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Communication technologies play a crucial role in battlefields. They are an inalienable part of any tactical response, whether at the battlefront or inland. Such scenarios require that the communication technologies be versatile, scalable, cost-effective, and stealthy. While multiple studies and past products have tried to address these requirements, none of them have been able to solve all the four challenges simultaneously. Hence, in this paper, we propose a tactical solution that is based on the versatile, scalable, and cost effective 5G NR system. Our focus is on the initial-access phase which is subject to a high probability of detection by an eavesdropper. To address this issue, we propose a novel approach that involves some modifications to the initial access procedure that lowers the probability of detection while not affecting standards compliance and not requiring any modifications to the user equipment chipset implementation. Further, we demonstrate that with a simple downlink power control algorithm, we reduce the probability of detection at an eavesdropper. The result is a 5G NR based initial-access method that improves stealthiness when compared with a vanilla 5G NR implementation., Comment: Submitted to IEEE MILCOM 2024

5G NR is touted to be an attractive candidate for tactical networks owing to its versatility, scalability, and low cost. However, tactical networks need to be stealthy, where an adversary is not able to detect or intercept the tactical communication. In this paper, we investigate the stealthiness of 5G NR by looking at the probability with which an adversary that monitors the downlink synchronization signals can detect the presence of the network. We simulate a single-cell single-eavesdropper scenario and evaluate the probability with which the eavesdropper can detect the synchronization signal block when using either a correlator or an energy detector. We show that this probability is close to $ 100\% $ suggesting that 5G out-of-the-box is not suitable for a tactical network. We then propose utilizing the uplink channel-state-information to beamform the downlink synchronization-signals towards the tactical user-equipment (UE) to lower the eavesdropper detection probability while not compromising the performance of the legitimate tactical UE., Comment: Submitted to IEEE MILCOM 2024

This paper proposes a novel hardware beamforming architecture, which is capable of utilizing a different number of Radio Frequency (RF) chains in different parts of the bandwidth. It also shows that a proportional fairness scheduler will effectively utilize the high rank part of the bandwidth in a multi-user setting, thus operating more efficiently and effectively than classical beamforming schemes., Comment: 10 pages, 14 figures. Submitted to IEEE Transactions on Wireless Communications

With the introduction of spectrum sharing and heterogeneous services in next-generation networks, the base stations need to sense the wideband spectrum and identify the spectrum resources to meet the quality-of-service, bandwidth, and latency constraints. Sub-Nyquist sampling (SNS) enables digitization for sparse wideband spectrum without needing Nyquist speed analog-to-digital converters. However, SNS demands additional signal processing algorithms for spectrum reconstruction, such as the well-known orthogonal matching pursuit (OMP) algorithm. OMP is also widely used in other compressed sensing applications. The first contribution of this work is efficiently mapping the OMP algorithm on the Zynq system-on-chip (ZSoC) consisting of an ARM processor and FPGA. Experimental analysis shows a significant degradation in OMP performance for sparse spectrum. Also, OMP needs prior knowledge of spectrum sparsity. We address these challenges via deep-learning-based architectures and efficiently map them on the ZSoC platform as second contribution. Via hardware-software co-design, different versions of the proposed architecture obtained by partitioning between software (ARM processor) and hardware (FPGA) are considered. The resource, power, and execution time comparisons for given memory constraints and a wide range of word lengths are presented for these architectures.

Satisfying the stringent 5G Quality of Service (QoS) requirements necessitates efficient resource utilization by the mobile networks. Consequently, we argue that Multi-Connectivity (MC) is an effective solution to leverage the limited radio resources from multiple base stations (BSs) in order to enhance the user throughput, provide seamless connectivity, or increase the data reliability. For this, we study different MC architectures, where distinct network entities and protocol layers are used to split or aggregate the user traffic. The benefits and challenges of MC are analyzed as well as the open issues that network/device vendors and mobile network operators (MNOs) have to address for its use. Finally, through experimental evaluations, we illustrate the importance of MC design decisions for the overall network performance., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

The reconfigurable intelligent surface is a promising technology for the manipulation and control of wireless electromagnetic signals. In particular, it has the potential to provide significant performance improvements for wireless networks. However, to do so, a proper reconfiguration of the reflection coefficients of unit cells is required, which often leads to complex and expensive devices. To amortize the cost, one may share the system resources among multiple transmitters and receivers. In this paper, we propose an efficient reconfiguration technique providing control over multiple beams independently. Compared to time-consuming optimization techniques, the proposed strategy utilizes an analytical method to configure the surface for multi-beam radiation. This method is easy to implement, effective and efficient since it only requires phase reconfiguration. We analyze the performance for indoor and outdoor scenarios, given the broadcast mode of operation. The aforesaid scenarios encompass some of the most challenging scenarios that wireless networks encounter. We show that our proposed technique provisions sufficient improvements in the observed channel capacity when the receivers are close to the surface in the indoor office environment scenario. Further, we report a considerable increase in the system throughput given the outdoor environment.

