Showing papers by "Nokia published in 2021"

Joint Design of Communication and Sensing for Beyond 5G and 6G Systems

Abstract: The 6G vision of creating authentic digital twin representations of the physical world calls for new sensing solutions to compose multi-layered maps of our environments. Radio sensing using the mobile communication network as a sensor has the potential to become an essential component of the solution. With the evolution of cellular systems to mmWave bands in 5G and potentially sub-THz bands in 6G, small cell deployments will begin to dominate. Large bandwidth systems deployed in small cell configurations provide an unprecedented opportunity to employ the mobile network for sensing. In this paper, we focus on the major design aspects of such a cellular joint communication and sensing (JCAS) system. We present an analysis of the choice of the waveform that points towards choosing the one that is best suited for communication also for radar sensing. We discuss several techniques for efficiently integrating the sensing capability into the JCAS system, some of which are applicable with NR air-interface for evolved 5G systems. Specifically, methods for reducing sensing overhead by appropriate sensing signal design or by configuring separate numerologies for communications and sensing are presented. Sophisticated use of the sensing signals is shown to reduce the signaling overhead by a factor of 2.67 for an exemplary road traffic monitoring use case. We then present a vision for future advanced JCAS systems building upon distributed massive MIMO and discuss various other research challenges for JCAS that need to be addressed in order to pave the way towards natively integrated JCAS in 6G.

The emerging clinical role of wearables: factors for successful implementation in healthcare.

Abstract: Wearable technologies promise to redefine assessment of health behaviors, yet their clinical implementation remains a challenge. To address this gap, two of the NIH's Big Data to Knowledge Centers of Excellence organized a workshop on potential clinical applications of wearables. A workgroup comprised of 14 stakeholders from diverse backgrounds (hospital administration, clinical medicine, academia, insurance, and the commercial device industry) discussed two successful digital health interventions that involve wearables to identify common features responsible for their success. Seven features were identified including: a clearly defined problem, integration into a system of healthcare delivery, technology support, personalized experience, focus on end-user experience, alignment with reimbursement models, and inclusion of clinician champions. Health providers and systems keen to establish new models of care inclusive of wearables may consider these features during program design. A better understanding of these features is necessary to guide future clinical applications of wearable technology.

MPEG Immersive Video Coding Standard

Abstract: This article introduces the ISO/IEC MPEG Immersive Video (MIV) standard, MPEG-I Part 12, which is undergoing standardization. The draft MIV standard provides support for viewing immersive volumetric content captured by multiple cameras with six degrees of freedom (6DoF) within a viewing space that is determined by the camera arrangement in the capture rig. The bitstream format and decoding processes of the draft specification along with aspects of the Test Model for Immersive Video (TMIV) reference software encoder, decoder, and renderer are described. The use cases, test conditions, quality assessment methods, and experimental results are provided. In the TMIV, multiple texture and geometry views are coded as atlases of patches using a legacy 2-D video codec, while optimizing for bitrate, pixel rate, and quality. The design of the bitstream format and decoder is based on the visual volumetric video-based coding (V3C) and video-based point cloud compression (V-PCC) standard, MPEG-I Part 5.

Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions

Abstract: Future wireless networks will provide high-bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from virtual reality to the Internet of Things. To this aim, edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 improves the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bit-rate streaming); an overview of mobile network architectures, enabling technologies, and applications for video edge-C3; video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications); and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined.

