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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Synchrophasor Data Anomaly Detection on Grid Edge by 5G Communication and Adjacent Compute

The fifth-generation mobile communication (5G) technology offers the opportunities to enhance the grid real-time monitoring. The 5G-enabled phasor measurement units (PMUs) features flexible positioning and cost-effective long-term maintenance, without constraints of fixing wire. This paper is the first to demonstrate the applicability of 5G in PMU communication, and the experiment was carried out at Verizon non-standalone testbed at Pacific Northwest National Laboratory (PNNL) Advanced Wireless Communication lab. The performance of 5G-enabled PMU communication setup is reviewed and discussed in this paper, and the paper presents a real-time dynamic linear model (DML) based synchrophasor data anomaly detection application. Last but not least, the practicability of implementing 5G for wide-area protection strategies is explored and discussed by analyzing the experimental results.

5G, Synchrophasor data, machine learning, anomaly ↗
DOE OSTI2021

Multi-User Capacity for Cyclic Prefix Direct Sequence Spread Spectrum with Linear Detection and Precoding

Cyclic Prefix Direct Sequence Spread Spectrum (CP-DSSS) is a promising solution for futuristic 6G ultra-reliable low latency communications (URLLC) and massive machine type communication (mMTC) applications, where the CP-DSSS waveform would operate as a secondary network at the same frequencies as the primary network but at much lower SNR. In this paper, we show per-user capacity for multi-user scenarios, where simple matched filtering (MF) is performed on the uplink (UL) and time-reversal (TR) precoding is used on the downlink (DL). When operating in the low SNR regime, CP-DSSS achieves per-user capacity near the optimum single-user capacity by using a MF detector at the receiver for the UL. TR precoding converges to the optimal capacity as the number of antennas at the hub/gateway increases. Given the near-optimal performance of MF detection and TR precoding for each of the users, CP-DSSS can be implemented with simple device transceiver structures, reducing per-unit cost for massively deployed 6G networks.

5G and Beyond Communications↗
DOE OSTI2020

Near Optimality of Matched Filter Detection for Cyclic Prefix Direct Sequence Spread Spectrum

Abstract—Cyclic Prefix Direct Sequence Spread Spectrum -DSSS) has been presented as a potential solution for ultrareliable low latency communications (URLLC) and massive machine type communication (mMTC), where the CP-DSSS waveform would operate as a secondary network at the same frequencies as the primary network but at much lower SNR. In this paper, we show that when operating in the low SNR regime, CP-DSSS achieves near optimum performance when using a matched filter (MF) detector at the receiver. Time reversal (TR) precoding at the transmitter is also analyzed. These results also carry forward into multi-antenna scenarios where array gain is preserved. With nearly optimal performance of MF detection, CP-DSSS can be implemented with simple device transceiver structures, reducing per-unit cost for massively deployed networks.

5G and Beyond Communications↗
DOE OSTI2020

Cyclic Prefix Direct Sequence Spread Spectrum Capacity Analysis

Cyclic Prefix Direct Sequence Spread Spectrum (CP-DSSS) is a novel waveform that has the potential to solve 5G objectives such as ultra reliable low latency communication (URLLC) and massive machine Type Communication (mMTC). This paper analyzes the capacity of the CP-DSSS waveform in comparison with Orthogonal Frequency Domain Multiplexing OFDM). CP-DSSS can be optimized to achieve the same capacity as OFDM when optimized by the water-filling algorithm. A significant advantage for CP-DSSS is that this capacity can be achieved with all symbols being transmitted with the same effective rate. As a result, stronger forward error correction codes can be used in a CP-DSSS implementation compared to an OFDM implementation with resource block constraints. In addition, the applicability of CP-DSSS as a waveform for a secondary network operating in the same frequency band as the primary network is discussed.

5G and Beyond Communications↗
DOE OSTI2020

CP-DSSS: A Novel Waveform for Multiple Access in IoT

Cyclic prefix direct sequence spread spectrum (CPDSSS) is a novel waveform with versatile characteristics that positions itself well as a secondary network to relieve the congested wireless spectrum. The underlying structure of CPDSSS allows for efficient and effective multi-access capabilites through frequency and time division schemes in a given system. The sum-rate capacity of the system is maximized when the spectrum is divided and allocated to users with the best signal-to-noise (SNR) ratio for the given channel slice. We propose an algorithm for dividing and allocating portions of the spectrum to multiple users with the final goal of maximizing the sumrate capacity of the network. We also propose and develop a precoding/equalization technique that reduces the length of the channel impulse response. This, when used along with a matched filter detector, leads to some improvement in the sum-rate capacity of the network

