user device configurations. Joint qualitative assessments of the results of 56 participants showed that participants gave higher ratings to the summaries generated using the proposed method. To the best of our knowledge, this is the first attempt in designing a fully client-driven personalized keyshot video summarization framework using thumbnail containers for feature-length videos.

cs.CV↗

arXiv2022

Coded Caching in Networks with Heterogeneous User Activity

This work elevates coded caching networks from their purely information-theoretic framework to a stochastic setting, by exploring the effect of random user activity and by exploiting correlations in the activity patterns of different users. In particular, the work studies the $K$-user cache-aided broadcast channel with a limited number of cache states, and explores the effect of cache state association strategies in the presence of arbitrary user activity levels; a combination that strikes at the very core of the coded caching problem and its crippling subpacketization bottleneck. We first present a statistical analysis of the average worst-case delay performance of such subpacketization-constrained (state-constrained) coded caching networks, and provide computationally efficient performance bounds as well as scaling laws for any arbitrary probability distribution of the user-activity levels. The achieved performance is a result of a novel user-to-cache state association algorithm that leverages the knowledge of probabilistic user-activity levels. We then follow a data-driven approach that exploits the prior history on user-activity levels and correlations, in order to predict interference patterns, and thus better design the caching algorithm. This optimized strategy is based on the principle that users that overlap more, interfere more, and thus have higher priority to secure complementary cache states. This strategy is proven here to be within a small constant factor from the optimal. Finally, the above analysis is validated numerically using synthetic data following the Pareto principle. To the best of our understanding, this is the first work that seeks to exploit user-activity levels and correlations, in order to map future interference and design optimized coded caching algorithms that better handle this interference.

cs.IT↗

arXiv2021

Stochastic Coded Caching with Optimized Shared-Cache Sizes and Reduced Subpacketization

This work studies the $K$-user broadcast channel with $Λ$ caches, when the association between users and caches is random, i.e., for the scenario where each user can appear within the coverage area of -- and subsequently is assisted by -- a specific cache based on a given probability distribution. Caches are subject to a cumulative memory constraint that is equal to $t$ times the size of the library. We provide a scheme that consists of three phases: the storage allocation phase, the content placement phase, and the delivery phase, and show that an optimized storage allocation across the caches together with a modified uncoded cache placement and delivery strategy alleviates the adverse effect of cache-load imbalance by significantly reducing the multiplicative performance deterioration due to randomness. In a nutshell, our work provides a scheme that manages to substantially mitigate the impact of cache-load imbalance in stochastic networks, as well as -- compared to the best-known state-of-the-art -- the well-known subpacketization bottleneck by showing its applicability in deterministic settings for which it achieves the same delivery time -- which was proven to be close to optimal for bounded values of $t$ -- with an exponential reduction in the subpacketization.

cs.IT↗

arXiv2021

Fundamental Limits of Stochastic Shared Caches Networks

The work establishes the exact performance limits of stochastic coded caching when users share a bounded number of cache states, and when the association between users and caches, is random. Under the premise that more balanced user-to-cache associations perform better than unbalanced ones, our work provides a statistical analysis of the average performance of such networks, identifying in closed form, the exact optimal average delivery time. To insightfully capture this delay, we derive easy to compute closed-form analytical bounds that prove tight in the limit of a large number $Λ$ of cache states. In the scenario where delivery involves $K$ users, we conclude that the multiplicative performance deterioration due to randomness -- as compared to the well-known deterministic uniform case -- can be unbounded and can scale as $Θ\left( \frac{\log Λ}{\log \log Λ} \right)$ at $K=Θ\left(Λ\right)$, and that this scaling vanishes when $K=Ω\left(Λ\log Λ\right)$. To alleviate this adverse effect of cache-load imbalance, we consider various load balancing methods, and show that employing proximity-bounded load balancing with an ability to choose from $h$ neighboring caches, the aforementioned scaling reduces to $Θ\left(\frac{\log(Λ/ h)}{ \log \log(Λ/ h)} \right)$, while when the proximity constraint is removed, the scaling is of a much slower order $Θ\left( \log \log Λ\right)$. The above analysis is extensively validated numerically.

cs.IT↗

arXiv2020

A Personalized Preference Learning Framework for Caching in Mobile Networks

This paper comprehensively studies a content-centric mobile network based on a preference learning framework, where each mobile user is equipped with a finite-size cache. We consider a practical scenario where each user requests a content file according to its own preferences, which is motivated by the existence of heterogeneity in file preferences among different users. Under our model, we consider a single-hop-based device-to-device (D2D) content delivery protocol and characterize the average hit ratio for the following two file preference cases: the personalized file preferences and the common file preferences. By assuming that the model parameters such as user activity levels, user file preferences, and file popularity are unknown and thus need to be inferred, we present a collaborative filtering (CF)-based approach to learn these parameters. Then, we reformulate the hit ratio maximization problems into a submodular function maximization and propose two computationally efficient algorithms including a greedy approach to efficiently solve the cache allocation problems. We analyze the computational complexity of each algorithm. Moreover, we analyze the corresponding level of the approximation that our greedy algorithm can achieve compared to the optimal solution. Using a real-world dataset, we demonstrate that the proposed framework employing the personalized file preferences brings substantial gains over its counterpart for various system parameters.

cs.NI↗

arXiv2018

On the Effects of Subpacketization in Content-Centric Mobile Networks

A large-scale content-centric mobile ad hoc network employing subpacketization is studied in which each mobile node having finite-size cache moves according to the reshuffling mobility model and requests a content object from the library independently at random according to the Zipf popularity distribution. Instead of assuming that one content object is transferred in a single time slot, we consider a more challenging scenario where the size of each content object is considerably large and thus only a subpacket of a file can be delivered during one time slot, which is motivated by a fast mobility scenario. Under our mobility model, we consider a single-hop-based content delivery and characterize the fundamental trade-offs between throughput and delay. The order-optimal throughput-delay trade-off is analyzed by presenting the following two content reception strategies: the sequential reception for uncoded caching and the random reception for maximum distance separable (MDS)-coded caching. We also perform numerical evaluation to validate our analytical results. In particular, we conduct performance comparisons between the uncoded caching and the MDS-coded caching strategies by identifying the regimes in which the performance difference between the two caching strategies becomes prominent with respect to system parameters such as the Zipf exponent and the number of subpackets. In addition, we extend our study to the random walk mobility scenario and show that our main results are essentially the same as those in the reshuffling mobility model.

cs.IT↗

arXiv2015

Throughput Bound of XOR Coded Wireless Multicasting to Three Clients

It is a well-known result that constructing codewords over $GF(2)$ to minimize the number of transmissions for a single-hop wireless multicasting is an NP-complete problem. Linearly independent codewords can be constructed in polynomial time for all the $n$ clients, known as maximum distance separable (MDS) code, when the finite field size $q$ is larger than or equal to the number of clients, $q\geq n$. In this paper we quantify the exact minimum number of transmissions for a multicast network using erasure code when $q=2$ and $n=3$, such that $q<n$. We first show that the use of Markov chain model to derive the minimum number of transmissions for such a network is limited for very small number of input packets. We then use combinatorial approach to derive an upper bound on the exact minimum number of transmissions. Our results show that the difference between the expected number of transmissions using XOR coding and MDS coding is negligible for $n=3$.

cs.NI↗