Experimental quantum speed-up in reinforcement learning agents

Abstract: Increasing demand for algorithms that can learn quickly and efficiently has led to a surge of development within the field of artificial intelligence (AI). An important paradigm within AI is reinforcement learning (RL), where agents interact with environments by exchanging signals via a communication channel. Agents can learn by updating their behaviour based on obtained feedback. The crucial question for practical applications is how fast agents can learn to respond correctly. An essential figure of merit is therefore the learning time. While various works have made use of quantum mechanics to speed up the agent's decision-making process, a reduction in learning time has not been demonstrated yet. Here we present a RL experiment where the learning of an agent is boosted by utilizing a quantum communication channel with the environment. We further show that the combination with classical communication enables the evaluation of such an improvement, and additionally allows for optimal control of the learning progress. This novel scenario is therefore demonstrated by considering hybrid agents, that alternate between rounds of quantum and classical communication. We implement this learning protocol on a compact and fully tunable integrated nanophotonic processor. The device interfaces with telecom-wavelength photons and features a fast active feedback mechanism, allowing us to demonstrate the agent's systematic quantum advantage in a setup that could be readily integrated within future large-scale quantum communication networks.

Physical-Layer Security in 6G Networks

Abstract: The sixth generation (6G) of mobile network will be composed by different nodes, from macro-devices (satellite) to nano-devices (sensors inside the human body), providing a full connectivity fabric all around us. These heterogeneous nodes constitute an ultra dense network managing tons of information, often very sensitive. To trust the services provided by such network, security is a mandatory feature by design. In this scenario, physical-layer security (PLS) can act as a first line of defense, providing security even to low-resourced nodes in different environments. This paper discusses challenges, solutions and visions of PLS in beyond-5G networks.

Transform Coding in the VVC Standard

Abstract: In the past decade, the development of transform coding techniques has achieved significant progress and several advanced transform tools have been adopted in the new generation Versatile Video Coding (VVC) standard. In this paper, a brief history of transform coding development during VVC standardization is presented, and the transform coding tools in the VVC standard are described in detail together with their initial design, incremental improvements and implementation aspects. To improve coding efficiency, four new transform coding techniques are introduced in VVC, which are namely Multiple Transform Selection (MTS), Low-Frequency Non-separable Secondary Transform (LFNST) and Sub-Block Transform (SBT), as well as a large (64-point) type-2 DCT. The experimental results on VVC reference software (VTM-9.0) show that average 4.5% and 3.6% overall coding gain can be achieved by the VVC transform coding tools for All Intra and Random Access configurations, respectively.

Repeated centralized multidisciplinary team assessment of resectability, clinical behavior, and outcomes in 1086 Finnish metastatic colorectal cancer patients (RAXO): A nationwide prospective intervention study.

Abstract: Background Resection of colorectal cancer (CRC) metastases provides good survival but is probably underused in real-world practice Methods A prospective Finnish nationwide study enrolled treatable metastatic CRC patients The intervention was the assessment of resectability upfront and twice during first-line therapy by the multidisciplinary team (MDT) at Helsinki tertiary referral centre The primary outcome was resection rates and survival Findings In 2012-2018, 1086 patients were included Median follow-up was 58 months Multiple metastatic sites were present in 500 (46%) patients at baseline and in 820 (76%) during disease trajectory In MDT assessments, 447 (41%) were classified as resectable, 310 (29%) upfront and 137 (18%) after conversion therapy Six-hundred and ninety curative intent resections or local ablative therapies (LAT) were performed in 399 patients (89% of 447 resectable) Multiple metastasectomies for multisite or later developing metastases were performed in 148 (37%) patients Overall, 414 liver, 112 lung, 57 peritoneal, and 107 other metastasectomies were performed Median OS was 80·4 months in R0/1-resected (HR 0·15; CI95% 0·12-0·19), 39·1 months in R2-resected/LAT (0·39; 0·29-0·53) patients, and 20·8 months in patients treated with "systemic therapy alone" (reference), with 5-year OS rates of 66%, 40%, and 6%, respectively Interpretation Repeated centralized MDT assessment in real-world metastatic CRC patients generates high resectability (41%) and resection rates (37%) with impressive survival, even when multisite metastases are present or develop later Funding The funders had no role in the study design, analysis, and interpretation of the data or writing of this report

Quantization and Entropy Coding in the Versatile Video Coding (VVC) Standard

Abstract: The paper provides an overview of the quantization and entropy coding methods in the Versatile Video Coding (VVC) standard. Special focus is laid on techniques that improve coding efficiency relative to the methods included in the High Efficiency Video Coding (HEVC) standard: The inclusion of trellis-coded quantization, the advanced context modeling for entropy coding of transform coefficient levels, the arithmetic coding engine with multi-hypothesis probability estimation, and the joint coding of chroma residuals. Beside a description of the design concepts, the paper also discusses motivations and implementation aspects. The effectiveness of the quantization and entropy coding methods specified in VVC is validated by experimental results.