5G and Beyond Communications↗
DOE OSTI2025

Best Practices for Grid Communications

As the grid evolves, the communications architecture will need to evolve with it. That architecture affords a structured means by which the evolving complexities of the modern electric grid can be managed. This document provides best practices that can be implemented in the grid of today and evolve towards the grid and grid architecture of the future. The evolving grid and its control communications increasingly rely on commercial communications providers and a variety of technologies, from wireless (e.g., 5G, microwave, Wi-Fi) to wireline (fiber, copper) to radio communications (P25, other repeater-based systems), and all these communications systems rely on electric power. A reliable and resilient grid must account for this complex set of interdependencies in its planning activities, especially those involving restoration and recovery. The participation of all relevant parties in both planning and exercising of plans can prevent unexpected conditions that impede the reliable operation and recovery of the grid. Best practices for grid communications include using a Network Management System to document the operational state, define and monitor baselines, detect changes, and accelerate response to abnormalities. If transitioning from SONET to IP/packet-based systems, translating grid requirements into communications requirements for latency, bandwidth and throughput, IP packet delay variation, packet loss, and availability should inform and drive technology planning and selection as well as that communication system’s Quality of Service (QoS) policies and Service Level Agreements (SLAs). Secure and reliable timing is another key component of a reliable and resilient grid that can operate through adverse events. A trusted internal NTP configuration, an integrated and diverse timing delivery system, optimizing the timing architecture based on the transport technologies of the communications system, and using established standards can deliver the level of timing accuracy required by a range of time-sensitive power system applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds

Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.

5G↗
DOE OSTI2025

Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds

Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.

5G↗
DOE OSTI2021

Efficient Precoding for Single Carrier Modulation in Multi-User Massive MIMO Networks

Abstract—Frequency domain (FD) multi-user detection (MUD) and precoding has been shown to be an effective means of approaching the theoretical per-user capacity for single carrier modulation (SCM) schemes in massive MIMO scenarios with highly dispersive channels. When a cyclic prefix is added to the SCM waveform, the circulant structures of the resulting convolutional channel matrix allows for relatively simple expressions for the FD detection and precoding. In this paper, we further refine the computationally efficient minimum mean squared error (MMSE) FD-MUD technique in a time-division duplexing (TDD) massive MIMO setup by pre-computing the scale factor needed to produce an unbiased estimate. The resulting scale factor and the matrix inverse that was calculated for the uplink is then reused for the downlink to form a computationally efficient precoding scheme with superior performance compared to zero-forcing (ZF) precoding. Performance analysis is provided and confirmed through simulation results.

5G and Beyond Communications↗
DOE OSTI2021

Ensuring reliable connectivity to cellular-connected UAVs with up-tilted antennas and interference coordination

To integrate unmanned aerial vehicles (UAVs) in future large-scale deployments, a new wireless communication paradigm, namely, the cellular-connected UAV has recently attracted interest. However, the line-of-sight dominant air-to-ground channels along with the antenna pattern of the cellular ground base stations (GBSs) introduce critical interference issues in cellular-connected UAV communications. In particular, the complex antenna pattern and the ground reflection (GR) from the down-tilted antennas create both coverage holes and patchy coverage for the UAVs in the sky, which leads to unreliable connectivity from the underlying cellular network. To overcome these challenges, in this paper, we propose a new cellular architecture that employs an extra set of co-channel antennas oriented towards the sky to support UAVs on top of the existing down-tilted antennas for ground user equipment (GUE). To model the GR stemming from the down-tilted antennas, we propose a path-loss model, which takes both antenna radiation pattern and configuration into account. Next, we formulate an optimization problem to maximize the minimum signal-to-interference ratio (SIR) of the UAVs by tuning the up-tilt (UT) angles of the up-tilted antennas. Since this is an NP-hard problem, we propose a genetic algorithm (GA) based heuristic method to optimize the UT angles of these antennas. After obtaining the optimal UT angles, we integrate the 3GPP Release-10 specified enhanced inter-cell interference coordination (eICIC) to reduce the interference stemming from the down-tilted antennas. Our simulation results based on the hexagonal cell layout show that the proposed interference mitigation method can ensure higher minimum SIRs for the UAVs over baseline methods while creating minimal impact on the SIR of GUEs.

3GPP↗