In-situ measurement methodology for the assessment of 5G NR massive MIMO base station exposure at sub-6 GHz frequencies

As the roll-out of the fifth generation (5G) of mobile telecommunications is well underway, standardized methods to assess the human exposure to radiofrequency electromagnetic fields from 5G base station radios are needed in addition to existing numerical models and preliminary measurement studies. Challenges following the introduction of 5G New Radio (NR) include the utilization of new spectrum bands and the widespread use of technological advances such as Massive MIMO (Multiple-Input Multiple-Output) and beamforming. We propose a comprehensive and ready-to-use exposure assessment methodology for use with common spectrum analyzer equipment to measure or calculate in-situ the time-averaged instantaneous exposure and the theoretical maximum exposure from 5G NR base stations. Besides providing the correct method and equipment settings to capture the instantaneous exposure, the procedure also comprises a number of steps that involve the identification of the Synchronization Signal Block, which is the only 5G NR component that is transmitted periodically and at constant power, the assessment of the power density carried by its resources, and the subsequent extrapolation to the theoretical maximum exposure level. The procedure was validated on site for a 5G NR base station operating at 3.5 GHz, but it should be generally applicable to any 5G NR signal, i.e., as is for any sub-6 GHz signal and after adjustment of the proposed measurement settings for signals in the millimeter-wave range

How Does 5G NR V2X Mode 2 Handle Aperiodic Packets and Variable Packet Sizes?

—5G NR V2X complements LTE V2X to support advanced V2X services for connected and automated driving. 5G NR V2X introduces novel features at the MAC layer that are designed to cope with potential packet collisions, and that could help address the LTE V2X MAC inefficiencies observed under aperiodic traffic of variable size. This is the case of the reevaluation mechanism that is a mandatory MAC feature of 5G NR V2X, and that seeks avoiding possible packet collisions detected before a vehicle transmits in selected resources. Evaluations conducted to date of 5G NR V2X do not consider the re-evaluation mechanism, and have focused on traffic patterns that do not fully account for the traffic variability of advanced V2X services. This paper extends the current state of the art with the first evaluation of a fully standard compliant 5G NR V2X implementation under the traffic patterns recommended by 3GPP for advanced V2X services. Our study shows that 5G NR V2X Mode 2 still faces MAC challenges when using semi-persistent scheduling (SPS) to efficiently support aperiodic traffic of variable size

Radio Resource Management for New Application Scenarios in 5G: Optimization and Deep Learning

The fifth-generation (5G) New Radio (NR) systems are expected to support a wide range of emerging applications with diverse Quality-of-Service (QoS) requirements. New application scenarios in 5G NR include enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low-latency communications (URLLC). New wireless architectures, such as full-dimension (FD) massive multiple-input multiple-output (MIMO) and mobile edge computing (MEC) system, and new coding scheme, such as short block-length channel coding, are envisioned as enablers of QoS requirements for 5G NR applications. Resource management in these new wireless architectures is crucial in guaranteeing the QoS requirements of 5G NR systems. The traditional optimization problems, such as subcarriers and user association, are usually non-convex or Non-deterministic Polynomial-time (NP)-hard. It is time-consuming and computing-expensive to find the optimal solution, especially in a large-scale network. To solve these problems, one approach is to design a low-complexity algorithm with near optimal performance. In some cases, the low complexity algorithms are hard to obtain, deep learning can be used as an accurate approximator that maps environment parameters, such as the channel state information and traffic state, to the optimal solutions. In this thesis, we design low-complexity optimization algorithms, and deep learning frameworks in different architectures of 5G NR to resolve optimization problems subject to QoS requirements. First, we propose a low-complexity algorithm for a joint cooperative beamforming and user association problem for eMBB in 5G NR to maximize the network capacity. Next, we propose a deep learning (DL) framework to optimize user association, resource allocation, and offloading probabilities for delay-tolerant services and URLLC in 5G NR. Finally, we address the issue of time-varying traffic and network conditions on resource management in 5G NR

Random Linear Network Coding for 5G Mobile Video Delivery

An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.Comment: Invited paper for Special Issue "Network and Rateless Coding for Video Streaming" - MDPI Informatio

ML-Enabled Outdoor User Positioning in 5G NR Systems via Uplink SRS Channel Estimates

Cellular user positioning is a promising service provided by Fifth Generation New Radio (5G NR) networks. Besides, Machine Learning (ML) techniques are foreseen to become an integrated part of 5G NR systems improving radio performance and reducing complexity. In this paper, we investigate ML techniques for positioning using 5G NR fingerprints consisting of uplink channel estimates from the physical layer channel. We show that it is possible to use Sounding Reference Signals (SRS) channel fingerprints to provide sufficient data to infer user position. Furthermore, we show that small fully-connected moderately Deep Neural Networks, even when applied to very sparse SRS data, can achieve successful outdoor user positioning with meter-level accuracy in a commercial 5G environment.Comment: 6 pages, 8 figures, Accepted to be published in IEEE International Conference on Communications 2023, Rome, Ital