Image Coding For Machines: an End-To-End Learned Approach

Abstract: Over recent years, deep learning-based computer vision systems have been applied to images at an ever-increasing pace, oftentimes representing the only type of consumption for those images. Given the dramatic explosion in the number of images generated per day, a question arises: how much better would an image codec targeting machine-consumption perform against state-of-the-art codecs targeting human-consumption? In this paper, we propose an image codec for machines which is neural network (NN) based and end-to-end learned. In particular, we propose a set of training strategies that address the delicate problem of balancing competing loss functions, such as computer vision task losses, image distortion losses, and rate loss. Our experimental results show that our NN-based codec outperforms the state-of-the-art Versa-tile Video Coding (VVC) standard on the object detection and instance segmentation tasks, achieving -37.87% and -32.90% of BD-rate gain, respectively, while being fast thanks to its compact size. To the best of our knowledge, this is the first end-to-end learned machine-targeted image codec.

Optical Heterodyne Analog Radio-Over-Fiber Link for Millimeter-Wave Wireless Systems

Abstract: Optical heterodyne analog radio-over-fiber (A-RoF) links provide an efficient solution for future millimeter wave (mm-wave) wireless systems. The phase noise of the photo-generated mm-wave carrier limits the performance of such links, especially, for the transmission of low subcarrier baud rate multi-carrier signals. In this work, we present three different techniques for the compensation of the laser frequency offset (FO) and phase noise (PN) in an optical heterodyne A-RoF system. The first approach advocates the use of an analog mm-wave receiver; the second approach uses standard digital signal processing (DSP) algorithms, while in the third approach, the use of a photonic integrated mode locked laser (MLL) with reduced DSP is advocated. The compensation of the FO and PN with these three approaches is demonstrated by successfully transmitting a 1.95 MHz subcarrier spaced orthogonal frequency division multiplexing (OFDM) signal over a 25 km 61 GHz mm-wave optical heterodyne A-RoF link. The advantages and limitations of these approaches are discussed in detail and with regard to recent 5G recommendations, highlighting their potential for deployment in next generation wireless systems.

Dynamic Multiconnectivity Based Joint Scheduling of eMBB and uRLLC in 5G Networks

Abstract: Multiple use cases are the dominant feature for the fifth-generation mobile communication system (5G). Enhanced mobile broadband (eMBB) and ultrareliability low latency communication (uRLLC) are considered as two main scenarios. Different from the eMBB, which requires high data rate, the uRLLC requires extremely high reliability and very low latency. With multiple demands, the coexistence of eMBB and uRLLC will be an urgent challenge for the 5G networking strategy. In this article, a dynamic multiconnectivity (MC)-based joint scheduling framework with traffic steering for eMBB and uRLLC is proposed. The eMBB and uRLLC are specifically sliced with each other, which avoids the queue of uRLLC. A modified effective capacity (EC) model is proposed to measure the performance of our framework, which improves the throughput of eMBB under the guarantee of uRLLC latency. The proposed EC model turns the analytical target to the queue of users, different from the queue of base stations in traditional EC model. By a two-step optimization, the typical mixed integer nonlinear programming conducted by the EC model is decomposed into the integral programming and the nonlinear programming. The system-level simulation results show the proposed framework enhances the system throughput effectively while guaranteeing the uRLLC latency.