Relational data present in real world graph representations demands for tools capable to study it accurately. In this regard Graph Neural Network (GNN) is a powerful tool, wherein various models for it have also been developed over the past decade. Recently, there has been a significant push towards creating accelerators that speed up the inference and training process of GNNs. These accelerators, however, do not delve into the impact of their dataflows on the overall data movement and, hence, on the communication requirements. In this paper, we formulate analytical models that capture the amount of data movement in the most recent GNN accelerator frameworks. Specifically, the proposed models capture the dataflows and hardware setup of these accelerator designs and expose their scalability characteristics for a set of hardware, GNN model and input graph parameters. Additionally, the proposed approach provides means for the comparative analysis of the vastly different GNN accelerators., Comment: ISCAS 2021

Graph Neural Networks (GNNs) have garnered a lot of recent interest because of their success in learning representations from graph-structured data across several critical applications in cloud and HPC. Owing to their unique compute and memory characteristics that come from an interplay between dense and sparse phases of computations, the emergence of reconfigurable dataflow (aka spatial) accelerators offers promise for acceleration by mapping optimized dataflows (i.e., computation order and parallelism) for both phases. The goal of this work is to characterize and understand the design-space of dataflow choices for running GNNs on spatial accelerators in order for mappers or design-space exploration tools to optimize the dataflow based on the workload. Specifically, we propose a taxonomy to describe all possible choices for mapping the dense and sparse phases of GNN inference, spatially and temporally over a spatial accelerator, capturing both the intra-phase dataflow and the inter-phase (pipelined) dataflow. Using this taxonomy, we do deep-dives into the cost and benefits of several dataflows and perform case studies on implications of hardware parameters for dataflows and value of flexibility to support pipelined execution., Comment: Accepted for publication at the 36th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2022)

The main design principles in computer architecture have recently shifted from a monolithic scaling-driven approach to the development of heterogeneous architectures that tightly co-integrate multiple specialized processor and memory chiplets. In such data-hungry multi-chip architectures, current Networks-in-Package (NiPs) may not be enough to cater to their heterogeneous and fast-changing communication demands. This position paper makes the case for wireless in-package nanonetworking as the enabler of efficient and versatile wired-wireless interconnect fabrics for massive heterogeneous processors. To that end, the use of graphene-based antennas and transceivers with unique frequency-beam reconfigurability in the terahertz band is proposed. The feasibility of such a nanonetworking vision and the main research challenges towards its realization are analyzed from the technological, communications, and computer architecture perspectives., Comment: 8 pages, 4 figures, 1 table

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data. Such an ability has strong implications in a wide variety of fields whose data is inherently relational, for which conventional neural networks do not perform well. Indeed, as recent reviews can attest, research in the area of GNNs has grown rapidly and has lead to the development of a variety of GNN algorithm variants as well as to the exploration of groundbreaking applications in chemistry, neurology, electronics, or communication networks, among others. At the current stage of research, however, the efficient processing of GNNs is still an open challenge for several reasons. Besides of their novelty, GNNs are hard to compute due to their dependence on the input graph, their combination of dense and very sparse operations, or the need to scale to huge graphs in some applications. In this context, this paper aims to make two main contributions. On the one hand, a review of the field of GNNs is presented from the perspective of computing. This includes a brief tutorial on the GNN fundamentals, an overview of the evolution of the field in the last decade, and a summary of operations carried out in the multiple phases of different GNN algorithm variants. On the other hand, an in-depth analysis of current software and hardware acceleration schemes is provided, from which a hardware-software, graph-aware, and communication-centric vision for GNN accelerators is distilled., Comment: 35 pages, 9 figures, 8 tables, 188 references

As the current standardization for the 5G networks nears completion, work towards understanding the potential technologies for the 6G wireless networks is already underway. One of these potential technologies for the 6G networks are Reconfigurable Intelligent Surfaces (RISs). They offer unprecedented degrees of freedom towards engineering the wireless channel, i.e., the ability to modify the characteristics of the channel whenever and however required. Nevertheless, such properties demand that the response of the associated metasurface (MSF) is well understood under all possible operational conditions. While an understanding of the radiation pattern characteristics can be obtained through either analytical models or full wave simulations, they suffer from inaccuracy under certain conditions and extremely high computational complexity, respectively. Hence, in this paper we propose a novel neural networks based approach that enables a fast and accurate characterization of the MSF response. We analyze multiple scenarios and demonstrate the capabilities and utility of the proposed methodology. Concretely, we show that this method is able to learn and predict the parameters governing the reflected wave radiation pattern with an accuracy of a full wave simulation (98.8%-99.8%) and the time and computational complexity of an analytical model. The aforementioned result and methodology will be of specific importance for the design, fault tolerance and maintenance of the thousands of RISs that will be deployed in the 6G network environment., Comment: Submitted to IEEE OJ-COMS