Silicon photonics for terabit/s communication in data centers and exascale computers

Abstract: Silicon Photonics Technology using sub micrometer SOI platform, which commercially emerged at the beginning of the century, has now gained market shares in the field of fiber optic interconnects, from Inter-to Intra-Data Center communications. With growing demands in terms of aggregated bandwidth, scalability, transceiver form factor, and cost, Silicon Photonics is expected to play a growing role, especially with the foreseeable need to co-package photonic transceivers with next generation Ethernet switches. This new paradigm will be possible only with an evolution of existing Silicon Photonics manufacturing platforms, in order to solve the challenges of 3D packaging, laser integration, reflow-compatible optical connectors and high efficiency, low footprint modulators. Achieving these challenges may pave the way to Terabit scale communications in Data Centers and High Performance Computing Systems (HPC).

The High-Level Syntax of the Versatile Video Coding (VVC) Standard

Abstract: Versatile Video Coding (VVC), a.k.a. ITU-T H.266 | ISO/IEC 23090-3, is the new generation video coding standard that has just been finalized by the Joint Video Experts Team (JVET) of ITU-T VCEG and ISO/IEC MPEG at its $19^{\mathrm {th}}$ meeting ending on July 1, 2020. This paper gives an overview of the VVC high-level syntax (HLS), which forms its system and transport interface. Comparisons to the HLS designs in High Efficiency Video Coding (HEVC) and Advanced Video Coding (AVC), the previous major video coding standards, are included. When discussing new HLS features introduced into VVC or differences relative to HEVC and AVC, the reasoning behind the design differences and the benefits they bring are described. The HLS of VVC enables newer and more versatile use cases such as video region extraction, composition and merging of content from multiple coded video bitstreams, and viewport-adaptive 360° immersive media.

Empirical Low-Altitude Air-to-Ground Spatial Channel Characterization for Cellular Networks Connectivity

Abstract: Cellular-connected unmanned aerial vehicles (UAVs) have recently attracted a surge of interest in both academia and industry. Understanding the air-to-ground (A2G) propagation channels is essential to enable reliable and/or high-throughput communications for UAVs and protect the ground user equipments (UEs). In this contribution, a recently conducted measurement campaign for the A2G channels is introduced. A uniform circular array (UCA) with 16 antenna elements was employed to collect the downlink signals of two different Long Term Evolution (LTE) networks, at the heights of 0-40m in three different, namely rural, urban and industrial scenarios. The channel impulse responses (CIRs) have been extracted from the received data, and the spatial, including angular, parameters of the multipath components in individual channels were estimated according to a high-resolution-parameter-estimation (HRPE) principle. Based on the HRPE results, clusters of multipath components were further identified. Finally, comprehensive spatial channel characteristics were investigated in the composite and cluster levels at different heights in the three scenarios.

An Overview of Omnidirectional MediA Format (OMAF)

Abstract: During recent years, there have been product launches and research for enabling immersive audio–visual media experiences. For example, a variety of head-mounted displays and 360° cameras are available in the market. To facilitate interoperability between devices and media system components by different vendors, the Moving Picture Experts Group (MPEG) developed the Omnidirectional MediA Format (OMAF), which is arguably the first virtual reality (VR) system standard. OMAF is a storage and streaming format for omnidirectional media, including 360° video and images, spatial audio, and associated timed text. This article provides a comprehensive overview of OMAF.

Experimental quantum speed-up in reinforcement learning agents.

Abstract: As the field of artificial intelligence advances, the demand for algorithms that can learn quickly and efficiently increases. An important paradigm within artificial intelligence is reinforcement learning1, where decision-making entities called agents interact with environments and learn by updating their behaviour on the basis of the obtained feedback. The crucial question for practical applications is how fast agents learn2. Although various studies have made use of quantum mechanics to speed up the agent’s decision-making process3,4, a reduction in learning time has not yet been demonstrated. Here we present a reinforcement learning experiment in which the learning process of an agent is sped up by using a quantum communication channel with the environment. We further show that combining this scenario with classical communication enables the evaluation of this improvement and allows optimal control of the learning progress. We implement this learning protocol on a compact and fully tunable integrated nanophotonic processor. The device interfaces with telecommunication-wavelength photons and features a fast active-feedback mechanism, demonstrating the agent’s systematic quantum advantage in a setup that could readily be integrated within future large-scale quantum communication networks. A reinforcement learning experiment using a programmable integrated nanophotonic processor shows that a quantum communication channel with the environment speeds up the learning process of an agent.