In this paper, we provide a novel application aware user association and resource allocation framework, i.e., AURA-5G, which utilizes a joint optimization strategy to accomplish the same. Concretely, our methodology considers all the real network constraints that will be prevalent in the 5G networks as well as practical deployment scenarios. Furthermore, AURA-5G, being an application aware framework, considers the resource requirements of both eMBB and mMTC services whilst performing the optimization task. We have demonstrated that our strategy performs significantly better than the baseline algorithm, given any of the multiple combinations of network constraints explored in this paper. In addition, we have also presented a novel computational complexity analysis for the AURA-5G framework as well as a solvability and convergence time analysis. Such an analysis will be beneficial for both industry and academia in determining the applicability and performance of the AURA-5G framework, given the scenario and constraints. Lastly, we have also provisioned a short study on the aspect of network re-dimensioning, wherein we demonstrate the efficacy of the AURA-5G framework in providing insights to the operators with regards to their deployment and how they can utilize it to optimize the performance of their networks., Comment: Submitted to Elsevier Computer Networks

We present a new multimodal question answering challenge, ManyModalQA, in which an agent must answer a question by considering three distinct modalities: text, images, and tables. We collect our data by scraping Wikipedia and then utilize crowdsourcing to collect question-answer pairs. Our questions are ambiguous, in that the modality that contains the answer is not easily determined based solely upon the question. To demonstrate this ambiguity, we construct a modality selector (or disambiguator) network, and this model gets substantially lower accuracy on our challenge set, compared to existing datasets, indicating that our questions are more ambiguous. By analyzing this model, we investigate which words in the question are indicative of the modality. Next, we construct a simple baseline ManyModalQA model, which, based on the prediction from the modality selector, fires a corresponding pre-trained state-of-the-art unimodal QA model. We focus on providing the community with a new manymodal evaluation set and only provide a fine-tuning set, with the expectation that existing datasets and approaches will be transferred for most of the training, to encourage low-resource generalization without large, monolithic training sets for each new task. There is a significant gap between our baseline models and human performance; therefore, we hope that this challenge encourages research in end-to-end modality disambiguation and multimodal QA models, as well as transfer learning. Code and data available at: https://github.com/hannandarryl/ManyModalQA, Comment: AAAI 2020 (10 pages)

Enabling users to move to different geographical locations within a network and still be able to maintain their connectivity and most essentially, continuity of service, is what makes any wireless network ubiquitous. Whilst challenging, modern day wireless networks, such as 3GPP-LTE, provision satisfactory mobility management (MM) performance. However, it is estimated that the number of mobile subscriptions will approximately touch 9 billion and the amount of data traffic will expand by 5 times in 2024 as compared to 2018. Further, it is expected that this trend of exponential growth will be maintained well into the future. To cope with such an exponential increase in cellular traffic and users alongside a burgeoning demand for higher Quality of Service (QoS), the future networks are expected to be highly dense and heterogeneous. This will severely challenge the existing MM solutions and ultimately render them ineffective as they will not be able to provide the required reliability, flexibility, and scalability. Consequently, to serve the 5G and beyond 5G networks, a new perspective to MM is required. Hence, in this article we present a novel discussion of the functional requirements from MM strategies for these networks. We present a novel qualitative assessment of the state-of-the-art mechanisms based on their ability to satisfy the reliability, flexibility and scalability criteria for future MM strategies. We then present a study detailing the research challenges that exist in the design and implementation of MM strategies for 5G and beyond networks. Further, we chart out the potential MM solutions and the associated capabilities they offer to tackle the persistent challenges. We conclude this paper with a vision for the 5G and beyond MM mechanisms., Comment: Submitted to Elsevier Computer Communications in November 2019. Revised Version Submitted in April 2020

The ongoing efforts in the research development and standardization of 5G, by both industry and academia, have resulted in the identification of enablers (Software Defined Networks, Network Function Virtualization, Distributed Mobility Management, etc.) and critical areas (Mobility management, Interference management, Joint access-backhaul mechanisms, etc.) that will help achieve the 5G objectives. During these efforts, it has also been identified that the 5G networks due to their high degree of heterogeneity, high QoS demand and the inevitable density (both in terms of access points and users), will need to have efficient joint backhaul and access mechanisms as well as enhanced mobility management mechanisms in order to be effective, efficient and ubiquitous. Therefore, in this paper we first provide a discussion on the evolution of the backhaul scenario, and the necessity for joint access and backhaul optimization. Subsequently, and since mobility management mechanisms can entail the availability, reliability and heterogeneity of the future backhaul/fronthaul networks as parameters in determining the most optimal solution for a given context, a study with regards to the effect of future backhaul/fronthaul scenarios on the design and implementation of mobility management solutions in 5G networks has been performed., Comment: IEEE Conference on Standards for Communications & Networking, September 2017, Helsinki, Finland