The fifth archetype of electricity market: the blockchain marketplace

Abstract: Enabling and empowering the diverse energy resources to have active yet efficient participation in the smart grid and energy market is an unrivaled challenge for the energy industry. This research expands the four dominant archetypes of business models in the energy and electricity market, creating a fifth archetype, the “blockchain marketplace”. The contributions of the study are to identify the extant electricity market designs and architectures as centralized and pseudo-decentralized while proposing a fully decentralized architecture enabled by the blockchain. The research contributes to the literature of smart grids and demand-side management and introduces the value configuration/architecture approach for the energy market and business model domains.

Spectral Efficiency Optimization for Hybrid Relay-Reflecting Intelligent Surface

Abstract: We propose a novel concept of hybrid relay-reflecting intelligent surface (HR-RIS), in which a single or few elements are deployed with power amplifiers (PAs) to serve as active relays, while the remaining elements only reflect the incident signals. The design and optimization of the HR-RIS is formulated in a spectral efficiency (SE) maximization problem, which is efficiently solved by the alternating optimization (AO) method. The simulation results show that a significant improvement in the SE can be attained by the proposed HR-RIS, even with a limited power budget, with respect to the conventional reconfigurable intelligent surface (RIS). In particular, the favorable design and deployment of the HR-RIS are analytically derived and numerically justified.

Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

Abstract: Segmentation methods can mutually exclude the location of the tumor. However, the challenge of complex location or incomplete identification is located in segmentation challenge dataset. Identificationof tumor location is difficult due to the variation of intensities in MRI image. Vairation of intensity extends up to edema. Confidence Region with Contour Detection identifies the variation of intensities and level set algorithm (Region Scale Fitting) is used to delineate among the region of inner and outer of the tumor. Automatic feature selection method is required due to data complexity. An improved Self Organization Feature Map. Method is required. Weighted SOM Map selects a deterministic feature. This feature is one higher trained accuracy feature. When this specific feature is combines with cluster therefore it is known as deterministic feature clustering. This method gives confidence element. Confidence Region with Contour detection is facing the issue due to extended variations of intensities. These intensities are segmented by hybrid SOM Pixel Labelling with Reduce Cluster Membership and Deterministic Feature Clustering. This hyhbrid method segments the complex tumor intensities. This method produces a potential cluster which is achieved through the hybrid of three unsupervised learning techniques. Hybrid cluster method segments the tumor region. Extended intensities are also segmented by this hybrid approach. Above methods are validated on MICCAI BraTs brain tumor dataset, this is a segmentation challenge dataset. Proposed hybrid algorithm is efficient and it’s accuracy can be seen with testing parameters like Dice Overlap Index, Jaccard Tanimoto Coefficient Index, Mean Squared Error and Peak Signal to Noise Ratio. Dice OverlapIndex is 98%, Jaccard Index is 96 percent, Mean Squared Error is 0.06 and Peak Signal To Noise ratio is 18db. The performance of the suggested algorithm is compared to other state of the art.

Associating machine-learning and analytical models for quality of transmission estimation: combining the best of both worlds

Abstract: By associating machine learning and an analytical model (i.e., the Gaussian noise model), we reduce uncertainties on the output power profile and the noise figure of each amplifier in an optical network. We leverage the signal-to-noise ratio (SNR) of all the light paths of an optical network, monitored in all the coherent receivers. The learning process is based on a gradient-descent algorithm where all the uncertain input parameters of the analytical model are iteratively modified from their estimated values to match with the SNR of light paths in a European optical network. The design margin is then reduced to 0.1 dB for new traffic demands.

Positioning Technology Trends and Solutions Toward 6G

Abstract: In the 4G era, cellular positioning was used for emergency services and services associated to lawful interception. In 5G, commercial use cases have gained momentum and use cases like factory automation, transportation, and logistics are included in 5G besides the regulatory use cases. Toward 6G, it is anticipated that positioning and location services will be fundamental part of the system demanded by most commercial applications, such as AR/VR/XR, gaming, sensing, low-cost tracking and new industrial applications with extremely high accuracy. As a result, positioning accuracy and latency requirements are anticipated to tighten further from 5G. Thus, positioning and location services needs to be designed as an integral part of 5G evolution to address these requirements in a scalable and efficient manner both for devices and networks. This paper discusses the technical trends and enablers to realize this vision towards 6G, such as latency and accuracy enhancements, and low-cost positioning.

Integrating Fronthaul and Backhaul Networks: Transport Challenges and Feasibility Results

Abstract: In addition to CPRI, new functional splits have been defined in 5G creating diverse fronthaul transport bandwidth and latency requirements. These fronthaul requirements shall be fulfilled simultaneously together with the backhaul requirements by an integrated fronthaul and backhaul transport solution. In this paper, we analyze the technical challenges to achieve an integrated transport solution in 5G and propose specific solutions to address these challenges. These solutions have been implemented and verified with pre-commercial equipment. Our results confirm that an integrated fronthaul and backhaul transport dubbed Crosshaul can meet all the requirements of 5G fronthaul and backhaul in a cost-efficient manner.

Security and Trust in the 6G Era

Abstract: A comprehensive set of security technology enablers will be critically required for communication systems for the 6G era of the 2030s. Trustworthiness must be assured across IoT, heterogenous cloud and networks, devices, sub-networks, and applications. The 6G threat vector will be defined by 6G architectural disaggregation, open interfaces and an environment with multiple stakeholders. Broadly decomposed into domains of cyber-resilience, privacy and trust and their respective intersection, we explore relevant security technology enablers including automated software creation and automated closed-loop security operation, privacy preserving technologies, hardware and cloud embedded anchors of trust, quantum-safe security, jamming protection and physical layer security as well as distributed ledger technologies. Artificial intelligence and machine learning (AI/ML) as a key technology enabler will be pervasive and of pivotal relevance across the security technology stack and architecture. A novel vision for a trustworthy Secure Telecom Operation Map is developed as part of the automated closed loop operations paradigm.

Learned Image Coding for Machines: A Content-Adaptive Approach

Abstract: Today, according to the Cisco Annual Internet Report (2018-2023), the fastest-growing category of Internet traffic is machine-to-machine communication. In particular, machine-to-machine communication of images and videos represents a new challenge and opens up new perspectives in the context of data compression. One possible solution approach consists of adapting current human-targeted image and video coding standards to the use case of machine consumption. Another approach consists of developing completely new compression paradigms and architectures for machine-to-machine communications. In this paper, we focus on image compression and present an inference-time content-adaptive fine-tuning scheme that optimizes the latent representation of an end-to-end learned image codec, aimed at improving the compression efficiency for machine-consumption. The conducted experiments targeting instance segmentation task network show that our online finetuning brings an average bitrate saving (BD-rate) of -3.66% with respect to our pretrained image codec. In particular, at low bitrate points, our proposed method results in a significant bitrate saving of -9.85%. Overall, our pretrained-and-then-finetuned system achieves - 30.54% BD-rate over the state-of-the-art image/video codec Versatile Video Coding (VVC) on instance segmentation.

Novel Iterative Clipping and Error Filtering Methods for Efficient PAPR Reduction in 5G and Beyond

Abstract: The physical-layer radio access of 5G New Radio (NR) and other modern wireless networks builds on the cyclic prefix (CP) orthogonal frequency-division multiplexing (OFDM), known to suffer from the high peak-to-average power ratio (PAPR) challenge. In this article, novel PAPR reduction methods are developed, referred to as the iterative clipping and weighted error filtering (ICWEF) approach. To this end, clipping noise is separated from the data signal in frequency domain and properly tailored frequency-selective clipping noise filtering is adopted to control the tradeoff between PAPR reduction and transmitted signal quality. Furthermore, as 5G NR networks support adopting different OFDM numerologies at different bandwidth parts within one channel bandwidth, the ICWEF approach is also extended to take into account and suppress the resulting inter-numerology interference—something that most existing state-of-the-art methods do not consider. To facilitate comprehensive performance evaluations, a software-defined radio based prototyping testbed including a high-power base station power amplifier is also developed and used for assessing the performance of PAPR reduction solutions. The proposed ICWEF-based PAPR reduction concept is thereon thoroughly validated with extensive numerical and experimental results and shown to outperform the existing state-of-the-art reference solutions.

DeepRadar: a deep-learning-based environmental sensing capability sensor design for CBRS

Abstract: We present DeepRadar, a novel deep-learning-based environmental sensing capability system for detecting radar signals and estimating their spectral occupancy. DeepRadar makes decisions in real-time and maintains continuous operability by adapting its computations based on the available computing resources. We thoroughly evaluate DeepRadar using a variety of test data at different signal-to-interference ratio (SIR) levels. Our evaluation results show that at 20 dB peak-to-average SIR, per MHz, DeepRadar detects radar signals with 99% accuracy and misses only less than 2 MHz, on average, while estimating their spectral occupancy. Our implementation of DeepRadar using a commercial-off-the-shelf software-defined radio also achieves a similarly high detection accuracy.

ML-Assisted UE Positioning: Performance Analysis and 5G Architecture Enhancements

Abstract: Artificial intelligence and data-driven networks will be integral part of 6G systems. In this article, we comprehensively discuss implementation challenges and need for architectural changes in 5G radio access networks for integrating machine learning (ML) solutions. As an example use case, we investigate user equipment (UE) positioning assisted by deep learning (DL) in 5G and beyond networks. As compared to state of the art positioning algorithms used in today's networks, radio signal fingerprinting and machine learning (ML) assisted positioning requires smaller additional feedback overhead; and the positioning estimates are made directly inside the radio access network (RAN), thereby assisting in radio resource management. In this regard, we study ML-assisted positioning methods and evaluate their performance using system level simulations for an outdoor scenario. The study is based on the use of raytracing tool, a 3GPP 5G NR compliant system level simulator and DL framework to estimate positioning accuracy of the UE. We evaluate and compare performance of various DL models and show mean positioning error in the range of 1-1.5 m for a 2-hidden layer DL architecture with appropriate feature-modeling. Building on our performance analysis, we discuss pros and cons of various architectures to implement ML solutions for future networks and draw conclusions on the most suitable architecture.

Virtual Queues for P4: A Poor Man’s Programmable Traffic Manager

Abstract: The advent of programmable network switch ASICs and recent developments on other programmable data planes (NPUs, FPGAs) drive the renewed interest in network data plane programmability. The P4 language has emerged as a strong candidate to describe a protocol independent datapath pipeline. With its supported architectures, the P4 language provides an excellent way to define the packet processing and forwarding behavior, while leaving other networking components such as the traffic management engine, to non-programmable fixed function elements, based on the capabilities of most programmable devices. However, network flexibility is essential to meet the Quality of Service (QoS) requirements of traffic flows. Thus, enabling programmable control for fixed-function elements like traffic management is crucial. Towards that end we propose the use of virtual queues in the P4 pipeline, investigate the application of virtual queue-based traffic management, and portability of the approach using different P4 programmable targets. Specifically, we focus on virtual queue based Active Queue Management (AQM) for congestion policing and meeting the latency targets of distinct network slices. The solution is compared to P4 built-in functionality for bandwidth management using meters, proving also that the additional dimensions of control are achieved without compromising the processing complexity of the solution.